PROCESS SYSTEMS ENGINEERING 2003 (Part A)
COMPUTER-AIDED CHEMICAL ENGINEERING Advisory Editor: R. Gani Volume 1: Volume 2: Volume 3: Volume 4: Volume 5:
Volume 6: Volume 7: Volume 8: Volume 9: Volume 10: Volume 11: Volume 12: Volume 13: Volume 14: Volume 15:
Distillation Design in Practice (L.M. Rose) The Art of Chemical Process Design (G.L. Wells and L.M. Rose) Computer Programming Examples for Chemical Engineers (G. Ross) Analysis and Synthesis of Chemical Process Systems (K. Hartmann and K. Kaplick) Studies in Computer-Aided Modelling. Design and Operation Part A: Unite Operations (I. Pallai and Z. Fonyo, Editors) Part B: Systems (I. Pallai and G.E. Veress, Editors) Neural Networks for Chemical Engineers (A.B. Bulsari, Editor) Material and Energy Balancing in the Process Industries - From Microscopic Balances to Large Plants (V.V. Veverka and F. Madron) European Symposium on Computer Aided Process Engineering-10 (S. Pierucci, Editor) European Symposium on Computer Aided Process Engineering- 11 (R. Gani and S.B. Jerrgensen, Editors) European Symposium on Computer Aided Process Engineering-12 (J. Grievink and J. van Schijndel, Editors) Software Architectures and Tools for Computer Aided Process Engineering (B. Braunschweig and R. Gani, Editors) Computer Aided Molecular Design: Theory and Practice (L.E.K. Achenie, R. Gani and V. Venkatasubramanian, Editors) Integrated Design and Simulation of Chemical Processes (A.C. Dimian) European Symposium on Computer Aided Process Engineering-13 (A. Kraslawski and I. Turunen, Editors) Process Systems Engineering 2003 (Bingzhen Chen and A.W. Westerberg, Editors)
COMPUTER-AIDEDCHEMICAL ENGINEERING, 15
PROCESS SYSTEMS ENGINEERING 2003 Part A
8th International Symposium on Process SystemsEngineering, China
Edited by
Bingzhen Chen Department of Chemical Engineering Tsinghua University Beijing 100084, China
Arthur W. Westerberg Department of Chemical Engineering Doherty Ha1123 11 Carnegie Mellon University Pittsburgh, PA 15213 USA
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Preface PSE2003 is the eighth in the triennial series of international symposia on process systems engineering initiated in 1982. The purpose of these meetings is to bring together the worldwide PSE community of researchers and practitioners who are involved in the creation and application of computing based methodologies for planning, design, operation, control, and maintenance of chemical processes. The composition of these meetings is international by design, with representation from the three main geographic zones of Asia and Pacific, Europe and Africa, and Americas. The conference was initiated by the Executive Committee of the Process Systems Engineering Symposium series, which draws its representation from the Asian Pacific Confederation of Chemical Engineering, the European Federation of Chemical Engineering, and the Inter American Confederation of Chemical Engineering. In keeping with the international scope of the conference series, the previous conferences in the series were held in Keystone, Colorado USA(2000); Trondheim, Nonvay(1997); Kyongju, Korea(1994); Montebello, Canada(l991); Sydney, Australia(l988); Cambridge, England(1985); and Kyoto, Japan(1982). PSE2003 is the first time the conference has convened in China. Selecting the conference theme was a first goal. All involved in organizing exchanged e-mails suggesting ideas. In the midst of a dampened business climate, an informal conversation involving Vladimir Mahalec (AspenTech), Bing Tjoa (Mitsubishi Chemicals) and Art Westerberg (Carnegie Mellon) at the AIChE meeting in Reno, Nevada, in November 2001, proposed supporting business decision making. We all knew this topic was an exciting possibility. An exchange of e-mails among the organizers led to the vision for a conference in which the participants would come more with questions than answers, a conference involving lively discussions and one where everyone would learn from that discussion. The suggestion arose to have two introductory plenary sessions from the "customer" - i.e., the business andlor government community. If we are to help them, they should define their needs. We next elected to have a first keynote lecture to relate the terminology of the business leaders to that of the PSE community and vice versa. Finally we asked all organizers and presenters to think how their contributions would speak to this theme. While we would have the typical topics of PSE for the sessions, each would be asking about supporting business decision making rather than simply reporting latest research results. A later conversation with Mike Doherty (Univ. of California, Santa Barbara) helped to set the desired tone for this conference. He noted that we as engineers like to think of ourselves as clever, which is often the basis for our presentations to management. Management already believes we are clever. We need to understand much better the kinds of decisions they must make and the type of information they need. We are useful to them when we can provide this information. This understanding should certainly alter what we do. The program features 2 plenary and 10 keynote papers as well as 225 regular conference papers. They cover topics on PSE and business decision making, product discovery and
design, enterprise and supply chain optimization, control and operations, design and operation, PSE education, PSE in formulation development, integration of business with information and process systems, information technology, and bio-chemicals and materials. The PSE 2003 conference is conducted under the auspices of the Executive Committee for PSE, chaired by Professor G. V. Reklaitis, Purdue University. The technical program was assembled with the active participation of the International Program Committee. We would like to express our gratitude to the highly professional volunteer efforts of the IPC in reviewing abstracts and manuscripts and providing feedback to authors. We would also like to acknowledge the participation of National Organizing Committee. Finally, we gratefully acknowledge the joint sponsorship of the conference by the China petroleum & Chemical Corporation, Petroleum Company Limited and Process Systems Engineering Society of China. Bingzhen Chen Tsinghua University Co-chair PSE 2003
Arthur W. Westerberg Carnegie Mellon University Co-chair PSE 2003
Executive Committee for PSE Gintaras V. Reklaitis (USA, Chair) (Japan) Iori Hashimoto John Perkins (UK)
Jeffrey J. Siirola Sigurd Skogestad Kun Soo Chang
(USA) (Norway) (Korea)
International Program Committee Co-Chairs Bingzhen Chen Arthur W. Westerberg
(China) (USA)
Members PSE & Business Decision Making Gintaras V. Reklaitis Lixing Wang
(USA, Chair) (China, Vice Chair) Vasilios I. Manousiouthakis (USA) Sunwon Park (Korea) Jeffrey J. Siirola (USA) Youqi Yang (China)
Herbert I. Britt (USA) Christodoulos A. Floudas (USA) (USA) Vipin Gopal Vincent G. Grassi (USA) Michael F. Malone (USA)
Challenges in the NEW Millennium Lorenz T. Biegler (USA, Chair) Efstratios N. Pistikopoulos (UK, Vice Chair) Paul I. Barton Jim Davis Furong Gao Shinji Hasebe Xiaorong He
Knut W. Mathisen (Norway) Masahiro Oshima (Japan) Gerhard Schembecker (Germany) (Korea) En Sup Yoon
(USA) (USA) (China) (Japan) (China)
CPI Applications Sigurd Skogestad Robin Smith
(Norway, Chair) (UK, Vice Chair)
David Bogle Min-Sen Chiu Jian Chu Peter Glavic Xavier Joulia
Andrzej Kraslawski (Finland) Mantked Morari (Switzerland) Maurizio Rovaglio (Italy) (China) Wen-Teng Wu Tezi Mokhtar Zadeh (USA)
(UK) (Singapore) (China) (Slovenia) (France)
Nontraditional Applications Rafiqul Gani Hirokazu Nishitani
(Denmark, Chair) (Japan, Vice Chair) William Y. Svrcek (Canada) Venkat Venkatasubramanian (USA) Xigang Yuan (China) (USA) Frank Zhu
Xiao Feng (China) Costas Maranas (USA) Jack W. Ponton (UK) Jose Romagnoli (Australia) George Stephanopoulos (Japan)
Support Technology Rene Banares-Alcantara (Spain, Chair) Barbara S. Nyland (USA, Vice Chair) Conor M. McDonald (USA) Wolfgang Marquardt (Germany) Ming Rao (Canada) Jan van Schijndel (Netherlands)
Joan Cordiner (UK) Wolfgang Gutermuth (Germany) Sten B. Jorgensen (Denmark) (Japan) Chiaki Kuroda
PSE Education Geoffrey Moggridge Yu Qian Chuei-Tin Chang (China) (USA) Michael Henson Andrew N. Hryrnak (Canada) Milan Kubicek (Czech)
(USA, Chair) (China, Vice Chair) In Beum Lee Peter Lee Katsuaki Onogi
National Organizing Committee Honorary Chair: Cheng, Siwei, NFSC, Honorary chair
(Korea) (Australia) (Japan)
Chair: Wang, Jiming, SINOPEC Vice Chair: Yuan, Qingtang, SINOPEC Secretary: Wang, Lixing, SINOPEC, General secretary Committee members: Hu, Renan, Automation Research Institute of Metallurgy Meng, chunxu, PetroChina Gu, Jifa, System Engineering Society of China Ye, liaoyuan, Kunming Univ. Of Sci. & Tech. Feng, Quanli, Yunnan Univ. Guo, Jingbiao, Research Institute of Petroleum Processing, SINOPEC Han, Fangyu, Qingdao Institut. Chern. Tech. Hu, Shanying, Tsinghua Univ. Jin, Yihui, Tsinghua Univ. Sheng, Lizhong, Kunming Univ. Of Sci. & Tech. Sun, Hongwei, NSFC Yao, Pinjing, Dalian Univ. Of Tech. Wang, Hongshui, Baoshan Steel Co. Wang, Jianhong, Beijing Univ. Chem. Tech. Wang, Mei, Sichuan Univ. Zhao, Jianhua, SINOPEC Conference Secretariat Process Systems Engineering Institute Department of Chemical Engineering Beijing 100084, China E-mail:
[email protected] URL: http://www.chemeng.tsinghua.edu.cn
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Contents Plenary Papers Wang, J.M. SINOPECfsReform and IT Development Kawachi, S. Technological competitiveness in the chemical industry Keynote Papers Ydstie, B.E., Jiao, Y. The Distributed Enterprize Integrates Business, IT and Process Systems Engineering Grossmann, I.E. Challenges in the New Millennium: Product Discovery and Design, Enterprise and Supply Chain Optimization, Global Life Cycle Assessment Edgar, T.F. Control and Operations: When Does Controllability Equal Profitability? Ng, K.M. MOPSD: A Framework Linking Business Decision-Making to Product and Process Design Seider, W.D., Seader, J.D., Lewin, D.R. PSE and Business Decision-Making in the Chemical Engineering Curriculum Kim, S. Informatics in Pharmaceutical Research Hasebe, S. Design and operation of micro-chemical plants - Bridging the gap between nano, micro and macro technologies Marquardt, W., Nagl, M. Workjlow and Information Centered Support of Design Processes Cordiner, J.L. Challenges for the PSE community in formulations Floudas, C.A., Siirola, J.J. A Summary of PSE2003 and the Impact of Business Decision Making on the Future of PSE
Contributed Papers PSE & Business Decision Making Aoyarna, A., Naka, Y. Multi-Scale and Multi-Dimensional Formalism for Enterprise Modeling Arellano-Garcia, H., Martini, W., Wendt, M., Li, P., Wozny, G. An Evaluation Strategy for Optimal Operation of Batch Processes under Uncertainties by Chance Constrained Programming Balasubramanian, J., Grossmann, I.E. Scheduling Multistage Flowshops with Parallel Units - An Alternative Approach to Optimization under Uncertainty
Batres, R., Lu, M.L., Wang, X.Z. Concurrent Process Engineering and Intergrated Decision Making Bezzo, F., Macchietto, S., Pantelides, C.C. An Object-Oriented Approach to Hybrid CFDMultizonal Modelling Cheng, H.N., Qian, Y., Li, X.X. Integration of Decision Tasks in Chemical Operation Process Cheung, K.Y., Hui, C.W., Sakamoto, H., Hirata, K. Marginal Values Analysis for Chemical Industry Elsass, M., Saravanarajan, R., Davis, J.F., Mylaraswamy, D., Reising, D.V., Josephson, J. An Integrated Decision Support Framework for Managing and Interpreting Information in Process Diagnosis Ender, L., Filho, R.M. Neural Networks Applied to a Multivariable Nonlinear Control Strategies Espuiia, A., Rodrigues, M.T., Gimeno, L., Puigjaner, L. Decision Support System for multi-layered negotiated management in Supply Chain networks Ha, J.K., Lee, E.S., Yi, G.B. Optimal operation strategy and production planning of multi-purpose batch plants with batch distillation process Heller, M., Westfechtel, B. Dynamic Project and Workjlow Management for Design Processes in Chemical Engineering Hugo, A., Pistikopoulos, E.N. Environmentally Conscious Planning and Design of Supply Chain Networks Huss, R.S., Malone, M.F., Doherty, M.F., Alger, M.M. Challenge Problem Approach to Business Dynamics and Decision-Making for Process Engineers Jia, Z.Y., Ierapetritou, M. Incorporation of Flexibility in Scheduling Decision-Making Jiang, L., Biegler, L.T., Fox, V.G. Design and Optimization of Pressure Swing Adsorption Systems with Parallel Implementation Kim, M.J., Lee, Y.H., Han, LS., Han, C.H. Quality Improvement in the Chemical Process Industries using Six Sigma Technique Kim, K.H., Ahn, S.J., Shin, M.W., Yoon, E.S. Risk Analysis of Chemical Process Using Multi-distinction Equipment Screening Algorithm Koshijima, I., Shindo, A., Umeda, T. Process Integration Framework for Business Decision-Making in the Process Industry Lee,Y.H.,Yun,K.U.,Kim,M.J.,Han,C.H. Application of multivariate statistical process control to supervising NOx emissions from large-scale combustion systems Li, Z.H., Hua, B. Study of Heat Storage Setting & Its Release Timefor Batch Processes Li, W.K.,Hui,C.W. Debottlenecking and Retrofitting for a Refinery Using Marginal Cost Analysis, Sensitivity Analysis and Parametric Programming Maravelias, C.T., Grossmann, I.E.
A General Continuous State Task Network Formulation for Short Term Scheduling of Multipurpose Batch Plants with Due Dates Martinez-Miranda, J., Aldea, A., Baiiares-Alcintara, R. A tool to support the configuration of work teams Mujtaba, I.M., Greaves, M.A., Hussain, M.A. A Quick Eflcient Neural Network Based Business Decision Making Tool in Batch Reactive Distillation Musulin, E., Bagajewicz, M., NouguCs, J.M., Puigjaner, L. Design of sensor networks to optimize PCA monitoring performance PintariE Z.N., Kravanja, Z. An Approximate Novel Method for the Stochastic Optimization and MINLP Synthesis of Chemical Processes under Uncertainty Reddy, P.C.P., Karimi, LA., Srinivasan, R. Short-term Scheduling of Refinery Operations from Unloading Crudes to Distillation Rogers, M.J., Gupta, A., Maranas, C.D. Real Options Based Approaches to Decision Making Under Uncertainty Romero, J., Badell, M., Bagajewicz, M., Puigjaner, L. Risk management in integrated budgeting-scheduling models for the batch industry Shi, L., Huang, J., Shi, H.C., Qian, Y. Modeling Cleaner Production Promotion with Systems Dynamics Methodology: a Case Study of Process Industries in China Shimizu, Y., Yoo, J.K., Tanaka, Y. Web-based Application for Multi-Objective Optimization in Process Systems Siirola, J.D., Hauan, S., Westerberg, A.W. Computing Pareto Fronts Using Distributed Agents Skogestad, S. Self-optimizing control: From key performance indicators to control of biological systems Stec, L.Z., Bell, P.K., Borissova, A., Fairweather, M., Goltz, G.E., McKay, A., Wang, X.Z. Reconfigurable batch processes: innovative design of the engineering side of chemical supply chains Wang, Y.J., Zhang, H., He, Y.R. Refinery Scheduling of Crude Oil Considering Two Different Uses of Naphtha Yamashita, Y. Dimensionality Reduction in Computer-Aided Decision Making Yen, C.H., Wong, D.S.H., Jang, S.S. Information Directed Sampling and Ordinal Optimization for Combinatorial Material Synthesis and Library Design Yi,H.S.,Kim, J.H.,Han,C.H. Balanced production cost estimation of coke oven gas in iron and steel making plant Yi, G.B., Reklaitis, G.V. Optimal Design of Batch-Storage Network with Recycling Streams Zeng, M.G., Hua, B., Liu, J.P., Xie, X.A., Hui, C.W. Investment Decision-making for Optimal Retrofit of Utility Systems Zheng, X.S., Feng, X., Cao, D.L. Design water allocation network with minimum freshwater and energy consumption Zyngier, D., Marlin, T.E. Monitoring, diagnosing and improving the performance of LPbased real-time
optimization systems
Challenges in the NEW Millennium Atmaniou, L., Dietz, A., Azzaro-Pantel, C., Zarate, P., Pibouleau, L., Domenech, S., Lann, J.M.L. A MultiObjective Genetic Algorithm optimization framework for batch plant design Briesen, H., Marquardt, W. Adaptive Multigrid Solution Strategy for the Dynamic Simulation of Petroleum Mixture Processes: A Case Study Chan, P., Cheung, K.Y., Hui, C.W., Sakamoto, H., Hirata, K. Electricity Contract Optimization for a Large-Scale Chemical Production Site Chang, M.H., Park, Y.C., Lee, T.Y. Iterative Dynamic Programming of Optimal Control Problem Using a New Global Optimization Technique Chen, C.L., Wang, B.W., Lee, W.C., Huang, H.P. The optimal profit distribution problem for a supply chain network Chen, H., He, X.R., Chen, B.Z., Qiu, T. A multi-period optimization model for refinery inventory management under demand uncertainty Cheng, L.F., Subrahmanian, E., Westerberg, A.W. Multi-Objective Decision Processes under Uncertainty: Applications, Problem Formulations and Solutions Choi, J., Lee, J.H., Realff, M.J. Simulation Based Approach for Improving Heuristics in Stochastic ResourceConstrained Project Scheduling Problem Dave, D., Zhang, N. Hybrid Methods Using Genetic Algorithm Approach For Crude Distillation Unit Scheduling Eden, M.R., Jergensen, S.B., Gani, R., El-Halwagi, M.M. Reverse Problem Formulation Based Techniquesfor Process and Product Synthesis and Design Foltz, C., Luczak, H. Analyzing Chemical Process Design Using an Abstraction-Decomposition Space Gisnas, A., Srinivasan, B., Bonvin, D. Optimal Grade Transition for Polyethylene Reactors Guilltn, G., Bagajewicz, M., Sequeira, S.E., Tona, R., Espuiia, A., Puigjaner, L. Integrating pricing policies and risk management into scheduling of batch plants
Heo,S.K.,Son,H.R.,Lee,K.H.,Lee,H.K.,Lee,I.B.,Lee,E.S. Production and Distribution of Polyvinyl Chloride Considering Demands of Warehouses Herder, P.M., Stikkelman, R.M. Decision making in the methanol production chain-A screening tool for exploring alternative production chains Hua, B., Yuan, J.B., Hui, C.W. The Coevolutionary Supply Chain Hurme, M., Tuomaala, M., Turunen, I. General Approach to Connect Business and Engineering Decisions in Plant Design Kang, J.S., Kim, H.D., Lee, T.Y.
Sharing Benefits of Eco-Industrial Park by Multiobjective Material Flow Optimization Kulay, L., JimCnez, L., Castells, F., Baiiares-Alchtara, R., Silva, G.A. A case study on the integration of process simulation and life cycle inventory for a petrochemical process Li, P., Wendt, M., Wozny, G. Optimal Production Planning under Uncertain Market Conditions Li, X., Shao, Z.J., Qian, J.X. Complexity Analysis for Hybrid DifSerentiation in Process System Optimization Li, S.J., Wang, H., Yang, Y.R., Qian, F. Multi-objective programming in refinery planning optimization Li, X.X., Qian, Y., Huang, Q.M., Jiang, Y.B. Multi-scale ART2 Used in State Identification of Process Operation Systems Lim, Y.I., Jorgensen, S.B. Application of a time-space CE/SE (Conservation Element/Solution Element) method to the numerical solution of chromatographic separation processes Lin, B., Chavali, S., Camarda, K., Miller, D.C. Using Tabu Search to solve MZNLP Problems for PSE Lin, X.X., Chajakis, E.D., Floudas, C.A. Continuous-Time Scheduling of Tanker Lightering in Crude Oil Supply Chain Lin, P.H., Jang, S.S., Wong, D.S.H. Dynamical Supply Chains Analysis Via a Linear Discrete Model ?A Study of ztransform Modeling and Bullwhip Effects Simulation Liu,H., Yin,K.K.,Yin,G.G. Making Decisions under uncertainty - Applications of Hierarchical Approach in Industrial Practice Mitova, E., Glasser, D., Hildebrandt, D., Hausberger, B. Finding Candidatesfor Multidimensional Attainable Regions Nishi, T., Konishi, M. An Integrated Optimization of Production Scheduling and Logistics by a Distributed Decision Making: Application to an Aluminum Rolling Processing Line O'Grady, A.R.F., Bogle, I.D.L., Fraga, E.S. An adaptive interval algorithm to identify the globally optimal process structure Roe, B., Shah, N., Papageorgiou , L.G. A Hybrid CLP and MILP Approach to Batch Process Scheduling Sand, G., Engell, S. Risk Conscious Scheduling of Batch Processes Seodigeng, T., Hausberger, B., Hildebrandt, D., Glasser, D., Kauchali, S. DSR Algorithm for Construction of Attainable Region Structure Shi, L., Yang, Y.Q. Green process systems engineering: challenges and perspectives Shimizu, Y., Wada, T. Logistic Optimization for Site Location and Route Selection under Capacity Constraints Using Hybrid Tabu Search Shin, D., Lee, G., Yoon, E.S. Distributed Multi-Agents and Cooperative Problem Solving for On-Line Fault Diagnosis Sugiyama, H., Hirao, M. Development of Process Design Methodology Based on Life Cycle Assessment Wan, X.T., Orcun, S., Pekny, J.F., Reklaitis, G.V.
A Simulation Based Optimization Framework to Analyze and Investigate complex supply chains Wang, X.Z., Garcia-Flores, R., Hua, B., Lu, M.L. Advanced information systemsfor process control and supply chain integration Wang, K.F., Salhi, A., Fraga, E. Cluster analysis and Visualisation Enhanced Genetic Algorithm Wu,L.Y.,Hu,Y.D.,Xu,D.M.,Hua,B. Solving Batch Production Scheduling Using Genetic Algorithm
Xue,D.F.,Li,Y.R.,Shen,J.Z.,Hu,S.Y.
Synthesis of eco-industrial system considering environmental value using adaptive simulated annealing genetic algorithms Yamaba, H., Tomita, S. On an Object-Oriented Modeling of Supply Chain and its Operational Strategy Zhang, J., Chen, B.Z., He, X.R., Hu, S.Y. Variable Decomposition Based Global-OptimizationAlgorithm for Process Synthesis
Zheng,X.P.,Hu,S.Y.,Li,Y.R.,Shen,J.Z.
The Approach of Multi-factor Life Cycle Assessment and Product Structure Optimization Zhou, W., Jiang, Y.H., Jin, Y.H. Coordination of Multi-Factory Production Planning in the Process Industry Based on Intemal-Prices Zhou, Z.Y., Cheng, S.W., Hua, B. A Hierarchical Architecture of Modelling on Integrated Supply Chain Optimization Systems of Continuous Process Industries Zhou, X., He, X.R., Chen, B.Z., Qiu, T. Computer-aided molecular design with BP-ANN and global optimization algorithm
Chemical Process Industry Applications Almeida-Rivera, C.P., Swinkels, P.L.J., Grievink, J. Multilevel modelling of spatial structures in reactive distillation units Arrimadas, C., JimCnez, L., Bafiares-Alckntara, R., Caballero, J.A. Inclusion of quantitative safety evaluations in superstructure optimisation Aziz, N., Hussain, M.A., Mujtaba, I.M. Implementation of Neural Network Inverse-Model-Based Control (NN-IMBC)Strategy in Batch Reactors Balkema, A.J., Preisig, H.A., Otterpohl, R., Larnbert, A.J.D. Augmenting design with sustainability Berard, F., Azzaro-Pantel, C., Domenech, S., Pibouleau, L., Hoquet, P. Combinatorial study of multipurpose batch plant planning Bildea, C.S., Kiss, A.A., Dimian, A.C. Stable plantwide control of recycle systems Brueggemann, S., Marquardt, W. Rapid Screening of Regular and Thermally Coupled Design Alternativesfor Nonideal Multiproduct Distillation Processes Cavin, L., Fischer, U., Hungerbiihler, K. Identifying the optimal design of a single chemical process to be implemented in an existing multipurpose batch plant by the use of Tabu Search Chatzidoukas, C., Kiparissides, C., Perkins, J.D., Pistikopoulos, E.N.
Optimal grade transition campaign scheduling in a gas-phase polyolejin FBR using mixed integer dynamic optimization Chen, Q.S., Feng, X. Potential Environmental Impact (PEI)Analysis of Reaction Processes Chen, Q.L., Wang, S.P., Yin, Q.H., Zhu, X.F., Hua, B. Environmental-exergoeconomicStrategiesfor Modelling and Optirnization of Energy Systems Chien, I.L., Chao, H.Y., Teng, Y.P. Design and Control of a Complete Heterogeneous Azeotropic Distillation Column System Chin, J., Lee, J.W. Critical composition regions and the feasibility of extractive distillation Choi, S.H. Calculation of Probability Distributions of Output Variables in Process Simulation Cziner, K., Hurme, M. Process Evaluation and Synthesis by Analytic Hierarchy Process Combined with Genetic Optirnization Dadhe, K., Engell, S., Gesthuisen, R., Scharf, T., Volker, M. Multi-Model Trajectory Optimisationfor Batch and Semi-Batch Processes Du, H.B., Yao, P.J. Closed loop robust stability of mimo dynamic matrix control with input saturation Du, J., Yu, H.M.,Fan,X.S., Yao, P.J. Integration of Mass and Energy in Water Network Design Eggersmann, M., Schneider, R., Marquardt, W. Understanding the interrelations between synthesis and analysis during model based design Ekawati, E., Bahri, P.A. Variable Redundancy Elimination and Automatic Structure Selection within Dynamic Operability Framework El-Farra, N.H., Mhaskar, P., Christofides, P.D. Hybrid Control of Uncertain Process Systems Goel, H.D., Weijnen, M.P.C Process design for reliability and maintainability:A case of multipurpose process plants Hoffmaster, W.R., Hauan, S. Generating and evaluating reactive distillation column designs using difference points and feasible regions Holland, S.T., Tapp, M., Hildebrandt, D., Glasser, D., Hausberger, B. Novel Separation System Design Using "Moving Triangles" Hu, Y.D., Xu, D.M., Li, Y.M., Wei, Q.Y., Hua, B. Hybrid genetic algorithmfor the optimum design of complex distillation column based on exergy economics objectives Huang, H.P., Wu, M.C. Monitoring and Fault Detectionfor Dynamic Systems Using Dynamic PCA on Filtered data Jia,X.P.,Han,F.Y. A Hierarchical Multiobjective Cooperative Problem Solving for Process Systems Synthesis and Optimization Jin, S.Y., Zhou, C.G., Zhao, W., Yang, C.H.
xviii Detecting Changes of Process Steady States Using the Period Extending Strategy Jin, X.M., Rong, G., Wang, S.Q. On Applying Model Predictive Control for Distillation System of Linear Alkylbenzene (LAB) Complex Kheahwom, S., Hirao, M. A methodology for environmentally benign process design under uncertainty Kim,M.J.,Han,I.S.,Han,C.H. Modified PLS Method for Inferential Quality Control Kim, J.H.,Yi,H.S.,Han,C.H.,Park,C.H.,Kim, Y.J. Plant-wide Multiperiod Optimal Energy Resource Distribution and Byproduct Gas Holder Level Control in the Iron and Steel Making Process under Varying Energy Demands Kittisupakom, P., Moolasartsatom, O., Arpomwichanop, A., Kaewpradit, P. Optimal operation and control scheme design of pervaporative membrane reactor Kong, M.F., Chen, B.Z., He, X.R. Gross Error Identification for Dynamic System Kwon, S.P., Song, S.O., Cho, J., Yoon, E.S. On-line Monitoring of Process Transient States by the Intelligent Multivariable Filtering System based on the Rule-Based Method Lang, P., Modla, G., Kotai, B. Batch Heteroazeotropic Rectification under Continuous Entrainer Feeding: II. Rigorous Simulation Results Lee, J.H., Lee, J.M., Tosukhowong, T., Lu, J. On Interj5acing Model Predictive Controllers with a Real-Time Optimizer Lee, J.M., Yoo, C.K., Lee, I.B. Efficient Fault Detection using Multivariate Exponentially Weighted Moving Average and Independent Component Analysis Li, Y.G., Han, F.Y., Zheng, S.Q., Xiang, S.G., Tan, X.S. An Automatic Approach for Designing Water Utilization Network Li,Y.,Du, J.,Yao,P.J. Wastewater minimization through the combination of process integration techniques and multi-objective optimization Li, W.K., Hui, C.W., Hua, B., Tong, Z.X. Material and Energy Integration in a Petroleum Refinery Complex Li, H.W., Hansen, C.A., Gani, R., Jorgensen, S.B. Analysis of Optimal Operation of an Energy Integrated Distillation Plant Li, X.N., Rong, B.G., Lahdenpera, E., Kraslawski, A,, Nystrom, L. Conflict-based Approach for Multi-objective Synthesis Li, Z.M. Pinch Analysis of Hydrogen System in Refineries Liefeldt, A., Engell, S. A Modelling and Simulation Environment for Pipeless Plants Lin, S.W., Yu, C.C. Interaction between Design and Controlfor Heat-Integrated Recycle Plants: Ternary System with Two Recycles Maurya, M.R., Rengaswamy, R., Venkatasubramanian,V. Qualitative Trend Analysis of the Principal Components: Application to Fault Diagnosis Modla, G., Lang, P., Kotai, B., Molnar, K.
Batch Heteroazeotropic Rectification under Continuous Entrainer Feeding: I. Feasibility Studies Morais, E.R.D., Toledo, E.C.V.D., Filho, R.M. Mixed Coolant Flow For Optimal Design of Fixed Bed Catalytic Reactors MCndez, C.A., Cerda, J. Short-Term Scheduling of Multistage Batch Processes Subject to Limited Finite Resources Papaeconomou, I., Jorgensen, S.B., Gani, R., Cordiner, J.L. Integrated Synthesis, Design and Modelling of Batch Operations Qian, Y., Jiang, Y.Y., Wen, Y.Q., Li, X.X., Jiang, Y.B. Novel Hybrid Representation of Knowledge in the Expert System for Real-time Faults Diagnosis Rejowski, R.Jr., Pinto, J.M. Efficient MILP formulations for multiproduct pipeline scheduling Rudinger, A.C.A., Spogis, N., Nunhez, J.R. Optimizing mixing and power consumption through the use of computationalfluid dynamics (CFD) Saha, P., Cao, Y. Globally optimal control structure selection using Hankel singular value through Branch and Bound method Sakizlis, V., Perkins, J.D., Pistikopoulos, E.N. Parametric Controllers in Simultaneous Process and Control Design Sato, C., Ohtani, T., Nishitani, H. Steps toward Novel Grade Transition Control for Gas-Phase Polymerization Process Sawaya, N.W., Grossmann, I.E. A cutting plane method for solving linear generalized disjunctive programming problems Smania, P., Pinto, J.M. Mixed Integer Nonlinear Programming Techniquesfor the Short Term Scheduling of Oil Refineries Tang, Y.T., Huang, H.P., Chien, I.L. Design of a Complete Ethyl Acetate Reactive Distillation Column System Tapp, M., Holland, S.T., Hausberger, B., Hildebrandt, D., Glasser, D. Expanding the Operating Leaves in Distillation Column Sections by Distributed Feed Addition and Sidestream Withdrawal Torgashov, A.Y., Park, K.C., Choi, H.C., Choe, Y.K. Stability analysis of distillation control using vector Lyapunov function Wang, F.Y., Bahri, P.A., Lee, P.L., Cameron, I.T. A Multiple Model Approach to Robust Control and Operation of Complex Non-linear Processes Wang, G.B., Liang, J.J., Own, S.M., Lee, H.Y. Robust Tuning of PI Controllers Based on Taguchi Method Wu,J.Y.,He,X.R.,Chen,B.Z.,Qiu,T. A General MILP Model for Scheduling of Batch Process with Sequence-dependent Changeover Xu, D.M., Hu, Y.D., Hua, B., Wang, X.L. Optimum design of water utilize systems featuring regeneration re-usefor multiple contaminants Xu,Q.,Chen,B.Z.,He,X.R.
Optimizationfor Semi-continuous Dynamic Process under Uncertainties Yamamoto, S., Yoneda, M., Yabuki, Y. The control of heterogeneous azeotropic distillation in industry considering entreiner ratio in reflux Yamashita, Y., Kidane, N., Nishitani, H. Observer design for nonlinear systems described by dzrerential-algebraic equations Yan, L.X., Wei, D.G., Ma, D.X. Line-up Competition Algorithm for Separation Sequence Synthesis Yao, Z.X., Qian, Y., Jiang, Y.B. A Description of Chemical Processes Based on State Space Yu, W.F., Hariprasad,, J.S., Zhang, Z.Y., Hidajat, J., Ray, A.K. Application of multi-objective optimization in the design of SMB in chemical process industry Yuan, X.G., An, W.Z. Synthesis of fully thermally coupled distillation columns for multicomponent separation via stochastic optimization Zhang, J. Reliable Optimal Control of a Batch Polymerisation Reactor Based on Neural Network Model with Model Prediction Confidence Bounds Zhao, W., Zhou, C.G., Jin, S.Y., Zhang, L., Han, F.Y. Studies of RSR system characteristic curves Zhu, P., Feng, X., Liu, Y.Z. Exergy-based environmental impact analysis in the industrial processes Zhuang, H.L., Ohshima, M., Chiu, M.S. A Multiple Models Based Predictive Control Strategy Applied In Polymerization Reactor Control Nontraditional Applications Aoyama, A., Naka, Y. Technological Information Infrastructure for R&D Management in Product Centered Manufacturing Chang, W., Lee, T.Y. Observation of Adsorption and Permeation Phenomena in Silica Membrane System through Molecular Dynamics Simulation Chen, H.P., Shao, M.J., Zhang, S.J. Modeling and prediction of crystal growth for vitamin C Dai, Z.Y., Jing, Z.H., Zhou, H., Liu, W. QSAR Studies of Cl-Bridged Flu-Cp Complexes of Zirconium Metallocene Eden, M.R., Jorgensen, S.B., Gani, R., El-Halwagi, M.M. Property Cluster based Visual Technique for Synthesis and Design of Formulations Karimi, I.A., Tan, Z.Y.L., Bhushan, S. An Improved MILP Formulation for Scheduling an Automated Wet-etch Station Kim, B.M., Kim, S.W., Yang, D.R. Cybernetic Modeling of the Cephalosporin C Fermentation Process Li, M.H., Shi, D., Christofides, P.D. Feedback control of HVOF thermal spray process: A study of the effect of process disturbances on closed-loop pe~ormance Lou, Y.M., Christofides, P.D.
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Real-Time Estimation of Growth Rate and Surjace Roughness in Thin Film Growth Using Kinetic Monte-Carlo Models Pinto, J.F., Filho, R.M. Computational prediction of the solid phase extraction of herbicides in water Thaysen, M., Jorgensen, S.B. Process SofnYare Sensor for Plant Optimization Vally, T., Hildebrandt, D., Rubin, D., Crowther, N., Glasser, D. Application of Process Synthesis Methodology to Biomedical Engineering for the Development of Artificial Organs Wang, Y.H., Huang, D.X., Gao, D.J., Jin, Y.H. Wavelet Networks Based Soft Sensors and Predictive Control in Fermentation Process Wang, Y., Sun, Z.C., Wang, A.J., Li, C., Li, X., Zhao, B., Yao, P.J. A Kinetic Study on Hydrodesulfurization of Dibenzothiophene Catalyzed by Sulfided Ni-Mo/MCM-41 Wu, W., Huang, M.Y. Flexible Output Regulation Designs for Nonlinear Bioreactors Zhang, X.P., Zhang, S.J., Li, C.S. Simulation of protein crystallization on the basis of thermodynamic model Zhou, H., Da, Z.J., Xu, W., Zhang, L.W., Mu, X.H. Applications of Molecular Simulation Techniques in Petroleum Processing Support Technology
Abbas, A., Romagnoli, J. A modelling environmentfor the advanced operation of crystallisation processes Bayer, B., Becker, S., Nagl, M. Integration Toolsfor Supporting Incremental Modifications within Design Processes in Chemical Engineering Bafiares-Alcintara, R., Kokossis, A., Aldea, A., JimCnez, L., Linke, P. A knowledge management platform to extract and process information from the web Du, W.L., Liu, Z.M., Qian, F., Liu, M.D., Zhang, K. 4-CBA Online Soft-measurement Via Fuzzy GMDH Networks Han,F.Y., Jia,X.P.,Tan,X.S. Two Key Support Toolsfor Environmentally Friendly Process Optimal Synthesis Jin, Y.H., Kurooka, T., Yamashita, Y., Nishitani, H. Modeling and Simulation of Human Errors in Plant Operations Kim, J.H., Yi, H., Han, C.H., Park, C., Kim, Y. The Development of the Real Time Plant-wide Optimal Byproduct Gas Supply Management System Kristensen, N.R., Madsen, H., Jorgensen, S.B. A Unified Framework for Systematic Model Improvement Lahdenpera, E., Li, X.N. A Cluster Computing Approach Using Parallel Simulated Annealing for Multiobjective Process Optimisation Li, W.M.,Li, W.K.,Hui,C.W. Integrating Neural Network Models for Refinery Planning Li,S.J., Yang,Y.R.,Qian,F. Mass Exchanger Network Synthesis Using Genetic-Alopex Algorithms Lu, M.L., Moore, E.
A Flexible Topology Management Software Component To Support Business Process Integration Lu, M.L. A Model Architecture For Concurrent Process Engineering Ngigi, G., Glasser, D., Hildebrandt, D. MaPS (Managed Process Synthesis). A methodology, integrated with the experimental programme, to develop aflow sheet. A first step Park, Y.C., Chang, M.H. , Lee, T.Y. A Novel Global Optimization Algorithm Using D.C Envelope and Convex Cut Function Reis, M.P.S.D., Saraiva, P.M. Multiscale Latent Variable Analysis of Industrial Data Romanenko, A., Oliveira, N.M.C., Santos, L.O., Afonso, P.A.F.N.A. A system for chemical process control and supervision based on real-time Linux Rong, B.G., Kraslawski, A., Turunen, I. Synthesis of heat-integrated thermally coupled column configurations for multicomponent distillations Shi, L., Chen, P., Shi, H.C., Qian, Y. Supporting cleaner production audit with systems approaches: a case study of PVC process Su,D.P.,Chen,W.M.,Feng, J.S., Wang,H.S.,Su,Y.C. Data Mining in Metallurgical Industry Process Sun,L.,Fan,X.S.,Yao,P.J. Study on Multi-Objective Fuzzy Optimization Algorithm for Chemical Process Tsai, C.S., Wang, S.Y., Chang, C.T., Huang, S.H. Multi-variate Run Rules Wen, J., Rao, M. Human, Machine and Management Integration through In-time Knowledge Processing to Ensure Process Operation Safety Wibowo, C., O'Young, L., Ng, K.M. Workjlow Management in Chemical Process Development Yan, L.X., Hu, S.H., Ma, D.X. Visual Method for Operating Optimization of Process System Yang, S.H., Chen, X., Yang, L.L. Global Support to Process Plants over the Internet Yang, L., Hu, S.Y., Shen, J.Z., Li, Y.R., Wu, Z.J., Wang, T. Development of a Management Information System for an Eco-industrial Park Yi, H.S., Kim, J.H., Han, C.H. Optimal Grade Transition of a HDPE Plant by the Modijied Two-Step Hierarchical Dynamic Optimization Yin, Q.H., Yin, Q.Y., Deng, T., Qian, Y., Hua, B., Chen, Q.L. Metasynthesis Methodology of Technology and Management for New Product Development Project of Petrochemical Enterprises Zhang, Q.H., Qian, Y., Xu, B.G. A Simulating System for Intelligent Control of Electric Drives Zhang, L., He, X.R. Extended Association Rule Extraction from Process Operational Data Zhao, C.H., Bhushan, M., Venkatasubramanian, V. A Multi-Agent Architecture for Automated Process Safety Analysis Zheng,S.Q.,Yue, J.C.,Li,Y.G.,Tan,X.S.,Han,F.Y.
Study on Multiobjective Process Synthesis in Modular Simulator Environment Zou, Z.Y., Cao, B.Y., Gui, X.J., Chao, C.G. The Development of A Novel Type Chemical Process Operator-Training Simulator
PSE Education Hausmanns, C., Zerry, R., Goers, B., Urbas, L., Gauss, B., Wozny, G. Multimedia-supported teaching of process system dynamics using an ontology-based semantic network Moggridge, G.D., Cussler, E.L. Teaching Chemical Product Design Orapimpan, O., Kurooka, T., Yarnashita, Y., Nishitani, H. Support Systems for Knowledge Inheritancefrom Video Images of Plant Operations Rao, M., Wen, J., Zhang, Y. Incident Prevention Training Simulator Srinivasan, R., Doiron, J.A.G., Song, M. Enhancing Process Control Education using a Web-based Interactive Multimedia Environment Author Index
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Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
SINOPEC's Reform and IT Development Wang Jiming China Petroleum and Chemical Corp., Beijing, China Abstract The theme of the 8 th Intemational Conference of Process Systems Engineering is to provide the process industry with PSE for sound and professional decision-making. With the fast development of information technology, the global economic, industrial and corporate structures are being remade, and enterprise business model and operation are also changing significantly. The vast application of information technology has been playing an important role in such changes and reform. After the early irrationality, a lesson has been learnt that only combined with the conventional industries can information technology show its vitality. IT development, as the Chinese government points out, is a necessity in China's industrialization and modernization. Recognizing this, China Petroleum & Chemical Corporation upgrades its businesses with information technology to meet challenges of globalization and WTO accession. 1. SINOPEC'S PRESENT SITUATION AND OPERATION PERFORMANCE Petroleum and petrochemical industry, as the energy and raw material provider, is the backbone of China's economy. In 1998, the government made the decision to strategically restructure petroleum and petrochemical industry. In July 1998, China Petrochemical Corporation (SINOPEC Group) was founded, a vertically integrated company with upstream and downstream businesses and domestic and foreign trade. In 1999, further restructuring of businesses, assets, finance, organization and personnel was carried out. On Feb. 22, 2000, China Petrochemical Corporation set up China Petroleum & Chemical Corporation (SINOPEC Corp) with its quality assets. SINOPEC Corp. was publicly listed in Hong Kong, New York and London in October 2000 and in Shanghai in August 2001, becoming the first Chinese company going public in four stock exchanges home and abroad. Through the public listing, SINOPEC Corp. not only raised capital, diversified its equity structure and increased company competitiveness, but also pushed itself towards more standard management and supervision and promoted mechanism reform within the company. A milestone in the history of China's petroleum and petrochemical industry, SINOPEC's public listing paved the way for the company's speedy take-off and the company embraced a new era of development. Over the past three years, SINOPEC Corp. has basically set up a corporate governance structure of unified rights and responsibilities, smooth operation and effective supervision. The company defined the functions of the headquarter, the strategic business units and the
subsidiaries as decision-making centre, profit generating centre and cost control centre, respectively. In this way, unified management over operation strategy, financing and investment, capital operation, major research work and information system has been achieved. Faced with the fierce competition and the pressure from the stock market and investors, SINOPEC has actively promoted resources strategy, marketing strategy, investment strategy, technology and human resources strategy in the past three years. As a result, the company's overall competitiveness and operation performance have been improved. Stable growth of economic volume. The output of oil and gas has kept growing steadily. The total newly added proven reserves of oil in the past three years was 585 million tons, and that of natural gas was 212.2 billion cubic metres. The output of crude and natural gas of the three years was 110.785 million tons and 11.95 billion cubic metres, respectively. To compare with 1999, the output of crude and natural gas increased by 10% and 127%, respectively in 2002, and the reserves was up by 14.3%. In 2002, the company processed 104.94 million tons of crude, an increase of 19.1% on 1999. The production of refined products was 62.42 million tons, an increase of 19.1% and that of chemical light was 15.05 million tons, up by 36.1%. By tech upgrading and product line readjustment, chemicals production also saw remarkable growth. The ethylene production of 2002 was 2.72 million tons, up by 32.6%. The production of synthetic resin, synthetic rubber and synthetic fibre also increased tremendously. Increase of market share and competitiveness. Through acquisition, building and rebuilding service stations and storage and transport facilities, a complete marketing network has been set up and refined products sales volume grows year on year, especially for the retail, direct sales and export. The sales volume of refined products in 1999 was 63.49 million tons. The number jumped to 70.09 million in 2002. Retailed refined products reached 34.73 million tons, up by 181.4% over 1999. The company's retail market share in its principal market reached 68%, up by 28 percentage points. To explore overseas market, the company exported 5.02 million tons of refined products in 2002, increased by 34.6%. Fruitful technological achievement. SINOPEC always attaches great importance to technology innovation, which provides strong back-up for economic efficiency and industry upgrading. There have been 20 major technology break-throughs in the three years, among which Loop Reactor Polypropylene Technology, VRFCC Technologies (vacuum residue fluid catalytic cracking) and RHT (residue hydrotreating technology) won the first prize of the national technology advancement award. Three other technologies won the second prize of the national invention award and another 13 won the second prize of the national technology advancement award. The company made 1,830 patent applications, and was granted 709 patent rights, adding its total patent rights to 3,610. Continuous cost reduction and rising profitability. Persisting in maximizing the company's profit and shareholders' return is SINOPEC's business goal. Therefore, the company has been reducing cost unswervingly. In 2001, the company reduced cost by RMB 2.28 billion, and further reduced RMB 2.5 billion of cost in 2002. Although competition grew fiercer and fiercer, the company achieved a relatively good operation result in the three years. Profit of 2002 was RMB 25.9 billion and that of 2001 was RMB 21.6 billion. The company's 2002 revenue was RMB 312 billion with a profit of RMB 21.84 billion. Over the past three
years, the company's total assets increased from RMB 276.9 billion to RMB 375.0 billion, debt-to-equity ratio reduced from 53.35% to 28.2%, and shareholders' equity increased from RMB 87.12 billion to RMB 148.5 billion. These achievements laid a solid foundation for the company's future growth. 2. SPEED UP IT COMPETITIVENESS
DEVELOPMENT
AND
IMPROVE
THE
COMPANY'S
The above achievements would not be obtained without information technology. Since the beginning, SINOPEC Corp. has set up the strategy of upgrading its business with information technology and has been committed to promoting IT construction. During the IT construction process, we focus on an all-round development, that is, along with the IT construction, management, technology, economic efficiency and marketing ability should also be promoted. We have set up database, performance evaluation system and cost control system. At the same time, we optimized crude procurement, production process and improved management in financial information, sales information and e-business for materials and chemicals purchase. We also implemented human resources management, oil field information gathering and other economic and technological indices gathering. These efforts played a vital role in improving the company's competitiveness. In the past three years, we have made achievements in IT construction in the following five aspects. 2.1. The implementation of ERP system fastened business process reengineering Since its founding in 2000, SINOPEC Corp. has set up an ERP programme. The company aims to build up a basic framework in three years and complete the project in five years. After the completion, further expansion and perfection of functions should be carried out according to market changes. The company achieved substantial results in its production and sales subsidiaries during the ERP pilot and promotion stage. Under the guiding principle of 'international standard combining with SINOPEC characteristics', the successful implementation of ERP at subsidiaries in Zhenhai, Yizheng, Tianjin, etc. integrated finance, materials, sales and planning, optimized business process, integrated materials flow, capital flow and information flow, reduced management layers and increased the subsidiaries' decision-making power. It also helped in reducing inventory, strengthening budgeting, and improving the company's internal control and responsiveness. Through these pilots, the company advanced its management concepts and internal reform, which is conducive to the full promotion of the ERP system. 2.2. The implementation of supply chain technology optimized resources utilization SINOPEC has a strong demand for the supply chain technology. The company's 33 refineries process more than 60 types of crude and the import of crude in 2002 accounted for 54% percent of the company's total annual throughput. The sales of refined products also cover a
wide area of 20 provinces through vessels, pipeline, train and other means of transportation. In crude processing, we set up production models with PIMS software in the 33 refineries and an integration model at the headquarter. This measure provides valuable information when making import plan or production plans, and proved effective in allocating resources. In sales, we formed a refined products sales information network, collecting price and inventory information in more than 20,000 service stations. We also set up storage-to-stations models for optimizing products transport and distribution, reducing transportation cost effectively.
2.3. The implementation of e-business elevated the company's management level SINOPEC's annual materials procurement is about RMB 50 billion and its annual sales of petrochemical products is close to RMB 100 billion. To standardize the procurement procedure, enlarge the scope of suppliers and strengthen supplier management, the company started a materials procurement e-business system. A petrochemical products sales e-business system was also started for better transparency of the prices, better understanding of the customer's needs and increased direct sales proportion. In August 2000, the chemical products sales e-business web site opened to the public. Product information is publicized and viewers can make inquiries on line. The web site provides a platform for collecting orders for products and feedback from customers. Currently, there are more than 700 customer subscribers and 35 manufacturer subscribers. More than 1,000 kinds of products are available on line. By the end of 2002, online transaction volume had reached 5.69 million tons, with a revenue of RMB 33.34 billion. On our B2B online material procurement web site, demand for certain materials and guiding prices are publicized, so are inquiries and bidding announcements. Contract negotiation and contract drafting can also be done on line. So far, materials procured online have extended to 56 categories and 116, 000 kinds or so. Online suppliers now total 2,500 and there are 6,100 subscribers. By the end of 2002, cumulative procurement volume had amounted to RMB 20.8 billion. With the business process standardized and optimized, more materials are procured online, which reduced procurement expenses by RMB 520 million. 2.4. The implementation of advanced control technology improved profitability Improving SINOPEC's major refining and chemical plants control is an effective way to enhance operation performance. We formed strategic alliance and partnership with automation and software suppliers like Honeywell and AspenTech. We set safety, stability, longer run length, full workload and optimization as our goals and utilized advanced technology to resolve problems such as high energy consumption, low yield and poor efficiency. With the new technologies, we achieved stable production, higher product quality, longer run length in distillation units, FCC and polyolefins units. In production operation, we are able to optimize the product line distribution and increase target products volume. We also use process control and optimization technology to work out the immeasurable variables and take them into consideration during production. We utilized these technologies on over 60 units and realized stable output. These technologies require low investment, but provide high return. In this way, we were able to reclaim our project investment within one year.
2.5. The infrastructure construction backed up the IT application To standardize the information code is an important task for the company in building its information system. Unified definition of data sources and a standardized code system are necessary for the company's integrated information system. The company set up an internal code system which employs some 130, 000 pieces of code for 108 kinds of articles in the categories of work unit, product, fixed asset, material, finance and personnel. Each code is unique. As for the network construction, we have set up the IT infrastructure within and between the headquarters and all our subsidiaries. The main computer network links the intranet in the headquarters and subsidiaries as well as internet. We have our own satellite system covering all SINOPEC subsidiaries. Firewalls and other anti-hacking software were also installed which safeguarded safety and reliability.
3. DEVELOPMENT DEVELOPMENT
GOALS
AND FUTURE
PLANNING
OF
SINOPEC'S
IT
According to the blue print of building a well-off society in an all-round way, China will continue to see a sustainable and fast economic growth. Given this, domestic demand for crude, natural gas, refined products and chemicals will increase by a large margin. This will provide SINOPEC with a valuable opportunity for development. The company will definitely seize the opportunity to enhance the company's profitability and international competitiveness. It will focus on resources, investment, marketing, technology and human resources to further restructure the company's operation and management. Reform and restructuring, technology innovation, scientific management and sound financial operation will serve together to improve the company's competence. The development goal for SINOPEC is to become an internationally competitive, world-class, integrated energy company with prominent core business, quality assets, diversified equity structure, innovative technologies, advanced management and prudent financial practice by 2010. With another ten years, we wish to see a much more competitive company with strong market exploration ability, technology innovation ability and profit making ability. In SINOPEC's IT development, the company aims to leverage the petrochemical industry with information technology. The company compiled SINOPEC's Technology Development Plan and SINOPEC's ERP System Plan, making sure that the information system develops in a fast and all-round way. In the years to come, SINOPEC will try its best to make breakthrough in upgrading technology, enhance responsiveness to the market changes, improve internal control and increase efficiency. The detailed objectives are to integrate streams of materials and goods, information and cash flow, to set up the three-centre management system (with the headquarter as the decision making centre, the strategic business units as the profit making centre, and the subsidiaries as the cost control centre), to execute performance evaluation system and cost assessment system and to fully carry out integrated supply chain management.
The company will also build a unified information technology platform for the subsidiaries to share the real-time production and operation information. During the 'tenth five-year-plan' period, the major tasks for the company are as follows. The company will utilize advanced information technology in resources allocation, exploration and development and production to a wider extent. Such technology will also be used in marketing and procurement, enabling the company to be more adjustable to market. In addition, the company will build an integrated ERP operation management platform and Manufacturing Execution System (MES) platform. Focusing on the company's resources strategy, marketing strategy, investment strategy and technology and human resources strategy, relevant research work should be done on corporate strategy management, asset management, risk management, technology management, human resources management and market predicting, which would form the supporting system for decision making. SINOPEC's information system mainly consists of ERP, MES and PCS (Process Control System). On the management level, apart from the conventional ERP, we have database technology and have set up performance evaluation system and cost control system. Meanwhile, ERP further integrates with e-business system and supply chain system. IT construction is also developing fast at the strategic business units and subsidiaries. With the special systems, streams of goods, capital and information are optimized and resources are properly allocated. Oil field information integration and customer relation management are applied. Decision-making is now based on more of data rather than experience. At the subsidiaries, system integration is carried out in the aim of reducing cost and improving product quality. Cost assessment is disciplined and production process is advanced. However, due to mechanisms, personnel and technology, SINOPEC is left behind by international petrochemical majors in the application of information technology. The company is yet to form a complete database and the system integration level needs improving. Therefore, we would like to learn from other companies and share experiences. 4. CONCLUSION The 21st century posts opportunities and challenges for SINOPEC. On the one hand, we are faced with much fiercer competition from international companies after the WTO accession. In addition, resources and environment are putting more pressure on us. On the other hand, we are given the opportunity to cooperate with international companies, to learn their management concepts and models, and to upgrade the company's competitiveness. In this respect, SINOPEC will adopt new development concepts in IT construction and constantly leverage petroleum and petrochemical industry through information technology. We believe all this will help build SINOPEC into a world-class integrated energy and chemical company in an early stage.
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
Technological competitiveness in the chemical industry
Satoshi Kawachi
Senior Managing Director, Sumitomo Chemical Company, Limited, Tokyo, Japan
Abstract This is to describe several issues of business environments surrounding the
chemical industry and suggest ways to reinforce competitiveness for winning race in the industry, in particular, various approaches or use of technologies in R&D, including actual experiences in novel process developments. Also explained are the necessity of responsible care activity to ensure environment, safety and health and its technology buildup which in turn leads to further reinforcement of competitiveness together with deepening of technology in specific discipline and fusion of technologies between different disciplines. 1. INTRODUCTION In year 2001, the Japanese chemical industry shipped 24.4 trillion yen of products, which accounted for about 4.8% of Japan's GDP, and its employees were over 370,000, while the value of shipment of the American chemical industry in the same year was 427 billion dollars, about 4.6% of GDP of the U.S. Namely, relative importance of the chemical industry in respective national economies was almost the same. When we see the sales of chemical companies in the world, the largest is that of BASF (30.8 billion dollars), and the second largest is that of DuPont (28.4 billion dollars). Among Japanese chemical companies, Sumitomo Chemical ranks Number 12 in the world (9.4 billion dollars), Mitsubishi Chemical Number 13 (9 billion dollars), and Mitsui Chemical Number 15 (8.7 billion dollars). Sumitomo and Mitsui have a plan to merge together, and when these two are consolidated, the new company will become Number 6 chemical company in the world. Recently alignment of chemical industries is progressing by concentrating business resources on comparatively advantageous fields respectively, and as a result, many companies have changed into those which specialize in specific fields such as petrochemicals, basic chemicals, specialty chemicals or life science chemicals.
On the other hand, Sumitomo Chemical has chosen the way of diversified chemical industry where I believe there are certain advantages. It has a portfolio which makes it easier to adjust itself to a change of business environment. Its technologies are commonly usable among various disciplines within the company. It sometimes brings about a new and unexpected discovery through fusion of knowledge between different disciplines. It is my belief that diversified industry is particularly advantageous in creating new technologies and developing human resources. 2. ROLES OF THE CHEMICAL INDUSTRY One of the characteristics of chemical industry is that they are immensely rich in varieties; pharmaceuticals, agricultural chemicals, plastics, etc. These are the products which are manufactured and processed down to the finished products by the chemical industry, while there are other types of chemicals which are used by other industries as raw materials for manufacturing their final goods. When we see chemical industry's customers, chemical industry itself is the largest in sales, and others are agriculture, food, paper, printing, metal, machinery, electronics, automobile and many other industries. I would say almost all kinds of industries are our customers. Talking about finished goods, clothing, cosmetics, pharmaceuticals, home electric appliances, automobiles and many other daily commodities are the finished goods for which chemical materials are used. Let me take a liquid crystal television as an example, chemicals are used in various parts; body, film, liquid crystal, and IC encapsulation. Recently technological innovation has taken place in display devices through inventions of new materials such as liquid crystal and organic LED. I do not think it is exaggeration to say that new functional materials or high performance products invented by the chemical industry are giving great impetus to other industries which are also eagerly working on new product development. The driving force to create high functional or high performance materials is nothing but R&D, and depending on R&D strategy, a great difference will appear in the company's future business. In order for us to win the race in new product development, we must satisfy the following three conditions. (1): to discover chemicals or materials which demonstrate such functions as are exactly required.
(2): to discover manufacturing process which makes it possible to supply a newly invented product stably at a reasonable price. (3)" to put it on the market before our competitors do. 3. R&D ORGANIZATION In launching a new product, design of product functions and discovery of its manufacturing process are inseparable. If either of them is missing, it is impossible to launch any new product competitively in the market. In Sumitomo Chemical, in order to feed back quickly requirements of the market to R&D for new product development, R&D groups are organized within business sectors, by which R&D are directly connected to marketing endeavors. With respect to required manufacturing process, if it is just a matter of improvement of current technology, we handle it at the sector laboratory simultaneously with development of new product, but if entirely new process is looked for, we usually study it at a corporate laboratory. When I say " corporate laboratories", in our case, they are Organic Synthesis Research Laboratory where they are engaged in discovery of new catalysts and new synthesis methods, Process & Production Technology Center which develops manufacturing processes and Environmental Health Science Laboratory where they assess product safety and its possible impact on human and environment. Again, speaking of manufacturing process, there are many fundamental technologies which commonly apply to various fields of products, and, therefore, we think it is meaningful to work on process development and build up company's technology base at a corporate level laboratory like our Process & Production Technology Center. Corporate level research is of course not limited to process development, but is expected to create entirely new business opportunities, as its major mission, through their inventions. Thus, in our case, new business creation and common technology development are done by corporate research laboratories while product development catering to customers' needs is done by division research laboratories, and this method is working well. 4. R & D
Combinatorial synthesis and high through put screening (HTS) method have been invented to accelerate discovery of new functional materials. Sumitomo Chemical has introduced an HTS unit for the purpose of speeding up development of new synthesis catalysts. In the field of pharmaceuticals, discovery of new drugs by genomics has started. Sumitomo also has
10 positively capitalized on this innovative approach, judging that employment of such a new method is indispensable for new drug discoveries. Even when a new functional material is invented, if it is not accompanied by manufacturing process which makes it possible to supply the product at an reasonable price, business is not feasible practically. In a reverse way, novel process sometimes brings new products. We have an experience in polyolefin case where new process together with novel catalyst brought about creation of new materials. It may well be said that R&D of manufacturing process or technology is one of the key elements for winning competitions. We are accelerating process development with simultaneous cooperation between manufacturing group and research group by utilizing process systems engineering techniques (modeling, simulation, optimization, etc.). Studies on existing processes are also important. Since product life of basic chemicals is generally long, its process improvement brings us substantial economic benefits, and it also gives great impact on the environment. Response to fluctuating component ratios of raw materials or products as a result of change in market conditions is another challenging job. Sumitomo has research programs in this particular area; for example, we have developed an innovative process to manufacture caprolactam which does not produce any by-products. A new process to solely produce propylene oxide is another example, whereas a conventional method is simultaneous production of propylene oxide and styrene. The third example is our newly developed technology to oxidize hydrochloric acid which is by-produced from process using chlorine and to recycle it back to chlorine, reducing the load to the environment substantially. 5. RESPONSIBLE CARE It is a responsibility of the manufacturing group to manufacture the products without fault, which is certainly one of the fundamentals for winning the industrial competition. In addition, responsible care (RC) is another fundamental element for doing business in the chemical industry. RC is a voluntary activity to earn the trust from the society by ensuring "environment, safety and health" regarding company's products throughout their entire life cycles and keeping communications with the society. It is classified into such activities as "maintenance of environment", "security & prevention of disaster", "occupational safety" and "product safety". In Sumitomo Chemical, one more activity is added, that is "quality assurance". This way of thinking is applied from the beginning of work for development, and our
11 assessment system is working up to manufacturing under the company's development and industrialization rule. Among assessment items, those which are related to product safety and impact on environment are handled by the Environmental Health Science Laboratory. If their assessment result is negative about any newly developed product, we may give up launching such product. Within our Process & Production Technology Center, we have organized a group for studying safety engineering, who study security & prevention of disaster and feed back to the process group new knowledge if they find any problems so that they can improve manufacturing process accordingly. Through such self assessment of our own products and processes, we are able to build up technologies to find problems and solve them, which leads to further development of our R&D. 6. DEEPENING OF T E C H N O L O G Y IN SPECIFIC DISCIPLINE AND T E C H N O L O G Y FUSION B E T W E E N DIFFERENT DISCIPLINES
Sumitomo Chemical has research programs not only for polymerization catalysts of polyethylene but also synthesis of pharmaceuticals. These two may appear to be different from each other, but in reality they are built on a common technology base called "metal complex catalyst". Likewise, knowledge about ligand and central metal which are key elements of metallocene catalysts are shared by fine chemicals specialists, and actually scientists of both disciplines are jointly carrying out a research for post metallocene catalysts. This is one of the areas where fusion of technology between different disciplines is expected to bear a fruit. One more similar example is a joint work between heterogeneous catalyst researchers and organic synthesis researchers of our company, through which they arrived at an idea of modification of the surface of catalyst by organic synthesis method, and as a result, their study made a remarkable progress. There is no question as to the importance of building up technology base of specific discipline in R&D and technology accumulation in RC for reinforcing competitiveness. In addition, technology fusion between different disciplines is also useful in creating new business for the company's future. 7. C O N C L U S I O N In the above, Sumitomo Chemical's case for approaches to reinforcement of competitiveness in the chemical industry was mainly described. As a matter of fact, decision making in R&D is quite complicated and extremely difficult. Sumitomo Chemical is committed to continued endeavors for new process developments by fully utilizing the company's long fostered technology and know-how.
12
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
The Distributed Enterprize Integrates Business, IT and Process Systems Engineering B. Erik Ydstie*, Yu Jiao t Carnegie Mellon Department of Chemical Engineering Pittsburgh PA 15213 A b s t r a c t : In this paper we discuss the convergence of business systems like TQM and 6sigma, Information Technology and Process Systems Engineering. We propose that the proper framework advancing the conflux of these ideas is provided by distributed network computing. We furthermore suggest that the integrated systems of software, hardware and physical devices should be viewed as a complex adaptive system which is ever changing in non-predicable ways. The prospect of embedding such a system into a rigid, hierarchical and centralized computational structure is therefore dim. In the paper we discuss very briefly the emergence of a theory for complex adaptive systems. We outline the basic tenets of business finance and emphasize the importance of using value analysis for strategic decision making. We motivate a network model for the value chain and relate this to an electrical circuit description. This description allows us to draw conclusions about stability and optimality which are not pursued further in this paper. We also describe very briefly the basis for evaluating business performance, process and product development and risk. These activities are supported by case studies. We finish by reviewing very briefly how the drive towards excellence can make it easier for universities and industry to carry out cooperative R&D. K e y w o r d s : Technology management, information technology, finance, business systems, process control, lean manufacture.
* To whom all correspondence should be addressed: Carnegie Mellon, D e p a r t m e n t of Chemical Engineering, 5000 Forbes Avenue, P i t t s b u r g h PA 15213, e-mail
[email protected]. t P P G Industries Inc. Glass Technology Research Center. An extended version of the paper with a complete list of references is available on request from the authors.
13
1
Introduction
The purpose of this paper is to review issues and tools that have (1) found wide-spread acceptance in industrial management and resource allocation and (2) indicate in the most minimal way possible how the PSE community can help sharpen and develop these tools further. In order to do this we need to review the nature of the value generating enterprize. The main goal of such an enterprize is to survive and grow over time by generating products and services that have value in the eyes of its customers, In order to contribute towards value generation the effective leader 1. defines clear business objectives, 2. develops plans to achieve the objectives, 3. systematically monitors progress against the plan and 4. adapts the objectives and the plans as new needs and opportunities arise, Books have been written and courses have been developed that instruct managers and inspire them to develop objectives, plans and monitoring systems, Most of the concepts and paradigms that have been developed arc highly intuitive, graphical and descriptivc. An even wider range of methods have been developed to show the manager how to distribute the decision making processes. Distributed decision making makes the enterprize more manageable and adaptive so that it can rapidly respond to changing pressures in the market place. These tools are also highly descriptive and intuitive. They all rely on the use of highly distributed Information Technology (IT) systems, IT encompasses hardware and software for data storage, retrieval and high bandwidth communication. IT as a field grew out of the computer revolution and is responsible for the most profound changes to affect our industry since mass production became common early last century. IT technology tools allow frequent monitoring. It can be used to track transactions, inventory and human resource allocation in real time. Use of such tools have led to enormous savings and improved efficiency. A well implemented IT system simplifies data entry, unifies information representations, keeps
track of transactions, smoothes data flow and facilitates information exchange. Business systems, under various guises like lean manufacturing, just in time production, Total Quality Management (TQM), pull scheduling etcetera, grew out of idea that globalization, increased competition and a changing customer base should motivate low inventory, high flexibility and low wastage. The classical example is the Toyota manufacturing system which developed into 6sigma. These techniques are now implemented and adapted for use in almost all major US manufacturing industries and contribute towards faster turn around, lower inventory and improved cost structure. IT and business systems do not as a rule rely on the use of models that have predictive capability. Process Systems Engineering (PSE) on the other hand provides the models and numerical methods needed to optimize products, processes and their operation. It is therefore natural to attempt to interface PSE, business systems and information technology. The convergence of the three areas has proven to be difficult for a variety of reasons. One important reason is that the development of PSE followed a different trajectory than that of IT and business systems like 6 sigma. Computer and information networks evolved and adapted so that they could be applied to highly distributed systems. These tools are therefore amenable for direct application for decision support in large organizational structures with little centralized governance. Most of the tools developed for analysis and design in the process systems area are not well adapted for distributed decision making since they rely on hardware and software architectures developed in the 1960ies and 1970ies. PSE tools are having a considerable smaller impact than what one might expect at the business level in the global chemical industry. An organization that consists of physical and computational devices t h a t act in concert and are coordinated over a high bandwidth communication network is called an the adaptive enterprize. The adaptive enterprize is build around the idea that decision making processes is distributed and organized in a manner t h a t allows the system to be flexible and agile so that it can respond and adapt to changing needs. In order to extend the reach of process systems engineering and make it truly useful for decision
14 support in an adaptive enterprize environment it's future behavior and structure of such objects is not imperative therefore to develop methods that take completely predictable and it may displays sensitivadvantage of distributed computing and high band- ity with respect to choice of initial conditions. Howwidth communication. We need to address issues ever, the very nature and complexity of the adapof concern to the business leader communicate so- tive organism makes it able to behave in a good lution in the language that he can appreciate. We manner to unforeseen circumstances. The classialso must develop new ways to think about the sys- cal example of an adaptive system is an ant colony terns we study, which grows and adapts to the changing environThe paper is organized as follows. In the next ment. Another good example is the development section we describe very briefly the main compo- of the internet based, industrial computer control nents of complex adaptive systems theory. Busi- systems. ness is about making money so we review the main The architecture for industrial control systems components of financial analysis to set set stage of during the 1960ies and 1970'ies were built around the next section which is about our approach to what was then a powerful centralized computer where modelling the distributed enterprize. In the follow- all information was stored and calculations took ing chapter we review the dynamics of competition, place in sequential, pipeline machine as shown in the motivation for lean manufacture and the cur- Figure 2. The computer, often build around hardrent focus on quality and cost. In the following ware and software developed by DEC (the VAX section we review the rationale for developing new series), would interface directly with field devices, processes and products and how to enter into new like sensors and actuators through the AD/DA inbusinesses through mergers and acquisitions. We terrace. During the 1980'ies this picture changed then give overviews of the problems of how to man- quite dramatically. The computer resources beage risk. We finish by explaining how the mission of came more segmented as Honeywell, Fisher and the university has changed and how this gives new other process control system vendors developed disopportunities for cooperative university- industry tributed control systems. More and more cornresearch, puters were linked together using a data highway based on a Token Ring concept. Deterministic message passing ensured high reliability and fault tolerance and the "smarts" of the system was now distributed throughout the plant as shown in Figure An adaptive system is a system that is able to adapt 3. These networks however, although highly disto its environment and changing needs. Most bio- tributed, were not adaptive since they were based logical organisms are adaptive, human beings are on proprietary technology. It became increasingly adaptive and business systems are to an increasing expensive and difficult to maintain and configure degree adaptive as well. An adaptive system is fur- such networks as their size and complexity grew. thcrmorc said to be complex if it has the ability to Similar difficulties emerged in the software induslearn new behaviors and develop in ways that do tries and hardware and software vendors were forced not simply depend on its internal working princi- to develop open architecture products. ples, but also on the stimulae it receives through The major new development, which took place sensors from the environment. The environment is during the last decade, was the development of the in a constant state of flux and may itself be adap- non-deterministic, very high bandwidth communitive. This means that a system consisting of two or cation system, which forms the basis for modern more adaptive organisms that adapt to each other computer networking through ethernet and wirecan evolve in an ever changing manner (Figure 1). less. This architecture for process operation and Complex adaptive systems are often made out control, shown in Figure 4 is highly flexible and of adaptive devices that in and of themselves may adaptable. Computers, software, and field devices be quite simple. However, complex behavior emerges can be added, modified, exchanged and integrated as these are connected together to form large, inteinto the system in a "plug and play" manner. The grated networks were the adaptive devices compete "smarts" of the system is now highly distributed or cooperate to meet distributed design goals. The and more and more calculations take place at the
Complex Adaptive Systems
15
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15 Proprietary Industrial Network iii HII iii
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17 device level. For example, instruments and actuators come with their own, onboard computer systems with models for calibration, error checking, linearization, model predction and fault detection. What is emerging are distributed devices as shown for example in Figure 5 Adaptability and flexibility is further enhanced by the development of new and cheaper sensors for accurate process state measurement and information sharing over the internet. All process information is in principle available anywhere and the need for a physical presence of trained personnel has been significantly reduced. It is important to note, however t h a t these hardware and software systems are highly distributed are not coordinated by a central computing facility. The study of complex adaptive systems has taken importance in many fields. Including biological systems, fluid mechanics, astronomy and geo-sciences, computer science and business systems. In fact, as we illustrate below, the dynamics of large enterprizes can be modelled using very simple ideas from electrical networks and analog computing. The PSE community has recently begun to study how such network systems behave and more importantly how we can modify their structure and organization so that the integrated network can respond in an agile manner to changing needs of the business and and remain competitive in the long run.
is the book value of the holdings of a specific asset at a specific location and the number V -
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assets represents the bookvalue of the company. There has recently been a trend towards activity based analysis [2]. In this approach we attach specific value to the benefit generated by an activity. The activity may represent a production line, business segment, movement of an asset or any form of service t h a t generates value. The value of the activity depends on the physical process in question and how it enters into the value chain. Activities can therefore be are characterized by the triple ~ - - {f, x, w}. The scalar f gives the activity rate, x gives the specification of the activity in question and finally w represents the value added. It is now important to note that assets and activities are related in a continuous m a n n e r so t h a t w - - cs - Cp
where Cp is the value of the asset before the activity takes place and cs is the value of the asset after the activity is completed. We view Cp as the internal purchase price and cs as the internal sales price per unit normalized to a common standard, for example mass or energy. The internal cost of the activity can be expressed by the formula
~ = fw
Business Finance The instantaneous cash performance of a business unit can be evaluated by tracking the flow and storage of assets and assigning an internal costs to each and every activity. Assets are characterized by the triplet ,4 = {v, c, z}. The function v represents the amount stored at a given location, c represents the value of one unit of a specific asset and finally, z represents the specification. The specification identitles an asset by its name, SKU-number, chemical composition, patent number or some other identitier which should be unique. All these variables are functions of time and geographical coordinates. In financial terms the number
(1)
We choose sign so that asset flow is positive in the direction of increasing value and negative in the direction of decreasing added value. The internal cost is then always positive but the added value from performing an activity may be negative. For example, the added value in going from destination a to destination b is the negative of the added value of going from b to a, indicating t h a t cyclical activity does not generate value, it only adds costs. A more detailed assessment is given by Taylor and Brunt [8]. The instantaneous business performance is evaluated by taking difference between revenues R and expenditures E so t h a t T=R-E,
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However, the rate of profit T, varies considerably from one instant to the next and it also ignores the value of asset accumulation, To overcome "jumpiness" it is common to integrate over a period of time and define the profit P during the period (t0, tl } so that P =
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For example, if {t0, tz } represents a quarter then P gives the quarterly net income from operations, A range of similar measures including profit margin, operating profit and ratios like Return on Assets, ROI, R O C E and Price Earnings ( P / E ) ratio arc also used to evaluate business performance [2]. The book value of the company therefore evolves according to the formula V(t) =
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There has therefore been a tendency to move away from the use of measures based purely on profit and past performance towards the use of measures that include value creation. For example, the financier, Warren Buffet [1] argues t h a t the performance should be evaluated using an underlying and potentially subjective measure he calls the "intrinsic value" of the company. The intrinsic value is defined to be the "discounted value of cash that can be taken out of a business during its remaining life [1]." This measure is expressed in mathematical terms by the conditional expectation ft+,H(t)= / aSE{T(t+s)lF(t)}ds+V(t),
s > 0 (2) The number 0 < a < 1 is the discount factor and the filtration F(t) represents all information available at time t that is used to estimate the future cash flow [3]. The book value of the company at time t is clearly F(t) measurable, in fact it has to Jt
These numbers represent faithfully the numbers used be according to the laws t h a t govern business conto represent the value of the company assets and are duct. However, the remainder of the formula relates to to the future and captures the best use of models used for tax and other reporting purposes. However, as argued in [2], focusing on account- and forecasts t h a t the business leader has available ing measures alone gives a myopic view of the value to make business decisions. of a business since it does not include the time The number II(t) clearly depends on a large value of accumulated assets and the future busi- number of factors inside and outside the reach of ness performance. In fact, savvy business leaders control. The forecast also needs to be adjusted and are more concerned about the future t h a n the past. updated as the context and changes. This is where
19 PSE come into the picture in full force.
Process systems engineering provides the integrated approach that allows us to combine predictive modelling, efficient solution procedures, optimization methods, distributed control and statistical techniques needed to evaluate and compare different alternatives as expressed by equation 2.
sions that allow the sustainable development so that the company can prosper and generate value over a long and indefinite period of time. The implied risk of a decision is therefore as important to consider than optimality, maybe more.
The Enterprize
as a Distributed
However, the emergence of complex networks N e t w o r k pose considerable challenges for the research and business community that transcend the mere ap- There are seven distinct factors that need to be plication of tools that have been developed for op- taken into account when we develop an abstract timization and control during the last decades. The model of an enterprize system in the commodity centralized (autocratic) approach which brings all chemicals industry. These include information together is likely to fail. W h a t is needed 1. Topology. are systematic tools that allow us to integrate predictive models, diverse information written in dif2. Transportation. ferent formats, different standards and distributed 3. Shipping and receiving. agent based optimization systems that reflect the structure of the adaptive system we seek to con4. Activities. trol. Early attempts based on genetic algorithms and simulated annealing showed that some prob5. Storage and holding. lem classes can handled efficiently using distributed 6. Forecasting and signal processing. computing. 7. Performance evaluation. Remarks: 1. The accuracy of the predictions depends on how much information one has available at a given time. More information is available for company insiders, especially with regards to the development of new technology and negotiation of contracts. Such developments are therefore guarded until they are consummated or protected, 2. The importance of evaluating intrinsic value as accurately as possible cannot be over estimated since it impacts the business strategy and may motivate significant changes in business operation, 3. It is important to consider the fact that we seek robust, rather than optimal solutions. The main objective of a large corporation is not simply make the most profit possible in a given period. This may lead to extreme solutions like breaking up the company and selling off all assets. As stated in the introduction, the objective is make business deci-
In addition one needs to consider intangible assets like legal position, know-how, intellectual property and how well this is protected, customer-supplier relationships and the quality of workforce and management. With the elements above we can represent the enterprize as a graph. A graph is normally characterized by a set of vertices and a set of edges. In light of the discussion above, we find it useful to introduce more structure and represent the topology of the supply chain using a graph with five classes of elements. These represent production, storage, terminals, routing points and activities. Transportation is represented by edges, while the other four elements are represented by vertices. More formally we have the graph G = (7), $, T, 7~, T{) Elements Pi E 7) , i = 1, ..., np represent the production sites where we assemble or disassemble chemical constituents or parts into pre-cursors or products. Elements s, E 8, i = 1,...,ns denote the
20 storage facilities. Elements ti C T , i = 1,...,nt denote terminals for receiving and shipping. Elements ri E ~ , i = 1, ..., nT represent points where material, energy, money and d a t a can be routed in different directions. Finally, hi E ~ , i - 1,..., nh denote transportation. An activyt is a collection of connected nodes and edges, By working on the basis of a common unit and the fact t h a t cyclical activity does not add value we can write the equations 0 0
=
asynchronous m a n n e r w i t h o u t referring to a central organizing structure. The system can now evolve and a d a p t to changing needs. However, in order to develop a theory for such a complex adaptive system and develop distributed control mechanisms for load balancing we need to address questions relating to stability, transient response and optimality of systems represented by network flow on a graph. Some of these are addressed in classical circuit theory and its extensions to network thermodynamics, a field of more recent origin.
~ w activities
(3)
routing point f
(4) Business Systems: The D y n a m -
The first equation corresponds to Kirchoffs voltage law and the second to the current law. In process modeling the first equation corresponds to the fact that pressures, chemical potentials and temperatures are equal at all interphases 1 One interesting feature of networks t h a t obey Kirchoffs laws is t h a t they have certain topological properties t h a t can be described in terms of
ics
of
Competition
A n u m b e r of business systems have been developed under the headings of 6 sigma, lean manufacturing, C F W (continuous flow manufacturing), T Q M , Just In Time (JIT) production and cycle time reduction. The idea in all cases is t h a t variations
in product quality, wastage, inventory level and overhead should be reduced while the service level Tellegen's theorem of electrical network theory [4]. should increase so t h a t the customer needs are satOne consequence of this theory is t h a t the simulated behavior of the network can give optimality isfied. Many of these ideas can be traced back to the developments at Toyota during the latter half of and stability, the 20th century [5]. I m p l e m e n t a t i o n of such busiA graph can bc used to represent information, ness systems requires a more highly skilled workmaterial and service flow of very complex manuforce t h a n the mass m a n u f a c t u r e t h a t dominated facturing proccsscs. The model can rcprcscnt a the 20th century industrial production. single business unit or a group of businesses workThe basic principles of lean production can bc ing together when free terminals are connected so summarized by the following five points that there is at least one continuous path from every terminal connecting raw material suppliers and 1. Value as defined by the customer. customers. All activities, including financial ones, 2. The value stream, which consists of the acshould be part of at least one such p a t h in order tions needed to bring the product/service to to be considered part of the business system. We the customer. also necd to map the internal activity costs so t h a t we can evaluate the relative merits of different ac3. The flow and continuity of products and sertivities as well as the relative merits of parallel acvices. tivities representing different contracts, technologies and work practices. Once the system dynamics 4. The responsiveness (pull) of the customer needs. and the cost structure is available we can develop 5. The pursuit of total quality (perfection, 6 sigma control systems to balance the load so t h a t all sysetc.) tem requirements and constraints are met and the activity costs are minimized in a distributed and These items are covered in depth in 6sigma or re1The theory can be extended to systems with disconti- lated courses at nearly all major US chemical comnuities and shocks when the minimum entropy principle is panies. Point 2 in the list above is especially iminvoked to select the appropriate characteristic at the shock front, p o r t a n t and typically illustrated using a diagram of
21 the type shown in Figure 6. More generally we find that the value generation may vary as a function of the throughput rate. There may be a minimum start up cost before any activity can start, beyond this the activity cost may vary in stepwise manner until at some point the activity saturates. At this level the activity is constraining and represents a bottle neck for further production improvement. Business systems of the type reviewed above are highly distributed and they can be used effectively to improve quality and reduce cost at every level in the organization. At a deeper level these systems are necessitated by the dynamics of competition, These can be understood at one level by making a comparison with network models, In the first case we consider a non-competitive market where the prices are stabilized by common understanding among the major producers (which is illegal), government regulation or price controls. In the network analogy we can model such a system by using resistors and capacitors where the per unit production cost increases as the production rate increases. There is therefore no incentive to expand the business. The integrated system therefore stabilizes at a point which corresponds to minimum dissipation. This follows by a simple application of Helmholtz minimum heat theorem. The profit will be distributed throughout the network but the customers suffer by having to pay high prices for services and products, In a competitive market onc or more the producer will try to expand his/her business by offering incentives by lowering the per unit cost as the production volumes increase. The minimum heat theorem no longer applies and what was originally a passivc system now dcstabilizcs as the market will tcnd favor thc low cost producer. The other producers will have to try to match the low cost produccr in quality and price to stay in business. This in turn leads to lower prices and an advantage for the end user. Case
Study:
Glass
Manufacture
til it is viscous enough to be lifted out as a continuous sheet. This sheet is about 2 mm thick and 9ft wide for production of automotive windshield glass and may be more than 20mm thick for other applications. Continuous and slow cooling after the tin-bath reduces thermal stress and finally the glass is checked for quality, cut, sorted and packaged. Scrap pieces and rejects are broken up and recycled. The objectives are to improve yield, maintain quality and stability and reduce product changeover costs. Diverse objectives and system complexity precluded us from simply "wrapping a loop around the entire process" and applying one particular brand of control theory. We therefore used the inventory based control approach to develop the control system structure. In this approach we write material and energy balances for each sub-activity so that dv d-t = r + p
where v represents, as explained above, the amount of stored assets (energy and materials), r = )-~, fi represents the net flow of assets to the control volume and p the net rate of production. We used a variety of models to find where to place new sensors, how to decompose the plant into smaller sub-sections, structure control systems and design estimators for unmeasured variables. The control system evolved over time and continues to be refined. It now includes nonlinear and constrained predictive control, Kalman filters, adaptation, stochastic control, vision systems with pattern recognition, and discrete feedback logic. The control loops are implemented on a number of different computer platforms, integrated via local area network and coordinated with the business system through the internet. The system has been implemented on most PPG glass plants and contributes towards significant yield improvements and improving the bottom line.
N e w P r o d u c t a n d P r o c e s s Tech-
In the glass process, sand, soda ash and other raw materials enter the batch house where they are mixed nology according to the given recipe and fed to the furnace (the hot end), where they are melted, refined and New process and product development often takes transported by fluid flow to the "tin-bath". The place inside the technical organization. One examglass there spreads out on molten tin and cools un- ple example is the development of HFC-134 series
22
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Figure 6: Implementation of the value stream mapping tool.
Figure 7: Implementation tool for project management.
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Figure 8: The control system for a large glass furnace was developed by dividing the process into subsections and representing the storage and flow of energy and materials from one sub-section to the next. The furnace itself was divided into five sub-sections as indicated in the Figure.
23 of refrigerants by duPont to respond to the environ- briefly described above is of relatively new origin, mental challenge posed by the growing ozone hole. however. Each process step represented a major inHowever, a large company may also be in a posi- novation and resulted in higher throughput, lower tion to buy technology and expand their sphere of cost and higher quality. The last innovation was the interest by purchasing new production capacity and development of the tin-bath process by the Pilkingexpand into areas where they have little expertise, ton brothers in the 1950'ies. The Pilkingtom proDifferent expansion projects can be represented cess allowed the production of distortion free glass as shown in Figure 9. Each project is represented without the need for time consuming and expensive by a larger circle whose area represents the intrin- polishing. It led to significant increases in throughsic value of the project as evaluated by equation put, lower cost and easier production. The R&D (2) and the area of the smaller circle represents the that went into the development of the tin-bath proNPV of the investment needed to carry out the cess was far from simple and cheap. It took over project. The area of the shaded doughnut rep- a decade before the first float glass was produced. resents the expected return relative to investing However, the development led to a tripling of proin monetary instruments. The coordinates are in ductivity through series of process improvements. this case labelled as old/new market and old/new The length of the production line was reduced by a product, an indication of the strategic impact of factor of two, the use of costly abrasives was elimthe investment decision. These coordinates can inated, the energy costs were reduced by 50% and be replaced by old/new process or low/high risk. the labor costs by 80%. The old method of making The representation illustrated here gives a quick glass was totally eliminated in matter of few years. overview of different strategic choices. A tool which can be refined using process sys- C a s e S t u d y : Solar Grade Silicon Protems engineering is the cost-curve, used to motivate d u c t i o n buying, selling or modernizing production equipment and for plant acquisitions. Such a curve is Silicon is prized for its semi-conductor properties, shown in Figure 10. Each bar represents a pro- its abilities to form polymer chains, and its exduction facility. The horizontal axis represents the cellent alloying properties with materials like aluproduction volume of a plant site. The total adds minum and steel. A by-product of the process, up to the world production if a global market is called microsilica, is used as a binder to form high considered. The vertical axis represents the pro- strength cement. However, these commodity marduction cost per unit produced. The lower the cost kets are fairly stable and even the market for electhe cheaper the production. Suppose for example tronics grade silicon (42,000 MT in 2000) is severely that a company owns plant sites with production depressed due to the slump in the micro-electronics characteristics indicated by the darker shade. It industry and the trend towards smaller and smaller might consider divesting the high cost site, invest- circuits t h a t use less silicon per transistor. ing in the large site in an effort to reduce cost and The market for photo-voltaics grade silicon is buy capacity in the geographical areas correspond- growing at a remarkable robust and stable rate of ing to the lower cost producers at the left of the 25-30% per year. This market is in part supported diagram, by government subsidies in countries like Japan and Germany, the energy needs of highly distributed Case Study: Glass Production wireless communication systems and third world countries with unreliable and poorly developed elecThe development of the Pilkington glass process trical grids. The supply chain leading to the manugives a good example of the development of disrup- facture of photo-voltaic grade silicon is highly comtive process technology. Glass is a very old ma- plex however and relies heavily on the use of reject terial, in fact it is one of the oldest manufactured materials from the micro-electronics grade indusmaterials. Exquisite examples of highly refined and try. This source of silicon is rapidly drying up due masterfully shaped glass artifacts have been discov- to the rapid growth and new sources and producered that goes all the way back to the Etruscans. tion technology is needed to support the growing The modern, continuous float glass process very solar cell industry. Many companies are engaged in
24
Figure 9: The figure shows the industry wide cost curve for silicon production units.
Figure 10: The figure shows the industry wide cost curve for silicon production units.
developing simplified and cheap routes for developing solar grade material. K P M G estimates t h a t if such a material is produced at a cost of around $15 per kg then large scale production methods will reduce the cost of electricity generated by solar panels sufficiently to compete with coal and oil-based electricity generating plants in countries like J a p a n
sure a reasonable rate of capital investment. Staying in the same line of business, markets and products for long period time generally lead to lower margins and reduced value of capital investment. Risk in a technical program can be assessed by the formula [9] R = p 9E
and Germany.
where p is the probably of failure and E is expenditure. This expression can be used to compare different technologies and programs and evaluate their relative risks. Another approach is approach is use a Pareto chart. The most common way to manage risk is to take break large programs with a substantial cost and pay-off, into smaller sub-programs with specific milestones. This approach distributes the risk and allows intermediate assessment of go and no -go decision criteria. In the area of process control it easier to motivate and implement a distributed control system where incremental gains can be realized along the way. The development effort behind a centralized control system is larger and more expensive. The
Managing Risk Risk can bc viewed as movement away from the current state in the three areas of new product, new market and new process technology as shown in Figure 11 [9]. Companies were very quick in adapting the concept of lean manufacture since the risk is low and the pay-off directly measurable using classical accounting tools. Risk plays a more i m p o r t a n t factor in new process technology, market or product, however the payoff is greater in these areas and businesses need from time to time enter new business areas to en-
25 New Product New Process Tecnology .~
New Market
% Current state
Figure 11" The figure shows three directions in the space of risks.
pay-off is offered at the end. The inherent risk is higher and the maintenance more cumbersome and costly, There are other ways to minimize risk, including developing partnerships and joint ventures, engaging consultants, out-sourcing and buying a company that has the capabilities needed. All these strategies lessen the impact of uncertainty. Significant risks are associated with the market outlook, cxchangc rates, legal and international trade agreements or lack thereof, Case study:
Carbothermic
Aluminum
ALCOA, ELKEM and Carnegie Mellon has for the past four years been working on the development of a new process for making aluminum. The new process is based on carbothermic reduction rather than electrolysis as is the case with the classical Hall-Heroult process. The new process is estimated to reduce emission of greenhouse gases. The capital and operating costs are estimated to be about 30% smaller than current technology. However, the new technology is expensive to develop and test since it involves extremely high temperature in the smelting stages. One of the stages is expected to operate at 2050 ~ C. A simple way to visualize the risk associated with the development of the carbothermic aluminum process is to express sensitives of the intrinsic value through a "tornado diagram" as shown in Figure 12. Each bar represents the variation of NPV as a function of a set of chosen sensitivity parameters, The calculations that go into generating such a diagram are clearly quite extensive since each point
in principle represents a specific plant design optimized and controlled to the given conditions. In this case the uncertainty in the London Metal Exchange (LME) price of aluminum dominates. This illustrates that market forecasts and market uncertainty dominate over technology in the area of finance.
Our experience is that managers trust their technologists, their ability to solve difficult technical prob lems and believe that they are able to design engineering systems that work well. They find it difficult justify why and how technical solutions can be used to generate value. Finally, it is easier for the technologist to learn to speak the language of finance than it is for the Chief Financial Officer to learn how to evaluate engineering solution based on its technical merits. An objective of PSE should therefore be to design better interfaces with business finance.
The
Drive
Towards
Excellence
Globalization of business and increased competition led to the development of business systems that emphasize quality and the needs of customer. Businesses that do not provide acceptable service level at competitive prices are not allowed to survive in the long run. This trend has led to re-structuring and significant shifts in the manufacturing industry. Similar trends have emerged in the universities. A significant shift in the mission and philosophy
26 I Base case value $00'0 ] LME price of Aluminum ($/Ib) Cost of power (S/kWh) Capital cost ($MM) Coke price ($/MT) Pilot development cost ($000) Hourly labor (hr/MT) Alumina (MT/MT) Basic R&D cost ($000)
I
[ I
I
NPV @ 15%, $000
Figure 12: The tornado diagram represents the sensitivity of the N P V with respect to variations in the expected financial parameters.
of higher education and university R&D has taken place. The classical idea of the university as a n a tional, cultural institution has all but disappeared [6]. Universities no longer primarily serve national interests, except in the narrowly defined areas like defence and homeland security, It has become very difficult to support theoret-
ical and knowledge-base research which cannot be directly linked to economic objectives that are realized in the short run. Most of the programs of the US Government are mission oriented and grow out of perceived industry and society needs. Universities therefore have become subject to the same market forces and competitive pressures as those that characterize industrial markets. They have become global in and compete for the best students, star professors and scarce resources. These trends have led a re-alignment of research interests and the establishment of centers of excellence t h a t to an increasing degree support industry as a whole, This is especially apparent in the area of PSE where such centers proliferate and grow. We find such centers at Imperial College, Kyoto University, Purdue , Carnegie Mellon, in Aachen and Lausanne. Larger, joint programs are also being formed. Examples include a joint program between University of Wisconsin and University of Texas, Austin and another one between Umass and UCSB. The centers provide service by leveraging industry and government research. T h e rationale for their existence is that university research can focus on generality, depth and education of high level technologists who
will contribute to value creation by developing new process technology, new products and new markets. However, there are m a n y hurdles to overcome and diverging interest put d e m a n d s on technology management, funding structure and communication t h a t university and industry R&D must prepare to meet. A common language and framework must be established so t h a t issues of the concern to the company, like technology protection, technology ownership, licensing and exclusive rights of use, have been dealt with. Financial implications must be analyzed. Academic concerns like right to publish results of general interest, degrees of freedom to pursue fundamental problems, opportunities to fulfill educational objectives and most i m p o r t a n t l y the opportunity to write MS and P h D theses, must also be taken into account. Publication challenge security issues as sensitive d a t a may become distributed more widely. Our experience however, is t h a t diverging objectives can be overcome and t h a t win-win situations are becoming more easy to establish as university R&D is becoming more market oriented and industry R&D has been become more decentralized, downsized and focused on short term business needs 2 2There are some signs that this discouraging trend is reversing and that at some larger companies are in the process of re-enforcing their centralized R & D organizations. Examples that come to mind include GE and IBM.
27 Summary and Conclusions
the way business is being conducted and how we think about pSe as a field. New paradigms will Distributed computing has emerged as the new parR- emerge. These will include distributed networks of digm for modelling complex financial and engineer- computational devices adapted to the physical sysing systems. Complex adaptive systems theory is tem through extensive sensory systems. These netslowly emerging as the paradigm for understanding works will be capable of modelling the system and how these systems can be designed and how they they can be generate forecasts that can be used for evolve over time and adapt to new and unforeseen resource allocation and strategy development. needs. Distributed computing arises naturally in several ways. The systems we want to model are distributed - we can no longer model process segments, R e f e r e n c e s business units and enterprizes in meaningful way [1] Buffett, W. (2002), "Berkshire without considering their geographical coordinates Hathaway, Shareholder Letters", and how they are integrated into the global market. Information, physical infra-structure and human resources are distributed across the globe and the computer networks we use for information exchange are also distributed. Moreover the topology of the network changes and adapts rapidly as
new needs arise and old sub-systems are exchanged with newer ones, new products and processes are brought on line and new businesses are added or old ones closed. Adaptive networks integrate physical devices, information systems, finance and personnel and they must be designed to operate in a stable and close to optimal manner.
~r~w.berkshirehathaway, com/letters, html
[2] Helfert, E. A. (2001), Financial Analysis Tools and Techniques, McGraw Hill, New York [3] Overhaus, M. A Ferraris, T. Knudsen, R. Milward, L. Nguyen-Ngoc, G. Schindlmayr (2002), Equity Derivatives, John Wiley & Sons Inc., Wiley Finance, New York [4] Penfield, P., R. Spence, S. Duinker (1970), Tellegen's Theorem and Electrical Networks, Research Monograph 58, The MIT Press, Cambridge, MA.
PSE has so far focused most of their efforts on scientific and large scale computing using modelling and optimization techniques and concepts based on methodologies, computer hardware and software architectures established in the 1960ies and 1970ies. What is needed now is the development of process systems engineering tools suitable for distributed decision making and network computing.
[5] Yasushiro Monden (1992), Toyota production system - an integrated approach
It will not entail a complete rethinking of the field or a need to abandon or change research directions that after all have been extremely successful. However, there is a need to broaden the scope of PSE and focus on the structural properties and architectures complex adaptive systems. What is is needed is a coherent and systematic theory that allows us to design, analyze and operate highly integrated and distributed systems of semi-independent devices and agents. Progress in this direction will allow the convergence of IT, business systems and PSE. A conflux of these three areas promise will change
[8] Taylor, D. and D. Brunt (2000), Manufacturing operations and supply chain management: The lean approach, Thomson Learning, London.
[6] Reading, B. (1996), The university in ruins, Harvard Press., cambridge Mass [7] Stern, C. W. and G. Stalk, Jr. (1998), Perspectives on Strategy from the Boston Consulting Group, John Wiley & Sons Inc., New York
[9] Steel, L. W. (1988), Managing technology: The strategic view, McGraw-Hill, New York.
28
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
Challenges in the New Millennium: Product Discovery and Design, Enterprise and Supply Chain Optimization, Global Life Cycle Assessment Ignacio E. Grossmann Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.
Abstract. This paper first provides an overview of the financial state of the process industry, major issues it currently faces, and job placement of chemical engineers in the U.S. These facts combined with an expanded role of Process Systems Engineering, are used to argue that to support the "value preservation" and "value growth" industry three major future research challenges need to be addressed: Product Discovery and Design, Enterprise and Supply Chain Optimization, and Global Life Cycle Assessment. We provide a brief review of the progress that has been made in these areas, as well as the supporting methods and tools for tackling these problems. Finally, we provide some concluding remarks.
Keywords: Process Systems Engineering, Process and Product Design, Supply Chain Optimization, Life Cycle Analysis. 1. INTRODUCTION When Professors Chen and Westerberg invited me to give a keynote lecture on research challenges in the new millennium at the PSE2003 meeting I was rather hesitant to accept their invitation since I was unsure whether I would be in a position to contribute to such an imposing topic. However, after realizing I did not have to provide a research agenda for the entire next millennium, but at best for the next few years, what also made me decide to accept were three major facts. The first is that the future of Process Systems Engineering (PSE) has been very much in my mind through an AIChE perspectives article that I coauthored with Art Westerberg (Grossmann and Westerberg, 2000), and through my involvement in a committee of the National Research Council, which is in the final stages of putting together the report "Beyond the Molecular Frontier: Challenges in Chemistry and Chemical Engineering in the 21 st Century." Second, having been department Head at Carnegie Mellon for the last eight and one half years, my interactions with employers and advisory boards has given me a useful perspective on the educational and research needs of the chemical industry. Third, I have had the opportunity to be involved in the Council for Chemical Research, and in the AIChE Journal as an associate editor. These activities, as well as the headship at Carnegie Mellon, have helped me to gain a broader appreciation of our profession. Therefore, it is in this context that I would like to take the opportunity to speculate about future research challenges in Process Systems Engineering.
29 The paper is organized as follows. We first define the landscape in industry by presenting several financial facts, economic and social challenges, and job placement data in the process industry. Next we discuss the major trends in the industry, as well as the expanding role of Process Systems Engineering. Finally, we discuss the future challenges identifying Product Discovery and Design, Enterprise and Supply Chain Optimization, and Global Life Cycle Assessment as major themes for future research. 2. L A N D S C A P E OF THE P R O C E S S I N D U S T R Y
Since Process Systems Engineering has as a major objective to develop new methods and tools that allow industry to meet its needs by tying science to engineering, it is important first to identify the major facts and trends in industry that motivate the challenges for future research in this area. In this section we consider first several financial facts about the chemical process industry. We then discuss some of the major economic and social issues that are being faced by the industry, and lastly we present some data for placement of chemical engineers. We restrict the data to the U.S. industry for the sake of consistency. 2.1 Financial facts. While it has become trendy over the last few years to question the future of chemical engineering and the process industry, it is important to note that the latter still remains a major sector of the economy. As an example, Chemical Engineering News reported in its June 24 issue of 2002 (pp.44-57) that the total revenues of chemicals in the U.S. in 2001 amounted to $447 billion. The breakdown by product types in billions is shown in Table 1. If we expand the process industry by adding the petroleum industry, the revenues reported by major U.S. oil companies in 2001 were $595 billion. Thus, between chemicals and petroleum, the total revenue in the U.S. is close to $1,000 billion per year. Table 1. Revenues of chemicals in the U.S. in 2001 (billions) Basic chemicals $146.8 Pharmaceuticals 119.9 Specialty chemicals 108.6 Consumer products 48.4 Crop protection 13.7 Fertilizers 10.4
Expenditures in capital spending of the top 25 U.S. chemical producers (i.e. basic and specialty chemicals) were $7.074 billions in 2001, which is considerably lower than the $9.333 billion in 1998. The R&D expenditures were $4.798 billions in 2001 versus $5.023 in 1998. In contrast, the pharmaceutical industry saw their R&D expenditures increase from $15.2 billion in 1998 to $20.9 billion in 2001. It is also interesting to note that in 2001 one in 12 researchers in industry worked for a chemical or pharmaceutical company, while the expenditure of R&D in the chemical sector was about 10% among all industrial sectors.
30 Table 2. Revenues of maior U.S. chemical companies in 2001 (billions) ExxonMobil $191.6 ChevronTexaco 99.7 Merck 47.7 Procter & Gamble 39.2 Johnson & Johnson 33.0 Pfizer 32.2 Dow 27.8 DuPont 26.8 Bristol-Myers Squibb 21.7 Amgen 3.5 Genentech 1.7 To provide another perspective, the revenue in 2001 of major companies in the process industry is shown in Table 2. From the figures in this table it is clear that the petroleum companies have the largest revenues followed by pharmaceuticals, consumer products and chemical companies. Biotechnology companies are last with relatively small revenues. In terms of profitability and approximate return, however, the order is first biotechnology (2030%), then pharmaceutical (15-20%), petroleum (6-10%) and finally chemical companies (5-
8%). 2.2 Economic and social issues. The chemical process industry faces very important economic and social issues (Siirola, 2000). Globalization of the industry has opened new markets. While potentially this can help to increase the standard of living throughout the world, globalization has also resulted in growing worldwide competition. Furthermore, the introduction of e-commerce is producing greater market efficiencies, while at the same time greatly reducing the profit margins. Added to these challenges are increased investor demands for predictable earnings growth despite the cyclical behavior inherent in most of the chemical industry, which tends to be capital intensive. Socially, sustainability and protection of the environment will become even more important challenges for the process industries. Many of the raw materials used, especially those derived from oil, gas, and some plants and animals have been, and in some cases continue to be, depleted at rates either large compared to known reserves, or faster than replenishment. Also, by the very nature of chemistry, there are always contaminants in the raw materials, incompletely converted raw materials, unavoidable byproducts, or spent catalysts and solvents that produce waste. These challenges also apply to the production of the energy from the fuels produced by or consumed by the processing industries. Another concern that has recently received great attention is the potential detrimental effects of carbon dioxide emissions to the atmosphere. Recent estimates indicate that the level of carbon dioxide in the atmosphere has increased by a third since the beginning of the industrial age, and that it currently contributes about 73% to the potential for global wanning. Finally, another concern is the management of water, which is expected to become a major problem in this century. Almost all chemical manufacturers in the U.S., and increasingly world-wide, now subscribe to a program called Responsible Care, a pledge by the manufacturers to make only products
31 that are harmless to the environment and to its living occupants, and by processes that are also environmentally and biologically benign. Closely related to the environmental challenges are the energy challenges. Currently about 85-90% of the world's energy is obtained by burning fossil fuels (petroleum, natural gas, and coal), but this must change at some point. Regarding alternative energy sources for the process industry, better ways need to be devised to use solar and wind energy to effectively convert them to electricity. Combustion needs to be eventually replaced by fuel cell technology, and the safe and economic use of hydrogen needs to be made a reality. There are also important future implications for petroleum companies if rechargeable batteries become practical in electric motor vehicles, obviating the need of gasoline engines. Finally, the industry also has to respond to diseases and poverty, particularly in the developing world. For instance, a major challenge is the development and manufacture of low cost drugs to combat diseases such as AIDS. 2.3 Placement of chemical engineers. Another angle that is important to analyze is the change in the job placement of graduates in chemical engineering, which has been a result of changes in the industry. Twenty years ago most graduating students would join petroleum and chemical companies. Nowadays this has greatly changed. Figs. 1(a) and 1(b) which are a result of a survey conducted by AIChE, show the distribution of jobs offered to graduates with B.S. and Ph.D. degrees in 2001. The data is for about 60% of the graduating students in each case. At the B.S. level the remaining 40% works for the government (2%), pursues a graduate or professional degree (12%), is unemployed (3.5%), or their employment status is unknown (20%). At the Ph.D. level the other 40% works for the government (0.8%), takes on a faculty position (17%), does postdoctoral work (13%), is unemployed (2.8%), or their employment status is unknown (6%).
What is striking from Fig. 1 is the diversity of industries that employ chemical engineers. At the B.S. level chemicals, fuels and food/consumer product companies hired almost 50% of the students. Electronics hired as many as the fuels companies (16%), while the share of biotechnology and pharmaceutical, and engineering services and consulting, was close to 10% for each. At the Ph.D. level the trends are similar, although chemicals, fuels and consumer products show a share of only 35%, which is only slightly above electronics at 30%. Also biotechnology and pharmaceuticals has a 16% share. To appreciate the significant changes that have taken place over the last ten years in the placement of chemical engineers, the percent changes at the B.S. and Ph.D. levels in the four major sectors is shown in Table 3. There is a striking decrease in chemicals, and increase in electronics. Bio/pharma also shows growth but at half the rate of electronics. The drop in fuels is also noticeable, but not as dramatic as in chemicals. Food and consumer products is fairly constant.
32
Ca)
(b)
Fig. 1. Distribution of 2000-01 industry placement of (a) B.S. and (b) Ph.D. graduates in the U.S.
Table 3. Change in percentaee placement B.S. 91 Chemicals 43.7 Fuels 21.2 Food/Consumer Prods. 7.2 Electronics 2.4 Bio/Pharma 3.1
of graduates in the U.S. B.S. 01 Ph.D. 91 23.3 46.0 15.7 15.3 10.6 5.5 15.9 4.8 9.3 4.0
Ph.D. O1 21.3 10.6 4.3 29.5 15.9
3. DISCUSSION OF TRENDS IN INDUSTRY The material that we presented in section 2 could be the subject of a separate article. Our intent here is to use these data and issues as a basis to motivate the future research agenda in Process Systems Engineering in terms of three major themes: Product Discovery and Design, Enterprise and Supply Chain Optimization, and Global Life Cycle Assessment. The data of the previous section clearly show that although the traditional chemical industry (i.e. chemicals, petroleum, consumer products) has greatly shrunk in terms of employment and R&D expenditures, its revenues are still very large (see Tables 1 and 2). The data also show that the biotechnology and pharmaceutical sector has become of significant importance even though their revenues are one quarter of the total industry in the U.S. (see Table 1). Surprisingly, the electronics sector has become almost as important as the other sectors in terms of employment. In this case, companies such as IBM (revenue $86 billion), HP (revenue $45 billion), and 1NTEL (revenue $27 billion), which are dominated by electrical and computer engineers, have hired chemical engineers largely for their process skills, which are important in chip manufacturing.
33 One implication of the above, and of the financial and placement data in section 2, is that it is important to recognize on the one hand the very large magnitude of the "value preservation" part of the industry (e.g., large-scale commodity chemicals, petroleum), and on the other hand the great potential of the "value growth" part of the industry (e.g., specialty chemicals, biotechnology and pharmaceutical products). Obviously, most companies are in fact dealing with both aspects, particularly since the specialty chemical today becomes the commodity chemical of tomorrow, and this transition seems to be accelerating, even in the case of the pharmaceutical industry. Therefore, for companies in the process industry to remain competitive and economically viable, this requires for the "value preservation" part of the industry the optimization of the enterprise and its supply chain by reducing costs and inventories, operating efficiently and continuously improving product quality. For the "value growth" part it means innovating and becoming smarter and quicker in product discovery and design, as well as in their commercialization, particularly in reducing the time to market. But the challenges posed by the energy and the environment discussed in section 2.2 must also be urgently addressed, as they are likely to have a profound effect on the long-term viability and acceptance of the chemical industry. It is here where global life cycle assessment will become an essential task that must be undertaken effectively. 4. EXPANDING THE SCOPE OF PROCESS SYSTEMS E N G I N E E R I N G
Process Systems Engineering has traditionally been concerned with the understanding and development of systematic procedures for the design, control and operation of chemical process systems (Sargent, 1991). However, as discussed in Grossmann and Westerberg (2000), the scope of PSE can be broadened by making use of the concept of the "chemical supply chain" shown in Fig. 2. The supply chain starts at the molecular level with of chemicals that must be discovered or synthesized. Subsequent steps aggregate the molecules into clusters, particles and films as single and multiphase systems that finally take the form of macroscopic mixtures. Through scale-up, we next move to the design and analysis of the production units that must be integrated in a process flowsheet. Finally, that process becomes part of a site with several plants that are connected through suppliers, warehouses and distribution centers, which ultimately defines a commercial enterprise. Based on Fig. 2, Grossmann and Westerberg (2000) define Process Systems Engineering as the field that is concerned with the improvement of decision making processes for the creation and operation of the chemical supply chain. It deals with the discovery, design, manufacture and distribution of chemical products in the context of many conflicting goals. A major change with this definition of PSE is the move away from process units and plants. In one direction the move is towards the molecular level in order to aid in the discovery and design of new molecular structures. In the other direction, the need is to move towards the enterprise level in order to help the coordination of the logistics for manufacturing and production planning with the entire supply chain. These two trends are consistent with the themes Product Discovery and Design, and Enterprise and Supply Chain Optimization, respectively. Furthermore, since the objective is to ultimately integrate from the R&D level through the process manufacturing level, and finally to the distribution level, this gives rise
34 to the theme of Global Life Cycle Assessment that requires covering all the length and time scales of the subsystems involved in Fig. 2.
Fig. 2. The "chemical supply" chain*. 5. FUTURE CHALLENGES Based on the previous sections, it would appear that Product Discovery and Design, Enterprise and Supply Chain Optimization, and Global Life Cycle Assessment are likely to emerge as major research challenges in the PSE area over the next decade. Methods and tools, traditional strengths of PSE, will also continue to be of central importance. Below we discuss each of these themes.
5.1 Product Discovery and Design. In order to move towards the molecular level, traditional process design is expanding to include molecular product design (Joback and Stephanopoulos, 1989). Promising work in this area, which has been labeled as CAMD (Computer-Aided Molecular Design), has been recently summarized in the book by Achenie et al. (2002). Good progress has been made here in developing optimization models for synthesizing molecular structures of solvents, refrigerants and polymers (e.g. see Pretel et al., 1994; Camarda and Maranas, 1999; Duvedi and Achenie, 1997; Sahinidis and Tawarmalani, 2000). A major challenge that remains is the need to develop more accurate predictive capabilities for properties of compounds in order to apply the optimization methodologies (Sinha et al., 2002). Ideally one would like to resort to molecular simulation models (De Pablo and Escobar, 2002), which are increasingly "This figure is courtesy of Professor Wolfgang Marquardt, RWTH Aachen (Marquardtet al, 2000).
35 providing very good predictions as for instance shown in the work by Bowen et al. (2002). Closely related to CAMD is the design and analysis of proteins and biological molecules (see Maranas et al, 2003), where predictive properties tend to be probabilistic as they large rely on experimentation. On the other hand, force-field and ab-initio models are being developed for protein structure prediction as reported by Floudas and Klepeis (2001), Klepeis and Floudas (2002) and Westerberg and Floudas (1999), who have made use of global optimization techniques to solve these problems. To support the expansion to R&D through product discovery, optimal planning and scheduling techniques for new product development are also receiving increased attention to better coordinate the pipeline of new products and their testing in the agrochemical and pharmaceutical industry (e.g. see Blau et al., 2000; Maravelias and Grossmann, 2001; Subramanian et al., 2003; Shah, 2003) At the macroscopic level, product design is also emerging as an intriguing area of research (Cussler and Moggridge, 2001; Westerberg and Subramanian, 2000), although industry has practiced product design for a long time. The emphasis in research is the tie of new products to market needs, and the systematic exploration of alternatives for developing new products, which normally must be accomplished in multidisciplinary teams that are composed by scientists, engineers of other disciplines and business people. An interesting problem here that has not received enough attention is the integration of product and process design, an example of which is the work by Vaidyraman and Maranas (1999) on synthesis of refrigeration synthesis and refrigerant selection. Also, the design of electronic and optic devices would seem to be a worthwhile area given the number of chemical engineers who are hired by the electronics industry. Process design will of course still involve significant research challenges. The lack of commercial tools for the synthesis of large-scale commodity flowsheets clearly indicates the need for new developments and approaches (e.g. see Gadewar et al., 2001; Ismail et al., 1999; Shelley and Halwagi, 2000; Wilson and Maniuosiouthakis, 2000; Yeomans and Grossmann, 1999). Also, among the assessment of operability measures safety is the one that still remains a major challenge (e.g. see Huang et al., 2002). The interaction between design and control also continues to attract attention (Bansal et al., 2002; Design and control of batch and biological processes will also become more prominent. Another related challenge is the area of process intensification that requires discovering novel unit operations that integrate several functions and that can potentially reduce the cost and complexity of process systems (Stankiewicz and Moulin, 2000). While significant progress has been made in the area of reactive distillation (e.g. see Nasoli et al. 1997; Lee et al., 2000; Jackson and Grossmann, 2001), there is not yet a systematic procedure for synthesizing more general units, let alone processes, that integrate functions in a novel way. Other areas that are likely to receive increased attention due to the growth in new industries include synthesis of micro-systems and design of micro-structured materials, where for instance crystallization is a problem of great significance in the pharmaceutical industry (e.g. see Winn and Doherty, 1998). Also, biological related processes, starting at the level of the genome (Hatzimaikatis, 2000), going through metabolic networks (Stephanopoulos, 2002) and finally to process units and flowsheets (Lee et al. 1997; Steffens et al, 2000) will give rise to design and control problems that are likely to attract attention, and lead to problems
36 that have not been investigated previously (e.g. synthesis of separation of very dilute systems). Finally, biomedical applications such as drug delivery (Parker and Doyle, 2001), and anesthesia (Morari and Gentilini, 2001), provide new opportunities for applications of process control methodologies. Surprisingly, there are only very few applications in the electronics area (Edgar, 2000), despite the fact that this sector hires a large number of chemical engineers as was seen in section 2.3.
5.2. Enterprise and Supply Chain Optimization. This area is attracting a great deal of attention from industry and academia, as was shown in the recent FOCAPO2003 meeting in Coral Springs. While the applications are aimed mostly at commodities, there are also increasingly applications in specialties, pharmaceuticals (Shah, 2003), and in the food industry (Masini et al, 2003). Major challenges in this area include development of models for strategic and tactical planning for process networks (Shapiro, 2003) that often require the solution of large-scale multiperiod optimization problems. Furthermore, these models must be eventually integrated with scheduling models. While very significant progress has been made, these models still lack sufficient generality despite significant advances made in this area (e.g. Bassett et al, 1997; Kondili et al., 1993; Pantelides, 1994; Ierapetritou and Floudas, 1998). Also, the efficient solution of these models and extension to rescheduling (Mendez and Cerda, 2003) is required for their application to real-time scheduling and their handling of uncertainties (e.g. Honkomp et al., 1997; Balasubramanian and Grossmann, 2002). The incorporation of uncertainty in planning and scheduling models through stochastic optimization still remains a great challenge due to the very large computational requirements that are needed (Sahinidis, 2003). However, this is clearly an area that might be ripe for significant progress. Another interesting challenge is the characterization of dynamics in supply chains and the application of control methodologies in order to improve responsiveness (Vargas-Villamil and Rivera, 2000; Perea et al, 2001; Nishi et al., 2002). Interestingly the work by Vargas-Villamil and Rivera deals with semi-conductor manufacturing. In order to fully address the enterprise and supply chain optimization requires a tighter integration with the operation at the plant level. Here the areas of data reconciliation and sensor location still offer interesting challenges for optimization (see Bagajewicz, 2003; Chmieliwski et al., 2002), while the areas of verification, abnormal events management, and synthesis of operating procedures (Barton, 2003; Venkatsubramanian, 2003) are being approached by knowledge-based systems, and increasingly with emerging methods for hybrid systems (Silva et al., 2001). The development of effective plant-wide model predictive control is also very relevant in order to provide a seamless integration of the plant with the planning and supply chain optimization models. The major pending research problem that still remains is the integration of planning, scheduling and control, whether at the plant level, or at the supply chain level. Major difficulty is ensuring consistency, feasibility and optimality across models that are applied over large changes in times scales (years, months, down to days and seconds). Another outstanding problem is the design of supply chains in the face of restructuring in the industry.
37 5.3. Global Life Cycle Assessment.
Supporting the goal of Responsible Care by the chemical industry will require development of systematic methods and tools for design of environmentally benign products and processes. At the process level significant progress has been made in the synthesis and optimization of water networks (for a review see Bagajewicz, 2000). Progress has also been made to better understand the implications of waste at the level of the synthesis and analysis of a process flowsheet (e.g. Pistikopoulos et al., 1994; E1-Halwagi, 1997; Cabezas et al., 1999; Linninger and Chakraborty, 1999; Skidar and E1-Halwagi, 2001). Little work, however, has been made to assess environmental implications at the level of product design, and the integration with processing. Examples have been the work by Sinha et al. (1999) for design of environmentally benign solvents, and the work by Hostrup et al. (1999) for design of solvents and synthesis of separation systems. More importantly, however, is the need to adopt broader approaches to the Life Cycle Assessment of Products and Processes in order to predict more accurately their long-term sustainability (Heijungs et al., 1996; Koreevar, 2001; Nebel and Wright, 2002). While few interesting measures have been proposed to support this assessment in the PSE community in terms of thermodynamics (Bakshi, 2000) and in terms of IT to document all the elements involved in the life cycle of a plant design (Schneider and Marquardt, 2002), an open question is still what are good measures for sustainability. Also more comprehensive approaches to sustainability are required as has been done in other disciplines (e.g. Hendrickson et al., 1998). An interesting related question is how to effectively recycle excess products by using a "reverse supply chain" (Biehl et al., 2000). Also, the consideration of atmospheric chemistry and global climate change (Seinfeld and Pandis, 1997; Seinfeld, 2000) should be incorporated to provide a truly global assessment. Furthermore, this would also provide a stronger basis for investigating the carbon sequestration problem (Johnson and Keith, 2001). 5.4. PSE Methods and Tools.
A comprehensive approach to Product Discovery and Design, Enterprise and Supply Chain Optimization, and Global Life Cycle Assessment will involve the solution of a number of challenging problems, including the integration of several parts of the chemical supply chain in Fig. 2. This will require the multi-scale modeling ranging from the atomic level to the enterprise level, as well as the development and improvement of supporting methods for simulation, optimization, control and information processing. While significant progress has been made in optimization in areas such as nonlinear and mixed-integer optimization (Biegler and Grossmann, 2002) there are still many outstanding problems (Grossmann and Biegler, 2002). The solution of very large-scale differentialalgebraic methods whether for dynamic simulation and real-time optimization of entire plants involving millions of variables, or for simulating systems at multiple scales (e.g. fluid mechanics and molecular dynamics) is a capability that is still at relatively early stages (Biegler et al, 2002). There is also need for methods for simulating and optimizing under uncertainty, a capability that is still limited to fairly small problems due to the great potential computational expense (Sahinidis, 2003). Related capabilities where good progress has been made are new approaches to flexibility analysis (Ierapetritou, 2001; Novak and Kravanja, 1999) and parametric programming (Dua and Pistikopoulos, 1999), although current methods are still restricted to relatively small problems. Another important capability will be advanced optimization tools that can handle mixed-integer, discrete-logic and quantitative-
38 qualitative equations to model synthesis and planning and scheduling problems more effectively (Grossmann et al., 2002; Grossmann, 2002). Generalized Disjunctive Programming (Lee and Grossmann, 2003) and Constraint Programming (Hooker, 2000) are novel approaches that offer alternative solution methods, with the former appearing to be especially useful for synthesis problems, and the latter on scheduling problems. An interesting problem here is the development of hybrid methods that effectively combine these techniques (Jain and Grossmann, 2001). Another very important problem that is still in its infancy is mixed-integer dynamic optimization (Bansal et al., 2001). Global optimization has also seen significant progress for solving problems with a specific structure (Adjiman et al., 1998; Ryoo and Sahinidis, 1996; Sahinidis, 1996). Nevertheless, there is still a need to effectively address the solution of large-scale problems, problems involving arbitrary functions, equations (Lucia, 2002) and problems involving differential equations (Papamichail and Adjiman, 2002). A very different approach that is being explored for the approximate global optimization is agent-based computations (Siirola et al, 2002), which has been motivated by the area of complex systems (Ottino, 2003) and that has been recently applied to supply chain problems (Julka et al., 2002) In the area of process control vigorous efforts continue in the areas of model predictive control (Rawlings, 2000; Morari et al., 2003), which is being increasingly applied to new systems such as simulated moving bed chromatography system (Natarajan and Lee (2000). Other efforts include system identification (Jorgensen. and Lee, 2002), process monitoring (Kourti and MacGregor, 1996), and fault diagnosis (Zhao et al., 1998; Chiang et al., 2001). Intriguing problems that merit further research is the use of passivity theory (Hangos et al., 1999; Ydstie and Alonso, 1997) for providing a stronger physical foundation to process control, and network theory for structured modeling in control (Mangold et al. 2002). A new interesting development in process control has been the consideration of PDE models (Baker and Christofides, 1999; Christofides, 2001) that in principle will allow tackling dynamic problems at a much greater level of detail, although the computational challenges are formidable. Another very interesting development has been hybrid dynamic systems that involve discrete and continuous variables (Avraam et al, 1998; Benmporad and Morari, 2001; Kowalewski, 2002; Morari, 2002), and that provide a framework for integrating regulatory control with higher level supervisory control functions. A challenging development in hybrid systems has been the incorporation of global optimization methods (Barton, 2003). Finally, the integration of measurements, control and information systems will emerge as a problem of increased importance with advances in IT (Ydstie, 2001). Modeling will continue to be a major focus of PSE research. New directions include greater integration of traditional algebraic or DAE flowsheet models with PDE models for Computational Fluid Dynamics calculations (Oh and Pantelides, 1996), integration of macroscopic models with molecular simulation models (Stefanovi6 and Pantelides, 2000) in order to support multi-scale computations. Also, modeling tools are needed for the natural specification of logic and discrete decisions (Vecchietti and Grossmann, 2000) as well as for accommodating hierarchical decisions such as in conceptual design (Douglas, 1985). Information modeling tools (Davis et al., 2001; Eggersmann et al., 2002), will also become increasingly important for supporting integration problems, and for problem solving by large and globally distributed teams (Eggesmann et al., 2003).
39 Advances in computing, both in performance growth of computing hardware and in object oriented software development will help to realize some of the supporting tools described above. Higher number of cycles and larger memories can be expected in the future, which will help in addressing a number of the larger problems described above (e.g. Mallya et al., 1999, for process simulation). The need for advanced computing has led to the development of cheap, high performance clusters, such as the Beowulf class computers (e.g. see http://beowulf.cheme.cmu.edu). These have leveraged the availability cost effective components (microprocessors, motherboards, disks and network interface cards) and publicly available, hardware independent software. Beowulf clusters allow the possibility of large-scale parallel computing for the price of standard components. Finally, wireless computing may also create new needs for effectively supporting team work by diverse and distributed specialists. 6. CONCLUDING REMARKS The financial trends, issues and changes in job placement, and the broadening of PSE, would indicate that in order to support "value preservation" and "value growth" in the process industry will require new advances and developments from PSE in three major areas: Product Discovery and Design, Enterprise and Supply Chain Optimization, and Global Life Cycle Assessment. Furthermore, to make progress in these areas continued work is required in basic PSE methods and tools. While this is not a surprising conclusion, we can make a few remarks and observations based on the data and review of recent work that we presented: 1. While a shift to product design is a welcome change in PSE to support "value growth," it should not be separated from process design, which is a core capability of chemical engineers and PSE. Furthermore, to support discovery it is paramount to connect the molecular level design with properties at the macroscopic level. 2. The area of enterprise and supply chain optimization offers a unique opportunity to PSE given its potential to impact the "value preservation" part of the industry, which is under great competition. Supply chain optimization of course also offers opportunities for the "value growth" industry such as pharmaceuticals. 3. Despite the great importance of sustainability and environmental issues, the research efforts from the PSE community have been rather timid. A bolder and more creative approach is clearly needed. One intriguing opportunity might be process intensification as a way to revolutionize chemical plants. Another opportunity could be stronger interaction between product and process design as part of a life cycle analysis of chemicals. 4. There are many potential and exciting possibilities in biological related research in PSE. However, industrial growth in that area is unlikely to become sufficiently large so that it should become the major focus of PSE research. 5. The electronics area has received be little attention by the PSE community despite the significant number of chemical engineers who have been hired by that industry. PSE should be able to contribute to the simulation and optimization of chip manufacturing processes, as
40 well as in the area of product design by exploiting our knowledge o f chemistry and chemical engineering. 6. Since many of the computational challenges of PSE tools arise with large problem sizes, there might be the temptation to think that faster and more advanced computers is all that is needed. While these developments will obviously have significant impact, the breakthroughs will come with synergy of new theories and algorithms as it has happened in the case of LP and MILP optimization (Bixby, 2002). Finally, we hope that this paper has shown that in PSE there are many problems that are intellectually challenging and that are relevant to industry. The important goal is to make sure that new research directions in the "new millennium" emphasize both points.
ACKNOWLEGDMENTS. Many thanks to Christos Maravelias, Conor McDonald and Startos Pistikopoulos for their useful comments and feedback on this paper.
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48
Process SystemsEngineering2003 B. Chen and A.W. Westerberg (editors) 9 2003 Publishedby Elsevier Science B.V.
Control and Operations" When Does Controllability Equal Profitability? Thomas F. Edgar Department of Chemical Engineering, University of Texas, Austin, Texas 78712, U.S.A.
Abstract The justification of process control in the context ofbusiness decis ion-making may include the following economic or operating considerations: increased product throughput, increased yield of higher valued products, decreased energy consumption, decreased pollution, decreased off-specification product, improved safety, extended life of equipment, improved operability, and decreased production labor. However, identifying a direct relationship between each type of economic benefit (profitability) and how controllers are designed or operated (controllability) is an elusive target. Perspectives of how process control has influenced business decision-making have changed radically over the brief history of process control (1950 to the present). Thus it is valuable to have an historical view of the changing role of process control in operations and profit/loss measures. Today the influence of process control on business decisionmaking is at its highest level ever, but there are still many challenges that must be met for process control to maximize its economic impact on an enterprise-wide scale. Keywords process control, profitability, optimization, business drivers 1. INTRODUCTION Profitability is the criterion by which most if not all decisions are made in the chemical industry. It is necessary to quantify profitability mathematically in order to apply modem tools used in process design, operations, and control. However, when process control strategies are to be determined or changed, the key economic considerations, or business drivers, are not easily formulated by a single objective function. Table 1 lists six business drivers for process control that are being used today. Different drivers have been emphasized at different times during the past 50 years. In the 1960s, a plant was considered successful if the products met customer specifications and could be manufactured reliably and more or less consistently (BD 1). The view of process control then was a minimalist one; plant operations personnel were mainly concerned that the controllers did not harm the process, which led to a mentality of "don't touch that dial" once the process was making the desired product. Automation systems in the 1970s utilized supervisory control based on rudimentary optimization tools to maximize profits, thus justifying the investment in computing equipment (BD2). By optimizing the steadystate operating conditions, the return on hardware investment was calculated from the increased profits, but process dynamics and feedback control (i.e., "controllability")
49 played no explicit role in determining economic feasibility. In the 1980s the statistical quality control movement focused on minimizing product variability in order to achieve profitability (BD3). The focus on quality recognized the importance of making the right product that met quality specifications the first time, which eliminated the negative effects on profitability of waste, rework, blending, or reduced selling price (when offspec products were made). Feedback control became a principal tool for achieving BD 1. Meeting safety and regulatory requirements via process control became much more important during the 1980s as well, because when violations occurred there was sometimes a large penalty cost (BD4). In the 1990s additional imperatives on manufacturing were added, name ly that process equipment should be used fully (maximum asset utilization) and that the plant should be operated as flexibly as possible, in order to adapt to market, raw materials, and energy considerations (BD5). This led to the watchwords of"good, fast, cheap, and clean" to describe the goals of manufacturing. Improving the efficiency of information and control systems and workforce productivity became an added driving force in the late 1990s (BD6).
Table 1 21 st Century Business Drivers for Process Control (adapted from Ramaker et al. [~] and Shunta TM) BD1.
Deliver a product that meets customer specifications consistently.
BD2.
Maximize the cost benefits of implementing and supporting control and information systems.
BD3.
Minimize product variability.
BD4.
Meet safety and regulatory (environmental) requirements.
BD5.
Maximize asset utilization and operate the plant flexibly.
BD6.
Improve the operating range and reliability of control and information systems and increase the operator's span of control.
This paper covers the chronology of how the business drivers for process control have evolved, in the context of major "epochs" since 1950: (1) (2) (3)
the early days (1950-70) the energy crisis and the adoption of digital control systems (1970-80) the quality movement and addressing health, safety, and environmental factors (1980-90)
50 (4)
the 21 st century enterprise view of process control (1990 - present)
An important dichotomy in relating process control to economic benefits occurs in batch vs. continuous processing; batch processing has a more natural connection to profitability through an explicit economic objective function, as explained in this paper. 2. PROCESS CONTROL IN THE EARLY DAYS In the 1950s the PID controller was the foundation for the practice of automatic process control in the process industries. However, the important controller design question, namely how to select the values ofKc, zi, and zD that give satisfactory closedloop performance, had no direct connection to process economics. Most operating plants used a combination of manual control and PID control in order to make the desired products. During the 1950s and 1960s, optimization theory and stability analysis received heavy emphasis in the development of control theory due to the aerospace programs in the U.S.S.R. and the U.S.A. This approach was called "modern" control theory, which provided a more sophisticated alternative to the classical control theoryused to design PID controllers. Oil and chemical companies began to recognize that more sophisticated mathematical tools and the use of digital computers held considerable promise to improve plant operations and thus plant profitability. However, the cost of a plant-wide computer control system for a large process plant could range from $250,000 to $1 million, which meant significant benefits had to be derived from steady-state optimization in order to justify such a large capital investment. In the 1960s steady-state optimization (such as linear programming) was normally performed in an off- line computer once a day or once a week, then the results were given to the operator who would change set points manually. Tests in 1957 using Direct Digital Control (DDC) were carried out in the Gulf Coast region by Texaco and Monsanto, but these early applications did not achieve the economic benefits predicted or hoped for. As a result, the chemical industry resisted changing from reliable PID controllers, which worked well enough, were not terribly expensive, and were easy to understand. Why trade them for extremely expensive systems that were unreliable and required extensive research and development as well as highly trained personnel for implementation? How could such systems increase profitability? In the 1960s and early 1970s there were a few documented studies on economic benefits resulting from process control. Barkelew TMreported a 17% reduction in utility costs and 25% increase in capacity with feedforward control. Harter [41 described an ethylene plant where a multilevel scheme with supervisory optimizing control and regulatory control was installed in 1969. With so few successful applications of digital computer control, it became clear that aerospace control technology was not easily transferred to the process industries, whose companies did not benefit from government funding and had processes that were difficult to model. Modem control theory generated controllers whose performance was quite sensitive to model inaccuracies, hence it was not considered to be practical for chemical processes. When it came to control system design, the chemical industry used maxims
51 such as "you can get 80% of the profit with 20% of the effort" and "what can go wrong will go wrong" (Murphy's "feedback" law). Once a plant was making a satisfactory product, any efforts to change the plant or to optimize the operating conditions were opposed. In spite of the resistance in industry, academic researchers continued to pursue the holy grail of optimal control during the 1960s and 1970s as the ultimate answer to industrial process control problems. Because of the plethora of academic papers published on the subject of optimal control, a well-publicized "gap" developed between control theory and practice. At that time it appeared that a number of important process control problems could be solved by the minimum principle, e.g., the minimum time startup of a batch reactor or the maximum conversion in a tubular reactor. Although fundamental models for such systems were fairly simple, implementation of an optimal controller on an actual process was a formidable challenge because of hardware and software limitations. It was not until the 1970s that such algorithms were successfully applied to pilot plants. Ten years later they became commercially feasible. Because the general optimal control solution was an open-loop rather than feedback type of control, there was considerable academic interest in the linear-quadratic problem (LQP), which is now called the LQG (linear-quadratic-Gaussian) problem. The LQG problem is formulated as minimizing a quadratic objective function subject to a linear state equation. This is an important type ofoptimal control problem for which there exists a closed-form analytical representation for the optimal controller in closed-loop or feedback form, u(t) = -Kx(t), where x and u are the state and control vectors, respectively. For LQG, the gain matrix K is obtained independent of the initial conditions. The key question is how to tune the controller performance by adjusting the state weighting matrix Q or the input weighting matrix R in the objective function
J = So' [x~ Qx + u ~Ru]dt
(1)
The use of an objective function in Eq. (1) misled some researchers into thinking it was a meaningful economic measure of control. Unfortunately, the cost of deviation of a state variable (e.g., a concentration) about a desired set point is usually different for positive deviations (profit reduction) and negative deviations (off-spec product). The cost of control can range from zero to the cost of utilities, which may be significant. When positive control changes incur a significant cost, a pure feedback control framework is not optimal except in some fortuitous cases where the control effort could be positive for all time, thus giving economic meaning to the urRu term. In general this will not happen, because a well-tuned output response will yield overshoot in both state and control variables. Explicit control weighting in the objective function can be eliminated by adding a quadratic term involving du/dt. Thus control effort is ignored but changes in the input are penalized. This approach effectively incorporates integral control into the overall feedback control law. Penalizing the rate of change of the control induces more inertia into the controller, causing it to change position less often, which is desirable because constant adjustment of a control valve causes faster valve wear (a hidden cost). Another hidden cost might be the disturbances caused in other units by an overactive control system.
52 Chintapilli and Douglas TMperformed a study of optimal control of a nonlinear reactor using various types of objective functions. For this system they found that a linear feedback controller derived from a quadratic objective function and linearized model was almost as good as the open- loop optimal controller computed for an actual profit function and the nonlinear model. Thus the LQG could be used to synthesize multivariable controllers having profitability implications. O'Connor and Denn[6] showed that the optimal solution for a first-order-plus-time-delay model and a quadratic error objective function could be put in the form of a PID controller, which has settings very similar to that for Ziegler-Nichols tuning. Subsequently other approaches such as Internal Model Control (IMC) or Box-Jenkins minimum variance control have derived similar tuning relationships (although with a smoothing filter). While an optimal control formulation can yield a familiar control structure like PID, it was not a "silver bullet" for ensuring profitability of a plant. Addressing profitability in the chemical industry required a two-pronged (or hierarchical) approach combining steady-state optimization and regulatory control. In the 1970s it became apparent that most ofthe direct financial gain arose fi'om steady-state optimization that maintained production within specifications and calculated improved set points when operating conditions changed. The main value of dynamic control was to provide satisfactory regulatory control and set point changes, although dynamic control could permit operation at unstable steady states which are desirable economically (e.g., in crystallization and polymerization). Once deviations became small enough, there was little economic value to make them smaller. The two-pronged approach also suggested that two types of models were required, a linear or nonlinear steady-state model and a linear dynamic model. 3. THE ENERGY CRISIS AND THE ADVENT OF DISTRIBUTED CONTROL SYSTEMS A number of events occurred during the late 1970s that stimulated the widespread introduction of computer control in the chemical and refining industries. Because of the high cost of computers and associated control equipment and the lack of demonstrated economic benefits from closed-loop computer control, early installations were justified based on a return on investment by using the computer to perform supervisory control (set point optimization), as discussed in the previous section. Typically a payback period of one year or less was required to justify a computer control project. A broader view began to emerge on potential financial benefits of process control, which included improvements in yield, throughput, quality, and energy usage. Process yield could be improved by reducing physical losses of materials in vents and waste streams and by reducing chemical losses due to excessive recycle of reactants. Throughput improvements offered tremendous benefits for a product where the market is sold out and additional products can be sold at the same price. Quality improvements were harder to quantify because quality changes were not always reflected in higher sales volume or selling price. The importance of energy savings relative to yield or quality depended on the nature of the process; specialty chemicals or pharmaceuticals typically do not have a high energy cost contribution, while refining processes are more energy-
53 intensive. All of these considerations can be incorporated into profitability calculating carried out on a daily or hourly basis, which is now called real-time optimization or RTO. In order to perform supervisory control or real-time optimization (RTO) for an operating plant, an objective function (or economic model) must be defined, which includes the costs of raw materials, values of products, and costs of production as functions of operating conditions, projected sales or interdepartmental transfer prices, and so on. An operating model must be developed that relates product variables to operating conditions (the set points). Both the operating and economic models typically include constraints on operating conditions, feed and production rates, storage and warehousing capacities, and product impurities. In addition, safety or environmental constraints might be added, such as a temperature limit or an upper limit on a toxic species. Once models and constraints are defined, optimization methods can be applied to find the optimal set points. 3.1 The Energy Crisis The tripling of fuel costs in the mid- 1970s due to the Arab States oil embargo was a shock to industry and individual consumers alike and caused a permanent change in thinking in the process industries, i.e., energy was no longer "cheap". This event provided the impetus for the initiation of wide-ranging programs on energy conservation, which ranged from housekeeping items such as checking steam traps or adding more insulation to computer control and RTO on equipment such as distillation columns, boiler systems, and refrigeration units. To estimate the potential significance of energy conservation via process control, a 1% energy savings due to improved control and a fuel cost of $2/MBtu would save around 300 million dollars per year (based on 15xl 09 Btu/year consumed in the U.S.A. process industries). An oft-cited figure that distillation columns consume 3% of all energy used in the U.S.A. gives an indication of the large incentives to optimize distillation energy usage. For example, most columns in the 1970s were manually operated at high reflux ratios in order to meet product quality specifications, which required a re-balancing of the economics of column operation using automation. Buckley[71 and ShinskeyTMdiscussed several design and operations steps to minimize energy consumption in distillation, based on improved control techniques and optimizing operating conditions. Adding equipment for heat recovery purposes entailed recovering or reusing heat contained in the column product streams, using multiple effect distillation or vapor recompression. One impediment at that time was the added control requirements for auxiliary condensers or reboilers, which was not recommended by Buckley (1981) because of added investment cost and increased instrumentation and control complexity. Turning auxiliary reboilers and condensers on and off was problematic so a small load on these heat exchangers was always maintained, which obviously wastes energy. An analysis by Luyben[91 showed that multi-point composition control yields minimum energy consumption in a distillation column. When the composition of both product streams from a binary distillation column are controlled simultaneously, the quantity of product which might be discarded is also reduced. Beyond distillation columns, energy management strategies were developed for boilers and steam systems in order to reduce costs (Bouilloud[l~ Edgar et all 1I]). During
54 the 1970s, DuPont reported that energy management systems were optimized for several types of problems [12]. In individual boilers, boiler excess oxygen was optimized using CO measurement, by adjusting excess air in the boiler feed. The steam load was distributed among multiple boilers by using linear programming. If the desired load could be achieved by running only six of seven available refrigeration machines, sometimes shutting down one unit reduces costs. Turning equipment on and off involved mixed-integer programming or penalty functions. However, computer control systems in the 1970s had to be extensively customized in order to implement such optimization strategies.
3.2 Computer Hardware Developments Development of the microcomputer and the cumulative effects of Moore's law started a revolution in the application of computing in the process industries. The reductions in hardware cost of process control computers (due to increases in computing speed by a factor of 100 each decade) was a significant impetus for adopting computer control and advanced control techniques. The emergence of standard software packages and architecture also facilitated applications in process control. Computer control algorithms based on optimization were not feasible for real-time computing in the 1970s but during the 1980s such methods became usable because of increased processing speed. Early digital installations used for process control were not failure-proof and required a totally redundant system in case of component failure. In most cases, the backup system was the analog (pneumatic) system used before the introduction of computer control, which involved extra costs. The distributed computer process control system (DCS) was pioneered during the 1970s by Honeywell. The DCS is still dominant in the process industries (although new architectures have emerged in the 1990s) and employs a hierarchy of computers, with a single microcomputer controlling 8 to 16 individual control loops. Detailed calculations such as supervisory control are performed using workstations that receive information from the lower-level devices. Set points are sent from the higher level to the lower level. The hierarchical design was compatible with different supervisory and regulatory functions and the need for database accessibility. Software could be located where calculations need to be made. Because the system was modularly designed, failure at any one point in the network did not shut down the entire systern[13] . 4. NEW PLANT OPERATING OBJECTIVES During the 1980s the effect of global competition on the chemical process industry caused a heightened awareness of the importance of product quality in affecting profitability. Process control began to be employed to ensure satisfactory product quality, in many cases using statistical process control rather than feedback control. Because past hazardous waste disposal practices created a number of pressing environmental problems, the trend of increasingly more stringent environmental regulations also began in this period. Chemical companies changed design and operating strategies (via process control) to minimize waste production because of the prohibition against discharge and/or disposal of toxic substances. New plants moved toward a "zero-
55 discharge" concept, and protecting the safety of operating personnel took on heightened emphasis after the Three-Mile Island, Chernobyl, and Bhopal incidents. Quality control (and its inherent variability) has an overriding effect on profitability, and quality can be controlled on a dynamic basis. Lower variability means that the process can be operated closer to constraints in order to maximize throughput []4]. Making the specified product also maximizes equipment utilization because equipment does not have to be shut down for blending or rework. With lower product variability, cycle time (the elapsed time between receipt of raw materials and shipping finished products) can be minimized. The effectiveness of the control system in ensuring consistent product quality depends on speed of response of the controllers and the effectiveness of the control strategy and the measurement system (e.g., all product quality variables are measured online). A 1988 study by E.I. DuPont de Nemours estimated that increased profits of $200 to $500 million dollars/year could be realized in their facilities through implementation of advanced control and optimized operating conditions. Downs and Doss [15] discussed the relationship between process variability and control system design using a reactor feed preheater. From a process control point of view, the control system relocates variability in the feed exit temperature to variability in the hot stream flow rate. As the control algorithm and/or the controller tuning change, different amounts of the variation are transferred. Depending on the process it may be more desirable to have all the variation in the flow rate of the hot stream (tight control) or all the variation in the feed exit temperature (controller in manual or no control at all). The control algorithm adjusts the amount of inlet temperature variation to be shared between the outlet temperature and the hot stream flow rate. However, understanding the objectives of the process are critical to the design and tuning of the controller. The use of advanced control techniques such as adaptive control or a nonlinear control may be warranted for tight control of the reactor feed temperature, but this requires a clear statement of the process objective. Decreasing the variability of the process became a principal way of quantifying the potential benefits of improved process control. Figure 1 shows a typical pattern for product quality variabilit~ TM. The limit represents a hard constraint, in other words, one that should not be violated, such as a product specification or an unacceptable impurity level. If the product quality can be maintained more consistently near the upper limit, the resulting cost savings are roughly proportional to the size of A 2 - A] in Fig. 1.
Limit
Limit ~
Operating Variable
Average, A2
Average, A1 Time (a)
Time (b)
Figure 1. Product variability over time: (a) before improved control; (b) after.
56 A corresponding frequency distribution or histogram for variations in product quality is another standard interpretation[2]' [131. While the use of computer control can sometimes be justified solely by safety and environmental concerns, the decreased variability of the process is a principal way of quantifying the potential benefits of improved process control[ 16]. The rebirth in the process control field in the 1980s, both in theory and practice, was highlighted bythe emergence of a new generation of model-based control theory that was tailored to the successful operation of modem plants. The most noteworthy algorithm was model predictive control (MPC), where a mathematical model is explicit in developing a control strategy that handles multivariable interactions and manipulated and controlled variable constraints. MPC is based on selecting the current and future control actions using a dynamic model to predict the process dynamic behavior. In MPC, control actions are obtained from on-line optimization (usually by solving a quadratic program, or QP), which handles the process variable constraints. MPC also unifies treatment of load and set-point changes via the use of disturbance models and the Kalman filter. MPC was essentially a straightforward extension of the LQG problem discussed earlier, but was more effective due to the greatly improved computational ability, especially for input constraints. 5. AN ENTERPRISE VIEW OF PROCESS CONTROL The latest era of process control has an equal emphasis on process modeling, control, and optimization and began in the 1990s. The capability of using more sophisticated mathematical models in automation and control has grown during the past twenty years. Given the current state of the art in control and optimization theory, the major uncertainty in controller design is selection of the model and its level of detail and complexity. Once the model is actually chosen and verified, there are usually several methods available to compute a control or optimization strategy. Edgar et al. [~11have shown there are five levels of control activities in a manufacturing process where various optimization, control, monitoring, and data acquisition activities are employed. Data from the plant (flows, levels, temperatures, pressures, compositions) as well as so-called enterprise data, consisting of commercial and financial information, are used to make decisions in a timely fashion. The highest level (level 5) deals with planning and scheduling, sets production goals to meet supply and logistics constraints, and addresses time-varying capacity and manpower utilization decisions; this is called enterprise resource planning (ERP). In a refinery the planning and scheduling model can be optimized to obtain target levels and prices for inter-refinery transfers, crude and product allocations to each refinery, production targets, inventory targets, optimal operating conditions, stream allocations, and blends for each re finery (Shobrys and White [17]). In level 4 RTO is utilized to coordinate the network of process units and provide costeffective set points for each unit, as discussed in previous sections of this paper. For multivariable control or processes with active constraints, set-point changes are performed in level 3 (e.g., model predictive control), while for single-loop or multi-loop control the regulatory control level is also carried out in level 3. Leve 1 2 (safety, environment, and equipment
57 protection) includes activities such as alarm management and emergency shutdowns. Level 1 (process measurement and actuation) provides data acquisition and on-line analysis and actuation functions, including some sensor validation. The time scale for decision-making at the highest level (planning and scheduling) may be of the order of months, while at lower levels (e.g., process control), decisions affecting the process can be made frequently, e.g., in fractions of a second. Even with the multi-level view of process control, RTO is still a significant economic driver; operating profits can exceed $200,000 per day (Bailey et al.[18]). The scale at which industrial RTO can be implemented is impressive, solving problems with over 100,000 variables and equality/inequality constraints. Other recent examples of integrating operations and business life cycles through optimizing asset utilization have been discussed by Howell et al. [19]. Marlin and Hrymak t2~ reviewed a number of industrial applications of RTO, mostly in the petrochemical area. They reported that typically there are more manipulated variables than controlled variables, so some degrees of freedom exist to carry out both economic optimization as well as establish priorities in adjusting manipulated variables while simultaneously carrying out feedback control. Skogestad [2~] and Perkins E22] have discussed the interplay of constraints, the selection of the optimal operating conditions and the preferred control structure. Skogestad identified three different cases for RTO: (a) constrained optimum (b) unconstrained flat optimum (c) unconstrained sharp optimum. Cases (a) and (b) are preferred. In both cases (b) and (c), if the process is operated at the optimum, a change in the sign of the process gain occurs around the optimum, which makes linear feedback control more difficult. Significant potential benefits can be realized by using a combination of MPC and RTO. At the present time, most commercial MPC packages integrate the two methodologies in a configuration such as the one shown in Fig. 2. The MPC calculations are imbedded in the prediction and controller blocks and are carried out quite often (e.g., every 1-10 min). The prediction block predicts the future trajectory of all controlled variables, and the controller achieves the desired response while keeping the process within limits. Set-Point I Calculations Set Points (Targets)
Prediction
Predictions
I Jnputs_I ] -1 Process
Control -' Calculations I Inputs
,
_l v[
Model
,,
,
I
Process
Outputs Model
Outputs ~
Residuals
Figure 2. Block diagram for Model Predictive Control. The targets for the MPC calculations are generated by solving a steady-state optimization problem (LP or NLP) based on a linear process model, which also finds the best path to
58 achieve the new targets (BackxI23] ). These calculations may be performed as often as the MPC calculations. Linear model predictive control based on a quadratic performance index has been successfully applied to many continuous plants, which has encouraged the consideration of control strategies based on nonlinear fundamental models. Bacl~ TM has stated that the performance and robustness of the control systems are directly related to the quality and accuracy of the prediction models in the control scheme. It is important that the models describe all relevant process dynamics and cover the full operating range, consisting of operating points as well as transition states or trajectories, which may not be possible with linear models. To generalize further, one can define objective functions that include profits earned along a trajectory plus a capital inventory term. For continuous processes this permits computing an optimal transition between operating conditions or the optimal path to recover from disturbances to the normal operating point. The control strategies based on an explicit economic objective function can change depending on different prices for product quality and on market conditions. 6. BATCH PROCESSING Batch process control has historically received much less attention than continuous process control from the process control community. Because the volume of product is normally small, large product demands are achieved by repeating the process on a predetermined schedule. It is usually not economically feasible to dedicate processing equipment to the manufacture of a single product due to the small product volumes. Instead, batch processing units are organized so that a range of products (from a few to possibly hundreds) can be manufactured with a given set or subset of process equipment. The key challenge for batch plants is to consistently manufacture each product in accordance with its specifications while maximizing the utilization of available equipment. Benefits include reduced inventories and shortened response times to make a specialty product (vs. larger continuous processing plants). Typically it is not possible to use blending operations in order to obtain the desired product quality, so product quality specifications must be satisfied by each batch. Maximization o f yield is a secondary objective to obtaining the specified yield. [24j Batch processing is widely used to manufacture specialty chemicals, metals, electronic materials, ceramics, polymers, food and agricultural materials, biochemicals and pharmaceuticals, multiphase materials/blends, coatings, and composites - an extremely broad range of processes and products. In the U.S.A. there are more batch chemical plants than plants using continuous processing, but this is not apparent from reviewing the process control literature. Batch control systems operate at various levels: (1) Control during the batch: This includes defining an optimal trajectory for the batch plus feedback control of flow rate, temperature, pressure, composition, and level, also called "within-the-batch" control (Bo nvin[25]). (2) Run-to-run control: Also called batch-to-batch, this is a supervisory function based on off- line product quality measurements at the end of a run. Operating conditions and profiles for the batch are optimized between runs to improve the product quality.
59 (3)
Batch production management: Scheduling of process units is based on availability of raw materials and equipment and customer demand. Bonvin[25] and Juba and Hamer [26] have discussed the operational challenges for optimal control during a batch, notably nonlinear behavior and constrained operation. There are several advantages of a batch process over an equivalent continuous process in meeting product quality requirements: 1. 2. 3.
The batch duration can be adjusted in order to meet quality specifications. Because a batch process is repetitive in nature, it offers the possibility of making improvements on a run-to-run basis. Batch processes tend to be fairly slow so that operating conditions can be optimized in real time.
In a batch process, frequently it is possible to use optimal control to determine the most economically advantageous trajectory for the process variables during the batch. For a multi-step reaction such as A + B ? C and 2A ? D in a fed-batch reactor, the feed-rate profile that maximizes the concentration of C after a specified batch time consists of a singular/bang-bang control, i.e., maximum flow rate followed by a period of unconstrained flow (singular arc) and then a time period at minimum flow. Usually it is possible to relate directly the conversion to C to profitability concerns. Another example of direct formulation of an economic objective function for a batch process is the minimum time addition of the reactant such that the undesirable product D is minimized at the final time. By minimizing the batch time, asset (reactor) utilization can be maximized. Bonvin et al. [271 distinguish between model-based optimization (MBO) used in batch processing and model predictive control (MPC) used in continuous processing. The goal in MPC is to choose inputs to track a reference signal, and it has an objective function (typically a quadratic error function) that reflects the quality of control. In contrast, MBO defines an actual cost function to be optimized. MPC applications almost always deal with continuous processes. One practical matter with batch processing is that there is no steady state operating point but rather a trajectory, which makes developing a linearized model problematic. In MBO the key issues are feasibility and optimal feedback control. MBO typically has solutions that lie on the constraints. In contrast, typically MPC is designed by introducing a compromise between tracking performance and input effort. 6.1. Run-To-Run Control
Recipe modifications from one run to the next are common in specialty chemicals manufacture. Typical examples are modifying the reaction time, feed stoichiometry, or reactor temperature. In run-to-nm control modifications are done at the beginning of a run (rather than during a run). Run-to-nm control is frequently motivated by the lack of on- line measurements of the product quality during a batch run. In batch chemical production, on- line measurements are often not available during the run but the product can be analyzed by laboratory samples at the end of the run[251. The task of the run-to-nm
60 controller is to adjust the recipe after each run to reduce variability in the output product from the stated specifications. In semiconductor manufacturing, the goal is to control qualities such as film thickness or electrical properties which are difficult, if not impossible, to measure in real time in the process environment. Run-to-run control is particularly useful to compensate for drifting processes where controlled variable fluctuations are correlated in time. For example, in a chemical vapor deposition process, the reactor walls may become fouled due to byproduct deposition. This slow drift in the reactor chamber condition requires occasional changes to the batch recipe in order to ensure that the controlled variables remain on-target. Eventually, the reactor chamber must be cleaned to remove the wall deposits, effectively causing a step disturbance to the process outputs when the inputs are held constant (Edgar et al. [281 ; Moyne et a1.[29]).
6.2 Batch Production Management A production run typically consists of a sequence of a specified number of batches using the same raw materials and making the same product to satisfy customer demand; the accumulated batches are called a lot. When a production run is scheduled, the necessary equipment items are assigned and the necessary raw materials are allocated to the production run. As the individual batches proceed, the consumption of raw materials must be monitored for consistency with the original allocation of raw materials to the production run, because parallel trains of equipment may be involved. Various optimization techniques can be employed to solve the scheduling problem, ranging from linear programming to mixed-integer nonlinear programming (Pekny and Reklaitis[3~ 7.0 CONCLUSIONS AND FUTURE CHALLENGES The business drivers for process control have evolved over the past 50 years from a single one (meeting product specification) to a multi-objective set that requires practical trade-offs in order to maximize plant profitability. Using a multilevel view of process control, greater efficiencies can be achieved through having all levels work together in harmony, rather than as a set of decoupled control functions. This is especially true at higher levels such as planning and scheduling and real-time optimization where large potential improvements in profits are possible. Increased usage of batch processing will permit process industries to emphasize rapid delivery of smaller quantities of differentiated products, which will allow plants to be downsized and located closer to customers. Such plants will also be more flexible (or agile) in operation and will more easily satisfy increasingly stringent safety, health, and environmental regulations, but they will require a more sophisticated level of process control. The increased usage of advanced batch process control may make direct optimization of an economic criterion more common compared to its use in continuous processing. REFERENCES [1]
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Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
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MOPSD" A Framework Linking Business Decision-Making to Product and Process Design Ka M. Ng Department of Chemical Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR Multiscale objective-oriented process synthesis and development, MOPSD, relates business decision-making to the design and development of products and processes. Business decisions are made in a hierarchical manner, from corporate goals, marketing decisions, product design, to plant design and development. To implement such a framework, the RATIO concept is introduced. The objective, information, tools, time needed, activities, and human and monetary resources for completing each step of the business project are identified. Keywords: Business Process, Decision-Making, Product Design, Process Design, Process Development 1. INTRODUCTION The chemical processing industry (CPI) is the largest global industrial sector with a total shipment of US$1.59 trillion in 1999 [1]. This is higher than the 2001 China GNP of US$1.16 trillion [2]. The CPI, similar to other industries, has been striving to innovate in response to new technological developments and changes in the world economy. During the 70s, improvement of equipment and process performance was the focus of much research and development, building on a better understanding of transport phenomena, and improved simulation and optimization techniques. In the 80s, the CPI made a significant amount of effort using the pinch technology to minimize energy consumption and advanced control to maximize productivity. These efforts have led to notable results. For example, between 1982 and 2001, the operating cost for downstream petroleum processing in the US has declined from US$10 per barrel to approximately US$4 per barrel in constant year 2000 dollars [3]. However, due to competition, the gross margin has also decreased by the same magnitude, resulting in no gain in net margin. It became clear in the 90s that one should look at the entire supply chain for additional savings. To meet this need, companies such as Aspentech [4], i2 [5], SAS [6] and PricewaterhouseCoopers [7] offer a wide range of tools for enterprise resource planning, demand, production and distribution planning, etc. In the past several years, much attention has turned to the design and manufacturing of differentiated products [8-12]. In hindsight, this is hardly surprising in view of the profit margin in different industrial sectors of the CPI. Most chemical companies have their profit margin hovering around 8%, whereas it is 12% and 20% for specialty chemical and pharmaceutical companies, respectively. This of course does not
64 imply that drug firms which tend to have a higher price-earning ratio are a better investment. The rationale for the numerous reorganizations, spin-offs, mergers and acquisitions of the CPI in the past decade was varied. Some such as ICI attempted to shift from commodity chemicals to specialty chemicals, thus placing more weight on productcentered processing rather than process-centered processing (Figure 1). Some mergers such as those between BP and Amoco, and Exxon and Mobil enhanced economy of scale. Spin-offs such as DuPont and Conoco, Kodak and Eastman Chemical, and Monsanto and Solutia resulted in an improved corporate focus. All of these M&A activities, particular the mega ones such as that of Pfizer, Pharmacia and Searle have significantly changed the landscape of the global CPI.
Corporate Strategy
Process-Centered Processing
Product-Centered Processing
Business Process Model
Figure l. The corporate strategy decides on the mix of high-volume or high-value-added products. This in turn affects the business process as well as corporate R&D. All of these changes, either technical or financial, are the results of deliberate decision making. Indeed, thousands of decisions are made every day in a corporation. Corporate-wide strategic decisions can have a life span of tens of years and affect stakeholders around the globe. Decisions made in business units, such as a pigment division or a monomer division, tend to have a shorter duration in time. For example, they tend to focus on seasonal, monthly, weekly, and daily demand and production planning. To meet these production targets, engineers and technicians have to make decisions on equipment operations. Business decision-making is not limited to management and manufacturing. The R&D effort has to be aligned with the corporate-wide strategies, business unit directions, plant operations and product requirements. Decisions in R&D also span a wide range of length and time scales. The researcher may have to consider the entire process, each equipment unit, the transport phenomena within each unit, and the molecular nature of the product [ 13-16]. Indeed, this multiple length and time scale approach is expected to play a key role in process systems engineering [ 17]. This article proposes a framework for viewing a chemical enterprise from a multiple length and time scale perspective. Similar to Douglas' procedure for conceptual design [ 18], this framework is hierarchical in nature, with decision-making divided into a number of levels. By making decisions more or less in the order of decreasing length and
65 time scales, iterations among the various levels are minimized. Thus, corporate goals guide marketing strategies, customer desires determine product attributes, which in turn dictate materials selection and process flowsheet. The objective, information, tools, time, activities, and resources in terms of personnel and money involved at each scale are also identified [ 19]. 2. M U L T I S C A L E O B J E C T I V E - O R I E N T E D PROCESS DESIGN AND DEVELOPMENT 2.1.
L e n g t h a n d T i m e S c a l e s in CPI Let us begin with a review of the length and time scales considered in this framework (Figure 2) [ 16]. The length scale spans from the size of a molecule to that of an enterprise. Here, 108m is roughly the circumference of the earth, suggesting a global company, whereas 109s is roughly 32 years, signifying a long-term corporate strategy. Following the enterprise, we have production plants, equipment inside the plant, transport phenomena within the equipment, and the molecules involved in the reactions. Multiple
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66 2.2. Relating Shareholder Value Added to the Objectives in the MOPSD Framework An international chemical company may have thousands of employees working for the company. The employees at each level may have a different objective. For example, the business VP has to balance the demand and production of a particular product, whereas a plant manager focuses primarily on ensuring smooth plant operations and product qualities, and improving uptime. Despite the diversity of job functions, shareholder value added (SVA) is perhaps the singular financial metric that should be shared by all employees: SVA = After Tax Operating Income - C o s t o f Capital x Net Investment
(1)
It captures the common goal of a corporation - creation of wealth for the shareholders. (The State-Owned Enterprises in China which have to meet certain social responsibilities are an exception.) It represents the gain above the amount that their investment could earn in the financial market. To relate SVA to plant design and operations, we can express the ratio of after tax operating income to net investment in terms of retum on net assets (RONA): R ONA =
(2)
Sales Volume x Selling P r i c e - C o s t s Net permanent investment + Working capital
This can be seen more explicitly in a corporate cash flow diagram (Figure 3). The after tax operating income is derived from the sales. Construction, financed with equity, borrowing, and operating cash flow, results in permanent investment.
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67 Equation (2) clearly shows that we can improve SVA by raising the price or increasing the sales volume, or alternatively by cost reduction. High-value-added products are more likely to have better selling prices. Product design methods such as Quality Function Deployment can be used to capture the customer and technical information. Commodity chemical companies tend to have capacity expansion and cost reduction as their business strategy. In addition, a company can improve the uptime to reduce the necessary permanent investment and minimize inventory to reduce working capital. For the latter, supply chain management plays an important role to achieve the desirable outcome by optimizing the entire cycle of buy, make, move, store and sell. Other financial metrics such as after tax profit margin, sales growth, controlled fixed cost productivity (CFC), etc. can also be used to relate SVA to the various technical objectives of the MOPSD framework. Consider controlled fixed cost productivity which is defined as follows: CFC =
Sa/es
(3)
C o n t r o l l e d f i x e d c o s ts
Here, the controlled fixed costs include payroll and benefits. For the performance of a batch plant, it serves as an important measure because its labor costs constitute a much larger percentage of the total cost than that in a continuous process. Thus, it provides a possible optimization objective in the design and scheduling of batch processes [20]. In general, the technical objectives in product and process design should be set with the cash flow diagram, SVA or other financial metrics in mind, if possible. 2.3. Individual Levels of MOPSD We need the participation of all organizational levels in the company to carry out the corporate strategy. The organizational levels are equivalent to the levels (or length scales) in MOPSD. The number of levels in MOPSD should be chosen according to the culture and capability of the company, business unit, plant site, research division, laboratory, etc. and thus is company specific. Nonetheless, let us illustrate this concept with a greatly simplified example (Table 1).
Table 1. Various objectives and length scales (i.e., organizational levels) in a typical chemical company are presented in column 1 and 2, respectively. The personnel involved at each level are also shown. The sub-columns of column 2 show the role of the personnel in meeting the various objectives. Organizational Level and Personnel Objectives
Corporate Goals
Corporation
Business Unit
CEO, CTO, CFO, Board members Set corporate goals and allocate resources
Business VPs, Marketing managers Identify business opportunities to meet
Manufacturing Site Plant Managers, Operating personnel Identify interbusiness operational improvements to
R&D Laboratories R&D Director, Chemists and Engineers Identify new products and processes across business units to
68 corporate goals Set business and marketing plans
Business Unit Strategy
Listen and review
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Listen and review
Listen and review
New Products and Processes
Listen and review
Listen and review
meet corporate goals Identify interproduction site operational improvements to meet business goals Reduce downtime, safety, quality assurance, etc. Listen and review
meet corporate goals Identify new products and processes to meet business unit goals Develop new methods and tools for manufacturing Allocate resources to meet long and short term R&D objectives
The broad objectives in Table 1 have to be reduced into sub-objectives for project planning. For example, the design of a new process by the R&D laboratory requires conceptual design, determination of basic data, process simulation and optimization, control system design, etc. 2.4. RATIO for the Implementation of MOPSD
RATIO is the acronym for objective, information, tools, time, activities, and resources (Table 2). It describes the key components in the execution of each subobjective in MOPSD. The broad objective as well as the sub-objectives has to be defined first. For business decisions, some objectives such as customer satisfaction cannot be measured quantitatively. Often, one has to deal with multi-criteria decision-making and Pareto-optimality. Table 2. The components in the execution of MOPSD - RATIO [ 19]
Define objective of the task Determine the input and output information Identify appropriate tools Estimate the time needed to meet the objective Identify the activities to be performed Identify human and monetary resources to perform the activities
Next, we identify and obtain the necessary input information. While historical data may be available in company archives, one has to take advantage of the human resources. Experience shows that chemists and engineers involved in similar projects can point out the right directions and potential pitfalls, thus greatly enhancing the chance of success and reducing the time and effort. Appropriate tools should also be identified. This
69 can be software such as the wide variety of computer programs for process simulation and modeling, and supply chain management. They can also be systematic design methods for process synthesis such as those for distillation [21 ], crystallization [22-24] and reactions [25-27]. Likewise, these can be experimental setups and procedures. For example, the many high-throughput screening techniques can expedite the identification of the best catalyst for a given reaction. The use of such tools by the people involved constitute activities, which can be estimation, synthesis, modeling, simulation, experimentation, etc. Finally, we allocate human resources and capital for these activities and tools. As mentioned, an objective can be further broken down into sub-objectives. Figure 4 shows that RATIO is applied to each sub-objective to achieve the overall objective. This represents the essence of this objective-oriented approach in which tasks are purposely performed.
Figure 4. A hierarchy of objectives. An accurate estimate of the time required to complete a given task or to achieve a certain objective is important in the implementation of MOPSD. Such estimates allow the maximum number of tasks to be performed concurrently and help predict the time needed for the overall project [ 16].
70
2.5. MOPSD: An Integration of Business Process Engineering and Process Systems Engineering Much has been achieved in business process engineering as well as business process reengineering [28]. Kirchmer [29] argued that there should be a market- and product-oriented design of business processes. Smart et al. [30] pointed out the five stages of a business reengineering methodology: Stage 1 Identify or create corporate, manufacturing and information technology strategies Stage 2 Identify key processes and process objectives Stage 3 Analyze existing processes Stage 4 Redesign processes Stage 5 Implement MOPSD follows a similar strategy but has two major differences. First, we follow the natural length and time scales of the entire business and manufacturing process. Therefore, we can more easily identify both business and technical sub-objectives. Second, we use SVA as the overall objective to ensure that the development of new products and manufacturing technologies is in alignment with the corporate directions. 3. AN EXAMPLE - MOPSD AND PRODUCT-CENTERED PROCESSING Let us consider Figure 5 which shows a systematic procedure for the synthesis and development of chemical-based consumer products [9, 31]. The Head Office has identified a family of products for which our company has a competitive advantage in terms of marketing, technical know-how and IP position. For this reason, we have decided to carry out a product and process development project. At the enterprise level, market trends are used to identify the product forms, the functionalities of the product, and the projected demand. At this stage, existing and potential competitors are identified as well. With an estimated product cost, capital budgeting is performed to determine the intrinsic rate of return. Assuming that the rough estimate satisfies the corporate financial return target, the project moves forward. The quality factors are identified. These are related to technical factors which are met by properly selecting the ingredients and by synthesizing the process alternatives for the transformation of the ingredients into the final product. In Figure 5, the round-comered rectangles represent the outcomes; i.e. the output information. The vertical arrows indicate the activities. The input information and tools for each activity are given in the rectangles on the right 4. CONCLUSIONS A conventional company tends to have a business ladder and a technical ladder for their employees. Often, there is limited interaction between business personnel, and chemists and chemical engineers within the company. This problem is compounded for a global enterprise for which business and technical decisions are made with people in different parts of the world. This gap has to be narrowed to produce the right product, improve product quality, lower production cost and reduce time-to-market. To this end, MOPSD provides a framework linking business decision-making to the synthesis and development of products and processes. In a hierarchical manner, from large scale to progressively smaller scales, company strategy is executed through all the organizational levels within the company. Process design is treated in a similar manner by including more fine details as one proceeds through the hierarchy.
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(Manufacturingprocess1 Figure 5. Step-by step procedure for product-centered process synthesis and development. To implement such a framework, a product and process design project is divided into a number of tasks, each with its own objective. These tasks should be executed concurrently if possible in order to minimize development time, but whether this is feasible depends on the required input information and the availability of resources. Thus, it is important to clearly identify the objective, information, tools, time, activities and resources (RATIO) for each task in planning a project. MOPSD attempts to integrate business process engineering and process systems engineering. With a changing global environment, the demarcation between disciplines has become blurred and process systems engineering is bound to expand its scope. Biology is now widely considered to be a foundation science of chemical engineering. Will management be next for PSE?
72 ACKNOWLEGMENTS My thinking on the relationship between business decision-making and process development has been influenced by many of my industrial collaborations. In particular, I have greatly benefited from my interactions with George Stephanopoulos, Haruki Asatani, Hironori Kageyama, Takeshi Matsuoka, Toshiyuki Suzuki, and many others at Mitsubishi Chemical Corporation, and Lionel O'Young, and Christianto Wibowo of CWB Technology. I would also like to thank Bruce Vrana for his teachings on corporate finance during my stay at DuPont Central R&D. Finally, the financial support of the Research Grants Council, HKUST6018/02P, is gratefully acknowledged. REFERENCES
[11 "Facts and Figures from the Chemical Industry," C&EN, June 26 (2000) 48. [2] International Monetary Fund, World Bank [3]
[4] [5] [6] [7] [8] [9] [10] [ 1l] [12] [13] [14] [15] [16] [17] [ 18] [ 19] [20] [21 ]
C. J. Kim, "Supply Chain Management in Process Industry," keynote presentation at PSE Asia, 2002, Taipei. www.aspentech.com www.i2.com www.sas.com www.pwcglobal.com C. Wibowo, and K. M. Ng, "Product-Oriented Process Synthesis and Development: Creams and Pastes," AIChE J., 47 (200 l) 2746. C. Wibowo, and K. M. Ng, "Product-Centered Processing: Chemical-Based Consumer Product Manufacture," AIChE J., 48 (2002) 1212. K. Y. Fung, and K. M. Ng, "Product-Centered Process Synthesis and Development: Pharmaceutical Tablets and Capsules," accepted for publication in AIChE J. (2002). A. W. Westerberg, and E. Subrahmanian, "Product Design," Comp. Chem. Eng., 24 (2000) 959. E. L. Cussler, and J. D. Moggridge, Chemical Product Design, Cambridge University Press, Cambridge, UK (2001). J. Villermaux, "Future Challenges in Chemical Engineering Research," Trans. IChemE 73 (part A) (1995) 105. A. V. Sapre, and J. R. Katzer, "Core of Chemical Reaction Engineering: One Industrial View," Ind. Eng. Chem. Res. 34 (1995) 105. J.J. Lerou, and K. M. Ng, "Chemical Reaction Engineering: A Multiscale Approach to a Multiobjective Task," Chem. Eng. Sci., 51 (1996) 1595. K. M. Ng, "A Multiscale-Multifaceted Approach to Process Synthesis and Development," ESCAPE 1l, Ed. R. Gani and S. B. Jorgensen, Elsevier (200 l) 41. I.E. Grossmann, and A.W. Westerberg, "Research Challenges in Process Systems Engineering," AIChE J. 46 (2000) 1700. J.M. Douglas, Conceptual Design of Chemical Processes, McGraw-Hill, New York (1988). C. Wibowo, L. O'Young, and K. M. Ng, "Workflow Management in Chemical Process Development," paper in preparation. L.T. Biegler, I. E. Grossmann, and A.W. Westerberg, Systematic Methods of Chemical Process Design, Prentice Hall, New Jersey (1997). M.F. Malone, and M. F. Doherty, "Separation System Synthesis for Nonidela Liquid Mixtures," AICHE Symp. Series 91 (1995) 9.
73 [22] C. Wibowo, and K. M. Ng, "Unified Approach for Synthesizing Crystallization-Based Separation Processes," AIChE J., 46 (2000) 1400. [23] K.D. Samant, and K. M. Ng, "Representation of High-Dimensional Solid-Liquid Phase Diagrams for Ionic Systems," AIChE J. 47 (2001) 861. [24] C. Wibowo, K. D. Samant, and K. M. Ng, "High-Dimensional Solid-Liquid Phase Diagrams Involving Compounds and Polymorphs," AIChE J. 48 (2002) 2179. [25] K.D. Samant, and K. M. Ng, "Synthesis of Extractive Reaction Processes," AIChE J. 44 (1998) 1363. [26] K.D. Samant, and K. M. Ng, "Synthesis of Prepolymerization Stage in Polycondensation Processes," AIChE J. 45 (1999) 1808. [27] V. V. Kelkar, and K. M. Ng, "Development of Fluidized Catalytic Reactors- Screening and Scale-up," AIChE J. 48 (2002) 1486. [28] A. W. Scheer, Business Process Engineering, 2nd ed., Springer-Verlag, Berlin (1994) [29] D.J. Elzinga, T. R. Gulledge, and C. Y. Lee, ed., Business Process Engineering: Advancing the State of the Art, Chapter 6, Kluwer Academic Publishers, Norwell, MA (1999). [30] D.J. Elzinga, T. R. Gulledge, and C. Y. Lee, ed., Business Process Engineering: Advancing the State of the Art, Chapter 12, Kluwer Academic Publishers, Norwell, MA (1999). [31] K. M. Ng, "Teaching ChE to Business and Science Students," Chem. Eng. Edu., Summer (2002) 222.
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
74
PSE and Business Decision-Making in the Chemical Engineering Curriculum Warren D. SeideP, J. D. Seader b, and Daniel R. Lewin c
aDepartment of Chemical and Biomolecular Philadelphia, Pennsylvania 19104-6393
Engineering,
University
of Pennsylvania,
bDepartment of Chemical and Fuels Engineering, University of Utah, Salt Lake City, Utah 84112-9203 CPSE Research Group, Department of Chemical Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel This manuscript discusses approaches for providing chemical engineering students a modem experience in product and process design with an objective of exposing them to process systems engineering (PSE) and business decision-making. After typical mechanisms for business decision-making are reviewed, a template is introduced that presents the steps in product and process design. These involve a blend of heuristic and algorithmic methods, with emphasis on the usage of modem computer tools, especially the process simulators. Then emphasis is placed on the use of case studies, design projects, and other aspects of product and process design courses, to teach students the role of these steps and computer tools in supporting the high-level business decisions required in the process industries. Abstract
Keywords product design, process design, simulators, equipment design, cost estimation 1. INTRODUCTION Most departments of chemical engineering teach courses in process design and process control as vehicles for introducing students to process systems engineering (PSE) techniques. Recently, the growing involvement of chemical engineers in product design has spurred interest in either including this subject in the process design course or adding it as a new course to the curriculum. In this paper, the objective is to consider the role of PSE and business decision-making in the chemical engineering curriculum through a close examination of a modem product and process design course(s). In this introduction, both the scope of business decision-making and the scope of product and process design are reviewed, followed by brief statements concerning the origin of design projects. Then, in the next section, the key steps in product and process design are examined, many of which result from or influence business decisions. Finally, the last section focuses on those steps and computer tools used by students (and practitioners) that support most directly high-level business decisions.
75 1.1
Business Decision-Making
Companies in the chemical industry, and many other commercial and govemmental organizations, have business decision makers, typically within high-level management, who receive inputs from the many sources discussed below. The many inputs are processed and decisions are issued in three principal categories: (1) the concept is approved with the authors of the proposals authorized to proceed to the next step; usually to prepare a more detailed evaluation, keeping preliminary capital limits in mind, (2) the concept is recycled for further study, given reviews that are the basis for the decision, and (3) the concept is rejected. Note, however, that rejected proposals are often not entirely rejected. In many cases, research and development managers find some combination of time, equipment, and motivated employees able to rework the proposal with a "new look." The inputs, or proposals, often come from business managers, whose teams work with current customers, seeking to learn about customer needs. Inputs also come from application groups, who interact closely with business managers, working to synthesize solutions using existing technologies and promising laboratory results. Often ideas from business managers and application groups are fed to research and development (R&D) groups, who work to invent new products and technologies. Their most promising ideas and concepts are sent usually to a business center in a request for a budget to carry out an engineering study. When approved, process systems engineering work is undertaken to carry out product and process synthesis, preliminary design studies, cost estimates, and profitability analyses. When promising results are obtained, these are the basis for proposals, or inputs, to the business decision makers. Another source of inputs for business decision makers comes from manufacturing sites, which often work to resolve operating problems, leading to ideas for variations on existing products, retrofits, or even new processing methods. Often these ideas are fed to R&D groups, with engineering studies undertaken, as described above. Here, also, the most promising results provide inputs to business decision makers. Finally, it is important to note that most business areas have a group of financial analysts who carry out detailed economic analyses, including sensitivity and uncertainty analyses, to accompany inputs to the business decision makers. 1.2
Product and Process Design
The design of chemical products begins with the identification and creation of potential opportunities to satisfy societal needs and to generate profit. Thousands of chemical products are manufactured, with companies like Minnesota Mining and Manufacturing (3M) having developed over 50,000 chemical products since being founded in 1904. The scope of chemical products is extremely broad. They can be roughly classified as: (1) basic chemical products, (2) industrial products, and (3) consumer products. As shown in Figure l a, basic chemical products are manufactured from natural resources. They include commodity and specialty chemicals (e.g., commodity chemicals- ethylene, acetone, vinyl chloride; specialty chemicals- difluor~ylene, ethylene-glycol mono-methyl ether, diethyl ketone), bio-materials (e.g., pharmaceuticals, tissue implants), and polymeric materials (e.g., ethylene copolymers, polyvinylchloride, polystyrene). The manufacture of
76 industrial products begins with the basic chemical products, as shown in Figure lb. Industrial products include films, fibers (woven and non-woven), and paper. Finally, as shown in Figure l c, consumer products are manufactured from basic chemical and industrial products. These include dialysis devices, hand-warmers, Post-it notes, ink-jet cartridges, detachable wall hangers, solar desalination devices, transparencies for overhead projectors, drug delivery patches, fuel cells, cosmetics, detergents, pharmaceuticals, and many others. Many chemical products are manufactured in small quantities and the design of a product focuses on identifying the chemicals or mixture of chemicals that have the desired properties, such as stickiness, porosity, and permeability, to satisfy specific consumer needs. For these, the challenge is to create a product that has sufficiently high market demand to command an attractive selling price. After the chemical mixture is identified, it is often necessary to design a manufacturing process. Other chemical products, otten referred to as commodity chemicals, are required in large quantifies. These are often intermediates in the manufacture of specialty chemicals and industrial and consumer products. These include ethylene, propylene, butadiene, methanol, ethanol, ethylene oxide, ethylene glycol, ammonia, nylon, and caprolactam (for carpets), together with solvents like benzene, toluene, pher~l, methyl chloride, and tetmhydrofumn, and fuels like gasoline, kerosene, and diesel fuel. These are manufactured in large-scale processes that produce billions of potmds annually in continuous operation. Since they usually involve small well-defined molecules, the focus of the design is on the process to produce these chemicals from various raw materials.
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77 Design projects have many points of origin. Often they originate in the research labs of chemists, biochemists, and engineers who seek to satisfy the desires of customers for chemicals with improved properties for many applications (e.g., textiles, carpets, plastic tubing). In this respect, several well-known products, such as Teflon (polytetrafluoroethylene), were discovered by accident. (At Dt&ont, a polymer residue that had accumulated in a lab cylinder of tetrafluoroethylene was found to provide a slippery surface for cookware and was capable of withstanding elevated temperatures, among many similar applications.) In other cases, an inexpensive source of a raw material(s) becomes available and process engineers are called on to design processes that use this chemical, often with new reaction paths and methods of separation. Other design problems originate when new markets are discovered, especially in the developing countries of Southeast Asia and Africa. Yet another source of design projects are engineers themselves who often have a strong feeling that a new chemical or route to produce an existing chemical may be very profitable, or that a market exists for a new chemical product. When a new chemical product is envisioned, the design project can be especially challenging, as much uncertainty often exists in the chemical(s)or mixture of chemicals best suited for the product, as well as the product configuration, the modes of manufacture, and the market demand. For the manufacture of a commodity chemical, the design project is usually less comprehensive, as the focus is usually on the design of the manufacturing facility or chemical process. 2
STEPS IN PRODUCT AND PROCESS DESIGN
In this section, the objective is to introduce a template that presents the steps in product and process design. These involve a blend of heuristic and algorithmic methods, with emphasis on modem computer tools, especially the process simulators. Note that, because the inputs to and responses from business decision makers are not always clearly positioned, the focus of Section 3 is on those steps and computer tools that support high-level decision-making in the product and process design courses. Figure 2 shows many of the steps in designing chemical products and processes. Beginning with a potential opporttmity, a design team creates and assesses a so-called primitive problem. When necessary, the team seeks to find chemicals or chemical mixtures that have desired properties and performance. Then, when a process is required to produce the chemicals, process creation (or invention) is undertaken. When the gross profit is favorable, a base-case design is developed. In parallel, algorithmic methods are employed to find better process flowsheets. Also, in parallel, plantwide controllability assessment is undertaken to eliminate processes that are difficult to control. When the process looks promising and/or when a configured industrial or consumer product is to be designed, the design team carries out detailed design, equipment sizing, and optimization. These steps are expanded upon in the subsections that follow. 2.1
Create and Assess Primitive Problem
Product and process designs begin with a potential opportunity, often a gleam in the eye of an engineer. Usually, the opportunity arises from a customer need, often identified by interviewing customers. Given an array of needs, an effort is made to arrive at specifications
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79 for the product; for example, desired density, viscosity, and latent heat of crystallization for a solution to be used in a hand-warmer. In many cases, a design team engages in a formal session to identify needs and generate ideas for the product. This involves brainstorming to arrive at concepts that are potentially promising in satisfying the needs. Given an array of ideas, an attempt is made to select the most promising from among them using the principles of thermodynamics, chemical kinetics, heat and mass transfer, etc. In so doing, designers and design teams create and assess primitive problems that are most worthy of further research and development. These steps are discussed thoroughly in Chemical Product Design by Cussler and Moggridge I1].
2.1.1 TypicalOpportunities and Primitive Problems As examples, consider the following three potential opportunities and the kinds of primitive design problems generated:
Example 1 Dialysis Device Consider the possibility of designing an inexpensive (say, less than $10) throw-away product for patients with temporary or permanent kidney failure; a device that provides the only treatment for patients with end-stage renal disease (ESRD), whose kidneys are no longer capable of their function. This treatment, which is required three times per week, for an average of 3-4 hours per dialysis, was performed on more than 200,000 patients in the United States in 1996.
Example 2 Drug to Counter Degradation of Blood In the manufacture of pharmaceuticals, consider the possible production of plasminogen activators, which are powerful enzymes that trigger the proteolytic (breaking down of proteins to form simpler substances) degradation of blood clots that cause strokes and heart attacks. Since the mid-1980s, Genentech, a U. S. company, has manufactured tissue plasminogen activator (tPA), which they currently sell for $2,000 per 100 mg dose, with annual sales of 300 million S/yr. Given that their patent will expire soon, Genentech has developed a next generation, FDAapproved, plasminogen activator called "TNK-tPA, which is easier and safer for clinicians to use. With a rapidly growing market, the question arises as to whether an opportunity exists for another company to manufacture a generic (i.e., without a brand name) form of tPA that can compete favorably with TNK-tPA.
Example 3
Vinyl Chloride Manufacture
Consider the need to manufacture vinyl chloride, H~
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80 an important polymer (usually refen'ed to as just vinyl) that is widely-used for rigid plastic piping, fittings, and similar products. An oppommity has arisen to satisfy a new demand for vinyl chloride monomer, on the order of 800 million pounds per year, in a petrochemical complex on the Gulf Coast, given that an existing plant owned by the company produces 1 billion pounds per year of this commodity chemical. Because vinyl-chloride monomer is an extremely toxic substance, it is required that all new facilities be designed carefully to satisfy govemmental health and safety regulations. Clearly, potential opportunities and primitive problems are generated regularly in the fast-paced corporate, govemment, and university research environments. In product design, especially, it is important to create an environment that encourages the identification of consumer needs, the generation of product ideas, and the selection from among these ideas of the most promising altematives; see Sections 3.2 and 3.3. 2.1.2
Selecting Alternatives - Assessing the Primitive Problem
Normally, the designer or a small design team generates many potential ideas for products and processes as potential solutions for the primitive problem, particularly if these individtmls are well familiar with the existing products or situation. Ideas may also come from potential customers, who may be frustrated with the existing products or situation. The ideas are best generated in a non-critical environment. Often, the best ideas may initially be those that might otherwise receive the most criticism. All of the ideas are collected, organized, discussed, and carefully assessed. Cussler and Moggridge [I] present extensive lists of ideas for several products. From the list of ideas, a selection of the most promising altematives is made, based upon technical or marketing considerations; for example, thermodynamics or advertising potential. For the three primitive problems above, Seider et al. TM present design altematives that are typical of those selected from lists of ideas that serve as a base on which to begin the engineering of a product or a process. At this stage, the alternatives require further, more detailed study, and hence it is important to recognize that, as the engineering work proceeds, some altematives are rejected and new alternatives are generated. This is a crucial aspect of design engineering. On the one hand, it is important to generate large numbers of ideas leading to a few promising alternatives. On the other hand, to meet the competition with a product designed and manufactured in a timely fashion, it is important to winnow those alternatives that might require too extensive an engineering effort to be evaluated; e.g., a process that requires exotic materials of construction or extreme conditions of temperature and/or pressure. 2.1.3
Literature Survey
When generating altemative specific problems, design teams in industry have access to company employees, company files, and the open literature. These resources include the SRI Design Reports, encyclopedias, handbooks, indices, and patents, many of which are available electronically, with an increasing number available on the Intemet. For product design, especially, patents are important sources with which the design team must be aware to avoid the duplication of designs protected by patents. After the 17 years that protect patented products and processes in the United States are over, patents are often helpful in the design of next-generation processes to produce the principal chemicals, or chemicals that
81 have fimilar properties, chemical reactions, and so on. Often patents are licensed for fees on the order of 3-6 percent of gross sales.
2.1.4 Auxiliary Studies While creating and assessing a primitive design problem, design teams often initiate studies of (1) technical feasibility, (2) marketing, and (3) business considerations. For a promising product, the technical feasibility study identifies existing and potentially new manufacturing methods, the advantages and disadvantages of each method, and eventually (as design proceeds), the reasons for selecting a specific method. In the marketing analysis, other manufacturers are identified, as well as plant capacities, price histories of the raw materials and products, regulatory restrictions, and principal uses of the product. Marketing considerations often far outweigh technical considerations. Many products or process designs are rejected by management for marketing reasons. For each promising design, business objectives and constraints are normally considered, usually in a business study. These include plant capacity, product quality, likely size of first plant expansion, mechanical completion and startup dates, maximum capacity available, maximum operating costs as a function of capacity, seasonal demand changes, inventory requirements, and minimum acceptable return on investment. As mentioned in Section 1.1, these studies often justify requests to business centers for a budget to carry out the engineering work, including product and process synthesis, prelinainary design studies, cost estimates, and profitability analyses.
2.1.5
Stimulating Innovation in Product Design
The invention and commercialization of new products, and chemical products in particular, benefits by corporate organization to encourage interactions between researchers, marketers, sales-people, and others. In this regard, companies like 3M and General Electric (G.E.) are noted for their corporate policies that seek to maintain a climate in which innovation flourishes. These hclude the fifteen percent rule, in which managers are expected to allow employees 15% of their time to work on projects of their own choosing, tech forums designed to encourage technical exchange and a cross-fertilization of ideas between persons working in many corporate divisions at widely disparate locations, stretch goals intended to stretch the pace of innovation (for example, at least 30 percent of annual sales should come from products introduced in the last four years), process innovation technology centers staffed with chemical engineers and material scientists to help researchers scale-up a new idea from the bench to production, and six sigma strategies for quality control in manufacturing. For discussions of these approaches, with examples, see the books by GundlingTM, Coe [4], and Seider et al. [2].
2.1.6
Pharmaceutical Products
Special considerations are needed for the design of pharmaceutical products. As the design team creates and assesses the primitive problem, the typical development cycle or time line for the discovery and development of a new chemical entity plays a key role, as discussed by Pisano TM. The key steps begin with discovery, in which exploratory research identifies molecules that prove safe and effective in the treatment of disease, usually involving the exploration of thousands of compounds to locate a handful that are sufficiently promising for further development, and the application of data mining techniques to locate
82 the most promising proteins, and cells within which they can be grown, fi'om numerous data bases of laboratory data. Next, in preclinical development, a company seeks to obtain sufficient data on a drag to justify the more expensive and risky step of testing in humans. Then, in clinical trials, which are administered over three phases (each of duration 1-2 years), the drag is tested on human volunteers. Dmfng the latter phase, the drag is administered to thousands of patients at many locations over several years. Finally, to gain approval, an application is prepared for the FDA requesting permission to sell the drug. Note that as Phases 1 and 2 of the clinical trials proceed, process design is undertaken to produce large quantities of the drag, first for Phase 3 testing and then for commercial operation. These steps are considered in detail for a plant to produce tissue plasminogen activator (tPA) in Seider et al. [2]. Note also that the profit margins are sufficiently high to accelerate the process design of the facility for Phase 3 testing, with little if any process optimization. Subsequently, when FDA approval is obtained, the Phase 3 process is used for commercial operation. 2.2
Find Chemicals Performance
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Having created and assessed the primitive problem, the design team often undertakes For those primitive problems in which desired properties and performance have been specified, it is often necessary to identify chemicals or chemical mixtures that meet these specifications. Examples include: (1) thin polymer films to protect electronic devices, having a high glass-transition temperature and low water solubility, (2) refrigerants that boil and condense at desired temperatures and low pressures, while not reacting with ozone in the Earth's stratosphere, (3) environmentally friendly solvents for cleaning, for example to remove ink pigments, and for separations, as in liquid-liquid extraction, (4) low-viscosity lubricants, (5) proteins for pharmaceuticals that have the desired therapeutic effects, (6) solutes for hand warmers that remain supersaturated at normal temperatures, solidifying at low temperatures when activated, and (7) ceramics having hightensile strength and low viscosity for processing. Often design problems are formulated in which the molecular structure is manipulated, using optimization methods, to achieve the desired properties. For this purpose, methods of property estimation are needed, which often include group contribution methods, and increasingly molecular simulations (using molecular dynamics and Monte-Carlo methods). The search for molecular structure is often iterative, involving heuristics, experimentation, and the need to evaluate many alternatives in parallel, especially in the discovery of pharmaceutical proteins, as discussed by Seider et al. [2].
molecular structure design.
For some chemical products, like creams and pastes, the specification of desired properties is a key to successful product design. Creams and pastes are colloidal systems that contain immiscible liquid phases, as well as solid particles. As described by Wibowo and Ng I6], the first step in their design involves the identification of product quality factors, including functional quality factors (e.g., protects, cleans, and decorates the body, delivers an active pharmaceutical ingredient, ...), rheological quality factors (e.g., pours easily, spreads easily on the skin, does not flow under gravity, stirs easily, coats uniformly, ...), physical quality factors (e.g., remains stable for an extended period, melts at a specified temperature, releases an ingredient at a controlled rate, ...), and sensorial quality factors (e.g., feels smooth, does not feel oily, appears transparent, opaque, or pearlescent, does not cause imtation, ...) Given these specifications, the second step involves product formulation,
83 which involves selection of the ingredients, the emulsion type (if applicable), the emulsifier (if necessary), and determination of the product microstructure. Then, the process creation step, as discussed below, and the product evaluation step follow. In a second paper, Wibowo and Ngiv1 expand upon these steps, and apply them for several products, including dry toner, laundry detergent, shampoo, and cosmetic lotion. 2.3 Process Creation
For those primitive problems for which a process must be designed to produce the chemical products, when funded by a business center, the design team carries out the process creation step. Since strategies for process flowsheet synthesis are well documented tS' 9, 2], especially as they apply to the manufacture of commodity chemicals, these steps are not reviewed herein. Note, however, that when process equipment is selected in the so-called task-integration step, the operating mode (that is, continuous, batch, or semi-continuous)is selected. For processes to manufacture specialty chemicals and pharmaceuticals, especially, the design and scheduling of batch processes gains importance.
2.3.1 Product Type Retuming to Figure 1, the manufacauSng processes shown differ significantly depending on the chemical product type. For the manufacture of basic chemical products, the process flowsheet involves chemical reaction, separation, pumping, compression, and similar operations. On the other hand, for the manufacture of industrial products, extrusion, blending, compounding, and stamping are typical operations. Note also that the focus on quality control of the product shifts from the control of the physical, thermal, chemical, and rheological properties to the control of optical properties, weatherability, mechanical strength, printability, and similar properties. Most strategies for process synthesis focus on the manufacture of basic chemical products, with much work remaining to elucidate strategies for the manufacture of industrial products. For the design of configured industrial and consumer products, where emphasis is placed on the design of three-dimensional products, chemical engineers are nomaaUy not involved in the design of the manufacturing process, which includes parts making, parts assembly and integration, and fmishing. 2.4
Development of Base Case Process
To address the most promising flowsheet altematives for the manufacture of basic chemicals, the design team is usually expanded or assisted by specialized engineers, to develop base-case designs. Again, since these strategies are well documented, they are not reviewed herein. Regarding computer-aided process simulation, it is noteworthy that batch process simulators (for example, BATCH PLUS and SUPERPRO DESIGNER) are gaining favor for the design of processes to manufacture specialty chemicals, especially pharmaceuticals. 2.5
Detailed Process Synthesis using Algorithmic Methods
While the design team develops one or more base-case designs, detailed process synthesis is often undertaken using algorithmic methods. For continuous processes, these methods include: (1) create and evaluate chemical reactor networks for conversion of feed to
84 product chemicals, separation trains for recovering species in multicomponent mixtures, and reactor-separator-recycle networks, and (2) locate and reduce energy usage, create and evaluate efficient networks of heat exchangers with turbines for power recovery, and networks of mass exchangers. For batch processes, these methods create and evaluate optimal sequences and schedules for batch operation. In the manufacture of industrial chemicals, such as films, fibers, and paper, processes are synthesized involving extrusion, blending, compounding, sealing, stamping, and related operations. These processes normally involve large throughputs, and consequently, are usually continuous, with some discrete operations that occur at high frequency, such as sealing and stamping. Methods of process synthesis rely heavily on heuristics and are not as well developed as for the manufacture of basic chemical products. For these processes, the emphasis is on the unit operations that include single- and twin-screw extrusion, coating, and fiber spinning. See, for example, the Principles of Polymer Processing by Tadmor and Gogos [10] and Process Modeling by Denn [11]. 2.6
Plant-wide Controllability Assessment
An assessment of the controllability of the process is initiated after the detailed process fiowsheet has been completed, beginning with the qualitative synthesis of control structures for the entire flowsheet. Then measures are utilized that can be applied before the equipment is sized in the detailed design stage, to assess the ease of controlling he process and the degree to which it is inherently resilient to disturbances. These measures permit altemative processes to be screened for controllability and resiliency with little effort and, for the most promising processes, they identify promising control structures. Subsequently, control systems are added and rigorous dynamic simulations are carried out to confirm the projections using the approximate measures discussed previously. See, for example, the books by Luyben et al. [12], Luyben[13], and Seider et al. [21. 2.7 Detailed Design, Equipment Sizing, and Optimization - Configured Product Design
Depending on the primitive design problem, the detailed design involves equipment sizing of units in a new process for commodity and specialty chemicals, that is, detailed process design, and/or the determination of the product configuration for a configured industrial or consumer product (that uses the chemicals or chemical mixtures produced). Here, also, the steps in equipment sizing and cost estimation for processes involving commodity and specialy chemicals are well documented[14' 21. When the primitive design problem leads to a configured industrial or consumer product, much of the design activity is centered on the three-dimensional structure of the product. Typical chemically-related, industrial and consumer products include, for example, hemodialysis devices, solar desalination traits, automotive fuel cells, handwarmers, multi-layer polymer mirrors, integrated circuits, germ-killing surfaces, insect repelling wristbands, disposable diapers, inkjet cartridges, transparencies for overhead projectors, sticky wall hangers, and many others. In many cases, the product must be configured for ease of use and to meet existing standards, as well as to be manufactured easily. Increasingly, when determining the product configuration, distributed-parameter models, involving ordinary and partial differential equations, are being created. Simple discretization algorithms are often used to obtain solutions, as well as the Finite-element Toolbox in MATLAB and the FEMLAB package.
85 The product invention phase begins in which ideas are generated and screened, with detailed three-dimensional designs developed. For the most promising inventions, prototypes are hailt and tested on a small-scale. Only aider the product passes these initial tests, do the design engineers focus on the process to manufacture the product in commercial quantifies. This scale-up often involves pilot-plant testing, as well as consumer lesting, before a decision is made to enter commercial production. Clearly, the methods of capital cost estimation, profitability analysis, and optimization are applied before and during the design of the manufacturing facility, although when the product can be sold at a high price, due to large demand and limited production, detailed profitability analysis and optimization are less important. For these products, it is most important to reduce the design and manufacturing time to capture the market before competitive products are developed. 3
FOCI ON STEPS AND C O M P U T E R TOOLS IN SUPPORTING HIGH-LEVEL BUSINESS DECISIONS
Given the need to expose chemical engineering students to the principal steps and computer tools in designing chemical products and processes, several vantage points can be taken. In the previous section, these steps and computer tools were elucidated, but the inputs to and responses from business decision makers were difficult to define explicitly, as they occur at various stages, depending on the product and/or process and company situation or policy. In this section, the focus is on imparting to students an appreciation of the steps and computer tools that support the high-level business decisions required in the process industries.
3.1
Case Studies and Design Projects
Virtually all students are engaged in open-ended problem solving in the core courses of chemical engineering. However, most do not solve a comprehensive design project until their senior year. When framed well, students are presented with a letter from management stating a societal need(s) and suggesting a primitive problem(s) to be solved in trying to meet this need(s). See, for example, the three examples in Section 2.1.1. Then, as discussed in Section 2.1.3, student design teams search the literature for competitive products, especially the patent literature for existing businesses and successful products. Also, as discussed in Section 2.1.4, students are expected to initiate studies of marketing and business considerations. These involve assessing consumer needs and the size of the market, as well as determining the sources of competitive product(s), the level of production, the selling prices, potential gross profits, and related issues of importance to business decision makers.
3.2
Management Stimuli
The actions by business decision makers, discussed in Section 2.1.5, contribute toward creating an environment that stimulates technical people to innovate and invent new products. These have been key elements of 3M's business strategy over the past century and have led to their success in introducing numerous successful chemical products TM. Rather than taking a passive role, management actively seeks to stimulate innovation.
86
3.3
Idea Generation in Response to Business Trends and Customer Needs
The idea generation phase, in which a design team brainstorms to arrive at long lists of potential ideas worthy of consideration in developing a viable product(s), is at the heart of product design [11. In addition, together with marketing people, the design team interviews potential customers to obtain their assessment of their most important needs. While difficult to carry out quantitatively in a university setting, it is important that students appreciate hese key steps in responding to business trends and customer needs in the commercial world.
3.4
Detailed Process Simulation, Equipment Design, and Cost Estimation
In solving their design projects, students are taught to formulate decision trees and to use screening measures to eliminate the least promising altematives, initially using approximate measures such as gross profits, and gradually refining these measures as the technical analysis becomes more quantitative. In carrying out this analysis, students often use process simulators and rigorous methods to size equipment and estimate costs. These usually involve the use of extensive data banks of thermophysical property data, equipmentsizing data, and purchase cost data. While business managers do not examine the details, the use of these packages provides business decision makers with a common basis for comparing the bottom lines of competitive designs; for example, involving the investor's rate of retum.
3.5
Sensitivity and Uncertainty Analysis
Furthermore, through the use of computer tools to compare competitive designs, students learn the ease and speed with which they can prepare detailed design studies. They learn to vary parameters in sensitivity analyses, as well as in optimization studies. By the same token, students also learn the ease with which these systems can be misused, possibly leading a design team to present management with a misguided recommendation for a business venture. The need to check calculations and fully document the underlying assumptions and approximations, with estimates of uncertainties, is another important lesson students must learn.
3.6
Design Reports
Training students to transmit their ideas, both orally and in writing, is a key aspect of their education. The experience of writing an extensive design report, with its letter of transmittal, is an important vehicle for teaching students to communicate with persons not involved in the technical details of their project. In addition, the preparation, delivery, and evaluation of an oral presentation to a critical audience, usually populated by faculty and industrial persons, is another key aspect of leaming to influence business decision makers. 4
YOUNG FACULTY PERSPECTIVES
Recent changes in young faculty perspectives suggests future impacts of PSE on business decision-making. With recent advances in experimental synthesis methods at the nano- or meso-scale, a large fraction of the young faculty have been completing doctoral research on structured materials, protein structures and interactions, among similar applied
87 chemistry, physics, and biology projects. These persons are likely to be more stimulated by the design of configured chemical products, such as biochemical sensors, integrated circuits, and multi-layer polymer mirrors, rather than the design of commodity chemical processes. The development of these products, which often command high selling prices (for example, specialized pharmaceuticals), can have a major impact on business decision-making. It seems clear that chemical engineers will focus increasingly on the design of these kinds of products, with significant business and legal considerations. 5
CONCLUSIONS
As the steps in product and process design are being elucidated and computer tools are gaining sophistication, the ties between PSE and business decision-making are being conveyed to undergraduate students more effectively. In addition to clarifying the steps and computer tools, this manuscript has focused on many of the ties. REFERENCES
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [ 11] [12] [13] [14]
E. L. Cussler and G.D. Moggridge, Chemical Product Design, Cambridge University Press 2001. W . D . Seider, J. D. Seader and D. R. Lewin, Product and Process Design Principles: Synthesis, Analysis, and Evaluation, Second Edition, Wiley, New York, 2003. E. Gundling, The 3M Way to Innovation: Balancing People and Profit, Kodansha Intemational, Tokyo, 2000. J.T. Coe, Unlikely Victory: How General Electric Succeeded in the Chemical Industry, AIChE, New York, 2000. G. P. Pisano, The Development Factory: Unlocking the Potential fo Process Innovation, Harvard Business School Press, Cambridge, 1997. C. Wibowo and K. M. Ng, Product-Oriented Process Synthesis and Development: Creams and Pastes, AIChE Journal, 47, 12 (2001) 2746-2767. C. Wibowo and K. M. Ng, Product Centered Processing: Manufacture of ChemicalBased Consumer Products, AIChE Journal, 48, 6 (2002) 1212-1230. J. M. Douglas, Conceptual Design of Chemical Processes, McGraw-Hill, New York, 1988. L. T. Biegler, I. E. Grossmann, and A. W. Westerberg, Systematic Methods of Chemical Process Design, Prentice-Hall, New Jersey, 1997. Z. Tadmor and C. G. Gogos, Principles of Polymer Processing, Wiley, New York, 1979. M.M. Denn, Process Modeling, Longman, New York (1986). W. L. Luyben, B. D. Tyreus and M. L. Luyben, Plantwide Process Control, McGrawHill, New York, 1999. W. L. Luyben, Plantwide Dynamic Simulators in Chemical Processing and Control, Marcel Dekker, New York, 2002. M. S. Peters, K. D. Timmerhaus, and R. West, Plant Design and Economics for Chemical Engineers, Fifth Ed., McGraw-Hill, New York, 2003.
88
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
Informatics in Pharmaceutical Research S. Kima
aVice President/Information Officer, Lilly Research Laboratories, Lilly Corporate Center, Indianapolis, Indiana 46285, United States of America. Abstract
The traditional role of information technology (IT) in pharmaceutical R&D has been facilitating cycle compression, i.e., speeding up the time-to-market and increasing the retum on R&D investments. In the post-genomic era, the great surge in data creates additional challenges and roles for IT. A new profile of the R&D pipeline is taking shape, based on the concept of massively parallel operations in Discovery research (target discovery and development, high throughput screening and lead optimization). This increase in opportunities, however, must be tempered with the realization that the later stages of R&D are most costly as viewed from the tracking of a particular molecular entity through the pipeline. Evolving in silico technologies to selectively encourage early attrition of the pipeline, i.e., by exploiting advances at the interface of chemistry, biology, and systems engineering (systems biology), are important parts of the modern landscape for pharmaceutical informatics. The presentation will highlight the observation that concepts from process systems engineering are especially relevant for organizing both scientific and management principles.
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
89
Design and operation of m i c r o - c h e m i c a l plants - Bridging the gap between nano, micro and m a c r o technologies Shinji Hasebe Department of Chemical Engineering, Kyoto University, Kyoto 606-8501, Japan E-mail:
[email protected] Abstract: The design and operation problems of micro chemical plants which are used for production are treated in this research. First, the design problems of micro chemical plants are classified into two sub-problems: the design of the micro unit operations and the design of the entire micro plants. Then, for each of the sub-problems the features of the micro systems are explained and the dominant problems which must be solved by process systems engineers are pointed out. Then, the characteristics of the instrumentation and control problems of the micro chemical plants are summarized. Keywords micro chemical plant, CFD simulation, numbering up, optimal design
1. INTRODUCTION Few Micro-Chemical Plants (MCPs) have been used for the production; therefore, engineers do not have experience in designing MCPs, and also do not have any systematic tools for their design and control. One of the dominant characteristics of the MCPs is that the research results can be transferred into production much faster. Thus, it is very important to elucidate the problems which occur in the design and control of MCPs and propose solutions for those problems. From this viewpoint, the emphasis is placed on explaining the characteristics of the MCPs and pointing out the future research subjects to be solved. The concrete techniques to solve the problems are not explained. 2. POSSIBILITY OF MCPs When an MCP is developed for real production, the reason for using micro devices must be clear. It is meaningless to say that a product which can be produced in a conventional plant can also be produced in an MCP. We must aim at the production of materials which cannot be produced in conventional chemical plants, or the production efficiency of which is drastically
90 improved by using an MCP. At present, many kinds of materials are produced by using micro devices [1 ] - [4]. However, the number of products which satisfy the above conditions is not clear. Scientists and engineers engaged in the research of MCPs must always evaluate their results from the viewpoint of real production. The production rate is one of the dominant problems of an MCP when it is applied to the production process. It is meaningful to discuss the amount of production of MCPs by using an example. Figure 1 shows a conventional batch plant. It is assumed that the batch size is 1.0 m 3 of product, and that ten kinds of different products are produced by changing the raw material and/or production conditions. It is also assumed that two days are requested for one batch of production, and the process is operated 320 days in a year. In this case, 16 m 3 of each product is produced in a year when every product is produced equally. Suppose the case of producing the same amount of products in an MCP. If one train of MCP is assigned to one product, the flow rate of each production train which is enough for the same amount of production with the batch plant is around 2000 cm3/h. This amount of production can be attained by 16 square channels each of which has a cross-sectional area of 600 ~m x 600 pm, if the average flow speed is 0.1 m/s (See Fig. 2). However, the residence time is one second if the length ofthe device is 10 cm. From this example, it becomes clear that the problem is not the size of the device but the residence time. In this case, much effort should be devoted to increasing the reaction rate. One of the features of MCPs is that precise temperature control is easy. If a device can be operated at a higher temperature, an increase of the reaction rate may be possible. This example shows that if a sufficient reaction rate can be achieved in micro-devices, the MCPs can be used not only for the production of small volume specialty chemicals but also for the production of commodity chemicals of medium volume.
Fig.1 Batch plant
Fig. 2 Micro chemical plant
91 3. DESIGN PROBLEM OF MCPs 3.1 Design of micro unit operations As the size of the device is drastically smaller than that of a conventional device, a new design method is required that is different from that of the conventional unit operations such as heat exchange, mixture, absorption, and adsorption. Therefore, to distinguish them from the usual unit operations they are hereafter called "micro unit operations (MUOs). Characteristics of the design problems of MUOs are explained in this section.
a) Design margin When chemical equipment is designed, some amount of design margin is added to each variable so as to compensate for unforeseen variations. This method can be used when the design margin always shows a beneficial effect on the production efficiency of the device. In MUOs, the size of the device strongly affects its function. In other words, the functional design and physical design cannot be executed separately. In this case, the design margin may not work as well expected. In the simple micro device shown in Fig. 3, the cross-sectional area and the residence time are assumed to be the dominant factors which affect the function of the device. It is obvious that the function expected from the device is not satisfied when the cross-sectional area is increased. If the device is lengthened or the number of channels is increased, the residence time is also increased. It is clear from this example that in the MCPs the uncertainties of the model and parameters may not be compensated by the design margins. Laminar flow characteristics are exhibited in a channel of micro-device. Recent advances of computer technologies and simulation algorithms enable us to simulate the flows with reaction in the micro-devices by using a Computational Fluid Dynamics (CFD) simulator. The advance of the CFD simulation algorithm creates new possibilities for embedding the CFD simulator into the design system and designing devices which do not require any design margin.
~ Optimalsize J li~iii~I ~/
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Fig. 3 Effects of design margin
92
b) Shape of the device In a conventional design problem, the unit operations are modeled by using terms such as perfect mixing, piston flow, steady state, and total heat transfer coefficient. Each unit operation is modeled by using terms whose values do not depend on the location in the device. Convection and diffusion in the device strongly affect the functions of the micro devices, and the convection and diffusion conditions are affected by the shape of the device. Thus, it is very important to include the shape of the device in the design variables. The shape of the device has large degree of freedom; thus, it is almost impossible to derive the best shape if no constraints are added to the shape. However, the introduction of the constraints to the shape interferes with the emergence of the devices designed by a completely new idea. We must carefully select the constraints which are embedded in the design problem. In addition to the constraints used in the design problem of the conventional unit operation, new types of constraints, such as those on the average residence time, the residence time distribution, and the temperature distribution in the device, are included in the design problem. Figure 4 shows a concept of the future design system in which the shape of the device satisfying all constraints is gradually improved so as to optimize the given objective function.
c) Robust design In a conventional chemical plant, a feedback control system is used to keep the plant at the desirable state. It is difficult to measure and control the flow condition in the micro devices, although the flow condition is affected by the change of the physical property of the fluid such as the viscosity. Therefore, the micro device should be designed so that the effect of the change of the physical property to the flow condition is as small as possible. One method is to design the optimal shape by taking the various operating conditions into account. The introduction of stochastic programming method is a more theoretical approach. A living organism can effectively adjust to the disturbances which occur inside and outside of the body without using any mechanical system. So an interesting research area would be the installation of a self-adjusting function in the device.
Fig. 4 Concept of a shape design system
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d) Reevaluation of the neglected terms Though a micro device is fairly small compared with conventional chemical equipment, it is still very large compared with atoms or molecules. In principle, the physical laws established in the conventional world can be used to describe the behavior in the device. However, it is probable that some terms which have been neglected in the conventional design cannot be neglected in the design of MUO. As an example, the results of the efficiency analysis of heat exchangers are explained in the next subsection.
e) Influence of physical properties of materials on heat transfer behavior [5] Plate-fin micro heat exchangers are representative of micro heat exchangers. Figure 5 shows the counter-flow plate-fin micro heat exchanger investigated in this work. A number of plates are stacked, and on each plate a hot or cold stream flows. The Fluent | code, which uses the control volume method to solve conservation equations for mass, momentum, and energy, has been used to calculate profiles of temperature inside plate-fin micro heat exchangers. Under the conditions shown in Fig.5, CFD simulations of micro heat exchangers were performed to analyze the influence of physical properties of materials on the heat transfer performance. Three types of materials -- copper, stainless steel, and glass -- were examined. Their thermal conductivities are listed in Table 1. Temperature changes of heat transfer fluids were used to evaluate the performance of micro heat exchangers. The simulation results summarized in Table 1 show that the heat transfer efficiency of micro heat exchangers made of stainless steel or glass is higher than that of copper.
Fig. 5 Schematic view of counter-flow plate-fin micro heat exchanger.
94 Table 1 Heat transfer performance achieved by using; three kinds of materials Materials Thermal conductivity [W m -1 K l] Temperature change [K] Copper 388 59.4 Stainless steel 16.3 72.7 Glass 0.78 64.8 [High thermal conductivity]
[Low thermal conductivity]
kTemp.
kTemp.
Hot s t r e a m ~
~
all
Wall
Fig. 6 Temperature profiles of heat transfer fluids
Fig. 7 Image of micro heat exchanger When the copper micro heat exchanger is used, the temperature profile inside the wall (device itself) becomes flat in the longitudinal direction due to high heat conduction as shown in Fig. 6 (left). The stainless steel or glass micro heat exchanger generates an appropriate temperature gradient inside the wall in this case study, as shown in Fig. 6 (fight). Therefore, higher heat transfer efficiency is not necessarily achieved by using materials with higher thermal conductivity, which leads to the conclusion that the heat transfer behavior depends largely on the longitudinal heat conduction inside walls. This result does not mean that materials with lower thermal conductivity are suitable for achieving higher heat transfer performance. In designing micro heat exchangers, it is necessary to select appropriate design and operating conditions that maximize their performance. Longitudinal heat conduction inside walls is ignored for the design of conventional macro heat exchangers. However, the effect of longitudinal heat conduction cannot be neglected in designing micro heat exchangers, because the ratio of wall volume to channel volume is large as shown in Fig. 7.
95
3.2 Process synthesis The study of process synthesis has been done by assuming that the unit operations which can be used in the process are given in advance. Under that condition, a method which derives the optimal structure of the process has been developed. On the other hand, in the MCPs the micro unit operations which can be used in the process synthesis are not clear. For example, there are no standard types of micro reactors or those of micro absorbers. Therefore, the process synthesis of the MCPs becomes the problem of deriving the optimal combination of the functions which are requested in the final process, not the problem of deriving the optimal combination of MUOs. After the combination of the required functions is decided, the physical structures of the devices and the plant are generated. The concept of unit operations has been effectively used to develop new plants. However, it is not suitable to generate a completely new structure of the device and plant. If the plant can be synthesized by using the idea explained above, it is possible to generate a new type of plant systematically by computers. Figure 8 shows an example of process integration [6], [7]. In this example, eleven units have been merged into one column consisting of the reaction, distillation, reactive distillation, and extractive distillation. This is not the case for MCPs. The number of devices in an MCP increases as the production rate increases. Thus, the development of a complex device in which various functions are executed is a very important problem in order to avoid complexity of the micro plant. A complex device such as shown in Fig. 8 is usually developed ad hoc, on the basis of an engineer's experience. In other words, there is no systematic procedure for developing a complex device. Systematic generation of complex device hardware from the knowledge of the process functions to be done in the device is a promising research area in process synthesis.
Fig. 8 Integration of the functions
96 Conventional
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Fig. 9 Four types of plant structures
3.3 Numbering-up Figure 9a shows a typical case of the numbering-up of unit micro plants. The production rate can be increased by increasing the number of unit micro plants operated in parallel. This is an easy way to increase the production rate, but this structure may not be economically or technologically optimum. Four types of structures are shown in Fig. 9. Figure 9b shows the structure in which the micro devices having the same function are collected to one aggregated device. If an efficient MUO does not exist for a type of unit operation, a combination of micro devices and a conventional device is also possible (See Fig. 9b). The structure shown in Fig. 9d is the hybrid structure of Fig. 9a and Fig. 9b. Many factors affect the optimal structure. It must be determined taking the following terms into account:
a) Types of micro-unit operations suitable for the process The MUOs or the train of MUOs which is suitable for the given process must be made clear. If a conventional unit operation is used in a part of the process, the structure shown in Fig. 9b must be adopted.
b) The time allowable to transfer to the next device In an MCP, the residence time in the connection device cannot be neglected. If a shorter residence time in the connection device is desirable, the structure in Fig. 9c [8] is better than that in Fig. 9b.
c) Operating temperature As is shown in section 3.1e), the heat transfer in the longitudinal direction cannot be
97 neglected in the micro device. Thus, it is not desirable to aggregate two MUOs which are operated at different temperatures. If two MUOs are operated at different temperature, the structures shown in Fig. 9b and Fig. 9c are better than that in Fig. 9a.
d) Requirement of actuators There are some proposals for the micro pumps. However, for the production process, it is desirable to use conventional pumps to transfer the materials among devices. If a pump is requested at every intersection of unit operations, the structures shown in Fig. 9a and Fig. 9c are not recommended.
e) The possibility of cost reduction by the aggregation Micro devices are manufactured in many ways. Usually, devices having the same function can be manufactured on the same board. In this case, the structures shown in Fig. 9b and Fig. 9c can be manufactured easily.
39 Flexibility of the production When we adopt the structure shown in Fig. 9a, the production rate can be easily changed by changing the number of unit plants to be operated. However, the production path cannot be changed.
g) Ease of instrumentation and control This term is explained more precisely in the next section. 4. INSTRUMENTATION AND CONTROL OF MCPs 4.1 Instrumentation of MCPs
A catalyst may progressively degrade with time, and the environmental temperature and pressure also change with time. In order to produce desirable products, information on the precise operating condition of the plant is indispensable. There is much research on the development of the analyzing systems using micro systems. There is also much research in which the measurement systems are used to analyze the behavior of the micro systems. However, most of them are not for the MCPs which are operated for a long time. For conventional chemical plants, it is possible to add new measurement devices after the construction of the plant. On the other hand, it is almost impossible to add new measurement devices to the MCP after construction. Thus, the plant and the instrumentation and control systems must be designed simultaneously. For the development of suitable measurement and control system design, all of the dominant disturbances which affect the process performance must be enumerated, and countermeasures against each of the disturbances must be developed. Then, the observed, manipulated and controlled variables are selected so as to be able to execute the countermeasures derived above. If there is no instrumentation or manipulation devices to some countermeasures, the structure of the process itself is changed. There is no experience on the operation of MCPs, so a dynamic simulator should be used intensively to evaluate the efficiency of using each countermeasure.
98 4.2 Control of MCPs
In conventional chemical plants, feedback control is predominantly used to keep the plant at the desirable condition. For example, the temperature of the reactor is controlled as shown in Fig. 10a. The temperature of the liquid in the reactor is measured, and the flow rate of the heating medium is adjusted so that the temperature of the liquid in the reactor becomes the predefined value. When the number of controlled variables is small in the MCP, it is possible to adopt a feedback control scheme. However, as the number of control variables increases, it becomes economically difficult to adopt the feedback control scheme (See Fig. 10b). Thus, the control variables must be selected carefully, taking the variation of the conditions and the effect of each variation into account. One possible approach is to design robust hardware for disturbances and changes in operating conditions. If such a robust device is designed successfully, the total number of sensors and actuators can be reduced. In this case, it may be possible to leave the variables as they are. Indirect control by using the characteristics of the micro devices is another approach. As shown in Fig. 10c, the temperature of the liquid in the micro device can be controlled by keeping the temperature of the heating medium constant because of the good thermal conductivity in the micro device.
Fig. 10 Two structures of temperature control systems
99
Fig. 11 Control systems of micro chemical plants
It is not difficult to embed many actuators and sensors in micro devices by using the techniques developed in the field of Micro-Electro-Mechanical Systems (MEMS). However, the number of data cables increases as the number of observed and manipulated variables increases. To avoid the "spaghetti condition," some functions should be assigned to the actuators and sensors (See Fig. 11). If valves which are driven by the deviation of the pressure, temperature, flow rate or concentration of some material can be developed, the flow rate can be directly controlled without using electrical signals. For example, the tube which shrinks and expands by the change of the temperature can be used as the valves. 5. CONCLUSION - Bridging the gap between nano, micro and macro technologies -
The dominant problems to be solved in the field of the design and control of the micro chemical plants were explained. In the design problems of the conventional chemical plants, it has been difficult to develop a precise model because of many uncertain factors. Thus, the latest results of chemical engineering research have not been used in the design problems. In most cases, it is possible to assume laminar flow in micro devices. Thus, the development of a precise model is much easier than that of conventional plants. That is, the design of micro chemical plants can be regarded as a most appropriate subject to apply a new design method based on chemical engineering science, systems science and computer technology. Such new design methods have not been proposed yet, and it is not easy to develop them. However, the author believes that micro chemical plants act as a bridge between chemical engineering science (micro area) and process systems engineering (macro area) (See Fig. 12).
100
Fig. 12
Innovation of process design
REFERENCES
[ 1] E. Ehrfeld (Edt.), "Microreaction Technology: Industrial Prospects," Proceedings oflMRET 3, Springer (1999) [2] I. Rinard (Edt.), Proceedings of IMRET 4, AIChE National Meeting, March 5-9, Atlanta (2000) [3] M. Matlosz, W. Ehrfeld & J.P. Baselt (Eds.), "Microreaction Technology," Proceedings of IMRET 5, Springer (2001) [4] P. Baselt et al. (Eds.), Proceedings of IMRET 6, AIChE Spring Meeting, March 10-14, New Orleans (2002) [5] O. Tonomura, et al., Proceedings of PSEAsia 2002, Dec. 4-6, Taipei (2002) 109. [6] A. I. Stankiewicz and J. A. Moulijn, Chem. Eng. Prog., 96(1), (2000) 22. [7] J. J. Siirola, "An Industrial Perspective on Process Synthesis," AIChE Symposium Series, No.304, Vol.91, (Proceedings of FOCAPD'94), (1995) 222. ACKNOWLEDGMENTS The author thanks Professors I. Hashimoto, K. Mae, M. Ohshima, M. Kano and M. Noda (Kyoto University) for their valuable discussion. The financial support of the Micro Chemical Process Technology Research Association is gratefully acknowledged.
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
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Workflow and Information Centered Support of Design Processes Wolfgang Marquardt a and Manfred Nagl b
aLehrstuhl ftir Prozesstechnik, bLehrstuhl fiJr Informatik III (Software Engineering), RWTH Aachen University, D-52056 Aachen, Germany Abstract Design process excellence is considered a major differentiating factor between competing enterprises since it determines the constraints within which plant operation and supply chain management are confined. The most important prerequisite to establish such design process excellence is a proper management of all the design process activities and the associated information. Starting from an analysis of the characteristics of chemical engineering design processes, some important open research issues are identified. They include the development of an integrated information model of the design process, a number of innovative functionalities to support collaborative design, and the a-posteriori integration of existing software tools to an integrated design support environment. Some of the results obtained and experiences gained in the last years in the collaborative research center IMPROVE at RWTH Aachen University are presented.
computer-aided design, information modeling, software engineering, tool integration, business processes, workflow, work process
Keywords
1. M A N U F A C T U R I N G A N D D E S I G N IN THE 21 st C E N T U R Y
The markets and hence the requirements on manufacturing in the process industries have been changing tremendously in the last decades. Growing market volume and limited, often largely local competition have been dominating manufacturing in the seventies and eighties. Today, the process industry is facing largely saturated markets in many geographical regions of the world. Internet technology has been successfully used in e-commerce solutions to achieve almost complete market transparency. At the same time, transportation cost have been decreasing significantly. Hence, every manufacturer is facing truly global competition. Economic success is only possible, if new ideas can be quickly transformed into new marketable products or if the production cost of established products can be diminished substantially to counteract decreasing profit margins. Product innovation, process design as well as manufacturing processes have to be continuously improved to reduce time to market of a new product, to minimize manufacturing cost and to establish a high level of customer satisfaction by offering the right product at the right time and location. 1.1 Two business processes
The value chain in any manufacturing oriented industry comprises two major business processes- manufacturing and design- which are highly interrelated [1]. These business processes are constrained by the socio-economic environment, in particular, the market, the legislation and the available process technologies (Fig. 1).
102 Value creation happens in the manufacturing process (Fig. 1, top), which is part of a supply chain including warehouses, distribution and procurement in addition to the production plants. Excellence in manufacturing is not possible without explicit consideration of the constraints and potentials resulting from interaction between the plant and the supply chain it is embedded into. The influencing factors from the supply chain on plant operation have to be exploited rather than rejected by model-based plant management considering all the manufacturing business processes across the whole supply chain [2]. The changing business environment can be addressed on a short time scale by adapting supply chain management and plant operation strategies for a fixed design. The manufacturing process is largely determined by the second business process, the design process, which comprises all the activities related to the design of a new product and the associated production plant including the process and control equipment as well as all operation and management support systems (Fig. 1, bottom). This business process starts with
Fig. 1. The two major business processes in the process industries: manufacturing and design. an idea on a new product and subsequent product design. Conceptual design, basic and detail engineering of the production plant are the major activities which follow, before the plant can be built and commissioned. Excellence in design requires consideration of the complete design lifecycle [3]. In particular, the interactions between different design lifecycle phases focusing on different aspects such as the chemical product, the process concept, equipment design, plant layout, or control structure selection need to be exploited. Only an integrated consideration facilitates the realization of synergies and the achievement of the true economical potential. The plant and the supply chain have to be continuously reengineered during their lifetime in order to adjust manufacturing to major changes in the market conditions and legislation, to adopt new process technologies and to profit from accumulated operational experience. Plant reengineering is only possible on a longer time scale as
103 compared to an adaptation of the manufacturing process for a given plant and supply chain design. 1.2 Value creation
The economic performance of an enterprise heavily relies on the quality of the products of these two business processes. Typically, the major focus is on the product of the manufacturing process, namely the chemicals, which are sold to customers and therefore are considered to generate the revenue to the enterprise. The manufacturing process and its associated supply chain, however, are considered as the cost generators. Profit can be increased on the short time scale with limited investment, if the manufacturing cost can be reduced by optimized strategies for plant operation and supply chain management. It is therefore not surprising, that the current industrial focus is on the reduction of manufacturing cost in order to counteract decreasing profit margins. This strategy does not seem to be sustainable in the long run, since cost reduction by means of better supply chain management and plant operation using existing assets is largely independent of a certain product portfolio and does not contribute to a ftmdamental understanding of the processing technology and its impact on chemical product characteristics. The employed operations research techniques apply to many businesses and may therefore evolve in a technological commodity. After a transition period during which these technologies are adopted, the differentiation between competitors with respect to manufacturing excellence vanishes. Hence, at least at this point in time, there is no adequate appreciation of the contribution of design excellence to the overall success of an enterprise. It is the design process which determines the design of a manufacturing plant. This design is largely responsible for the achievable quality of the chemical product and for the order of magnitude of the production cost. The design also constrains the operational envelope and hence the flexibility to react to changing market conditions. Ideally, an integrated consideration of plant and supply chain design on the one and supply chain and plant management on the other hand should be addressed [2]. However, such an approach would have to generalize and extend the problem of an integrated design and control of a single plant, which itself has not yet been solved satisfactorily. We hypothesize that design excellence is becoming a major differentiating asset in the future which, to a large extent, will decide on the economical success of an enterprise. Of course, for this hypothesis to be true, design has to be interpreted in a broader than the traditional sense. In particular, not only the process flowsheet and equipment, but also the operation support system as well as the chemical product itself have to be considered part of the integrated design business process. The quality of the design process is strongly depending on the available knowledge about the chemical process and products and its longterm management. However, we claim that design process excellence in addition requires a profound understanding of the integrated design process itself. Such an understanding forms the basis for identifying shortcomings in available knowledge and established work processes. It is therefore a prerequisite for design process reengineering to establish better process design practices as well as for the implementation of a suitable software environment to support the activities in the design process in an integrated manner. 1.3 Overview on the paper
In the following we focus in this paper only on apart of the design process, namely to the early phases of the chemical process design lifecycle, the conceptual design and front-end engineering, for pragmatic reasons to avoid excessive complexity. Further, we believe that
104 many of our findings will carry over to the more complicated integrated design and manufacturing problem - not only in chemical engineering but in many other related fields. The next section continues with a discussion of the character of the chemical process design process. The key research questions are formulated and the interdisciplinary research center IMPROVE is introduced subsequently. The major results of the research work together with a discussion of our experience constitutes the last part of this contribution. 2. THE C H A R A C T E R OF C H E M I C A L PROCESS DESIGN P R O C E S S E S
The plant lifecycle can be subdivided into six major phases which comprise conceptual design, basic engineering, detail engineering, construction and commissioning as well as maintenance and continuous reengineering (Fig. 1). Conceptual design and front end engineering (the early phase of basic engineering) constitute those parts of the lifecycle with the most significant impact on the lifecycle cost. In this early design phase, almost all of the conceptual decisions on the raw materials and the reactions, the process, the equipment, the plant and even on control and plant operation are taken. Though only a small fraction of the total investment cost of the plant is spent in these early lifecycle phases, the consequences on the total cost of ownership of the plant are most significant. The results of this early lifecycle phase form the basis for the subsequent refinement during basic and detail engineering. These early phases of the design lifecycle constitute the focus of this contribution due to their significance for the whole plant lifecycle. 2.1 Status of industrial design processes The design process is carried out by a team of multidisciplinary experts from different organizational units within the same or different companies. The team is formed to carry out a dedicated project, it is directed by a project manager. Usually, a number of consultants are contributing to the design activities in addition to the team members. All team members are typically part of more than one team at the same time. Often, the team operates at different, geographically distributed sites. The duration of a single project may range from weeks to years with varying levels of activity at a certain point in time. Hence, the team and the status and assignments of its members may change with time in particular in case of long project duration. Inevitably, there is no common understanding about the design problem in the beginning of the project. Such a common understanding, called shared memory in [4], has to evolve during collaborative work. The design process constitutes of all the interrelated activities carried out by the team members while they work on the design problem [5]. This multi-disciplinary process shows an immense complexity. It has to deal with the culture and paradigms from different domains. Complicated multi-objective decision making processes are incorporated in the design. They rely on the information produced in the current and previous design activities. In particular, conceptual design processes show a high degree of creativity, they are of an inventive nature and do not just apply existing solutions. Creative conceptual design processes are hardly predictable and can therefore only be preplanned on a coarse-grained level. A work process definition- even coarse-grained- is mandatory to establish simultaneous and concurrent engineering to reduce the total time spent on a design. The lack of precise planning on a medium-grained level inevitably results in highly dynamic work processes. They show branches to deal with the assessment of alternatives and to allow for a simultaneous work on only loosely related subtasks. Iterations occur to deal with the necessary revision of previous decisions and solutions. They are due to new insight or due to evolving design requirements. A revision may either address a problem
105 which has instantly been recognized or it may serve to exploit an identified potential. A strict definition of the work process in conceptual design (as accomplished in many administrative business processes [6]) is not only impossible but also highly undesirable. It would largely constrain the creativity of the designer with obviously undesirable consequences for the quality of the resulting design. The team of experts typically uses a multitude of resources in the various phases of the design process. For example, web-based text retrieval and browsing systems are used to search the scientific and patent literature or internal archives for information on the materials or processing technologies. Lab-scale or pilot-scale experiments allow the investigation of specific questions related to physical properties, kinetics, scale-up of equipment or the accumulation of impurities in recycles and their impact on the process behavior. All kinds of software tools with diverse and often overlapping functionality have been increasingly used in the last two decades to support different design activities. First, there are standard software tools such as word processing, spreadsheet or groupware systems, which are completely independent of a specific application domain and hence are established in all industrial segments. Second, there are domain specific tools which support specific chemical process design activities. Such tools include, for example, block or equation oriented process modeling environments, equipment rating and design or cost estimation software. Often, different tools are in use for the same or similar tasks within a typically globally acting enterprise. This diversity and heterogeneity of software tools may even show up in a geographically distributed design team. Often, these tools rely on some mathematical model of the chemical process to perform a synthesis or analysis step in a model-based fashion. These models are of differing coverage and rigor, but contain a lot of process knowledge in a formalized and structured manner. In the course of the design process, a complex configuration of different types of information is created. This information appears in multiple ways. There are, for example, standardized documents including equipment specification sheets or design reports, informal texts like e-mail or telephone notes, or input or output files of certain software tools containing problem specifications or result summaries in a formal syntax. This information is typically held in a decentralized manner in the local data stores of the individual software tools, in document management systems or in project databases. Typically, the relationship between the various information units is not explicitly held in the data stores. Information is exchanged in the design team by means of documents, which aggregate selected data relevant to a certain work process context. Though a large amount of information is created and archived in some data store during the design process, there is typically no complete documentation of all the alternatives considered during the design. However, a full documentation of the final conceptual design has to be compiled from the information created during the design process. Typically, this documentation is handed over to an engineering contractor and to the operating company. The contractor employs this design documentation to continue the design process during basic and detail engineering, whereas the operating company uses the conceptual design package to prepare maintenance and asset management procedures.
2.2 Analysis of current design practice and supporting software tools The analysis of current design practice reveals a number of weaknesses which have to be overcome to successfully establish design process excellence. The most import issues are the following: 9 There is no common understanding and terminology related to the design process and its results.
106 9 Creative design processes are not properly understood. There is no systematic reengineering and continuous improvement process in place. 9 Design processes and their results are not sufficiently well documented. This lack of documentation prevents the tracing (i) of ideas which have not been pursued further for one or the other reason, (ii) of all the alternatives studied, (iii) of the decision making processes and (iv) of the design rationale. 9 Reuse of previous solutions and experiences at a later time in the same or similar design projects is not supported. 9 The creation of knowledge through learning from previous experience is not systematically supported by information technologies. 9 There is no systematic evolution of requirements and no assessment of design objectives with respect to the requirements. 9 A coherent configuration of all the design data in the context of the work process is not available. Time spent for searching and interpreting information on a certain design in the course of the plant lifecycle is enormous. Often, it is less effort to repeat a task. There is no systematic management of conflicts between design information or change propagation mechanism between design documents. 9 There is no systematic integration of design methodologies based on mathematical models of the chemical processes with the overall design work process. In addition to these work process oriented deficiencies, there are also serious shortcomings with respect to the software tools supporting the design process. Some important considerations are the following: 9 Tools are determining the design practice significantly, because there has been largely a technology push and not a market pull situation in the past. Tool functionality has been constrained by technology, often preventing a proper tailoring to the requirements of the design process. Usually, the tools are providing support functionality for only a part of a design task or a set of design tasks. 9 There is a limited integration between tools largely focusing on those of a single vendor or its collaborating partners. The integration of legacy tools into such an environment or the integration of the software infrastructure of a company is costly. 9 The heterogeneity of the software environment impedes cooperation between organizations. 9 Design data are represented differently in the various tools. There are not only technical, but also syntactic and semantic mismatches which prevent integration. 9 There is a lack of managing relations between data and documents produced by different tools in different design activities. 9 Project management and administration software is not at all integrated with engineering design support software. Hence, proper planning and controlling of creative design processes is difficult. 9 Tool integration is largely accomplished by data transfer or data integration via a central data store, neglecting the requirements of the work processes. 9 Communication in the design team is only supported by generic tools like e-mail, video conferences, etc., which are not integrated with engineering design tools. 9 The management of creative design processes is not supported by means of domain specific tools. These two lists clearly reveal high correlation of the work processes itself and the supporting software tools. Both have to be synergistically improved and tailored to reflect the needs of the design process in a holistic manner. We believe that a work process oriented
107 view on design and the required information technology support is a major prerequisite to achieve design process excellence. In addition, a further development of model-based chemical process design methodologies, algorithms and tools has to take place. 3. THE C O L L A B O R A T I V E R E S E A R C H C E N T E R I M P R O V E
About six years ago, the interdisciplinary collaborative research center (CRC) 476 (IMPROVE) has been established at RWTH Aachen University. It is funded by Deutsche Forschungsgemeinschaft (DFG, the German science foundation) to address some of the issues identified in the last section. Computer scientists and engineers from six disciplines are collaborating with substantial financial and human resources in this long term research effort. The focus is on new concepts and software engineering solutions to support collaborative engineering design processes [7]. Research is concentrated on the early phases of the design lifecycle due to its significant impact on total cost of ownership and due to the challenges resulting from the creative and highly dynamic nature of the work process. A scenario-based research approach has been used in IMPROVE in order to identify the requirements based on a concrete chemical process design case study. The selected scenario comprises the conceptual design of a polymerization process for the production of polyamide6 from caprolactam [8]. This process is well documented in the literature and of significant industrial relevance. The polymerization domain has been chosen because there are much less mature design support tools as compared to petrochemical processes. Therefore, tool integration and work process support are of considerable interest in the end user as well as software vendor industry. The process consists of a number of polymerization reactors followed by a number of units to separate water and monomer from the reaction products and a compounding extruder. The extruder is not only used for compounding but also for degassing of the monomer remaining in the melt. Typically, polymerization, separation, and extrusion are designed in different organizational units of the same or even different corporations using different approaches and supporting software tools. An integrated solution of this problem has to overcome the traditional gap between polymer reaction engineering and polymer processing with its different culture as well as the problem of incompatible data and software tools. Hence, the scenario poses a challenge for any integrated conceptual design process and its supporting software environment. The design support software tools employed in the scenario are of a completely different nature. They include commercial as well as legacy tools. Examples are Microsoft Excel, various simulators such as Polymers Plus from Aspen Technology, gPROMS from PSE, Morex, BEMflow and BEMview from Institut for Kunststoffverarbeitung at RWTH Aachen, the project database Comos PT from Innotec, the document management system Documentum as well as the platform Cheops for run-time integration of heterogeneous simulators, the repository ROME for archiving mathematical models and the modeling tool ModKit, all of Lehrstuhl fOr Prozesstechnik at RWTH Aachen. The major research issues considered in IMPROVE include 9 the improvement of design processes by either integrating yet largely isolated design activities or by defining innovative design processes, 9 the development of an integrated information model of the complete design process in the sense of an ontology,
108 9 the development of novel computer science concepts and their prototypical implementation for information and collaborative work process management in engineering design processes, 9 the implementation of a demonstrator of an integrated design support system to illustrate the synergy of integration and to prove the additional benefit to the end user by means of an industrially relevant and realistic design scenario, and 9 the development of software technologies for the a-posteriori integration of existing tools and their functional extensions with an emphasis on the automatic generation of wrappers to homogenize interfaces. Some results of IMPROVE will be presented in the remainder of this contribution. More detailed information with numerous references to publications originating from IMPROVE can be found at http://www-i3.informatik.rwth-aachen.de/research/sfb476/. 4. MODELING OF DESIGN W O R K PROCESSES AND THEIR PRODUCTS
A major objective of our research in IMPROVE is the development of an integrated information model which covers the work processes, the resources employed, and the resulting design (or product) data, which are typically organized in documents reflecting the context of a certain activity during the design process. Such a modeling activity is not selfsufficient. The resulting model can be used in a number of ways. For example, deficiencies of established design processes may be identified as a prerequisite for their improvement and reengineering. Further, new innovative work processes may be developed from an analysis of existing approaches in order to better integrate traditionally separated activities. Examples include the tighter integration of mathematical modeling and cost estimation with the increasing refinement of the design in a continuous manner, despite the constraints imposed by current tool function. Another example relates to the improved integration of different design domains such as polymer reaction, monomer separation and polymer processing. Besides these engineering related use cases, the information model is the basis for a
Fig. 2: Different perspectives on an integrated information model for the representation of design process information.
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model-based top-down design of new software tools with innovative functionality and for the integration of these new and of existing tools to a design support environment. The envisioned information model not only has to cover work processes and the information generated and used, but has also to describe the design process and the associated information from various perspectives with differing levels of detail. Fig. 2 shows some relevant perspectives on the information managed during the design process on various levels of detail and with various degrees of formalism [9]. First of all, the major process design concepts have to be represented on a conceptual level (Fig 2, top) to address the needs of the designers in the team. For example, such a conceptual model facilitates a common understanding of the design process and its results, a prerequisite for improving the design process or for formulating requirements on appropriate design support software tools. The conceptual information model can be transformed into a design model (Fig. 2, middle). It serves the needs of the software engineer during software development and also determines the user interface of tools. Finally, the design model is implemented by means of some technology resulting in the implementation model of the design support software (Fig. 2, bottom). In addition to these levels of detail and degrees of formalization, we also distinguish between the data itself (Fig. 2, left) and the documents as carriers of data related by a certain design context (Fig. 2, right). Hence, documents link contextual design data to the work process. In the sequel, we will discuss some of the information models developed and their relation. For the sake of clarity, the focus will be largely on the conceptual level. Besides such a conceptual model, various more refined and strongly formalized implementation models have been derived from or related to the conceptual model in IMPROVE.
4.1 Work process modeling during empirical studies The investigation of existing and the definition of recommended work processes is supported by means of the work process model C3. It is based on the Unified Modeling Language (UML), but includes a number of specific extensions [10, 11 ]. C3 supports work process modeling in a hierarchical manner on an arbitrary level of granularity. It covers the roles of the members of the design team, the order of activities carried out in a certain role, the information used, modified or generated, as well as the resources (software tools, in particular) employed during an activity. C3, implemented by the Workflow Modeling _System (WOMS), facilitates the acquisition and documentation of actual work processes by industrial designers with little extra effort due to its easily accessible and illustrative graphical notation [12]. The weak degree of formalization is considered a strength of C3. It minimizes the modeling effort to a minimum which is essential for being accepted by always time constrained industrial designers. The C3 work process model can form the starting point for further extension and refinement to a conceptual work process model which itself can further be transformed in the sense of Fig. 2 in order to assist the development of software supporting the design process in geographically and institutionally distributed teams [ 13]. 4.2 The conceptual information model CLiP and its applications The conceptual information model CLiP has been developed to clarify the most important concepts and their relations for the description of chemical process design processes in the sense of an ontology [14]. The design of CLiP is based on ideas from general systems theory [15], which have been successfully applied to represent complex structured systems in various domains. Its design philosophy is detailed in [ 16].
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Fig. 3: The conceptual information model CLIP: Meta model and partial model structures. The development of CLiP aims at a well structured and therefore extensible information model, which ultimately covers all the design data produced during the design process, the mathematical models used in the various model-based design activities, the documents for archiving and exchanging data between designers, collaborating institutions, or software tools, as well as the design activities with the resources they use. CLiP is not planned as an information model which fixes all the details of the universe of chemical process design in a comprehensive manner. Rather it is understood as a modeling framework in the first place to provide a coarse structure for the very diverse types of data occurring in the design process. Such a model framework has to be open for extensions driven by the requirements of a certain application. Further, it has to been designed to allow for an integration of already existing data models. CLiP integrates the representation of work processes and the resulting design information. In particular, design documents are explicitly distinguished from design data. Fig. 3 gives an overview on the structure of CLIP. A more detailed description can be found elsewhere [9, 17, 18]. Meta modeling has been used a first structuring mechanism, in order to allow for an efficient representation of symmetric and recurrent model structures. This way the coarse structure of the information model can be fixed and a simple categorization of the most important modeling concepts becomes feasible. We distinguish the meta meta class level, which only defines the concept of a general system, the meta class level, which holds the major categories of concepts for our domain, and the simple class level, which defines concepts related to different tasks in the design process. The meta class level comprises a technical system with its constituents device and connection, the material, the social system consisting of the members of the design team, the activities carried out during the design process and the documents associated to the various activities. The open model structure of CLiP is achieved by grouping the concepts on the simple class level to related logical units. The resulting partial models relate to design tasks which
111 are typically addressed independently in parallel or in sequence during the design process. The concepts in the partial models can be introduced and maintained largely independently from each other. However, since the same real object is often referred to in different design tasks from different perspectives with differing degree of detail, overlap, partial redundancy, conflicts, and even inconsistency can hardly be avoided. Existing relationships between concepts are explicitly captured by means of association links. These links are defined by means of integration classes (or documents) to specify relations not only between concepts in different partial models but also between the associated data. To reduce the specification effort and the complexity of the resulting information model, only those relations are represented which are of any relevance in the course of the design process. This principle of systematic, task-oriented decomposition and subsequent selective reintegration is considered an essential prerequisite to successfully deal with the inherent complexity of an integrated information model covering the whole design lifecycle. CLiP is implemented by means of different modelingformalisms. The meta model and some of the concepts of the simple class level have been implemented in ConceptBase [19]. This system nicely supports meta modeling and offers a sound logical foundation with basic deductive reasoning capabilities to assist schema development and maintenance. All the partial models of the simple class level are represented by means of UML [10]. This formalism is well-suited for large data models due to its graphical notation. The contents of documents are represented by means of XML [20]. The information units within documents are linked to CLiP classes and their attributes by means of associations. This link is the prerequisite for explicitly relating information stored in a project database to that contained in design documents, typically stored in a document management system. Currently, CLiP is being enhanced by additionalformal semantics for various reasons. First, the associations between partial models can only be specified if a precise meaning of the concepts and attributes is established. Second, model development is facilitated and third, the model can be directly used by a reasoner based on description logics. This way, new data and concepts can be classified and introduced in an existing database. Also, browsing and retrieval of data can be assisted across heterogeneous data sources, if the semantically enriched data model is used as a homogenization layer. Still, a coarse conceptualization by means of UML is accomplished first, before the refinement and further formalization of the UML concepts is addressed by means of some ontology language such as DAML+OIL [21 ].
4.3 Application of C L i P - From conceptualization to implementation The software implementation of design support functionality requires a refinement and transformation of this conceptual information model according to Fig. 2. This refinement may be organized by means of various horizontal layers on the simple class level. Such layers serve as an additional structuring mechanism to maintain transparency and to support extensibility. The specific refinement of the model is determined by the envisioned application and the target software platform. There may be more than one refined model, if different tools for the same of similar tasks are being used in an integrated software environment. Often, available data models are subject to reuse and integration. These data models can either be those used in the tools to be integrated, or some standardized data model such as the application protocols of STEP [22] which have been developed for data exchange between the software environments of different organizations. Different ways of integrating existing data models with the information model framework CLiP have been discussed in [23 ]. There have been a number of data modeling activities in the process engineering domain (see [24] for a critical review) without a proper validation. Information model validation is difficult in principle since there is very little theoretical foundation to decide
112 upon the quality of a certain information model. Validation is only feasible if such a data model is implemented in a variety of different ways. An information model may be considered valid, if such implementations are possible without major effort and if the resulting software matches the cognitive model of the user to facilitate the use of the tool. For this purpose, CLiP has been used in various software development projects in IMPROVE. CLiP has been used for example to extend the database schema of the project database Comos PT of Innotec to also cover conceptual design data [9]. Originally, the database schema of Comos PT has been focusing on detail engineering and maintenance only. The case study revealed the versatility of CLiP and its simple integration with existing data models. Another case study carried out in IMPROVE is related to the integration of different software tools [25]. CLiP is refined into the data model for the specification of so-called integration documents which explicitly model the relations between the schema and the data of the implementation models of different tools. This way, an integration of tools is facilitated by a selective data homogenization approach without the need for defining and implementing a centralized data store (see Section 5). Such an approach avoids the problems of data centered tool integration, in particular, the maintenance and implementation of the necessarily complex data model of the complete design process. In contrast to this tool-to-tool data integration, CLiP is also being used in IMPROVE as a basis for the implementation of a data warehouse to integrate heterogeneous data sources such as tool specific file systems or databases which inevitably occur in an integrated design support environment. Such a process data warehouse not only archives all data generated, but also the work processes operating on these data (see Section 5). Besides the application of the product data model of CLiP for the implementation of information management functionality, e. g. for archiving of the design data generated during a design project and for the exchange of data between tools, the integrated data model can also be used as a starting point for the implementation of tools which support the execution of work processes during design. Such tools can be considered generalized workflow systems which, in contrast to existing workflow systems, satisfy the needs of highly dynamic and creative engineering design processes. At least in the medium time range, such systems are considered of high industrial relevance. The focus will shift from information management to an efficient support of the execution of high-quality design processes. Two work process support approaches are pursued in IMPROVE (see Section 6). They aim on the one hand at the guidance of an individual designer during the execution of unstructured and not properly planned personal work process, and on the other hand on the administration and management of the complete design process carried out by the design team. 4.4 Some lessons learned and future challenges in information modeling Four major and largely independent issues will be briefly sketched in the sequel. They relate to empirical studies of design processes, the integrated modeling of data, documents and work processes, the structuring of an integrated information model and its application. Work process oriented information modeling has to rely at least in part on empirical studies of real industrial design processes. These empirical studies, however, should not only be confined to clarify the social context of a design process [26]. Rather, they should be related to the concrete engineering domain and to the information technology support of design, either desired or actually used. According to our experience, empirical studies have to be goal oriented towards an in-depth understanding of the design process relating organization, management, resources, requirements, tasks, and results produced. The understanding is at best cast into an information model. Since it is impossible to completely formalize (on a fine-
113 grained level) creative conceptual process design, the information model has to remain and coarse-grained (and hence vague) in parts. Such a focus on understanding and modeling is comparable to inductive (empirical) mathematical modeling of chemical processes. Acquisition of real work process data is most effective if it is carried out by the designers themselves. WOMS has proved to be a useful tool to support such work process data acquisition. As in mathematical modeling, this bottom-up approach of empirical studies has to be complemented by some deductive component as in fundamental chemical process modeling. Obviously, such a top-down component of modeling a design process requires a "design theory" or, more pragmatically, a good understanding of current design practice or preferred design processes. A meaningful combination of both approaches remains a challenge for future research [27]. As soon as an information model of the existing design process is available, techniques from business process engineering may be applied to improve the design process and to formulate requirements for computer-aided support [28]. The integrated consideration of data, documents and work processes together with the resources used and the organizational structures involved seems to be appropriate. Still, a lot of conceptual as well as technical issues of developing and validating such an integrated information model have to be addressed by future research work. A much better capturing of the real design process seems to be possible, if documents of all kinds are systematically considered to link design data and work processes. Documents are not only used to archive a design, but they also play a dominant role in the exchange of design results between people across organizational boundaries. Hence, they are closely related to the human part of the work process. Further, documents can be interpreted as input files and they are the result of the execution of some software tools. Therefore, documents relate to the computer-assisted part of the work process. Documents always define the context of a work process and provide a situated view on the design data. Separated documents, however, do not allow a comprehensive and consistent presentation of the whole configuration of the design data. Hence, more work has to be done to clarify the conceptual relations between different types of documents and their data items. An integrated information model of the design process lifecycle has an immense inherent complexity. An appropriate structure of a multi-faceted information model is crucial to facilitate transparency, extensibility, and maintainability. The evolving formalisms and languages for information model representation are further complicating the problem. Last but not least, the collaborative work process of information modeling has to be properly defined, managed, and supported by suitable tools. The resulting information model not only provides a common understanding of the domain of interest within the design team. It is also mandatory for a fully model-based topdown design of design support software systems. There are many applications which can benefit from the same integrated information model such as tool development, integration of existing tools, data exchange between tools and organizations, homogenization of heterogeneous data sources, or the realization of the semantic web to create the knowledge base of an enterprise. Ideally, all this software should be generated automatically from a formal specification. There is obviously a long way to go due to the complexity of the design domain.
5. ARCHITECTURE OF A FUTURE INTEGRATED DESIGN ENVIRONMENT The information models introduced in the previous section are indispensable for a top-down design and for the implementation of integrated design environments. Before we discuss
114 advanced cooperative design support under development in IMPROVE in Section 6, we present and discuss a coarse software architecture which is suitable for the work process oriented integration of existing and novel software tools. 5.1 An exemplary architecture Fig. 3 depicts a sketch of a software architecture of a future design support environment. A prototype of such an environment with partial functionality has been implemented and evaluated in IMPROVE. The environment comprises ex&ting tools typically employed in industrial practice which stem from different sources, either commercial or in-house. Tool integration is still and will remain of substantial interest for the operating companies despite the substantial effort of major vendors to integrate their own tools with each other and with those of selected collaborating partners. The end users in the operating companies are interested in customizing their design support environments by integrating additional tools and data bases provided by other vendors or in-house development groups in order to differentiate their technology from that of their competitors. The software to be integrated can therefore be either "complete" design environments from some major vendor or highly specialized tools or data bases from niche providers. The tools are wrapped by thin software layers to provide standardized interfaces for data exchange and method invocation employing state of the art middleware technology [29]. The interface definition is guided by the conceptual information model of the design process discussed in the previous section. The design documents and their evolution during the work processes determine the interface definition to a large extent, since they are providing the context for tool interoperation in a natural manner. The architecture in Fig. 3 suggests interoperation of very different types of software modules in an integrated design support environment. There are, for example, general purpose process modeling environments (e. g. Aspen Plus from Aspen Technology or gPROMS from Process Systems Enterprise) as well as dedicated simulation tools (e. g. Morex for the simulation of extrusion processes). In addition to the various simulation capabilities various data bases need to be integrated. For example, a project database (e. g. Comos PT from Innotec) is required to store the major product data during a design project. Such a project
Fig. 3: A coarse sketch of a software architecture of a future integrated design environment as partially implemented in the CRC IMPROVE.
115 database may offer a flowsheet centered graphically supported portal to access the design data stored as well as interfaces to a limited number of design tools. Alternatively, a separate flowsheet tool with extended functionality [30] could be integrated in order to serve the needs of other tools integrated in the environment. In addition to the project database, a physical property database (e. g. DIPPR) with raw experimental data as well as parameters for physical property correlations and a repository for storing mathematical models of different kinds (such as ROME [31]) are part of the integrated environment. A commercial document management system is used to serve as an archive for all design documents. A process data warehouse captures the design data in the context of the work process [32]. In order to support the execution of distributed design process, the management system AHEAD [33] of Informatik III at RWTH Aachen is integrated. It assists the project manager in allocating and monitoring the resources (e.g. the members of the design team and the tools they use), in providing a consistent set of documents produced during the design project, and in keeping track of all the activities carried out during the design process on a medium-grained level. An extended middleware platform developed as part of CRC IMPROVE provides load balancing, error handling and service management for the integrated design environment which is typically operated in a distributed wide area network.
5.2 Integration approach The software integration approach chosen is driven by the characteristics of actual design processes, the resulting product data distributed in documents of various kinds and the relations between those documents, or the data items they contain. It is not intended to extract the design data, completely or in parts, from the native data stores of tools in order to duplicate them for example in a central data warehouse and store them together with the relevant associations existing across the various tools. Rather, in contrast to such a data centered integration approach followed by all commercial integration solutions, we preserve the native data stores of the tools to be integrated. Hence, integration is achieved by means of a-posteriori homogenization of heterogeneous data sources. For this purpose, the data and communication layer of the architecture (see Fig. 3) is equipped with dedicated mediators [34] which map the data instances between data sources and sinks. The process data warehouse stores the meta data which are required to trace work processes and the resulting product data for documentation purposes and to facilitate later reuse in the same or in a different project [32]. Such an integration approach has been advocated by a requirements analysis of a number of German operating companies [35]. If integration considers both, the work processes as well as the data handled in a particular design context, implementation and maintenance effort of integrated solutions is limited.
5.3 Providing new functionality for collaborative design New design support functionality has to be provided by means of a functional extension of existing software tools (e. g. a simulator or a project database). These extensions have to be accomplished without reengineering existing tools which is typically not feasible because of commercial as well as technological constraints. Hence, the functional extensions of existing tools are implemented as separate and self-contained software components. These software components are subsequently wrapped by a thin software layer to implement logically as well as technically matching interfaces to facilitate integration with existing tools. Examples of such new functionality under development in IMPROVE will be discussed in Section 6. In many cases, some desired functionality is already available as part of an existing tool. Often, the level of sophistication of the available implementation is too limited in order
116 to apply it for a related purpose for which it has not been designed originally. In such cases, it would be desirable to isolate and extract the available generic functionality from the existing tool in order to offer its service to other tools in the integrated environment after the required extensions and modifications. For example, most computer-aided process engineering tools include some software module for the specification, representation and visualization of flowsheets. Typically, the level of abstraction and the information content covered is determined by the specific task addressed by the tool in the design process. It is obviously preferable from a usability as well as from a maintenance point of view to centralize all the flowsheet functionality in a single dedicated tool. Such an advanced flowsheet tool (see Fig. 3) is designed to fulfill all the requirements for managing flowsheet representations on various levels of granularity and for browsing and retrieving flowsheet related design data [30]. In practice, the extraction of some functionality from existing code may not be possible. There are at least two reasons: the source code may not be available, or the functionality to be extracted may be tightly linked to other tool functions such that the extraction is impossible without complete reimplemention of the tool. In those cases, the functionality is not extracted, but it is bypassed instead. An extended functionality superseding the existing capabilities is provided by a new dedicated tool as part of the integrated design support environment. 5.4 Some lessons learned and future challenges in tool integration A number of challenging issues have come up during our studies on the development of integrated design support environments. Some of them are briefly sketched in the sequel. The a-posteriori integration of existing tools into an open integrated design support environment is meeting the expectations of the end users but is, at least to some extent, contradicting the objectives of the software vendors. The latter want to offer their own integrated solutions to extend coverage and market share. Especially, their tools do not offer transparent interfaces which easily allow tool integration. The data structures may not be documented or the data can not be exported. Existing tools often combine too much functionality in a single software systems due to historical reasons. Typically, the tools have not been designed for integration. Rather, they have been created in an evolutionary extension process which steadily extended the functionality of a monolithic tool. Obviously, a redesign and modularization of the tools would not only facilitate integration into open environments but would also reduce software maintenance cost. Both issues, the lack of transparent interfaces and appropriate modularization are hard problems for tool integration. Middleware and wrapper technology has come a long way and is nicely supporting the control and platform integration aspect of tool integration [36] on a technical level. However, the interfaces are only standardized on a syntactic level, which is not sufficient for tool integration. Rather, standardization on a semantic level is required to ensure proper function and meaningful data exchange between tools. Such a semantic standard may be accomplished by ontologies which are tremendously pushed by semantic web approaches [37]. Ultimately, the classical tool integration dimensions [36] have to be extended by a work process dimension to provide context to the integration exercise. If such a work process orientation is lacking, tool integration is unnecessarily complex and costly to develop and maintain. Hardware and software platforms are rapidly changing. The technological progress in information technology is driven by the mass consumer markets and not by the requirements of engineering design applications. The level of sophistication and functionality of the service layer on top of traditional operating systems is steadily increasing. Improved services simplify the implementation of integrated design environments and allow more advanced functionality. For example, multimedia services can be used for advanced communication between design
117 team members. However, a careful modularization of the application becomes crucial to allow the absorption of consolidated new software technologies. In summary, the integration of tools into useful design support environments at reasonable cost requires careful architectural considerations. Both, the integration of existing commercial as well as in-house legacy software and the absorption of evolving software technologies have to be accomodated. Vendors have to design their tools systematically for aposteriori integration to satisfy the needs of their customers and to reduce their own development and maintenance cost. 6. NEW DESIGN SUPPORT FUNCTIONALITY A work process oriented integration of existing design support software tools requires novel functionality if a new quality of support for collaborative design is aimed at. Subsequently, a selection of such novel support functions are discussed.
6.1 Semantic support of individual designers A designer has accumulated a substantial amount of experience during previous design projects. The quality of the design processes can be improved tremendously if this implicit knowledge can be converted into explicit knowledge which is amenable to a later reuse by himself or by a colleague in a completely different context either within this or another design process. There have been numerous attempts to acquire implicit knowledge from experts by means of formal techniques in artificial intelligence. These techniques typically require a basic understanding of the business processes of interest. Since creative design processes are, at least in part, not sufficiently well understood to effectively guide such knowledge acquisition processes and since experts are not always cooperating well, a new approach formerly suggested in the context of requirements engineering was adopted to apply to engineering design processes in IMPROVE. We briefly sketch the idea in the following and refer for details to [38]. Instead of acquiring knowledge a-posteriori by means of structured interviews, reviews of past design processes etc., the design process is recorded automatically during its execution. The recording results in so-called process traces which capture all the major steps carried out during the design process together with the data and documents which have been handled. These traces are stored in the process data warehouse of the integrated design support environment (see Fig. 3). The traces are not only used to document the work processes in detail. Rather, they provide the basis for interactively extracting repetitively occurring process chunks applicable in a certain design context. As in the area of mathematical process modeling, such an identification task can be supported if the purely data driven identification is complemented by some a-priori knowledge. While such knowledge is comprised by model structures derived from the fundamental laws of physics in mathematical modeling, it is not that obvious what kind of a-priori knowledge can assist the discovery of design process chunks. We are currently investigating to what extent specific parts of a design process can be modeled on an abstract level in order to provide parameterized chunks which could guide the discovery process based on process traces. The process chunks are supposed to be implemented in the design process guidance and enactment tool of the PRIME environment [38] which assists the individual designer during repetitive activities. For this purpose, the guidance and enactment tool has to analyze the current context of the design process first. Next, it has to match it with similar contexts stored in the process data warehouse. If a matching context has been found, applicable
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process chunks are retrieved from the process data warehouse and suggested to the designer. After his approval and after providing lacking context data, the process chunk is enacted. 6.2 Administration and coordination of the complete design process Individual designers are typically contributing to different design processes simultaneously. All these processes are administrated and coordinated by a chief design engineer, the manager for short. Obviously, the individual design processes are not independent but highly interrelated by the documents they work with and by the resources they share. The resources include time and budget, team members, experimental facilities and available software tools. Inevitably, the inherent complexity of the design processes requires a management support tool to effectively monitor and coordinate the design processes and the associated activities, to keep track of the resulting design documents and their relationships, and to administrate and allocate the available resources. The strong relation between resources, activities, and documents has to be taken into account for a proper allocation of resources to specific design tasks as well as for consistency management of the documents. AHEAD, a software tool to support the management of cooperative design processes anal their interdependencies on a coarse-grained level, provides functionality for two different kinds of users, the manager and the designer [33]. The manager is supported by three fully integrated tool sets. Dynamic task networks with control and data flow interrelations are provided to implement activity management. Version control, configuration management and an explicit notion of the dependencies between documents are provided to facilitate management of the products of the design process. The resource management allows for the definition of the organizational structure of the design teams working on the various design processes. The designer is supported by an agenda tool, which displays the upcoming tasks to be carried out by the design team members, and of a work context tool to manage the documents and the software tools required to carry out a certain design task. The design support offered by AHEAD is purposely limited to coarse-grained activities in order to facilitate the link between the actual design work carried out by the design teams and the management of related design processes. Hence, it differs in scope from the work process support offered by the PRIME environment which focuses on guiding and supporting activities of an individual designer on a fine-grained level. The implementation of AHEAD directly addresses the inherent dynamics of a design process. In particular, the task networks can be modified at any time during project execution to reflect changes in the design process as consequences of emerging insight into the design problem or handling of problems and mistakes. Further, an adaptation of the functionality to the peculiarities of a given domain of application is possible by means of a modeling environment which facilitates the representation of domain specific knowledge, for example, related to the capabilities of the tools employed. Domain specific code is generated to customize the management tool to the domain. 6.3 Multimedia communication in distributed design teams Geographically distributed design teams already use a multitude of services including e-mail, groupware systems, joint workspaces or even video conference systems in order to facilitate synchronous and asynchronous communication. Typically, these services are not integrated among each other, and more importantly, with the engineering design tools of a given domain. Hence, the available communication support systems do not offer sufficient functionality to effectively assist the members of distributed engineering design teams. For example, during the design of an extruder as part of a polymer production process, the potential separation of remaining monomer from the polymer melt during polymer
119 processing in the extruder has to be assessed in order to decide on the degree of monomer separation in the evaporation unit following the polymer reactors. This question can only be resolved effectively, if the chief engineer, the extrusion expert and the separation e x p e r t - all working at different locations and in part in different institutions- can easily communicate via multimedia services which are seamlessly integrated with the design support environment. Only then, all the team members have access to the same set of currently valid design documents and to all the required software tools to jointly carry out the necessary design studies during their virtual conference. For example, they may carry out a CFD simulation of the degassing melt flow in the extruder and a process simulation to study the effect of shifting the monomer separation partly from the evaporator to the extruder. The results of the simulations have to be discussed immediately to decide on the required equipment design modifications of the extruder given the multiple domain specific requirements. In order to support such a scenario effectively, the system KomPaKT has been developed in the CRC IMPROVE and evaluated on the basis of the polyamide-6 design case study [39]. KomPaKT offers a set of modular services in a homogeneous environment to support the needs of multimedia conferencing in engineering design applications. Communication is supported asynchronously, for example by e-mail and audio messages, and synchronously by means of a whiteboard and video streams. Floor control and conference management functions are also provided. KomPaKT is integrated with AHEAD in order to support spontaneous as well as planned conferences. AHEAD provides information on the organizational data of the project, the tools and the documents of a design context of interest. Communication on design issues is supported by application and event sharing mechanisms. In application sharing, the output of a design tool residing on the computer of one designer is presented to all participants of a multimedia conference. Often, communication bandwidth is not sufficient if 3D images or movies have to be transmitted. In those cases, event sharing is more appropriate. An instance of the design tool is then available on every team member's computer and only control information is communicated to synchronize the different instances of the tool during communication in the multimedia conference.
6.4 Document oriented tool integration Tool integration is always possible via input and output data which form a certain configuration of the product data denoted as documents, if the data contained in the documents of two different tools can be mapped to each other in a consistent manner at any time during the design process. Despite the independent creation and incremental revision of such documents by individual design tools, there exist a large number of fine-grained dependencies between the data contained in different documents. For example, the abstraction of the process flowsheet used to define the steady-state simulation problem has to match the real flowsheet stored in the project database. Inconsistencies between the various documents are unavoidable. However, a certain level of consistency has to be established as soon as two tools of a design support environment are used in a cooperative manner. The manual reconciliation of the content of associated documents is time-consuming and error-prone. Hence, integration tools are preferable which automate such a reconciliation process to the extent possible. It should be noted that a fully automated integration is not feasible in many cases because of a potential semantic mismatch between the data models employed by the tools to be integrated. This mismatch can only be resolved manually. Obviously, document oriented integration tools are crucial for the implementation of a design support environments (as suggested in Fig. 2) which do not rely on integration via a centralized design data store.
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Document oriented integration functionality is subject to research and development in IMPROVE [30]. The integration tools developed assist the user in consistency analysis of two documents, in browsing document content and in the necessary transformations between documents. They operate in an incremental manner and propagate only the increments between documents in a bi-directional manner. They are interactively used by the designer in order to control the transformation process. The reconciliation of the documents is automatic if possible; it can also be assisted by manual interaction of the designer in those cases, where the integration mechanisms fail. The reconciliation is rule based. The rules build on an information model of the documents to be integrated. The objects of the two models are related to each other by means of an integration document, which holds the links between the data items in the two documents. These links are derived by refining the associations between concepts in different parts of the conceptual information model defined in CLIP. Because of a model-based design, the integration tool can be customized to the peculiarities of the tool documents to be reconciled, if the conceptual information model covers the data objects in the documents semantically. Various integrators between different tools have been developed and tested as part of the activities in IMPROVE by employing a common reference architecture. 6.5 Advanced tools for mathematical model development, maintenance and reuse
Chemical process design has been quickly moving towards solutions which heavily rely on mathematical models. Process simulation is used on a routine basis during conceptual design today assisting the analysis of design alternatives. Tomorrow, the generation of a design alternative itself is routinely supported by short-cut methods and partly automated by rigorous structural optimization employing a multitude of tailored mathematical models. The variety of mathematical models requires their management across the design process lifecycle [3]. Two objectives can be distinguished, namely the integration across the process of mathematical modeling to reuse existing model knowledge downstream in the design process and to integrate existing models at runtime to facilitate multi-model, multimethod and multi-platform integration of simulation and optimization tools. Until recently, traditional heuristics and experienced based design have been largely separated from modelbased design. Consequently, the software environments used in both areas are not integrated, neither conceptually nor technically. Both issues, the management and integration of mathematical models across the lifecycle as well as the integration of design data, mathematical models and the results produced during simulation experiments are addressed as part of the IMPROVE project. For the support of mathematical modeling, three complementary software systems are under development. ModKit [40] supports the generation of tailored mathematical models which cannot be found in the library of a simulator. The model can either be exported into the proprietary format of a commercial process modeling environment or in a neutral format derived from Modelica [41] to facilitate model exchange between applications. Models generated by either ModKit or any other commercial modeling environment can be stored in their native form in the model repository ROME [31 ]. Hence, ROME stores symbolic models in a neutral format or in any proprietary format of a commercial simulator, declarative equation-based models as well as executable block-oriented models. Links between models in a flowsheet or between models from different sources are kept at this point on a coarsegrained level only in the database schema which derives from the appropriate partial model in CLIP. Models can be checked out in their native form to be processed by the appropriate simulation tool. However, models from different sources can be linked to a single flowsheet and integrated during runtime by means of Cheops [42]. Cheops allows steady-state as well
121 as dynamic equation-oriented and modular simulation using existing dedicated simulators which have been developed for specific parts of a process. For example, in the polyamide-6 case study, Polymers Plus may have been used for polymer reactor modeling, gProms for monomer separation from polymer melt in a wiped-film evaporator, and the legacy tool Morex for the modeling of the extrusion process. These simulators are wrapped by standard interfaces and integrated with a configurable simulation strategy (modular, simultaneous, or mixes thereof) to form a simulator of the complete flowsheet showing a recycle of the unconverted monomer. This reuse of individual models is possible without the need for a costly and error-prone reimplementation in a single process modeling environment. Mathematical models and their results have to be related to the design process and in particular to the design data. However, mathematical models and design data are kept in different tools without explicitly accounting for relations between them. Obviously, there is a significant overlap and the risk of inconsistencies in these two data sets. Further, tracing of the design process and its rationale requires an explicit relation between design data and mathematical models [43]. Such an integration is currently being carried out using ROME as a model repository to archive models from various simulators in a coherent manner in the first place and Comos PT which serves as the project database storing relevant design data [44]. This kind of integration may be considered a special case Of the homogenization of related data from different sources as discussed already above. 6.6 Discussion The advanced functionality discussed in the previous subsections is not meant to be the only necessary to effectively upgrade current design environments for collaborative and geographically as well as organizationally distributed conceptual design processes in the process industries. Many other support functions to improve the efficiency of collaborative design are conceivable. We have limited our attention on those activities which are currently being studied in IMPROVE. There is yet very little experience with those functionalities which impact the way a designer works. This is not just a matter of human-computer interaction which is essential for both, acceptance and high productivity. An interesting question also concerns the social implications of such an extended design functionality (see [45] for a general discussion). More and more activities are becoming computer-based, the interaction between humans is changing in quality with unforeseen consequences, for both, the quality of the design and the satisfaction of the designer. Further, the full transparency of the design process results in an almost complete assessment of the performance of a designer. Any inefficiency or any mistake is documented. Obviously, such transparency has to be handled with care by management. 6. CONCLUSIONS This contribution has attempted to show that information technology support of engineering design processes (not only in the chemical process domain) is a complex and far reaching problem. It goes well beyond the classical problem of data exchange or of data centered integration of tools to a design environment. IMPROVE addresses this problem area in a long-term project. The objective of the research work is the clarification of work process oriented support of engineering design by means of information technologies. This objective is considered to be the guiding paradigm of the research work and determines the concrete research projects in the center to a large extent.
122 Some of these research issues together with results obtained and experience gained have been summarized in this contribution. Despite the long term and fundamental research focus of IMPROVE, some of the concepts and technologies have already reached a level of maturity which is sufficient to start transfer into industrial practice in focused joint research and development work with the software and end user industries. ACKNOWLEDGEMENTS The authors thank the German Research Foundation (DFG) for the financial support of the Collaborative Research Center CRC 476 (Sonderforschungsbereich SFB 476) and all members of the CRC for their fruitful collaboration, without which the results presented in this paper would not have been possible. REFERENCES
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Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
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C H A L L E N G E S F O R THE PSE C O M M U N I T Y IN FORMULATIONS. J.L. Cordiner
Syngenta, Global Specialist Technology, T&P, Earls Road, Grangemouth. Stirlingshire. Scotland FK3 8XG
1. I N T R O D U C T I O N Process Systems and computer aided process engineering has over the last few decades very successfully tackled many of the issues from chemical processes. These range from modelling reactors, distillation columns to whole refineries and chemical manufacturing plants. Meanwhile to support this physical property models for the process fluids have been developed from SRK in the seventies through UNIFAC for predictions, Chen and Pitzer models for electrolytes. Recently with increasing computer power and improved mathematical optimisation techniques [1,2] methods have been applied to more complex problems. Rational solvent selections over whole processes [3,4,5] and computer aided molecular design [6,7] have been very successfully applied to industry problems. In addition more complex fluids, such as: associating fluids [8], polymers [9], surfactants [ 10] and electroyes [ 11,12] have been modelled by advanced property methods. Molecular dynamics, QSAR, data-mining and mathematical techniques taken from "biology" e.g. neural networks and genetic algorithms have also been used extensively in modelling complex systems. With this expanded "toolkit" the Process Systems Engineer can begin to tackle some very different types of problems. In recent years there has been much encouragement to broaden the chemical engineering discipline to meet the needs of the new industries, e.g. microprocessors, biochemical, biomedical, food etc. Along with this, academic funding is focussing on partnerships and cross-functional work [13]. Therefore the time is right to exploit the "toolkit- modelling capability and thinking" of the process systems community in these new fields.
126
2. F O R M U L A T I O N S IN I N D U S T R Y The chemical process industry produces many millions of products from relatively few raw materials. Currently the PSE community is focussed on the few thousand active ingredients and bulk commodities. Final products that people use are formulated from these active ingredients and are present in all walks of life from personal care, hygiene, pharmaceuticals and agrochemicals. Formulated products use specifically chosen mechanisms to serve the customer needs by accurately exerting their desired features that can be performance related (such as crop protection, surface protection) and/or convenience (such as controlled release, ease of handling). These formulations are designed for different markets and purposes. The successful design of such formulations can have a huge impact on sales and profitability of a company. With markets tightening and growing competition many of these products need to be made more efficiently and with reduced costs. Therefore the optimisation and design of these formulations is critical to business success. This presentation will focus on the challenges for the PSE community in agrochemical formulations, though many of the issues are directly relevant to drug delivery, personal care, hygiene products and speciality/effect chemicals.
2.1 Agrochemical Market The agrochemicals market is estimated to be $40bn+/year, which is expected to grow as the world population, and pressure on food production grows as shown in figure 1.
Figure 1. The pressure on the agrochemical industry is increasing with advancing competition from Asia and much consolidation has happened as shown in figure 2.
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Figure 2. Industry is focused on finding new products and formulations that will expand their market and increase their market share. It typically takes at least 10 years of intense research and development from the discovery (- first synthesis) of an active ingredient until it's market introduction as an agro-chemical product. A new formulation can be on the market much quicker. In addition, it has become increasingly more and more difficult to find better actives, that are more user friendly, safer, cheaper to manufacture and increase the effacy of the active. Therefore the design of the formulation becomes more business critical.
3. C H A L L E N G E S IN M O D E L L I N G M A N U F A C T U R I N G PROCESSES
FORMULATION
Starting with unit operations as this is familiar to the PSE community. These unit operations for formulations, shown in figure 3, are in some part very different to active ingredient or bulk commodity manufacturing. Typical examples being of bead mills, coaters, high shear agitators as shown in figure 4. However some items are familiar to the PSE community e.g. fluid bed dryers, spay dryers, mixing tanks and agitators. The characterization of the familiar equipment though is more difficult due to high viscosity, solids, slurries and fluids such as surfactants, wetters, dispersants, polymers and complex active ingredients. Formulation manufacturing is typically more globally spread in more numerous smaller sites than active manufacture as can be seen in figures 5-6. This brings added complications of scheduling and differentiation for each market. The number of slightly different formulations may be large and therefore a system for designing formulations, their manufactures and optimising them easily for each market becomes important.
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Figure 3" Unit operations in formulation processes. Provided by Paul Bonnett Syngenta.
Figure 4 Coater, Dynomill- bead mill and high shear emulsion agitator from left to right.
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Figure 5
Figure 6
4. F O R M U L A T I O N TYPES AND ISSUES Pesticide products are split into herbicides, insecticides and fungicides. Herbicides have to penetrate deeply into the plant to kill it. Many Insecticides need to sit on the Surface of the plant leaf to contact insects attacking the plant. Contact many be by direct pick-up or ingestion. Protectant Fungicides in general form a protective layer on the wax of the leaf and therefore require slow uptake into the leaf cuticle but rapid uptake into the surface wax [ 14], although systemic fungicides require reasonable uptake for redistribution in the xylem (for example azoxystrobin). These products are split into a number of different formulation types as shown in table 1 and the characteristics of some of these formulations are shown below.
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Solid formulations Wettable Powder ( W P ) Wettable Granules ( W G ) Soluble Granules (SG) Granules (GR) Tablets (TB)
Liquid formulations Suspensionconcentrates (SC) Emulsionconcentrates (EC) Soluble liquids (SL) Emulsions in water (EW) Microemulsion concentrates (MEC) Microemulsions (ME) Capsule suspensions (CS) Suspoemulsions (SE)
Table 1: Formulation types 4.1 Solid Formulations Wettable Powders (WP) have a solid, active ingredient - or a liquid active ingredient coated on an inert carrier that is mixed up with all other formulation inerts (dispersants, surfactants, fillers). This is then dry milled (i.e. jet-stream milling) to reduce the particle size to about 2-5 microns. Redispersion upon dilution with water by the farmer results in a suspension of the active ingredient/filler particles as spray solution. Dispersants and surfactants ensure fast redispersion to single particles upon dilution in water to form the spray solution and prevent the single particles from agglomerating and/or sedimenting in the spray solution
4.2 Liquid formulations: Suspension concentrate (SC) :A solid active ingredient with the help of added dispersant and thickner is suspended in water then wet-milled to reduce the particle size to about 2 microns. Surfactants in this case protects the particles from crystal growth and agglomeration They also ensures fast redispersion to single particles upon dilution in water to form the spray solution and prevent agglomeration and/or sedimentation of particles in the spray solution. The addition of a thickener (for rhelogy and structure adjustment) prevents the sedimentation of particles during storage.
Emulsion concentrates (EC). The active ingredient is dissolved in a waterimmiscible organic solvent along with the addition of a co-solvent if not liquid already. Emulsifiers are added to ensure spontaneous emulsification of the formulation upon addition to water to form the spray solution. Emulsifiers also prevent emulsion droplets in spray solution from particle growth, agglomeration, creaming and sedimentation and prevent recrystallisation of the active ingredient upon dilution in water to form the spray solution. Capsule suspension (CS). The liquid active ingredient or solid active ingredient is dissolved in water-immiscible organic solvent and emulsified in water. Oil droplets contain an (at least bifunctional) purely oil-soluble monomer in addition of an (at least bifunctional) purely water-soluble monomer starts an interfacial polymerisation (figure 7). This reaction results in polymer capsules filled with active ingredient "Controlled release" formulation. Emulsifiers stabilise emulsion droplets before polymerisation to prevent
131 sedimentation of the polymer capsules in the suspension. This also ensures fast redispersion to single particles upon dilution in water to form the spray solution and prevents polymer capsules from agglomeration and sedimentation in the spray solution. The polymer chosen need to provide a polymer wall that is mechanically stable upon drying of spraysolution,where applicable, and allows release of active ingredient at the desired speed and amount. A cut out section of a capsule is shown in figure 8. Careful selection of the solvent system can adjust the strength and structure of the wall changing the release rates.
Figure 7 Emulsion before polymerisation and
Capsule suspension after polymerisation
Figure 8: Cut out section of a capsule showing the wall structure taken with Scan Transmission X-Ray Microscopy. Originally presented by Daryl W. Vanbesien, Harald D.H. St6ver, McMaster University, Hamilton, Canada in a internal Syngenta Presentation. Used with permission.
Emulsions in water (EW): Are essentially the first stage in an encapsulation. They are complex multiphase systems where the surfactant typically creates a third phase (micelles) or changes the interfacial tension sufficiently to increase the solubility of the active and increase bio-availability. The control of the water/oil or oil/water emulsion and the path to achieve the emulsion will change the droplet size distribution and the ability to form a stable emulsion as shown in fugure 9. The HLD (hydrophilic-lypophhilic deviation ) scale as described in [15] is written as a sum of the contributions of effects and therefore would lend itself to a group contribution method.
132 Small Drop Size clue to best formulation compromise Small Drop Size due to high internal phase ratio effect
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~
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~
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ill
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Two-dimensional fomaulation-composition (water-to-oil ratio) map showing ~he (shaded) zones where smalle~ drop size is attained, when varying formulation or composition at constant stirring energy. Inserted plots show the aspect of the drop size distribution in different points of the map,
Figure 9: Reprinted from [ 16].Used with permission.
4.3 Generic issues with formulations These formulations typically need to have a shelf-life of at least 3 years which means no (or minimal within regulatory limits) chemical degradation of the active ingredient and/or formulation inerts, no change in physical appearance of the formulation i.e. lump formation in WP's, sedimentation or creaming of SC's and recrystallisation of active ingredient in EC's. There must be no change in redispersability upon dilution in water to form spray solution. And finally no agglomeration or sedimentation in the spray solution. Farmers oftern use mixtures of formulations and therefore compatability between these typical mixtures is also important. Therefore a formulation design can include wetters, emulsifiers, dispersants, polymers, surfactants as well as the active ingredient. The potential number of mixtures is vast with the choices of all the formulants possible. As was mentioned before, the design of a new formulation can be business critical and therefore formulations are often tuned to the needs of specific markets, allowing differentiation of the products to maximise sales potential. A well-designed formulation can increase the activity by a number of means, for example increased uptake from careful selection of surfactants. UV protection reduced environmental impact and reduced pytotoxicity can make the product more attractive. Increased retention and spreading characteristics can reduce usage rates. Any of these can also make the product sufficiently beneficial over a competitor's to increase sales. In addition a novel formulation can be used to extend patent coverage preventing generic competition taking over the market when the active ingredient patent runs out.
133 The current practice in designing formulations is to employ a trial and error approach based on past knowledge and expertise. Therefore, although the needs may be satisfied, there is no guarantee that the solution is optimal.
5. P L A N T S T R U C T U R E AND E F F E C T ON UPTAKE. An important step for the efficacy of an agrochemical is the uptake of the active ingredient into the target organism. Therefore building the right mixture into the formulation can enhance the speed and/or amount of uptake of the active ingredient into the target organism and therefore enhance the activity. The right mixture can enhance the chemical stability of the active ingredient, i.e. protect it against photodegradation by UV radiation and weaken the negative impact of environmental factors like heavy rainfalls on the efficacy of the agrochemical. The formulation can influence positively the toxicological profile of the agrochemical like reducing leaching,skin irritation or inhalation. Often something simple like a pH change can substantially improve the suitability of a given formulation. The pesticide, for example, needs to travel from the surface through the epicuticular wax, through the cutin and pectin layer before reaching the target cells as shown in figure 10
Figure 10: reprinted from [ 14] with permission.
This epicuticular wax is a complicated mixture which can be homogenous or show varying levels of crystallinity [ ] (figure 11) dependant on species, which slows the diffusion of the pesticide [ 17]. The intracuticular wax layer is generally accepted to be the main barrier to pesticide uptake [ 18,19,20]. Young leafs have more wax crystals possibly due to the fragile cuticles that are more permeable to water. The waxes differ between plant species. Two examples of the composition of the waxes are shown in figure 12.
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Figure 11: Pea Leaf epicuticular wax provided by Dr C Hart, Syngenta. Further information is presented in [21] This wax layer forms a 2 micron barrier that the active ingredient has to to travel through. Diffusion through the leaf wax can be improved significantly by careful selection of the surfactant [22-29]. The HLB scale [30] is used to classify and select surfactants much like solvent classifications Parker [31] and Chastrette [32] for reaction solvents [33,34]. There are several postulations about why the surfactant aids the passage of the active ingredient, for example the surfactant could be solubilizing the wax and in particular the crystalline structure reducing the tortuosity of the actives' path through to the target cells.
135 [] n-alkanes [] iso-alkanes ~] anteiso-alkanes 9n-alkenes 9iso-alkenes 9anteiso.alkenes [] n-alkanals [] n-alkanoic acids [] unsaturated alkanoic acids [] branched alkanoic acids 9ketones 9n-alkanols 9secondary alkanols [] branched alkanols [] diols [] alkyl~sters [] methyl-esters [] glycerine esters [] al kyl.coum arates [] trite rpe ne esters [] triterpenes (~ constituents with basis peak 8: constituent w i ~ basis peak 123 ra constituent with basis peak 18( [] constituent w i ~ basis peak 117 [] unidentified
Figure 12 9The chemical composition as well as the chain length of the components of the cuticular wax of sunflower leaves (upper chart) and rape (lower chart) and is given in %
5.2 Cutin Composition The cutin layer is made up of an insoluble polymer matrix. This is generally whydroxy-fatty acids of chain length C16-C18 [35-37] and 1-2microns in depth.
6. M O D E L L I N G UPTAKE IN PESTICIDES. Some modelling has been attempted. Essentially the problem is a familiar one with solubility of the active chemical in the water droplet and the formulation mixture on the leaf, then a set of membranes to cross and diffuse through before reaching the target cells. Fickian diffusion can be used to model this diffusion. Foliar uptake of a pesticide has been modelled by using partition coefficients and diffusion coefficients across the plant cuticle[38,39]. This has been extended to the effect of the tortuosity of the actives through the crystallinity of the epicuticular wax [ 14]. This needs estimations for Partition Coefficients, diffusion coefficients, cuticle thickness, Solubility of Leaf Wax in Droplet and the Molar Volume of AI.
136 Whole plant models have been developed [23] which considers the cuticular membrane solubility, solute mobility and tortuosity and the driving force. Further models have been developed for adhesion [23] onto the plant leaf and retention [23] this is especially important for insecticides. Clearly the formulants selected need to aid faster diffusion through the wax in the cases of fungicides and herbicides. Insecticides ideally should diffuse very slowly into the plant and also be well retained on the surface wax. Encapsulated formulations, like microcapsules for drug delivery are being used which present some different modelling and design tasks. The solubility of the active ingredient in the polymer and the diffusivity through it becomes important. The ability to select and optimise the polymers used for efficiency, environmental and cost reasons become the objective and are very familiar to the PSE community being similar to solvent selection and design as used in the CAMD approaches. The careful selection of solvent, for encapsulation can change the release rate and structure of the capsule. This is as a result of the solubility of the monomer in the solvent and therefore the amount available for the polymerisation. A new generation of models and model-based PSE-tools would be needed. Also, formulation design problems, could in principle, be formulated as Computer Aided Mixture Design problems - the limitations at this moment are the models to predict the properties that design the mixtures.
7. CURRENT RESEARCH BY FORMULATORS, PLANT SCIENCE AND RELATED DISCIPLINES. Formulation research is focused on areas as shown in figure 3 using some new advances such as high throughput screening and combinatorial chemistry. Robots are used to rapidly generate large datasets [40] to facilitate more fundamental understanding of what processes are happening as a herbicide, insecticide or fungicide comes in contact with it's target organism. A mixture of methods is used to study the transport processes and mechanisms. Surfactant or oil/solvent enhanced uptake is studied by reconstituted or isolated waxes along with measurements of wax composition versus uptake, cuticle architecture. Clearly genomics is being exploited to build significantly new markets beyond traditional formulations of classical active ingredients. Some of these "tools" could potentially be useful for building PSE type models with high throughput screening able to generate vast quantities of data, which may be needed to build more fundamental understanding of systems or generate group contribution type methods.
137
Figure 13
8. C H A L L E N G E S F R O M F O R M U L A T I O N DESIGN. As formulations can be a complicated mixture of surfactants, dispersants, emulsifiers, polymers, buffer agents, antifoams, oil concentrates and inorganic salts models for these will need to be further developed. Many of the systems are formulants such as solvents and surfactants are actually mixtures and therefore models need to capable of handling these appropriately. Models for surfactants need improvement. An understanding and representation of surfactant concentrated phase behaviour phase behaviour is required [23] e.g. liquid crystals where deposits on leaf surfaces dry, or gel phases in waxes. Models for the solubility of active ingredients in these phases are required. The solubility can change rapidly when the deposit dries. A typical surfactant structure is shown in figure 14.
~O~o~O~o~O~o~O~o/~/O~o'~O',H 0 Figurel4: Typical surfactant: "Akypo RML 100" surfactant molecule: C12, EO l0 COOH. Alkyl chain ranges, EO number typically gaussian distribution with mean at 10. Below pH 3.65 effectively non-ionic but anionic above pka of acid group Control of the rheology and interfacial properties can be the key to size control of particles, stability of emulsions and process ability. As mentioned above, the solubility of complicated actives in polymers along with the solubility of the polymers in solvents are also required. Solubilities of the complex actives in the surfactant and in the leaf wax are required. Even solubility of the complex actives in water is important and at the bounds of
138 what is currently possible. Improved models for solubility of complex multi-functional molecules would be very beneficial in development of active ingredients purifications and separations as well as formulation design. Many of the systems are electrolytic, as are many active ingredient processes. However no reasonable predictive models exist for such systems. Such predictive models would be extremely useful in Active process optimisation [34]. Predictive models are needed for all the systems m e n t i o n e d - current models are unable to handle interfacial phenomena and the properties related to this. Models need to consider the phenomena as well as the physics and chemistry (that is, thermodynamic properties, molecular structural properties as well as mass transport, interfacial phenomena with or without reactions - in the case of electrolytes, there are reactions). Without suitable models, development of PSE or CAPE tools would not be possible (these tools can contribute by providing the means to investigate solutions that otherwise would not be possible - but, they require models, which are not available, currently). The interaction between the species and a fundamental understanding on how they affect the active ingredients joumey to the target organism is required. How does the surfactant and other additives really affect uptake. The PSE toolkit allows for rationalization of large datasets and to fit models to the data. Perhaps this can be used to generate more fundamental understanding of the systems in formulation or the effect on the target organism. The ability to model the effect of the different formulants is necessary in-order to be able to optimise and generate and handle the large amount of data required. Models are required for partition coefficients and diffusivity from the drop into the wax and from the wax into the cutin, through the pectin. Models are also required for retention on the leaf surface, spreading and loss, to allow optimisation of formulations and conditions to maximise active ingredient uptake and beneficial bio-availability. If such models can be developed or approximated from analysis of large data sets potentially available from high throughput experiments, then the ability to optimise formulation design and design like the CAMD approach for solvents would be feasible. However, what's needed in this case is computer aided mixture design and has a much higher potential benefit than CAMD, as the composition space is much larger than for solvent selection. Global optimisation techniques may well be appropriate to rapidly assess such large composition spaces for optimal solutions. Empirical models and geometrical techniques [41,42 and 34] have proven very useful in design of active ingredient plant design. Perhaps there is an opportunity to use these types of models to develop our understanding and selection of formulation mixtures. Clearly developing understanding of the effect of formulants in the mixture and the ability to select as required in a Computer aided formulation mixture design tool would also lead to better understanding of the impact of the Active ingredient design and manufacture. Typically active ingredients are made to a specification and designed for the best process for the active. However, this artificial boundary between the active and
139 formulation manufacture can lead to less than optimal designs. The boundary must be removed and any designs need to take into account the formulation ability of the product and optimise across this wider whole process. An understanding of the properties that are important in an active to ensure successful formulation is required.
9. C O N C L U S I O N S . A series of challenges for the PSE community have been presented showing the need for more fundemental understanding of the impact of each formulant on uptake, effacy and the ability to model the systems. The critical need to develop physical property models to handle the complex mixtures was highlighted. The aim being to be able to use PSE models to design and optimise formulations and invent new formulation types. A rational selection tool or computer aided formulation mixture design tool being the goal to radically improve formulation design. Such tools and the ability to tune formulations specifically, optimising their design will reduce costs, reduce environmental impact and allow product differentiation giving potential for sales growth. Given the increasing difficulty to find new agrochemicals that show advantages over those currently available. The ability to design new and improved and cost effective formulations will be the key to business growth and therefore critically important. The PSE community needs to work in partnership with plant scientists, surface scientists and those working in formulation development to build understanding of the systems bringing their toolkits and thinking together.
REFERENCES 1. Adjiman C.S., Adjiman, Androulakis I.P., Maranas C.D.,and Floudas C.A., Computers in chemical engineerings 20:$419-$424,1996 2. Pantelides C.C. Proc 2nd conf on foundations of computer aided process operations. Edited by Rippen D.W.T. and Hale J. Cache Publications, 253-274, 1994 and 1997. 3. O. Odele and S. Macchietto Fluid Phase Equilibria. Vol 82, pp 57-54, 1993 4. E.N. Pistikopoulos ,S. K. Stefanis Computers Chem. Eng 1998 Vol 22, pp 717-733, 5. Giovanoglou A.,Barlarier J., Adjiman C.S., Pistikopoulos E.N., Cordiner J., submitted to AIChemE Joumal and presented at Fall AIChE Meeting 2002. 6. Hostrup, M., Harper, P.M., Gani, R., Comput Chem Eng, 23, pp1394-1405, 1999. 7. Gani R., Achenie L.E.K., Venkatasubramanian V. Editors. Computer Aided Molecular Design: Theory and Practice. Elsevier Science, Amsterdam. 8. W. G. Chapman, K. E. Gubbins, G. Jackson and M. Radosz,, Industrial and Engineering Chemistry Research, 29, 1709-1721 (1990) 9. Arlt W., Distillation and Absorption Conference Sept 2002 10. A. Galindo, G. Jackson, and D. Photinos, Chemical Physics Letters, 325, 631-638 (2000) l l.A. Galindo, A. Gil-Villegas, G. Jackson, and A. N. Burgess, Journal of Physical Chemistry B, 103, 10272-10281 (1999)
140 12. A A. Gil-Villegas, A. Galindo, P. J. Whitehead, S. J. Mills, G. Jackson and A. N. Burgess, Journal of Chemical Physics, 106, 4168-4186 (1997) 13. Howell J, Cordiner JL et al Report to EPSRC "Strategy and Stucture of Chemical Engineering research" 14. N. M. Sune, R. Gani, G. Bell & J. Cordiner, AIChE Fall Meeting, 2002. 15. Salager J.L.,Forgiarini A.,Marquez L.,Pena A., 3rd World Congress on Emulsion Lyon Sept 2002 16. Perez M., Zambrano N., Ramirez M.,Tyrode E.,Salager J.l., J Dispersion Science and Technology, 23(1-3)55-63 (2002). 17. Merk, S., A. Blume, et al. Planta 204(1): 44-53, 1997 18. Sch6nherr J 1976 19. SchOnherr J 1984 2o. Schreiber, L. and M. Riederer. Plant, Cell Environ. 19:1075-1082. 1996 21. Friedmann A.,Bell G.,Hart C. IUPAC Int. Congress on the Chemistry of Crop Protection. Basel 2002 22. Friedmann A., Taylor P., Bell G., Stock D. SCI Conference 3.12.2002 23. Zabkiewicz J.A. IUPAC Int. Congress on the Chemistry of Crop Protection. Basel 2002 24. Holloway, P. J., Edgerton B. M., Weed Res. 32:183-95, 1992. 25. Holloway, P. J., Silcox D. Proc. Br. Crop Prot. Conf.-Weeds: 297-302, 1985. 26. Holloway, P. J., Wong W. W. C. etal. Pestic. Sci. 34: 109-18, 1992 27. Stock, D., B. M. Edgerton, et al. Pestic. Sci. 34: 233-42, 1992 28. Van Toor, R. F., A. L. Hayes, et al. FRI Bull. 193: 279-284. 1996 29. Burghardt M., Schriber L., Riederer M.J., Agric. Food Chem 46, 1593-1602, 1998 3o. Holmberg K., Editor Handbook of Applied Surface and Colloid Chemistry. 2001 John Wiley and Sons, Ltd. 31. Parker, A.J, 1973, J. Am. Chem. Soc., 95,408 32. Chastrette JACS Vol 107 No 1 1-11 1985 33. Cox, B. G., 1994, Modem liquid phase kinetics, Oxford Chemistry Primer Series 21, Oxford University Press 34. Cordiner J.L., Comput Chem Eng Volume 9 Proceedings of Escape 11 35. Baker 1982 36. Holloway 1982 37. Holloway 1993 38. Lamb et al 2001 Syngenta internal report 39. Stock, D., P. J. Holloway, et al. Pestic. Sci. 37: 233-45. 1993 40. Baker E., Hayes A., Butler R., Pest. Sci. 34, 167-182 (1992) 41. M Doherty and J Perkins Chemical Engineering Science 1982 Vol.37 381-392 42. Bek-Pedersen, E., Gani, R., Levaux, O., Comput Chem Eng. 24 (2-7) pp253-259, 2000
ACKNOWLEDGEMENTS Permission to publish from Syngenta is gratefully acknowledged. Thanks to a great many friends and colleagues for advice and information, especially: Dr Gordon Bell, Dr Alan Hall, Kenneth McDonald, Brian Lauder, Paul Bonnett, Dr Adrian Friedmann, Dr Cliff Hart and Dr Stefan Haas of Syngenta Dr Claire Adjiman of Imperial College and Prof Rafiqul Gani of Danish Technical University, Denmark.
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
141
A Summary of PSE2003 and the Impact of Business Decision Making on the Future of PSE Christodoulos A. Floudas a, Jeffrey J. Siirola b aDepartment of Chemical Engineering, Princeton University, Princeton, NJ 08544-5263, USA bEastman Chemical Company, PO Box 1972, Kingsport, TN 37662-5150, USA
The authors of this paper are to prepare it based on these proceedings and the presentations at PSE2003. Thus a copy cannot be included here.
142
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Published by Elsevier ScienceB.V.
Multi-Scale and Multi-Dimensional Formalism for Enterprise Modeling Atsushi Aoyama, Yuji Naka Chemical Resources Laboratory, Tokyo Institute of Technology Nagatsuta Midori-Ku Yokohama 226-8503, Japan
Abstract This paper proposes a modeling formalism that integrates various phenomena with different level of accuracy, temporal and spatial scale and dynamics. Multi-scale and multi-dimensional formalism (MSMDF), in which model is expressed as a combination of hierarchically and/or horizontally structured modules and their links, has been proposed. The MDF concept defines a model as an interaction of structural, behavioral, and operational dimensions (modules). The MSF concept further enables a hierarchical/horizontal combination of MDF modules. Multi-scale and multi-dimensional formalism (MSMDF) integrates high-level business models with day-to-day operation models, and enables a business decision making (BDM) based on more precise understandings of the state of the business.
Keywords multi-scale modeling, object oriented modeling, lifecycle engineering, concurrent engineering 1 INTRODUCTION The business environments surrounding global chemical industries are undergoing tremendous changes. One trend is a heightening concern on environmental impacts and global sustainable issues. An immediate attention has to be paid to development and improvement of product lifecycle that reduces environmental impact and energy consumption. These activities for sustainability require modeling and simulation of product lifecycle to evaluate environmental impacts (e.g. global warming, ozone layer depletion). Another trend in process industries is the shift to manufacturing of low-volume, higher added value products. In commodity chemical industries, the improvement of process efficiency such as energy saving, cost saving, controllability and operability during the product manufacturing is the most important issue. However, in fine chemical industries, these issues are marginal and how quickly to manage a
143 business flow from research and development to manufacturing and marketing becomes crucial. Concurrent engineering is a solution to this paradigm shift from the process centered manufacturing to the product centered manufacturing. Performances of concurrent engineering depend on accurate business flow model that can express multidisciplinary processes with different levels of accuracy, temporal and spatial scale and modes. The above-described tasks require a modeling scheme that can integrate phenomena ranging from government policy, business decision-making and supply chain management to individual process equipment and molecular interaction. The purpose of this paper is set to present a multi-scale modeling formalism to integrate models with not only different range of characteristic times and lengths but also vast ranges of dynamics and business and engineering activities. The proposed scheme is based on multi-dimensional formalism (MDF) [ 1]. Multi-scale and multi-dimensional formalism (MSMDF) is a conceptual expansion of MDF to enable multi-scale modeling where a lower layer MDF module is defined to be a behavioral dimension of upper layer MDF module. The next section introduces the concepts of multi-scale modeling. Section 3 describes multi-dimensional formalism (MDF). Section 4 describes the conceptual expansion of MDF to form multi-scale and multi-dimensional formalism (MSMDF) for multi-scale hierarchical modeling. Section 5 briefly summarizes the results. 2 MULTI-SCALE MODELING Physical and chemical behaviors arise from the interaction of atoms, molecules and nanometer-structures. Chemical engineering's traditional focus, behaviors of unit operations arise from the combination of physical and chemical behaviors. And the behavior of plants and business enterprises is a combined behavior of unit operations. A multi-scale model is a composite mathematical model formed by combining modeling modules with different characteristic lengths and time scales. Each modeling module expresses some aspect or part of the overall model. The links are defined between modeling modules to exchange information. The multi-scale modeling framework describes the way in which modeling modules are integrated, or linked, to form an overall model. This section looks into the classification of various multi-scale modeling frameworks and conceptual issues related to multi-scale modeling frameworks. Ingram [2] divides multi-scale modeling frameworks into five classes: serial, simultaneous, hierarchical, parallel and multi-domain following the classification proposed by Pantelides [3]. The notion of "micro-scale" and "macro-scale" in the following description is based on the usage in the Ingram's paper. 1. Serial: The micro-scale model is used to generate some parameters, data or simple relationship that is later used by or as the macro-scale model.
144 2.
Simultaneous: The micro-scale model is used to simulate the system in its entirety. The macro-scale model is simply some averaging or integrating procedure that is used to summarize the detailed micro-scale results 3. Hierarchical: The micro-scale model is 'formally embedded' in the macro-scale model. That is, the micro-scale model exists within the macro-scale model and it provides some relationship between macro-scale model quantities. The chief advantages of hierarchical integration are micro-scale realism coupled with a reduced computational burden and the 'natural appeal' of this style of modeling. 4. Multi-domain: The micro-scale and macro-scale models describe the processes in a small number of distinct but adjoining spatial regions of the system; there is an interface between them. 5. Parallel: Both the micro-scale and macro-scale models span the entire spatial domain. The micro-scale model treats some phenomena thoroughly, while other phenomena are treated in a minimal way. The micro-scale model is complementary to the macro-scale model in the thoroughness with which it treats the various phenomena. We consider serial framework is essentially equivalent to hierarchical framework from the viewpoint of model structure. It is reasonable to suppose that serial framework is proposed only to avoid the computational burden of simultaneous micro-scale and macro-scale computation. Simultaneous framework is considered to be a special case of hierarchical framework where macro-scale model is very thin. Multi-domain framework and parallel framework are classified as a rather simple model division. So we chose hierarchical framework as the main focus of our research for multi-scale modeling. Our proposal also supports multi-domain framework and parallel framework. 3 MULTI-DIMENSIONAL FORMALISM (MDF) Multi-dimensional formalism (MDF) expresses model as a combination of structural, behavioral, and operational dimensions. The structural dimension defines the boundary of model. The structural dimension has attributes to define characters of the model (e.g. size, capacity and connectivity). The behavioral dimension comprises objects and activities with properties that come out as an interaction between structural dimension parameters and internal states. The behavior is not what the object is supposed to do but refers to the internal states as is processed, contained, or conveyed in the structure. The operational dimension includes knowledge and activities for managing, controlling, operating and manipulating parameters of structural dimension. In MDF, structural, behavioral, and operational dimensions are configured to perform roles in a similar way to those played by the equipment, controllers, processed material and energy in a real plant. Figure 1 shows an example of MDF model, a chemical company. As shown in Figure 1, operational dimension does not directly exchange information with behavior
145 dimension. Because MDF supports highly structured modeling, the resulting model has a high level of modularity and hence makes the management of change very simple. MDF allows the presence of more than one kind of behavioral and operational dimension associated to a particular structural dimension and enables the development of simulation environments in which different ways of management scheme can be explored with the same simulation models. MDF introduces the concept of meta-model to enable a hierarchical structure in behavioral dimension. Behavioral dimension is formulated with a behavior entity called meta-model. Meta-models have properties of aggregation and hierarchical decomposition. Hierarchical decomposition allows a meta-model to be decomposed into a number of other meta-models. Aggregation is the property of combining and linking meta-models.
Figure 1 MDF Model
4 MULTI-SCALE AND MULTI-DIMENSIONAL F O R M A L I S M (MSMDF) As described in the previous section, MDF does not clearly show the framework for hierarchical structure in operational dimension and structural dimension, or a heterogeneous hierarchical structure of all three dimensions. This paper proposes a framework in which all three kinds of modeling dimensions (modules) are linked to form an overall model. The framework also specifies how information propagates between modeling modules. The basic concept of MSMDF is created based on the observation that it is possible to define an internal structure with structural, behavioral and operational dimension to behavior dimension of MDF model.
146 Since a behavior dimension of MDF model can be a MDF model, defining the structure and operational dimension of the MDF model as the upper layer module and the behavior dimension of MDF model as the lower layer module constructs a layered structure of overall model. The concept of module is developed so as to support the creation of libraries of reusable modules that can be exchanged between simulation tools. In addition, a module can be replaced by another module of different fidelity or coarseness without modifying the overall model structure. MSMDF allows the presence of more than one lower layer modules associated to particular upper layer module so as to support multi-domain and parallel framework of multi-scale modeling.
Figure 2 Layered Structure of MSMDF Model Three kinds of module are defined: structural-operational module, structural module and behavioral module. Structural-operational module has structural dimension and operational module, operational module can change predefined parameter values of corresponding structural dimension. Structural module is used to model a component that cannot be directly operated, manipulated or controlled. Modules that do not have lower layer module are called behavior modules. Modules are interconnected via ports. The following eight links are defined for transferring information, energy and mass between modules. 9 Between upper layer structural module and lower layer structural module 9 Between upper layer structural module and lower layer behavioral module 9 Between upper layer structural module and lower layer operational module 9 Between behavioral module and structural module in the same layer
147 9 Between behavioral module and behavioral module in the same layer 9 Between behavioral module and operational module in the same layer 9 Between structural module and structural module in the same layer 9 Between operational module and operational module in the same layer The port ensures the consistency of information exchanged between modules and transforms the fidelity and/or coarseness of information if necessary.
Figure 3 Modules and Links 5 CONCLUSIONS This paper proposed a novel modeling formalism, multi-scale and multi-dimensional formalism (MSMDF). MSMDF not only supports simple management of change and multiple modelling but also enables multi-scale hierarchical modelling, and can be used to model product lifecycles and concurrent engineering activities for better business decision making (BDM). REFERENCES [ 1] R. Batres, M. L. Lu and Y. Naka, Concurrent Engineering Research and Application,
No. 7 (1996) 43. [2] G. D. Ingram and I. T. Cameron, The Proceedings of the 9th APCChE Congress and CHEMECA 2002 (2002). [3] C. C. Pantelides, European Symposium on Computer Aided Process Engineering11 (2001), Elsevier, Amsterdam 15.
148
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
An Evaluation Strategy for Optimal Operation of Batch Processes under Uncertainties by Chance Constrained Programming H. Arellano-Garcia, W. Martini, M. Wendt, P. Li and G. Wozny Institute of Process and Plant Technology, Technical University of Berlin, KWT 9 10625 Berlin, Germany
Abstract: Previous studies have applied deterministic approaches for simulation and optimization of batch processes with constant parameters treated as known. Nevertheless, the existing uncertainties may have a significant impact on the operation developed by a deterministic approach. In this work, chance constrained programming is used to cope with those uncertainties for the development of both optimal and robust operation policies. We hereby focus on a reactive semi-batch distillation process with purity constraints, which is known as a complex dynamic nonlinear process. For this purpose, a new approach is proposed to compute single and joint probabilities and their gradients. The relationship between the probability level and the corresponding values of the objective function is used as a measurement for evaluating and selecting operation policies. Keywords: Uncertainty, chance constraints, single and joint probabilities, probabilistic programming, batch distillation. 1. INTRODUCTION Robust decision making under uncertainty is considered to be of fundamental importance in numerous discipline and application areas. Specifically in complex dynamic processes there are parameters which are usually uncertain, but may have a large impact on the targets like the objective value and the constrained outputs. Deterministic optimization approaches based on constant model parameters have mostly been used in the past. One way of handling uncertainties in optimization approaches is the definition of chance constraints. For this purpose, efficient approaches for linear systems had been proposed (Pr6kopa, 1995) and applied for linear process optimization and control (Scharm and Nikolaou, 1999; Li et al. 2002). An approach to nonlinear chance constraint problems for steady state process optimization under uncertainty was proposed (Wendt et al. 2002). Efficient approaches to chance constrained programming for nonlinear dynamic processes are not available, although it is required for developing and evaluating operation policies for nonlinear dynamic processes, e.g. batch processes. In this work, we focus on chance constrained optimization of a reactive semibatch distillation process of industrial scale with product purity constraints. The kinetic parameters of the reaction rate and the tray efficiency are considered as uncertain parameters. In this work, they are assumed to be multivariate normally distributed involving correlations to each other. The probability levels can be regarded as a measurement of the robustness of the optimized control strategies. With a higher probability level, the optimized value of the objective function will be degraded. Therefore, a trade-off decision has to be made between robustness and profit concerning the objective function value. In order to achieve this target, one important step for complex dynamic systems is the development of an efficient numerical method to compute the probabilities and their gradients. Since there is a monotone relation between the tray efficiency as the uncertain input and all constrained outputs, the method can
149 be generally based on the approach proposed by Wendt et al. (2002), but modified with an efficient search algorithm of bounds of the uncertain input. 2. C H A N C E C O N S T R A I N E D O P T I M I Z A T I O N P R O B L E M S
Generally, a nonlinear dynamic minimization problem under uncertainty can be formulated as follows: min f ( x, u, ~) s.t. g(2,x,u,~) =0
h(2,x,u,~)~i "~ then the equivalent presentation will be P{~s > ~:sL}> or. Based on given values of the other uncertain variables ~,,, ( s = l , . . . , S - 1 ) ,
the bound of the constrained output y/SP and the control
variables u, the bound ~sL can be computed as follows:
~ :F-'(r162
(3)
150 and this leads to the following representation **
**~s L
(4)
p{y, < ySP}= I"" I I p(~:'''''' ~s-,, ~s)d~sd~s-,"" d~, --oo
_o._oo
Furthermore, computation of the gradients, needed for an NLP framework, can be implemented based on the following representation:
OP{y, X D, 1 ~ and also r/$ ~ X A,NST ~,. According to (1)-(5) /7L can be used as the upper bound for the uncertain variable r/ in the numerical integration of both probabilities P of the complementary event of the original constraints in (10). The actually desired probability will be found by P = 1- P.
152 o
....................................
tl al
Od
0
=
99
%
o 40%
30~
50%
60%
70%
Probability r
80%
90%
100%
Fig. 1: Objective value to probability limits Since the results are to be used for finding trade-off decisions between robustness (reliability of fulfilling the constraints) and the benefit of the objective value, the optimized values are computed for different probability levels. Here the probability level of the distillate product purity is fixed at a~ = 99%. It represents the case that almost no risk can be afforded towards a violation of the distillate product purity. We change the confidence level for holding the bottom purity restriction. The resulting profile is illustrated in Fig. 1. It is worthwhile to note the significant increase of the objective value, from the confidence level of 90% on. From this point on, it is obvious that not much reliability can be gained by increasing the batch time. The opposite thing occurs in regions of lower probabilities. For a trade-off decision, the point, at which the low increase ends and the significant increase begins, can be chosen. The consideration of a joint chance constraint means that the two single chance constraints in (10) will be replaced by one chance constraint with the following formulation: P
t
XD"(t")>XD" SP
X A, NST
(tf) < XA, NST
}
>
Due to the relations 7/1" ~
a'
(11)
XD,~ 1" and r/1" ~ XA.NSr ,1,, both purity restrictions induce an
upper bound of r/ in the integral for computing the probability of violation (or a lower bound for the probability of being feasible). Follwing the principles in eq. (3) and (8), we have also the convenient case, i.e. there is only an upper bound but no lower bound. This means that in each step, the reverse function is computed for each purity restriction according to eq. (4) so that we have for each one a corresoponding upper bound for r/:
~L,1 = F-,(~,...,~s_,, yslP,u)=rl L
(12a)
~:~.2
(12b)
= F-,
(~: , . . . , 4 S _ l
,Y2s , ,u)=rl~
Then the higher one will be taken as the upper bound for the integration: ~'s = max{q~, q~}
(13)
It is worthwhile to note different values of r/f and r/~ generated through reverse projection at different values of the other uncertain parameters. Taking the last point from the curve in Fig. 1 (i.e. a, = 99%, a 2 = 93%), some corresponding curves of r/( and r/~ are illustrated in Fig. 2. It can be seen that the higher value switches between r/f and r/~ in different situations.
153 Frequency Factor 1 = 37266
Educt
/b
~
-
9
t Frequency Factor 1 ,, 47064
~'
12500
13500
145oo 155oo Frequency Factor 2
165oo
Product
[
14500
tSSO0
15500 Frequency Factor 2
17500
18500
Fig. 2a-b." Tray efficiency over frequency factors
Fig. 3." Single and joint probability Due to this changing behaviour, it can be concluded that the joint probability resulting from a determined operation policy is always significantly lower than both single constraints. This fact can be confirmed in Fig. 3, showing the results of the two purities by the optimal operation policy and 1000 samples of the uncertain paramerters through Monte Carlo simulation. Moreover, it can be seen that the optimal policy will result in a higher reliability for holding the product alcohol purity than that of the educt ester purity. 6. CONCLUSIONS In this work, a new approach has been developed to chance constrained optimization problems of batch processes. The novelty of this approach lies in the efficient computation of single and joint constraints and their gradients. It has been applied to a reactive semi-batch distillation process. Uncertainties of some model parameters such as kinetic parameters and tray efficiency have been taken into consideration. It leads to a dynamic nonlinear chance constrained optimization problem. For performance evaluation, this problem is solved with different probability levels and their corresponding objective values can be received. Furthermore, a comparison between the effect of single and joint constraint has been made. These results can be used for a trade-off decision between robustness and profitability to select optimal and robust operation policies. We thank the Deutsche Forschungsgemeinschaft (DFG) for the financial support under the contract WO 565/12-1. REFERENCES
[1] [2] [3] [4] [5]
A. Pr~kopa, Stochastic Programming, Kluwer, Dordrecht, The Netherlands (1995). A. T. Schwarm, M. Nikolau, AIChE J., 45(1999) 1743. P. L, M. Wendt, H. Arellano-Garcia and G. Wozny, AIChE J., 48(2002) 1198. M. Wendt, P. Li, G. Wozny, Ind. Eng. Chem. Res., 41 (2002) 3621. P. Li, H. Arellano-Garcia., G. Wozny, E. Reuter, Ind. Eng. Chem. Res., 37(1998) 1341
154
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
Scheduling Multistage Flowshops with Parallel Units An Alternative A p p r o a c h to Optimization under U n c e r t a i n t y J. Balasubramanian and I. E. Grossmann Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15217, U.S.A.,
Abstract The prevalent probabilistic approaches to modeling processing time uncertainties in production scheduling problems result in mathematical programming models that are computationally expensive to solve. We present a non-probabilistic treatment of scheduling optimization under processing time uncertainty, where the latter is described using fuzzy set theory, and derive Mixed Integer Linear Programming (MILP) models for the scheduling of multistage flowshops with parallel units. Results indicate that these models are computationally tractable for reasonably sized problems. We also describe tabu search implementations for larger problems. 1. Introduction A number of papers in recent years have addressed scheduling in the face of uncertainties in different parameters - for e.g., demands [ 1-3] and processing times [4,5]. The prevalent approach to the treatment of uncertainties is through probabilistic models that describe the uncertain parameters in terms of probability distributions. However, the evaluation and optimization of these models is computationally expensive, either due to the large number of scenarios resulting from a discrete representation of the uncertainty [6], or complicated multiple integration techniques when the uncertainty is represented by continuous distributions [7]. In this work, we draw upon concepts from fuzzy set theory to describe the imprecision and uncertainties in the durations of batch processing tasks. Indeed, this approach has been receiving increasing attention recently (see Ref. 8 for an excellent overview). However, most of the work in applying fuzzy set theory to scheduling optimization has focussed on using heuristic search techniques such as simulated annealing and genetic algorithms to obtain near-optimal solutions. We show how it is possible to develop MILP models for the scheduling of multistage flowshop plants with parallel units, when the processing times of the tasks are modeled using a fuzzy-set representation of uncertainty. The value of the MILP approach is that it can be used to rigorously obtain optimal solutions or at least provide bounds on the best possible solution, as well as predict the most likely, optimistic and pessimistic values of metrics such as the makespan, total lateness etc. We show that the MILP models can be solved to optimality with reasonable computational effort, and discuss heuristic search algorithms for larger problems. 2. Overview of Fuzzy Sets and Numbers Here we review key concepts from the theory of fuzzy sets relevant to scheduling models.
155 2.1. Definitions Zadeh [9] introduced the concept of a fuzzy set in which an element's membership to a set need not be just binary-valued (i.e., 0-1), but any value over the interval [0,1 ] depending on the degree to which the element belongs to the set. The higher the degree of belonging, the higher the membership. A fuzzy set A of the universe X is specified by a membership function #,~ (x) which takes its value in [0,1 ]. For each element x of X, the quantity #~i(x) specifies the degree to which x belongs in A. Thus, fi~ is completely characterized by the set of ordered pairs shown in Eq. (1). The a-level set (a-cut) of a fuzzy set A is a crisp subset of X given by Eq. (2). A fuzzy set is said to be convex if all its a-level sets are convex. A fuzzy number is a convex normalized fuzzy set with piecewise-continuous membership function [ 10]. Triangular Fuzzy Numbers (TFNs) and Trapezoidal Fuzzy Numbers (TrFNs) are commonly used fuzzy numbers.
7t = {(x, pA(x)) l x c X } A,~ = {x e X lp;a(x ) >_ a}
(1)
Va e (0, 1]
(2)
2.2. Operations on Fuzzy Numbers Arithmetic operations on fuzzy numbers are defined through the extension principle [9], that extends operations on real numbers to fuzzy numbers. In scheduling, the principal arithmetic operations that are involved are addition (computing the fuzzy end-time of a task given the fuzzy start time and duration) and maximization (computing the start time of a task as the maximum of the end times of preceding tasks). We also need to compare metrics like makespan, total lateness etc., which are fuzzy numbers. We summarize the relevant operators. a. Addition" If X and Y are two fuzzy numbers, their addition can be accomplished by using a-level sets, X~ = [x~, x~] and Y,~ = [y~, y~]. The addition of X" and Y, Z, is obtained as in Eq. (3), which clearly shows that the lower bound of Z is the sum of the lower bounds of ~" and Y and similarly, the upper bound of Z is the sum of the upper bounds of 2( and Y.
2~ = 2 o ( + ) ? ~ = [x~ + y~,~on + yR~]
w e (0, 1]
(3)
b. Maximum: The maximum o f ) ( and 1/can also be obtained by using a-level sets as:
2~ = max()~a, Ya) = [max(x L, yL), max(xn, yff)]
Va e (0, 1]
(4)
In general, the maximum operation requires infinite computations, i.e., evaluating maxima for every a E (0, 1]. However, good approximations can be obtained by performing these computations at specific values of a, rather than at all values. Of course, the number of a-levels at which the computations are performed affects the quality of the approximation. e. Area Compensation Operator: If we want to select a schedule with the minimum makespan, we have to compare the fuzzy makespans of potential schedules. We use the area compensation integral [ 11 ] for comparing the fuzzy makespans. When minimizing the makespan, a schedule with fuzzy makespan X1 is preferred over a schedule with fuzzy makespan X2 if Ac(f(1) < AC(X2). The optimization models use a discretization approximation for the calculation of the one-dimensional integral; see Eq. (5).
AC(f() = 0.5.
(x L + x R) do~ ,~ ( S S / 3 ) . (1/2). ~ SCa . (XaL + X~R) a
(5)
156
Figure 1. Multistage flowshop
Figure 2. Membership functions of total lateness of two schedules
Thus, the interval (0,1] is discretized to points {al, a 2 , . . . , aA}, with step-size S S = 1/(A - 1) and the integral in Eq. (5) is approximated by a summation of the function values at specific values of a. The SCs denote the Simpson coefficients [ 12] used in approximating the integral. By choosing a sufficiently fine discretization, the errors in the integral approximation do not affect the comparison between different solutions (a post-optimization evaluation of a few solutions with finer discretization can be used as a check on the validity of the obtained solutions). With the discretization approximation, the optimization models are formulated as MILPs, with timing-related continuous variables and sequencing- or assignment-related binaries. 3. Multistage Flowshop with Parallel Units
We address the problem of optimizing the scheduling of multistage flowshop plants which have parallel units in several stages (see Fig. 1) - an extension of two classical scheduling problems (flowshop and parallel machines problems). Given a production horizon with due dates for the demands of several products, the key decisions that are to be made are the assignment of the products to the units as well as the sequencing of products that are assigned to the same unit. Each product is to be processed only once by exactly one unit of every stage that it goes through. We present an MILP model (M) for obtaining the schedule with minimum total lateness, when the processing times of the orders are given by fuzzy numbers. The assumptions involved in the model are that (i) due dates are deterministic, (ii) there is unlimited intermediate storage between the stages, and (iii) transition times are deterministic and equipment dependent. Much work has been done on deterministic versions of this problem of scheduling flowshop plants with parallel units [ 13-15]. These models have used tetra-indexed (stage-order-slot-unit) or triindexed (order-order-stage) binary variables. Model (M) is a generalization of the tri-indexed model [15] and in it, the timing computations are performed for different a-levels, with the objective being a discrete approximation of the area compensation integral of the total lateness. 3.1. Tri-indexed model- (M) Given are products (orders) i c I and processing stages I c L with processing units j c Jr. Each product i must be processed on stages L i, and can be processed only on units Ji. The parameters of interest are the processing times Tijae, transition times SUj and due dates DDi. In parameters Tijae, the a subscript refers to the specific a-level and the e subscript refers to the
157 left (L) or right (R) end-points of the interval at a-level a. These Tijae can be derived from the fuzzy-number specification for 7~ij. The model presented in [15] uses tri-indexed binary variables Xil,i2,t to indicate if product il is processed before product i2 in stage I. Binaries s~j are used to represent the assignment of product i to the first processing position in unit j, while continuous variables wij are used to represent assignment of product i to unit 3". Here we present the model for total lateness minimization. Continuous positive variables tsilae and te~t~ respectively denote the start- and end- times of product i in stage l at a-level a. The lateness at different a-levels is computed through positive slack variables slpi~e and slniae. Objective Function: The objective is to minimize the area compensation of the total lateness. min ZTotn,ue -- (SS/3) * 0.5 * ~ ~ S C a . (snian + sniaR) a
(6)
i
Assignment Constraints: Eq. (7) state that every order has to be processed by a unique unit in each stage, while Eq. (8) state that each unit processes at most one starting order. Eq. (9) specify that if orders i and i' are consecutive orders and order i is assigned to unit j, then order i' is also assigned to unit j, while Eq. (10) specify the relation between the assignment and s variable. Note that with Eq. (9) and (10), the assignment variables, wij, can be relaxed as continuous variables with 0 and 1 as lower and upper bounds (for proof, see [ 15]). E Wij = 1 je(J, nJt)
s,j _< 1
V(i e I, l e L)
(7)
V(j e J,)
(8)
iCI
wiy +
~
wi,j, + xii,z l; i C I; a C A; e E E)
(13) (14)
158 te~t~ < (1 - x~i,l) " U + ts~,t~ V(i, i' E I, i r i';1 E L; a E A; e E E ) tei~ae + slpi~ -- s l n i ~ = D D i
V(i E I; a E A; e E E)
(15) (16)
Note that although model (M) resembles the scenario approach used in probabilistic models, a crucial difference is that the number of c~-points at which the discretization is performed can be chosen independently of the number of uncertain parameters (since with 10-point discretizations, accurate approximations - within a few percent - of the A C value can be obtained). With scenario-based approaches, the number of scenarios one must consider grows exponentially with the number of uncertain parameters (assuming independence of parameters).
3.2. Computational Results MILe model (M) can also be modified to reflect different objectives. Thus, rather than minimizing the area compensation of the total lateness, we can minimize the maximum lateness among all orders. Fig. 2 displays the membership functions of the total lateness of two schedules for a 5-order, 3-stage, 7-unit (2-2-3) problem where the processing times were given by TFNs, triplets (a, b, c), with a representing the lower bound (optimistic estimate), b the most likely value, and c the upper bound (pessimistic estimate). Schedule 1 is optimal with respect to the area compensation of the total lateness (ZTotLate 57.6), but the maximum lateness among all orders is 31.3. On the other hand, Schedule 2 has a considerably higher area compensation of the total lateness (ZTotLat~ = 67.7), but it is the schedule with the minimum maximum lateness among all orders (25.3). We can also see from Fig. 2 that Schedule 1 has a most likely total lateness of 54, with an optimistic estimate of 48.8 and a pessimistic estimate of 58.3. These models were solved in under 2 minutes of CPU time with GAMS / CPLEX 7.5 on a Pentium 111/930 MHz machine running Linux. Thus, model (M) can be easily adapted to optimize schedules with respect to different objectives in the face of processing time uncertainties. Results from more examples for total lateness minimization are presented in Table 1. These examples were solved with a resource limit of 5000 CPU secs. The number of binary variables required in model (M) increases quadratically with the number of orders; thus, larger problems are very hard to solve to optimality. Computational difficulties with model M arise more from the NP-hard nature of the underlying scheduling problem rather than the number of discretization points. For larger problems, we implemented a local search algorithm called Reactive Tabu Search (RTS) [16] in Java [17]. Since the evaluation of a given solution (schedule) under a fuzzy-set description of uncertainty can be performed very easily, local search algorithms like RTS (which explore a large number of feasible solutions every iteration) are particularly relevant. Other attractive features of the RTS algorithm are that (i) the CPU time for one iteration of the algorithm scales polynomially in the size of the problem, and (ii) the termination criteria can be modified as necessary (for e.g., terminate after a fixed number of iterations or when there has not been a change in the best solution for a given number of iterations). Although the algorithm finds very good solutions quickly, it cannot guarantee the quality of the solution. However, this can be verified with the use of model (M). With a limit of 5000 iterations (,~, 1000-2500 CPU secs), the RTS algorithm found the optimal solution for the smaller problems below, and, for the larger problems, found better solutions than through the MILe model. Since the RTS algorithm is computationally less expensive, a two-step approach can be conceived: RTS is utilized to provide a few high quality solutions, which are then used as bounds for improving the branch and bound algorithm for model (M). =
159 Table 1 Characteristics of model (M) for parallel flowshop problems N-L-(Units in Stgs) 5-3-(2,2,3) 10-3-(10,8,7) 15-3-(10,8,7)
Binaries 95 520 1005
LP Rlxn. 30.91 158.78 0
Best Solution Obtd. 57.63 165.77 6.84
Best Possible 57.63 159.2 0
4. Conclusions
We have addressed the problem of optimizing the schedules of multistage flowshop plants with parallel units under processing-time uncertainty. Using a fuzzy-set description of the uncertainty, MILP models were formulated for minimizing the total or maximum lateness of orders. Results indicate that these models are computationally tractable for problems with reasonably large number of uncertain parameters. Furthermore, due to the ease of evaluating given solutions, local search techniques can be used for larger problems. Acknowledgement
We gratefully acknowledge financial support from the NSF under Grant CTS-9810182. REFERENCES
1. M.G. Ierapetritou and E.N. Pistikopoulos, Ind. Eng. Chem. Res. 35 (1996), 772. 2. S.B. Petkov and C.D. Maranas, Ind. Eng. Chem. Res. 36 (1997), 4864. 3. G. Sand, A. Engell, A. M/irkert, R. Schultz, R. and C. Schulz, Comput. Chem. Eng. 24 (2000), 361. 4. S.J. Honkomp, L. Mockus and G.V. Reklaitis, Comput. Chem. Eng. 23 (1999), 595. 5. J. Balasubramanian and I.E. Grossmann, Comput. Chem. Eng. 26 (2002), 41. 6. R.J.-B. Wets, SIAM Rev., 16 (1974), 309. 7. C. Schmidt and I.E. Grossmann, Eur. J. Oper. Res. 3 (2000), 614. 8. R. Slowinski and M. Hapke, M. (eds.), Scheduling under Fuzziness, Physica Verlag, Heidelberg, 2000. 9. L. Zadeh, Info. Contr., 8 (1965), 338. 10. H.-J. Zimmermann, Fuzzy Set Theory and its Applications, Kluwer Academic Publishers, Norwell, 1990. 11. P. Fortemps and M. Roubens, Fuzzy Sets Syst. 82 (1996), 319. 12. M. Abramowitz and I.A. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, Dover Publications, New York, p.886., 1972. 13. J. Pinto and I.E. Grossmann, Ind. Eng. Chem. Res. 34 (1995), 3037. 14. C.M. McDonald and I. Karimi, Ind. Eng. Chem. Res. 36 (1997), 2701. 15. C.-W. Hui, A. Gupta and H.A.J. van der Meulen, Comput. Chem. Eng. 24 (2000), 2705. 16. R. Battiti and G. Tecchiolli, ORSA J. Comput. 6 (1994), 126. 17. K. Amold and J. Gosling, The Java Programming Language, Addison-Wesley, Reading, 1998.
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Publishedby ElsevierScience B.V.
160
CONCURRENT PROCESS ENGINEERING AND INTEGRATED DECISION
MAKING
Rafael Batres a, Ming L. Lu b and Xue Z. Wang c "Tokyo Institute of Technology, R1 Bldg., Midori-ku, 226-8503 Yokohama, Japan bAspen Technology Inc. 10 Canal Park, Cambridge, MA 02141, USA CDepartment of Chemical Engineering, Leeds University, Leeds, LS2 9JT, UK
Abstract Concurrent Engineering (CE) is a systematic methodology based on multi-disciplinary teams that collaborate in parallel processing activities to continuously consider all product, process, and plant related aspects from their every individual domain perspectives. This paper reviews the main developments of concurrent process engineering and highlights its contribution to integrated decision making. 1. I N T R O D U C T I O N To compete in the ever-expanding global market, as well as to meet increasingly tighter safety and environmental constraints process industries are being compelled to develop safer, operable and reliable plants and processes that result in safer high-quality products in shorter time and less cost. Therefore, different approaches are needed that address all of these requirements from the very beginning. To achieve this, it is important to consider a number of aspects simultaneously. For example, analyses of market demand and raw material forecast are needed during product development. Similarly, the use of performance history of existing unit operations and control strategies in safety and process design leads to the synthesis of inherently safer process structures. In the same way, bringing operational issues such as the simulation of plant startup to the design stage and design modification during the operation stage are ways to make sure that plant is easier to operate. Once the plant is built and the process is in operation, it is also important to simultaneously observe all these aspects to make timely and correct business and operational decisions. In product, process and plant development, it is often necessary to evaluate and screen a number of alternatives. Unfortunately, engineers frequently face a number of uncertain factors that complicate the evaluation [ 1]. Life-cycle considerations can be integrated at early stages of the design when later potential problems can be attenuated or eliminated by proposing better decision alternatives. Traditionally, however, assessment of life-cycle issues including safety and operability analysis tend to be performed at relatively late stages of the design and development process. Others factors such as environmental analysis of the value chain are rarely done and computational support is almost non-existent. On the other hand, significant progress has been made in Concurrent Engineering (CE), a systematic methodology based on multi-disciplinary teams that collaborate in parallel processing activities to continuously consider all product, process, and plant related aspects from their every individual domain perspectives. Customer involvement and computer support environment are
161 additional elements that are essential to Concurrent Engineering, which allows customers and team members to have access to relevant information and tools to collaboratively make engineering decisions. During last ten to fifteen years, Concurrent Engineering has been under continuous development and successfully applied to several different industries and engineering fields. The core concepts of CE are integration and concurrency. Integration means simultaneously considering issues of different life cycle stages and solving problem consistently while concurrency highlights the interactions between system components of the value chain and different life cycle stages. The underlying assumption of this paper is that these concepts facilitate the decision making process at different levels of abstraction, ranging from government-based material recycling strategies, virtual enterprises down to plant operation. The structure of the paper is as follows. The concept of concurrent process engineering will be defined and a systems approach to the assessment of life cycle issues will be presented. Then a concurrent engineering approach for design of chemical plants and integrated production will be described, followed by examples and critical review of the challenges.
2. C O N C U R R E N T P R O C E S S E N G I N E E R I N G The application of CE in chemical engineering started from chemical process and plant design such as heat exchanger network synthesis and heat exchanger design [2], FCC process design [3], control system design [4]. Then CE extended to cover integration between several pairs of different life-cycle activities such as between design and control, and between operation and maintenance [5]. The complexity of chemical process systems and its operation have attracted quite some researchers to apply and extend CE and systematically develop more domain specific methodologies and models which have resulted in the emergence of Concurrent Process Engineering (CPE) as a new area of research. The holistic nature of CPE can be explained by the diverse (and often interrelated) value chains that are characteristic of the process industries. A value chain describes the full range of activities that are required to bring a product from discovery through the intermediate phases of production (involving combinations of physical and chemical processes), delivery to final consumers, recycling of materials or products and disposal after use. Extended value chains are networks of a number interacting value chains. Holistic approaches have a profound impact in ensuring a sustainable development 1 as material, energy, information and economical flows of the value chains are interrelated. CPE takes into account aspects that concern the society such as safety, energy and the environment. CPE involves cross-functional teams (including representatives from key suppliers and customers) that work jointly on activities around products, processes, and plants. Cross-functional teams are multidisciplinary in nature, which implies sharing a common, high-level goal to achieve a defined objective. Teams are composed of experts from chemistry, engineering, production, marketing, and other domains that interact and perform designated tasks to achieve common goals. In addition, CPE has a cross-organizational structure as dynamic networks of companies collaborate by creating synergies of computational resources and people. Simultaneity in CPE is needed in order to reduce the time to bring high quality products to the market. Contrary to sequential approaches, process-engineering activities are allowed to occur in parallel and exchange information continuously. It can be expected that CPE will be accompanied by information that flows more rapidly with more complex interactions than those in traditional engineering approaches. As a result, CPE is
1 Sustainable development encompasses activities that combine environmental protection and resource conservation with social responsibility and value creation.
162 optimum so that with CPE enabled tools, more alternatives can be analyzed and evaluated so that better (if not the best) design alternatives in terms of the life-cycle aspects can be obtained. 3. L I F E - C Y C L E
SYSTEMS
Following a systems theory approach based on a multi-dimensional object oriented model [6] [7], integration between product, process, and plant life cycles can be seen as interacting material, processes, value chain components, and human systems. Each system is composed of other subsystems that are defined based on their structural, behavioral, and operational aspects. Structural descriptions of a system are characterized by the interconnections between system components. For example, the plant structure system describes the equipment and the equipment topology in a plant network. The structural description of value chains would represent a network of suppliers, producers, consumers, research institutions, government and recycling facilities. The behavior of a system indicates how a system reacts in its relation to other systems. The behavioral description of an industrial process system defines physicochemical behaviors that are influenced by exogenous variables illustrates this concept. Similarly, the behavior of a value chain refers to the changes in the flows of material, energy, emissions to the environment, and money. Operational aspects define the activities (intentional processes) aimed at satisfying specific goals of a system. For example, the plant operation system defines actions that manipulate valves and other devices that satisfy production and safety requirements. The operational description of the value chain include the decision making processes that will define the flows of material, energy, emissions to the environment, and money. A typical example is the selection of recycling technologies and the design of material flows along value chains. Based on this methodology, information, activities and software can be defined that support concurrent engineering. Tables 1 and 2 show examples of information and activities organized into the three aspects. Table 1 Information models and knowledge System Behavior Value Logistics, production, Chain inventory and demand models Plant Corrosion models
Subsystem category Structure Topology in terms of possible flows of information, material, energy and currency Facility, Equipment
Process
Process structure
Process behavior models Material Physico-chemical property models
Composition, molecular structure
Management / Operation Business models, negotiation models Operating procedures, batch recipes Synthesis methods, design rationale Recycling policies, design rationale
A key concept is that process behavior refers to the physicochemical phenomena that are manifested through changes in the properties of the material that is processed in the plant. In other words, process behavior is defined from a material perspective. This definition implies the existence of physicochemical product behavior models that can be defined independently of where the modeled phenomena occur. For a given piece of equipment, engineers can combine multiple process behavior models to represent proposed, assumed, or actual behaviors as well as functional information (roles or expected behaviors).
163 The process-management-and-operation subsystem defines concepts for equipmentindependent activities that describe the chemical process. Plant behavior subsystems include corrosion behaviors, mechanical stress or thermal expansion. On the other hand, the plant structure refers to the description of the components of which the plant is built as well as its topological (component connections), mereological (part-whole) and spatial representation. The plant management and operation subsystems cover activities that range from planning and scheduling, plant-wide control, through to local advanced and regulatory control. Knowledge and activities are defined for controlling, and operating actionable devices (such as control valves) from the plant structural dimension. In the product life-cycle, structure subsystems include the molecular structure and their properties such as sterilization capabilities, transparency. In the management and operation dimension of product processing constraints can be defined. For example, for a material to be used for the food and beverages industry, a processing constraint may look like 'sterilize with dry heat, don't use toxic chemicals).' Activities include design of experiments. In the product-behaviordimension, models and measurement containers describe material properties in terms of variables of the process (such as temperature, pressure, etc.). Next to the definition of subsystems, the relationships between subsystems should be identified. For example, process behavior (behavior subsystem in the process life cycle) takes place in a piece of equipment (plant structure subsystem). Simulation models based on this system classification have a number of advantages over traditional approaches including the possibility of modeling matter or energy flows that do not flow through defined pipes or channels, such as a vessel leakage or sunshine heating over the equipment [7]. Table 2 Examples of activities based on the three subsystem categories System Subsystem category Structure Behavior Value Changes in consumer Selection of a production Chain preferences, changes in the route, design of the topology flow of requested raw of the value chain materials Plant Simulate equipment Develop piping and instrumentation diagram deterioration, predict mechanical stress Develop process flow Process Simulate process, monitor process variables diagram, develop process design networks Material Estimate physical properties Determine product specifications
Management/Operation Definition of governmental policies and regulations Control and manipulate valves, maintain plant Determine ramping trajectories for startup Plan experimental work
4. DECISION MAKING TECHNIQUES Decision making is selecting a course of action from among alternatives. Approaches in concurrent engineering are needed that manage alternatives proposed from different life-cycle perspectives and evaluation criteria. Design rationale methods have been developed to assist engineers in solving decision-making problems by capturing the argumentation (pros and cons) of alternatives that are proposed as potential solutions of an engineering problem. IBIS (Issue Based Information Systems) [8] is probably the most popular design rationale techniques. IBIS has a methodology and a knowledge
164 model. The methodology starts with problems that are formulated for which solutions are proposed and then evaluated with supporting and refuting arguments. The knowledge model defines a graph composed of three kinds of three kinds of nodes namely issues (problems), positions (potential solutions), and arguments. Eight types of edges are defined to add semantic content to the graph, namely supports, objects-to, replaces, responds-to, generalizes, specializes, questions, and suggested-by. Changes in assumptions or objectives of a design artifact have an effect on a part or all of the constraints taken into account along the design. Management of change consists in consequence analysis and reconfiguration. Consequence analysis identifies the assumptions that became invalidated after the change. Reconfiguration refers to changes in the design of the artifact to adjust to the changes or objectives. Design rationale tools can be developed to support consequence analysis. For example, the design support system l~gide [9] implements a management of change functionality based on dependency-directed backtracking. Egide verifies that all the issues have their best positions selected, which in turn identifies the segment of the design rationale that became invalidated. For the invalidated design rationale segment, the tool evaluates alternative arguments that provide a new set of active positions. Figure 1 illustrates an application of the dependency-directed backtracking in the design of a safety protection system for a pressure vessel. The IBIS network shows a segment of a design rationale record that is to be reused in the UK in a fictitious scenario. Argument A-4 that objects to position P-2 became invalidated, for which position P-2 is preferred over P- 1. The life-cycle systems approach discussed in Section 3 can be used to guide the development of IBIS networks. Firstly, positions can be proposed at different levels among the value chain,
Figure 1. A fragment of an IBIS network of the design of a safety protection system plant, process or material systems. For each level, positions can be proposed based on structural, behavioral or management aspects. Similarly, arguments can be proposed following the different levels and the three system aspects. 5. C H A L L E N G E S A N D F U T U R E D I R E C T I O N S The field is huge and needs much more effort to be made, which needs both time and funds. Unfortunately, research in CE needs more funding and industry does not see immediate benefit, not to mention the existence of constraints in existing investment and legacy systems. In addition, technical challenges are 1) development of consistent and complete engineering activity model
165 that allows dynamic change and management of engineering workflow; 2) development of consistent and inherent data models that can be used to manage past, current, and future prediction data; 3) integrated value chains that integrate R&D chains with production chains, suppliers' chains, demand chains, recycling subsystem, EPC contractor/operator organizations, academic institutions, etc. 6. C O N C L U S I O N S Extensive research effort has been made in applying CE in different activities of process and plant life cycle. Integration and concurrency between different product, process and plant life cycle activities and a multi-dimensional framework have built the foundation for creation of a domain specific CE - CPE. Now more than a dozen leading research laboratories are conducting research in this area. Several standardization efforts have been promoting the development of common data models that form the basis for sharing and exchanging information across tools (a critical issue in concurrent engineering). However, these efforts while effective in data exchange as snapshots such as between design and production offer little support for interoperability of software components that share and exchange data concurrently in time. On the other hand, efforts that aim at realizing the interoperability of simulation components have produced successful stories, but still little has been done to integrate such components with CAD tools, equipment rating software, safety design packages and control systems. Additionally, none of these integration efforts addresses the need of a paradigm shift in the way of carrying out the engineering activities, which may bring new requirements for the information used in the design and operations as well as for the integration of tools. REFERENCES
[1] Herder, P. M. & Weijnen, M. P. C. 2000. A concurrent engineering approach to chemical process design. Int. J. Production Economics, Vol. 64, pp. 311-318 [2] McGreavy, C., M. L. Lu and E.K.T. Kam. 1993. A Concurrent Design Procedure for Heat Exchangers in Integrated Systems, Heat Exchange Engineering ,Vol. IV, Chapter 17, Ellis Horwood Ltd, Oxford, UK. [3] Wang, X.Z., M.L. Lu and C. McGreavy. 1994. Concurrent Engineering Application in Industrial Fluid Catalytic Cracking Process Design. Proc. of The 5th International Symposium on Process System Engineering, PSE'94, pp.381-386, Korea [4] Yang, S.A., Y. Hashimoto, M.L. Lu, X.Z. Wang and C. McGreavy. 1994. Concurrent Design of Control System with Associated Process. Proc. of The 5th International Symposium on Advanced Multi-Variable System and Technologies(AMST'94), pp.41-47, UK. [5] Zhao, Y., Lu, M. L., Yuan, Y. 2000. Operation and maintenance integration to improve safety. Proceedings of the Process Systems Engineering conference. [6] Lu, M. L., Batres, R., Li, H. S., Naka, Y. 1997. A G2 Based MDOOM Testbed for Concurrent Process Engineering. Comput. Chem. Engng., Vol. 21, Suppl., pp. S 11-S 16 [7] Batres, R., Lu, M. L. and Naka Y. 1999. A Multidimensional Design Framework and Its Implementation in an Engineering Design Environment. Journal of Concurrent Engineering, 7(1) [8] Conklin, J. and Burgess-Yakemovic, K. 1995. A Process-Oriented Approach to Design Rationale. In Design Rationale Concepts, Techniques, and Use; T. Moran and J. Carroll, (eds), Lawrence Erlbaum Associates, Mahwah, NJ, pp. 293-428. [9] Bafiares-Alc~intara, R., King, J.M.P. and Ballinger, G. H. (1995). Egide: A Design Support System for Conceptual Chemical Process Design. AI System Support for Conceptual Design. Springer-Verlag, Edited by John E. E. Sharpe. Presented at the University of Lancaster, UK 1995
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
166
An Object-Oriented Approach to Hybrid CFD/Multizonal Modelling F. B e z z o a, S. M a c c h i e t t o b, C . C . P a n t e l i d e s b aDIPIC "I.Sorgato" - University of Padua, via Marzolo 9, 35131 Padova, Italy bCPSE, Imperial College of Science, Technology and Medicine, London SW7 2BY, UK
Abstract. Process simulation and Computational Fluid Dynamics (CFD) are well-established tools in the process industry. The two technology are largely complementary and their combined application can lead to significant industrial benefits. In view of the advantages and limitations of process simulation and CFD modelling, it is natural to consider hybrid approaches that attempt to combine the two. This may bring great advantages in the process and product design, in the equipment scaling up and down, in the capability of optimising the process and delivering solid technical criteria for business decision making. A few works have recently appeared demonstrating the feasibility of a combined approach where critical parameters are exchanged between a CFD and a process simulation model. In this paper a novel design for hybrid CFD/multizonal modelling is considered in terms of an object oriented approach. This generic modelling approach allows an easy-to-use and effective representation of the process by a synergic use of available technologies.
1
Introduction
One of the key challenges facing process modelling today is the need to describe in a quantitative manner the interactions between mixing, fluid flow and other phenomena such as chemical reaction, heterogeneous and homogeneous mass transfer and phase equilibrium. This is particularly important in complex operations (such as polymerisation and crystallisation) because it is often these interactions that determine the quality of the product (e.g. in terms of the distributions of molecular weights or crystal sizes and shapes) and, eventually, its profitability. Both process modelling tools and CFD techniques, if used independently, cannot grasp the complex interactions between hydrodynamics and the other physical and chemical phenomena (Bezzo, 2002). Some of the problems can be addressed by hybrid approaches that attempt to combine process simulation models with CFD calculations (e.g. Bauer and Eigenberger, 1999, 2001 in their study of gas-liquid bubble columns, Urban and Liberis, 1999 for the model of an industrial crystallisation process). Bezzo (2002) presented a first formal framework to address the above difficulties by the definition of a compact and generic structure which can largely be standardised within currently available software. The entire domain is mapped by means of a multizonal model which is independent of the specific model for each zone and which is topologically defined by means of a set of interfaces establishing the connectivity between zones. The coupling between the
167 multizonal and CFD models is achieved by characterising fluxes of material between zones and properties affected by fluid mechanical mixing processes by means of CFD calculations, while properties of the fluid needed by the CFD model are, in turn, computed by the multizonal model. In this paper we want to include the above methodology within a more generic description of the overall framework for the integration of process simulation and CFD for a better approach to simulation design. The definition of a object-oriented description will demonstrate the usefulness of a clear subdivision of modelling specifications and the possibility of an integrated use of existing modelling tools.
2
Spatial partitioning
The first target of a declarative process for achieving a good hybrid model representation regards the definition of a more comprehensive modelling design by assessing the best modelling tools for the description of each piece of equipment and operation. We call this spatial partitioning. This idea moves away from the current push towards multiscale integration which aims at considering all relevant phenomena by means of a single modelling tool. Here we consider a solution which exploits the present effort of modelling tools towards open architecture and standardisation (Braunschweig et al., 2000) as we believe that in the near future major benefits may be obtained in product design and business decision making in the process industry by "properly" using the simulation tools which are already available. Some CFD and process simulation companies have already moved together for the definition of a more comprehensive design allowing unit operation coupling. Process simulation and CFD unit operation models are used separately and the only connection is obtained by common inlets/oulets. This idea can be pushed even further as long as we start viewing process models as objects, which are transparent to a set of inputs/outputs. From this perspective, an object does not necessarily describes a whole unit operation. For instance, consider a jacketed reactor: on the one side we may use a CFD model of the mixing tank and on the other side a process simulation model may take care of jacket, other ancillary equipment and operating and control procedures (Bezzo et al., 2000). Also, we may refer to membrane operations and/or fuel cells where there is a clear separation between phenomena occurring in the fluid and on the membrane: a spatial partition approach may be used to define a process simulation model for the membrane and a CFD model for the fluid phenomena.
3
Model partitioning
Each spatial partition is an object defined by a set of public variables and parameters (inputs/outputs), internal variables and modelling equations and, finally, suitable numerical methods to solve the model equations. This is not very different from the modular approach to process simulation. However, in this case, we clearly state that a) different pieces of software and numerics will be used and that b) spatial partitions may occur within a single piece of equipment. Furthermore, we assume that a spatial partition may itself be defined through a hybrid use of modelling techniques dealing with the same spatial domain, but taking care of different phenomena. We define this approach as model
168
partitioning according to which we split the constituent equations of a single process system into two submodels. Here we will refer to the methodology for hybrid multizonal/CFD models defined by Bezzo (2002), which was briefly described above. A generic approach is to define two different objects representing the CFD model ( CFD object) and the multizonal model (MZ object). This two models are contained within the hybrid object defining the spatial partitioning. According to the object-oriented approach the CFD and MZ objects define a set of public variables representing the critical variables exchanged between the two independent packages. For instance, we may consider a stirred tank bioreactor. The CFD object would solve the fluid flow equation and set as public properties the mass flowrates between zones and the non-Newtonian viscosity field. The MZ would use viscosity values to calculate the mass transfer coefficient and return nonNewtonian law parameters based on composition in the tank. 3.1
The
MZ
object
In order to construct our multizonal model, the spatial domain of interest is divided into a number of internal zones representing spatial regions in the process equipment. Each single zone is considered to have a fixed volume and to be well-mixed and homogeneous. Two zones can interact with each other via an interface that connects a port of one zone with a port of the other. The flow of material and/or energy across each interface is assumed to be bi-directional, with both directions potentially being active simultaneously. The MZ object provide a number of public properties. These are: i n p u t s : zone network topology (to be commented on in w mass flowrates between internal and environment zones, zone volumes, a set of fluid flow dependent propcrties o u t p u t s : a set of zone intensive properties such as physical properties, temperature, composition The internal structure is not public and is the encapsulated part of the model. In fact, the internal model deal with public properties, but in general it will also contain a number of variables and parameters required to define the state of the system. The MZ object is defined by means of other lower-level entities establishing the zone network structure. These are the internal zones and the interfaces. An internal zone models is a self-contained object (IZ object). Each internal zone represents a portion of the physical domain and is represented by the same encapsulated model. The set of public properties is defined by: i n p u t s : number of ports, mass fluxes, zone volume o u t p u t s : records of intensive quantities to be exchanged with other zones, intensive properties required by CFD calculations. For instance, in the bioreactor example mentioned above the set of intensive properties required by the CFD calculations is represented by the parameters for the non-Newtonian law, while the set of properties exchanged with other zones comprises biomass, oxygen, product concentrations. At the same level as the zone objects, we define the last set of objects within the multizonal model, i.e. the interfaces. The interface objects (II object) do not perform
169 any calculations but are used to link internal zones through their ports. There are no cncapsulatcd properties. After defining the two sides of the interface as 0 and 1, the public properties are: inputs: internal zone on side O, internal zone on side 1, port on side O, port on side 1, mass flowrates between zones o u t p u t s : none. 3.2
The
CFD
object
The CFD object is responsible for determining the fluid flow behaviour within the physical domain by solving the mass and momentum conservation equations. The CFD object public properties include: inputs: cell-to-zone map (to be commented on in w set of cell intensive properties, set of hydrodynamic parameters (e.g. agitation speed in a stirred tank reactor) o u t p u t s : mass flowrates between zones, zone volumes, set of hydrodynamic related properties. The above protocol essentially defines the behaviour of a general class of computational fluid dynamic foreign objects. Specific instances of this class may correspond to individual items of equipment or spatial regions, each modelled separately. Each instance is charactcrised by its own geometry and also by the way this is represented within the CFD package (c.g. the discretisation grid). The definition of an instance in terms of this information must be dcclared before the combined simulation is initiated. 3.3
Topology
declaration
The IZ and II objects within the MZ object requires the definition of the zone network topology. We assume that this is achieved by means of a third object (TD object) delivering the required data. The TD object makes the number of zones and interfaces as well as their connectivity available. This information is used to set the right values of mass flowratcs, properties, number of ports in the IZ objects and the 0 - 1 zones in the II objects. The TD object is also used to define the map between cells and zones required by the CFD object to determine the correspondence between the two geometric representations (each internal zone is constituted by a subset of CFD computational cells). The calculation of mass flowrates between zones and zone mixing-related parameters needs the map between cells and zones. Similarly, fluid properties calculated within each internal zone had to be passed to the cells belonging to that zone. The encapsulated part of the TD object may be empty or it may be constituted by properties and models capable identifying homogeneous and well-mixed regions (i.e. internal zones) from the results of CFD calculations. For example the distribution of the eddy turbulent energy of dissipation may be used to define homogeneous and well-mixed zones. More details for these autozoning methods can be found in Bezzo (2002). The TD object after including autozoning procedures contains the following public properties:
170 Distillation Column Process Simulation)
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Figure 1: Spatial partitioning can be used to model several unit operations according to different techniques (e.g. process simulation for a distillation column, CFD for a tank reactor, hybrid multizonal/CFD for a bioreactor). The same approach is used to divide the representation of a fuel cell: three physical domains are identified within the same unit. Each partition is defined as an object showing a set of public properties. The definition of a multizonal/CFD model for a bioreactor implies a model partitioning approach: the same spatial domain is simultaneously described by CFD and process simulation to take into account different phenomena. Once again an object representation is adopted.
inputs: outputs:
results of C F D calculations multizonal model topology, cell-to-zone map
Figure 1 illustrates the main ideas concerning the use of an object-oriented approach to modelling.
4
C a l c u l a t i o n flow
Few words will be dedicated to consider the computational process. This is a complex issue and here we will just mention what is required for a solution to be obtained. First of all a master program is required to manage the overall flux of information between objects. In the case of a steady-state analysis this may appear to be rather similar to the approach used for sequential modular simulators. However, we cannot forget that the use of different numerical approaches within each object requires special attention to ensure the robustness of solution since the level of approximation and the numerical fragility may vary among different tools (the solution scheme may need filtering procedures).
171 Furthermore, the efficiency of solution becomes a critical issue since the iterative solution of CFD models may become unfeasible even for the present computational capabilities. All these issue becomes even more stringent whenever a dynamic simulation (or optimisation) is needed. In that case special assumptions should be taken into account to separate time scales of different phenomena. For instance, fluid flow dynamics are usually instantaneous compared to polymerisation or crystallisation processes and and may be treated as a sequence of steady-state simulations. Some of these issues have been considered and solved for dynamic simulation of hybrid multizonal/CFD models in the work by Bezzo (2002).
5
C o n c l u d i n g remarks
Business decisions in the process industry are often related to the capability of predicting and optimising process behaviour and product design. This has always been one of the principal scopes of process simulation. Nowadays, tighter competition and complexity of products require a more precise understanding of processing dynamics. This paper identified a different approach to simulation design applied to CFD-process simulation integration. An object oriented approach has been used to show that open architecture and standardised software for modelling purposes may greatly improve the current modelling capabilities.
References [1] M. Bauer and G. Eigenberger. A concept for multi-scale modeling of bubble columns and loop reactors. Chem. Eng. Sci., 54:5109-5117, (1999). [2] M. Bauer and G. Eigenberger. Multiscale modeling of hydrodynamics, mass transfer and reaction in bubble column reactors. Chem. Eng. Sci., 56:1067-1074, (2001).
Design of a general architecture for the integration of process engineering simulation and computational fluid dynamics. PhD thesis, University of London,
[3] F. Bezzo.
United Kingdom, (2002). [4] F. Bezzo, S. Macchietto, and C.C. Pantelides. A general framework for the integration of computational fluid dynamics and process simulation. Comp. chem. Engng., 24:653658, (2000). [5] B.L. Braunschweig, C.C. Pantelides, H.I. Britt, and S. Sama. Process modeling: the promise of open software architectures. Chem. Eng. Progr., 96:65-76, (2000). [6] Z. Urban and L. Liberis. Hybrid gPROMS-CFD modelling of an industrial scale crystalliser with rigorous crystal nucleation and growth kinetics and a full population balance. Proc. Chemputers 1999 Conference, Diisseldorf, Germany, (1999).
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
172
Integration of Decision Tasks in Chemical Operation Process Huanong Cheng, Yu Qian ~, Xiuxi Li Chemical Engineering School, South China University of Technology, 510640, E R. China
Abstract: In real industrial process, operation decisions conflict easily due to different domain knowledge and different time response scales, which make it difficult to integrate different decision tasks to the optimal process operation. In this paper, a multi-agent approach of decision integration is presented. And an experimental platform is developed. A case study, TE process, is used to demonstrate the decision integration approach. Keyword" process operation, decision task, agent, integration 1 INTRODUCTION In chemical industries, many computer aided process operations (CAPO) are developed for operation decisions, such as fault diagnosis, advanced control, on-line optimization and scheduling et al. These CAPOs are designed respectively for some an operation domain and lack of collaboration. Consequently, it is necessary to integrate the different CAPOs to computer integrated process operation system (CIPOS) for the optimal operation. Main challenge in CIPOS is task integration. Decision tasks conflict easily due to differences of domain knowledge and response time scales, which makes it difficult to coordinate different decisions efficient. Recently, more concentration is focused on agent-based approaches in decisions collaboration in the process system, especially in process design. The agent-based approaches are used to eliminate the decision conflictions among the multi-disciplinary teams and achieve the aim of concurrent engineering [1,2,31. Compared with prominent progress in process design, the complexity of operation system causes agent-based approaches in process operations are still applied in single domain, such as modeling of supply-chain [41, cooperation of fault diagnosis I51, abnormal management [61 and on-line product and financial scheduling E71. 2 SIMPLIFICATION OF PROCESS OPERATION SYSTEM It is still impossible to implement the integration of all process operations by one step in the current situation [81. One approach is to divide the process operation system into several sub-systems. Integration is implemented in these sub-systems respectively, then to integrate the sub-systems into a whole system. One sub-system is the integration of data reconciliation, advanced control, steady-state simulation and online optimization I91. The online optimization is executed by the advance control (such as MPC). These operations interact closely and time response is from about half an hour to a day or more. So they are treated as a category. One sub-system is to combine data reconciliation, monitoring, diagnosis with control i To whom corresponding should be addressed,
[email protected] 173 systems [101. Abnormal features are caught by process monitoring from the process data, then are treated to find the cause by fault diagnosis. According to the causes, the control parameters are modified and process transform to normal state. These operation tasks respond in time-scale from seconds to minutes. Usually, process monitoring and fault diagnosis are correlated in domain knowledge. Thus, process monitoring and fault diagnosis are set to a category. Another sub-system is scheduling system, whose time-scale is weeks or months. We define the scheduling system as another category jill. The above sub-systems can be found in real industries, so we use the idea in multi-agent based decision integration. First, process operation system is divided into three sub-systems. Second, FDD (Fault Detection and Diagnosis) agent, CO (Control and Optimization) agent and SC agent are built respectively. The system division approach is illustrated in figure 1. The plant and basic control system are passive elements, which are modeled as objects.
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Figure 1 Dividing and simplifying of the process operation system 3 INTERACTION DIAGRAM OF AGENTS Cooperation of agents is a procedure in which agents dynamically distribute sources, knowledge and information, negotiate and cooperate with each other to eliminate the confliction among different operation decisions. For the collaboration, it is essential for an agent to know when inform/request other agents, the content of the inform/request. So a basic prerequisite for an operation agent model is to have enough knowledge and information about the related agent status and the common objective. These knowledge and information are stored in the internal database of agent. Therefore, construction of internal knowledge base is the solution for the integration of operation tasks. In this work, the performance of the operation system is decided with individual agents and activities emerged in their interactions. We depict the activities of agents in interaction diagram using extended unified modeling language (UML) [12], in which agent is added as the new component corresponding to object. How to construct the interaction diagram will be illustrated in following sections. When constitution of the interaction diagram of agents in process operation system, one more issue should be addressed: varies kinds of agents and objects would cause too complex behavior under different outside conditions, such as market, environment and society. To this problem, we use some main affairs as intriguing messages. For a example, a fault occur could be a intriguing message, then we use the interaction diagram to represent the cooperation
174 behavior of agents and objects in the operation system. 4 I M P L E M E N T OF MULTI-AGENTS
In this work, a multi-agent system includes [~31. the standard agent architecture, the communication platform of agents, communication language and protocol, and data format of the communication language. The standard agent consists of the internal knowledge base (KB), an executor, a set of function modules and a communication module. The internal KB represents the states of agent itself, environment and neighborhood agents. The executor evokes the different function modules under different conditions. The function modules exchange messages by the executor to perform activities, decisions, communication and learning of the agent. The function modules are the compiled executable files, which are implemented with the different program languages. The only pre-requirement is that the compiled function module must support some application interface protocol. The communication module receives and sends the messages from/to the other agents. Common Object Request Broker Architecture (CORBA) is used as a middle ware of the information exchange of agents [141. CORBA connect the different functional agents on heterogeneous computer platforms and operation systems. On the CORBA platform, agents are independent physically from each others and easy to be removed and added. So the multi-agent system with CORBA is an open architecture, which is essential to the requirements of the process operation system integration. Knowledge Query Message Language (KQML) has been widely accepted as the language and protocol of the information exchange between agents. Similar to Wang and co-workers [41, we use the idea and the philosophy provide by standard for exchange of product data (STEP) to be the data standard of the internal content of KQML. The internal content is expressed in EXPRESS, an information modeling language. 5 CASE STUDY 5.1 Experimental platform of TE process In this work, TE process t151with PI control strategy [16] is used as a case study. Category of raw materials and architecture of flow sheet are fixed, and factors need to be considered are price of products and faults of process. In the case study, we cancel the SC agent and add the price factor in CO agent model. Therefore, the decision integration of TE process operation is focused on the cooperation of CO agent and FDD agent. TE process and basic control system are simulated with TE process simulator. The TE process simulator, CO agent and FDD agent are located in three different computers, which connected with local networks. Fault diagnosis function module in FDD agent is based on PCA algorithm [17]. Optimal function module in CO agent uses the tools of MINOS 5.1 [18]. TE process simulator and CO agent are developed with Borland C++ Builder 5. FDD agent is built by Delphi 6 software. The information among the three components is transformed by CORBA middleware, which is developed with Borland VisiBroker tool. 5.2 Interaction diagram of operation agents in TE process The behavior of CO agent and FDD agent are depicted in the following interaction diagram (Fig. 2): The process data are transferred to CO agent and FDD agent. FDD agent analyzes the process data and abstract features. If an abnormality is detected and the cause of the fault
175 is found, the fault information will be send to CO agent. At the same time, the control action is send to the process operation to eliminate the fault. In addition, the optimization result is transfer to the fault detection and diagnosis to avoid mis-warning. Activities of CO agent receive the fault information, unit optimization results and market data to make out decisions of production modes and optimal set points. When CO agent and FDD agent could not make decisions based on the abilities themselves, they will consult with the operators
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Bottleneck at the stream, same as M P , 0.
180 3.1. Problem Definitions An example production site shown in Figure 1 is employed in the case study. The example site consists of five production plants (ETY, VCM, PVC, PP, PE) originally. After three new plants are built (Plants A, B and C), the total electricity demand in the production site is increased greatly. This reduces the cogeneration ability of the utility plant as well as the total site profitability. To resume the capability of the utility plant, new turbines and/or boilers may be required.
Currently, the utility plant contains two boilers (B 1 and B2), two back-pressured turbines (T1 and T2) and a condensing turbine (T3). Options of new turbines and boiler (TX1 to TX6 and BX1, shaded in Figure 2) are suggested for new installation. A site-model, which includes all the information, is then applied to identify the best investment option.
Figure 1: Sample production site.
Figure 2: Utility plant configurations.
3.2. Site-Model Definitions The site-model is a linear programming model, which contains all the mass balances, energy balances and plant unit interactions in the chemical production site(s). The basic components used for the site-model are defined as follows. Indices:
p m a t s r
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Plant or unit. Material (includes utilities). Alternative (variable properties). Time period (month). Shift in a time period, t. Balance equation index.
Parameters: Er,p,m,a,t,s Zp, m,a,l,s
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181
Positive Continuous Variables: Fp,m.a.t,s Variable of material m with altemative a at plant p in period t, shift s. Continuous Variables." Profitt, s Profit in period t, shift s. TProfit Total profit of the production site during the planning period. Mass and Energy Balance Equations." All mass and energy balance equations are arranged by the index r in the site-model. With a predefined set of (p,m,a), the equations can be represented by (1). ~_,(Fp ..... t,s X Ep ..... t,s) =0, FER, t E T , s E S . (1)
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Profit Calculation. The following equation calculates the site profit in period t shift s.
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peP, m eM, a eA, te T, s eS.
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Objective Function." The site-model objective function is to maximize the total profit in the planning period.
max(Tprofit = ~-" profit,,s l
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The site-model is actually a general model, so that, it can be easily modified to use on investment decision making as well as other applications. 3.3. Base Case
Base case shows the production site's conditions when there is no any new equipment. The annual total site profit is 11,134.24 million Yen. The utility marginal values under this situation are studied and shown in Figure 3.
MCF values of all utilities vary regularly throughout the year, except in maintenance periods (April and September). During the normal operation periods, it is observed that the electricity MCF has relatively high values when comparing to the importation prices. (MCF: Day: 21,000 Yen/MW, Night: 20,000 Yen/MW, Mid-night: 6,000 Yen/MW; Price: Day: 21,000 Yen/MW, Night: 12,000 Yen/MW, Mid-Night: 3,000Yen/MW). The higher MCF values suggest that additional production as well as profit can be induced by extra electricity supply. Besides this, low pressure (LP) steam MCF value in day shift equals to zero, which indicates purge of LP steam. Excess steam is produced because of maximizing the existing turbines electricity generation. However, steam purge is a kind of energy loss and it should be avoided.
182
Figure 3: Utility MCF in base case.
Figure 4: Utility MCF in case 1.
3.4. Case 1: Low Pressure Condensing Turbine To improve the situation in base case, a low pressure condensing (LP-CT) turbine, TX2, is therefore proposed to utilize the LP steam surplus. After the site-model calculation, the generation capacity of turbine TX2 is determined as 4.374 MW. It requires the capital cost of 84.99 million Yen. The total-site profit increases to 11,278.20 million Yen, it is about 1.3% more than the base case. The electricity MCF of this case is similar to the base case (Figure 4). However, the steam MCF values are increased about 2.5 times to about 2,500 Yen/Ton. The value is much higher than its production fuel cost. It suggests that both electricity and steam are limited in the site. After adding a new turbine TX2, the existing boilers already operate at their maximum loading. There is no more steam for extra electricity generation, so that, the new turbine contribution is restricted. 3.5. Case2: Low Pressure Condensing Turbine & Boiler In order to overcome the limitation, a new boiler BXI is then proposed to operate together with the LP-CT turbine, TX2. The result capacity of turbine TX2 is 14.336 MW and the capacity of boiler BXI is 180.55 T/H. Although the total capital cost is greatly increased to 274.97 million Yen, the overall profit still increases to 11,456.05 million Yen (about 2.9% increment with respect to the base case). This is because the new boiler BX1 can provide more steam for the new turbine TX2 to generate electricity. The turbine TX2 then can have a larger generation capacity, and it can support additional production as well as reducing the electricity importation amount. Combining all the effects, a net profit increase is obtained. The utility MCF is studied again to explore any improvement opportunity (Figure 5). The electricity MCF remains at a high level and the electricity supply still acts as a pinch in the site. Figure 5 also shows that the MCF of different pressure steams have the same value at day and night shifts. It indicates that improper steam balance occurs in the utility system. Large amounts of VHP and HP steam letdown to LP level for utilizing TX2 without further electricity generation. Such inefficient generation path increases the electricity production cost and makes addition production be non-profitable. 3.6. Case 3: Very High Pressure Condensing Turbine & Boiler To prevent the improper steam balance, a very high pressure condensing (VHP-CT) turbine, TX6, is suggested to substitute the LP-CT turbine, TX2. The boiler BX1 is kept unchanged. With this new combination, the VHP is well utilized for electricity generation in the turbine TX6 without letting down. The result turbine capacity is 72.837MW and the boiler capacity is 290.87T/H. This combination provides a huge selfelectricity generation capacity and can reduce the electricity importation cost a lot as well as maximizing the production level. The total profit is therefore increased to 13,260.55
183
Figure 5: Utility MCF in case 2.
Figure 6: Utility MCF in case 3.
million Yen. It is about 19% more than the base case. In this case, the MCF values of different kinds of steam are no longer the same (Figure 6). The electricity MCv also decreases to a low level. These two facts indicate the pinch removal from the utility system. 3.7. Case 4: Mixed Integer Linear Programming Model Conventionally, engineers apply mixed integer programming to obtain the optimum solution. In order to compare the conventional method and the marginal value analysis, the linear programming site-model is modified to include binary variables for optimizing the design options as a mixed integer linear programming (MILP) model. After optimization, the MILP site-model provides the same results as the case 3. It includes the new turbine TX6 and the new boiler BX1. This proves that the MVA is also possible to obtain the optimal solution. In fact, the MVA requires less computational effort than the MILP model, as increasing the number of design options will slow solving process down. It is especially unfavorable in industrial scale problem. In addition, the marginal value analysis can give a clear explanation during the utility plant configuration changes. It makes the results much more meaningful than only providing the optimum solution. 4. CONCLUSIONS The marginal value analysis (MVA) is introduced in this paper, which can be used to determine the utilities and the intermediate materials prices, make appropriate business decisions and locate or remove bottlenecks in a chemical production site. A case study of investment decision making is performed in this paper by adopting MVA. The MVA solution is then compared with the MILP optimal solution. It is believed that the MVA can also obtain a near best solution or even the optimum solution without requiring huge computational efforts. ACKNOWLEDGMENTS The authors would like to acknowledge financial support from the RGC (Hong Kong) and the Major State Basic Research Development Program (G2000026308) and technical support from Mitsubishi Chemical Corporation. REFERENCES [1] A.P. Rossiter, and S.M. Ranade, IChemE Symposium Series, 109, (1988), 283-301. [2] S.M. Ranade, S.C. Shreck, and D.H. Jones, Hydrocarbon Processing, 68(9), (1989), 81-84. [3] C.W. Hui, Computers and Chemical Engineering, 24, (2000), 1023-1029. [4] J.C.M. Hartmann, Hydrocarbon Processing, 78(2), (1999), 64-68.
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Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Published by Elsevier ScienceB.V.
An Integrated Decision Support Framework for Managing and Interpreting Information in Process Diagnosis Michael Elsass (Ohio State University), Saravanarajan (UCLA), James F. Davis (UCLA)*, Dinkar Mylaraswamy (Honeywell Labs), Dal Vernon Reising (Honeywell Labs) and John Josephson (Ohio State University) In this paper we describe and demonstrate a comprehensive decision support framework for rapid operator understanding of abnormal plant situations. The operational objective is to manage fault situations that are on a trajectory to exceed the capabilities of distributed control (or optimization), but have not yet reached alarm limits. This is accomplished through early detection of an abnormal situation, assimilation of relevant information for quick understanding, rapid assessment and diagnostic localization. The early detection, assimilation and assessment components are briefly described in the context of the operator Graphical User Interface (GUI). The paper focuses on diagnostic localization, diagnosis to a level of explanatory detail that is just sufficient for operator action. Abstract
* James F. Davis (
[email protected]) author to whom all correspondence to be made 1. INTRODUCTION Management of abnormal situations is a challenge in the chemical industry accounting for $10 billion in lost revenue in the US alone [1]. The Abnormal Situation Management| Consortium (ASM| was formed in 1992 to create solutions that reduce the number and severity of abnormal situations +. One area pursued by the ASM Consortium is operator assistance with early event detection and situation assessment to avoid prolonged events, alarms, and potential safety hazards. The consortium perspective is holistic, encompassing the plant and the people that operate it. Decision support therefore includes tools that help the operator explore and quickly localize abnormal situations. 2. THE O P E R A T O R GUI A N D THE DECISION SUPPORT SYSTEM In any abnormal situation, the role of the operator is to accurately detect, diagnose and take proper corrective action. The operators' interface is, therefore, a key system element for rapid understanding and is also a key motivator for an integrated approach [2]. Figure 1 shows a prototype interface for a decision support system developed for a demethanizer, the industrial case study used in this work. The interface shows four distinct areas: the polar star GUI, key variable trends for rapid assessment, a process flow sheet showing system and device localization and malfunction hypotheses for diagnostic localization. +
Honeywell, Celanese, ChevronTexaco,ConocoPhillips, ExxonMobil,, NOVA Chemicals, Shell, Brad Adams Walker Architecture, P.C., TTS Performance Systems,User CenteredDesign Services, LLC and UCLA
185
Figure 1. Elements of a decision support system interface In the top left panel, a polar star is shown as one GUI for presenting plant functionality by displaying the status of functions at each point on the star. Early detection and rapid assimilation of information is facilitated by a functional view of the plant operation based on a definition of plant objectives and how they are achieved. Each plant function aggregates key sensors into a multivariate detector called a state estimator (SE) [3]. This functional assessment provides a first level diagnosis by virtue of the distributed functional organization itself and the associated SEs. Key trends are shown in the right hand panel. ASM studies place high value on relevant trend plots that are readily available to help operators better understand a given abnormal situation. Upon detection, operators can view and manipulate sensor displays grouped according to functional abnormality. Diagnostic localization is a form of diagnosis that evaluates process behavior to narrow the focus to those sub-systems and devices that are in fault or failed modes. While the SEs run continuously, diagnostic localization is triggered and runs only during an abnormal event. The upper right hand panel in Figure 1 displays a list of possible localized fault and failure hypotheses that are updated throughout the duration of the event. The lower left hand displays the device and system level contributors. 3. DIAGNOSTIC L O C A L I Z A T I O N The task of diagnostic localization involves aggregating evidence from multiple sources, such as SE output and sensor measurements, and applying the information to a process model. Using these input sources, a localization algorithm operates on a causal process model to assimilate data into possible process behaviors and generate diagnostic hypotheses.
186
3.1 Functional Representation A causal process model is built as a Functional Representation (FR), a modular, device-centered formalism that models structure, mode of operation, function and behavior [4]. Function and behavioral knowledge is stored within the device models, and is expressed in a process sense when the models are connected to reflect process topology. Process behavior is modeled as a highly distributed set of device level Causal State Digraphs (CSDs) and Causal Process Diagrams (CPDs). Figure 2 shows the FR model for a control valve. The CSD is shown at the bottom of the diagram. It is composed of a set of connected device port states (inlet, outlet, and an intemal state) with each state consisting of qualitative process variable descriptions. Process variables can be material, energy or information. Material refers to any process fluid characteristics such as flow, temperature, phase, etc. CSD states are causally connected to model the range of device states for an instant in time, giving a 'snapshot' of the device behavior. Behavior modeled with this static perspective significantly decreases the possible causal linkages a device can exhibit in a CSD. In the valve example, a typical representation would have a behavior describing high inlet flow and low outlet flow (caused by the valve closing), a behavior occurring over a period of time. However for an instant in time, the valve inlet flow must equal the outlet flow. While the CSD represents all device behaviors, a Causal Process Diagram (CPD) represents a single device behavior. Each device has a distributed set of CPDs, each of which models a single consistent path through the CSD. Only a few of the valve CPDs are shown in the figure for the purposes of clarity. Function groups CPDs, (i.e. individual device behaviors) based on various userdefined categories relating to device operation or the internal variable transformations. In Figure 2, control valve functions model behavior around valve aperture since this directly influences the flow through the valve. CPDs can be linked to multiple functions, a particularly useful capability when categorizing the very large number of possible behaviors associated with complex devices, such as a reactor.
Figure 2. FR representation of control valve (partial)
187
Modes are located at the top of the FR construct (see Figure 2) and organize functions and CPDs. In this work, modes characterize failure, fault and normal behaviors. Normal refers to a behavior in which all pertinent process variables are within a normal range. Failure refers to abnormal behaviors associated with an internal malfunction. Fault refers to an abnormal behavior resulting from abnormal input. A normal behavior can be restored when a deviating input is returned to normal. Each device model is composed of a linked set of the above constructs. By connecting the device models, these constructs are brought together into a highly distributed process model that decomposes behavior into failure, fault and normal categories. This kind of decomposition allows us to operate on the model in a highly selective manner. 3.2 Causal Link Assessment (CLA) Because of the modularity of the FR process model, the information system can explore all feasible behavioral links based upon sensor measurements. Diagnostic localization is achieved by applying the CLA algorithm to the FR process model to first generate hypotheses and then discriminate among them at a point in time and over time. 3.2.1 Hypothesis Generation Hypothesis generation produces a set of possible static state descriptions using abstracted sensor readings as input. An abstracted sensor reading can have the values of low, normal, or high. Resulting process states comprise the list of individual device behaviors (CPDs) that are linked. The CLA algorithm produces process states through an exhaustive device-bydevice analysis of the entire process model that assembles feasible device behaviors into an overall process behavior. Figure 3 shows a simple example process containing a temperature sensor and valve with an initial boundary state of [high temperature: normal flow]. The boundary state is applied to the temperature sensor inlet, and matched to the inlet states of the temperature sensor's CSD.
Figure 3. Process state generation
188 Since only temperature is modeled in the temperature sensor device, the [temperature high] state is matched to 'high inlet temperature' in the CSD, while the [flow normal] description is stored and passed on to the valve device. Every CPD containing the 'high inlet temperature' state is tagged as a possible behavior for the sensor, e.g. [high temperature, high signal], [high temperature, normal signal] and [high temperature, low signal]. For these CPDs, signal refers to the sensor measurement. Even though the process fluid temperature is high for all these states (due to the inlet condition), a sensor malfunction can result in a low or normal measurement. Sensor behaviors are then matched against the actual sensor measurement. Process states are built by accumulating device states starting from the initial boundary state. In the figure the [high temperature, normal signal] CPD is added to the [high temperature, normal flow] boundary state. The outlet state of the sensor CPD [high temperature] is combined with the stored variable description [flow normal], and used as the inlet to the control valve ([high temperature, flow normal] state). Since flow is the only variable of concern in a control valve, the [flow normal] state is matched to the inlet states of the control valve CSD. Three CPDs match the normal inlet flow state as shown in the figure. Because there are three possible behaviors, the system branches into three different process states, each corresponding to a different valve behavior (with the sensor behaviors the same for all). The control valve example demonstrates how the procedure branches as it progresses from device to device and how multiple unique process states develop. Consequently, many process states will be generated that show multiple malfunctioning devices, and in the extreme, there will be process states indicating that a majority of devices are malfunctioning. Note that CLA is not causal propagation. In a general sense, causal propagation uses upstream conditions to predict downstream behavior, a procedure that examines a process over space and time. CLA uses current sensor values to examine the plant at a static point in time. A hypothesis is established by analysis of repeated static views throughout the duration of an abnormal event. Branching in CLA is not about exploring feasible propagation paths, but about exploring feasible paths of static causal links. It is in this context that the branching process needs to be managed. The static basis of the state descriptions lets the branching be effectively managed by ordering the investigation and matching against snapshot sensor information. Order of investigation is a strong factor in managing process state branching by constraining the number of possible behaviors a device can exhibit by constraining the number of inlet conditions considered. A relatively simple device such as a valve will have a small number of possible behaviors whereas a complex device will have a large number. By setting the states of the simpler devices around complex devices, the complex device inlet possibilities will be constrained, thus constraining the number of possible behaviors the complex device can exhibit. Matching sensor devices with actual sensor measurements constrains the signal inlet and reduces the possible sensor behaviors (as shown in Figure 3).
3.2.2 Hypothesis Discrimination Finally, CLA generates diagnostic hypotheses by accessing modes related to each device CPD in the set of process states. Any device CPD linked to a failure mode is considered a feasible hypothesis in that the failure device is the malfunction hypothesis. The set of feasible malfunction hypotheses is then evaluated to determine the most probable
189 hypotheses using three criteria: number of simultaneously malfunctioning devices, number of time-steps a malfunction persists, and comparison with SE outputs from the rapid assessment module. Malfunction hypotheses that contain a single malfunctioning device are considered more probable than hypotheses with two or more malfunctioning devices, and are therefore ranked higher. The CLA algorithm is constructed so the user can specify the number of simultaneous malfunctions to consider. For the case study described in this work, the threshold was set at three. Those malfunction hypotheses that persist over time are ranked higher than those appearing sporadically throughout the duration of the abnormal event. Finally, each malfunction hypothesis is compared to the state estimator output, with unmatched hypotheses rejected. While both state estimator and FR systems use sensor data as input, they constitute independent models that should be in agreement. 4. CASE STUDY An industrial demethanizer unit was used to evaluate prototype performance. The unit had several hundred sensors that recorded readings once per minute. In this work, a subset of 30 key sensors was used to drive both the state estimators and the CLA in the information system. A case study involved an increasingly erratic condenser level sensor caused by an oil leak that slowly upset the process over a period of two days. Operators initially misdiagnosed this situation and took improper corrective action that further exacerbated the upset. Applying plant data to state estimation and diagnostic localization demonstrated that the SE detected an abnormal situation within seven hours of the initial malfunction. Once a functional abnormality was detected, the CLA system was triggered. Initially, hypotheses were about several unrelated malfunctions. As the malfunction further manifested itself, the CLA algorithm localized the malfunction to the overhead section. Soon after, the malfunction was narrowed to the condenser control system and finally the condenser level sensor malfunction. This was a blind case study analyzed by the decision support system. REFERENCES
[ 1] Nimmo, I. "Adequately Address Abnormal Operations." Chemical Engineering Progress, 91 (9) pp. 36-45 (September 1995). [2] Cochran, E., C. Miller and P. Bullemer, "Abnormal Situation Management in Petrochemical Plants, NAECON (1996). [3] Bullemer, P., D. Mylaraswamy and K. Emigholz, "Fielding a Multiple State Estimator Platform, NPRA conference (2000). [4] Elsass, M. J. "Multipurpose Sharable Engineering Knowledge repository", PhD thesis, The Ohio State University, (June 2001).
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Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
Neural Networks Applied to a Multivariable Nonlinear Control Strategies La~rcio Ender a, Rubens Maciel Filho b
aDepartment of Chemical Engineering, Regional University of Blumenau, Brazil; bLaboratory of Optimization, Design and Advanced Process Control (LOPCA), Faculty of Chemical Engineering, State University of Campinas (UNICAMP), Brazil. Abstract The Artificial Neural Networks (ANN) are computational tools with great number of applications in modeling and process control techniques. The neural networks can learn sufficiently accurate models and give good nonlinear control when model equations are unknown or only partial state information are available. Neural network approach allows taking into account non-linearities of the process as well as variable interactions. The proposed work has as objective to explore the use of the neural networks in multivariable control strategies in which ANN are used as dynamic models in the generation of predictions, as well as in the definition of adaptive control strategies based on neural networks. Keywords: neural networks, control strategies, on-line learning, catalytic reactor, soft sensor.
1. INTRODUCTION The ability of the neural networks to represent nonlinear behaviour and noisy data has been attracting the community of process control. Bhat and McAvoy Ill were among the first ones to use ANN's to model nonlinear chemical processes.The abilities of the nets to representing the dynamic behavior of processes have been used to solve problems applied in engineering for the generation of predictive dynamic models as well as in the definition of control strategies [21. Multilayered feedforward neural networks represent a special form of connectionist model that performs a mapping from an input space to an output space. They consist of massively interconnected simple processing elements arranged in a layered structure. Bearing this in main, this work has as objective the discussion of some applications of the multilayered feedforward artificial neural networks in adaptive control strategies. 2. NEURAL NETWORKS APPLIED IN CONTROL PROCESSES Process control has been by far the most popular area of neural network applications chemical engineering. The following characteristics and properties of neural networks are importantt31: a) Nonlinear systems. Neural networks have greatest promise in the realm of nonlinear control problems. This stems from their ability to approximate arbitrary nonlinear mappings; b)Parallel distributed processing. Neural networks have a highly parallel structure which lends itself immediately to parallel implementation; c) Learning and Adaptation. Networks are trained using past data records from the system under study. A suitable trained
191 network then has the ability to generalize when presented with inputs not appearing in the training data. Networks can also be on-line adapted; d) Data fusion. Neural networks can operate simultaneously on both quantitative and qualitative data; e) Multivariable systems. Neural networks naturally process many inputs and have many outputs. Baughman and Liu[3]classify various approaches into three categories: a) Direct network control: training a neural network as the controller and determining the controller output directly; b) Inverse network control: Training a neural network as an inverse model of the process, predicting the process inputs necessary to produce the desired process outputs; c) Indirect network control: training a neural network to serve as a model of a process, or to determine a local controller. Psichogios and Ungar [21 classified the applications of neural networks as direct and indirect method. In the direct method, a neural network is trained with observed input-output data from the system to represent its inverse dynamics. In the indirect method the neural network is trained with input-output data from the dynamic system to represent the forward dynamics. This work considers only two classifications: direct and indirect control. Direct control whenever the neural network acts as controller and indirect control when the neural network is used as models to prediction the states or future dynamics behavior. 3. ON-LINE LEARNING OF THE NEURAL N E T W O R K The on-line learning presents limitations due to the number of necessary iterations to attend the error criterion adopted as well as the necessity of the learning to be in real time. A maximum number of iterations were allowed to outline these limitations. Two vectors formed with the last inputs/outputs of the process compose the patterns of the on-line learning. The vector that contains the oldest information is used in the training the neural networks and the vector that contains more recent information it is used to evaluate the obtained nets. To guarantee the good representation of the process through neural networks, a strategy formed by three nets acting parallelly was adopted. The first is formed by weights of the off-line learning, here denominated of standard weights; the second, is initialized with the standard weights and it is submitted at the on-line learning. Whenever the standard weights present better performance, this net has its weights substituted by the standard weights. The third is initialized with the standard weights and continually is submitted to the on-line learning at each sampling time. The on-line learning procedure is carried out with data set provided by the reactor model emulating a real plant. This structure formed by three neural networks will be used in the representation of the dynamic behavior in control strategies, as well as in control strategy where the network acts as a controller. The neural network that presents the smallest quadratic error in the representation of the vector that contains the more recent inputs/outputs (patterns) of the process is used in the control strategy in this sampling time. This procedure is repeated at each sampling time. 4. CONTROL S T R A T E G Y - DIRECT ADAPTIVE N E T W O R K C O N T R O L The proposed control strategy is formed by two optimization procedures named controller optimization and setpoint optimization, based on neural networks [a' 5] showed in the Fig. 1. Historical input-output data were used to train two dynamical neural networks of the control strategy [5] and a stationary neural network to estimate the concentration and temperature in the soft sensor. The first of the dynamical network is trained to represent the forward process dynamics. The inputs of the network are the current and past values of the controlled and manipulated variables and the outputs of the network are the one step ahead
192 prediction of the process outputs. The second dynamical neural network is trained to represent de inverse process dynamics and acts as a controller of the strategy. The inputs are the setpoints of the closed loop for the next sampling time; past controlled and manipulated variables and the outputs of the neural network are the manipulated variables for the next sampling instant. The stationary neural model is used as soft sensor to predict the concentration of the reactor in the setpoint optimization procedure. The neural networks used in the proposed control strategy are on-line trained through an appropriate methodology described in the sequence.
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The controller is based on a neural network that represents the inverse dynamics of the system, which is on-line trained through an optimization routine. The controller optimization routine adjusts the weight of the neural controller using the estimated global error of the closed loop at each sampling time, based on a dynamic model of the process, represented by a neural network with on-line learning ESI. The dynamic model of the process is on-line trained with the last ones inputs/outputs data of the process stored in a vector. The controller design uses the same inputs of the neural controller at each sampling time. This optimization routine adjusts the weights of the neural network of the controller in such way to minimize the estimated global error (e yset_ y pred). Considering that the estimated error is based on a neural model, it is necessary to have a model that represents with fidelity the dynamic behavior of the process. When the quadratic error of the neural model outputs is smaller than the desired tolerance, this model is used in the optimization routine. If the quadratic error becomes larger in relation to a determined error, the controller makes use of the standard weight (weight of the off-line learning) to generate the control action for this sampling instant. The global error (e) cannot backpropagate directly because of the location of the process information (it is available process exit data). The global error is propagated back through the plant using the Jacobian matrix of the process [4' 51. These control actions deviations are used in the on-line learning of the neural network of the controller. The proposed setpoint optimization strategy is based on a neural network representing the stationary model of process and the optimization procedure using Sequential Quadratic Programming algorithm (SQP). As a case study a fixed bed catalytic reactor for production of acetaldehyde by ethyl alcohol oxidation over Fe-Mo catalyst proposed by McGreavy and Maciel Filho [6] and Toledo [7] was used. The neural model able to represent the stationary model of the process is used to predict the concentration and temperatures of the reactor, acting as soft sensor. The inputs of this neural network are state variables that present strong influence on the process and the outputs are the concentration and temperatures of the reactor. =
193 The output variables are used in the objective function in the optimization routine and the obtained temperatures are the setpoints for the control algorithm. A sensitivity analysis through complete factorial design is performed in order to estimate the influence of the process parameters on the output variables. By this analysis the parameters are determined and they will be used as inputs and outputs of the neural network. The inputs in the ANN are feed mass flow (GMo), feed temperature (Tfo), entrance cooling fluid temperature (Tg0) and air/ethanol relationship (R). The outputs of the neural network are the prediction of the concentration in the third (C(3)) and seventh (C(7)) axial orthogonal collocation points and temperatures in the first (Tpred(1)) and third (Tpred(3)) axial orthogonal collocation points. In the control strategy the manipulated variable are feed mass flow (GMo) and air/ethanol relationship (R) and the controlled variables are the temperature of the first and third axial orthogonal collocation points. The setpoint optimization procedure is accomplish by SQP which manipulates GMo and R to take C(3) and C(7) to the concentration setpoint. The obtained values of GMo and R are not the control actions of the strategy, but they are the stationary values for these variables to lead the process to the desired concentrations in the actual conditions. The temperatures obtained (Ypred(1) and Tpred(3)) in the optimization procedure are the temperature setpoints for the control strategy. The optimization procedure is accomplished in a time interval larger than the sampling time of the control strategy or is executed when a significant disturbance is detected. This procedure is used to avoid many changes in the setpoint of the closed loop. The objective function in the optimization procedure is given by: J(k) :)--'~ -Csp(i)-C(i,k) )3 ,T(i,k)2,103
(1)
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206 Where U is sufficient large positive number. For the scheduling of a sequential multi-purpose batch plant consisting of batch distillation under MPC and UIS policy, the objective function is set to minimize the makespan.
Min'Makespan > ET~
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Therefore, the optimization problem is composed of minimizing the makespan for the sequential multi-purpose batch plant consisting of batch distillation under MPC and UIS policy, as represented equation (7), subject to constraints equation (1)-(6). We formulated this problem as an MILP model. We have tested the proposed model against several examples to show its effectiveness. 3. EXAMPLE The scheduling problem in this paper is solved on PC, Intel Pentium III 800 MHz, using the solver Hyper LINGO 6.0. The structure of example process is shown at Figure 1. Processing time, production path and batches of each product is given in the following Table 1. Table 1. Data for example ~uct unit
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207 P4-P3-P8-P2-P7-P6-P5, as seen in Figure 2, with the minimum makespan, 45h. slot 1
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Figure 2. Optimal schedule of example. 4. CONCLUSION We investigated a production scheduling of multi-purpose plants consisting of batch processes and batch distillation for the manufacturing of fine chemical products. For the short-term scheduling of a sequential multi-purpose batch plant consisting of batch distillation under MPC and UIS policy, we proposed a MILP model based on a priori time slot allocation. The developed methodology will be especially useful for the design and optimal operations of multi-purpose and multiproduct plants that is suitable for fine chemical production. ACKNOWLEDGMENT The authors would like to acknowledge the financial support from Korea Science and Engineering Foundation (R01-2002-000-00007-0). REFERENCE
[1 ] Pinto, J.M. & Grossmann, I.E., Ind. Eng. Chem. Res., 34 (1995) 3037 [2] Jung, J. H. & Lee, H., Comput. Chem. Engng., 20 (1996) 845 [3] Moon, S. & Hrymak A. N., Ind. Eng. Chem. Res., 38 (1999) 2144 [4] Bok, J-.K. & Park, S., Ind. Eng. Chem. Res., 37 (1998) 3652 [5] S. B. Kim, H. K. Lee, I. B. Lee, E. S. Lee, B. Lee., Comput. Chem. Engng., 24 (2000) 1603 [6] Pinto, J.M. & Grossmann, I.E., Ind. Eng. Chem. Res., 35 (1996) 338 [7] Pinto, J.M. & Grossmann, I.E., Comput. Chem. Engng., 20 (1996) S 1197 [8] Jung, J. H., Lee, H., Yang, D. R., & Lee, I., Comput. Chem. Engng., 18 (1994) 537
208
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Publishedby Elsevier ScienceB.V.
Dynamic Project and Workflow Management for Design Processes in Chemical Engineering Markus Heller a, Bernhard Westfechtel a aComputer Science III, RWTH Aachen, D-52056 Aachen, Germany
Abstract Design processes in chemical engineering are hard to support. The design process is highly creative, many design alternatives are explored, and both unexpected and planned feedback occurs frequently. Thus, it is inherently difficult to manage the workflow in design processes, i.e., to coordinate the effort of experts working on tasks such as creation of flow diagrams, steady-state and dynamic simulations, etc. Conventional project and workflow management systems support the management of design processes only to a limited extent. In contrast, the management system AHEAD is designed specifically for dynamic design processes.
Keywords process systems engineering, business decision making, workflow management 1. INTRODUCTION
Design processes in chemical engineering are hard to support. Since design processes are highly creative, they can rarely be planned completely in advance. Rather, planning and execution may have to be interleaved seamlessly. In the course of the design process, many design alternatives are explored which are mutually dependent. Furthermore, design proceeds iteratively, starting from sketchy, coarse-level designs to detailed designs which are eventually needed for building the respective chemical plant. Iterations may cause feedback to earlier steps of the design process; it may be necessary to revoke inadequate design decisions. Finally, design involves cooperation among team members from different disciplines and potentially multiple enterprises, causing additional difficulties concerning the coordination of the overall design process. Technical tools such as flowsheet editors, simulators for steady-state and dynamic simulations, etc. are crucial aids for effectively and efficiently performing design tasks. In addition, managerial tools are required which address the coordination of design processes. In fact, such tools are crucial for supporting business decision making. In the course of the design process, many decisions have to be made concerning the steps of the chemical process, the relationships among these steps, the realization of chemical process steps by devices, etc. To perform these decisions, design alternatives have to be identified and elaborated, and the respective design tasks have to be coordinated regarding their mutual interfaces and dependencies. To support business decision making, managerial tools must provide chief designers with accurate views of the design process at an adequate level of granularity, offer tools for planning, controlling, and coordinating design tasks, thereby taking care of the dynamics of design processes. Currently, many management tools are available which, unfortunately, often have not been developed for supporting complex and dynamic design processes. In fact, most of these tools are generic in the sense that they can be applied to any kind of business process (there are a few process support tools designed for chemical engineering such as e.g. n-dim [ 1] and KBDS [2]). For example, project management systems [3] assist managers in project planning and control, assuming that the project can be represented by a partially ordered set of activities. However, iteration and feedback cannot be represented in project plans. Furthermore, important
209 information is missing concerning e.g. the products of design tasks such as e.g. different kinds of flow sheets and simulation models. To some extent, project management systems are useful for high-level planning and control at the level of milestones. But to support decision making effectively, other sources of information have to be exploited, as well. Workflow management systems [4] have been developed for supporting routine processes performed e.g. in banks, insurance companies, administrations, etc. A workflow management system manages the flow of work between participants, according to a defined procedure consisting of a number of tasks. It coordinates user and system participants to achieve defined objectives by set deadlines. To this end, tasks and documents are passed from participant to participant in a correct order. Moreover, a workflow management system may offer an interface to invoke a tool on a document either interactively or automatically. Workflow management systems differ from project management systems since they address rather detailed execution support rather than only high-level planning. Their most important restriction is limited support for dynamic design processes. Many workflow management systems assume a statically defined workflow that cannot be changed during execution. This assumption does not match the characteristics of design processes, which are highly creative, dynamic, and iterative, and therefore cannot be controlled by a static workflow defined in advance. Though this problem has been recognized in the workflow community [5], so-called adaptive workflow management systems provide at best partial solutions (e.g., based on exceptions, which, however, have to be pre-defined). Still, many of the commercial tools hardly address the problem of evolving workflows. In this paper, we present a management system which has been developed to support engineering design processes. This system is called AHEAD (Adaptable and Human-Centered Environment for the MAnagement of Design Processes [6, 7]). It has been developed in the context of the long-term research project IMPROVE [8] which is concerned with models and tools for design processes in chemical engineering. AHEAD equally covers products, activities, and resources and therefore offers more comprehensive support than project or workflow management systems. Moreover, AHEAD supports seamless interleaving of planning and execution a crucial requirement which workflow management systems usually do not meet. Design processes are represented by dynamic task nets, which may evolve continuously throughout the execution of a design process. Dynamic task nets include modeling elements specifically introduced for design processes, e.g., feedback relationships for iterations in the design process which cannot be represented in project plans. This way, AHEAD improves business decision making since it offers a more natural, realistic, and adequate representation of design processes. -
-
2. SYSTEM DESCRIPTION Figure 1 gives an overview of the AHEAD system. AHEAD offers environments for different kinds of users, which are called modeler, manager, and designer. Please note that "modeler", "manager", and "designer" denote roles rather than persons. In particular, one person may play multiple roles. For example, the chief designer of a project may act both as a manager and as a designer. Similarly, management and modeling may be performed by the same person. The management environment supports project managers in planning, analyzing, monitoring, and controlling design processes. In particular, planning and execution may be interleaved seamlessly so that the dynamics of design processes may be taken into account. Since the management environment is coupled with the work environments for designers, the manager is provided with accurate and current information about the state of his project. Management comprises activities - - the design tasks and their relationships, products n the design doc-
210
Figure 1. Architecture of the AHEAD system.
uments and their dependencies, and resources - - both human resources, i.e., the design team, and technical resources, i.e., the software tools used by the team. The management environment offers different kinds of graphical representations, e.g., trees, diagrams, and tables. The work environment supports designers in their work as far as the coordination aspects are concerned. Management and work environments are integrated through a common database which stores a graph representation of the respective design process. The work environment provides an agenda tool which displays the tasks assigned to a designer in a table containing information about state, deadline, expected duration, etc. The designer may perform operations such as starting, suspending, finishing, or aborting a task. Furthermore, he may open a work context which manages the documents and tools required for executing a certain task. The designer is supplied with a workspace of versioned documents. He may work on a document by starting a tool such as e.g. a flow diagram editor, a simulation tool, etc. The workspace may change dynamically during task execution (which may take hours, days, or weeks rather than minutes); e.g., a new version of some input document may arrive during task execution. Management and work environment are used at project run-time to execute design processes. The modeling environment allows incorporation of domain-specific knowledge about design processes into the AHEAD system. Domain knowledge is expressed in the Unified Modeling Language. With the help of class diagrams, design processes may be modeled on the type level. For example, task classes may be defined for designing flow sheets, performing steady-state or dynamic simulations, etc. In addition, recurring patterns of task instances may be defined with the help of collaboration diagrams. Using the UML model, process support may be tailored towards the respective domain. These models may evolve during execution, as well [9].
211 3. APPLICATION In the IMPROVE project, we are studying the design of a chemical plant for Polyamide6 as a reference process against which the developed tools (e.g., AHEAD) are evaluated. The process focuses on the early lifecycle phases, namely basic engineering and conceptual design. It is based on interviews conducted with an industrial partner, study of literature, and own research. To some extent, the reference process reflects current industrial practice, but it also contributes innovative aspects such as simultaneous engineering and inter-organizational cooperation. For a comprehensive description of the reference process, see [ 10]. Here, we focus on the managerial parts of this process, which are supported by the AHEAD system. We illustrate this process support by discussing the task net displayed in Figure 2. This task net evolves during execution, but this evolution is not shown graphically due to the lack of space. Task nets are shown to the user of the management environment of AHEAD in roughly the same way as in the figure. Tasks are represented by rectangles. Each task may have inputs and outputs shown as white and black circles, respectively. Tasks are connected by control flows which resemble precedence relationships in project plans. Feedback flows represent feedback in the design process and are oriented oppositely to control flows. Data flows connect task outputs and inputs. Hierarchical relationships are not shown explicitly in the figure; rather, they are expressed by placing subtasks below their supertasks. At the start of the design process, the manager creates an initial task net comprising only the task Preparation and its refinements. In the preparation phase, the requirements to the chemical plant are defined, a literature research is carried out, and an initial abstract flow diagram is created. Based on this information, the alternatives batch and continuous operation are compared, and a decision is performed towards continuous operation. The result of the preparation phase is an abstract flow diagram, according to which the design process may be detailed further. To this end, the manager extends the task net with tasks for designing the reaction, the separation, and the compounding (using an extruder), which are the major steps of the respective chemical process. To design the reaction, a process flow diagram is created which defines alternatives for this part of the chemical process. Only then may the task net be extended with design tasks for elaborating these alternatives (a CSTR reactor, a PFR reactor, or two reactor cascades). Simulations are carried out to evaluate these alternatives; if necessary, laboratory experiments are performed to validate the simulation models. Finally, a decision is performed with respect to the selected alternative (task Compare). The subnet for designing the separation is structured in a similar way; here, the alternatives extraction and evaporation are considered. To start the investigation of the separation step as soon as possible, an initial estimate of the output of the reaction step is made (simultaneous engineering). The subnet for designing the extruder is structured in a different way; special-purpose simulation tools are used to this end. Please note the feedback flow from DesignExtruderto DesignSeparation: it is not clear from the beginning to what extent the extruder can be used for the separation of remaining input substances. This requires negotiation between the designer of the separation step and the designer of the extruder. After having designed all parts of the chemical process, the overall concept is jointly discussed in a final decision step. 4. CONCLUSION The sample process sketched above shows how managers and designers are supported in making business decisions concerning the plant design. It shows the typical structure of the design process: elaborate alternatives, evaluate them by simulations, laboratory experiments, and cost
212
Figure 2. Task net for the polyamide6 design process.
213 calculations, select an alternative, and proceed by iterative refinement of the design. Decision steps are explicitly modeled as tasks. The overall task net evolves during execution. Different aspects of dynamics are taken into account, such as product-dependent task nets, simultaneous engineering, and feedback. Please note that managerial and technical activities are tightly integrated; this is necessary to provide effective decision support. Business decision making of this kind cannot be provided by conventional project and workflow management systems. In a project management system, the manager may define milestones based on a simple conceptual model of partially ordered activities. But project plans do not reflect well the characteristics of highly dynamic, iterative, and creative design processes. Furthermore, the link to the actual design activities is missing. Workflow management systems would typically constrain the design process very tightly and tend to automate the design process too far. The approach realized in the AHEAD system provides the required flexibility and promises more adequate decision support at the managerial level. REFERENCES
[ 1] Arthur W. Westerberg, Eswaran Subrahmanian, Yoram Reich, Suresh Konda, and the ndim group. Designing the process design process. Computers & Chemical Engineering, 21 (S):S 1-$9, 1997. [2] R. Bafiares-AlcS.ntara and H.M.S. Lababidi. Design support systems for process engin e e r i n g - II. KBDS: An experimental prototype. Computers & Chemical Engineering, 19(3):279-301, 1995. [3] Harold Kerzner. Project Management: A Systems Approach to Planning, Scheduling, and Controlling. John Wiley & Sons, New York, 1998. [4] Peter Lawrence, editor. Workflow Handbook. John Wiley & Sons, Chichester, UK, 1997. [5] Dimitrios Georgakopoulos, Wolfgang Prinz, and Alexander L. Wolf, editors. Proceedings of the International Joint Conference on WorkActivities Coordination and Collaboration (WACC-99), volume 24-2 of ACM SIGSOFT Software Engineering Notes, San Francisco, CA, 1999. ACM Press. [6] Dirk J~iger, Ansgar Schleicher, and Bernhard Westfechtel. AHEAD: A graph-based system for modeling and managing development processes. In Manfred Nagl, Andy Sch/irr, and Manfred M/inch, editors, AGTIVE m Applications of Graph Transformations with Industrial Relevance, LNCS 1779, pages 325-339, Castle Rolduc, The Netherlands, 1999. Springer-Verlag. [7] Manfred Nagl, Bernhard Westfechtel, and Ralph Schneider. Tool support for the management of design processes in chemical engineering. Computers & Chemical Engineering, 27(2):175-197, 2003. [8] Manfred Nagl and Wolfgang Marquardt. SFB-476 IMPROVE: Informatische Unterst/itzung/ibergreifender Entwicklungsprozesse in der Verfahrenstechnik. In Matthias Jarke, Klaus Pasedach, and Klaus Pohl, editors, Informatik '97: Informatik als Innovationsmotor, Informatik aktuell, pages 143-154, Aachen, Germany, September 1997. Springer-Verlag. [9] Ansgar Schleicher. Management of Development Processes ~ An Evolutionary Approach. Deutscher Universit~its-Verlag, Wiesbaden, Germany, 2002. [10] B. Bayer, M. Eggersmann, R. Gani, and R. Schneider. Case studies in process design. In B. Braunschweig and R. Gani, editors, Software Architectures and Toolsfor Computer Aided Process Engineering. Elsevier Publishers, 2002.
Process Systems Engineering 2003 B. Chen and A.W. Westerberg(editors) 9 2003 Published by Elsevier Science B.V.
214
Environmentally Conscious Planning and Design of Supply Chain Networks A. Hugo and E . N . Pistikopoulosl Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College, London, SW7 2BY, UK Abstract In this paper, a methodology is presented for the explicit inclusion of environmental impact assessment criteria as part of strategic investment decisions related to supply chain networks. At this level, the earliest opportunities exist for a company to influence both its future market competitiveness and environmental performance. Trying to give consideration to not only the traditional economic criteria, but also to the multiple environmental concerns, inevitably results in the design task being formulated as a multi-objective optimisation problem. In this way, the simultaneous consideration of multiple selection criteria provides an ideal platform for exploring trade-offs upon which environmentally conscious business decision-making can be based. K e y w o r d s Long-Range Planning, Life Cycle Assessment, Multi-Objective Optimisation
1
Introduction
Although sustainable development can be defined in a number of ways, the fundamental ideal that it pursues, whereby economic development, improved environmental quality and social prosperity are seen as complimentary, has become widely accepted. For the chemical processing industry, this paradigm shift means that step-change improvements are demanded in the type of processes developed, the efficiency with which resources are utilised and the products manufactured. However, despite the consensus about the relevance and benefits of adopting more sustainable business practices, the greatest challenge still lies in the practical application of its principles in pursuit of technological innovations. With one of the main obstacles preventing its wide-scale implementation in the past being the difficulty in measuring the progress towards sustainability, a number of initiatives have responded by proposing environmental management guidelines and performance indicators (see for example [1]). The natural next step is the application of these management strategies, aimed at the operations level, as business decision-making tools during the early stages of conceptual process and product development. As such, this paper presents a methodology for the inclusion of environmental performance criteria as part of a model for the strategic investment planning and design of supply chain networks. During these stages a manufacturing company has the earliest opportunity to influence both its future market competitiveness and environmental performance.
2
Supply Chain Analysis and the Environment
In the past, the design and management of supply chains have largely concentrated on improving market competitiveness through the use of objectives such as (i) minimising costs, delivery delays, inventories and investment, or (ii) maximising profit, return on investment (ROI) and customer service level. Decisions are generally grouped either as operational (related to production schedules, inventory levels and transportation logistics) or strategic (related to, for example, the 1To whom correspondence should be addressed. Tel.: (44) (0) 20 75946620, Fax: (44) (0) 20 75946606, E-marl:
e. pistikopoulos@ic,
ac. u k
215 location of production facilities and allocation of suppliers to plants), with time horizons ranging between hours and years [2]. Increasingly, researchers and practitioners have started taking note of the impact that supply chains have on the natural environment. However, despite this growing awareness, challenging research problems still remain. In a review aimed at informing operational researchers and environmental scientist of the possibilities of exchanging skills, the challenges of linking life cycle assessment (LCA) and operations research (OR) models in pursuit of more responsible supply chain management are highlighted [3].
3
Problem Formulation
Taking these challenges into account, the problem that will be consider here for the inclusion of environmental concerns within the process selection and supply chain optimisation task can be stated as follows: Given: 9 a set of markets (distributors or customers) and their demands for a set of chemicals over a given time period (planning horizon) 9 a set of potential plants using known technologies to produce the desired products 9 a set of potential sites for locating the plants, and the availabilities of the raw material and utility suppliers over the planning horizon
then the task is to: 9 design the supply chain network of the integrated production facilities that would satisfy the demand over the entire planning horizon,
such that both the: 1. net present value of the capital investment evaluated at the end of the planning horizon is maximised, and the 2. impact that the entire network has on the environment is minimised.
Finding the optimum solution to this multiple criteria problem involves (a) selecting the most appropriate technologies and their production profiles over time (b) allocating the technologies to the potential sites, and (c) assigning the products produced by the selected plants to the demands at the markets. This formulation contains elements of multi-product, multi-site supply chain network design problems [4], while also resembling strategic decision-making models for technology selection [5] and the long-range investment planning within the process industry [6].
4
Multi-Objective Optimisation Model
The mathematical programming model developed here is based upon a supply chain network superstructure consisting of a set of N M existing markets - representing either distribution centres or customers - demanding a set of N I chemical products. Also given is information regarding the location and availability of a set of N R chemical feedstock suppliers. At the centre of the superstructure is a set of N J known chemical processing technologies (plants) that can perform the conversion of the raw materials into final products. Strategic decisions included are the selection and allocation of the plants to a set of N S potential sites, as well as the assignment of transportation links between the selected sites and existing markets. These decisions are performed in terms of a finite number of time periods, N T , constituting the long-range planning horizon during which prices, demands and availabilities of
216 the chemicals, and fixed investment and operating costs of the plants can vary. At the operational level, optimal plant expansion capacities, production profiles and the flows of materials between the various components within the supply chain are determined over the entire planning horizon. Unlike most traditional approaches where only an economic criterion is considered, the model developed here also aims at finding the design that minimises the environmental impact of the entire supply chain. Consequently, the formulation results in a multi-objective mixed integer linear programming (MOMILP) problem, allowing the inherent trade-offs between the conflicting economic and environmental objectives to be explored. As a measure of the profitability of the network, the expected net present value (NPV) of the investment required to install and operate the plants is used as the economic objective function. In contrast, the environmental objective function is based upon the environmental impact resulting from the operation of the entire network over the entire planning horizon. This is achieved by adopting the principles of LCA and using a recently developed method of damage modeling, the E c o - I n d i c a t o r 99 [7], to assess the environmental impact of the network. Guided by the scope and boundary definition step of LCA, the network boundaries are expanded to incorporate a set of N K life cycle stages that includes the (a) production plants within the network, (b) transportation of raw materials to sites and products to markets, (c) generation and supply of utilities (electricity, thermal energy, etc.), (d) production of raw materials and (e) acquisition of natural resources. For each site over the entire horizon, the environmental burdens of the life cycle stages related to its operations are characterised in terms of a set of N E impact categories/indicators representing various environmental concerns. Taken from the E c o - I n d i c a t o r 99 methodology, the 9 indicators incorporated in the model are: (i) carcinogenic effects, (ii) respiratory illnesses caused by organic substances, (iii) respiratory illnesses caused by inorganic substances, (iv) climate change, (v) ozone layer depletion, (vi) ecotoxic emissions, (vii) acidification and eutrophication, (viii) extraction of minerals, and (ix) extraction of fossil fuels. Arriving at the final measure of environmental performance, requires the normalisation of the impacts into the three main categories- Human Health, Ecosystem Quality and Resource Depletion - and then tile aggregation of these normalised categories into a single E c o - I n d i c a t o r 99 Score. These normalisation and valuation steps are performed using tile weighting factors corresponding to one of the three "perspectives" (Hiararchist, Individualist or Egalitarian) provided. Mathematically, the resulting two objective function version of the MOMILP problem can be expressed in a simplified form as: { min U
fl(x) = - N e t Present Value, } f2(x) = E c o - I n d i c a t o r 99 Score
subject t o : x E ~'
(P1)
where U is the utility function and, for notational convenience, x represents the vector of both continuous and discrete variables belonging to the feasible region of both equality and inequality constraints, X. Note that the minimisation of the negative NPV is equivalent to maximising its original formulation. Further details concerning the exact nature of the constraints, variables and parameters defined by x E X are presented elsewhere [8]. Solving the multi-objective optimisation model results in the set of trade-off solutions commonly referred to as the efficient, non-inferior or Paxeto set of solutions. By definition, a point is said to be efficient (or Pareto optimal) such that any other point within the set improves the value of one objective function while compromising at least one other objective. One way of obtaining this set of efficient solutions is by reformulating it as a parametric programming problem where only one objective function remains as the utility function while the others enter as inequality constraints [9]. The advantage of such an approach is that it does not demand setting preference weights or goals before performing the optimisation. Instead, the decision-maker is
217 presented not only one solution, but a set of alternatives from which he/she can then further explore interesting trade-offs.
5 5.1
Illustrative Example Network Description
In the case study that will be used to illustrate the applicability of the proposed model, two chemical products, vinyl chloride monomer (VCM) and ethylene glycol (EG), are demanded at two markets. The possible raw materials from which these chemicals can be produced are ethylene and chlorine via a total of six potential processing technologies. Details concerning the material and energy utilisation of each technology are taken from [5]. In addition, the problem considers three potential sites for the installation and expansion of the potential technologies, while the investment will be evaluated over a period of five years. Concerning the forecasted growth patterns of the chemicals, it is worth noting that three of the chemicals (VCM, EG and chlorine) appear on the Toxic Release Inventory (TRI) of the US EPA. It can, therefore, be expected that governmental legislation and pressure from the public will demotivate their future industrial use. Accordingly, the forecast scenario is used where the availability and demand of these toxic chemicals decrease annually by 2%. 5.2
Two Objective Functions
Solving the two objective function problem (P1) using the Hiararchist perspective, results in the efficient set of solutions as presented in Figure 1. Clearly a conflict exists between a network design that achieves minimum environmental damage and one that achieves maximum net present value. It shows that an improvement in the environmental performance is only possible if the decision-maker is willing to compromise the net present value of the investment. It is also worth highlighting that the discontinuities in the efficient set of solutions originate from the strategic decisions included in the model and corresponds to the optimal solution switching from one network structure two another. This provides the opportunity to achieve a significant improvement in investment performance at a marginal increase in environmental damage by merely reconfiguring the supply chain network structure. For example, at the region on Figure 1 with a f2-value of approximately 1.709 • 109, compromising the environmental impact by only 0.5% can improve the net present value of tile investment by 25%. 5.3
Weighting Factor Sensitivity and Disaggregation
A topic in LCA that continues to receive increasing attention, is the step during which vastly different environmental impacts are weighted into an aggregated score. While this can facilitate the decision-making process, caution must be taken and the practical implications must be noted. Within this context, the E c o - I n d i c a t o r 99 methodology relies upon the concepts of Cultural Theory to manage the subjectivity of valuation and provides three different sets of weighting factors instead of one. The network design can, therefore, be assessed using three different perspectives, allowing the sensitivity of the model to the various weighting factors to be analysed as shown in Table 1. From the results of solving Problem (P1) also with the Egalitarian and Individualist perspectives, it can be concluded that for this particular case study, the set of efficient solutions depends heavily on the chosen perspective. While the differences between the Egalitarian and Hierarchist perspectives are not that pronounced, using the Individualist perspective results in the region over which the solutions are efficient being significantly shifted.
218 x 107 i__~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
r--
s
z
1.74 1.76 1.78 -~ 7-f~ ..... 1.72 f2 - Eco-lndicator 99 Score [Points]
1.8f~81 x 2109
Figure 1: Pareto Set of Solutions- Two Objective Functions Table 1: Sensitivity of Eco-Indicator 99 Region to Different Weighting Perspectives fL [109 Points] f u [109 Points]
Hiararchist 1.706 1.805
Egalitarian 2.143 2.271
Individualist 67.868 71.180
One way of circumventing the model's sensitivity to the weighting factors, is to interrupt the impact assessment procedure before the normalisation stage and thereby avoiding aggregation all together. Consequently, the single score can be disaggregated and the MOMILP problem can be reformulated in terms of the individual impact indicators which are neither normalised nor weighted. While recently developed multi-parametric programming algorithms can be used to solve the reformulation, the computational effort required by a 10 objective function problem (1 Economic and 9 Environmental) is still considerable. Selection of the sources that contribute most significantly to the overall impact can facilitate the reduction of the problem to a more solvable size [10]. Analysis of the results shows that the carcinogenic wastes and emissions emitted from the VCM and EG production facilities and resource depletion resulting from the operation of the network carry the greatest contributions. This allows the MOMILP problem to be expressed accordingly in terms of these environmental concerns as Problem (P2) with the efficient set of solutions presented as Figure 2. { min U
fl(x) = - N e t Present Value, } f2(x) = Carcinogenic Plant Emissions, f3(x) = Network Resource Depletion
subject to: x c ~'
6
(P2)
Conclusions
The foundations for incorporating multiple environmental impact criteria into a mathematical programming based model for the strategic investment planning of chemical supply chains is presented. The model provides an ideal platform for exploring the inherent conflict between the emerging environmental concerns and the traditional economic incentives. Through the application to a case study, the issues surrounding the aggregration and valuation of vastly different environmental concerns into a single score of performance was emphasised. It was proposed that these issues can be effectively addressed using recently developed multi-parametric optimisation
219 algorithms in conjunction with LCA contribution analysis techniques. ....... : , . ~ X 1U
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where ~ = /~iPAij - A~cj; p corresponds to the leaf node where the dual variable of the objective function (A~) equals to 1, while AP is the vector of dual multipliers at leaf node p;
229 u~ and ~-'Pu#denote the lower and upper bound of xj at node p, respectively, zp is the objective value at node p, and Azp -- z* -- Zp. The information that needs to be extracted from the Branch and Bound solution procedure of the deterministic problem is the optimal dual solution at each node for the case that sensitivity analysis is required for all the parameters involved. The computational requirements are reduced if the parameters for which sensitivity analysis is needed are known a priori. In this case only the dual information of the nodes that correspond to nonzero dual variables of the required parameters in needed. The information obtained from the sensitivity analysis is essential for understanding the effects of uncertainty in the scheduling problem and is obtained by exploiting the information that has already been obtained from the solution of the primal problem as it will be illustrated in the example that follows in the next section. Thus, no additional computational time is required. 4.CASE STUDIES 4.1 R o b u s t O p t i m i z a t i o n Example 2 described by Ierapetritou and Floudas [13] is considered here. Uncertainty arises in the processing times of reactions in reactor 1. The mean processing times of T ( r e a c t i o n l , reactorl ), T(reaction2, reactorl ), T(reaction3, reactorl ) are 2hr, 2hr and lhr, respectively. Assume that those parameters vary by 50% about their nominal values following a uniform probability distribution. Maximum, nominal and minimum values of each processing time are considered, thus 27(3 x 3 x 3) scenarios are generated. The proposed model is solved with different values of/~ and w. The assignments of tasks and units to event points are illustrated in Table 1, whereas the optimal values of expected makespan, solution robustness and model robustness are shown in Table 2. Different sequences of tasks executed in units are obtained, when the weights of model and solution robustness terms change, thus leading to more robust scheduling in terms of model and solution robustness. 4.2 S e n s i t i v i t y A n a l y s i s Example 1 in [13] considers a single production line consisting of a mixer, a reactor and a purificator. For this example the Branch and Bound tree is constructed and the dual information is stored at each node. Based on these data and the corresponding system of inequalities, (11), (12) the following interesting results are obtained. First, the problem is solved with the objective of maximizing the production within the time horizon of 8 time units. It is found that the most critical unit of the production line is the purificator. By increasing its capacity, production will increase accordingly. The same information can be used to determine the allowable purificator size reduction that will result in limiting production decrease. For example it is found that the purificator can decrease by up to 4.24 units without reducing the profit by more than 5%. Important sensitivity information can be obtained by a simple primal analysis of the MILP problem that results in an upper bound on the optimal value given a perturbation of the problem data. In this way it is determined that a 10% increase in purificator size from 50 to 55 units, will result in a maximum of 2.75% increase in production profit from 71.158 to 73.118 units (upper bound on the objective function). The results of the sensitivity analysis can be also used to analyze the effects of product price in the objective function in order to investigate the relative importance of different products as well as to take decisions regarding the production levels. For this example it is
230 Table 1 Values of binary variables of optimal schedules .................................... (task, unit) nO nl n2 n3
.(heating, . . . . . . heater) . . . . . . . . . . . ]-..... ( -
n4
n5
(task, unit)
nO
n1
n2
n3
n4
n5
0 -6
0
0
1 0
! 1
0 0
1 0
0 0
0 0
1 0
0 0
0 1
0 0
0 0
0 0 0
(reaction 1, reactor 1)
!
0
0
0
0
0
(heating, heater) (reaction 1, reactor 1)
(reaction 1, reactor 2) (reaction 2, reactor 1)
1 0
0 1
0 0
0 0
0 1
0 0
(reaction 1, reactor 2) (reaction 2, reactor 1)
(reaction 2, reactor 2)
0
0
1
0
1
0
(reaction 2, reactor 2)
I
0
0
0
1
(reaction 3, reactor 1) (reaction 3, reactor 2)
0 0
0 0
0 0
1 1
0 0
0 0
(reaction 3, reactor l)
0
0
0
l
0
0
(reaction 3, reactor 2)
0
0
l
0
0
0
(seperation, still)
0
0
0
0
1
0
(seperation, still)
0
0
0
0
1
(Deterministic Model)
0 ........
( ~, = 10, co = 0.1)
nO
nl
n2
n3
n4
n5
nO
nI
n2
n3
n4
1
0
1
1
0
0
(heating, heater)
1
1
1
1
0
0
0
0
0
0
0
0
(reaction 1, reactor 1)
0
1
0
0
0
0
(reaction 1, reactor 2)
1
0
0
1
0
0
(reaction 1, reactor 2)
1
0
0
0
0
0
(reaction 2, reactor l)
0
0
1
0
1
0
(reaction 2, reactor 1)
0
0
1
0
1
0
(task, unit) . . . . . . -iheating,heater) (reaction 1, reactor 1)
(task, unit)
n5
(reaction 2, reactor 2)
0
l
0
0
l
0
(reaction 2, reactor 2)
0
1
0
1
0
0
(reaction 3, reactor 1) (reaction 3, reactor 2)
0 0
0 0
0 1
1 0
0 0
0 0
(reaction 3, reactor 1) (reaction 3, reactor 2)
0 0
0 0
0 I
1 0
0 0
0 0
(seperation, still)
0
0
0
0
1
0
(seperation, still) 0 0 0 0 I 0 . . . . . . . . . . . . . . . . . . . . . . . . . .
( Z. = 0.5. co=5)
()~=0.5, co = 0.1) Table 2 Objectives o f deterministic model and proposed model with different weights o f robustness Deterministic Model E(makespan)
~, =10 co =0.1
~, =0.5 03 =0.1
~ =0.5 03 =5
8.40
9.26
7.28
9.23
Solution Robustness
--
0
0.1
0.32
Model Robustness
--
3.94 0.74 ............................
0
found that there is one to one correspondence between price increase and objective function increase. In particular, a 5% price increase would result in also 5% increase in the objective function. The question of how demand fluctuation affect the profit function and the production time is also analyzed. In order to consider this factor, the required dual information involves the dual variables associated with the demand constraint at the nodes that this constraint is active. It is found that a demand change from 60 to 65 units (8.3%) can be accommodated within the production line resulting in an equivalent increase of production profit but also in an increased, by 2.6%, makespan, 11.593 time units from 11.291 units. 5. S U M M A R Y A N D F U T U R E D I R E C T I O N S In this paper the problem of scheduling under uncertainty is addressed through robust optimization and inference-based sensitivity analysis. The objectives of solution and model robustness are incorporated using the proposed robust optimization model, while the importance of the variables and constraints is determined through sensitivity analysis. Work is under progress to develop an integrated framework for the efficient consideration
231 of uncertainty at the scheduling stage in order to determine a more flexible schedule and provide a priori guidance of the optimal decision when uncertain parameters change. ACKNOLEDGMENT The authors gratefully acknowledge financial support from NSF under the CAREER grant 9983406. NOMENCLATURE Indices i = tasks; j = units; n = event points; s - states; k = scenarios Parameters Vmin(i,j) = minimum capacity of unit j when processing task i Vmax(i,j) = maximum capacity of unit j when processing task i pP(s, i), pC(s, i) = proportion of state s produced, consumed from task i, respectively r(s) = market requirement for state s at the end of the time horizon price(s) = price of state s H = time horizon pk = probability of scenario k Variables wv(i,n) = binary variables that assign the beginning of task i at event point n yv(j,n) = binary variables that assign the utilization of unit j at event point n b(i,j,n) - amount of material undertaking task i in unit j at event point n d(s,n) = amount of state s being delivered to the market at event point n st(s,n) = amount of state s at event point n Ts(i,j,n) = starting time of task i in unit j at event point n Tf(i,j,n) = finishing time of task i in unit j at event point n REFERENCES [1]H.J.Reinhart and D.W.T.Rippin. AICHE Annual Meeting, 1987. [2]N.Shah and C.C.Pantelides. Ind.Eng.Chem.Res.,31(1992) 1325. [3]D.A.Straub and I.E.Grossmann. Comput.Chem.Engng., 17(1993) 339. [4]M.G.Ierapetritou and E.N.Pistikopoulos. Ind.Eng.Chem.Res.,18(1994) 163. [5]M.G.Ierapetritou and E.N.Pistikopoulos. Ind.Eng.Chem.Res.,35(1996) 772. [6]S.T.Harding and C.A.Floudas. Ind.Eng.Chem.Res.,36(1997) 1644. [7]M.L.Liu and N.V.Sahinidis. Ind.Eng.Chem.Res.,35(1996) 4154. [8]U.M.Diwekar and J.R.Kalagnanam.Comput.Chem.Engng., 20(1996) $389. [9]J.M.Mulvey, R.J.Vanderbei and S.A.Zenios. Oper.Res.,43(1995) 264. [10]R.L.Daniels and J.E.Carrillo. IEE Trans., 29(1997) 977. [ll]J.P.Vin and M.G.Ierapetritou. Ind.Eng.Chem.Res.,40(2001) 4543. [12]J.N.Hooker. In Lectures Notes in Computer Science 1118, Springer, 1996, pp.224-236. [13]M.G.Ierapetritou and C.A.Floudas. Ind.Eng.Chem.Res.,37(1998) 4341. [14]S.Ahmed and N.V.Sahinidis. Ind.Eng.Chem.Res.,37(1998) 1883.
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Published by Elsevier Science B.V.
232
Design and Optimization of Pressure Swing Adsorption Systems with Parallel Implementation Ling Jiang a, Lorenz T. Biegler a, V. Grant Fox b a Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA b Air Products and Chemicals, Allentown, PA, USA
Abstract We implement a Newton-based approach with accurate sensitivities to directly determine cyclic steady states with design constraints. We also design optimal PSA processes by means of state-of-the-art SQP-based optimization algorithms. The simultaneous tailored approach can incorporate large-scale and detailed adsorption models and is robust and flexible. In order to improve the computation efficiency, we apply parallel computing technique with Message Passing Interface (MPI). The implementation of parallel computing is based on the fact that sensitivity evaluation with respect to each parameter is independent and thus can be separated. Applications of several non-isothermal bulk gas separation processes are illustrated. Keywords
pressure swing adsorption, optimization, parallel computing, sensitivity
1. INTRODUCTION Over the past twenty years, Pressure Swing Adsorption (PSA) processes have gained increasing commercial acceptance as an energy efficient separation technique. With extensive industry applications, there is significant interest for an efficient modeling, simulation and optimization strategy. PSA systems are distributed in nature, with spatial and temporal variations that are mathematically represented by Partial Differential Equations (PDEs). These include Langmuir equilibrium isotherms, mass and energy balances, as well as detailed thermodynamic and transport models. Fixed bed systems are typically operated in a cyclic manner with each bed repeatedly undergoing a sequence of steps such as pressurization, adsorption, pressure equalization, blowdown, desorption and pressure equalization. After a start-up time, the system reaches Cyclic Steady State (CSS), at which the conditions in each bed at the start and end of each cycle are identical, and lead to normal production. Due to the intrinsic dynamics of PSA systems, the convergence rate to CSS is very slow. The traditional way to approach the cyclic steady state is to simulate a series of complete cycles until the bed conditions change very little from cycle to cycle. This successive substitution method simulates the true operation of a real plant but can be time-consuming. Several studies have attempted to optimize the PSA systems. Most of them can be classified either as Black
233 Box or Equation-Oriented approaches. The Black Box approach is robust and capable of dealing with complicated models but the repeated CSS convergence makes it very time consuming. The equation-based approach using gPROMS tll is efficient for simple models but non-differentiable terms and steep fronts from more complex bed equations often lead to failure of the Newton solver. In our work we develop a flexible and reliable optimization strategy that incorporates realistic, detailed process models and rigorous solution procedures. 2. SOLUTION STRATEGY 2.1 PDE discretization In order to ensure the accurate approximation of the adsorption models, the selection of discretization method is essential. Finite volume method E21 is used to spatially discretize PDEs into differential algebraic equations (DAEs). It is a conservative method, which preserves the mass and energy balance in the spatial direction. In order to mitigate the numerical noise and avoid the physically unrealistic numerical smearing or osciallation near steep adsorption fronts, we use the modified Van Leer flux limiter [3]. 2.2 DAE Solver and Sensitivity Evaluation DASPK 3.0 [4,5] is used as the DAE solver and sensitivity package. DASPK solves initial value problems of the form F(t,z,z,p)=O using a combination of Backward Diffentiation Formulae (BDF) methods and a choice of linear system solution methods. It also performs a sensitivity analysis upon the parameters dz/dpi contained within the DAE system simultaneously. In the direct approach, the sensitivity equations and the state equations are solved together. The disadvantage is that the computational cost increases linearly with number of parameters and can be quite time-consuming when the number of parameters is large. Therefore we adopt parallel computing to speed up the sensitivity evaluation. 2.3 CSS Convergence Acceleration In the so-called direct determination approach, CSS is written as a two-point boundary condition: zo- Z(tcycte) = 0 and Newton-based method is used to solve for z0. The Jacobian comes from the sensitivity evaluation in 2.2. Theoretically, a Newton based approach attains quadratic convergence rates near the solution. It has been applied successfully by LeVan E61 and Kvamsdal E71to solve relatively simple cycles. For more complicated systems with steep adsorption fronts, multi-layer adsorbents and non-isothermal effects, a trust region approach with proper scaling should be applied to ensure robust convergence tal. Furthermore, the direct determination approach can be easily extended to find operating parameters to satisfy design constraints. The design targets are met at the same time when the cycles are converged. 2.4 PSA Optimization We develop the simultaneous tailored approach for optimization. In this optimization framework, the convergence of CSS is incorporated as a constraint in the optimization problem while the detailed bed models are solved in an inner loop, in order to obtain values of
234 the constraints and objective function. Also accurate gradients with respect to parameters are obtained through 2.2. The CSS is not converged until the optimal solution is reached, thus the time consuming CSS convergence loop is eliminated. The optimization algorithm in the simultaneous tailored approach is reduced space Successive Quadratic Programming (rSQP) [8,9]. Since the number of decision variables is relatively small, rSQP is well suited because it exploits the problem structure. In rSQP, the variables are partitioned into independent and dependent variables and the search direction is a combination of these two directions, dk = Y~r + Zkpz. Here pr updates the dependent variables, which serve to improve the solution of the equality constraints, while pz updates independent variables, which act in the null space of the constraints and serve to optimize the objective function. 3. PARALLEL COMPUTING WITH MESSAGE PASSING INTERFACE (MPI) 3.1 Algorithm In our case studies, the sensitivity calculation is a very time-consuming process and is the dominant cost of design and optimization. However since the sensitivity variables are independent of each other, this makes it possible to use parallel computing. Zhu and Petzold [10] compare several parallel implementation schemes for sensitivity calculation and find the distributed parameter only (DPO) approach is the fastest. In DPO approach, the sensitivity parameters are divided into different sets and distributed to different processors, with each processor running a copy of the state equations and computing a subset of the sensitivity variables. In our implementation, we use the master-slave paradigm and give the master the maximum control over the calculation process. The master is responsible for function evaluation, and new point tests for line search or trust region. Whenever the master needs to do expensive computation such as sensitivity calculation, it broadcasts the variables and all other necessary information to the slaves and gathers the results from slaves when they are done. The slaves are mostly working independently, with very little synchronization and communication cost.
3.2 Message Passing Model The message-passing model [11] posits a set of processes that have only local memory but are able to communicate with other processes by sending and receiving messages. The data transfer from local memory of one process to the local memory of another requires operations to be performed by both processes. The message passing model has the advantages of universality, expressivity, ease of debugging and high performance. MPI addresses the message-passing model and remains a collection of processes communicating with messages. 3.3 Computer Facility The parallel computing work is performed on the Beowulf computer cluster, which is built and maintained by the Chemical Engineering department at Carnegie Mellon University. Currently, the Beowulf cluster has 32 computers, with 2 servers and 30 computing nodes. Most nodes have dual 1 GHz Pentium III processors with between 0.5 and 2 GB RAM.
235 4. CASE STUDY AND COMPUTATIONAL RESULTS Applications of a single-bed, 3-step 02 VSA cycle (Case 1) and two single-bed 6-step 02 VSA industrial cycles (Case 2, 3) are used for illustration. For details on adsorption models and design and optimization problem formulation, see Ref. 3. For Case 1, we choose to maximize 02 recovery (RC) at desired purity (YP) at cyclic steady state. Product tank pressure (Ptank), valve constant (CV), step time of step 2 and 3 (T2, T3) are chosen as decision variables. The optima achieve 18% more recovery than the base conditions and 27% more recovery than the design conditions while satisfying the purity constraints. For Case 2 and 3, we choose to minimize the specific work while maintaining 95% O2 purity at cyclic steady state, subject to some pressure constraints. Flow rates F1, F2, step times Tl and T4, valve constants CV2 and CVt are chosen to be the decision variables. Table 1 and 2 show the data under base, design and optimization conditions of Case 1-3.
Table 1 Optimization results for case 1 [3] Base Design Opt.
CV 1690 834.5 966.3
Ptank
T2 (s)
T3 (s)
1.124 1.124 2.03
15.0 15.0 100
5.0 5.0 98.26
YP (%) 30.55 35.00 35.00
RC (%) 71.94 62.24 89.83
Table 2 Optimization results for case 2-3 [3]
FI
F2
CV2
CVt
Tl (S)
T4 (s)
YP (%)
Work
Design, Case 2
9819
14354
244.5
41.66
25
10
95.00
1.965
Opt., Case 2 Design, Case 3 Opt., Case 3
8322 9426 8272
14610 14424 14610
257.5 238.1 242.2
36.6 32.29 29.48
25.6 25 26.40
4.97 10 7.25
95.00 95.00 95.00
1.808 2.346 2.286
Table 3 Wall time (speedups) with multiple processors (in hours) No .of processors Design, Casel
1 1.86
2 0.86(2.2)
4 0.38(4.9)
8 0.18(10.3)
16 0.11(17.1)
32 0.07(26.4)
Design, Case 2
2.91
1.61 (1.8)
0.81 (3.6)
0.50 (5.8)
0.32 (9.2)
0.23 (12.9)
Design, Case3
6.04
3.19(1.9)
1.95(3.1)
0.93(6.5)
0.52(11.6)
0.31(19.6)
Opt., Case 1
40.8
18.0 (2.1)
8.7 (4.8)
4.7 (9.6)
1.9 (17.0)
1.3 (31.2)
Opt., Case 2
38.0
21.2 (1.9)
8.4 (4.1)
3.7 (8.7)
2.6 (14.5)
1.6 (22.8)
Opt., Case 3
68.6
37.2 (2.1)
14.8 (4.6)
9.6 (7.7)
5.2 (14.9)
4.2 (24.8)
236 Table 4 No. of iterations with multiple processors for optimization No .of processors Case 1 Case 2 Case 3
1 55 34 51
2 52 36 57
4 56 31 50
8 61 29 55
16 43 33 58
32 54 33 77
Table 3 shows the wall time (speedups) with multiple processors for design and for optimization. The speedups for optimization are calculated based on time per iteration. With varying number of processors, the step sizes for the integrator are slightly different, which results in the different levels of accuracy of sensitivities and leads to the variance in the number of the rSQP iterations that the optimizer takes, as shown in Table 4. Design performance isn't affected that much, mainly because design is less sensitive towards the accuracy of Jacobians as long as the constraints are converged well. We find that two factors affect the speedup factor simultaneously. With fewer variables to work with, each local integrator takes fewer time steps and nonlinear iterations and larger step sizes. Thus, the sensitivity calculation exhibit super-linear speedups. On the other hand, the non-parallelized parts such as successive substitutions, Newton iteration and function evaluation limit the potential for parallelization. The percentages of non-parallelized parts in design and optimization are 1.72% and 0.57% for case 1, 4.86% and 1.03% for case 2 and 1.91% and 0.86% for case 3. For each case, the speedups for optimization are typically larger than for design, due to the smaller percentages for optimization. For the same reason, the speed-up factors among the case studies are: Case 1 > Case 3 > Case 2. All speedup factors are sub-linear when the number of processors is large enough because the super-linear effect from integrator is limited and the non-parallelized effect dominates. Since the integrator takes different number of time steps with varying number of processors, the Amdahl's law shouldn't be used here to estimate the potential for speedup factor. Amdahl's law requires that the serial and parallel programs take the same number of total calculation steps for the same input [~2] 5. CURRENT W O R K Currently we are incorporating an industrial HYCO process into the optimization framework. HYCO is a five-bed system and each bed is undergoing eleven steps. There are five gas components, H2, CO, CO2, CH4 and N2. H2 is the primary product and all the others are traces. We use both uni-bed and multi-bed approaches to simulate the bed behavior. The uni-bed approach consists of only one set of bed equations but with different inputs corresponding to all of the steps in the cycle. To simulate the interactions between steps among the different beds, storage buffers are used. On the other hand, multi-bed approach considers only a portion of the cycle with several beds solved simultaneously and matching conditions imposed among
237 exit and entrance conditions among beds. Multi-bed approach requires a larger work space but is easier for sensitivity purposes. For optimization, the objective is to maximize the H2 recovery within purity and pressure constraints. The decision variables include the bed geometry, adsorbent diameter, valves, flow rates and step times. Fig. 1 and Fig. 2 show the mole fraction profiles of H2 and CO at the end of each process step in the cycle at cyclic steady state.
Fig. 1. Mole fraction of H2 at CSS
Fig. 2. Mole fraction of CO at CSS
Acknowledgment The authors gratefully acknowledge the financial support of the NSF/GOALI program and Air Products and Chemicals Inc. The authors would also like to thank Prof. Steinar Hauan for the support on the computing facility and Dr. Shengtai Li for his inputs to this research.
REFERENCE [1 ] S. Nilchan, Ph.D. dissertation, Imperial College, London (1997) [2] P. A. Webley, J. He, Computers and Chemical Engineering, 23 (2000) 1701 [3] L. Jiang, L. Biegler, V. Fox, submitted to AIChE J. (2002) [4] S. Li, L. Petzold, Design of New DASPK for Sensitivity Analysis (1999) [5] S. Li, L. Petzold, W. Zhu, Applied Numerical Mathematics, 32 (2000) 161. [6] D.Croft, M. LeVan, Chemical Engineering Science, 49 (1994) 1821. [7] H. Kvamsdal, T. Hertzberg, Computers and Chemical Engineering, 21, (1997) 819 [8] P. Boggs, J. Tolle, Journal of Computational and Applied Mathematics (2000) [9] D. Temet, L. Biegler, Computers and Chemical Engineering, 22, (1998) 963 [ 10] W.Zhu, L. Petzold, Concurrency: practice and experience, 11, (1999) 571 [11] W. Gropp, E. Lusk, A. Skjellum, Using MPI: portable parallel programming with the message-passing interface, Cambridge, MIT press (1999) [12] Y. Shi, Computer and information sciences department, Temple University (1996)
238
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
Risk Analysis of Chemical Process Using Multi-distinction Equipment Screening Algorithm Ku Hwoi Kim 1, Sung Joon Ahn 2, Myung Wook Shin 3 and En Sup Yoon 2 lInstitute of Chemical Processes, Seoul National University, Korea 2School of Chemical Engineering, Seoul National University, Korea 3KOGAS, Korea ABSTRACT In this research, we has been trying to suggest how to reduce both the factors behind an accident and the damages caused by all kinds of accidents which might take place in Chemical Process, in other words, by using a number of data in Process such as material, operation condition, flow rate, failure rate, we suggest an algorithm to decide ranking about degree of danger per device in Process. Ultimately we can obtain the hazard ranking of all devices in Process by an algorithm above mentioned. The risk ranking getting through qualitative method will be able to contribute to the following field. Firstly, it uses for mending and maintenance in real industrial field. Secondly, suggesting several ways to distinguish the initial hazard devices from all devices in Process in order to estimate both qualitative and quantitative hazard evaluation in itself industrial facilities. Thirdly, reducing the total performance evaluating time as providing the right guideline for distinguishes risk. And finally contributing to establish the foundation of the risk management system, that is MESA (Multi-distinction Equipment Screening Algorithm), which should be able to reduce the danger to a minimum in artificial disaster and a natural calamity.
Keywords: MESA, QRA, Risk Ranking 1. Introduction In this research, we have tried to focus on these purposes. Firstly, by using item to distinguish the degree of risk in Process from facilities point of view such as material, operation condition, flow rate, failure rate taking its age into account, we'll realize which device is implying the worst danger. Secondly, after combining all conditions and distinguishing degree of danger per device in Process, it'll supply good information to make a decision for users. And finally, after distinguishing the worst dangerous device through the degree of risk according to accident rate, it'll help user to be prepared to cope with any situation. All of this purpose has been interested in real industrial field and operation step. Here we assume that the possibility of danger which was caused by the following devices and anything else don't give consideration to it.
239 1.Devices which even though it might have a little bit of danger in itself, it might have a farreaching influence to another equipments when it spreads rather than danger in itself (Pump, Pipe and Compression). 2.Devices which try to solve danger for itself as some kind of safeguard (Control valve) 3.Devices report the danger in advance (Alarm, Control variable Monitoring system (including sensor)). 4.Devices, which minimize the danger when the accident takes place (Mitigation Equipments - Rupture Disc, Safety Valve, Safety barrier, Emergency By-pass System). 5.Devices, which prevent the source of accident from happening (Prevention device - reaction restrainer and counteractive injection device). 6.Elements, which might give outside dangerous influences (Operating Air-Pressure of Compressor). 7.Natural calamity (Earthquake, Landslide and Flood) and extraordinary danger (terror, war and strike). The main purpose of this research is to find out the risk having itself in each device and especially which devices could have a far-reaching influence to another equipments when it spreads. But of course, not mention to preparations for this and another effectible devices. The reason which is used Screening Algorithm in order to distinguish between devices with latent maximum degree of danger is.
2.MESA (Multi-distinction Equipment Screening Algorithm) The evaluation of the risk we have been interesting was omitted from Screening Process because danger having in it is much less serious than what we try to consider it in Pump and many other things. Each one in Material Property, Flow Rate, Operating Condition and Failure Rate (Age) was ranked as three levels like (A: dangerous, B: cautious, C: safe) and then each device was gotten the risk from Matrix explaining the risk, namely Expert System DB. After resorting the results according to the standard of the risk in Analysis Algorithm list, we can find out which equipment is the worst dangerous as final results and then input this data as the accident propagation effect of the reasoning model. (Fig 1. MESA (Multidistinction Equipment Screening Algorithm) outline) The Matrix related to the influence of the risk and the association of each devices would be made on the basis of above mentioned MSA and then according to the association of the risk per device, Screening method in the general of the risk was deduced the following conclusion, as an example, from Matrix DATA BASE based on the Expert System. (Table 1. The Matrix related to the influence of the risk)
2.1. Material Property The judgment of the risk about material property consider both Nf(Flammable hazard rating) and Nr (Reactivity hazard rating) in NFPA (National Fire Protection Association) code and then make a Matrix about raking it as A, B and C. The content is like this.
240
Fig 1. MESA (Multi-distinction Equipment Screening Algorithm) outline Table 1. The Matrix related to the influence of the risk Eq3
Eq4
Eq5
Eq6
R
R
R
(~
A
I Eql ]Eq2 Classification Of the risk
~A
A
Eq7
N
(~
A
R
A
(~
R
F1
~
~
~
A
("
R
R ("
F r (A'~
~
~
A
A
A
O,
C'
Tc~t~I
A
~
A
A
R
A
R
2.2. Flow Rate Calculate the risk index in flux flow rate with following steps.
1. Calculate a correspondent radius after converting a device volume into a spherical volume. 2. Calculate a circle area with it 3. Calculate Heat Propagation Flux (H.P.F. = (Mass of storage + Flow rate * 60 second)/Circle Area) in which the standard of Heat Propagation Flux is based on the KOSHA (Korea Occupational Safety Health Agency) Code. Heat Propagation Flux is calculated by flow rate and storage. So, the magnitude of material affects risk index in flux flow rate. Above deduced virtual spherical explosive Heat Propagation Flux is divided like the following thing, which is on the basis of two dangerous situations prescribed in KOSHA. That is, Anatomical injury standard which exposed to Heat Flux as much as 37.5[kW/m 2] for 10 minutes results in the death and exposed to Heat Flux as much as 4[kW/m 2] for 10 minutes results in getting burnt. But we use 1 minute for calculating with SFPE (Society of Fire Protection Engineers) Guidance 2.3 Operating Condition With regard to the range of Temperature and Pressure which is one of operating conditions in
some Process, Temperature is divided into three steps on the risk, which is based on FP (Flash Point) and BP (Boiling Point) and Pressure is distinguished from the risk, which is based on 10Kg/cm2 and 0Kg/cm 2 as a standard. In case of distinguishing Temperature of the risk Matrix,
241 the standard is based on FP (Flash Point) and BP (Boiling Point), which is about to change phase. And In case of distinguishing Pressure, the standard is based on An Method of management Enforcement Ordinance of High pressure Gas Safety heralded in 1984. And for Pressure, a vacuum range, -1 < P < 0, include the risk in itself during operating according to material property and phase (High explosive reaction possibility with air if reactant suddenly happen to leak under vacuum condition, Nfis and 1 Nr is 2"--3). Thus let this condition be "caution needed". And as each the range of Temperature and Pressure is proportioned to the risk, that is, the more the dangerous, Matrix related to it is like this. (Only if Input is two phases (Gas-Liquid), have to apply to Liquid phase Matrix included all of outbreak danger.)
2.4. Failure Rate (Age) With regard to the standard distinguishing Risk from Failure Rate per device, Failure Rate totally seems like data including pressure durability (inner, outer), causticity and the stress durability of the outside-air-temperature change. Here the accident expectation results from multiplying this Failure Rate by Age (Guidelines for Process Equipment Reliability Data, CCPS of AIChE). Assuming that Risk is proportional to Failure Rate per device with time as the standard of setting up scope related to the accident expectation and considering that the standard of Failure Rate per device in 106hour(about 114 years) based on Guidelines for Process Equipment Reliability Data in CCPS of AIChE are divided into 0.01 -3000, let the standard of main equipment set as the expectation of happening just one time over 106hour and also the distinguishing standard of danger index was set with being based on the expectation of happening just one time over 100 times. Matrix related to the distinguishing standard of danger index is like this. And the formulation of Failure Rate per device is like this. FR = (Failure Rate per device) X (Expected Life span of device)
2.5. Rule base incentive or penalty We try to suggest in this research is basically weight factor based method, which firstly adds up all points, given according to the weight factor, and then append incentive or penalty to it based on several rules. This method has got some advantage to make the ranking of the risk easily and intuitionally according to some rule base instead of spending waste of time in order to get the weight factors which can explain all several cases. 3. Case study (LPG underground storage and shipping) Fig 2. is Process map which is being received LPG from a pelagic-fishing vessel and keeping it on underground storage space and then forwarding through the ground tank. And we tried to evaluate the risk per device on the basis of the theory introduced in main.
242 Unloncrmg/urn
-'~ . . . . . . . . %.,, Unload
~PiG
Boosting
Loacl
Heater I
t . _ . .
Inhibitor
. . . . . . . .
Ground
Water stripper
Air
N~ Storaoe Tank
P-e
.......... " ~ " = = r ' : :
............
Fig 2.LPG underground storage and shipping processing outline and detail map Materials used to this case study are Propane (C3Hs), Nitrogen (N2), Methanol (CH3OH), Dimethylsulfide ((CH3)2s), and Water (H20). After examining Nf and Nr of each one, we determine the hazard rank. In flow rate, fundamentally in case of storing material in equipment to some degree, this amount should be calculated firstly and then flow rate is multiplied by combustible heat. And devices used in Entire Process divided into pump, heater, tank, T-2(Dry Tank), valve and so on and then calculation of the cut area of volume or flux per each device, that is, calculation of imaginary sphere radium and surface area and finally each is divided by flow rate multiplied by combustible heat. And, after examining Temperature and Pressure on the basis of material condition, which is flowing in each device, the hazard rank was made a decision. Because there isn't any information related to Failure Rate about each device in LPG Process, Failure rate hazard rank was determined from assuming that LPG Process DATA is similar to LNG Process DATA. Here let each rank A (25 point), B (15 point) and C (5 point) and then Failure rate hazard rank was determined after giving the weight factor to each one according to Heuristic rule. Viewed in deciding overall ranking with this way, it seems that inner pump in carven, T-2(Dry Tank) and T-2(Dry Tank) tank are the most dangerous element and thus quantitative estimation about each device should be done above all. 4. Conclusion
It appears that the risk in P-2 (pump), T-2(Dry Tank) and T-3(Dry Tank) are high according to applying Multi-Distinction Equipment Screening Algorithm for LPG storage equipment. After analyzing the influence and the cause resulted from derivative operation per some special equipment mentioned in case study, that information will be able to be made use of the accident scenario related to quantitative risk analysis. Therefore, in considering this information on Process Design Steps, saving effect of human and material resources could be achieved, and also Scenario Decision Method based on equipment and material property could be built up on the part of Quantitative Process Safety
243 Evaluation System, and also this Scenario Decision Method will be able to apply influence evaluation in outside of industrial zone. (Table 2. The dangerous ranking per equipment) Table 2. The hazard ranking per selected equipment
References
EPA. (1996). RMP Offsite Consequence Analysis Guidance. EPA. Murphy, J.F., & Zimmermann, K.A. (1998). Making RMP hazard Assessment Meaningful. Process Safety Progress, 17(4), 238-242. CCPS. (1994). Guidelines for Evaluating the Characteristics of Vapor Cloud Explosions, Flash Fires, and BLEVEs. CCPS of the AIChE. CCPS. (1992). Guidelines for Hazard Evaluation Procedures. CCPS of the AIChE, (2 nd ed.). AIChE. (1994). Dow's Fire & Explosion Index Hazard Classification Guide. AIChE, (7 th ed.), New York. Catino, C.A., & L.H., Ungar. (1995). Model-Based Approach to Automated Hazard Identification of Chemical Plants. AIChE J., 41, 97-109. Durkin, J. (1994). Expert Systems-Design and Development, Macmillan, Publishing Company, New York. Oh, Y.S, K.H., Kim, Y.H., Yoon, & E.S., Yoon. (1997). The Changing Trend of Gas Safety Management System in Korea. Proceeding of the 1st International Symposium on Gas Safety, 1-9. Society of Fire Protection Engineers. (1995). The SFPE Handbook of fire Protection Engineering, National Fire Protection Association, (2nd ed.), 5, 111.
244
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
Quality Improvement in the Chemical Process Industry using Six Sigma Technique Minjin Kim, Young-Hak Lee, In-Su Han, Chonghun Han Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk 790-784, Korea ISYSTECH, Ltd, Pohang, Kyungbuk 790-784, Korea
Abstract Although Six Sigma has been so successful in many organizations, the successful applications of Six Sigma are rare for the chemical processes due to highly nonlinear causality. This paper provides a successful case for the chemical process industry in which degraded quality has been improved by performing the proposed Six Sigma strategy. The strategy consists of required techniques in each phase and the techniques include not only existing tools such as 5M1E approach, partial least squares (PLS), and design of experiment (DOE) but also a new methodology to discover exact key sources of quality degradation. This case study has been a great success; from 3.5 initial sigma to 5.5 sigma has been achieved by applying the proposed methodology to the quality degradation problem. Keywords Quality Improvement, Six Sigma, Multivariate Statistical Analysis, Design Of Experiment 1. INTRODUCTION In the last century, there has been a tremendous evolution of the ideas to improve quality in any organization. Conventional tools for product quality improvement, such as quality control (QC), total quality control (TQC), and total quality management (TQM), concentrate on reducing off-specialties of the final products rather than on removing defects during a process of production. It has cost a great deal grounded in fact that the amount of total produced products is two times or more the amount of a final shipping products. Six Sigma is so successful when other traditional methods often fail, since Six Sigma focuses on bottom line success and looks directly at the tools required to achieve this success [ 1]. The concept of implementation of Six Sigma methodology was pioneered at Motorola in the 1980s with the aim of reducing quality costs. It has since been adopted and generalized by a number of companies, such as Texas Instrument, Allied Signal, GE, Sony, ABB, Nokia, LG, and SKC [2]. It is a business decision making (BDM) strategy used to improve profitability, to drive out waste in business processes and to improve the efficiency of all operations that meet or exceed customers' needs and expectations. It is clear that Six Sigma is a great strategic framework for a quality innovation. However, it is poor to investigate the detail cause of defects and to guide the direct ways to improve process and quality. Actually, it is rare for Six Sigma to be applied to the large and complex chemical processes successfully because of its highly nonlinear causality. In the chemical process, many kinds of factors have been shown to influence quality of the final products,
245 but definition of quantitative relationships involving factor parameters has been elusive. Montgomery asserts that the next crucial goal of Six Sigma is to expand the base of problems and projects [3]. In the long run, to really accomplish the simultaneous objectives of Six Sigma, practitioners will not only have to gain a solid understanding of additional statistical tools, but knowledge of process engineering and operations research techniques [3]. The traditional quality improvement methods of chemical process can be enhanced by the use of Six Sigma strategy. Many of the chemical processes are already being used by systems engineers including cost modeling, multivariate statistical analysis, DOE, and design for manufacturing and assembly. Integrating these and other standard tools into an effective cost reduction discipline is a job best performed by systems engineers. The proposed methodology is also organized into five parts like a general Six Sigma, namely the definition phase, measurement phase, analysis phase, improvement phase, and control phase [4-5]. The method begins by defining that aspects of the process are "critical to quality (CTQ)". The second phase covers Six Sigma measurement using basic tools such as filtering, sensor reconciliation, normality test, and so on. The logical order within this phase is as follows: translate the problem into a measurable characteristic; and validate the measurement system. The analysis phase pursues two objectives: 1) identify variation sources through seasonality analysis, feed analysis, disturbance analysis, and alarm/shutdown analysis; and 2) screen potential causes that are taken along in the experiments in the next phase using MSPC, PCA, PLS, and correlation analysis. The order of the improvement phase is as follows: 1) discover variables relationships by DOE, plant implementation, analysis of variation (ANOVA), response surface method (RSM), and core group interviews; and 2) establish operating tolerances using the validated model. If the result does not meet the objectives, a return to a previous step is required. Finally, monitoring and control procedures are put in place to ensure continuing quality. The target process of this paper is a purified terephthalic acid (PTA) process of Samsung Petrochemical Corporation. Customers have complained about narrow and inconsistent particle size distribution (PSD) of PTA, and thus our goal specification is decided to increase the current PSD by six percent and to maintain that consistently. After improving the quality, the sigma level of the target quality has been changed from 3.5 sigma to 5.5 sigma. Finally, 92 percent of the goal has been achieved by applying the proposed quality improvement methodology. 2. THEORITICAL BACKGROUND 2.1. Six Sigma Six Sigma is a disciplined and highly quantitative approach to improving product or process quality. The original motivation for Six Sigma at Motorola was centered around manufacturing improvement, and this was also hos Six Sigma was originally introduced in GE. However, important as manufacturing cost reductions may be, they often do not readily translate into improvements that are transparent to the customer. GE management came to realize this [2]. It typically involves the four stages: Measure, Analyze, Improve, and Control, with and up-front stage (Define) sometimes added (DMAIC). In brief, these steps are as follows [2]: 1) Define the problem to be solved, including customer impact and potential benefits. 2) Identify the critical-to-quality characteristics (CTQs) of the product or service, verify measurement capability, and baseline the current defect rate and set goals for improvement. 3) Understand root causes of why defects occur, identify key process variables that cause defects. 4) Quantify influences of key process variables on the CTQs, identify acceptable limits of these variables, and modify the process to stay within these
246 limits, thereby reducing defect levels in the CTQs. 5) Ensure that the modified process now keeps the key process variable within acceptable limits, in order to maintain the gains long term. 2.2. Partial Least Squares Methods Partial least squares method is a sort of the multivariate statistical projection methods, which are recognized as state-of-the-art analysis and modeling techniques and are quite popular in chemical industry [6-7]. In this study, PLS method will be mostly used among these multivariate statistical projection methods. PLS method has been widely used as a powerful tool for constructing empirical models from lab and field measurement data because the resulting models are typically more robust and reliable than those using other modeling tools, such as ordinary least squares method, particularly when the data are noisy and highly correlated with each other [7]. 3. PROCESS DESCRIPTIONS The PTA manufacturing process is used to purify terephthalic acid produced by the bromine-promoted air oxidation ofp-xylene. The main impurity in the oxidation product is 4-formylbenzoic acid (4-CBA) and the process removes this to less than 25 ppm. Metals and colored organic impurities are also almost completely removed by the purification. Fig. 1 shows a simplified process flow diagram of the process consisting of major five unit processes. Crude terephthalic acid and water are fed to a mixing tank to form a slurry of at least 15 wt% terephthalic acid. The slurry is pumped to heat exchangers, which raise the slurry temperature sufficiently for the terehpthalic acid to dissolve. The solution flows through a hydrogenation reactor that contains a palladiumon-carbon support catalyst. Reactor temperature is held above the partial pressure of steam to maintain a liquid phase. In the reactor, 4-CBA is hydrogenated to p-toluic acid, and various colored impurities are hydrogenated to colorless products. The catalyst is highly selective; the loss of terephthalic acid by carboxylic acid reduction or ring hydrogenation is less than 1%. The overall effect of the hydrogenation is conversion of impurities to forms which remain in the mother liquor during the subsequent crystallization step. The terephthalic acid is purified by crystallization in a series of vessels where the pressures and therefore the temperature is sequentially decreased. As was noted above, impurities remain in the mother liquor for the most part. The purified terephthalic acid crystals are recovered by centrifugation or filtration, followed by drying of the wet cake. Overall yield of a whit, free-flowing powder is greater than 98%.
Fig. 1. Simplified process flow diagram of the purified terephthalic acid process.
Fig. 2. Quality improvement procedure with Six Sigma techniques.
247 4. QUALITY IMPROVEMENT The process is defined by five major steps like a typical procedure of Six Sigma: Define, Measure, Analyze, Improve, and Control. These steps are set up by combining several techniques into an effective quality improvement tool (Fig. 2). 4.1. Definition Associated with the wider scope of application is customer focus. This is emphasized repeatedly in Six Sigma in terms of CTQ; improvements will make sense only if they are directly related to some CTQs. Defining what is required from the target process is the key to quality improvement. In this study, the CTQ is identified as the particle size distribution (PSD) which is one of the important quality variables and which affects the product quality in the down stream processes that manufacture poly ethylene terephthalate, polyester, filament, fiber or film from PTA. From the analysis of historical data, it has found that the performance of the current process is around 3.5 sigma. By investigating the voice of the customers, it was determined that we have to improve the product quality up to at least 5 sigma. 4.2. Measurement Various types of information such as qualitative data, observational quantitative data, and experimental quantitative data are exploited in the identification step. A useful tool for the collection of the qualitative data to identify possible sources of quality degradation (without any prejudice to the level of their effect on output) is to interview the relevant personnel for the possible inputs that may affect the output. A good approach for a chemical process is to use the 5M1E (Man, Materials, Manufacture, Machine, Measurements, and Environment) format. Interviews have been done with a total of fourteen people including operators and engineers. The real-time database (RTDB) system and the laboratory information management system (LIMS) have been running to collect the observational quantitative data on the process and quality variables for the whole process. 4.3. Analysis After completing the measurement phase, the key sources that significantly affect the degradation of PSD should be identified to improve PSD. First, core sources have been qualitatively identified using a fishbone diagram. The process is analyzed to generate clues about degradation sources, thereby exploiting results from the interview. Fig. 3 indicated the inputs which can potentially affect the output. Note that the nine variables (DP 1, DP2, TA, TS, P1, P2, DP, DP5, and TF) with large score values greater than 2 are selected as potential input variables. Second, a quantitative identification of degradation sources has been performed using multivariate statistical analysis methods such as regression, variable selection, discriminant analysis, correlation analysis, histogram, cycle-time analysis and variable trend analysis. To identify the correlations between the PSD and a total of 22 process variables for the slurry preparation tank, the purification reactor and the six crystallizers, PLS models are built from the collected quantitative data in the measurement step. After performing cross-validations using the data matrices, the final PLS model has five latent variables and explains 78.8% of the variance in PSD. Note that the PLS modeling results are very good if we consider the fact that there should exist lots of sensor faults and measurement errors in the process and quality variables of actual processes and that the PLS models attempt to explain the "common-cause" variations [9] in the process data and to exclude the random variations and measurement errors that are uncorrelated
248 with other process and quality variables. We can select the candidates for key sources among the total variables by using a variable importance on the projection (VIP) plot (Fig. 4). Note that the eight variables (DP2, DP, TA, TS, DP1, DP5, TF, and P1) with large VIP values greater than 1 are selected as the candidates since they are most relevant to explain Y. To identify as many as possible sources of degradation, some selection of the major sources of degradation is made on the basis of prior knowledge and statistics. The resulting two factors (DP1 and TS) are obtained by comparing the results of qualitative identification (5M1E), quantitative identification (VIP), and controllability, which are shown in Table 1. 4.4. Improvement and Control Two kinds of small single factor experiments were performed after designing a DOE. Obviously, interaction terms are not considered in these experiments. The first experiment was carried out to determine if there was any possible relationship between the pressure difference variable of the first crystallizer and the PSD. Decompression of the first crystallizer was carried out at five different pressures of the first crystallizer and at the average condition of the other factors for the past one year. Fig. 5 shows that the results of an experiment to correlate the first crystallizer decompressed versus the change of the PSD. I M~.,act~e I
I M. . . . . eme.ts I
t,,a. \ - -
+.c,~,,,-, . k
//
/'00
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8.
/,
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tI
/
I ,,,,o
it,1/.,/ .
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_ f/-1
(Q,)
(4)
C,., Q: Activity based unit cost of the class (i) manager and the class (j) manager, where
C/>G.
255 N,., ~ : Number of the class (i) managers and the class (j) managers f,~,: Work efficiency function of the class (i) manager and the class (j) manager The condition Eq.(1) defines a constraint to specify the take-over point Q,. In the actual situation, the number of manager's ratio is (N/Ni) > 1 and their cost ratio is (C/Ci)TcM. 2) According to the priority principle of direct heat exchange and the concept of the greater temperature difference of heat transfer the more exergy dissipation, judging whether there is hot stream HI or not at the time interval with cold stream C 1 can be matched with C 1, if yes, heat storage release is forbidden, go to 3); otherwise, judging whether the heat storage can be matched with stream C 1, if yes, heat storage release is allowed at the time interval with stream C 1; otherwise, judging stream C2, C3, ..., CM in turn at the same rule. 3) Matching the cold stream C1 with the hot stream HI obtained from above step 2), supposing the residual cold stream is CR, To place cold stream CR and all cold streams from time interval 9 +1 to N in an arrangement in according with the order of the temperature. Supposing the sequence is as following: Tcl> Tc2...>TcR.-.>TcM, go to 2).
265
The step of heat storage release is shown in figure 1 place all cold streams from time interval x +1 to N in an arrangement in according with the order of the temperature. Supposing the sequence is as following: Tcl > Tc2> Tc3>...>TcR...>TcM.
no
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,
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end
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I, place residual ,. cold stream and all cold streams from time interval 9+I to N in an arrangement in according with the order of the temperature. Supposing the sequence is as following: Tcl> Tc2> Tc3> ... >TcR'">TcM
Figure 1 Sketch o f the heat storage release
266 4 I I I u s t r a t ions The problem data is shown in table 1. Time series sketch of process streams is shown in Fig.2. At time interval [0, 0.5], there are hot stream H 1 and cold stream C2. They are matched directly as shown in Fig.3. At time interval [0.5, 1], there are cold stream C3 required 250 kWeh heat load and hot stream H2 and H3 with 710 kW.h heat load together. So, hot stream H3 can be matched directly with cold stream C3. In the view of economy, heat storage can be set for hot stream H2 based on the proposed method. The heat exchanger network at this time interval is shown in Fig.4. At time interval [1, 2], only cold stream C1 is available. The above heat storage releases matched with cold stream C 1 is shown in Fig.5. Table 1 The problem data Stream No. 1 2 3 4 5 6 t(~
Stream Original type Temperature(~ HI 180 H2 300 H3 250 CI 200 C2 40 C3 120 A
Target Temperature('C) 60 210 150 280 160 220
Heat capacity flowrate(kW/~ 4 8 7 4 6 5
HII80~
H2
300 280
Start time (h) 0 0.5 0.5 1 0 0.5
End time (h) 0.5 1 1 2 0.5 1
Heat load (kW.h) 240 360 350 320 360 250
~ Steam
Q=240kWeh O=120kWeh
CI H3 C3
250
60~ Fig. 3 Heat exchan~:er network at time interval [0, 0.5]
HI 220
Heat storage~ 260"C C2
180 160 0
.. 9 0.5
1
2
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Fie.2 Time series sketch of nrocess streams C~ 120~
.L Cold water
210~ Fig. 5 Heat exchanger network at time interval [1, 2 H2 , 300~ COld190 ~
220 ~
~ Steam
Q=200kWeh Q=120kWeh
~Q=360
kW.~ 280Hot ~
210~ Fig.4 Heat exchanger network at time interval [0.5, 1]
5 Discussion
C o m p a r e d with the p r o p o s e d m e t h o d , the m e t h o d presented in literature [8] d e t e r m i n e s heat storage setting and its release time by heuristic m e t h o d before heat e x c h a n g e n e t w o r k synthesis, and the effect o f batch process practical factor is not taken into a c c o u n t in the method. For instance, the pipe i n v e s t m e n t cost, dispersal heat e x e r g y dissipation cost and dissipation cost m a y be increased while heat storage is far from the heat e x c h a n g e match, so,
267 g e n e r a l i n v e s t m e n t c o s t w i l l be i n c r e a s e d a n d the p e r i o d o f i n v e s t m e n t r e c l a m a t i o n will be p r o l o n g e d . A t the p r o p o s e d m e t h o d , h e a t e x c h a n g e m a t c h is d o n e in a d v a n c e at e v e r y t i m e interval. H e a t s t o r a g e s e t t i n g a n d its r e l e a s e t i m e are d e t e r m i n e d b a s e d o n A I m e t h o d a n d the economy analysis criterion for residual heat. Then match heat exchange networks taking heat storage stream as accessory stream. The proposed approach considers not only the energy degrade but also the effect o f b a t c h p r o c e s s p r a c t i c a l factor. F u r t h e r , the economy analysis criterion and the period of investment reclamation are taken into account to provide the economy criterion for investment decision-marker.
NOTATION
Ccap Cf Ch Cp Cpow F g L N Na
OD On Oi Op Os Oz
Expense coefficient related with the capacity of heat storage equipment Electricity price, u 9h Unit price of heat exergy dissipation, Y/kW Unit price of pipeline, Y/m Expense coefficient related with the release and sorb power of heat storage equipment flowrate, kg/s Acceleration of gravity, m/s 2 Pipeline length, m General number of time interval the release and sorb power of heat storage equipment ,kW Power dissipation cost, u Annual economy benefit, u General investment cost, u Pipeline cost, u heat exergy dissipation,, u General cost of heat storage equipment, u
P~ P2 QR t T To Tb v~ v2 w z~ z2 ]E hf A Qst p x
Inlet pressure, MPa Outlet pressure, MPa Heat dissipation quantity, kW Ordinal number of time interval Temperature, K Benchmark temperature, K(288K) Liquid temperature inside pipe, K Inlet velocity of flow, m/s Outlet velocity of flow, m/s Pump efficiency Upright distance of inlet flow from datum plane, m Upright distance of outlet flow from datum plane, m Energy dissipation of unit mass flow, kW The capacity of heat storage equipment, kWeh Density, kg/m 3 Time interval
Reference 1. 2
VecchiettiAR, Montagna J., Comp. & Chemical Engineering, 1998, Vol.22:$801--4 Eva Sorensen, Sigurd Skogestad. Chemical Engineering Science, 1996,51(22):4949----4926
3
Musier,R.F.H., UB.Evans, Batch process management, Chemical Engineering Progress, 1990, 86(6):66-77
4
N.Sadr-Kazemi,G.T.Polley, Chemical Engineering Research and Design, July 1996 Voi.74, Part A: 584--596
5
Y.P.Wang,R.Smith, Chemical Engineering Research and Design, Nov. 1995 Vol.73,Part A: 905--914
6
I.C.Kemp and A.W.Deakin, The cascade analysis for energy and process integration of batch processes, Part 1: Calculation of energy targets, Chemical Engineering Research and Design, 1989 Vol.67:459--509
7
JaneA. Vaselenak, Ignaclo E. Grossmann and Arthur W. Westerberg, Heat integration in batch
processing, Ind. Eng.
Chem., Process Des. Develop., 1986, Vol.25(2): 357--366 8 Zhang Zhaoxiao [Ph.D.thesis], The theoretical and application researches of energy diagnosis and optimization in batch processes, 1998,Xi'an Jiaotong University.
268
Process SystemsEngineering2003 B. Chenand A.W.Westerberg(editors) 9 2003Publishedby ElsevierScienceB.V.
Debottlenecking and Retrofitting for a Refinery Using Marginal Value Analysis, Sensitivity Analysis and Parametric Programming Li Wenkai, Chi-Wai Hui Chemical Engineering Department, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. Abstract A new analytical method called "marginal value analysis" is used in this paper to provide economic information for all stream flows inside a refinery. Three types of marginal values are defined that represent respectively the de-bottlenecking effect, production cost and product value of an intermediate material flow. Important insights are generated using the analysis to find production bottlenecks, assist in decision making and price intermediate materials, etc. Sensitivity analysis and parametric programming are studied in this paper to provide more comprehensive information for pricing, retrofitting and investment evaluation. Several case studies are used to illustrate the research on marginal value analysis, sensitivity analysis and parametric programming. Keywords Refinery, Marginal value, Sensitivity Analysis, Parametric Programming 1. INTRODUCTION Marginal values have been used in various economic evaluations and accounting activities for a long time. To get information for debottlenecking, retrofitting, pricing and investment evaluation by detailed analyzing of the additional economic information forms the subject of Marginal Values Analysis (MVA). Ranade, Shreck & Jones (1989) took into account the paths through which the stream was generated and used to calculate marginal values of a particular stream. This localized method may yield incorrect results since production and utility plants are closely interacted. Hui, C.W. (2000) proposed two novel definitions of marginal values: MCF and MCp that have important significance in bettering the understanding of the economic structure of the system. 2. DEFINITION OF MARGINAL VALUES There are several definitions of marginal value in literatures, whose abilities of revealing the economic structure of the system are different. 2.1 Traditional Definition of Marginal Value Traditional definition of marginal value (MV) is defined as the change in overall profit caused by a small variation of a stream flow (Ranade, Shreck & Jones ,1989). That is, MV =Aprofit/Astream flow. The MVs are usually reported by the standard output of a LP solver.
269 2.2 New Definitions of Marginal Values New definition of marginal value is defined as the variation in overall profit by adding or taking away a small amount of a stream (Hui, C.W., 2000). According to the above new definition, there are two types of marginal values as following: I. The marginal value of adding a small amount of a stream flow (MCl~). II. The marginal value of taking away a small amount of a stream flow (MCp). 3. P R O B L E M DESCRIPTION
The configuration of the problem studied in this paper is shown in Fig. 1. Fig.1 contains three production units: CDU (Crude Distillation Unit), GB (Gasoline Blending) and DB (Diesel oil Blending). Crude oil is separated into three fractions by CDU. Gasoline and MTBE enter GB to produce two products: 90# gasoline and 93# gasoline. Diesel oil and naphtha enter DB to produce another two products: -10# diesel oil and 0# diesel oil. The market prices of crude oil and MTBE are 1400 and 3500 Yuan/ton respectively; the market prices of 90# Gasoline, 93# Gasoline, -10# Diesel Oil and 0# Diesel Oil are 3215, 3387, 3000 and 2500 Yuan/ton respectively. The CDU transfer ratios of crude oil to gasoline, diesel oil and naphtha are fixed at 0.2, 0.3 and 0.5 respectively. The market demand for each product is 200 ton/day. A small amount of material (DF1) is added to the intermediate stream "gasoline". The material of DF1 is the same as the intermediate stream and its upper bound is set at a very small number: 1E-6 or zero. So are DF2, DF3. A small amount of material (DP1) is taken away from the intermediate stream "gasoline". The material of DP 1 is the same as the intermediate stream and its lower bound is set at a very small value: 1E-6 or zero. So are DP2, DP3.
MTBE
G
90 # GASOLINE
GASOLINE
CRUDE OIL v
C D U
B
"~
93 # GASOLINE
D
~
- 10 # DIESELOIL
v
DIESEL OIL DP2L
DF
DP3~
DF~
~/~,-
w,-
]bottleneck
Fig. 1. Basic Configuration
O# DIESEL OIL
270 Table 1. The solution results of case 1 and case 2 Overall Profit (Yuan) MCF of Diesel Oil NO TANK (Case 1) 308,772.7 3710.2 WITH TANK (Case 2) 337,008.3 3590.1
MCF of Naphtha -611 0.0
4. CASE STUDIES FOR MVA 4.1 Single Period Two cases are used for single period scenario. Case 1 is the same as the problem described in section 3. One intermediate tank (naphtha tank) and four final product tanks are added in case 2. The capacity of each tank is 200 ton. After solving the model, we found that the MV of 0# diesel oil in case 1 is 1544 Yuan/ton because its production rate has reached its upper bound. Here exists the bottleneck. The results of case 1 and 2 are shown in Table 1. Some interesting results were obtained. MCF of diesel oil is 3710.2 Yuan/ton, which is higher than the prices of all raw materials and all products. It means that this intermediate stream is even more valuable than the most expensive final product. On the contrary, MCF of naphtha is -611.0 Yuan/ton, which is lower than the prices of all raw materials and products. It is negative which means that we can increase the overall profit by taking away naphtha from the system. If the market price of diesel oil is lower than 3710.2 Yuan/ton, then more profit can be made by buying diesel oil from external suppliers. For naphtha, on the contrary, no matter how low the market price is, the company should not buy it. MCF and MCp can give very important information for business decision of a refinery. From Table 1, it can be seen that the overall profit in case 2 increased by 9% compared to that of case 1. The reason is that some of the naphtha is stored in naphtha tank in case 2. In casel, MCp of naphtha is negative, so putting some naphtha inside the tank will increase the overall profit. Notice that the profit making by adding intermediate naphtha tank in the system is only valid for a short period because the capacity of the naphtha tank is limited. 4.2 Multi-Period Considering the changing factors comprehensively may obtain better profit. Thus it is important to do marginal value analysis in multi-period case. In this section, case 3 and case 4 are introduced. The system configurations of case 3 and case 4 are the same as in case 2 (with tank) except that 7 days are considered comprehensively. Multi-period models are used to get the optimal solutions for these two cases. Table 2. The solution results of case 3 and case 4 case 3 case 4
Overall Profit (Yuan) 2,251,130 3,077,820
MCF of Diesel Oil 3677.6 3590.2
MCF of Naphtha -445.1 0.0
271 Naphtha Tank
200 ~/ ,..150
-----__
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case 4
r
Fig. 2 Inventory of Naphtha Tank (case 3 and case 4) The market demands of products are fixed at 200 ton/day in all days in case 3. In case 4, it is assumed that the market demands for all other three products are fixed at 200 ton/day except that the market demands for 0# diesel oil increase up to 400ton/day on days 3,6 and 7. The results of case 3 and case 4 are shown in Table 2. From Table 2, we can see that the overall profit of multi-period model with changing demand (case 4) is higher because the company can sell larger quantity of 0# diesel oil. The inventory of naphtha tank in case 3 and case 4 is shown in Fig. 2. In Fig. 2, the inventory in case 3 keeps increasing in all 7 days. The inventory level is limited by the capacity of the tank (200 ton). If the capacity of the tank increases, the overall profit will further increase. The inventory curve of naphtha tank in case 4 have peaks and valleys instead of keeping increasing. On days when the demand is large (days 3,6 and 7), the previously stored inventory is sold, thus the inventory decreases. Arranging the inventory level in these tanks maximizes the overall profit. In case 4, different from single period case, the inventory level of product tanks is not zero. Product tanks are useful in increasing the overall profit at changing market demand case. 5. SENSITIVITY ANALYSIS AND PARAMETRIC P R O G R A M M I N G 5.1 Introduction Sensitivity Analysis and Parametric Programming are methods for studying the influence of parameters, such as unit capacity, product specification, raw material and products prices, on overall profit. They provide more comprehensive information for pricing, retrofitting and investment evaluation. In this paper, these two methods are studied using the new definition of marginal values proposed. From section 4, by adding a small amount of material to a process stream or by taking away the material from the stream may increase the overall profit. However, it is impossible to add the material to the process stream or take away from it as much as we want. A refinery may want to know the range within which the influence maintains unchanged and what is the influence beyond this range. The information will of great importance to business decisions. The problem in this section is the same as case 4 except that another two intermediate tanks are added as buffers for gasoline and diesel oil respectively.
272 Table 3 Right-Hand Side Ranges DFlt 1o up tl 0.0 17.42 t2 0.0 17.42 t3 0.0 17.42 t4 0.0 17.42 t5 0.0 17.42 t6 0.0 17.42 t7 0.0 17.42 ,,.
. . ,
..
MCF 3062.13 3062.13 3062.13 3062.13 3062.13 3062.13 3062.13
1o 0.0 0.0 0.0 0.0 0.0 0.0 0.0
DF2t up 14.04 15.71 15.71 14.04 15.71 15.71 21.95
MCF 3173.82 3173.92 3174.02 3173.81 3173.83 3173.93 3173.75 ,
DF3t lo up 0.0 24.50 0.0 24.50 0.0 8.17 0.0 5.41 0.0 5.41 0.0 5.41 0.0 5.41
MCF 2116.27 2116.37 2116.47 2116.33 2116.43 2116.53 2116.63
5.2 Sensitivity Analysis To find out the right-hand range of the dummies added in diesel oil (DF2) and taking away from it (DP2), corresponding constrains should be added in the model. Table 3 lists the right-hand ranges and MCFS of gasoline, diesel oil and naphtha. In Table 3, "1o" column lists the lower limit of right-hand side range while "up" column lists the upper limit. Variables "DF 1t" and "DF3t" represent the dummy added to gasoline and naphtha respectively. The everyday right-hand side range of gasoline is (0.00 to 17.42 ton/day) and the corresponding MCF is 3062.13 Yuan/ton. That means, within this range, adding one ton of gasoline will increase the overall profit by 3062.13 Yuan. Different from gasoline, the upper limits of diesel oil and naphtha have different values on different day. It reflects the influence of changing market demand of 0# diesel oil. 5.3 Parametric Programming Sensitivity Analysis can tell us the limits within which the influences of dummies maintain unchanged. To find out the influence of dummies beyond these limits is part of the task of Parametric Programming. The procedure used in this paper is by setting the RHS value to previous limit to obtain a new limit. Then by fixing the value of the dummy to the limits found, the MCF or MCp of the corresponding stream can be obtained. Fig. 3 shows the MCF of diesel oil vs. the amount of diesel oil added on day 1. From sensitivity analysis, the upper limit of right-hand side range is 14.04 ton/day on day 1. From Fig. 3, we can see that the MCF only has minor change beyond this limit. However, if the flowrate of dummy increases up to 218.0 ton/day, MCF starts to decrease. The MCFs on day 2 to day 7 is almost the same as those of day 1. Notice that to maintain the feasibility of the model, the amount added cannot exceed 664 ton/day. Some detailed works have to be done to determine the MCF or MCp value between two points in Fig. 3. For example, we know that the MCFS at 218.0 ton/day and 260.0 ton/day are 3173.750 and 2499.8 Yuan/ton respectively; adding a small value (1.0E-3 was used here) to 218.0, and then fix the dummy to this bigger value, the MCF can be found to be 2499.8 Yuan/ton. Thus the MCF between two points (218.0 ton/day and 260.0 ton/day) is 2499.8 Yuan/ton. Fig. 3 tells us how the profit making decreases as the amount added increases. If a refinery buys
273 MCF of diesel oil 3200 , , 2800 2400 2000 -
~ 1600 -
~-T--
1200 800 400 0 0
40
80 120 1 6 0 2 0 0 2 4 0 2 8 0 3 2 0 3 6 0 4 0 0 4 4 0 4 8 0 5 2 0 5 6 0 6 0 0 6 4 0 amount
added
Fig. 3 the MCF of diesel oil vs. the amount being added diesel oil more than 621.1 ton/day, the profit making will be zero. Fig. 3 can help a refinery to decide the appropriate amount of diesel oil they should buy. From the figure of MCp of diesel oil vs. the amount being taken away, the refinery can compare the profit earned (the market price) and the profit loss (the value of MCp) to decide whether they should sell diesel oil or not. 6. CONCLUSIONS This paper proposed the research work on MVA, sensitivity analysis and parametric programming for a refinery. Important insights are generated using MVA to find production bottlenecks, assist in decision making and price intermediate materials, etc. The influences of market demand and tanks on the total profit were analyzed using MVA. Sensitivity analysis and parametric programming are also studied. ACKNOWLEDGMENTS The authors would like to acknowledge financial support from the Research Grant Council of Hong Kong (Grant No. HKUST6014/99P & DAG00/01.EG05), the National Science Foundation of China (Grant No. 79931000) and the Major State Basic Research Development Program (G2000026308). REFERENCES [1] Ranade, Saidas M. Shreck, Scott C. Jones, David H. Know marginal utility costs. Hydrocarbon Processing, 68(n9) Sep. pp. 81-84 (1989) [2] Hui, Chi-Wai, Determining marginal values of intermediate materials and utilities using a site model, Computers and Chem. Eng., 24(2-7), 1023-102 (2000)
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Published by Elsevier Science B.V.
274
A General Continuous State Task Network Formulation for Short Term Scheduling of Multipurpose Batch Plants with Due Dates Christos T. Maravelias and Ignacio E. Grossmann* Department of Chemical Engineering, Carnegie Mellon University Pittsburgh, PAl 5213, USA Abstract: A new continuous-time MILP model for the short term scheduling of multipurpose batch plants with due dates is presented. The proposed model is a general State Task Network (STN) formulation that accounts for variable batch sizes and processing times, various storage policies (UIS/FIS/NIS/ZW), utility constraints (other than units), and allows for batch mixing and splitting. Its key features are: (a) a continuous, common for all units time partitioning, (b) assignment constraints are expressed using only the binary variables defined for tasks, (c) start times of tasks are eliminated, (d) a new class of tightening valid inequalities is added to the MILP formulation, and (e) a new disjunctive programming formulation is used for the matching of due dates with time points. The proposed model is more general than the previously reported models and is computationally efficient. Keywords: Scheduling, Multipurpose batch plants, Scheduling with due dates
1. INTRODUCTION The problem of short-term scheduling of multipurpose batch plants with release dates for the raw materials and due dates for the final products is a very challenging and practically important problem. Due to its complexity, the models that have been proposed [1,2] do not account for all the features of the general problem. Three commonly made assumptions, for example, are that there are no utility requirements, that each order comprises a single batch and that orders can be pre-assigned to time points/events. In this work we propose a model that addresses the general problem, i.e. it accounts for complex plant configurations (batch splitting and mixing, recycle streams), various storage policies (UIS/FIS/NIS/ZW), variable batch sizes, processing times and utility requirements, and multiple release and due dates. A State Task Network [3] MILP model is proposed with continuous-time representation [4].
2. PROBLEM STATEMENT We assume that we are given: (i) a fixed or variable time horizon (ii) the available equipment units and storage tanks, and their capacities (iii) the available utilities and their upper limits (iv) the production recipe for all tasks (mass balance coefficients, utility requirements) (v) the initial amounts and prices of all states (vi) the deliveries of raw materials and orders of final products (amounts and time) *To whom all correspondenceshould be addressed. E-mail:
[email protected] 275 The goal is to determine: (i) the assignment, sequence and timing of tasks taking place in each unit (ii) the batch size of tasks (i.e. the processing time and the required utilities) (iii) the amount of states purchased and sold Various objective functions such as the maximization of additional production, or the final inventory of intermediate states can be accommodated within the proposed model.
3. M A T H E M A T I C A L FORMULATION The general continuous STN MILP model of Maravelias and Grossmann [5] is used as basis. In the proposed model the time horizon is divided into N time intervals of unequal and unknown duration. Also, tasks that can be assigned to different units are treated as individual tasks for each unit assignment. In this model, assignment constraints are expressed through task binaries Wsi. and Wf.. Binary Wsi. (Wf.) is 1 if task i starts at (finishes at or before) time point n, T.. The batch size of task i that starts at, is being processed at, and finishes at or before time point n is denoted by Bsi.. Bpi. and Bf., respectively, and the amount of state s The amount of state s consumed (produced) by task i at time point n is benoted by Blis. (B~ at time point n is denoted by Ss., and the amount of utility r consumed by various tasks at time point n is denoted by Rr.. The start, processing and finish time of task i that starts at time point n is denoted by T&., Tp~., and Tf., respectively. The parameters of the model include the time horizon H, the minimum/maximum batch size BiM1N/BiMAx, the storage capacity C~, the mass fractions pZjpO, and the coefficients for the fixed and variable term of processing time and utility requirements (ai, fl~, 7~, 6~). The basic constraints of the model of Maravelias and Grossmann are the following:
3.1. Assignment constraints
~_. ~"(Ws,.,-Wf,.,) l
:
(17)
Vi, Vr, Vn
YirWZn -1- r
Rrn'-Rrn-l-Z i
(16)
1(s)
Vs, Vn
3.5. Utility constraints R/r. "-TirWSin -~-l~irsBSin Vi, Vr, Vn R,~
(13)
ROirn-, -]'ZRilrn i
V F , VFI
Rr. < R MAx Vr, Vn
(18) (19)
(20) (21)
3.6. Time ordering constraints
T,,=, =0
(22)
T,,=ful = H
(23)
T.+, > T.
Vn
(24)
3.7. Tightening constraints ~" ~" rp,. < H Vj
(25)
tel(j) n
~Tp,., l (33) k~ L(s)
k~ K (s)
i~ O(s)
i~ 1 (s)
where ADk. is the amount of order (delivery) k at time point n; i.e. it is non-zero if order (delivery) k takes place at time point n and zero otherwise, and it is calculated as follows" AD,. = AD, Yk. Vk ~ K, Vn (34) 3.9. Objective function If in addition to meeting the due dates the objective is to maximize the revenue from the sales of extra production, the objective function is given by (35) where FP is the set of final products and (s is the price of state s, maxZ = ' ~ (sS,I,vl
(35)
sE F P
If there are orders for which there is no due date, the objective is to minimize the makespan and the objective is given by (36), where MS is the makespan. min MS
(36)
In this case, H is an upper bound on the makespan and it is replaced by MS in constraints (23), (25) and (26). Finally, the model can be used to minimize the inventory level of final products over time (Eq. (37)) or the final inventory of intermediate states (Eq. (38)), where INT is the set of intermediate states: min ~
~ S,,
n
(37)
s~FP
min ~ S,l~vl
(38)
s~ I N T
The proposed MILP model (M) consists of equations (1) - (15), (17) - (27), (29) - (34), and one of (35), (36), (37) and (38), where Wsin, Wfn, Yk. ~ {0,1}, and all the continuous variables are non-negative.
3.10. Remarks In the case where the release and due dates are distinct, constraints (31) and (32) can be simplified into (39) and (40); i.e. fewer constraints and variables. T. = ~ 7".* + T. Vn (39) k
T- < H ( 1 - ~ Y,.)
Vn
(40)
k
The timing and relative order of deliveries and due dates can be used to fix some of the Y,,, binaries, and derive valid inequalities that reduce the feasible space. For the latest order, for example, we can fix Yklul = 1, or if TDk < TDk., inequality (41) is valid, ~--]Y,., > ~--'~Yk,., Vn (41) n' 20 T
(3)
but a more restrictive sufficient condition can be obtained if the following norm is used:
II
Dx-TPr|
II l
=O'max Dx-TPrO/
/
(4)
where O'max (o) is the maximum singular value of a matrix. Thus, a sufficient condition to fault detectability is: 1
lifji[ >-lifriij = 2~
(D? Pr|
(5)
In turn, the SPE statistics is given by:
s~ -Ilex,II ~ = tlex0 + eo,f, II=
(6)
Therefore, a sufficient condition to SPE detectability is:
Ileo, f;ll >_=~x,~
(7)
but a more restrictive condition can be written as follows:
Ilfs..ll,: 2Crm~(CO,)6sPE and IlfseEils are wher~ I1s
(8) said to be the critical fault magnitudes (CFM). They are
approximations to the minimum fault magnitude IIf~ll detectable by these tests. 3
OPTIMIZATION MODEL
One possible objective function to locate sensors is to minimize the cost, subject to the detectability of certain faults above a certain threshold value (Bagajewicz, 2000). Let a certain set of J faults of interest {Fs,~g
j=l'
and let far, be the minimum magnitude of these faults that
one is interested in detecting. Below those threshold values, one is ready to accept the system behavior as normal, even though the fault may exist. For each design (number of sensors and location), one would have to evaluate the behavior of PCA for this set of faults, each occurring one at a time and a feasible solution is the one that can detect faults of the threshold size or larger. One can also add two faults occurring in pairs at the same time and request detectability on those too. This can be done by defining a new fault consisting of the two
294 other faults occurring simultaneously and revert to the single fault model. The model would be Minimize
f
N }
SensorNetworkCost
= ~ " c k " qk
k=l
(9)
s.t.
fm% -< fi%
J = 1,2 ..... J
where qk is a binary variable which is one if the kth sensor is located, ck is the cost of the kth sensor and fmin~is de minimum critical fault magnitude when fault j is introduced alone (eq 10). fmin, = min
{11s IIs }
o).
For small problems, the optimum can be obtained by simple enumeration, whereas for large systems, some numerical procedure to explore the feasible region is needed. The above described model has one important drawback: because very small thresholds may make the problem either infeasible or the sensor network too costly a designer has to determine some reasonable value of the fault thresholds. To do so, one would have to decide what is the economic loss or safety violation corresponding to each fault and determine the threshold accordingly. It seems therefore more straightforward to incorporate the cost of the magnitude of the fault at the time to be detected in the objective function directly. The cost of a fault is therefore modeled as follows:
f
0
if
F a u l t C o s t j = crj (fm,n, -- finfj ) if
oO
if
fm%< f.r, fi,q < fminj < fsupj fmin,>
fsupj
(11)
Above f~upj the cost is considered infinite, that is, the fault is not tolerated. The fault cost assigned to a sensor network that begins to detect the fault at this magnitude is called the superior fault cost and is noted by FCsup . Extensions of this model are easily made. Then the sensors are selected to minimize a weighted sum of the sensor and the fault costs. To solve the optimization problem Genetic Algorithm (GA) are used (Goldberg, Holland, 1970). Each chromosome (individual) is a string of bits representing the absence of not of one potentially located sensor. The length of the chromosome is equal to the number of process variables that can be potentially measured. The fitness of an individual, represented by the chromosome is evaluated using the inverse of the total cost.
1 i = Total
1
m
Cost i - ~
2.,
k=l
J ck 9qk + ~ F a u l t C o s t j
(12)
j=l
For each chromosome, the corresponding matrix |
needs to be calculated. Therefore, faults
are introduced in a simulator and deviations in the potentially measured process variables (fj) produced by a fault (Fj) are studied. Because of the noise, we consider that not all deviations in the variables are due to the fault. If the variable take less than 10% of the total deviation the
295 correspondent element o f |
is set to 0 (i.e. this process variable is considered not to be
affected by the fault), otherwise the element is set to 1. Figure 1 illustrates one instance o f the procedure. ~
. . . . . . . . . . . . . . . . . . . . . . .
, .................
, . . . . . . . . . . . . . . . . . . . . . . . . .
~02 =
..
"i. _ |
!1
[i~ ~17617617617617 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
-,,i I...... _--
Figure 1: (a) Deviation in the process variables (f/) due to Fj (b) O/obtained from this propagation
The values of f,~j and fsupj are obtained as the norm of the deviation in the process variables (fj) caused by faults of minimum size below which the cost will be considered zero (fi.f,), and the maximum size above which the fault will not be tolerated at all (fsupj). In turn, the proportionality constant aj is obtained using the cost o f the maximum size fault FCs~p , that is:
FC~up cr = J "' f~u~,, - f, or,,
(13)
Selection of mating pairs is done by roulette wheel method, the crossover is multi-point and mutations are made applying to the genes the NOT operator (switching a one to a zero or vice versa). The terminating criteria was been chosen as the number of generations or convergence (no improvement after a few generations), whichever is achieved first. 4
CASE STUDY
We consider a plant consisting of a reactor and a stripper (Figure 2). Fresh feed, consisting of reactant A and some o f product P, is fed to the reactor. The reactor is a continuous stirred tank. The irreversible reaction that occurs is of first order: A ~ P. The reactor output is fed to the stripper. Most of unreacted A is separated from product P there. The plant's product, with a small mol fraction of A (XAB) is obtained at the stripper's bottom. The stripper's output at the top is recycled to the reactor. The plant is designed around a fixed feed composition of XA00 = 0.9. Table 1 lists 13 potentially measured process variables. The following fault conditions are considered" F~ = drift in level sensor Vr, F2 = bias in level sensor Vr F3 = Steam Leak in the reboiler and F4 = Pump Failure.
296 Table 1. Possible sensor location and cost.
sP D,X~
:."~).................-. . . . . . . .......
~ I
Fre,.~ Fe~l
. . .J. .I
a,~t~u,,,t
~~
.......
I~-
jj
........... ~
i'-~...1~'~1 ...,
9
,
i .. 4
I/ '1 ''
....
I/ I sP ___~-~:" : ................... ~"'r"~ ~ s t ~
Lcu~,x~-n." sit. C ~ r a l ~ r set point LT Level ~
~
(I:,C ~
.."""q,
rn~t
9L T
~
~
9
Index
SensorDescription
Cost
l
Column input composition
100
2 3
Distillate Flow
10
Opening of condenser valve
1
4
Condenser level
1
,
Product flow
10
Opening of the column base valve
1
67
Column base level
1
Steam flow
10
8
i
9
:
.Product B,X~
9
12 13
Opening of the steam valve
1
Output composition
1
Reactor level
1
Reactor output flow
1
Reactor input flow
10
Figure 2. Chemical plant with recycle
Only some o f the measured variables (index: 2,3,6,8,9,11,12,13) will be affected by the faults. Then the search space can be reduced to take into account only the perturbed process variables because sensors placed in other points do not add information about the faults but noise. Table 2 depicts the values obtained forf~,fj ,fsupj and FCs,p, for each fault. T a b l e 2. f~,r, F1
[0.0011 -0.0071 -0.0017 -0.0019 0.0167 0.0064]
F2
[0.0032 0.0172 0.0032 0.0034 0.0033 0.0018]
F3
[0.0111 0.0025 0.0048 0.0051]
F4
[-0.0077] fsupj
FC.... ($)
F1
[0.0467 0.2593 0.0588 0.0626 0.0336 0.0238]
10000
F2
[0.0195 0.0643 0.0147 0.0157 0.0283 0.0288]
10000
F3
[0.0541 0.0178 0.0314 0.0334]
10000
F4
[1.3448]
10000
The algorithm was applied with an initial population of 130 initial individuals. The best individual o f the last generation was selected as solution. This solution is to put sensors 2, 9 and 11 (i.e. Distillate Flow, Opening of the steam valve, Reactor level).
297 Another case was solved by supposing that the sensors cost are near to the maximum fault cost FCostj. The solution is to locate sensors 4, 7, 9 and 10 (i.e. in Condenser level, column base level, Opening of the steam valve and output composition). After each optimization was finished, the solution was observed for example that the solutions obtained were able when more sensors are added the faults that have influence less efficiently detected. This is because more variance is needed to detect these faults. 5
tested using simulations. It was to detect faults efficiently and that in a small number of variables are introduced by signals that are not
CONCLUSIONS
The problem of locating sensors at minimum cost for the use of PCA was presented in this paper. Because unneeded sensors introduced extra variance, smaller number of sensors, when feasible, behave more efficiently. Therefore, not only the cost is improved, but also the PCA performance is improved. A C K N O W L E D G M E N T S : Support in part by the European community (project n ~ G1RDCT-2001-00466) is acknowledged. Financial support for Dr. Bagajewicz sabbatical stay at ETSEIB, provided by Spain's Ministry of Education is also acknowledged. REFERENCES
[ 1] Pearson, K. "On lines and planes of closest fit to systems of point in space." Phil. Mag. (6),2,559-572 [2] Hotelling, H "Analysis of a complex of statistical variables into principal components." J. Educ. Psychol.,24,417-441,498-520." 1933 [3] Nomikos P. MacGregor Monitoring Batch Process using Multiway PCA AIChE J 40,8.1365 1994. [4] MacGregor, J. F., C. Jaeckle, C.' Kiparissides, and M. Koutoudi, "Process Monitoring and Diagnosis by Multiblock PLS Methods," AIChE J., 40, 827 (1994). [5] R. Dunia, S. J. Qin, T. F. Edgar, and T. J. McAvoy, "Identification of Faulty Sensors Using Principal Component Analysis," AIChE J , 42, 2797 (1996).+ [6] Raghuraj, R.; Bhushan, M.; Rengaswamy R., "Location sensors in complex chemical plants based on fault diagnosis criteria." AIChE J. 1999, 45 (2), 310. [7] Bushan, M. ; Rengaswamy, R., "Design of sensor network based on the signed directed graph of the process for efficient fault diagnosis " Ind. Eng. Chem. Res. 2000, 39 (4), 999. [8] Wang, H. ; Song, Z. ; Ping, L.,"Fault detection behavior and performance analysis of principal component analysis based process monitoring methods" Ind. Eng. Chem. Res. 2002, 41 (10),2455-2464. [9] Bagajewicz M. Process Plant Instrumentation. Design and Upgrade. Technomic Publishing Company. Now CRC Press (2000). [10] Holland, J. Adaptation in natural and artificial systems. The University of Michigan Press - Ann Arbor - 1975.
Process Systems Engineering2003 B. Chen and A.W.Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
298
An approximate novel method for the stochastic optimization and MINLP synthesis of chemical processes under uncertainty Zorka Novak Pintari~ and Zdravko Kravanja
University of Maribor, Faculty of Chemistry and Chemical Engineering, Smetanova 17, SI-2000 Maribor, Slovenia Abstract In this work we present a novel central basic point (CBP) method for the approximate stochastic optimization and MINLP synthesis of chemical processes under uncertainty. The main feature of this method is that the expected value of the objective function is evaluated by solving a nonlinear program at one central basic point with lower bounds on design variables, while feasibility is ensured by simultaneous solution of the NLP at critical vertices. The central basic point and lower bounds are obtained through a set-up procedure which relies on a one-dimensional calculation of the objective function's conditional expectations for each uncertain parameter. On the basis of this method, a twolevel MINLP strategy for the synthesis of flexible chemical processes is proposed. Keywords
flexibility, uncertainty, conditional expected value, approximate method
1. INTRODUCTION The main problem in stochastic optimization is to achieve an accurate estimation of the expected objective function, which requires integration over a multi-dimensional space of uncertainty. Different integration schemes have been presented in literature, e.g. Gaussian quadrature formula and Monte Carlo simulation [11. Recently, a specialized cubature technique [z] was developed and a hybrid parametric stochastic approach TM.The purpose of this work is to reduce the problem's dimensionality in order to evaluate the expected objective function efficiently and accurately for large-scale models with a significant number of uncertain parameters, for instance 10 to 30. 2. BACKGROUND In our previous w o r k [41 w e developed a method called the Reduced Dimensional Stochastic (RDS) method, for stochastic optimization with reduced dimensionality. Fig. 1. briefly presents the main steps of RDS strategy. In this RDS method the expectancy of the objective function is approximated by a linear combination of objective functions evaluated at a set of basic vertices, while considering the critical vertices as feasibility constraints. Critical vertices are defined as those which require the largest values of design variables in the sequential scanning of extreme combinations of uncertain parameters. This method was incorporated into a two-level strategy for MINLP synthesis under uncertainty tSl which can handle nontrivial medium-scale optimization problems with a considerable number of uncertain parameters within a reasonable period of time. The selection of basic vertices, however, is not unique and many suboptimal solutions can be generated.
299
['Superstructure[ New structure §I-. Set-up procedure Identificationof basic and critical vertices. Determinationof weights.
NLP Problem
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
i
d
Design stage
~
~[ ...................
"~
Operating stage
Selection of design No~__._.___~EC variables by direct ~ search method, I Yes
Selectionof operatingvariables. Optimizationat basic and critical vertices. Estimationof the expected value EC.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .m lai.O tp]. . . . . . . . . d. .e. .s.i.g. .n. . ~. . ,. _. .~. .~. . . . . . . . . . . . . . . . . . . . . . . IFeasibility test. | ~ ..~Yes [ MILPMaster Problem] Fig. 1. Two-level strategy for RDS synthesis, tSJ The aim of this work is to improve and to simplify the set-up procedure so that more accurate approximations of the expected value can be obtained and more uncertain parameters can be handled than by the RDS method. The main idea is to perform the optimization at one central basic point in which the value of the objective function would be close to the expected value. 3. OUTLINE OF THE CENTRAL BASIC POINT (CBP) M E T H O D In the novel CBP method, the expected value of the objective function is evaluated at a point close to the nominal point. This feature eliminates the drawback of the RDS method of estimating the expected objective function at the extreme basic points. The uncertain parameters can be described by any distribution function and the nominal point can have a mean or mode values. The method is performed using the following steps: 3.1. Selection of critical vertices
Optimal oversizing of design variables is crucial for the flexibility and optimality of process schemes. We assume that in the convex problems the largest values of design variables appear at the "worst" combinations of uncertain parameters, i.e. at some extreme points (vertices). In order to determine a vector of critical vertices, the optimization of the original model (NLP 1) is performed sequentially at 2 Up extreme points, where Np is a number of uncertain parameters: min C(xi,di,O i ) s.t.
f(xi,di,Oi)=O g(xi,di,Oi) _A ( X i , O i )
x i , d i ~R, 0 i
E{0 LO, 0 UP}
(NLP1)i i=I,'",2NP
300 C is the economic objective function, x and d represent the vectors of the operating and design variables, respectively, 0 is the vector of uncertain parameters. The vertex with maximal value of particular design variable is selected after optimization and included in the vector of critical vertices, 0 c. In this way, the maximal number of critical vertices, Nc, is equal to the number of design variables, which often represents significant reduction in problem dimensionality. The scanning procedure, however, could become inefficient when the number of uncertain parameters is really high, e.g. higher than 30. 3.2. Calculation of the conditional expectations for each uncertain parameter A multi-dimensional evaluation of the expected value can be extremely exhaustive. On the other hand, performing the evaluations over a one-dimensional space in order to obtain information about the influence of each uncertain parameter on the objective function is much easier. Conditional expectation of the objective function for a particular uncertain parameter, EC(Oi), is obtained by the simultaneous optimization of the original problem at No Gaussian integration points (usually 5) and Arc critical vertices (NLP2):
min EC(Oi) = E C(xi,di,Oi,ojn]j#:i
-" ~_, C(Xi,k,di,Oi,k,Oj[j.Tt:i). Vi,k k=l
s.t.
fk(Xi,k,di, Oi,k, ojn]j~i ) = 0
fk(Xi,k,di,OCk) -" 0
gk(xi,k,di,Oi,k,ojnff~i) < 0 k = 1,...
gk(Xi,k,4,0Ck) -fdk(Xi' 'k'Oi' k'Onjlj ~')
ag _ A,k (x~,k,0~,)
(NLP2)i i=I,..,Np j=l,..,Np
Xi,k,di ~ R The left group of constraints (for k= 1,...,NG) in model (NLP2) represent the optimization at Gaussian integration points of particular parameter (0/,k), while other uncertain parameters are held at the nominal values (0jn [j~i). The values obtained are then used for estimation of the conditional expected value by using coefficients, v, obtained from the density functions. The right group of constraints (for k=l .... ,Nc) refer to the critical vertices (Okc) determined as described in section 3.1. Note, that the design variables are optimized uniformly for the whole set of points since, in reality, the dimensions of the installed process units cannot change. 3.3. Determination of the central basic point
The approximate functions, C(0/), can be developed for each uncertain parameter by a simple curve fitting based on the realizations of the objective function at the NG quadrature points obtained by solving (NLP2). The derived regression function C(0/) represents the dependency of the objective function on a particular uncertain parameter 0i. It is then possible from this function to predict a central value of uncertain parameter (0/B) by using a simple back calculation. For this central value the objective function is equal to the corresponding conditional expectation obtained in (NLP2)i:
c(oB)=EC(Oi)
i = 1,...,Np
(1)
When the fitting functions, C(Oi), are developed and Eq. (1) is applied to all uncertain parameters, their central values, 0/B, are determined and arranged as components of a vector
301 0B, called a central basic point (CBP). At this point the NLP optimization of the problem will be finally performed in order to obtain the approximate expected value. Steps 3.2. and 3.3. can be repeated iteratively until the convergence on 0B is achieved. 3.4. Determination of lower bounds on design variables Since CBP method deals with one single basic point instead of many quadrature points, the appropriate trade-off between capital and other costs over the entire uncertain space cannot be obtained. This can be overcome if the trade-off is rebalanced by enforcing design variables to the values obtained at calculation of conditional expectations, where a valid onedimensional trade-off is obtained for each uncertain parameter. The most suitable way of enforcing design variables to these values is to set their lower bounds to maximal values obtained in all previous (NLP2)i problems. A vector of lower bounds, d L~ obtained in this way represents a 'conditional overdesign'. The latter can be interpreted as an approximation of the exact overdesign which would be obtained by simultaneous optimization at quadrature points. 3.5. Approximate stochastic optimization at the central basic point The approximate stochastic optimization of the problem is finally performed at the central basic point with the lower bounds on design variables while considering feasibility constraints at critical vertices:
min C(x,d,O B) s.t. f(x,d,O B) = 0
fk(xk,d, OCk)= 0] /
g(x,d,O B) < 0
gk(xk,d, OCk)< O~ k--1,...,N C
(NLP3)
!
a>_A(x,O B) d> d L~
a>_A,k(xk,O~) J x, xk,d eR
4. ILLUSTRATING EXAMPLE The CBP method is illustrated by the optimization of a small heat exchanger network (Fig. 2.) with a fixed structure t4, 51. Three uncertain parameters are distributed by normal distribution functions with the following means and standard deviations: TIN,el N[388, 3.33] K, T~,H2 N[583, 3.33] K, Tcw N[298, 1.66] K. The total costs obtained at the mean values of uncertain parameters and with feasibility constraints at the extreme points amount to 45 100 S/yr. The problem was solved stochastically by using CBP method through the following steps: a) Problem comprises 8 vertices. Three of them were recognized as critical since at these vertices the largest values of design variables were observed. The critical vertices are as follows: 0c={(378, 573,293) K, (378, 573,303) K, (398, 593,303) K}. b) The conditional expected values of the objective function EC(TrN,cl), EC(TIN,H2) and EC(Tcw) were obtained by solving (NLP2) for three uncertain parameters and amount to (45 210, 45 256, 45 100) $/yr, respectively. c) Regression functions were developed based on the values obtained at the Gaussian quadrature points, and the components of the central basic point were determined: 0~=(T~,cl B, Try,H2B, TcwB)=(387.45, 582.73, 298.00) K. Since Tcw does not affect the objective function, its nominal point is considered as the central value. d) Maximal values of design variables were obtained by calculating the conditional expectations in b) and stated as lower bounds: AL~ 2.433, 6.385, 2.062, 2.343) m 2.
302 CF / (kW/K)
Tcw--298 [-H-~620
350 T
1
p'
CTo,~ = 0 (initial required cleaning time) (4)
~ x p ....
0
J
.
0
.
.
.
.
.
~__
5
10
.=
==
15
Weeks
Figure 1. Comparison of the integrated model for scheduling&planning and budgeting with the use of independent models.
320
Figure 2. Comparison of the planning results for the integrated model (right-graph) with the sequential resolution results (left-graph). kl to k13 are the 13th week-period planning and each gray-scale tone is a different product. 4. STOCHASTIC M O D E L Two essential features characterize the stochastic model: the uncertainty in the problem data and the sequence of decisions. Here, as for the planning model product demand is considered a random variable with a normal probability distribution. As for the short-term budgeting model, the delivered product payments and the 'others' costs of production aside from raw liabilities are also considered random. In the long-term budgeting model, the expected production profit and production cost are random. First stage decision variable are the ones concerning the planning & scheduling meanwhile the variables concerning the budgeting are considered second stage. 5. FINANCIAL RISK The Financial risk associated with a specific planning solution under uncertainty is defined as the probability of not meeting a certain target profit level, referred to as s (Barbaro and Bagajewicz, 2002a, b). The use of the concept of downside risk, D-Risk(x,~), in the way introduced by Eppen et al. (1989), is applied in this work. D-Risk(x,W) is used to control financial risk at different targets W. The details of the implementation are in Barbaro and Bagajewicz (2002). The financial risk curve obtained for the stochastic model is shown in Figure 2.
321
Figure 3. Risk curve when no risk minimization is used. 5. CONCLUSIONS This paper has addressed the importance of integrating scheduling and budgeting models. By means of a comparison using a case study, it has been shown that significant improvements are possible as compared to the use of scheduling models followed by budgeting models. It has also been illustrated how a stochastic model can be used to manage financial risk.
Acknowledgements Financial support from Generalitat de Catalunya (CIRIT) and European Community by VIPNET (G1RD-CT-2000-003181) project is gratefully acknowledged. The support of the ministry of Education of Spain for the sabbatical stay of Dr. Bagajewicz is also acknowledged.
References Badell, M., L. Puigjaner, "Discover a Powerful Tool for Scheduling in ERM Systems", Hydrocarbon Processing, 80, 3, 160, 2001. Badell, M., Puigjaner, L., "A New Conceptual Approach for Enterprise Resource Management Systems". FOCAPO AIChE Symposium Series No.320. (Eds. Joseph F. Pekny and Gary E. Blau), American Institute of Chemical Engineers (AIChE), New York, ISBN 08169-0776-5, V 94, pp 217- 223 (1998). Barbaro A.F., M. Bagajewicz. "Managing Financial Risk in Planning under Uncertainty". AIChE Journal. Submitted (2002). Baumol, W. J., "The Transactions Demand for Cash: An Inventory Theoretic Approach," Quarterly Journal of Economics, Vol. 66, No.4 (1952), pp. 545-556. Funk, G., "Enterprise integration: join the successful 20%", Hydrocarbon Processing, 80, 4, 2001. Miller, M. H., And Orr, R., "A Model of the Demand for Money by Firms," The Quarterly Journal of Economics, Vol. 80, No.3 (1966), pp. 413-435. Orgler, Y. E., "An Unequal-Period Model for Cash Management Decisions", Management Science, Vol. 20, No.10 (October 1970), pp. 1350-1363. Shah, N., "Single and Multisite Planning and Scheduling: Current Status and future Challenges". FOCAPO AIChE Symposium Series 94(320), 91-110 (1998). Srinivasan, V.,1986, "Deterministic Cash Flow Management", Omega, Vol. 14, No.2, pp.145-166.
322
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
Modeling Cleaner Production Promotion with Systems Dynamics Methodology: a Case Study of Process Industries in China Lei SHI a, Jing HUANG b, Hanchang SHI a, Yi QIAN a a(State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084,PR China) b(School of Environment and Natural Resources, Renmin University of China, Beijing, 100872, PR China) ABSTRACT By applying systems dynamics methodology, this paper primarily models two key aspects of cleaner production (CP): adoption and diffusion. In the modelling of the adoption of CP, focuses are stressed onto the differences between CP and end-of-pipes (EOP). In the modelling of the diffusion of CP methodology, Bass model is adopted to be the basic model. Based on this model, policy analysis can be carried out by adding peripheral loops to the basic loops. Though this work is just at the beginning, promising roles can be observed to find out what are main factors influencing CP promotion and in what way they influence.
Key words: Cleaner production, systems dynamics, business decision making 1. INTRODUCTION The last decade has seen remarkable progresses in the implementation and promotion of CP in process industries in China. A great deal of stories came forth, no matter what was a success or failure, in state-owned enterprises or SMEs (small and medium enterprises), promoted by governments or carried out voluntarily. Though these implementations and practices have been reviewed and disseminated extensivelyt~'21, further studies especially in a quantitative way are still needed. By introducing Systems Dynamics (SD) which is a methodology for better understanding the interrelationships and complexities, this paper aims to find out what are main factors influencing CP promotion and in what way they influence. Followed by introduction of the state-of-the-art of CP implementation and promotion in China, two key aspects of CP are modelled by using SD methodology. The first one is to model the adoption of CP and end-of-pipes (EOP); the second is to model the diffusion of CP methodology. Both aim to understand and analyse influencing factors and their interrelationships to promote and implement CP. 2. THE-STATE-OF-THE-ART OF CP PROMOTION IN CHINA As early as 1980s, some Chinese enterprises were engaged in technical innovations called 'zero-waste techniques'. These successful cases had provided experiences for China to
323 promote cleaner production later. In 1993, the Second National Working Conference on Industrial Pollution Prevention and Control highlighted cleaner production as a critical measure for harmonizing the environment with economic development, thereby attaining sustainable development. It means that the strategy for industrial pollution control should be shifted from the end of pipe control approach to whole production process management and adoption of cleaner production. Promotion of cleaner production was also documented into some important documents, such as China's Ten Strategic Policies on Environment and Development and China's Agenda 21. At the same year, China initiated its first CP demonstration project, referred to by National Environmental Protection Agency (NEPA) as the "B-4" project under the supports of the World Bank and United Nations Environmental Programs (UNEP). Since then, many efforts have been made in training and awareness raising, policy review, demonstration projects, and international cooperation on cleaner production with various provincial and governmental agencies. Some remarkable progress has been made. For example, many multilateral/bilateral cooperation projects on cleaner production have been carried out with the World Bank, Asia Development Bank, UNEP, and the governments of Canada, the United States, Norway and the Netherlands; National and more than 10 sectoral or local cleaner production centers have been established; Jiangsu, Liaoning province and Taiyuan city have systematically promoted and implemented cleaner production at provincial or municipal level. These early-stage efforts prepared the basic capacity and awareness for wide-adoption of cleaner production in China and led to many significant steps in government policies. In May 1997, SEPA issued the policy documents on promoting cleaner production in environmental protection activities. In May 1999, SETC issued the 'Notice on Promoting Cleaner Production via Demonstration Projects'. Ten cities and five industrial sectors have been identified as national demonstration sites on cleaner production. These cities are: Beijing, Shanghai, Tianjin, Chongqing, Shenyang, Taiyuan, Jinan, Kunming, Lanzhou and Fuyang. The sectors include petrochemical industry, metallurgical industry, chemical industry (nitrogen fertilizer, phosphate fertilizer, chlor-alkali and sulphuric acid), light industry (pulp and paper, fermentation and beer-brewery) and ship building. These important decisions show that China has scaled-up cleaner production strategies from the enterprise level to the regional and sector levels. In April 2000, the Taiyuan cleaner production Law, the first local cleaner production law in China, took into effect. More recently, a law "Cleaner Production Promotion Law" was issued by the National People's Congress on June 29, 2002 and came into effect as of January 1, 2003. Total 6 chapters are included: 1) general Provisions; 2)realization of cleaner production; 3) implementation of cleaner production; 4) inducement measures; 5) legal liability; and 6) supplementary articles. However, the promotion of CP in China is still far from satisfied. The implementation of cleaner production is unbalanced across the country and in different sectors. The majority of provinces and cities in China have not established a mechanism to promote cleaner production. The principles of cleaner production and sustainable development has not been effectively integrated into the policy systems of all government agencies. With "Cleaner
324 ProductionPromotion Law" taking into effects, China's cleaner production is stepping into
a new era where opportunities and challenges coexist. 3. MODELING AND DISCUSSION SD is a perspective and set of conceptual tools that enable us to understand the structure and dynamics of complex systems. Meanwhile, SD is also a rigorous modeling method to help us build computer simulation of complex systems and then use them to design more effective policies and organizations TM. To promote and implement CP is really a complex problem. The 10 years' efforts to implement CP in China have revealed several obstacles to further CP promotion. These obstacles exist both within and out of the enterprise. Generally, these can be categorized into the following types: 1) awareness; 2) management; 3) policy; 4) regulation and 5) technological aspects. To help to understand and analyse influencing factors and their interrelationships to promote and implement CP, two aspects of CP are modelled in this paper by using SD methodology. 3.1 Modelling the adoption of CP and EOP EOP technologies include the use of a variety of technologies and products (chemicals) to treat wastes and liquid and gaseous effluents. The major characteristic distinctions between CP and EOP have been extensively discussed and are not listed here. There exist many influencing factors for enterprises to adopt CP or EOP, such as the technological maturity, expected economic and environmental benefits, fund availability, and environmental regulations, etc. To model the adoption of CP or EOP at enterprise level is a challenging job. One simple SD model is illustrated in Fig. 1 which only considers the effect of cost, benefit and network compatibility. The resulted adoption ratio and market share are shown in Fig. 2. The model is just to illustrate the usefulness of SD methodology and too simple to reflect the real world. A more complex model is expected in our later work. 3.2 Modelling the diffusion of CP Methodology To promote the diffusion of CP methodology (CP audit here), a set of policies has been developed to promote CP in China based on the B-4 project, China-Canada CP cooperation project and other projects. An integrated policy framework has been shaped through surveys, case studies and reviews from China's current environmental and industrial policies and regulations, as well as its technological renovation policies and strategies. This CP policy framework composes of compulsory, incentives, pressure and supportive mechanisms (Fig. 3). To model the diffusion of CP, the Bass model is adopted as a base model. There are two basic feedback loops existing in Bass model. One is market saturation which has balancing
325 effect; the other is word of mouth which has reinforcing effect. Bass model is a widely-used diffusion model and has been used to model the diffusion of new product and technology. Its main advantage lies in that it can solve the startup problem of diffusion models. Fig. 4 illustrates the basic construction for modeling the diffusion of CP methodology. This model can help us to learn how the adoption rate and market share are affected by influencing factors. Say, different demonstration effectiveness can lead to different schemes of adoption rate and market share, which brings a useful tool to evaluate CP projects. Based on this model, then, subsequently, one by one, additional factors can be added and their influence on the dynamics of the process is studied. For example, the supportive policy will change the contact rate between CP adopters and potential adopters; we can learn what is the role of this policy by changing the contact rate. Applying this model to analyze city-level CP actions in Nantong city and Changzhou City in Jiangsu Province [4], we can primary understand how the elements of Jiangsu's CP program takes into effect. The results will be shown later.
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326
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...>ik=l V~, Wr, Crx, ~, ~x, ~. A~'il...ik and BYkil...ik are coefficients of the model. The factor is f~, ...ik is a symmetric factor. Figure 2 is a typical merit function landscape of a RPV model with C=3, N=O, M=15 and Qx=2. The piecewise continuous and nonlinear nature is evident. The complexity of the problem can be increased by increasing the dimension of variable and number of phases. Given the N-K model and the RPV model, the sampling algorithm for optimization in combinatorial synthesis and library design can be benchmarked. 3. METHODOLOGY 3.1 IFEDSA The Information Free Energy Directed Annealing (IFEDA) procedure proposed is described as follows: 1. A set of 100 initial configurations are sampled randomly and the merit functions are sampled. 2. The data are trained to generate a meta-model using the generalized regression neural network (GRNN, [12]) 3. A simulated annealing search is performed using this GRNN meta-model. i. The existing minimum is selected as the reference and its information free energy is calculated. ii. A potential test candidate is generated and its information free energy is calculated. iii. If information free energy of the candidate is less than that of the reference a. The candidate is collected as a new test point b. The reference point is replaced the candidate c. If NT candidates have been collected go to step iv, otherwise go to step ii otherwise, go to step ii 4. The actual merit functions at this new batch of candidate solutions are evaluated (tests are performed). 5. If enough tests have been performed and the optimum found is satisfactory, the process is stopped. Otherwise, the temperature is reduced and then go to step 2 so that the modeling-selection-testing cycle is repeated.
367
3.2 Information Energy Given a model of all the presently available experimental data, we define the information energy index of any candidate point in the search space
u (x) = ~ ~ (x) for menimizaton problems [-~" (x) for maximizationproblems
(4)
At any candidate point, the better the predicted fitness function, the lower is its information energy. The more valuable is the information at that point.
3.2 Information Entropy Our knowledge of a candidate point can be measured by the information entropy
S(x)--i=~I(X--xi)TA-I(x--xi)expI--(x--xi)TA-I(x-xi)]
(5)
i~=exp[-(x-xi)rA-'(x-xi)] A-z is a scaling factor matrix. The information entropy is just the average of the square of the distance between a candidate point and all existing data points. The higher the information entropy, the more we know about the candidate point.
3.4 Information free energy and temperature annealing A candidate is worthy of experiment if it has a potential of having a good fitness value (low information energy), or it neighborhood has not been sufficiently explored (high information entropy). During the initial stages when the number of experiment is small, the model predictions are of little value, experiments should be devoted to sample un-chartered search space. When sufficient information has been gathered, only samples that are potentially important should be tested. Chen et al. (1998) proposed an information free energy index: F(x)- U (x)- rS(x) (6) T is an annealing temperature proportional to the number of experiment. The proper convergence of the sampling procedure to the global minimum depends on the annealing schedule. In this work, the very fast annealing scheme proposed by Ingber [ 13] was used:
T = TO 9exp(-a. N o )
(7)
with To, a, b being adjustable constants and N is the total number of the experiments. 3. RESULTS Table 1 shows the results of solving several N-K and RPV problems after 1000 function evaluations. It was found that our IFEDA approach is much more effective than simple SA for both the N-K and R-P-V problems. Table 1:
Search Results for the NK and RPV problem using SA and IFEDSA RPV NK
M:15,N=3, Z:O,Q=2 Avg. a SA -1.71 IFEDA -1.83 average of 10 runs,
Stdb 0.08 0.01 b standard
M=37,N=6 Z:O,Q=6
N=20,K=4
Avg." Stdb ,, Av~. a -1.02 0.12 1.54 -1.62 0.02 1.13 deviation of 10 runs
Std b 0.08 0.00
N=30,K=5 Avg. a 1.88 1.58
Stdb 0.11 0.07
368 Fig. 1 illustrates the decrease in the objective function with number of batches of experiments for a RPV model. An undirected simulated annealing search causes the system to deviate from the current minimum so that the search process will avoid being trapped in a local minimum. However, aided by the GRNN, the IFEDSA is able to locate the correct minimum almost 1 order of magnitude faster. .9o . s
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Ordinal optimization theory [9] found that the improved alignment of top ranked candidates by the use of a model depends strongly on (i) how the fitness function is distributed, shape of the ordered performance curve, but weakly on (ii) accuracy of the model, and (iii) selection rule, how the model is used. Both the N-K and RPV models exhibit what is called a bell-shaped distribution curve, which is most amenable to help by a model. A parameter W defined as
maxlY-r~j
W = rmax - rmin
,
(8)
was used in their studies to represent model accuracy. Lau and Ho [14] found that when W-0.5, the alignment can be increased by 6--8 times if a horse-race selection rule is used. Our IFEDSA is just another form of selection rule. For both N-K and RPV model, we found that W-0.3 to 0.5. Therefore approximately one order of magnitude increase in efficiency is expected. Fig. 2 shows the distribution of data sampled in the 1st "~10 th batch, 21 st to 30 th batch and 51 st to 60 th batch for a N-K problem. It is obvious that distribution of data sampled shifted towards low energy end. Such changes were not found for simple SA. This shows that IFEDA allows us to perform importance sampling in the early stages of the search.
Fig. 2: Distribution of data sampled different stages of optimization for the N-K model
369 4. CONCLUSION In this work, we have demonstrated the necessity of true importance sampling in solving optimization problem of high dimension. Our results show that while brute-force combinatorial techniques may be powerful, but the technique becomes much more effective if we can organize the present knowledge periodically to direct the search. The organization of knowledge need not be done in a theoretical or a precise manner. A simple empirical model that memorizes all existing data is sufficient. A "rough" model together with appropriate consideration of model uncertainty is able to significantly improved search efficiency. REFERENCES
[1] M.E. Davis, "Combinatorial Methods: How will they integrate into Chemical Engineering?," AIChE J., 45(11), 2270, 1999 [2] X.D. Xiang, X. Sun, G. Briceno, Y. Lou, K. A. Wang. H. Chang, W.G. Wallace-Freedman, S.W. Chen and P.G. Schultz; "A Combinatorial Approach to Materials Discovery," Science, 268, 1738, 1995 [3] B.M. Cole, K.D. Shimizu, C.A. Kreuger, J.P.A. Harrity, M.L. Snapper, and A.H. Hoveyda, "Discovery of Chiral Catalysts Through Ligand Diversity: Ti-Catalyzed Enantioselective Addition of TMSCN to Meso Epoxides," Angew Chem Int. Ed. Engl., 35, 1668 (1996) [4] B.M. Gordon, M.A. Gallop, and D.V. Patel, "Strategy and Tactics in Combinatorial Organic Synthesis. Applications to Drug Discory," Acc. Chem. Res., 29, 144, 1996 [5] L. You, and F.H. Amold. "Directed Evolution of Subtilisin E in Bacillus Subtilis to Enhance Total Activity in Aqueous Dimethylformamide," Protein Eng., 9(1), 77,1996 [6] E.J. Pretel, P.A. Lopez, B.B. Susana, and E.A. Brignole, "Computer-aid molecular design of solvents for separation processes," AIChE J., 40(8), 1349, 1994. [7] P.J.M. van Laarhoven, and E.H.L. Aarts, "Simulated Annealing: theory and applications", Rediel, Dordrecht, The Netherlands, 1987. [8] J.H. Chen, S.S. Jang, D.S.H. Wong and S.L. Yang, "Product and Process Development Using Artificial Neural-Network Model and Information Analysis," AIChE J., 44, 876, 1998. [9] Y.H. Ho, "Tutorial on ordinal optimization" http://hrl.harvard.edu/people/faculty/ho /DEDS/00/ OOTOC. html [10] S.A. Kauffman, and S. Levin, "Towards a general theory of adaptive walks on rugged landscapes," J. Theor. Biol., 128, 1 l, 1987. [11 ] M.M. Falcioni and W. Deem, "Library Design in Combinatorial Chemistry by Monte Carlo Methods", Physical review E., 61(5), 5948, 2000. [12] D.F. Specht., "A General Regression Neural Network," IEEE Transactions on Neural Network, 2(6), 568, 1991. [ 13] L. Ingber, "Very Fast Simulate Annealing," Math. Comput. Modeling, 12, 967, 1989. [14] T.W.E. Lau, and Y.C. Ho "Universal alignment probability and subset selection for ordinal optimization.", Journal of Optimization Theory and Applications, 93(3), 455, 1997
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
370
Balanced Production Cost Estimation for Byproduct Gases in Iron and Steel Making Plants Heui-Seok Yi a, Jeong Hwan Kim b, Chonghun Han b aSchool of Environmental Engineering, Pohang University of Science and Technology, San 31, Hyoja, Pohang, Kyungbuk, 790-784, Korea bDepartment of Chemical Engineering, Pohang University of Science and Technology, San 31, Hyoja, Pohang, Kyungbuk, 790-784, Korea
Abstract A process industry consists of many individual process plants and each plant produces the various byproducts and final products. The byproduct in a plant can be used as feedstock or energy source in another plant. The cost estimation of the byproduct is critical to calculate the cost of final product when the byproduct is used as feedstock or energy source. However, the unbalanced characteristics of process measurements make the cost estimation of the final product incorrect due to the inaccurate cost estimation of byproduct. In this paper, balanced production cost estimation is implemented for an iron and steel making plant using data reconciliation and multiple gross error estimation. The byproduct cost is estimated from the error-corrected data so that the byproduct cost is consistent between the producer and consumer in the plants. Mixed integer nonlinear programming is applied to estimate the magnitudes of gross errors and remove random errors in process measurements simultaneously. Balanced cost estimation solves any discrepancy between the production cost of producers and the production cost of consumers. Keywords cost estimation, gross error estimation, data reconciliation, byproduct gas 1.
INTRODUCTION
An industrial plant complex is composed of several individual plants. Each plant makes byproducts, intermediate products and the final products from raw materials. Intermediate products and byproducts in a plant can be used for energy sources or raw material in another plant [ 1]. The operational data for energy and raw material consumption and production rate are used for production accounting. However, the random and gross errors of process measurements result in the discrepancies of the generation/consumption amounts between production plants and consumption plants, which make it difficult to estimate the exact production cost. For the production accounting, the error-corrected data must be used to make the financial data from operational data consistent [2]. Measurement errors can be removed by gross error estimation and data reconciliation. Data reconciliation adjusts the process measurement to satisfy the conservation law under the assumption of no gross error. There has been much research work to compensate the gross errors. Serial elimination is simple to implement but loses redundancies during the procedure. Serial compensation keeps redundancy but its accuracy depends on the size of gross errors. Simultaneous compensation is more correct than the serial method, however, much computation time is required [3]. The modification of the simultaneous compensation was proposed to reduce computation time [4]. In this paper, balanced cost estimation of byproduct gases for an iron and steel making plant is proposed using the error-corrected data. Random and gross errors are removed
371 simultaneously by mixed integer nonlinear programming (MINLP). Error-corrected data are used to estimate the production cost of byproduct gases. Estimation results show no discrepancies between the production plant and consumption plant of byproduct gases. 2.
BALANCED E S T I M A T I O N OF BASIC UNIT COST
Pl~ Balanced measurements are .__._~. essential to estimate the basic unit R1 cost. Figure 1 shows the process P2/,' flow network. Plants A, B, C and D R2 ~ V ~ I2 ] ~'I~ i consume raw materials, R1, R2, R3 IT ~ IMT / I M 2 ~ V ~ _ _ . ~ and R4 respectively, and produce P 1, ~P3~t P2, P3 and P4 as the final products, R3 ~[ C [ and I1, I2, I3 and I4 as intermediate p~ products. The intermediate products R4 I4 ,,..are mixed and consumed in the "~ plants E, F and G. The final Figure 1. Simple process network to show the balanced cost products, P5, P6 and P7 are made estimation. from the mixed intermediate products IM1, IM2 and IM3. Table 1 shows the true values and the measurements of the measured streams. Streams IT and IMT are not measured and all the Table 1. Measured values and true values other streams are measured. The plant E makes the final Measured product, P5, from raw Streams True Measured Streams True values values values values material, IM1 that is the R1 121 128 I4 90 95 mixed intermediate product. IM1 180 185 R2 165 171 The basic unit costs of IM2 100 95 R3 98 102 Pi (i = 5,6,7)for raw material IM3 140 136 R4 96 104 I1 have two different kinds S1 36 32 P1 21 25 of views: from the $2 20 17 P2 15 18 production plants A and $3 28 30 P3 18 15 from the consumption plant P5 144 151 P4 6 4 P6 80 76 I1 100 95 E. For example, the basic P7 112 121 I2 150 155 unit cost for P5 from points I3 80 83 of the plants A, B, C and D's views can be defined by Eq. (1). Table 2. Reconciled values P5
Cps,II, A =
IM 1
(1)
II ~-'IM, The basic unit cost of P5 for raw material I1 from the point of plant E's view can be defined by Eq. (2). P5 Cps.11.L. =
11 IM~ ~ I , t
(2)
Streams R1 R2 R3 R4 P1 P2 P3 P4 I1 I2 I3
Reconciled value 121.7 169.2 98.7 100.2 25.2 18.0 15.1 4.0 96.5 151.2 83.7
Streams I4 IM1 IM2 IM3 S1 $2 $3 P5 P6 P7
Reconciled value 96.2 186.9 94.6 146.0 32.2 17.1 29.7 154.7 77.5 116.3
The value of Cp5,:i,~ is equal to the value of Cp5,11, E with balanced data of mass flow rates. For example, the values of Cp5,11,A and Cp5,1~,E are 3.36. However, the value of Cp5,11,A is 3.500 and the value of Cp5,11,E is 3.677 with the measured data of mass flow rates. The error of basic unit cost with unbalanced data is
372 about 4.5 percent. Therefore, the calculation of basic unit cost or other accounting must use balanced flow rates. Assuming that the measured data in Table 1 have no gross error, data reconciliation gives balanced data for mass flows. Table 2 shows the results of data reconciliation. The basic unit costs using balanced data give the identical results (Ces,ll,A=Ces,il,~,=3.667). Therefore, the basic unit cost must be implemented using balanced data that are obtained by data reconciliation of measured data. 3.
GROSS E R R O R E S T I M A T I O N AND DATA R E C O N C I L I A T I O N
To obtain balanced measurement data, data reconciliation and gross error estimation must be implemented. The simultaneous estimation of the multiple gross error magnitudes and the reconciled values is used. The procedure consists of two steps: the first step is to identify the location of the multiple gross errors and the second step is to estimate the gross error magnitudes and the reconciled values. 1 st step In the first step of the proposed algorithm, the candidates of the gross errors are identified by measurement test. The vector of measurement adjustment can be defined as:
a=y-i (3) where y is the vector of measurement and i the vector of reconciled estimation. The measurement test requires data reconciliation before the identification. The test statistics, Za,j, follows a standard normal distribution under H o that no gross error is present in the system.
la l
z a,j = X/-~
(4)
where W~, is diagonal element of covariance matrix of a. Other identification methods can be used in the step 1 such as nodal test.
Ir,I
(5)
where r, is the i th element of the residual vector, r = Ay, and V,, is the diagonal element of the covariance matrix of r . The identified gross errors are formulated as integer variables to confirm their existence and estimate their magnitudes in the second step. 2 ad step
The model for the flow measurements and gross errors can be given by y =x+e+6 (6) where y is the vector of measurements, x the vector of true value, e the vector of random errors and 6 the vector of gross errors. The simultaneous estimation of the gross error magnitudes and the reconciled value is the solution of minimization problem given by mi.
Z
Jt
(y -x y j.t
streams
O'j
/=identified streams
subject to Ai = 0
(8)
373
]dkl < UkBk
(9)
ls l>-e g B
(10)
~,>0
(11)
Bk e Binary (12) where k is the stream that gross error is identified, V is the covariance matrix of constraint residuals, l~ the covariance matrix of measurements. U is chosen as arbitrary large value that can be considered as the upper limit on the bias magnitudes. The value of Bk must be fixed as zero if the measurement or nodal test in the first step does not identify any gross error. The values of ek must be chosen such that the values of ekUk is some times of standard deviation of the measurements [5]. 4.
INDUSTRIAL APPLICATION
Balanced cost estimation by data reconciliation and gross error estimation is applied to byproduct gases in an iron and steel making plant. The iron and steel making plant consumes much energy, and the basic unit cost for energy is very important for the economical production and energy management. Energy can be purchased in the form of liquefied natural gas, coal, heavy oil and electricity and be byproduced in the processing of iron ores as well. There are four kinds of byproduct gases: BFG (blast furnace gas), CFG (COREX furnace gas), COG (coke oven gas) and LDG (Linze Donawitz gas). These gases are not released to the environment because they are very toxic, and are consumed as energy sources in the downstream processes. The schematic diagrams of BFG, CFG and LDG distribution flows are shown in Figure l(a). Solid line shows 5,6 ~o';eT"'~ ................ ii................. i the BFG flow, dotted line CFG flow plant ] . _ . . _ . ~ 4coke i [ Corexfumace ] and dash-dotted line LDG flow. BFG 4furnace 7/ [: i~ / 5 / . _. . . _ ~-------__ 3 ~/ 3 coke .... i i ~t. / t F/S i s by-produced i n b l a s t furnaces and F furnace-~------~ - - , i ........ ~"-'---4 2coke ] : (HD consumed in coke plants and power 2F/S/ ~ ......... ~:" .... : : z " : ' " " : ' , . . . . i ' ]-plants. The remaining BFG is l...._~ -----5........... l coke i t ..................... pressurized and then mixed with COG [~}~uman~ ~-=-:~_& and LDG. CFG is by-produced in U ~ g ~ - ~ , _ _ _ _ / ~ 1F/S COREXfumace a n d i s c o n s u m e d i n [~fumyi~ ~ ~ power plants. The remaining amount is ) mixed with BFG directly. LDG is by~ - - -~;k--~~'H"~iWG/s --~ ] rT~,x produced in steel making plants. LDG _Z~x~ generated in the first steel making ~ / ~ [~-~-~) I t zM, ~ 12power 1 _~_____-:~_ plant is consumed in the first and second
power
plants.
However,
LDG
generated in the second steel making plant is pressurized and then consumed in low-pressure boiler or mixed with BFG and COG. Figure l(b) shows the schematic diagram of COG distribution flow. COG is byproduced in coke plants and consumed in furnaces, power plants, chemical plants, steel making plants, etc. The remaining COG is pressurized and mixed with BFG and LDG. The mixed
I-l"/"plant
~
1
I
--~_.. . . . . . ~3.4-po--wer-~ 2BFG/S 2LDG/S , ,, plant =--- :::--~t'HD) I ' Pi_ 1 !.-- 9-- 9~[{}7~ -~ - ..... " -ste----Lei 1 mak!ng_J
i
j.~.
I
_~
I---.~-T'Nlix-j . p.~W---~,x
[ LP boiier 3 LDG/S ..j
( HD )
Y ~m2kSt~l ingJ F i g u r e 1 (a). T h e s c h e m a t i c d i a g r a m o f B F G , C F G a n d L D G d i s t r i b u t i o n lines
374 gas is consumed in the plate rolling mill (PL) and wire rod rolling mill (WRM) and hot strip mill (HSM). The magnitudes of multiple gross errors and the reconciled values of measured streams are estimated by MINLP formulation and their results are shown in Table 3. The parenthesized values are the magnitudes of gross error estimated. The gross errors are identified in the production plants and no gross error is found in the consumption plants. Using the results of error-corrected data, balanced estimation of the basic unit cost can be computed. The basic unit cost for all plants is not required to show the significance of balanced estimation. The basic unit costs for some of BFG consumption plants in the point of no. 1 BFG plant's view are only shown in this paper due to the limited space of paper. Table 4 compares the basic unit cost of some BFG consumption plants in the point of no. 1 BFG plant's view by the error-corrected data with that by the measured data. The basic unit cost with balanced data from the point of the production plant has no differences with that from the point of the consumption plant. However, the basic unit costs with unbalanced data from the point of no. 1 BFG plant's view are not equal to that from the point of the consumption plant. The estimation error is about 1.14%-~ 3.60% compared with the basic unit cost using balanced data. Cost estimation with the unbalanced data is inconsistent and should be implemented with the error-corrected data. 5.
CONCLUSIONS
Balanced estimation of basic unit cost or production cost is implemented using the error-corrected data. To remove the measurement error, simultaneous gross error estimation and data reconciliation by MINLP is used. The results show that the basic unit cost using the error-corrected data shows no errors. However, the basic unit cost using the measured raw
375
data shows estimation errors about 1.14%-~ 3.36%. Therefore, the estimation of production cost must be implemented using the error-corrected process data. Balanced cost estimation could solve the discrepancy between the production cost for producers and the production cost for consumers. Table 4. The comparison of the basic unit costs Basic unit cost for each plant with Balanced estimation of the basic unit cost unbalanced data ................................................................................ From the point of From the point of From the point of From the point of the no. 1 BFG plant's the consumption no. 1 BFG plant's consumption plant view plant view 1 HSM 39.3 37.5 38.0 38.0 2 HSM 39.0 37.3 37.7 37.7 2 PM 139.7 133.3 134.8 134.8 3 PM 845.7 806.9 816.2 816.2 Billet 542.2 517.3 523.2 523.2 1 WRM 509.8 486.4 492.0 492.0 2 WRM 472.4 450.7 455.9 455.9 3 WRM 275.4 262.8 265.9 265.9 Plants
[C
....
5, 6 power],n I[ TP~T' ~
I c -il b sts i ~1
fu . . . . .
F fu~aeeJ
I,f~.... ~ P[CI 1
chemical plant 4 coke
I
___~3
chemical plant
]
chemical plant
]
___~2
F/S
I
I T M II ICAL ___~i
3 coke ]
t
E
Iio'~l~
l ~,,,o, I-L........................
~ 1 - ~ 3s'~
[,fu .... ] V'~
~------{1smt
I ~PM I
i
2 coke ]
I
I PL ,]J
~ ~
j
I,-~ll 11
~ s~s~, i
i......... ~.-.~~ 17i w~M
~i
I11
EsM i
STS 2
[ I SMPI
1 BCCP
~--J
i
11~2" ~
[ 3, 4 p.... I l pl~tnt J
~l,J~176
i......... 1 -q~.sM /
R st
....l c.,cP
'
'
Figure 1(b). The schematic diagram of COG distribution line REFERENCES
[ 1] D. P. Lal, Chem. Eng. Prog., 98 (2002), 72. [2] B. Harkins and K. Mills, Chem. Eng. Prog., 97 (2001), 58. [3] Q. Jiang and M. J. B a g a j e w i c z , Ind. Eng. Chem. Res., 38 ( 1 9 9 9 ) , 2119.
[4] H.-S. Yi, H. Shin, J. H. Kim and C. Han, Proceedings of ICCAS 2002, International Conference on Control, Autimation and Systems, Oct. 16-19 (2002) Muju, Jeonbuk, Korea. [5] T. A. Soderstrom, D. M. Himmelblau and T. F. Edgar, Control Eng. Practice, 9(2001), 869.
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
376
Optimal Design of Batch-Storage Network with Recycling Streams Gyeongbeom Yi* Gintaras V. Reklaitis** * Department of Chemical Engineering, Pukyong National University, Busan, Korea 608-739 School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, U.S.A.
Abstract An effective methodology is reported for determining the optimal capacity (lotsize) of batch processing and storage networks which include material recycle or reprocessing streams. We assume that any given storage unit can store one material type which can be purchased from suppliers, be internally produced, internally consumed and/or sold to customers. We further assume that a storage unit is connected to all processing stages that use or produce the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. The objective for optimization is to minimize the total cost composed of raw material procurement, setup and inventory holding costs as well as the capital costs of processing stages and storage units. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two subproblems. The first yields analytical solutions for determining batch sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks. For the special case in which the number of storage is equal to the number of process stages and raw materials storage units, a complete analytical solution for average flow rates can be derived. The analytical solution for the multistage, strictly sequential batch-storage network case, which was previously reported by the authors, can also be obtained via this approach. The principal contribution of this study is thus the generalization and the extension to nonsequential networks With recycle streams.
Keywords : Optimal, Design, Batch, Storage, Network, Recycle 1. INTRODUCTION The purpose of this study is to suggest an effective methodology to determine the optimal capacity(lot-size) of general batch-storage network including recycling streams. We already have developed the compact analytical solution of the optimal lot sizing of multiproduct, sequential multistage production and inventory system with serially and parallel interlinked storage units and processes[ 1]. In this study, we enlarge the network connection structure of the storage units and the processes to the most general form. We assume that any storage unit can be connected to any process as feedstock and/or product. A practical advantage of this study over our previous work exists in that we can deal with the network structure involving non-sequential recycling streams, which is very popular in chemical processes. In spite of general presentation, analytical solution is still available as a special case. 2. O P T I M I Z A T I O N M O D E L
Chemical plant, which converts the raw materials into final products through multiple physicochemical processing steps, is effectively represented by the batch-storage network, as shown at Fig.1. The chemical plant is composed of a set of storage units (J) and a set of batch processes (I) as shown in Fig. l(a). The circle ( j ~ J ) in the figure represents a storage unit, the square (i ~ I ) represents a batch process and the arrows represent the
377 material flows. Each process requires multiple feedstock materials of fixed composition ( f J) and produces multiple products with fixed product yield ( g / ) as shown in Fig. 1(b). Note that storage index j is superscript and process index i is subscript. If there is no material flow between a storage and a process, the corresponding feedstock composition or product yield value is zero. Each storage unit is dedicated to one material.
~~
,~
~ (a) Process and Storage Set f'
20%
~
(b)Feedstock
RawMaterials 60% 30% b,~ 10%
80% ~ Feed Composition
g/
Product Yield
i Composition and Product Yield
......
Finished Products
supp,ir k ,
I C. . . . . . . .
J
(c)MaterialMovement around Storage
Fig. 1. General Structure of Batch-Storage Network Each storage is involved with four types of material movement, purchasing from suppliers ( k ~ K ( j ) ) , shipping to consumers ( m ~ M ( j ) ) , feeding to processes and producing from processes as shown in Fig. l(c). Note that the sets of suppliers K(j) or customers M(j) are storage dependent. The material flow from process to storage (or from storage to process) is represented by the Periodic Square Wave (PSW) model[l]. The material flow representation of PSW model is composed of four variables: the batch size B,, the cycle time co,, the transportation time fraction x,lit (or x~
the start-up time t,lit (or t,o u t ).
According to the same discussion in Reference [1], we assume that the feedstock feeding operations to the process (or the product discharging operations from the process) occur at the same time and their transportation time fractions are the same among feeding or discharging flows. That is, the superscript j is not necessary to discriminate the storage units in the x,. . .(or . x, , ) and t~it (or t ~ ) . The material flow of raw material purchased is J transportation time fraction x/, and start-up represented by order size B[, cycle time cok, time t~ tilt (or t~
All transportation time fractions will be considered as parameters
whereas the other will be the design variables as used in this study. The material flow of J J m J in the same way. The arbitrary finished product sales is represented by B~,com,Xm,t periodic function of the finished product demand forecast can be represented by the sum of ) Xm' J tm. ) periodic square wave functions with known values of B~, COrn,
378 The feedstock flows from predecessor storages and the product flows to successor storages are of course not independent. From the fact that one production cycle in a process is composed of feedstock feeding, processing and product discharging, there exists the following timing relationship between the time delays of feedstock stage and the time delay of product stage. t,out = t~in + co,(1 - x~out \)
(1)
Let D, be the average material flow rate through process i, which is Bj divided by co,. The average material flow through raw material storage and finished product storage are denoted by D~, Dmj respectively. The overall material balance around the storage results in the following relationships; I~1
IKO)I
I~1
IMO)I
J=l
k=l
t=l
m=l
Zg/D, + ZD/, = Z f 'D, + ZD~
(2)
The size of storage j is denoted by VJ.
The initial inventory of storage j is denoted by
V j (0). The inventory hold-up of storage j at time t is denoted by V j (t). The inventory hold-up can be calculated by the difference between the incoming material flows from supply processes and the outgoing material flows into consumption processes. Special properties of the periodic square wave function are required to integrate the detail material balance equation [1 ]. The resulting inventory hold-up functions for storages are;
v.,(,) = v~(o)+ Z,,:, 8~
[intr":l +minl,__resF ' '--;lll ' L 04 J
+ ;(g/B,)/lnt| ,=, -Z m=l
t ' x~~
+min
L(o, jJJ
1,4res x,
[ Lt_,] {1 F,4 ll
B]m int
+min
COJm
L co,
1,~res
(3)
[ E "] f ',,
(f/B,) int t-t, XJm L (Dm JJJ ,=1 cO,
+min
1,~res x;
cO,
l}l
The upper bound of inventory hold-up, the lower bound of inventory hold-up and the average inventory hold-up of Eq. (3) are calculated by using the properties of flow accumulation function[ 1]. (4) k=l
i=l
Ixo)l I11 VtZ = V J ( O- ) Z v ~', ~j- Z g / ~ , k=l
t=l
m=l
It;"+ co, (1- x , out)]
t=l
Izl
IMO)I
t=l
m=l
+Ef/D,[t;"-(1-x;">,]+ ZDJm['Jm--(1--XJm)COj ]
(5)
3'/9 IxO)l III (1 o . t I/I ~) J -VJ - = V ; ( 0 ) + I I(1 - x k ) D kj c ojk - Z Dktk j j + Z ~- x, ) g / D , co, - ~_, g / D , t ~ k=! 2 k=l t=l t=l IM(J)l(l_Xm) J
m
_ t=l
~ ( 1 - x , ) f, JD, co, + ~ f , JD, t~~ - ~ 2 t=l m=l
2
J
J
IM (J)l
Dmcom+ ~ D m tj m;
(6)
m=l
The purchasing setup cost of raw material j is denoted by A~ S/order and the setup cost of process i is denoted by A, S/batch.
The annual inventory holding cost of storage j is
denoted by H j S/year/liter. The annual capital cost of process construction and licensing can not be ignored in the chemical process industries. In this article, we will assume that capital cost is proportional to process capacity in order to permit analytical solution. The objective function of designing batch-storage network is minimizing the total cost consisted of the setup cost of processes, inventory holding cost of storages and the capital cost of processes and storages.
TC:ZZ j=i
Ak +a~D~COk
+
k=l
+ a , D , co,
+
t=l
(7)
HJ-V-7+bJVffb j=l
where a kj is the annual capital cost of the purchasing facility of raw material j, a, is the annual capital cost of process i and b j is the capital cost of storage j. Without loss of generality, the storage size VJ will be determined by the upper bound of inventory holdup, V~.
Therefore, Eq. (4) is the expression for storage capacities.
The independent variables
are selected as cycle times o kJ , co,, initial time delays t~,t,In and average processing rates D~,/9,.
The inventory holdup V j (t) should be confined within the storage capacity.
Sufficient conditions are
0__Pdem n
(8)
k
@Steam balance f demand and supply: Z E . F . .... + Z E z F k n
....
+F.,m>_F.,aem,m
(9)
k
(6) Constraint of consumption ratio: ....
(10)
P < Pmax
(11)
Ff~Ff
Fs, m < F s . . . . . .
(12)
The optimization model was developed using MS m E x c e l 2000. Like other applications in process systems, the utility system should be formulated as a Mixed Integer Non-Linear Programming (MINLP) problem. However, without scarifying too much accuracy, this problem can be solved by using MSmExcel 2000. Application of this method to investment decision-making for utility systems projects in a petrochemical corporation indicates that this method is effective and rational. 5 CASE STUDY The methodology just described has been applied to a continuous plant. The petrochemical corporation is made up of fertilizer factory, refinery and chemical plant. It consists two
386 utility systems separately for fertilizer factory, refinery. Based on process integration and integrated hierarchical structure of process systems, optimal investment projects are presented for conceptual design and retrofit of utility. In order to minimize the overall cost, the fuel consumption and power generation in the utility system are normally the crucial cost components to consider. According to expert experience, some other feasible projects are recommended. By the method of computer-aided calculation, the results of every project are shown in Table 1. From the analysis of results, we conclude that the rational investment project should include retrofit of fertilizer (coke), new turbine of fertilizer and new gas turbine of fertilizer. Table 1. Project Project 1 Project 2 Project 3 Project 4 Project 5 Project 6
The results of every project for retrofit of utility systems Content of project Investment c o s t Annualcost (108u (108u Currentbasic operation _ 5.00 Retrofitof fertilizer (off-gas) 0.6 4.66 Retrofitof fertilizer (off-gas), new turbine of 0.7 4.55 fertilizer Retrofitof fertilizer (off-gas), new turbine of 0.8 4.19 fertilizer, pipe-line for high pressure steam Retrofitof fertilizer (coke), new turbine of 4.0 4.39 fertilizer Retrofit of fertilizer (coke), new turbine of 5.5 3.98 fertilizer, new l~asturbine of fertilizer
6 CONCLUSIONS Based on process integration and hierarchical structure of process systems, retrofit investment decision-making has been studied. A general formulation and an optimal strategy for the synthesis of utility systems have been presented. The objective for systematic investment evaluation is operation cost minimization. Sottware for scientific decision-making and optimal design has been developed on MSuExce12000. A case study is presented applying this method to investment decision-making for retrofit utility systems in a petrochemical corporation. It is shown that the new options considered may reduce significantly investment and operating cost. REFERENCES [1] Hua B., Zhou Zhangyu & Cheng S., hierarchical model architecture for enterprise integration in process industries. Chinese Journal of Chemical Engineering. Vol 9, No.4, 371-376, 2001. [2] Zeng Mingang, Yin Q., Hua Ben et al, Study on the Operation and Management Decision-making for Process Industry, Chemical Techno-Economics, Vol 18, No.8, 5, 42-46, 2000. [3] Douglas J. M., Conceptual Design of Chemical Processes, p. 569. McGraw-Hill, New York (1988). [4] Linnhoff B. et al., User Guide on Process Integration for the Efficient Use of Energy.
387 IChemE, U.K. (1982). [5] Dhole and linhoff, Total Site targets for fuel, co-generation emissions and cooling. Computers chem. Engng 17, S 101-S 109 (1992) [6] Hui C. W. and Ahmad, S., 1994, Total Site Heat Integration Using the Utility System, Computers and Chemical Engineering, Vol. 20, supple, 729~742, 1994. [7] Papoulias S. A. and I. E. Grossmann, A structural optimization approach in process synthesis, Computers chem. Engng 7, 695-734 (1983) [8] Hua B., A Systematic Methodology for Analysis And Synthesis of Process Energy Systems, AES, 2-1,57(1986) [9] Zhang Guoxi, Hua Ben Liu Jinping, Optimal Synthesis and Operation of Utility Systems, Modem Chemical Industry, Vol 20, No 7, 43~45, 2000. NOTATION C ~ T o t a l annual operation cost E n ~ b i n a r y variable, 1 if project n in operation, 0 otherwise E k ~ b i n a r y variable, 1 if Equipment k in operation, 0 otherwise 13n ~ I n v e s t m e n t payback ratio for Project n In--Investment cost for Project n D k ~ D e p r e c i a t i o n cost for Equipment k Cf,i Cost of fuel i Ff,i Consumption of fuel i C s , i ~ P r i c e of purchase of steam F s j ~ F l o w rate of purchase of steam C~--------Price of purchase of power P ~ F l o w rate of purchase of power In,limit~Max Investment cost of project n F k , i n ~ F l o w rate of steam from equipment in to equipment k Fk,out~Flow rate of steam from equipment k to equipment out Fk,in~Enthalpy of steam from equipment in to equipment k Fk,out~Enthalpy of steam from equipment k to equipment out W k ~ W o r k of equipment k Fk,out~flow rate for output of equipment k Fin,k~flOW rate for input of equipment k Q k ~ Q u a n t i t y of heat of equipment k W ~ Q u a n t i t y of power from equipment P ~ Q u a n t i t y of power from purchase Pdem~Quantity of demand Fk,s,m~flOW rate of steam level m from equipment k F s , m ~ f l o w rate of steam level m from purchase Fs,dem,m~flOW rate of steam level m demand Ff,m a x ~ M a x flow rate of fuel from outside P m a x ~ M a x flow rate of steam from outside Fs,m,max Max quantity of power from outside
Subscripts n ~ P r o j e c t number
k ~ E q u i p m e n t number
ACKNOWLEDGEMENTS Financial support from project 79931000 of NSFC and project G2000263 of the State Major Basic Research DevelopmentProgram is gratefully acknowledged.
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Publishedby Elsevier Science B.V.
388
Design water allocation network with minimum freshwater and energy consumption
X.-S. Zheng, X. Feng and D.-L. Cao Department of Chemical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Abstract
In this paper, water minimization as well as energy minimization is taken into account at the same time. Some new concepts are put forward, such as substream, favorable substream, favorable water network and favorable water system. Because of the complexity of coupling water system with energy system, the problem is divided into two steps. First, the optimal water allocation networks are obtained. Since the result may be not unique, those optimal water networks that are favorable to heat integration should be selected. In the selection, choosing those favorable water networks to perform subsequent heat integration. Second, the heat exchanger network of the selected optimal water network is constructed. If the selected network can satisfy the demands of the favorable water network, the determined minimum utilities can guarantee global optimality. Considering the particularity of heat integration on favorable water network, a method to design heat exchanger network on favorable water network is introduced in this paper.
Keywords 1.
water allocation, water network, heat exchanger network, heat integration
INTRODUCTION
Water and energy are widely used in process industry, and they play important roles in the sustainable development of society. Generally speaking, if the level of contaminants of wastewater from one process can satisfy the demand of the inlet stream in another process, the wastewater from the former process can be a source of water for the latter one. In this way, a large amount of freshwater may be saved. This procedure is called water allocation planning. Moreover, temperature may be one of the demands for inlet stream. The inlet stream should be heated or cooled to a certain temperature in order to satisfy the demand of the operation. So heat integration is involved at the same time. Till now, research in the area about Simultaneous Energy and Water Minimization is fairly few. In such research, the solving process is divided into two steps: perform the water system integration first, and then integrate the energy system. The research either cannot guarantee the global optimum [ 1], or can only be applied to single contaminant water-using
389 system [2]. In this paper, a new approach is proposed. The solving process is also divided into two steps, but after the water system integration, the optimal water-using network that is favorable to heat integration is selected. Heat integration is performed for the selected optimal water-using network. This method can be used to not only single contaminant water-using systems but also multiple contaminant systems. 2.
PROBLEM STATEMENT
Given is a set of water using or water disposing processes which require water of a certain quality and temperature. It is desired to design a water-using network to achieve the minimum freshwater consumption, while the processes receive water of adequate quality. At the same time, it is desired to construct a heat exchanger network to achieve the minimum utility. 3.
INTEGRATION PROCEDURE
In this paper the process of designing energy efficient water-using network is decoupled into two steps. Step 1: water system integration. This step involves two sub-steps. First perform the water system integration to obtain the optimal water-using networks consuming the minimum freshwater. Then select the proper water-using network that is favorable to heat integration from the optimal ones. Step 2: heat integration. Construct the heat exchanger network for the selected water-using network. 4.
WATER SYSTEM INTEGRATION
By using the water pinch technology or solving the mathematical program, the minimum freshwater consumption as well as the network of interconnection of water streams can be obtained. Detailed discussions of this process are given elsewhere [3-5]. It should be noticed that the optimal water-using networks obtained by any of the two methods may be not unique, and different water-using network may have different energy target. Thus it is preferred to select some favorable networks to perform energy integration so that the energy target can be minimized. 5.
SELECTION OF WATER-USING NETWORK
Although the number of optimal water-using networks may be not unique, and even may be infinite, the number of water-using network structures is finite. It is water-using network structure that mainly affects the energy target. If the optimal structure that is favorable to heat integration can be found, the heat exchanger network constructed on the basis of this water-using network will be optimal. To find out the water-using network structure that is favorable to heat integration, firstly,
390 a new conception, substream, is defined. A substream is such a stream with unchangeable flowrate that starts from fresh water or a water-using process and ends at wastewater or a water-using process 9 Any water stream in the water-using network can be regarded as a blend of some independent substreams. The total flow rate of these substreams should be equal to the flow rate of the water stream. The whole water system can be considered to consist of a set of substreams, instead of water streams 9 If there are no water generation or water loss in the processes, each substream starts from fresh water and ends at wastewater. If there are water losses in some processes in the water system, some substreams must end at those processes. Otherwise, if there are water generations in some processes, some substreams must start from those processes. The following are some examples to explain this concept more clearly 9 For the water-using network shown in Fig. 1, if there are no water generation or water loss in the water system, all the substreams in the system are given in Table 1. 20 t.h 1, 40~ , 20 t.h 1 43 75 t.h ~, 75~ i " ~1 Process 3 [ 43.75 t.h ~"1, wastewater _ Freshwater [ 2 2 7 ~ Process l [ 2 82.5 t.h_l [ ~
o --16 20 C
.
12.St.h.1
t 1-150 t'h31 ,"
9
I l l 1.25 t.h.,
50~
7
oo 10 e C
- 4 ~ [ ~roc
ss 2 I/38. 6 tlh-l[' Process 4
~ . ~
, 30o ' h ]
138.75 t ' 8 ~
Fig. 1. Water-using network without water generation or water loss processes
Table 1 Substreams of the water-using Substreams Flow rate NO. (t.h 1) 1 20 2 12.5 3 11.25 4 38.75
network shown in Fig. 1 Passing process sequence Freshwater, Freshwater, Freshwater, Freshwater,
Process Process Process Process
1, Process 3, Wastewater 3, Wastewater 2, Process 3, Wastewater 2, Process 4, Wastewater
Fig. 2 shows the processes with water generation or water loss. It can be seen that, in process 1 there is a water loss, while in process 2 there is water generation. The whole water system can be considered to consist of the following substreams, as shown in Table 2. I
Process 1
[ 3 t.h -~
~!
10 t.h -1
Freshwater
11 t.h ~
~~'i
Process2 ] 8 t ' h l .--.---I
~
Wastewater
Fig.2. Water-using network with water generation and water loss processes
391 Table 2 Substreams of the water-using network shown in Fig. 2 Substreams Flow rate Passing process sequence NO. (t'h-') Freshwater, Process 1, Wastewater 1 3 Freshwater, Process 2, Wastewater 2 5 Freshwater, Process 1 3 2 Process 2, Wastewater 4 3
According to the definition, the flow rate of each substream is a constant. And the total flow rate of those substreams that enter a certain process is equal to the inlet flow rate of the process. On the other hand, the total flow rate of those substreams that flow out a certain process is equal to the outlet flow rate of the process. In this way, the analysis of the water streams is transformed to the analysis of the substreams, which can make the analysis more convenient. Since different substreams flow through different processes, each substream has its own temperature variation. If the temperature variation of a substream meets any of the demands listed below, the substream is called a favorable substream. 1 The temperature of the substream increases from the beginning to a certain maximum value, and then decreases until the end, and the outlet temperature is higher than the inlet one. 2 The temperature of the substream keeps increasing until the end. 3 The temperature of the substream decreases from the beginning to a certain minimum value, and then increases until the end, and the outlet temperature is lower than the inlet one. 4 The temperature of the substream keeps decreasing until the end. 5 The temperature of the substream keeps a constant. Favorable substreams have an important feature that only one kind of utility (cold or hot utility) is needed to meet the temperature demand of the substream. If the inlet temperature of the substream is lower than the outlet one, only hot utility is needed. Otherwise, only cold utility is necessary. The minimum utility duty is equal to the product of the heat capacity and the temperature difference between inlet and outlet. This is very easy to demonstrate. Assume a substream, whose inlet temperature is Ti,, and outlet temperature is Tout (Tin< Tout), has a maximum temperature Tm,~. This substream belongs to the first kind of favorable substreams. The hot part of the substream, whose temperature range is from Tmox to Tout, can be used to heat the cold part, whose temperature range is from Tm,~- (Tout-Tin) to Tin. The rest part of this substream, whose temperature range is from Tmax- (Tout-Tin) to T,,,~ (ATmi,,~ Tout-Tin), can be heated directly by the hot utility. As to this substream, only hot utility is needed. The case of other kinds of substream can be demonstrated in the similar way. If the temperature variation of a substream can't satisfy any of the above demands, in other words, the temperature of the substream increases and decreases for several times, the substream is called an unfavorable substream. An unfavorable substream usually needs to be cooled and heated by other substreams or utilities.
392 If all the substreams in the water-using network are favorable ones and their outlet temperatures are higher than their inlet ones, only hot utility is needed for this system. Otherwise, if the outlet temperatures of all the favorable substreams are lower than the inlet ones, only cold utility is needed. Such water-using networks are called favorable water-using networks. Those water-using systems with the minimum freshwater consumption, for which at least a favorable water-using network can be found, are called favorable water-using systems. A favorable water-using system can guarantee that the minimum freshwater and energy consumptions can be achieved simultaneously. In other words, the energy target of a favorable water-using network is no higher than that of any other optimal water-using networks. But if unfavorable substreams exist in the water-using network, the system is likely to need not only hot utility but also cold utility. Therefore, it is preferred to select favorable water-using networks to perform heat integration so that the utility can be minimized. 6.
HEAT INTEGRATION
The pinch technology as well as LP model can be used to identify the minimum utility duty, or the target. The heat-exchanger network can be designed by heuristic rules or by solving a MILP model. The detailed information can refer to Ref. 6-7. Here, a simplified method of designing heat exchanger network is proposed to construct the heat-exchanger networks for favorable water-using networks. In a favorable water-using network, the proper hot part of each substream can be used to heat the corresponding cold part. The rest part can be directly heated or cooled by utilities. An example is shown in Fig. 3. Deal with all the substreams with this method, and then a heat-exchanger network with minimum utility duty can be obtained.
Tin
,..._ Tmax-( Tin- Tout)
Ti,, < Tout and Tout-Tin >1 A Train To~,~
Tm~ Fig.3. Heat transfer for a favorable substream
7.
CONCLUSIONS
In this paper how to design an energy efficient water-using network is discussed. Because of the complexity of the problem, it is decoupled into two steps. In the first step, the water system integration is performed and optimal water-using networks are obtained. Then those networks that are favorable to heat integration are selected. In the second step, heat integration is performed for the selected networks sequentially. Selecting the water-using networks favorable to heat integration is most important in the
393 method. The favorable water-using networks composed of favorable substreams can guarantee that the minimum freshwater and energy consumptions can be achieved simultaneously. For favorable water-using system, a simple method for heat integration is also proposed in this paper. The minimum utility duty is equal to the product of the heat capacity and the temperature difference between freshwater and wastewater. The proper hot part of each substream can be used to heat the corresponding cold part. The rest part can be directly heated or cooled by utilities. Deal with all the substreams in this way, and then a heat-exchanger network with minimum utility duty can be obtained. ACKNOWLEDGEMENT The financial supports for this research provided by the National Natural Science Foundation of China under G20176045 and the Major State Basic Research Development Program under G2000026307 are gratefully acknowledged. REFERENCES [ 1] [2] [3] [4] [5]
L.E. Savulescu and R. Smith, AIChE Annual Meeting, Miami Beach, Florida, 1998. M. Bagajewicz, H. Rodera and M. Savelski, Comput. Chem. Eng., 26(2002) 59. Y.P. Wang and R. Smith, Chem. Eng. Sci., 49(1994) 981. M. Savelski and M. Bagajewicz, Waste Manage., 20(2000) 659. C.H. Huang, C.T. Chang, H.C. Ling and C.C. Chang, Ind. Eng. Chem. Res., 38(1999) 2666. [6] R. Smith, Chemical Process Design, McGraw-Hill, New York, 1995. [7] W.D. Seider, J.D. Seader, and D. R. Lewin, Process Design Principles: Synthesis, Analysis and Evaluation, Wiley, New York, 1998.
394
ProcessSystemsEngineering2003 B. Chen and A.W.Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
Monitoring, diagnosing and improving the performance of LPbased real-time optimization systems D. Zyngier and T.E. Marlin Dept. of Chem. Eng., McMaster Univ., 1280 Main St. W, Hamilton, ON, L8S4L7, Canada
Abstract Operations optimization seeks to track a changing true plant optimum by maximizing a model-based calculated profit in closed-loop real-time optimization (RTO). The model is updated using recent plant data before the economic optimization is performed. This paper presents a monitoring method for RTO that compares the plant performance to the best possible (maximum) profit and a diagnostic method that uses profit-based experimentation to improve RTO performance, when needed. The proposed methodology for monitoring and improving the performance of RTO systems is evaluated on a gasoline-blending case study. Guidelines for RTO applications to blending operations are provided and insights into future research are presented. Keywords real-time optimization, performance monitoring, experimental design 1. INTRODUCTION
1.1. Real-Time Optimization (RTO) Systems A model-based real-time optimizer determines operating conditions that maximize profit while obeying a set of constraints. Opportunity for RTO exists when there are degrees of freedom available for optimization after safety, product, quality, and production rate goals have been satisfied. Potentially large benefits are possible when the optimum operation point changes often, i.e., there are significant disturbances in the process or changes in economicsIll. Many successful RTO industrial applications indicate that large improvements are possible when the RTO system is functioning well E21.However, methods for monitoring RTO performance are not yet available; online performance evaluation is the topic of this paper. Blending is a very important process in, among others, petroleum processing, cement manufacturing and food processing. Because of its economic importance and relatively simple models, blending was one of the first applications of RTO. This paper will present a monitoring method tailored to the blending process. 1.2. Blending RTO using Linear Programming Blending refinery streams to produce gasoline is a non-linear process; however, a linear model can be formulated when blending indices compensate for some non-linearities, models are formulated using flow-quality units, and the component flow ratios remain within established limits. Thus, blending RTO becomes a linear program t3]'t41. Usually, the feedback measurements are used to correct the model by adjusting a "bias" in the model, similar to the feedback used in model-predictive control, as shown in Fig. 1.
395 Measured:
Reformate
"-'lows 31end )ctanc
n-Butane FCC Gas ~ _.~ i ~ - . iiinli.iiiii..i-i-..:'9 Alkylate ~ . _ _.-._..................................... 31end RVP :. I
I EFi*Qi -B, A-->C. The objective is to find optimal control profile maximizing desired product B at final time, t,,at = 1. max J(u(,)) : xB(t#,o, )
-+
+0
28 = UXA 0j; .
O;
J~- Tfin~_tj
(9) (1 O)
V l, j
(11)
471
Tinild+, = Tfin O
V l, j
(12)
Tfin u < H
(13)
Z bintp >-Z bin,+,.p p
l = 1...L - 1
(14)
p
Z bintp -< 1
VI
(15)
p
topo = ~ top pj . bin~p
V l, j
(16)
p
Opt =
~] bs~ . bintp . bin2tt
V p,t
(17)
1
bs, = ~] bsp . bintp
Vl
(18)
p
bin2,, .T,_ 1 -0
(6) (7)
Qt shows the quality of water. The economic effect of n company by constructing an EIP is considered as follows. f . = g. + h.
(8)
where
g. = ~~[a s (U s - uj )-(~-] cj, xj, + E eJkwj~)] jedn
i
h, = ~-][bkVk -(~-]d,y,k + ~.ejkwjk + ~f]bmvmk)] keKn
t
(9)
keK'j
jeJ'k
(10)
,
and g, is saving cost of wastes treatment in n company and h, is saving cost of buying raw materials of n company. Overall saving is represented like below.
502
F =.~_.w,f,
(11)
n
where w, is a weighting factor according to the size of a company. In the viewpoint of EIP, it may be effective to maximize F but the saving cost of some company may not be considered resulting in a difficulty of inducing their participation. This observation shows us the importance of constructing EIP to make each company feel the need of EIP. This kind of investment problems that the interest of each industry conflicts have been studied for a long time in finance starting from the portfolio selection theory of Markowitz [4]. In this study, EIP is formulated as a by-objective optimization problem which has overall cost saving objective, F, and the lowest cost saving of an industry, F*, based on partial mean model by Fishburn [5]. max
(F, F*)
(12)
subject to (1)-~ (11) where
F* = ~-" w. min{f.,f" }
(13)
n
and f '
is a target value of companies with low cost saving.
4. RESULT AND DISCUSSION In this study, an EIP is composed of 5 wastes flows from 5 companies, 4 reuse material flows, and 3 recycling units. The modeling system GAMS [6] was used to implement the model. The objective function (Eq. 12) of the bi-objective problem can be reformulated as follows. max
(1-a)F
+aF"
(14)
Fig. 2 describes the relationship between cost saving of each company and the average total cost saving by changing c~ from 0 to 1. Dotted lines show cost saving of each company and solid line shows average total saving considered the size of the company. If we are interested in maximizing average total cost saving (ct=0), the maximum cost saving among companies is 402.5 Mwon/yr and the minimum cost saving is 153.58 Mwon/yr. When each company has similar amount of cost reduction (ct=l) by maximizing the least cost saving, the average total cost saving decreases 9.3 % and the least cost saving increases from 153.58 Mwon/yr to 239.66 Mwon/yr, 56 %. Generally, the final solution which is Pareto optimal and satisfies the needs and the requirements of the decision maker should be decided from compromise between individual company and government based on the analysis above. However, it is expected to choose the final solution where most companies can get reasonable satisfaction. In this case, it is desirable to select the average total cost saving 258.78 Mwon/yr which is 0.05 % less than the maximum average total cost saving with and 56 % of increment
503 in the least cost saving.
r 0
&9 9 '~ 9 9
350
Company 1 Company 2 Company 3 Company 4 Company5
. . . . . ..I
..It 300
(1) t'(D
250
tY1
200
.
9 el
9
.' ..
-o' .A
. 9
- "
9 ..-
.#
... . . . .
-'9
~
. . . . . .
~
.o
. -' 9 "
9~ , .
:':
..
: :g.. ....... "w ....
250
255
260
265
270
275
F Fig. 2. Benefit of companies with F 5. C O N C L U S I O N In this study, the economic effectiveness of EIP is represented by proposing a material flow optimization model. The objective function consists of the average total cost saving in EIP and partial mean by Fishbum [5] so that this proposed model can consider cost reduction and benefit distribution, together. NOMENCLATURE
aj
cost of throwing a waste j" away outside EIP
b~
cost of buying a raw material k from outside EIP
Cj~
cost of treatment of a waste j in a recycling unit i
d,k
cost of buying a treated material in a recycling unit i inside EIP
elk F
cost of j -~ k in trading inside EIP
L
cost saving of n company
g. h.
cost saving of waste treatment in n company cost saving of buying raw materials in n company
J.
set of wastes produced in n company
K. Uj
set of raw materials needed in n company the amount of a waste j ~ d. produced in n company
Uj
the amount of a waste j ~ d. thrown away from n company
v~
the amount of a raw material k ~ K. needed in n company
Vmk
the amount of a raw material k ~ K. needed among materials m bought from
cost reduction inside EIP
outside EIP
504
Wjk
the amount of j ~ k in trading inside EIP
Xjt
the amount of a waste j treated in a recycling unit i
Ytk
the amount of materials used for a raw material k which is produced in a recycling unit i the amount of water discharged from a recycling unit i outside EIP
Indices i j k l m n
recycling unit wastes raw materials 1- temperature, 2- pH, 3-COD, 4-SS materials bought outside EIP companies
ACKONWLEDGEMENT
This work was partially supported by the Brain Korea 21 Project, by the Korea Science and Engineering Foundation(KOSEF) through RRC/NMR Project, and by grant No. IMT200000015993 from Ministry of Commerce, Industry and Energy (MOICE). REFERENCES
[ 1] R. Chisholm, Developing Networking Organizations: Learning from Practice and Theory, Addison-Wesley, Reading, Massachusetts (1998). [2] E. Lowe, Designing, Financing and Building the Industrial Park of the Future Workshop, San Diego (1995). [3] M. Suh and T. Lee, Ind. Eng. Chem. Res., 40(2001) 5950. [4] H. Markowitz, The Journal of Finance, 7(1952) 77. [5] P.C. Fishburn, The American Economic Review, 67(977) 116. [6] A. Brooke, D. Kendrick and A. Meeraus, User's Guide, The Scientific Press, Redwood City, USA (1988).
Process SystemsEngineering2003 B. Chenand A.W.Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
505
A case study on the integration of process simulation and life cycle inventory for a petrochemical process L. Kulay a, L. Jim~nez b, F. Castells b, R. Bafiares-Alcfintara b and G. A. Silva a Chem. Eng. Dept., University of Sao Paulo, Av. Prof. Luciano Gualberto tr.3 - 380, 05508900 Sao Paulo, Brazil. E-mail: {luiz.kulay, gil.silva}@poli.usp.br
a
b Chem. Eng. Dept., University Rovira i Virgili, Av. dels Paisos Catalans 26, 43007 Tarragona, Spain. E-mail: {ljimenez, fcastell, rbanares }@etseq.urv.es
Abstract This paper presents the use of process simulation and the eco-matrix methodology to obtain the environmental impacts of a chemical process. The case study was the i-pentane purification process from the REPSOL-YPF refinery (Tarragona, Spain). First, the model was validated with plant data. Second, the key results establish the life cycle inventory, where the consumption of natural resources and the releases to the environment were quantified. The environmental aspects included in the eco-vector were divided into two categories: consumption of natural resources and wastes. The environmental loads were evaluated and compared for eight alternative scenarios of electricity generation and steam production. Cogeneration and heat recovery were the best alternatives from the environmental point of view. Although the life cycle approach does not allow the differentiation between local and global impacts, it is a valuable tool for strategic decision-making at a managerial level. Keywords life cycle assessment, simulation, process analysis 1. INTRODUCTION During the last decade many efforts have been done to perform the Life Cycle Assessment (LCA) of products. In this context, several methodologies to quantify the environmental loads and translate their effects on the ecosystem and the human health have been proposed. The interest of companies to gain new markets offering green products reinforces the application of the environmental evaluation of a product throughout its entire life cycle as a support tool at different levels of decision-making, i.e. management, technical and operational. The LCA approach has to be followed by an environment analysis to estimate the local, regional and global damages. For example, the CO2 is used as a indicator for the climate change in the global warming potential. One kilogram of CO2 generated by a industrial process in the different stages of a product life cycle contributes equally to the climate change. This is not the case for site-dependant impacts, as the potential impact of the acidification measured as H+. To do that, weighting factors across the endpoints have to be selected, what is far beyond the objective of this work Ell. This paper presents a methodology in which the evaluation of the environmental inventory of products is carried out to differentiate among scenarios in order to prioritize the environmental profile of different alternatives of electricity generation and steam production for the chemical process industry.
506 2. B A C K G R O U N D
According to ISO 14040, the LCA methodology comprises four steps[2]: goal definition and scope, inventory analysis, impact assessment and interpretation. In the first one we should state the system boundaries, the objective(s) of the study and the audience. The inventory analysis involves data collection and calculation procedures to quantify the relevant inputs and outputs of a process, called Environmental Loads (EL). Typical EL include the use of natural resources and the releases to air, water and land. One of the most effective approaches to obtain the Life Cycle Inventory (LCI) of a process is the eco-matrix, proposed by Sonneman et al. [1]. An eco-matrix has various eco-vectors. An eco-vector consists of mathematical operators whose elements depend on the EL selected. Each process stream (input, output, intermediate or waste; material or energy) has an associated eco-vector whose elements are expressed as environmental loads (EL, i.e. SO2, NOx ...) per functional unit (i.e. ton of main product). All input eco-vectors (material or energy streams) have to be distributed among the output streams of the process (or subsystem). In this sense a balance of each EL of the eco-vector can be stated similarly to the mass-balance (inputi -- outputi + generationi). This is the reason why all output streams are labeled as products or emissions. The eco-vector has negative elements in the streams of emissions and/or waste, corresponding to the pollutant they contain. Figure 1 illustrates an example of a chain of three processes that produces a unique product. The third aspect of LCA is the impact assessment to consider the significance of the potential impacts using the results from the LCI. This process associates inventory data with specific environmental impacts and helps to understand the effect of those impacts in human health, natural resources and the ecosystem. Finally, the results from the inventory analysis and the impact assessment are combined. In the case of LCA, the conclusions should include recommendations or corrective actions. 3. METHODOLOGY The methodology seeks to integrate process analysis and environmental assessment. To compute the LCI all environmental loads of the process have to be quantified. The use of process simulators to obtain the LCI guarantees a robust approach that allows LCA to exploit their advantages in terms of availability of information and planning. This approach favors both, the design of new processes and the optimization of existing installations, and minimizes the uncertainty of the information being used, as average values from the literature are used just for environmental loads at trace level. Nevertheless, the predictions of all models in our case study (for the naphtha plant, utility network and heat recovery system) were validated using plant data.
Fig. 1. Life cycle inventory analysis according to the eco-vector principle.
507 The methodology to compute the LCI is as follows: first, the model of the plant were built using Hysys.Plant | [3], and their predictions were validated with plant data. This model includes some modifications to consider fugitive emissions. The key simulation results were transferred to a spreadsheet (Microsoft| Excel) through macros programmed in Visual Basic t4]. Finally, the environmental loads of the LCI were evaluated in order to provide the environmental profile of the process. TM
4. CASE STUDY The methodology was applied to the debutanizer and depentanizer columns of a naphtha mixture processed in the REPSOL-YPF refinery located in Tarragona (Spain). A C4-rich naphtha stream (= 28.3 ton-h -1) is fed to the first column to remove n-butane and lighter components (= 0.50 ton'h~). The bottom product is mixed with a C5-rich naphtha (= 71.5 ton.h l ) stream and fed to the second distillation column, where i-pentane (- 16.3 ton.h 1) is obtained as the top product. The bottom stream (-- 83.0 ton.h ~) is sent to the blending section of the refinery. The plant has four heat exchangers, and two of them (HX-1 and HX-3) recover heat from the process. Both condensers are air coolers, and therefore plant operation requires electric power and steam. The production of these two utilities consumes additional natural resources and generates additional releases to the environment, and thus they need to be included in the overall analysis. For each one of the scenarios, the eco-vector was divided into the three different plants: steam production, electricity generation and naphtha plant. Figure 2 presents a simplified diagram of the system. Despite the fact that emissions were produced in different locations (e.g. those related to the extraction, transport and refining of the raw materials), the eco-vector is unique for each stream, and thus it does not considers site-dependant impacts. The aspects included in the ecovector were divided into two categories: 9 Consumption of natural resources: depletion of fossil fuels (fuel-oil, gas-oil, carbon, natural gas, oil) and consumption of electricity and water. The plant consumes high pressure steam and produces medium and low pressure steam that are used within the petrochemical complex. The eco-vector that corresponds to these streams is also considered. The environmental loads of the process inputs were retrieved from the ETH Report [5] and the TEAM database [6]. TM
Raw material
I
I
..~1 Electricity q generation
9 9
I "
~Wastes
!
Wastes ... v
C4-rich naphtha | C.~-rich naphtha
r] Distillation
.3
] 9
vt
~
I
Raw material
J |
9 i
[
|
| l
i
9 i
" I
process
I
Steam production 9 I
9
i
i-Pentane ,.. By-products
] I 9
i
9
i
r
~Wastes
I
System bounda~.
Fig. 2. Simplified block diagram of the plant from a LCA perspective
508 Table 1 Main characteristics of the scenarios studied Scenario Electricity generation I II III IV V VI VII VIII
Steam production Downgrading of quality of the high pressure Cogeneration steam Expansion of the steam Cogeneration Downgrading of quality of the high pressure Cogeneration + heat recovery Expansion + heat recovery Cogeneration Fuel oil & fuel gas burning Expansion of the steam in a turbine Fuel oil & fuel gas burning + heat recovery Spanish energy matrix Fuel oil & fuel gas burning Spanish energy matrix Downgrading of quality of the high pressure Spanish energy matrix + heat recovery
9 Generated wastes: air (CO2, SO2, NOx, and volatile organic compounds (VOC) were estimated as fugitive emissions), wastewater (chemical oxygen demand, COD) and solids (particulate matter and solids). The use of different scenarios allows the comparison of altemative environmental performances. The scenarios were configured by considering different sources of steam and types of electricity generation (Table 1). Concerning the electricity generation, three alternatives were explored: cogeneration, expansion of steam and data supplied by the Spanish energy matrix (that is, average environmental loads to produce 1 kW of electricity in Spain). Three of them focus on the environmental impacts of the original process with some variations related to steam production (scenarios VI, VII and VIII). All other cases compare alternatives for future possible implementation, e.g. those considering cogeneration to produce electricity. Also lamination was considered (downgrading of the quality of the high pressure steam to fit the plant needs). 5. ANALYSIS OF RESULTS The environmental loads for the different scenarios considered were compared and evaluated. Some of the scenarios do not contribute to the consumption of some raw materials and/or environmental loads, as they may not be present in the scenario. Figure 3 shows that most of the consumption of fossil fuels is found in scenarios V (fuel oil and fuel gas) and VII-VIII (coal, crude oil and electricity). The best alternatives in terms of water consumption were obtained for scenarios III, IV, VI and VIII, where heat recovery was considered. The natural gas consumption by the cogeneration process in scenarios I and II must also be highlighted. The best alternative concerning resource consumption results from the use of cogeneration and downgrading of quality of the high pressure steam with heat recovery (scenario III). The most significant emissions concerning the atmospheric releases were observed in scenario VII (Figure 4). Nevertheless, the releases of NOx, SO2 (scenario V) and VOC's (scenario VIII) must be highlighted. Scenarios I-II show the highest indices of water contamination, while the worst performance regarding solid waste generation was observed in scenario VIII. The study suggests that the best alternative is the cogeneration one using natural gas together with downgrading of quality of the high pressure or expansion with heat recovery (scenarios III and VI). Scenario III is the best one if all impacts are considered simultaneously, while the
509 worst altemative is the one when electricity is obtained from the Spanish matrix and the steam is produced by fuel oil and fuel gas combustion (scenario VII). 6. IMPACT OF THE APPROACH ON BUSINESS DECISION MAKING The consideration of Life Cycle Inventory and Environmental Loads prediction in the early stages of a design or retrofit project would be a valuable addition to the traditional criteria already being used, i.e. cost and technical criteria.
Fig. 3. Percentage of the total EL with respect to raw materials consumption for each scenario
Fig. 4. Percentage of the total EL with respect to waste generation for each scenario
510 One of the main advantages of the proposed methodology resides in its simplicity of application and the availability of the input information it requires, namely data produced by a commercial simulation system (such as Hysys.Plant| and easily accessible data on the environmental loads of the process inputs (from the ETH Report [51 and the TEAM database[6]). The analysis of the results from the case study indicate the sort of insights that managers and designers can derive from the application of the methodology. TM
7. CONCLUSIONS The methodology has proved to be adequate in establishing the environmental product profile and in comparing several alternatives. The LCA approach should be improved with the inclusion of local, regional, and global impacts and their specific effects in human health (for example, computing the years of life lost or the acute mortality). These indicators appear to be very useful in deciding among different alternatives. The potential impacts derived from Figures 3 and 4 can be used to compute the damage impact assessment by considering a transport model of the pollutants on a local scale or the concentration and deposition of acid species on a wide scale. Concerning the case study, the use of cogeneration and downgrading of quality of the high pressure with heat recovery would significantly improve the environmental product profile. ACKNOWLEDGEMENTS One of the authors (L. Kulay) thanks the Coordenar de Aperfeir de Pessoal de Nivel Superior (CAPES) from the Ministry of Education of Brazil for the financial support. We also acknowledge the cooperation of REPSOL-YPF, and Hyprotech (now part of Aspentech) for the use of an academic license of Hysys.Plant| REFERENCES [1]
[2] [3] [4] [5] [6]
G.W. Sonneman, F. Castells and M. Schuhmacher. Framework for environmental of an industrial process chain, Journal of Hazardous Materials B77 (2000), 91. International Organization for standardization. Environmental management - life cycle assessment: principles and framework: ISO 14040. Genrve, 1997. AEA Technology. User Manual for Hysys.Plant| AEA, Calgary, Canada, 1997 I. Herrera, L. Kulay, L. Jimrnez, and M. Schuhmacher. 2nd Meeting of the International Environmental Modelling and Software Society. Lugano, Switzerland, 2002. R. Frischknecht, U. Bollens, S. Bosshart, and M. Ciot. ETH Ziirich, PSI Villigen, 1996. Ecobilan Group, TEAM | Paris, France, 1998.
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
511
Optimal production planning under uncertain market conditions P. Li, M. Wendt and G. Wozny
Institut ftir Prozess- und Anlagentechnik, Technische Universit/at Berlin, KWT 9, 10623 Berlin, Germany Abstract: We propose to use a dynamic stochastic optimization approach to address production planning problems under uncertain market conditions. The problem is formulated as a dynamic mixed-integer chance constrained optimization problem which can be relaxed to an equivalent deterministic MILP formulation. Using this approach, a quantitative relationship between profit achievement and risk of constraints violation can be received, through which the sensitive uncertain variables can be identified. An optimal decision with a desirable trade-off can be made for the future purchase, sales and operation.
Keywords: Production planning," market condition; uncertainty; chance constraints; optimization 1. INTRODUCTION In the present economical environment, a company always has some uncertain supplies (inflows) such as feedstocks as well as utilities, and uncertain product demands (outflows). In production planning, the availability of the uncertain inflows has to be considered. Besides, it is very important to satisfy customer's demands. If their demands are not satisfied, they will resort to other suppliers. A conservative decision is usually made due to overestimation of the uncertainties, which leads to a considerable profit cutback. In other cases, because of profit expectations, an aggressive decision may be taken, which will probably result in constraint violations and lead to unstable operations. A proper production planning should be decisions with which the profit will be maximized, and meanwhile the uncertain inflow availability as well as the uncertain outflow demands will be satisfied. The current industrial operations planning is based on analysis of scenarios due to varying loads in comparison to base cases. Moreover, buffer tanks are commonly utilized to dampen variations of flows in the process industry. An extra degree of freedom, the storage stock, can be used for the profit optimization: different storage stocks can be allocated in different time periods. The current industrial situation is that buffer tanks tend to be sized via intuition and experience. There are hidden buffers of materials and capacity throughout the system as people protect themselves from uncertainty [1]. Therefore, it is necessary to introduce systematic methods to develop optimal dynamic production policies to enhance the efficiency of buffer tank capacities. The most previous studies on process planning under uncertainty used the two-stage solution approach [2-4]. It allows constrain violations which will be penalized in the profit function. A proper penalty function is required but it is in most cases not available. Batch process planning under chance constraints was considered in [5]. Linear and nonlinear chance constrained programming was used for single unit operations [6, 7]. In this work, a dynamic chance constrained MILP problem is formulated for production planning of existing continuous processes under market uncertainty. It is relaxed to an equivalent deterministic MILP problem. The solution provides robust purchase, sales and operation strategies for the future time horizon.
512 /3 ~ uncertain demands Q J
uncertain supplies {
P
~ sales to be decided Q )
purchases to be decided {
FR~ ~ F vo vr )
from upstream unit { l '
" " "~" WR .).
to downstream unit
} to disposal
Wv
Fig. 1: F l o w s u p e r s t r u c t u r e o f an o p e r a t i o n unit
2. P R O D U C T I O N P L A N N I N G P R O B L E M
We consider the planning problem of a company to decide in advance its investment for its production in the future time horizon T, e.g. a year, which is divided into sub-time periods (i = 1,I), e.g. months. The company consists of several operation units ( n = 1,N). Fig. 1 shows the superstructure of the flows of one of the traits. Some suppliers can supply as much as demanded, and some consumers may purchase how-much-ever produced by the company. These can be taken as decision variables, including some raw materials Rt, m (m= 1,M), utilities U,,~ (l = 1,L), and material products P,,j (j = l, J ) as well as energy products Q,,k, (k = 1,K). The internal flows to be decided are the input and output flows of both materials RIN , 1Si',ROUT U and utilities for each unit, F,,,j,, . .... jou, , "]Si',UIN .... h, , F,,,sou, ( j i n = 1,JIN; j o u t = 1 , J O U T ; lin = 1,LIN; lout = 1 , L O U T ; n = 1 , N ) , respectively. In addition, each individual unit can be started up or shut down during the planned time horizon, thus 1• N binary variables y,,, ~ {0,1} will be decided (y,,, = 0 means not in operation and y,,, = 1 means in operation).
Moreover, if the downstream unit is shut down, the outflows of the upstream unit become waste flows ( W,,,,,jo,,t, R U ) and have to be disposed. W,,,,to,, Due to the future varying market conditions, the demands of some other material products, fi,,j (j = 1, J ) , and some energy products, Q,,~ (k = 1,K), in the future periods can not be determined in advance. The supply of some other raw materials, /~,,~ ( ~ = 1, ~ t ) , and utilities, U,,i ([ = 1,L-), may also be uncertain. These supplies may be so important for the production that the company will purchase the amount which the suppliers can provide. Such uncertain flows are known as stochastic variables, i.e. they can not be determined in advance but their distribution may be available by statistical data analysis. Fig. 2 shows different possible distributions of dynamic stochastic variables in the future time horizon ( a - stepwise; b - peak-like; c - oscillating). The aim of the planning is to determine the decision flows under the uncertain supply availability (/?,,~, U,,i) and the uncertain demands (fi,.~, Q,,~ ), so that the expected total profit of the production in the future horizon will be maximized.
T 9 (a)
'0
~
(b) (c) Fig. 2: P o s s i b l e f o r m s o f f u t u r e p r o f i l e s o f u n c e r t a i n f l o w s
-T~
513 J
Z
K
M
P
j =1
L
Q
Ct,jet,J "1- Z
R
Ct,kat, k -- Z
k=l
Ci,met, m - Z
m=l
ctU,IUt, l
1=1
(1)
max Z et,,,,y,,. "q-
-
N FJow -
j
, n , j t n . . ] _ Z . 7 ,,.,rrT.,,.,h,, UIN Pt,n , F t R1N jm=l hn=l
,~ow
.... ,o.,W,,~
]
+ Z rlt.,.loutW,.,.lout lout =1
In (1), the first line represents the part due to the external decision flows, while the second line denotes the part due to the uncertain in- and outflows which can not be determined a priori. The third line in (1) is the sum of internal costs of the operation units, while the last line is the total cost of waste disposals. Although the values of prices (c e, c ~ c R, c u, c W) and cost factors ( a , fl, y, r/R, r/u ) in the future horizon may be uncertain, we assume that their expected values are known. The problem is subject to the following mass and energy balances as well as the restrictions of flows. For the purpose of planning linear balance equations can be used. Inflow allocationsN
Mass and energy balances:
N
Rt,m - - Z
e ......
Ut,! = Z
n=!
Capacity restrictions:
0
,)
> - p,,-~ '
> -P,,),
Pr I~n=l
Pr I, n=l
U't,n,l- --< U t j Q : ,, ,~ > -
> --
Pij
Q,,~ _> p,,f
(20) (21)
And for (19), the stored volume is to be probabilistically ensured inside the lower and upper limit of the tank capacities under the uncertain flows: Pr{gmr'min
< v , ~,
-< g ~ ' m a x } >-
, Pr{~-u'min
Pr/V jP'min ~ V,,j ~ ~ V)P'max -> -,,j, Pr
,mm
< v Oi,l < -- g-U'max "I } >__
~/r~,k 0- - p,,~
(22)
(23)
In (20)-(23), p e (0,1) is a user-defined confidence level to guarantee the reliability of the operation. This means, due to the existing uncertain properties, one can not ensure a 100% success of the planned production, i.e. a risk of the decision has to be taken into account. According to the practical situations, different confidence levels can be assigned for different flows and periods. To describe the uncertain flows, it is natural to assume that they have a multivariate normal distribution with the nominal (base) value as the mean and a known standard deviation (e.g. 10% of the mean). As shown in Fig. 2, an uncertain flow may have different means and deviations in different time periods. Single chance constraints can be readily relaxed to equivalent deterministic inequalities. For example, the first inequalities in (20) and (21) can be written as N
~--' R;,, . ~ < - ~ , (1- p,.~), n=l
N
~--,p,l ( p ,,) ) , , . , j -> ~ - t,~ n=l
(24)
515 9 -~ is the inverse probability distribution function of a normal distribution. For given p values, the right-hands of the inequalities in (24) can be easily calculated. To relax the chance constraints (22)-(23), from (15) one can rewrite the first inequality in (22) as the following:
" IN
Pr
OUT
R,,~ < t. 11=1 ^ m
Pr
R,max- V0,~ R
R,,~ + Vm
> Pi,~
(25)
11=1
R11,~ >
R , ~ _ ER
ovr
R11,~ + V~
i1=1
>
(26)
o,~ - P,,~
For ii = 1,...,I, the term inside the probability representation in (25) can be represented as T~
(27)
< Tz~ + g~
where 1 T=
0
"'"
9
i| 1
0
If~ZN1,'~ R2,-~ {-~= "IN "IN~ ~Rz,
I
9"
We define ~" = / ~ .
If ~
(RI,OU--~ T) IR~ I
N OUT
\ t,-~ j
V.2 max __ V, R o,~ I V R,max _V, R 0,~ v~,ma• _ ~ n o,~
conforms a multivariate normal distribution,
i.e.
~ - ~ N ( l z ~ , 2 ~ ) , then ~"-~ N(TI, t ~ , T Z ~ T r ) , where ~t~ and 2~ are the mean vector and the
covariance matrix of ~ .
Since the elements in g~ and z~ are either known parameters or
decision variables, the inequalities in (25) and (26) can be computed in the same way as (24). Now the dynamic stochastic operations planning problem is transformed into the general deterministic MILP formulation which can be solved with a standard MILP solver. 4. C O M P U T A T I O N E X A M P L E As shown in Fig. 3, we consider a company with three operation units. An inflow R and an outflow fi are uncertain flows, while another inflow R can be decided. Two tanks are used to dampen the uncertain flows 9 The data of the units, tanks and mean values of the flows are given in Table 1-3, respectively. The standard deviations of both uncertain flows are set to 10% of the mean values. Correlations between the flows are considered as well 9 5 time periods are considered for the planning. The profit will be maximized under the chance constraints to hold the lower and upper limits of the two tanks. As shown in Fig. 4(a), the profit will be decreased if the required confidence level increases. It is interesting to note that it decreases stepwise at some points where the switch (on or off) strategy of the units has to be changed. This can be seen in Fig. 4(b), where
Yl=~'~y ....Y2=~-'y ....Y3=2y,, 3 denote i=l
e=l
the total
i=l
number of on-periods of the three units, respectively. To achieve a proper trade-off between reliability and profitability, the optimal decision should be chosen at the point just before that step change. Fig. 4(c) shows the optimal decisions with the confidence level as 0.93.
Fig 3." F l o w s h e e t o f the example c o m p a n y
516
n 1
2 3
_
-
a,
,8,
150 205 100
10 5 12
a 2
F ~"~
3 3
350
V ~ 9
VZ" 3
V "~
1
2
3
4
5
10
m 1
15
/~
4.0
5.0
6.0
5.0
4.0
20 20
2
130
60
200
/3
50.0 60.0 70.0 60.0 50.0
300
.......
250
. . . . . .
200
-,
,,oo
,.
. . . . . .
. . 0,84
0,88
0,92
Confidence level
(a)
--~-------f~
0,96
1
0,8
0,84
0,88
"~
~
__ 0,92
0,96
F2
~
----t
20
,
,~
~ .
25
I
g
. . .
50 0 ~- . 0,8
i
"~
-, ....
~-15o
Table 3: Mean of the flows in time periods
Table 2: Data of tanks
Table 1: Data of operation units
..._
1
_rL
---
................
j 1
0 t,~ 0
t~ 1
...... ,-,2
Confidence level
(b)
- --~3
",,,Z4
5
T i m e period
(c)
Fig. 4" O p t i m i z a t i o n results by different c o n f i d e n c e levels
5. CONCLUSIONS Production planning under market uncertainty by the dynamic chance constrained MILP approach has the following features: (1) By solving the problem with different confidence levels, the decision can be made with a desired trade-off between profitability and reliability. (2) The solution provides purchase decisions for some raw materials and utilities as well as sales decisions for some products in the planned time horizon. These decisions will be robust to the changes of uncertain feed supplies and uncertain product demands. A high robustness is highly desired, since these decisions are usually realized in the form of contracts with external companies and thus variations are rarely allowed. (3) The solution provides a robust operation strategy for the internal units (in- and outflows, units on and off) in the future time horizon. It implies that hardly any changes to the planned operations will be required. This is advantageous for a stable operation. Of course, changes of operations of internal units can be made according to the realization of the uncertain flows. (4) It is possible to identify a p r i o r i the uncertain flows that are sensitive to the solution and recognize their impacts on reliability as well as profitability. This leads to guidelines to modify the process (a sense of debottlenecking), e.g. considering a larger tank. (5) A moving horizon can be used to modify the decided operations. A re-optimization will be made in the beginning of each time period for new operation strategies, based on the current information of supplies, demands and tank capacities. The approach presupposes the availability of the quantification of uncertainties and the model of a process. These two issues are being addressed, i.e. data analysis and modelling are more and more emphasized in the process industry. REFERENCES
[1] [2] [3] [4] [51 [6] [7]
D.E. Shobrys and D.C. White, Comput. Chem. Eng., 24(2000), 163. M.L. Liu and N.V. Sahinidis, Ind. Eng. Chem. Res., 35(1996) 4154. M.G. Ierapetritou and E.N. Pistikopoulos, Ind. Eng. Chem. Res., 35(1996) 772. R.L. Clay and I.E. Grossman, Comput. Chem. Eng., 21 (1997) 751. S.B. Petkov and C.D. Maranas, Ind. Eng. Chem. Res., 36(1997) 4864. P. Li, M. Wendt, H.G. Arellano and G. Wozny, AIChE J., 48(2002) 1198. M. Wendt, P. Li and G. Wozny, Ind. Eng. Chem. Res., 41 (2002) 3621.
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Published by ElsevierScienceB.V.
517
Complexity Analysis for Hybrid Differentiation in Process System Optimization Xiang LI, Zhijiang SHAO, Jixin QIAN Institute of Systems Engineering, Department of Control Science and Engineering, Zhejiang University, 310027, P. R. China Abstract Hybrid differentiation approach, employing different differentiation algorithms for different parts of a process model, has been presented and developed recently to achieve high performance differentiation in process system optimization. However, a convenient and efficient approach to choose differentiation algorithms is absent. In this paper, some measurement of complexity is defined as a criterion to choose differentiation algorithms, and an approach to evaluate the complexity in an extended automatic differentiation procedure is developed. An ad hoc approach to estimate the complexity of symbolic differentiation is particularly discussed in details. Numerical results validate the complexity analysis approach and demonstrate the high efficiency of hybrid differentiation.
Keywords
Complexity analysis, hybrid differentiation, process system optimization, redundant computation, similar terms 1
INTRODUCTION
Jacobian Evaluation is one of the most time-consuming steps for process system optimization (PSO) tl], which severely blocks the improvement of the optimization efficiency. Currently, finite-difference (FD), symbolic differentiation (SD) and automatic differentiation (AD) are the main approaches for Jacobian evaluation, among which AD is currently prevailing and widely considered as the most promising one in PSO. However, when modeled in simultaneous equations, PSO problems usually have approximately equal numbers of variables and equations because of their few degrees of freedom. As a result, for automatic differentiation the ratio of time for Jacobian evaluation to time for residual evaluation is bounded by 3n (n denotes the number of independent variables), while for finite-difference and symbolic differentiation that is roughly n [2]. Thus AD does not always have significant advantages over the other two differentiation approaches in PSO problems. According to the advantages and disadvantages of the three algorithms, hybrid differentiation (HD) approach, employing different differentiation algorithms to deal with different parts of a process model, was presented and developed by Li etc [3] and Shao etc [4]. It can exploit the model structure much better than a single differentiation approach so as to achieve higher efficiency. HD includes three steps. First, the optimization model is partitioned into different modules. Here the term of module merely refers to a group of codes or subroutines, and does not mean the blocks corresponding to some unit operation model subroutines. Then the best
518 differentiation approach for each module is selected. Finally, the overall derivative of the model is accumulated according to the derivatives of the modules. Among the three steps, the second one is the most important but the most difficult. In Section 2, some measurement of complexity is defined for a differentiation algorithm to characterize the number of scalar computations required to evaluate Jacobian matrix in scalar architecture, so that the best differentiation algorithm can be found by comparing the different complexities of different differentiation algorithms for each module. Then an extended AD approach is presented to evaluate the complexities. In Section 3, an ad hoc approach to detect redundant computations and the combination of similar terms are presented for estimating the complexity of SD. The complexity analysis approach for HD is applied to an optimization problem of an industrial batch polycondensation reactor in Section 4. Numerical results illustrate both the validity of complexity analysis and the significant advantages of applying HD to the optimization problem. 2
C O M P L E X I T Y ANALYSIS IN AN EXTENDED AD P R O C E D U R E
In a scalar architecture, the number of the scalar computations required for an algorithm to differentiate a module could be used to reflect the efficiency of this algorithm. It is called complexity of the differentiation algorithm for the module in this paper. Therefore, the best differentiation algorithm for a module can be selected through the comparison of their complexities for this module. Here we only discuss the complexity analysis for Jacobian evaluation. In HD framework, symbolic automatic differentiation (SAD) tSl, a symbolic variant of AD is adopted as an SD approach, because it can differentiate subroutines that are usually used to describe complex chemical process system models while traditional SD tools can not. In addition, the AD algorithm incorporating Jacobian-compressing technique t6'71, called sparse AD in this paper, is often adopted to exploit the sparsity of a model. The sparsity of variables in a model, which is necessary for the Jacobian-compressing technique, could be obtained during a quasi-AD procedure [61. Therefore, considering that the complexity analysis for FD is easy, the whole complexity analysis approach for different differentiation algorithms in HD could be united into a framework similar to that of AD. In operator-overloading AD, all the elementary computations (addition, subtraction, multiplication, etc.) during the evaluation of a process model are overloaded and the results of the elementary computations are regarded as temporary variables. The temporary variables that are dependent on the independent variables, called active variables t8l, are declared to be of a particular class named avariable. The value of an independent or temporary variable, its Jacobian, the related operator, and other useful information are encapsulated in class avariable. During the computation of a module, each operator is overloaded to compute both the resulting variable of the operation and its Jacobian. Class avariable can be extended to a class savariable (super active variable), which contains additional parameters that are indispensable for choosing the differentiation algorithms. As a result, the AD procedure is extended to perform more computational jobs (shown in Fig. 1). Unlike AD, sparse AD and FD, evaluating the complexity of SAD is tough work because
519 the elimination of the redundant computations are hard to detect. These redundant computations, including computations between constants, multiplications between 1 and any quantity, additions between 0 and any quantity, etc, are executed during the generation of the symbolic derivatives and need not to be performed in optimization. Furthermore, as each temporary variable denotes an expression in SAD, computations between the variables may incur combinations of the similar terms in the corresponding expressions. Such combinations should be taken into consideration for more accurate evaluation of complexity. Variable calculation Complexity evaluation for FD Complexity evaluation for SAD
~,~
Class
savariable ~
Complexity evaluation for sparse AD
Derivative calculation
Sparsity evaluation
evaluation Complexity evaluation forAD
Note: * denotes the number of scalar computations for calculating the temporary variable itself
Fig. 1 Diverse computational jobs in the extended AD procedure 3
COMPLEXITY ANALYSIS FOR SAD
The terms in the expression corresponding to a temporary variable could be grouped into three categories. The first is the nonconstant terms that will incur redundant computations when multiplied by a constant (except 0, 1, -1). The second is the nonconstant terms that will not incur redundant computations when multiplied by a nonzero constant. The third is the constant term. The numbers of the terms of each category are p, q, k. In this article, p, q, k, are encapsulated in class savariable to count discount, the number of eliminated redundant computations relevant to the temporary variable. Initially, each of the independent variables is assigned with p-0, q=l, k=-0. Then p, q, k of each temporary variable can be propagated during the operator-overloading procedure. Suppose that there is a multiplication between a variable Zl and a constant z2 (that is to say, p2=O, q2=0) with a result of z3. When the operator is overloaded, the p3, q3, k3 of z3 and discount of this operation can be evaluated according to thepl, ql, kl ofzl and the k2 ofz2 by the algorithm described in Fig. 2. The task of identifying all similar terms presented in a system of equations is very difficult[7]. Since linear equations are common in process models and are easy to find, we only consider the combination of linear terms associated with the independent variables. Two parameter lists, named index and coeffi, are encapsulated in class savariable to count combination, the number of eliminated computations during combination of the linear terms. The parameters in the list index are the indices of the independent variables in the linear
520 subexpression of the temporary variable, while those in the list c o e f f are the relevant coefficients of the independent variables. Initially, each of the independent variables xi is assigned with i n d e x = i and c o e f f = 1. Then the two lists of each variable during differentiation will be obtained when the relevant operator is overloaded. Since the linear subexpression is part of the temporary variable, updating the lists will probably influence the values of p, q. Suppose that there is an addition between variables Zl and z2 with a result of z3. The parameter c o m b i n a t i o n relevant to the addition can be obtained with i n d e x 1 and c o e f f l of Zl as well as i n d e x 2 and c o e f f 2 of z2 by the algorithm described in Fig. 3. The above approach may not find all the redundant computations and all the combinations of the similar terms. Therefore the efficiency of SAD will be underestimated. However, since SAD is not so convenient as AD and costs more memory, the user usually will not replace AD by SAD unless the latter greatly surpasses the former. Hence this approach is acceptable.
ifk2= 0 if kl = 0 discount = 2pl+q1
else discount = 2pt+qt+!
end p3=O, q3=O, k3=O
elseifk2= 1 or-1 discount = 1 P3 = Pi, q3 = ql, k3 = klk2
else if kl = 0 discount = p l + 1
else discount =p1+2
for i = 1 to length (index1) if indexl (i) is found in index2 at positionj if both coeffl(i) and coeff20") are 1 or-1 if coeffl (i)+coeff20")=O combination=2
else combination=l
end elseifeither coeffl(i) or coeff20") is 1 or-1 if coeffl (i)+coeff2 0)=O combination=3
elseif coeffl (i)+coeff20)=l or -1 combination=2
else combination=l
end elseif neither coeffl (i) nor coeff20) is 1 o r - 1 if coeffl (i)+coeff20)=O combination=4
end
elseif coeffl (i)+coeff20)=l or -1
p3 =pl+qt, q3 = O, k3 = klk2
else
end
combination=3 combination=2
end end end end Fig. 2 Partial view of the algorithm for obtaining d i s c o u n t 4
Fig. 3 Partial view of the algorithm for obtaining c o m b i n a t i o n
N U M E R I C A L RESULTS
The testing problem is an industrial polycondensation process optimization problem having been discussed in details by Li etc TM. The model contains a polycondensation process submodel and a flash distillation submodel, of which the former is continuous and the latter is discontinuous. The optimization was performed on Celeron 400MHZ PC with Microsoft Windows 2000 operating system. The Successive Quadratic Programming (SQP) algorithm was realized with Optimization Toolbox 2.1.1 in MATLAB. AD, sparse AD and SAD were realized with XADMAT ES], which was developed by Li etc. based on ADMAT E61. FD was
521 executed in a forward difference approach. Table 1-2 display the differentiation results of the two submodels by SAD, AD, sparse AD and FD. The estimated complexities approximate the actual ones so that can reflect the computing time. The complexities in the parentheses are estimated without considering the combination of similar linear terms. The considerable great difference between the two complexities indicates that the combination of linear terms is frequent during the differentiation and should be taken into consideration. The flash distillation submodel can not be differentiated by SAD and sparse AD because the discontinuous module does not have unique symbolic Jacobian and sparsity pattern. Sparse AD achieved much higher efficiency than AD did because this equation-oriented model is highly sparse. SAD performed much better than spare AD did because it exploited the sparsity more effectively, avoided redundant computations and combined similar terms. According to Table 1-2, HD should employ SAD for the polycondensation submodel, and FD for the flash distillation submodel. The results of the optimization based on FD, AD and HD are shown in table 3, which highlights the overwhelming advantage of HD. Table 1. Complexity and efficiency of each differentiation algorithm . . . . . . . . . . in polycondensation submodel Differentiation SAD algorithm Actual Estimated AD Sparse AD FD Complexity 24174 33045 (40517) 2251272 116181 2143743 Computing time(s) 0.24 76.24 11.41 194 Table 2. Complexity and efficiency of each differentiation algorithm in flash distillation submodel Differentiation SAD FD algorithm Actual Estimated AD Sparse AD Complexity / / 1709778 / 882659 Computing time(s) / 53.30 / 2.49 ,,
Table 3. Results of polycondensation process optimization with diverse differentiation al[[orithms Differentiation Algorithm
Iterations
Time for one differentiation(s)
Differentiation time (s)
Optimization time (s)
Ratio*
FD AD HD
9 13 21
196.17 136.82 2.73
1702.1 1778.7 53.16
2083.5 2254.4 519.67
81.69% 78.90% 10.23%
,,,,,,
,,,
,,
,
,,,,,
,
,,
,
,,,
,,
,
Note" *denotes the ratio of differentiation time to optimization time. 5
CONCLUSION
In scalar architecture, the complexity defined to choose differentiation algorithms for the modules of a process model can reflect the actual computing time. The approach to estimate the complexity presented in this paper is effective. Integrating HD to SQP algorithm can dramatically reduce the time for differentiation and significantly improve the efficiency of optimization.
522 ACKNOWLEDGEMENTS This research was supported by the National Natural Science Foundation of China (No. 20276062) and partially supported by the National High Technology Research and Development Program of China (No. 2002AA412110) REFERENCES 1 Wolbert, D., Joulia, X., Koehret, B., Biegler, L. T. Flowsheet optimization and optimal sensitivity analysis using analytical derivatives, Computers and Chemical Engineering, 18(11), 1083-1095 (1994). 2 Tolsma, J. E., Barton, P. I., On computational differentiation, Computers and Chemical Engineering, 22(4/5), 475-490 (1998). 3 Li, X., Shao Z., Zhong W., Qian, J., Polycondensation process optimization based on hybrid automatic differentiation, Journal of Chemical Industry and Engineering (China), 53(11), 1111-1116(2002). 4 Shao Z., Li, X., Qian J., Hybrid differentiation algorithm in chemical process system optimization, Journal of Chemical Industry and Engineering (China), accepted. 5 Li, X., Zhong W., Shao Z., Qian, J., Applying extended automaitc differentiation technique to process system optimization problems, Proceedings of the American Control Conference 2001, 4079-4084 (2001). 6 Verma, A., Structured Automatic Differentiation, Ph.D. Thesis, Cornell University (1998). 7 Tolsma, J. E., Barton, E I., Efficient calculation of sparse Jacobians. SIAM Journal on Scientific Computing, 20(6), 2282-2296 (1999). 8 Griewank, A., Juedes, D., Utke, J., ADOL-C: a package for the automatic differentiation of algorithms written in C/C++, ACM Transaction on Mathematical Software, 22(2), 131-167 (1996).
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
523
Multi-objective programming in refinery planning optimization Shaojun Li a, Hui Wang a, Yongrong Yang b, Feng Qian" aEast China University of Science and Technology, Shanghai 200237, China bDepartment of Chemical Engineering, Zhejiang University, Hangzhou 310027, China
Abstract The aim of this paper is to present a multi-objective genetic algorithm approach to optimize production planning in circumstance of uncertain price. The characteristic feature of this approach proposed is that various objectives are synthetically considered in the solving process. According to multi-objective programming, uncertain parameters in a mathematical model are considered triangular possibility distributions. Thus the uncertain optimizing problem can be transfer to a three objectives optimizing problem. An example was used to show the feasibility of the approach. Keywords multi-objective, planning, genetic algorithm 1 INTRODUCTION Oil refinery is one of the most complex chemical industries, which involves many different and complicated processes with various possible connections. The objective in refinery operation is to generate as much profit as possible by converting crude oils into valuable products such as gasoline, jet fuel, diesel, and son on. From the managerial level, managers need to decide which crude oils to process, which products to produce, which operating route to use, which operating mode is the best for each process, etc. But there are many related problems remain unsolved in supporting high-level decision-making, because more undermined factors are involved in this level. These factors, such as the randomness of arriving orders, the uncertainty in a competitive environment, present a special challenge for effectively modeling the real-world situation by using traditional mathematical programming technology. In this paper a multi-objective programming approach is used to solve the uncertain problem, especially the price uncertainty, in process planning for oil refinery plant. 2 MATHMATICAL MODEL OF PRICE UNCERTAINTY The aggregated production planning is a top-level long-range planning problem in a manufacturing enterprise. Other planning activities such as the annual sales planning, resource planning and raw material planning depend on the aggregated production planning. Due to the uncertainty of market, especially, the uncertain price of products and raw materials, the aggregated production planning is very difficult to be made. The mathematical model of refinery production planning in the circumstance of price uncertainty can be built as follows [ 1],
Max
(ZCiXi-ZC'jXj-ZC'kXk) i
s.t.
j
EXi =ZXj i
j
(1)
k
(2)
524 y ' Xik < M k
(3)
i
X, =~X,j
(4)
J X i = YifXf
(5)
b~
(6)
n' E N.
(9)
If a lightering vessel (v) serves tanker (t) at event point (n) and tanker (t') at an earlier event point (n'), i.e. z(t, v, n) = 1 and z(t', v, n') = 1, the constraint enforces that the task at event point (n) start no earlier than the time at which the task at event point (n') finishes. If either of the two tasks does not take place, the constraint is relaxed. Objective: Minimization of total cost dr. Ira(t)- t~] + ~ tET
vEV
woo. ~
~
z(t, v, n)
(10)
n E N tET
The objective is to minimize the total cost consisting of the tanker demurrage costs, which is proportional to the number of hours each tanker stays at the lightering location before finishing being lightered, and the fleet voyage costs, which is fixed per trip for each lightering vessel. A more detailed mathematical model that addresses several classes of lightering problems can be found in [7]. 4. C O M P U T A T I O N A L STUDY The mathematical formulation described above results in a Mixed-Integer Linear Programming (MILP) problem. We apply it to the following case study. A fleet of four lightering vessels is
551 Table 1: Data of tankers in the case study. Tanker number Arrival time Destination Refinery (hrs) (distance (miles)) 1 16.0 R1 (67) 2 21.0 R2 (88) 3 35.0 R1 (67) 4 38.0 R2 (88) 5 47.0 R1 (67) 5' 47.0 R2 (88) 6 55.0 R3 (49) 7 117.0 R1 (67) Table 2: Lightering fleet data in the case study. Vessel Capacity Speed number (thousand barrels) (miles/hr) R1 R2 R3 to refinery to anchorage 1 370 370 245 8.0 11.0 2 225 225 225 7.0 9.5 3 260 260 195 9.0 12.0 4 90 90 90 7.0 9.5
Lightering requirement (thousand barrels) 335.0 340.0 215.0 222.0 185.0 135.0 177.0 320.0
Pumping rate (thousand barrels/hr) to vessel to refinery 60.0 20.0 38.0 15.0 50.0 20.0 16.7 7.3
Cost ($000/trip) 6.20 4.50 8.20 19.00
available to lighter seven tankers in a horizon of seven days. Data of the tankers and the vessels are shown in Table 1 and Table 2, respectively. Each lightering vessel has different capacities for different refineries due to the different depths of the refinery ports. Note that Tankers 5 and 5' refer to the same tanker which carries crude oil for two different refineries. Furthermore, in this case study, each lightering vessel is available from the beginning of the horizon and is capable of lightering all the tankers involved. The scheduling model is formulated and solved with GAMS/CPLEX on an HP J-2240 workstation. Three event points are introduced to model the lightering process and the MILP model consists of 60 binary variables, 191 continuous variables and 620 constraints, which is solved to optimality in 30 CPU seconds. The optimal schedule is shown in Figure 2, which also includes the arrival time, lightering requirement, and departure time of each tanker. Below the name of each vessel are its maximum capacity and voyage cost, respectively. Each task by the vessel is represented with a sequence of bars each designating a specific operation of the vessel. The tanker being served, the lightered volume, the corresponding event point, the starting time and finishing time of the whole task by the vessel are also labeled in the Gantt chart. The schedule requires the lightering fleet to take nine trips in total. Each of the two larger vessels, Vs 1 and Vs3, lighters three tankers, Tk2, Tk4, Tk7 in sequence, and Tkl, Tk6, Tk5' in sequence, respectively. Vs2 lighters two tankers, Tk3 and Tk5, sequentially. Vs4, with the smallest capacity and highest voyage cost, is assigned to lighter only one tanker, namely Tkl. The resulting total cost is $190,405, with a demurrage cost of $119,205 and a fleet voyage cost of $71,200. Extensive computational studies on a variety of lightering problems can be found in [7]. 5. CONCLUSIONS The scheduling problem for tanker lightering is introduced and addressed. A novel continuoustime mathematical model is developed based on the concept of event points proposed in earlier
552 I mount tanker l pump on oil
U dismount tanker 9 travel to refinery 9 dock port I pump off oil
~ undock port
9 travel to anchorage
Figure 2: Gantt chart of the solution in the case study. work on the scheduling of chemical processes. A sequence of event points is introduced for each lightering vessel and binary variables are defined to determine whether or not the vessel is to start a task at each event point, while the task consists of mounting/dismounting a tanker, pumping on/off oil, traveling between the lightering location and the refinery, and docking/undocking the refinery. The formulation leads to a Mixed-Integer Linear Programming problem which can be solved to optimality efficiently. A case study is presented and demonstrates the effectiveness of the proposed approach. ACKNOWLEDGMENTS The authors gratefully acknowledge support from the National Science Foundation. REFERENCES [1] M.G. Ierapetritou and C.A. Floudas, Effective Continuous-Time Formulation for ShortTerm Scheduling: 1. Multipurpose Batch Processes, Ind. Eng. Chem. Res., 37 (1998) 4341. [2] M.G. Ierapetritou and C.A. Floudas, Effective Continuous-Time Formulation for ShortTerm Scheduling: 2. Continuous and Semi-Continuous Processes, Ind. Eng. Chem. Res., 37 (1998) 4360. [3] M.G. Ierapetritou, T.S. Hen6, and C.A. Floudas, Effective Continuous-Time Formulation for Short-Term Scheduling: 3. Multiple Intermediate Due Dates, Ind. Eng. Chem. Res., 38 (1999) 3446. [4] X. Lin and C.A. Floudas, Design, Synthesis and Scheduling of Multipurpose Batch Plants via an Effective Continuous-Time Formulation, Comp. Chem. Engng., 25 (2001) 665. [5] X. Lin, C.A. Floudas, S. Modi and N.M. Juhasz, Continuous-Time Optimization Approach for Medium-Range Production Scheduling of a Multiproduct Batch Plant, Ind. Eng. Chem. Res., 41 (2002) 3884. [6] E.D. Chajakis, Sophisticated Crude Transportation, OR/MS Today, 24 (1997). [7] X. Lin, E.D. Chajakis and C.A. Floudas, Scheduling of Tanker Lightering via a Novel Continuous-Time Optimization Framework, Ind. Eng. Chem. Res., in press (2003).
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
553
Dynamical Supply Chains Analysis Via a Linear Discrete M o d e l - A Study of z-transform Modeling and Bullwhip Effects Simulation Pin-Ho Lin a, Shi-Shang Jang b, David Shan-Hill Wong b aChemical Engineering Department, Nanya Institute of Technology, Tao-Yuan, Taiwan bChemical Engineering Department, National Tsing-Hua University, Hsin-Chu 30043, Taiwan
Abstract: In this work, a model of a supply chain system is derived using material and information balances and discrete time analysis. Transfer functions for each unit in the supply chain are obtained by z-transform. The stability of the linear system is studied. We proved that intuitive operation of supply chain system with demand forecasting causes bullwhip effect. Furthermore, we show that by implementing a PI or a cascade inventory position control, and properly synthesizing the controller parameters we can effectively suppress the bullwhip effect.
Keywords:
z-transform, bullwhip effect, cascade control, frequency analysis.
1. INTRODUCTION Supply chain management has attracted many attentions among process system engineering researchers recently. One of such areas is the analysis of the logistic problem of a supply chain using system control theory. A supply chain system is nothing but material balances of products and information flow [I]. In this work, a supply chain model is analyzed using z-transform. The objective of this work is to build up a dynamic model for the supply chain system and study the effects of the ordering strategy and forecasting the demand to the system dynamics. Analytical forms of the closed loop transfer functions are obtained. The causes of bullwhip [21 become quite apparent using the model and stability analysis [31. A PI and a cascade control structures are proposed and controllers are synthesized and tuned accordingly, to eliminate the bullwhip effect. 2. DISCRETE DYNAMIC MODEL Consider a simple supply chain that has no branch as shown in Figure 1. There are three
554 Material flow ~ ~ .
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Figure 2. The block diagram of node j of a supply chain logistic echelons: warehouse (W), distributing center (D) and retailer (C) between the producer (P) and customer (C).The material/information balances of a node can be modified based on the previous work [11. The z-transform of the modified discrete model is given by: z
Ij(z) = ~2-i_1 (z-LYij (Z) - Yjk (Z))
(1)
Ipj (z) = ~ _ 1 (Yij(z)- Yjk (z))
(2)
z
Oj (z) : ~--~_1 (U kj(Z)-- Yjk (Z))
(3)
U ji (z) = K j (SPj (z)- IPj (z))
(4)
I o Yjk (Z) = z-'Oj(z) [ z-'Ij(z)
o 0 _ 1, there exists at least one Izl _>1. Therefore, then the ultimate gain of the feedback loop Kju is equal to 1. 4. BULLWHIP EFFECT
Bullwhip effect for a node of Figure 1 is defined by Uji(z)/Uki(Z)>l, where Uji(z) is the order of a node, Uki(Z), is the demand from its down stream. When there is sufficient supply and high stock, we get: Uji(z) = Kj x ( z - l ) Kj SPj(z)+ ~ z-l+Kj z-l+Kj
Ukj(z )
(10)
If the set point of inventory position of a node, as shown in Figure 2, is fixed, mathematical manipulations find that the condition, Uji(z)/Uki(Z)> 1, is met only if Kj> 1. If Kj~
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x~.j z - 1
(13)
Figure 5, gives the dynamic simulation of using the above tuning parameters. Cascade Control An obvious alternative to be used is a cascade control scheme. In the cascade scheme, the set point of the inventory position is raised (or reduced) if the filtered "long-term" trend of the difference between actual inventory position and the demand however, Figure 5 shows the case that lower controller gain (Kj=0.7) gives no is less than (or greater than) zero. However, this target is only loosely pursued in the inner loop. Figure 6 shows that the cascade control works very well without bullwhip effect. The cascade control scheme results in much faster response than pure PI. The period with backorder and the magnitude of back order are both smaller, hence custom satisfaction is also higher compared with the case of PI.
558 .~
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Figure 6. Dynamic results of cascade control at outer loop Kcj = 1.0, ~7~cj-5, and inner loop control gain Kj= 1.0 for L=3 6. CONCLUSIONS Stability of the system was investigated. Bullwhip effect is also analyzed. The study proves that bullwhip effect is inevitable if the standard heuristic ordering policy is employed with demand forecasting. Several alternative ordering policies were formulated as P-only, PI and cascade control schemes. By implementing a PI controller, the bullwhip effect of a supply chain unit can be suppressed but long term trends in customer demand can be tracked. The cascade control scheme not only provides efficient control of the inventory position of a supply chain unit without causing bullwhip effect.. REFERENCES
[ 1] Perea-L6pez, E., Grossmann, I. E., Ydstie, B.E. and Tahmassebi, T. Dynamic modeling and decentralized control of supply chains. Industrial & Engineering Chemistry Research, 40 3369-3383. (2001). [2] Lee, H. L., Padmanabhan,V. and Whang, S. The bullwhip effect in supply chains. Sloan Management Review 38.93-102. (1997) [3] Towill, D.R. Dynamic analysis of an inventory and order based production control system. International Journal of Production Research 20 671-687.(1982). [4] Chen, F., Ryan, J.K.and Simchi-Levi, D The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics 47 269-286. (2000). [5] Coughanowr, D.R., and Koppel, L.R. Process Systems Analysis and Control. McGraw-Hill International. (1965).
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
559
Making decisions under u n c e r t a i n t y - applications of a hierarchical approach in industrial practice Hu Liua*~ K. K a r e n Yin~ at and G. G e o r g e Yin b t ~Department of Wood and Paper Science, University of Minnesota, St. Paul, MN 55108, USA bDepartment of Mathematics, Wayne State University, Detroit, MI 48202, USA A b s t r a c t This work is concerned with production planning of manufacturing systems under uncertainty. The dynamic systems are formulated by differential equations with Markovian disturbances. The random demand and random production capacity are modeled by two finite-state continuous-time Markov chains. To obtain the optimal feedback policies requires minimizing a discounted cost function, for which numerical solutions may be infeasible for large-scale systems. To address the issue of "curse of dimensionality", we resort to hierarchical approach and seek nearly optimal solutions. Application examples are provided for illustration. K e y w o r d s Planning; Markov chain; Optimal policy; HJB equation; Hierarchical approach. 1. I N T R O D U C T I O N Operating an enterprise requires making timely decisions in production planning and scheduling, process control, and inventory management subject to uncertainty from various sources such as raw material variation, customer demand fluctuation, and unpredictable equipment failure, etc. To better understand and more effectively deal with uncertainty requires sound mathematical models capable of capturing the salient aspects of the system and the unique feature of each major event while permitting the use of efficient algorithms to handle large-scale systems. In a recent work [1], the scheduling of a papermachine operation was formulated as a stochastic optimal control problem, the random demand and random production capacity were modeled by two continuous-time finite-state Markov chains, numerical solutions for obtaining the optimal policy were obtained by minimizing the discounted surplus and production costs. The computation needed in numerically solving such dynamic programming equations increases with respect to the number of Markovian states. In many cases, the computational requirements to obtain an optimal policy are staggering to the point that a numerical solution becomes infeasible. This is the so-called curse *Fax: (612) 625-6286; Email:
[email protected] tCorresponding author. Tel.: (612) 624-1761; Fax: (612) 625-6286; Email:
[email protected] tTel.: (313) 577-2496; Fax: (313) 577-7596; Email:
[email protected] 560 of dimensionality [2]. In this work, we resort to hierarchical approach for solving such problems. A general description of the system is given first, followed by the problem formulation in Section 3. Section 4 provides application examples for illustration. 2. T H E P R O D U C T I O N
SYSTEM
Consider a manufacturing system that produces r different products. Let u(t) E l~ r denote the production rates that vary with time and the random machine capacity. With the total surplus (the inventory/shortage level) x(t) E N r and the random demand rates z(t) C N ~, the system is given by a differential equation, which states that the rate of change of the surplus is the difference between the rate of production and the rate of demand. Our objective is to seek the optimal production rate, u*(.), to minimize a discounted cost function, subject to the system dynamics, the machine capacity c~(t), and other operation conditions. Owing to random breakdown and repair, the machine capacity is modeled by a continuous-time finite-state Markov chain c~(.) = {c~(t) 9t >_ 0} with state space C = {al,...,a.c}. The demand process z(.) = {z(t)" t :> 0} is another finite-state Markov chain having state space Z = { z l , . . . , zd}. The generators of the Markov chains a(.) and z(.) are given by Qc = (q~j) e IRc•162and Qd = (qd) e N dxd, respectively. Recall that for any functions r on C and r on Z,
QCr
= ~
qj~, [ r
r
Qer
= ~
Jl •J
qj~j, [ 0 ( j l ) - r
(1)
Jl Ts
For additional properties of continuous-time finite-state Markov chains, see [3-5]. We consider the optimal control problem under a joint stochastic process /3(t) = (a(t),z(t)), the capacity and demand pair. Note that/3(.) is also a Markov chain which has a state space M
"-- {(oll,z1),'''(o/'C,z1),''',(o/'l,zd),'''(o/'c,zd)}
-- . / ~ 1 U . . . U
./~d,
(2)
and a generator Q, an m x m matrix (m = c x d). Note that Adi is given by J ~ i --~ {( O~1 Zi), .-- , ( O~c, Z i )}.
In view of the well-known result in stochastic control [6], an application of the dynamic programming principle [5, Appendix A.5] leads to that the value function v(.) given by v(x,/3) =
inf g(x, u(.), /3), /3 CAd u(.)e~
(3)
satisfies a system of partial differential equations known as HJB (Hamilton-Jacobi-Bellman) equations, where ,4 is the set of admissible controls; x and/~ = (c~, z) are the initial surplus and the initial (demand, capacity) pair, respectively. 3. H I E R A R C H I C A L
CONTROL
AND NUMERICAL
PROCEDURE
Note that unlike the usual situation in controlled diffusion, instead of one HJB equation, we need to solve m HJB equations. Due to the large state space of the joint stochastic process/3(t), the computation required to solve the HJB equations is intensive, which often renders numerical solution infeasible. Considering that in many manufacturing systems,
561 the rates of changes of the random events involved are markedly different [5], Sethi and Zhang developed hierarchical approaches that led to multilevel decisions [7]. They showed that such results are asymptotically optimal. We adopt the idea of hierarchical decision making in this work. 3.1. T h e H i e r a r c h i c a l A p p r o a c h In many manufacturing processes such as papermaking, the machine breakdowns and repairs take place much more frequently than the changes in demand. To reflect the different transition rates, we introduce a small parameter e > 0 into the system by assuming that the generator of/3(t) is Q = Q~= (q~j) 9 R rnxm, and that
Qe l ~, + ~. def 1 ( Qc =
)
=
c
~
Q~
+
I qd111c qUl2Ic """ qdldlcl " dt
"
d
qdl c qd2Ic
"
,
(4)
qddlc
where Q and Q are of the same order of magnitude. Observe that the introduction of separates the system into two time-scales, in which Q/e dictates the chain's fast changing part and Q governs its slowly varying part. Note that Q is nothing but the Kronecker product Qd | Ic, and the block diagonal matrix ~) = diag(Q c, Q ~ , . . . , Q~). Assume that Qc is irreducible. Then the equilibrium distribution of Q~, u = (~'1,~'2,...,u~), is the unique solution of ~,QC=0
and
~'i=
1.
(5)
i=1
Now, we are in a position to give a precise formulation of the problem in which we designate the quantities involved by the small parameter e. i.e., both tie(t) ~ Q~ and x~(t) are e dependent, and the surplus is given by x~(t) = fl~(t)(u(t),--Id)'= c d ( t ) u ( t ) - z(t),
~ ( o ) = (~(o),~(o))= ~ = (~,~.),
x~(O) = x, where
(6)
Id is the
J~(x, u(.),fl)
d • d identity matrix. Let the cost function be
=E
/0 e-pt[h(x~(t)) + 7r(u(t),~)]dt, fl e .A4
(7)
where p > 0 is the discount rate, u(t) = (ui(t), i < r) is the "normalized" production rate satisfying 0 < ui(t) < 1, h(.) is the holding cost, 7r(.) denotes the production cost, and x and/3 are the initial surplus and the (demand, capacity) pair, respectively. Note that for notational simplicity, we have normalized u(.) so that u~(t) 90% chlorobenzene will also be accepted as a valid product stream. Semi-sharp distillation columns, with key recovery of between 97 and 99.9%, are to be used for the separation. The column designs are based on the Fenske-Underwood-Gilliland equations. The capital cost of heat exchangers is also taken into account based on the energy requirements of condensers and reboilers. The aim is to identify the flowsheet structure with the lowest capital cost for this separation. The adaptive scheme solved this problem in 26 seconds using an 850 MHz Pentium III computer with Java 1.4. The solution yielded is shown in Fig. 3. Below each column, the light and heavy key components are shown. In the solution, distillation with benzene and chlorobenzene as the keys is carried out in two columns. These two separations are coarser than the very fine split required from one column. The algorithm has shown, through comparing cost bounds of solutions that using the three column structure will incur the least capital cost. Benzene
Benzene
Chlorobenzene B/C1B
B/C1B Waste C1B/C12B Fig. 3. Optimal structure for the benzene problem
The maximum number of box splits before a solution was isolated was found to be 18. The finest discretisation in the percentage key recovery variable, as a fraction of the allowed key recovery range, was found to be 1.5x 105. This fraction corresponds to a width of about 1/600000 th of the possible range. Using the uniform discretisation technique, it would not be possible to solve the problem to this level of discretisation. In order to highlight further the efficiency of the adaptive algorithm, the problem was attempted using 18 discretised key recovery intervals for each discrete unit choice. This is the finest uniform discretisation possible. Table 2 Comparison of adaptive and uniform discretisations Adaptive algorithm Uniform discretisation Number of problems 884 246836 % problem reuse 26 96 Time (s) 27 3650
581 A comparison between the adaptive algorithm and uniform discretisation is shown in Table 2 demonstrating the efficiency attained by the adaptive approach. Further insight may be gained by the discovery of the regions where it is necessary for the adaptive procedure to discretised most finely. This is the subject of ongoing research. 4. DISCUSSION The process structure identified by the adaptive algorithm, in the case study presented above, demonstrates the potential benefits of the new adaptive procedure. A novel design that is not immediately obvious to the designer may be uncovered. If altematives were to be incorporated into a superstructure, it is likely that this superior altemative would be omitted as only one split between any two components would be defined. The case study has also demonstrated the enhanced efficiency of the procedure. There is a substantial improvement in solution time when compared with the finest achievable level of uniform discretisation, a level orders of magnitude coarser than the finest level reached by the adaptive algorithm. In the new scheme, it is unnecessary for the user either to specify a level of discretisation or to examine the results before deciding if finer discretisations are necessary. In addition, the globally optimal solution structure is assured. Overall, the accessibility of the system to a nonexpert user has been improved while retaining quality assurance. These factors are extremely important if automated process design tools are to be routinely used in industry. REFERENCES
1) H. Yoemans and I.E Grossmann, Computers Chem. Engng., 23(1999) 709. 2) E.S. Fraga, M.A. Steffens, I.D.L. Bogle and A.K. Hind, An object orientated framework for process synthesis and optimization, Foundations of Computer-Aided Process Design, M.F. Malone, J.A. Trainham and B. Carnahan (eds.), AIChE Symposium Series 323(2000), 446. 3) R.E. Moore, Interval Analysis, Prentice Hall, Englewood Cliffs, New Jersey, 1966. 4) E. Hansen, Global Optimization Using Interval Analysis, Marcel Dekker, New York, 1992. 5) C.A. Schnepper and M.A.Stadtherr, Computers Chem. Engng., 20(1996) 187. 6) R.P. Byrne and I.D.L. Bogle, Ind. Eng. Chem. Res., 39(2000) 4296. 7) A.R.F. O'Grady, I.D.L. Bogle and E.S. Fraga, Chem. Pap.,55(2001) 376. 8) A.R.F. O'Grady, I.D.L. Bogle and E.S. Fraga, Interval analysis for identification of potential process structures in early design, European Symposium on Computer Aided Process Engineering- 12, Grievink J and Van Schijndel J (eds.), Computer-Aided Chemical Engineering, Elsevier, 10(2002) 271. 9) E.S. Fraga, Chem. Eng. Res. Des., 74(1996) 249.
582
Process SystemsEngineering2003 B. Chenand A.W.Westerberg(editors) 9 2003 Publishedby ElsevierScienceB.V.
A H y b r i d C L P a n d M I L P A p p r o a c h to B a t c h P r o c e s s S c h e d u l i n g Benjamin Roe a*, Nilay Shah a and Lazaros G. Papageorgioub ~Centre for Process Systems Engineering, Imperial College, London, SW7 2AZ, U.K. bCentre for Process Systems Engineering, UCL(University College London), London WC1E 7JE, U.K. Abstract In this work a novel hybrid CLP/MILP algorithm for scheduling production in complex multipurpose batch processes is presented. The scheduling problem is decomposed into two sub-problems: first an aggregate planning problem is solved using an MILP model, and then a sequencing problem is solved using CLP techniques. The CLP model avoids the complexity of explicitly stating material balance constraints by instead using precedence constraints between batches to ensure the schedule is feasible. The algorithm used is summarised and encouraging computational results for a scheduling example are shown.
1. INTRODUCTION Scheduling complex multipurpose processes is a computationally hard problem. Much research in the area has concentrated on using pure MILP as an optimisation method. While mixed integer linear programming(MILP) is a flexible method, the computational effort needed to solve large-scale problems can be prohibitive. The combinatorial nature of the scheduling problem suggests that a decomposition of the general problem into a number of sub-problems has the potential to outperform traditional single-level methods. Each sub-problem can then be solved using the method that is most effective for that specific class of problem thus leading to a hybrid approach. The work of Harjunkoski and Grossmann[ 1] has shown that a hybrid formulation, combining constraint logic programming(CLP) and MILP, has potential as a scheduling method in multistage flowshop scheduling. The formulation decomposes the problem into two sub-problems: a machine assignment optimisation sub-problem, solved using MILP, followed by the generation of a feasible schedule subject to this assignment using CLP. In this paper the objective is to present a more general hybrid MILP/CLP algorithm for scheduling multipurpose batch processes. 2. OVERALL ALGORITHM The overall structure of the scheduling algorithm is shown in Fig. 1. The input to the first stage is a list of deliveries to customers that must be met and the State-Task Network description of the process. The objective is to determine the minimum set of batches required to meet these deliveries and to allocate the batches to the units available. The MILP formulation used in this *Author to whom all correspondenceshould be addressedemail:
[email protected] 583
Figure 1. Overall algorithm structure
stage is described in detail in Section 3. The second stage of the algorithm then performs the actual scheduling of these batches; the process of assigning a start time to each batch so as to create a feasible schedule subject to all process constraints. This stage is described in Section 4. 3. MILP AGGREGATE FORMULATION
The main constraints in the MILP formulation are the material balances. Total production must be greater than or equal to demand:
EE
-
iGIs jGUi
Vs
-
(1)
iEls jEUi
The total extent of each task is limited by the number of the task performed and the unit size:
Vm '~3nNJ"i
-< E i j
-
T,~ in the current solution, and add/~n' to B~p. Retum to step 2. The objective function is defined as the total value of product states delivered to customers. Product states can only be delivered to customers if they are produced before the specified delivery time. In order to perform optimisation, the precedence constraints for batches that precede deliveries are replaced with reified constraints (Eqn. 9). A solution that maximises
585 .
1
.
.
.
L_ _ _ _ a
/~ \
_
- }
_
f-~,
_
L_____l
,.
0.636
2
-
!.0 ~
.....
1.0
K_4 / \ 0 . 4 3 2
5
I
.
-
] \
-
~
_
j t
0 468 ~
.
.
.
.
~
...... I
1.0
/~\ \
/
0.814~
~
I
,.o
Figure 2. Ordering search tree
. . . . . . .
, L
-
,.o ._D
Figure 3. Case study STN
the amount of product delivered can then be found by minimising the sum B. This sum is the only objective function used during CLP search; the MILP and CLP formulations are entirely separate.
Pdn= ~ 0---~ T~ + Li.,j. < T~ t 1 ~ T,~+Li.,j. > T~
V d c D , n C B~
(9)
dED nEB~
Standard CLP optimisation search proceeds by repeatedly finding a solution and tightening the bound on the objective function value until no further solution is found[3]. As the only variables affecting the objective function value are the Pun binary variables, these are included with the batch start times in the list of variables to be instantiated. Given a constraint on the upper bound of the objective function value, the search procedure first instantiates the Pd,~ variables to meet this constraint and then attempt to instantiate the Ts variables subject to the constraints implied by the values of the binary variables. This procedure can waste much time attempting to find schedules for combinations of values of Pdn which are infeasible. If this is the case, incomplete tree search can increase solution efficiency by ignoring parts of the search space. The most effective method found is to use depth-bounded search followed by bounded-backtrack search. This will explore the search tree completely down to the level at which all Pan variables are instantiated. Below this, only a limited number of backtracks within a branch are allowed before the branch is pruned and search continues with the next branch. As a solution is usually found in relatively few backtracks if one exists, this allows the search to ignore parts of the search tree which do not contain a solution. 5. CASE STUDY The use of the hybrid approach is now illustrated with a process scheduling example. The hybrid algorithm is compared to a standard discrete-time MILP formulation, using the gBSS
586 Table 1 Unit task suitability data Unit Task Suitability A F_I, K_5_6, T_3 B E2b, K_4, T_4 C F_3, K_8 D F_2a, K_7 E T_l, T_2
Table 2 Solution data Solver Pure MILP Hybrid
for case study Objective Value Solution Time 49.3 10hrs 51.3 45mins
scheduling package [4],[5] using the XPRESS MILP solver. The hybrid algorithm was implemented using the ECLiPSe package developed at IC-PARC [2], also using the XPRESS solver for the MILP stage. The process state-task network and unit data are shown in Figure 3. Production is to be scheduled for a two-year horizon using a weekly time interval; the processing time for every task is approximated as one week. No maximum inventory level constraints are required due to the small scale of production. The tasks related to each product are considered as members of the same family. A cleaning time of 2 weeks is required on a unit between tasks from different families. Many deliveries of each of the three products are possible during the problem horizon. The objective function value is the total amount of all of the product states delivered to customers as described in section 4; this is calculated as the total possible value of deliveries (87.2) minus those not made, as defined in the CLP objective function. The LP relaxed value of the objective function is 78.3. Table 2 compares the objective function value and solution time for the two methods. The solution time for the hybrid algorithm is over an order of magnitude shorter than for the single-level approach. Approximately 99% of the hybrid solution time is spent in CLP search, the majority of this time being spent generating solutions with progressively better objective function values.
6. CONCLUDING R E M A R K S The results show that for processes a process in which the main complexities relate to cleaning and task allocation, the hybrid approach can outperform a traditional discrete time MILP formulation both in search speed and solution quality. Complex process constraints can be easily modelled using CLP, and the ability of the solver to propagate constraint effects provides a very efficient solution method. The search algorithm used in the CLP optimisation is an important factor in the efficiency of the method - the algorithm used here avoids the generation of multiple degenerate solutions and prunes branches of the search tree unlikely to contain a solution effectively. For processes in which inventory level constraints are tighter, the global nature of the pure MILP method allows it to outperform this hybrid approach at present. Further improvements of both the MILP and CLP stages of the method are possible, however, including better handling of inventory level constraints.
587 NOMENCLATURE
MILP Formulation Symbols Set Description Ui Tj I~ Fa
Ff Variable Eij
Nij Wfj Dj L
Parameter Vjr a i n Vjmax f~s f/~s
ds CsI
li H
Units that can perform task i Tasks unit j can perform Tasks that interact with state s Set of all task families Set of tasks which belong to family f
Description Total extent of task i on unit j Number of times task i is performed on unit j Binary variable indicating whether a task in family f occurs on unit j Duration of batches on unit j Makespan Estimate
Description Minimum capacity of unit j Maximum capacity of unit j Fraction of extent of task i produced as state s Fraction of extent of task i consumed as state s Total demand for product state s Initial Inventory of state s Duration of task i Number of time points in problem horizon Changeover time between tasks of different families on unit j
Cj Additional CLP Formulation Symbols Description Set BT Set of all batches to be performed D Set of all deliveries to be made
B~
Set of batches to be performed on unit j Set of batches of task i Set of batches which must precede batch n C BT
Variable
Description
~max
TnS,min
T2 in
fn
Maximum value of T,~ Minimum value of T~s Start time of batch n Task which n is an instance of Task family of which batch n is a member
REFERENCES 1. 2. 3. 4.
I. Harjunkoski, I.E. Grossmann, Comput Chem Eng 26 (11) (2002) 1533-1552. J. Schimpf, K. Shen and M. Wallace,www.icparc.ic.ac.uk/eclipse (1999). K. Marriott and P.J. Stuckey, Programming with Constraints (1999) 114-116. L.G. Papageorgiou, N. Shah and C.C. Pantelides, Advances in Process Control III, IChemE, Rugby (1992) 161-170. 5. N. Shah, K. Kuriyan, L. Liberis, C.C. Pantelides, L.G. Papageorgiou and P. Riminucci, Comput Chem Eng, 19S (1995) $765-$772.
588
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Published by Elsevier ScienceB.V.
Risk conscious scheduling of batch processes G. Sand and S. Engell
Process Control Laboratory, Dortmund University, 44221 Dortmund, Germany Abstract We consider real-time scheduling problems of flexible batch processes under the special consideration of uncertainties. Any decision has to be made subject to a certain risk since it affects the future evolution of the process, which is not precisely predictable. This difficulty can be faced by a moving horizon approach with frequent re-optimisations. We propose the use of a model framework from stochastic programming to reflect the uncertainty and the potential of recourses realistically. The framework is applied to a real-world process from the polymer industries, a decomposition algorithm is sketched and numerical results are given.
Keywords real-time scheduling, flexible batch processes, stochastic integer programming 1. INTRODUCTION During the operation of flexible batch plants, a large number of discrete decisions has to be made in real time and under significant uncertainties. Any prediction about the evolution of the demands, the availability of processing units and the performance of the processes is necessarily based on incomplete data. Resource assignment decisions must be made at a given point of time despite the fact that their future effects can not be foreseen completely. The multi-product plant is the most popular flexible plant concept in the chemical industries, especially in the growing market for specialty chemicals, where small volumes of highvalued products in several complicated synthesis steps are produced. Its flexibility enables the production of a variety of products on a single plant and for rapid and cost efficient adaptations of the product supply to the customer demands. To operate such a flexible plant in a dynamically changing environment, a large number of management decisions and control activities is needed, so efficient process management and control systems have a strong impact on the profitability of the plant. For the problems on the lower control levels, standardized solutions exist, and new plants are often highly automated on these levels. Nevertheless, the management of process operations, in particular planning and scheduling activities, can hardly be standardized, since these higher-level problems are dominated by complex interactions which are highly plant specific. Therefore, computer-aided planning and scheduling is still a topic of extensive academic research and so far seldom realized in industry. A large number of publications demonstrate that the theory of mathematical programming (MI(N)LP) provides promising methods to model scheduling problems adequately and to solve them efficiently (for an overview see Ref. 1). An appropriate strategy to schedule highly coupled processes online is based on a moving horizon approach, similar to model predictive control (MPC): The problem is solved for a certain horizon, and the first decisions are applied. Due to modelling inaccuracies or disturbances of the process, the computation must be iterated after some period taking new infor-
589 mation into account. While this is a "closed loop" strategy with decisions in the feedforward and observations in the feedback branch, the models used are often based on an "open loop" view, which neglects the optimisation potential of re-optimisations subject to feedback information. Undoubtedly, the quality of scheduling decisions can be increased by modelling the uncertain future evolution along with corresponding reactions more realistically. 2. RISK CONSCIOUS MODELLING
2.1. Motivation In recent years, several publications appeared which address the issue of uncertainty (e.g. Ref. 2-6). However, so far the following essential aspects of uncertainty conscious scheduling models received only little attention: 1. Plant managers who face uncertainty will try to maximize the mean profit but they will also try to avoid the rare occurrence of very unfavourable situations, e.g. heavy losses. Naturally they aim at a compromise between expected profit and accepted risk. 2. In any iteration, only the first few decisions in the horizon are really relevant. Due to temporal couplings, the remainder of the decisions has to be anticipated, but they are never applied since the solution will be modified in the next iteration. The second aspect cannot be reflected by open loop models since these models do not differentiate between "here and now" and "recourse" decisions. Compensations to possible disturbances can only be considered, if the model reflects different possible scenarios of the process evolution with corresponding degrees of freedom to react to certain realisations. Concerning the first aspect, the maximum expected profit for a number of possible process evolutions can in general not be determined by calculating the expected evolution and solving an optimisation problem for the mean values. Using a scenario based model usually leads to higher expected profit and provides sensitivity information to control the risk. 2.2. Stochastic integer programming The mentioned aspects exactly fit into the modelling framework of two-stage stochastic integer programming. For linear models and a scenario-based representation of uncertainties, a deterministic equivalent of a two-stage stochastic integer program (2-SSIP) can be written as a large mixed-integer linear program (MILP): f~ max
X,Yl ..... Ya
s.t.
cTx + E
T
~o=17r'~176y~
(1)
To~x + W~yo~ = ho~,x ~ X , Yo~ ~ Y, o~ = 1. . . . . f2.
In this framework the uncertain evolution is represented by a finite number of scenarios co with corresponding probabilities nc0. The variables are assigned to 1st and 2 nd stage vectors x and Y0~, which belong to polyhedral sets X and Y with integer requirements. The x-vector represents "here and now"-decisions which are applied regardless of the future evolution. It therefore is identical for all scenarios. In contrast, the yo-vectors model scenario-dependent recourses under the assumption that the respective scenario materializes. The uncertainties may affect any parameter of the model, such that f2 different matrices and fight hand sides To , Wo and ho may arise. The classical objective is to maximize the first stage profit plus the expected second stage profit computed as a weighted sum of x and Yc0 subject to the weighting-vectors c and q0~-
590 2.3. R i s k A v e r s i o n
The expected value criterion does not utilize the full information about the shape of the probability distribution of the objective function over the scenarios. This may lead to results with a high expected profit while a few scenarios give very low values of the objective function. To control the risk that the profit falls below a certain threshold e, Eq. (1) can be extended by a excess probability: f2
max x,Yl ," ",Yo,
T cTx + ~ 7r.o~qo~yo~ - 5 ~ r%uco (o=1
(2)
r
U 1 ,...,Uf2
s.t.
Tcox+Wcoyo~=hco,cTx+qTyco>_~.-Mu~,xeX, yo~ eg, u~ e {0,1}, o~= 1.... ,~.
The idea is to compute the probability that the profit falls below a threshold e by using binary indicator variables uo in a big-M inequality, and to reduce the expected value proportionally. The parameter 5 weights the risk relative to the mean value. This extension fits into the 2SSIP framework and increases the model size only marginally. From the syntactical point of view, any deterministic MILP-model can be regarded as a single-scenario base-model, which can be extended to a 2-SSIP according to Eq. (1) or (2) under two conditions: Firstly, the uncertainty must only affect the parameters and secondly, the decisions must not affect the probabilities. (E.g. models which represent time by an index cannot reflect temporal disturbances.) If these conditions are fulfilled, the scenarios are able to represent any probability distribution, e.g. tree-structured evolution estimations and empirical distributions. It should be noted that even coarsely approximated uncertainty representations have an advantage over mean value representations, since it is mathematically proven that 2SSIPs lead to better (lSt-stage -) scheduling decisions than obtained for the corresponding mean value problems (Ref. 7). 3. A B E N C H M A R K
PROBLEM
The described modelling methodology was applied to a flexible batch process from the polymer industries: The multi-product batch plant shown in Fig. 1 is used to produce two types (A/B) of expandable polystyrene (EPS) in 5 grain size fractions each. The preparation stage and the polymerisation stage are driven in batch mode whereas the finishing is done continuously. A polymerisation batch is produced according to a certain recipe (out of ten possible ones), which determines the EPS-type and the grain size distribution. The resulting mixture of ,
F
r
li
--a
, I,
IL~J~--44! I~
E
i
I
~.~
..................
,,
c% c% c%
LJ LJL#O
,- ................ 1
~J Preporotion.~
I"
' I
,, ~A2 ~
q
A5
B1
I ~>B3
' ng
Fig. 1. Flowsheet EPS-process
I
B4 B5
591 grain sizes is buffered in one out of two mixing vessels and then continuously fed into the two separation stages, which must be shut down temporarily if a minimal flowrate cannot be maintained. Scheduling decisions to be made are: 1. choice and 2. timing of the recipes of the polymerisations, 3. outflows of the mixing vessels, and 4. start-up- and shut-down-times of the finishing lines. They are subject to resource constraints and non-linear equality constraints describing the mixing process. The objective is to maximize the profit calculated from revenues for satisfying customer demands in time and costs for polymerisations, start-ups/shut-downs of the finishing lines, inventory, and penalties for demand shortages. The uncertainties can be classified into endogenous and exogenous uncertainties, which are or are not linked to process events, respectively. Endogenous disturbances comprise polymerisation times and yields; disturbances in the plant capacity and in the demand are regarded to be exogenous in nature. 4. MASTER SCHEDULING MODELS 4.1. The model family The scheduling problem is decomposed into a detailed scheduling and a master scheduling problem (DS/MS), which are implemented in a cascaded feedback structure (see Ref. 8). A deterministic base model for the DS problem was developed by Schulz (Ref. 9), so in the following we will focus on the master level. We developed a family of MS base models which comprises several model instances for the process and for the profit. It uses a time representation which is based on three fundamental considerations: 1. The problem is formulated on a finite moving horizon of reasonable length. By shifting the horizon, some of the former recourse decisions Yco become here and now decisions x. This auto-recourse, i.e. the property, that the same model is used throughout, gives rise to a uniform model structure over the entire horizon. 2. According to its horizon, the MS model reflects uncertainties with long-term effects, i.e. uncertain demands and capacities, which are both exogenous in nature. Since the probability of the occurrence of an exogenous event within a certain period of time depends on the period length, only the consideration of fixed time periods allows for the definition of uncertainty scenarios co with fixed probabilities no0. 3. An appropriate time representation is a multi-period grid with fixed time intervals, and the period lengths have to be chosen such that the probability of a disturbance is significant. Since the need for re-optimisations is determined by the same criterion, the iteration period is synchronous with the time grid. A reasonable choice is a horizon of 14 periods of 2 days each. The first 2 intervals are defined as the 1st stage, they serve as a guideline for the DS level. 4.2. Illustrative instances To give an impression of the models we present the key ideas by some illustrative constraints and refer to Refs. 8, 10 for more details. Scheduling decisions to be made on the master level are 1. the rough timing of start ups/shut downs of the finishing lines, 2. the rough timing of polymerisations and 3. the assignment of recipes. Given I fixed time periods i, the degrees of freedom are represented by the variables Zip ~ {0,1} and N% ~ IN, which represent the operation mode of the finishing
line p in i and the number of polymerisation starts according to recipe rp ~ {1... Rp } in i,
re-
592 spectively. The relevant constraints are the capacity of the polymerisation stage and of the finishing lines. It turned out that the interaction between the periods is of major importance. Considering the constraint for minimal throughput of the finishing lines, the formulation for decoupled periods would read as follows (C- mixer levels, F - feed rates, m i n - minimum, m a x - maximum, 0 - initial state):
Rp Z Nirp >-vp c m i n +zipF; m rp=l
(3)
Vi, p.
The technique to model the couplings is to constrain sums of periods (the non-linearity can exactly be linearized):
Zi" ZRp Ni'rp >-zi"pg(i"+l)pCpin - [z(i-1)pzipcpIi Cf p~ i=l i'=irp=l
max else} + ~i'=' iZ"i , p F p m .
Vi, i " , p l i < i " .
(4)
The use of Eq. (3) instead of Eq. (4) leads to more shut down-procedures if a finishing line is driven at its lower capacity limit and to significantly higher costs. An essential target of profit oriented scheduling is to maximize the sales subject to demand and supply constraints. W i t h Mif p E IR+ denoting the sales of productfp in i, Bif p E IR+ the demand and 9fprp c IR+ the yield offp according to a certain recipe rp, Eq. (5) defines the demand and the supply constraints, respectively: i
i
i
i
Z Mi'fp _0 ( j = l , . . . , n )
(3)
j=!
(4)
Yk,k = q k=l
(p--q+l)yk,k--s
k >_0 ( k = l , . - . , n )
(5)
j=l
Yj,k = xj,j ( j = 1,...,n)
(6)
zk, t = l (l = l , " ' , q )
(7)
z,,, : y,,~ (k : 1,..., , )
(s)
k=l
k=l q
i=!
Yk,k
~~(x,,jyj,kd,) 1.7• 1012. Koontz et al. (1975) described dynamic programming and branch-andbound algorithms which reduce the number of clusters which have to be examined in
645 order to locate an optimal one, but even with such saving a global search makes too heavy a demand on computing facilities. In this work, the algorithm is improved by visualisation based on 2-D: (1) the user defines, interactively, the optimal number of clusters by inspection; (2) the user also adjusts the radius for each cluster directly. Thus, the two parameters are guided by interaction with the user using visualisation. The cluster knowledge identified in the reduced 2-D is then converted back into the original higher dimension space, which are shown below: 2 E
2
( o k -- Z 2 - D , g k
~2 ---~ 2-D,g'
g = 1,2,...,G
k=l ( X t -- Zn-D, gt
)2 < /,.2 --
n-D,g ,
g = 1,2..... G
(8)
1=1
r,-o,g
G(N~-l)~,~ llx,-xp, ll/n
Where xi (i = 1..... n) denote the ith co-ordinate of the vector X. To generate Z,,-D from Z2-D, the same ANN is used; however, the input and the output of the net will be opposite of those in the former net. The objective function for training is
E = ~_~-~N (x'p, - Xp,/2
(9)
p=l t=l
where ANN.
x'~
(i = 1,...,n) denote the i t" co-ordinate of X, whose value is estimated by the
3. GA OPTIMISATION W I T H THE C I R C L E - C L U S T E R - K N O W L E D G E Applying a GA to a new problem requires the definition of the crossover and mutation genetic operators. In the new mutation operator, an individual is selected. It is mapped to 2-D to identify the cluster g to which it belongs out of all the clusters identified by the cluster analysis procedure. Cluster g is the cluster which includes the selected individual, or the nearest one if the selected individual is not within the feasible regions. A Gaussian mutation (Goldberg, 1989) is then carried out using the knowledge of the feasible regions for the randomly selected solution by emulating a coin toss for each variable in the genome. For example, in the cluster g, the variable of xi of the selected individual is mutated within the dynamic feasible region of
Zn_D,g , --
r._ o g
(xj j=l,j;~t
Zn_D,g I
Zn_D,g ' -st-
.-D,g --
(Xj /=l,jct
Zn_D,g I
The new crossover is applied to the selected parents as usual (Goldberg, 1989). If the resulting children do not belong to the identified clusters, the nearest clusters are selected and mutation is applied to the children as above. For the results presented below, the mutation rate is 0.1 and the crossover rate is 0.9. An overlapping population is used: newly generated offspring are added to the population and subsequently the worst individuals are destroyed. New offspring may or may not make it into the population depending on whether they are better than the worst in the
646 population. The termination function monitors the best individuals in each generation. If the best solution's value remains constant for a pre-specified number of generations (10 in the results presented below), the GA is terminated. 4. CASE STUDY AND CONCLUSIONS An Oil Stabilisation Process (OSP) (McCarthy et al., 2000) is used to demonstrate the effectiveness of the new clustering procedure. Fig. 1 shows the flow sheet structure, in which 1, 2, 3 and 4 represent flash vessels, M a mixer, and V a pressure valve. The feed consists of L "-[--~-] "12 hydrocarbons. There are n=5 continuous -optimisation variables: the flash temperature for the feed to each flash, Xl, x2, x3, x4, with normalised initial search region [0,1], and the target pressure for the valve, x'se [1,30] atm, represented as x5 ,, below in a normalised context (i.e. xse [0,1]). The optimisation problem is to maximise annual profit. The main constraint is a vapour pressure specification on the oil product and the amount of Fig. 1. OSP flowsheet, heavy components in the gas product. The objective function value of infeasible points is given an infinite cost, 1020; the value of feasible points is typically negative indicating a profit. In evaluating the objective function, infeasible points arise primarily for three reasons: (1) The process isn't valid: The temperatures for the flash vessels may be outside the ranges allowed by fundamental physical properties or the utilities available are not sufficient for the request. In this case, the flowsheet is given an arbitrary annualised cost of 1020 (i.e. infinity); (2) the process is valid, in so far as the individual processing units are concerned, but the gas product stream doesn't meet the specification imposed. In this case, the process is also given an infinite cost, i.e. 1020; (3) both the processing units and the gas product stream are fine but the oil product stream doesn't achieve the desired vapour pressure. In this case, the process can be costed fully but the product stream is given no value. Therefore, the process cost will be positive in value. For a feasible process (i.e. all of the constraints are satisfied), the cost will typically be negative, as said earlier, assuming enough oil product is generated. Jacaranda (Fraga et al., 2000) is used both for the generation of the 10" initial data within the initial search region, of which 1% are typically feasible, and for the evaluation of the objective function in Table 1 Solution obtained for OSP problem the GA. The initial (A) (B) (C) feasible points are Best Solution (106) 6.50 -360 -363 used to get the circle Average Solution (106) 6.53 -354 -361 knowledge about the Standard Deviation (106) 0.03 3 2 feasible regions. Worst Solution (106) 6.60 -349 -357 The generation to find the best 10 22 26 To evaluate the effectiveness of using the knowledge gained about the search space, we now consider applying the targeted GA, as described in Section 3. Three cases are considered: (A) a GA with no feasible region knowledge; (B) a
647 GA with linear clustering knowledge about the feasible regions (Wang et al., 2002); and, (C) a GA using the circle knowledge about the feasible regions. The population size is set to 100. Ten runs are performed for each case and the distribution of results, as well as the generation in which the converged population was first obtained, are shown in Table 1. In Table 1, the greater number of generations required for convergence are needed in Case (B) and (C). Moreover, considering the additional computation efforts needed for knowledge discovery, it is concluded that more computation requirements are needed in (B) and (C). However, in case (A), the GA is not able to find a feasible solution (no profit possible). In cases (B) and (C), feasible solutions are found and the behaviour is consistent in terms of the values obtained. Even if the GA parameters are adjusted (e.g. we increase the population size for (A) and decrease it for (B) and (C)), the relative behaviour remains consistent with that presented in Table 1. Cases (B) and (C) consistently find feasible solutions. We think the solution quality and consistency is more than efficiency, from this context, the combination of visualisation, knowledge (linear and circle) discovery and a GA results in a process optimal design tool with more effectiveness. Comparing (B) and (C), the results of linear and circle knowledge based approaches are similar. However, the visualisation of linear knowledge (Wang et al., 2002) is based on the Parallel Coordinate System (PCS) whereas the new method uses the 2-D Cartesian Coordinate System (CCS). The latter is easier for the user to understand intuitively and enables direct interaction with the user. The procedure actively involves the user to determine the number of clusters to define and the circle radius for each cluster, thus reducing the computational effort. Furthermore, the direct involvement of the user helps the user gain an understanding of optimisation problem, especially the feasible space. The result is a method with readily explainable results. The combination of automation with direct visual interaction can lead to improved results, as recently proposed by Wegner (1997) where it was proven that the combination of interaction with algorithms can solve a larger class of problems than algorithms alone.
Acknowledgements Funding provided by the EPSRC and valuable input from BP Amoco are gratefully acknowledged. REFERENCES Fraga E.S., M.A. Steffens, I.D.L. Bogle and A.K. Hind, Foundation of Computer-Aided Process Design, AIChE Symposium Series, 96:446-449, 2000. Fortier J.J. and H. Solomon, Multivariate Analysis, Academic Press, New York, 493-506, 1996. Goldberg D.E., Genetic Algorithms in Search Optimisation and Machine Learning, Addison Wesley, Reading, 1989. Gordon A.D., Classification, New York, Chapman and Hall, 1981. Kohonen T., Self-Organising Maps, Springer, Berlin, Heidelberg, 1995. Koontz W.L.G., P.M. Narendra and K. Fukunaga, IEEE Trans. Comput., C-24: 908-915, 1975. McCarthy E. C., E. S. Fraga & J. W. Ponton, Computers Chem. Engng., 22(Suppl.): 877-884, 1998. Wang K., A. Salhi and E. S. Fraga, European Symposium on Computer Aided Process Engineering-12 (ESCAPE12), 1003-1008, Hague, May, 2002. Wang K., T. Lohl, M. Stobbe, S. Engell, Computer and Chem. Engng, 24: 393-400, 2000. Ward J.H., J. Am. Statist. Assoc., 58: 236-244, 1963. Wegner P., Comm ACM 40(5): 80-91, 1997. Wishart D., Biometrics, 25: 165-170, 1969.
Process SystemsEngineering2003 B. Chen and A.W. Westerberg(editors) 9 2003 Published by Elsevier ScienceB.V.
648
Solving Batch Production Scheduling Using Genetic Algorithm** Lian-Ying Wu a Yang-Dong Hu** Dong-Mei Xu* Beng Hua b a College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao, 266003, P. R. China. b Institute of Chemical Engineering, South University of Technology, Guangzhou 510640, E R. China. Abstract The optimal scheduling of multi-product batch process is studied. The relationship between production scale and production cost is analyzed. A new mathematics model is proposed which takes the maximum profit as objective function, which can be solved by the modified genetic algorithm (GA) with mixed coding (sequence coding and decimal coding) developed by us. The PMX crossover and reverse mutation are used for the sequence coding. At the same time, the arithmetic crossover and heteropic mutation are used for the decimal coding. An example is solved to demonstrate the effectiveness of the method. Keywords
1.
production scheduling; batch process; combinatorial optimization; genetic algorithm
INTRODUCTION
Batch processes are widely used in the chemical process industry and are of increasing industrial importance due to a great emphasis on low-volume, high-value-added chemicals and the need for flexibility in a market-driver environment. Production scheduling aims at allocating available manufacturing resource for the required manufacturing tasks and identifying the sequence and timing parameter values to accomplish these tasks. Effective production scheduling in the industry has the potential to achieve high economic retums. Scheduling has been attracting increasing attention in recent years because the competitive of products becomes more serious. Up to now, two main approaches to production scheduling, non-stochastic algorithm and stochastic algorithm, are addressed. Some scholars (X.Lin and C.A. Floudas ill ; Minseok Kim et[2l.; Hong-ming Ku and Iftekhar Karimi[3l; I.B.Tjoa et[4l; M.G. Ierapetriton and C.A. Floudas [51) addressed the production scheduling with MINLP or NLP approach. In recent years, the stochastic searching algorithm has caused increasing "Corresponding author: Yang-DongHu Tel: +86-532-2032141 E-mail:
[email protected] **Supportedby the National Fundamental Research DevelopmentProgram of China (No.2000026308)
649 attentions for its simplicity and suitability for the large-scale problems. Kefen.Wang,et [61 and Jae. Iiak Jung [71 studied the scheduling of a multi-product batch plant using genetic algorithm. Y. Murakami et Is], Jun-Hyung Ryu.et [91 analyzed the production scheduling of batch chemical process using the simulation anneal. Two aspects, modeling and developing the effective algorithm, are equivalent important for production scheduling. In this paper, a novel mathematical formulation is presented for the scheduling of batch plants and a modified genetic algorithm featuring mixing coding (sequence coding and decimal coding), is developed, which can be used to solve the above problem effectively.
2. PRODUCTION SCHEDULING MODEL Although a number of diverse mathematical formulations are presented for solving the scheduling, most of them target the shortest total operation time for producing all products, i.e. the so-called make-span. However, the shortest make-span can't always ensure the maximum economic benefit for the enterprise, to overcome the deficiency, we formulate the scheduling model with the maximum profit as follows: F=Max(/3 / CNM-TF)
( 1)
For N products producing on M units, the completed time is calculated as follows: For j=l: Ci l--C (i-I)I-~-~ t (i-I),+aio+S i (i-I)k+ t i l+ai i
i-1, 2 ......N
(2)
Cil ~
i - l , 2 ...... N
(3)
C (i-l) l + a i o + t i l + A t ( i - I ) i + a i l + S i ( i - l ) l
For j=2. 3 ...... M:
C,j=Max[Ci(j-l) , C(~-,)j+s(i-,)~j+ai(j-,)]+t~j+a~j
i=l, 2 ...... N" j=2, 3 ..... .M
(4)
Cij~/ Ci(j-l)q- tij + aij
i=1, 2 ...... N" j=2, 3 ..... "M
(5)
i=l, 2 ...... N" j= 2, 3 ..... -M
(6)
Cij ~ C(i-l)jq-s(i-l)ij+t ij+aij
C,=Max [CN(Ml) , C(~-I)M+S(N-,)NM+aN(M-I)]+ t . + a .
(7)
N M
TF= y~ ~-" ct,(j_,)jS,(j_,)s +K
(8)
:=1 y=l
0 6i(j-l)j= C,j-C,o_l)
Cv < C,(j_I) C 9 ~ C,(j_l)
(9)
650 3. MODIFIED GENETIC ALGORITHM (MGA) Genetic algorithm is a stochastic search algorithm based on the mechanisms of natural selection and heredity. It can explore different regions of the search space simultaneously and hence are less prone to terminate in local minima. A difficulty of solving the combination-optimization problem using genetic algorithm lies in the coding of variables. For the above scheduling problem, there exist two kinds of variables in the model, which is inconvenient to be coded using binary coding simultaneously. Accordingly here a modified genetic algorithm featuring mixed coding, which uses two different crossover and mutation operators for the above two kinds of variables, is shown as follows.
3.1. Coding The chromosome based on mixing coding can be described as: { (pl, p2...... PN), (Ah, At2 ...... AtN) } Where the sequence coding (pl, p2...... pr~) , the first part of chromosome, represents the production sequence, and the decimal coding ( A h , At2 ...... A tN ) , the second part of chromosome, represents the time interval in the first unit from the finishing of a product processing to the beginning of the next one, which is restraint to the posterior processing and has a effect on the overall process.
3.2. Crossover Since coding of the above scheduling problem is very similar to that of the multi-machine layout studied by Cheng R. and M. Gen tl~ which adopts the PMX crossover and arithmetic crossover in solving process, therefore we also adopt the same crossover method, which can be described as follows. A. PMX crossover 1) Random select two individuals and determine the cross point: parent l-
1 2 ~ 4 5 ~ 7 8 9
parent2. 5 4 16 9 2 II 7 8 3 2)
Cross the above sub-blocks: Original offspringl: 1 2 [6 9 2 1[ 7 8 9 Originaloffspring2:5 4 ~ 4 5 ~ 7 8 3
3)
Determine the mapping relationship:
651 1-~--~ 6 ~ ' ~ 3 2~ 5 9 ~
4)
3 4 5 6 Legalize the offspring using mapping relationship: offspringl: 3 5 16 9 2 11 7 8 4 offspring 2- 2 9 ~ 4 5 ~ 7 8 1
B. Arithmetic crossover For the two arbitrary individuals V~, V2 (two parents), the offspring could be found as follow: offspring I:V(= V~+ ( 1 - k ) V 2
(10)
offspring 2: V~ = V2 + (1 - L)V~
(11)
The arithmetic crossover could hold the diversity of offspring and avoid prematurely convergence and local optimum. The PMX crossover and Arithmetic crossover are applied to different variable of the chromosome simultaneously in the evolution process. It is found that, there exists weak coupling between the sequence coding and decimal coding, consequently the preferable convergence can be ensured using the above crossovers, which can eliminate unfeasible offspring in the process of evolution. 3.3. Mutation The similar situation also appears in the mutation process. Here the reverse mutation [~] and the heteropic mutation Ill] are used for the sequence coding and decimal coding respectively. This mutation not only ensures the legalization of offspring but also improve greatly the efficiency of mutation. Table 1 Processing time for products t 0 and storage cost of unit time ( a ij) nit 1 2 3 4 5 6 7 8 9
1
23(0.2) 21(0.2) 15(0.3) 18(0.5) 12(0.2) 21(0.4) 12(0.1) 18(0.6) 20(0.3)
2
3
4
19(0.3) 13(0.6) 9(0.9) 8(0.1) 7(0.4) 6(0.3) 5(0.2) 5(0.1) 9(0.2) 10(0.6) 21(0.3) 12(0.1) 8(0.3) 14(0.2) 8(0.4) 17(0.2) 9(0.4) 16(0.3) 16(0.4) 14(0.5)21(0.2) 12(0.1) 19(0.3)10(0.4) 12(0.2)10(0.1)19(0.5)
5
18(0.1) 18(0.2) 10(0.4) 9(0.2) 7(0.5) 16(0.1) 6(0.3) 5(0.2) 8(0.6)
6
7
8
12(0.2)21(0.2) 17(0.7) 23(0.3) 21(0.5) 18(0.4) 15(0.5) 11(0.5) 23(0.2) 24(0.4) 12(0.3) 24(0.5) 20(0.1) 16(0.2) 18(0.1) 6(0.7) 19(0.6) 12(0.3) 9(0.2) 7(0.1) 17(0.2) 20(0.3) 20(0.4) 6(0.4) 18(0.4) 21(0.3)23(0.1)
After mutation, the selector operator selects only the best individuals among the parents and
652 the offsprings, which made the best the individuals be survived in one generation and the optimum solution be obtained step by step. According to the theory of schema theorem, the schema order could be lowered and the schema length could be diminished by using above method of crossover and mutation, thus they are not easy to be broken in the process of evolution, then the stability and convergence can be ensured. It must be denoted that two layers iteration to solve the problem, which the outer iteration is used for product order and the interior iteration is used for the interval time, is difficult to convergence and the result is not rational, which is proved in the solution process. So the simultaneous optimization is adopted for the two kinds of variable and the quality of convergence is improved obviously 4,
EXAMPLE
To investigate the performance of the approach above mentioned, a large problem of nine products and eight units is studied. The processing time of each job is listed in Table 1.The result is shown in Table 2. The result shows that the optimal scheduling changes with product price or storage cost of unit time of intermediate products. Apparently, different optimal sequences correspond to different time interval. The shorter the total completion time is, the higher the total storage cost due to the prolonged stack of intermediate products will be, vice versa. When the price of the product increases, the maximum profit can be achieved by raising productivity. In this case, the production cycle is shortened, although the total production cost rises up due to the increase of storage cost, which results the reduction of the profit of single product, the gross profit rise as a consequence of the high yield. On the other hand, when the price of the product decreases, the maximum profit can be obtained by diminishing yield. In this case, the production cycle is prolonged, i.e. the period of storage is cut down, which results the reduction of the storage cost, accordingly the maximum profit will be attained due to the cost cutting. 5, CONCLUSIONS The production scheduling of batch process with objective function of maximum profit has Table 2: Optimal result Case The optimal scheduling 1 { (3,5,2,6,4,9,1,8,7) (0,4.2,1,0,2,3.2,0,6.6,7.9)} 2
{ (3,8,4,1,9,2,6,5,7) (0,1.6,1.3,3,4,0,1.4,15.6,6)}
CNM 276.2
TF 32.1
264.9
40.5
3 {(3,5,2,6,4,1,9,8,7) (0,4.4,0,0,1,2.7,4.6,6.5,7.9) } 279 Notes: case 1:13 =10000 case 2:13 =20000 case 3: 13=10000,
31.0 a 3.4=1.0
been studied using the modified genetic algorithm. The mixed coding method has been put
653 forward and a novel strategy of crossover and mutation is adopted in the searching process. It could ensure that the optimal solution is searched in the feasible region, which results a steady convergence. It is found that, the production sequence and completion time are influenced by the production scale and operating cost. The maximum profit is always a trade-off of the production scale and operating cost. NOTATION random number between 0 and 1. the total completion time. average price of all products 13 TF the cost of production completion time of the ith product in the sequence in batch unit j where the product is CO finished transforming out of unit j and filling in unit j+ 1. processing time of product i in batch unit j tij transfer time of product i out of batch unit j to batch unit j+ 1 aij setup time required for product i-1 after product i in batch unit j S(i-l)ij the storage cost of intermediate product i between unit j- 1 and unit j in unit time a i (j-l) j the storage time of intermediate product i between unit j-1 and unit j i(j-l)j CNM
REFERENCES: [1] X. Lin, C.A. Floudas Computers and Chemical Engineering 2001,Vo125, 665-674 [2] Minseok Kim et, Ind. Eng. Chem. Res. 1996 Vol 35 4058-4066 [3] Hong-ming Ku and Iftekhar Karimi. Ind. Eng. Chem. Res. 1990 Vol 29 580-590 [4] I.B. Tjoa, et. Computers Chem. Engng. 1997 Vo121,S 1073-S 1077 [5] M.G.Ierapetritou.and C.A. Floudas Ind. Eng. Chem. Res. 1998 Vol 37 4341-4359 [6] Kefen. Wang, et. Computers and Chemical Engineering. 2000. Vol 24 393-400 [7] Jae. Iiak Jung et. Computers Chem. Engng. 1998 Vo122(11), 1725-1730 [8] Y. Murakami et. Computers Chem. Engng. 1997,Vo121, S1087-S1092 [9] Jun-Hyung Ryu,et. Ind. Eng. Chem. Res. 2001 Vol 30 228-233 [10] Cheng R. and M. Gen., Genetic algorithm for multi-row machine layout problem. Engineering Design and Automation, 1995 [11] Cheng Run-Wei et. ((Genetic Algorithm and Engineering Design)), Science Publishing Company 2000, 1
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
654
Synthesis of eco-industrial system considering environmental value using adaptive simulated annealing genetic algorithms XUE Dongfeng *~, LI Yourun, SHEN Jingzhu and HU Shanying Center for Industrial Ecology, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, P. R. China
Abstract: This paper elaborates the establishment of the model of eco-industrial system including selection of members, environmental value and construction of eco-industrial chains. The model is formulated into a large-scale mixed integer non-linear programming (MINLP) problem. The hybrid adaptive simulated annealing genetic algorithm (ASAGA) is proposed to solve the problem of synthesis of eco-industrial system. A case study shows that the model is applicable and the ASAGA algorithm is effective.
Keywords: eco-industrial system, model, environmental value, MINLP, adaptive simulated annealing genetic algorithm
I. Introduction With the rapid growth of population and economy, the problems, such as environmental deterioration, natural resource depletion and economic issues, become worse at a global scale, which menace the survival of human and the development of society. As a result, developing "cyclical" production systems that increasingly reuse and recycle all materials causes great attention. Similar to biological ecosystems in which one organism's waste is the resource of food for the another, we can develop industrial systems in which the by-products or wastes from one unity can be effectively turned useful to the other. This recognition has lead to the concept of industrial ecology (IE) and the development of eco-industrial system. IE is a novel approach to achieve sustainable development. It aims to optimize the consumption of natural resources and energy and minimize the generation of waste. The means are addressed by which humans maintain a desirable carrying capacity given continued economic, cultural and technological evolution. In the field of IE, the methodology of ecology is used to study industrial production. The economy is viewed as a closed system similar to the natural ecological system in which the waste generated by one entity is or can be the nutrient for the other. The key feature of eco-industrial system is the construction of industrial chains, which indicates the relationship among industrial enterprises in an industrial ecosystem. Significant advancements have been made since Frosch and Gallopoulos introduced the conception of IE (1992). Lowe et al. (1995) provided an overview of the field of industrial ecology. Connelly and Koshland (2001a, b) used the thermodynamic interpretation of ecosystem evolution to strengthen the analogy of biological-industrial ecosystem. Anderson adopted IE as a framework to do the energy analysis using fish as a case study. Koenig and "Corresponding author. Fax. 0086-10-62770304. E-mail:
[email protected]. Supported by National Science Foundation of China (No. 29836410)
655 Cantion (1998) presented a defining mathematical theory of IE. Chen et al. (2002) developed a mixed integer non-linear programming (MINLP) model of Eco-industrial Park. Many experiences of IE practice have been reported (Potts Carr, 1998; Co^te" and Cohen-Rosenthal, 1998; Lambert and boons, 2002). Most of the contributions, however, focus on the conception or application. This paper details the model of eco-industrial system considering environmental value with the superstructure approach. The model is often formulated into a large-scale MINLP problem. The traditional methods of solving a MINLP problem often fail to achieve the satisfactory results. For the non-convex mathematical form and complicated superstructure, it is necessary to develop an effective method to solve the problem. Genetic Algorithms (GA) and Simulated Annealing Algorithms (SA) have many advantages in dealing with MINLP problems. The intrinsic disadvantages, however, limit their further applications in combinatorial optimization, such as premature termination, long computing time and weak efficiency of hillclimbing in GA, and the neglect of historic information and slow exploitation in SA. This paper studies the eco-industrial system considering environmental value with the superstructure approach. The modified Adaptive Simulated Annealing Genetic Algorithm (ASAGA) is proposed to solve the MINLP problem. Furthermore, a case study illustrates the applicability of the model and the effectiveness of the algorithm. 2. Eco-industrial system
The essence of IE is to provide the optimal network of eco-industrial chain comprising of enterprises to utilize resources and energy completely within the industrial system, instead of only dealing with the terminal waste streams. The problem of synthesizing the IE is stated as follows: Given a series of enterprises generating pollutants in forms of terminal gaseous and liquid wastes, it is desired to identify a maximal-benefit strategy of pollutant reduction, to recycle and reuse the wastes within the eco-industrial network in order to utilize the resources and energy and reduce the pollutant load to the largest extent. An eco-industrial system consists of various eco-industrial chains formed by several industrial enterprises to minimize pollutant emissions. The idea is to use the wastes generated by one resident company or service unit as an input or raw material for the other. It is important to form the eco-industrial chains in the study of eco-industrial system. The problems of construction of the eco-industrial system include selection of members within the system, reuse or recycle of wastes, and introduction of environmental value. 2.1 Selection of members There are different members in the eco-industrial system similar to the producer, the consumer and the disintegrator in the natural ecosystem. Selection of members is fundamental to the eco-industrial system. The core enterprises are determined usually according to local dominant resources. Other members are chosen from several candidates with the proper evaluation in order to form the co-generate relation and make it exchange resources and wastes. The basic steps of member selection are as follows: (1) The relationship degree is obtained from the relation matrix of the candidates. (2) The index system of selection is established including the relationship degree of product, the consumption of resource and energy, and the economic profit.
656 (3) The judgment matrix method on the base of expert marking is used to determine the weight of various indexes. (4) The evaluation system of member in eco-industrial system with fuzzy mathematics method is set up to deal with the uncertainty and randomicity. As the data of the candidates are input, the members of eco-industrial system are chosen using above fuzzy evaluation approach. 2.2 Environmental value
IE purchases total or real benefit, including economic benefit, environmental benefit and social benefit. Therefore, the environmental value is introduced in this model. The environmental value is a dynamic concept, which means that environment can provide necessary foundation of material and energy and mental satisfaction for the survival and development of human (Li et al., 1999). It comprises of substantial resource value and abstract ecological value. It is environmental pollution that causes the losing of environmental value. Generally, the total losing caused by the environmental pollution consists of contributions from four aspects: the losing of production, capital cost, human health and environmental quality. The last item is usually ignored in the traditional cost accounting. The total losing of environmental pollution = production losing + capital cost losing + human health losing + environmental quality losing (1) The total losing of environmental pollution can be quantified as the commercialized value. Here, each item will be analyzed in detail. (1) The production losing caused by environmental pollution Generally, the production losing means production reduction of some products, quality decline of some products or increasing of product cost. (2) The capital losing caused by environmental pollution The capital losing refers to shortening of use life or increasing of upkeep-costs of fixed assets such as machines, buildings, ductwork or equipment. (3) The health losing caused by environmental pollution The health losing is the largest losing caused by environmental pollution. It means increasing the incidence of disease and death rate. Human capital approach is usually used to estimate the health losing. (4) The environmental quality losing caused by environmental pollution The restore-cost of original environmental quality or the losing of amenity service can substitute the losing. It is estimated by the treatment cost restoring the polluted environment to the origin state. The sum of the total losing of environmental pollution and treatment cost of pollution is the total expense of environmental pollution. As pollutants are reused and recycled, the corresponding environmental benefit is obtained. In this paper, environmental value is introduced as a part of objective functions in order to estimate real benefit in the system. 2.3 Model of eco-industrial chain
The model of eco-industrial chain aims at determining the connection of resource, energy and information flow among various members. It includes the route, amount and composition of flow and the selection of resource-processing or waste treatment technology. Part of the parameters is considered as 0-1 variables, such as the route, technology and composition. The
657 others are continuous variables. The logic expressions are introduced to denote the connection constraints. Together with other constraints and conditions, the problem of construction of eco-industrial chain is formulated into a large-scale MINLP model.
3. Adaptive simulated annealing genetic algorithms For this non-convex mathematical form and complicated superstructure, the traditional methods for solving a MINLP problem often fail to achieve the satisfactory results. It is necessary, therefore, to develop an effective method. As stochastic optimization methods, GA and SA have many advantages, but their intrinsic deficiencies limit their further applications. In order to take advantage of GA and SA, the hybrid ASAGA is proposed. 3.1 Essential ideas of ASAGA The essential ideas of ASAGA are as follows: (1) SA has a two-loop structure whereby alternative configurations are generated at the inner loop and the temperature is decreased at the outer loop. GA, generally accepted to be good at global optimization, is embedded into SA to strengthen its inner optimization. (2) In order to improve the performance of GA, the strategy of adaptive crossover and mutation probability is introduced, which can balance the diversity of population and the pressure of selection. It is useful to perfect the proposed hybrid algorithm. (3) In order to maintain the ratio of acceptance to rejection of new solution in the process of random selection in the simulated annealing algorithms, the strategy of adaptive step adjustment is introduced. It can make use of the historic information and improve the efficiency of the hybrid algorithms. (4) In order to deal with the MINLP problem, the integer variable is obtained through the dispersion of continuous variables. Then, the MINLP problem is transformed to a non-linear programming problem and it can improve the efficiency of the algorithms and the quality of solution greatly. 3.2 Basic procedure of ASAGA The basic steps of the GAA are as follows: Step 1. Initialize various parameters, including the initial temperature, stopping criterion, coefficient of annealing cooling, adjustment period of step, adjustment period of temperature, number of evolution generations of GA in the inner loop and other parameters. Step 2. At the inner loop, neighborhood solutions are generated N times along various coordinate directions in the current temperature. Principle of Metropolis and the strategy of adaptive step adjustment are applied in this process. Then, the A certain number of solutions are selected as the initial population and GA is performed for the global optimal in which the number of generation is fixed and the strategy of adaptive mutation rate and crossover rate. Step 3. At the outer loop, the annealing temperature is adjusted according to the expression of adaptive annealing cooling rate. Step 4. The algorithms end if the termination criterion is satisfied, or else return to step 2. Tested by various types of test functions, ASAGA is proved more effective and stable in constraint optimization than other methods, such as GBD approach and modified GA. This algorithm has been applied in solving several typical MINLP problems in the chemical process successfully. (Xue, 2001).
658 In order to apply ASAGA to solve the problem of IE, a suitable coding is defined. The coding of individuals of the population should be able to represent the searching space and all of the feasible solution. The independent variables of the system should be identified, which are coded with real numbers. To express a superstructure containing all potential solutions, a real number of [0, 1] is employed instead of binary to denote whether a unit is selected. If the flow rate equals to zero, this unit corresponding to the number will not be selected. The objective function is to maximize the total profit of eco-industrial system. 5. Case study
Synthesis of eco-industrial system by ASAGA has been programmed in a case study of the coal-based eco-industrial system in China. There are coal excavating, coal washing, coal power plant, coking, coal-gasification, smelting plant, foundry and methanol in the coal-based industrial system. For using coal resource widely in the industries and residents, 802, CO2, gangue and fly ash are the main pollutants causing the wasting of resource and worsening of ecological environment. For the convenience of computation, the value of total input of coal resource is fixed. The MINLP model is established by the superstructure approach. This problem has 7 binary variables and features nonlinear and non-convex. In addition, there are 24 continuous variables and 248 constraints in this problem. The solution of the IE problem is obtained by the proposed algorithm. An industrial system is established, which consider coal as raw material, electric power, methanol, DME, coke as leading products, and cement, water-purificant as supplement product. In this system, material exchange, information communication and manage cooperating of every producing flow of coal are realized. Integrated coal gasification combined-cycle (IGCC) system is adopted in the coal power plant. Aluminum in the gangue from the coal washing plant is utilized to produce A12(SO4)3 together with SO2 from the tail-gas of coking. Fly ash from the power plant is used to produce the brick. Fly ash, SO2 and wastewater will be settled or improved while the coal resource is made full use of. The total profit considering environmental value is higher 30% than the simple sum of the profit of each plant. 6. Conclusion
The model of eco-industrial system considering environmental value is developed in this paper. The hybrid adaptive simulated annealing genetic algorithm (ASAGA) is proposed to solve the formulated MINLP problem. Case study illustrates that the established model is applicable and ASAGA is effective. REFERENCE [1 ]Chen D. J., Li Y. R., Shen J. Z. and Hu S. Y.(2002). A MINLP Model of Eco-Industrial Parks, The Chinese Journal of Process Engineering, 2(1):75-80. [2] Co^te" R. P. and Cohen-Rosenthal E.(1998). Designing eco-industrial parks: a synthesis of some experiences, Journal of Cleaner Production 6:181-188. [3] Connelly L. and Koshland C. P.(2001). Exergy and industrial ecology. Part 1: An exergy-based definition of consumption and a thermodynamic interpretation of ecosystem evolution. Exergy Int. J. 1(3) 146-165.
659 [4] Connelly L. and Koshland C. P.(2001). Exergy and industrial ecology. Part 2: A non-dimensional analysis of means to reduce resource depletion, Exergy Int. J. 1(4) 234-255. [5] Frosch R. and Gallopoulos N. (1992). Towards an industrial ecology. In: Bradshow A. D. et al.(eds.), The treatment and handling of wastes, Chaoman and Hall, London, pp269-292. [6] Koenig H. E. and Cantlon J. E.(1998). Quantitative industrial ecology. IEEE transactions on systems, man, and cybernetic, Part C, Vol.28, No. 1, pp 16-28, 1998 [7] Lambert A.J.D. and Boons F.A.(2002).Eco-industrial parks: stimulating sustainable development in mixed industrial parks, Technovation 22: 471-484. [8] Li J. C., Jiang W. L., Jin L. S. and Ren Y.(1999). Theory of ecological value, Chongqing University Press, Chongqing. [9] Lowe E. A., Warren J. L. and Moran S. R.(1995). Discovering Industrial Ecology m An Executive Briefing and Sourcebook, Battelle Press, Columbus, Ohio. [10] Potts Carr Audra J.(1998). Choctaw Eco-Industrial Park: an ecological approach to industrial land-use planning and design, Landscape and Urban Planning 42 : 239-257. [ 11] Xue D.F.(2001). Study on mass integration for waste minimization. Ph.D. dissertation. Dalian University of Technology, Dalian.
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Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
On an Object-Oriented Modeling of Supply Chain and its Operational Strategy Hisaaki Yamaba and Shigeyuki Tomita Miyazaki University, 1-1, Gakuen Kibanadai Nishi, Miyazaki, Japan Abstract In recent years, researchers have devoted a great deal of attention to supply chain management (SCM) and various production models have been proposed. In such chains, a lot of decisions have to be made to achieve the production objectives under a variety of uncertainties. In order to operate SCM efficiently, it is indispensable that some rational and sound operational strategy for realizing cooperative operation among such chains. A simulation-based approach is one of the most effective ways as a design method of a production model together with its strategy under uncertain conditions. In this research, an attempt was made both constructing such a environment by means of Object-Oriented approach and, utilizing such a environment, exploring the possibility of a new production model between a maker and suppliers where both "Build-To-Order" and "Build-To-Stock" are used to suit the customer's needs. The validity of the model and the strategy proposed here was examined through computational experiments by using a simulator developed based on this approach. Keywords Supply Chain Management, Object-Oriented analysis/design, simulation 1. Introduction In recent years, researchers have devoted a great deal of attention to supply chain management (SCM) and various production models have been proposed. In such chains, a lot of decisions have to be made to achieve the production objectives under a variety of uncertainties such as arrival of urgent jobs and delay in arrival of materials. However, all decision-making in a certain department are currently carried out independently of those of other departments, and this causes low performance of the whole system. Thus, in order to operate SCM efficiently, it is indispensable that some rational and sound operational strategy for realizing cooperative operation among such chains. A simulation-based approach is one of the most effective ways as a method of performance evaluation of such strategies under various configurations of supply chains. However, a support environment that generates simulators is indispensable in order to deal with modification of a
661 simulator, which is needed to meet changes of applied strategy candidates. In this research, an attempt was made both constructing such a environment by means of Object-Oriented approach and, utilizing such a environment, exploring the possibility of "BTOS": a new production model between a maker and suppliers where both "Build-To-Order" and "Build-To-Stock" are used to suit customers' needs. 2. Basic Concept of BTOS The idea "BTOS" is a hybrid of the one of"BTO" (build-to-order) and "BTS" (build-to-stock). The basic concept of BTOS is derived from configuration-to-order (CTO), which offers high customer satisfaction by customization of products accepting requires of customers. BTO manufacturing process was introduced in order to reduce dead stock. For example, some PC vendors compose PCs of parts and require other companies, which are called "(Parts) Suppliers", to produce the parts. These assembly dealers have achieved zero-product-stock, because they have parts stock enough by ordering parts in advance according to the forecasting of demands from customers. While the forecasting is correct, since they have appropriate amount of parts, they can start production as soon as they accept orders from customers. Here, the cost for stock of parts is required under this model. In this work, we call this model "BTO-BTS". On the other hand, we call another manufacturing process "BTO-BTO" model: assembly dealers order parts every time they receive orders. This model does not require the cost for stock of parts, however it makes lead times longer. It depends on the characteristics of the target market which model should be adopted. In general, if quick delivery is needed than low price, "BTO-BTS" model may be more suitable. On the other hand, if low prices needed than quick delivery, "BTO-BTS" model may be better. It is assumed that "BTOS" model introduced here allows customers to indicate which is prefer, "low price" or "quick delivery". In case that quick delivery is required, the order is achieved using parts stock. On the other hand, needed parts are ordered for the requirement when the requirement is given when quick lower price is selected. 3. Behavior of BTOS production system It is assumed that the production system has one and only assembly dealer. And also, it is assumed that each kind of parts can be supplied by one part supplier. Furthermore, each supplier is assumed to have materials enough to achieve required orders. An assembly dealer and a part supplier are also consist of several sector; order receiving sector, planning sector, stock sector, production sector, etc (Fig. 1). A customer tells an assembly dealer the desirable date of delivery together with the specification of his order; the performance of the PC and/or peripherals he needs. At first, the dealer replies the estimated price and delivery date. Since each company has its productivity limitation, a replied delivery date may not answer the request. In case a customer is not satisfied an estimation, he can modify
662
Partssupplier
Assembledealer ~
3 ~ PlanningSector ~
Orders ~
Order Receiving Sectol ~
Orders of Parts
9
,nestimatec priceand eliverydate
"f OrderReceiving1 ~ Sector
te Progress T
Products~ I ~, [ Warehouse ]) ProductionSector /
Pa~l
I Schedules
Shipment~ ProductionSector j
Fig. 1.
A draft of the "BTOS" production system.
the date or the specification of his order again and again. An assembly dealer makes a production plan every week. It orders parts needed in the plan to suppliers. Each order has to be specified its kind, amount, and delivery date. Each supplier receives orders from an assembly dealer, produces the parts, delivers them at their delivery date. It is assumed that a stock of parts or products costs in proportion to the amount of them. Therefore, it is assumed that just-in-time production is oriented. An example of a timetable of the production system employed under this model is shown in Fig.2. The buffering span of orders is set to 1 week. Whether parts-stock are used or parts-order are made for each order is determined by its delivery date: in case an order received at the i th span, the delivery date of the order is 9 included the i+1 th span : parts-stock are used, 9 after the i + 2 th span : the parts are ordered to suppliers. The former is called "Quick delivery" orders and latter is called "lower price" orders in this work. So, "Quick delivery" orders given in the i- I th span and "lower price" orders given in the i - 2 th span are scheduled to be produced in the i th span. An assembly dealer make a purchase plan of parts according to the production plan. Parts for "lower price" orders are ordered after receiving the requirements, however, parts for "Quick delivery" orders have to be ensured to be supplied in advance. Therefore, forecasting of orders requested in the next span has to be made. After all, parts for "lower price" order and parts in order to supplement the estimated consuming amount of parts according to the forecasting of customers' requirements.
663
Fig. 2.
A timetable of the operation of the example production system
4. A Simulator G e n e r a t i n g E n v i r o n m e n t
A simulation model is used rather than analytical approaches under an environment with demand uncertainty. However, it is a very hard task to construct simulators for every structure chains and operational strategies. Therefore, a computer-aided environment for generating such simulators automatically is indispensable. One of the purposes of this work is to provide generic components of which various simulators can be composed. Such components should be designed so as to distinguish interactions among domain objects from activities of each one; it is desirable that a component is independent of other components in order to deal with changes of a domain model. Components of simulators with various configurations are designed by means of Object-Oriented approach. Components of simulators are classified into two groups: "Models" and "Strategies". Each "Model" represents a component of production systems such as a supplier, a maker and so on. Furthermore, each function of suppliers and/or makers is achieved by an object of the corresponding class: "Models" are composed of Scheduler class, some Manager classes, and so on. This achieved that various type of components in supply chain can easily be build from those objects. On the other hand, interactions among "Models" are encapsulated in "Strategies". Each method of the classes is described according to the Object-Oriented analysis of the target system. And also, other classes such as Order class and Plan class are introduced in order to represent various data used in SCM.
664
Fig. 4.
Fig. 3.
The Result of the experiments (BTO-BTS).
The result of the experiment (BTOS).
Concretely, the components of simulators are designed and implemented by Java language. These Java classes are used as super classes of concrete classes implemented for each domain. "Strategies" were implemented as subclasses of Class Thread in order to realize the simultaneous parallel operations. 5. Experiment
Simulation experiments were carried out by making use of the simulators composed of these classes. A PC vendor and a parts supplier were assumed. Customers can choose a mother-board from 3 types, a CPU from 3 types, a hard disk from 4 types and a memory from 3types. A price
665 Table 1: The number of orders whose delivery dates were delayed.
Type
N u m b e r of Orders
BTO-BTO
1388
BTOS
1116
BTO-BTS
911
of each parts is different each other. It is assumed that orders of each parts set are given according to some exponential distribution. Therefore, too many orders can be given to the dealer and some of their delivery dates have to be delayed because of the productivity limitation of the company and lack of part-stock. The purposes of the experiments are as follows: 9 A production system adopting the BTOS process can be operated 9 Comparison between BTOS and "BTO-BTS" from the viewpoint of amount ofpart-stock an assembly dealer should have. 9 Comparison between BTOS and "BTO-BTS" from the viewpoint of how many orders a dealer delays their delivery dates. First, it was confirmed that several configuration of supply chains were easily built and ran normally under BTOS process. Next, comparison of performances between 2 "BTOS" and "BTO-BTS" were examined through 50 weeks simulation experiments. The required amounts of parts-stock are shown in Fig.3 and 4. The number of orders whose delivery dates were delayed are shown in Table 1. These results show that a production system operated by BTOS process could certainly reduce the amount of parts-stock rather than a system operated by BTO-BTS process. However, a BTOS system caused much production delay in return to the advantage. 6. Conclusion
An attempt was made to implement generic components that compose simulators for supply chains in order to realize a generic computational environment of such simulators by means of Object-Oriented approach. Objects were classified into two groups: "Models" and "Strategies". The components of simulators are designed and implemented by Java language together with a simulator generating system, which compose simulators of such components. Some operational strategies that should be adopted in some occasion of decision-making were examined using these simulators, and a rational operational strategy was proposed. Finally, the validity of the model and the strategy proposed here was examined through computational experiments by using a simulator developed based on this approach.
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
666
Variable decomposition based global-optimization algorithm for process synthesis Jian Zhang a, Bingzhen Chen a, Shanying Hu a, Xiaorong He a aDepartment of Chemical Engineering, Tsinghua University, Beijing 100084, China Abstract The key-task of process synthesis is to solve a mixed-integer non-linear (MINLP) model. As the number of integer variables increase, one is faced with a large combinatorial problem. At the same time, due to the nonlinearities the MINLP problem is tended to converge to a local optimal solution. To overcome these difficulties, a variable decomposition based global-optimization algorithm is presented, in which the integer variables and the continuous variables are treated sequentially. There are three steps in the algorithm: The first step is to classify the logical variables into independent and dependent variables, meanwhile, the logical constraints are classified into deductive constraints and restrictive ones. Logical deductive graph is generated to get the feasible logical variable sets. The second step is to construct a non-linear continuous (NLP) sub-problem for each feasible logical variable set and to solve the sub-problem using general global optimization algorithm. The third step is to obtain the global optimum solution. A process synthesis example is presented to compare the algorithm with the standard Branch-and-Bound algorithm. Keywords:
global optimization algorithm, logic-based MINLP model, process synthesis
1. INTRODUCTION Process synthesis is an important part of process systems engineering. The mixed-integer non-linear programming (MINLP) model is widely used in process synthesis. In an MINLP model, continuous variables are used to model the technical parameters in chemical process, such as flows, temperatures and compositions. The 0-1 variables (only have value of 0 or 1) or logical variables can be used to model, for instance, altemative candidates and the topological relations among equipments. There are two types of MINLP model: algebraic MINLP model and logic-based MINLP model [11. The disadvantage of the algebraic MINLP model is that the topological relations expressed by 0-1 variables are not direct, and hard to modeling. It limits the industrial usage of the algebraic MINLP model. In a logic-based MINLP model, the topological relations are expressed clearly by logical relations. Heuristic knowledge would also be integrated in the model to eliminate candidate flow sheets that conflict with technical practice, and would accelerate the solving process. So the novel global optimization algorithm proposed in this paper is based on the logic-based MINLP model. The global optimization MINLP algorithm includes Branch-and-Simplify method,
667 Hybrid Branch-and-Bound and Outer Approximation method, txBB algorithm [21 and symbolic reformulation algorithm. L31 For each iteration of the branch strategy, only one from the integer variables is fixed to 0 or 1, and the rest ones are relaxed into continuous variables. So the number of iterations is exponential with the number of logical variables. Furthermore, the relaxation of integer variables leads to an increase of the number of continuous variables and the scale of model, also, it may make some linear constraints transfer to nonlinear constraints. These make the relaxed model hard to solve. In this paper, a variable decomposition based global-optimization algorithm is presented. This algorithm is described in the second section. The specific steps of the algorithm are listed in section three. A reaction process synthesis example is presented in section four to test and compare the algorithm. The fifth section is a conclusion and discussion. 2. A L G O R I T H M DESCRIPTIONS The key of the variable decomposition algorithm is to treat the logical variables and continuous variables of the MINLP model sequentially. A set of logical variables whose values satisfy all logical constraints is called a feasible logical variable set. Locating the feasible logical variable sets is an exact reasoning problem. The logical variables' values of each feasible solution of the original MINLP model certainly are in the feasible logical variable region. Corresponding to each feasible logical variable set, a non-linear continuous (NLP) sub-problem is constructed. The global optimization solution of the original MINLP model is obtained from solving these sub-problems. In a process synthesis problem, although the combination number of integer variables may be very large, the number of feasible sets is few. For example, in a reaction process synthesis problem t4] there are 13 logical variables, and the combination number is 213=8192. But the number of feasible logical variable sets is 32, which is 0.39% of the combination number. For an ethylene refrigerant synthesis problem, I51 there are 25 logical variables, and the combination number is 225=33554432. But the feasible number is only 265, which is 0.00079% of the combination number. Especially, when integrating the heuristic knowledge, the flow sheets that do not satisfy technical experience are eliminated, and the number of feasible logical variable sets will be further reduced. 3. A L G O R I T H M The algorithm includes 3 steps: getting feasible logical variable sets; generating continuous variables NLP sub-problems corresponding to the feasible logical variable sets and solving them using the NLP global optimization algorithm; obtaining the global optimization solution. In a logic-based process synthesis model, the constraints include equipment constraints, connection constraints, and logical relations among equipments t6]. 3.1. Locating the feasible logical variable sets In process synthesis, some types of logical constraints are often used, and these
668 constraints would deduct logical variables directly: 1) f(y2,ys,...,yn)-~ y~: in this constraint, the value of yl
is partly restricted by
f(Y2,Y3 .... ,yn): if f(YE,Y3 .... , y ~ ) = l , then yl-1; if f(yE,Ya,...,y~)=O, then yl would have the value of 1 or 0. 2) f(Y2,Y3 ..... y,,)~--~ y~: in this constraint, the value of yl is fully determined by
f(Y2,Y3 ....,Y,). 3) yl v Y2 v Y3 v . . . v yn : this constraint can be transferred to ~(Y2 v Y3 v which is the first type. 4)
Y~ @Y2 EDY3~)'"EDYn:
--n(y 2 V
Y3 V . . . V y, ) ~
in
this
constraint,
variable
yl
can
be
...
V y , ) --~ y~,
expressed
by"
y~, which is the second type. And the original constraint changes to
Y2 @ Y3 ~) .-. ~) Y, ~ -n(Y2 v Y3 v ... v yn). 5) Y2 ~ Y3 ~) " " E~)y, @-'flY2 v Y3 v
... V
Yn). in this constraint, variable y2 can be expressed
(Y3 V . . . V Yn) --~ -lYE, which is the first type. And the original constraint changes to Y3 @ Y4 ~ -.- ~ Y, ~ ~ ( Y 3 v . . . v yn ). The new constraint has the same form with the original constraint, thus more dependent variables would be derived from it. All the deductive constraints form an oriented non-loop graph, which called deductive graph. Each node in a deductive denotes a logical variable, and the arc that goes into a node denotes the deductive constraint that deducts the logical variable, and the arc that goes out from a node denotes the constraint that is used to deduct other logical variables. Not every logical constraint can be transferred to deductive constraint. In some logical constraints, all member variables are dependent variables, or the deduction would cause loops in the deductive graph, so they could not be transferred to deductive constraints. These constraints restrict the existing logical variable sets, and are called restrictive constraints. A restrictive constraint would not deduce a logical variable, but it could be used to check whether the deduced logical variables are satisfied with it, so as to reduce the number of logical variable sets. When all logical constraints were transferred to deductive constraints or restrictive constraints, there would be some residual logical variables that were not set to dependent variables. These variables are called independent variables and used as the starting nodes of deduction. The meaning of "independent" is that when generating the feasible logical variable sets, the values of these variables are chosen freely. After the classification of the logical variables and logical constraints is completed, the feasible logical variable sets will be generated as follows: the nodes of independent variables, as starting nodes, have no arcs entering them, and the values of these variables are chosen freely. Then these nodes and their associated arcs that go out from them are hidden, and new starting nodes would appear in the graph. The value of a node is determined through its associated arcs that enter the node. When the values of the nodes are determined, these nodes and their associated output arcs would be hidden. If a restrictive constraint is met, existing variable sets would be restricted by the constraint and infeasible variable sets would be eliminated. Repeat these steps until all the nodes are treated with, then the finally existing logical variable sets are the feasible logical variable ones.
by:
669 It should be noticed that different sequence of logical constraints and logical variables would generate different deductive graph. But different deductive graph would only affect the intermediate results, and would not affect the final feasible logical variable sets. The feasible variable sets are only correlated to logical constraints of the model.
3.2. Generating the continuous variables NLP sub-problems For each feasible logical variable set, a corresponding continuous variables nonlinear sub-problem can be generated. The sub-problem is used to optimize the technical parameters when the flow sheet is determined. The constraints of the NLP sub-problem include equipment constraints and connection constraints, and the objective function of the sub-problem comes from the original objective function. The symbolic reformulation global optimization algorithm [3] is applied to solve the NLP sub-problem, and the global optimum for each sub-system will be obtained. 3.3. Obtaining the global optimum solution The global optimum solution is obtained from solving all the sub-problems as follows: 1) If this sub-problem has no solution, it will be discarded. 2) If the lower bound of this sub-problem is higher than the current solution, this sub-problem will be discarded. 3) If the optimal solution of this sub-problem is lower than the current one, the current solution will be updated. After repeating these steps for each feasible logical variables set, the final solution is the global optimum one of the original MINLP model. 4. PROCESS SYNTHESIS EXAMPLE Consider a process synthesis example [41 shown in Fig. 2. The equations of equipments yl-y8 and logical constraints are listed below. Yl :x3 - ln(x2 + 1)
1) 1 ~2), Pi is resource individual index. (2) Energy consume index (EI) The method of calculating energy consume index is the same as resource consume index. (3) Pollutant emission index (WI & AI) Here the water pollution index (WI) and air pollution index (AI) are taken as the example. 9 Individual index
PEIP, = C,
(3)
S, Where Pi is the individual index, whose unit is cubic meter per unit of product. Ci is the weight of pollutant generated for producing one unit of product. Si is the government limit value of this kind of pollutant and its unit is the weight of pollutant per cubic meter of air or water. 9 Compositive index For water pollution,
675
(4)
WI = ~.~wl x PEIP~ i=I
Where WI is the water pollution index, cubic meter polluted water per unit of product, wi is the weight of all kinds of pollutants. For air pollution, (5)
AI = ~ w, x PEIP, i=i
Where AI is the air pollution index, cubic meter polluted air per unit of product, wi is all kinds of pollutants. 2.2.3 Social indexes Five indexes are defined for the social stage, which are listed in Table 5. Table 5 Social Indexes . . . . Description Index name Employment The employment volumeper unit of industrial scale can offer. Volume Index 1 means absolute safety. The value Of this index will be estimated Safety Index empirically. Estimated by the cost caused by health problem per unit of Health Index industrial scale. The requirement level Of technology of the product. Compareall Index the products involved in the assessment and give each a mark. L Policy Index Two value index. 1 means illegal. 0 means legal.
T. olo
the weight of
Index type Positive Converse Converse Converse Converse
3. METHOD OF CALCULATING COMPOSITIVE INDEXES Generally, there are three steps for calculating compositive indexes, which are index conversion, dimension eliminating and index synthesis.
3.1 Index conversion Just as showed in the previous tables that there are two kinds of index: Positive and Converse. Before calculating compositive index, all the indexes should be of same kind, normally positive. The reciprocal of converse index is positive index.
3.2 Dimension eliminating It is required that all the indexes involved in calculating compositive index must have same dimension. This requirement is difficult to meet in this model, so all the index must be eliminated their dimensions. Here the method of linear threshold value is applied. Ind =
I d - min(possible) max(possible)- min(possible)
(6)
Where Id is the value of index with dimension, min(possible) is the minimum value of this index, max(possible) is its maximum value and Ind is the value of index without dimension. After dimension eliminating, all the indexes become relative indexes. 3.3 Index synthesis In this model there are three categories of index: environmental, economic and social. Indexes of same category are synthesized by multiplication, while indexes of different categories are
676 synthesized by addition. (1) Synthesize economic/environmental/social indexes of every life cycle stage For example, to synthesize the economic indexes of the stage of primary resource extraction and processing, the addition method will be used to get the eompositive economic index of this life cycle stage. P1 _ E C O I = W ! x P I + W 2 x r 1 + W3 • S D I + W 4 x Q I
(7)
Where PI_ECOI is the compositive index, Wl, W2, W3 and W4 are weights. The summary of all the weights equals one. (2) Synthesize economic/environmental/social indexes through all the life cycle stages To synthesize the economic indexes of the five life cycle stages, the multiplication method will be used. ECOI =
fn
),
I
Pi_ECOI
w'
w,
,-i (8) Where ECOI is the compositive economic index of the product life cycle and wi is the weights of every life cycle stage. (3) Synthesize economic, environmental and social indexes There are three indexes from step (2), which are economic index (ECOI), environmental index (ENVI) and social index (SOCIOI). To synthesize these three indexes, the addition method will be applied. SUMI = WI x ECOI + W 2 x ENVI + W 3 x SOCIOI
(9)
Where SUMI is the compositive index of the product, Wl, W2 and W3 is the weight of each index. 4. APPLICATION OF THE MODEL Here the model is applied to optimize the organic silicone product structure. All the organic silicone products can be mainly classified as four categories: silicone oil, silicone rubber, silicone resin and silane coupling agents. The original product structure includes four product chains, each representing one class of silicone products. This product structure includes fifteen products. Their assessment index values are calculated according to the method proposed previous, which are showed in Table 5. Table 5 The value of indexes of organic silicone products Product Economic Environmental Social Dimethyldichlorosiiane 0.0646 0.0225 0.0223 Octamethylcyclotetrasiloxane 0.0817 0.0603 0.0296 Dimethicone 0.0772 0.0296 0.0289 Trimethylchlorosilane 0.0763 0.0225 0.0240 Hexamethyldisiloxane 0.0773 0.0282 0.0222 M ethyl dichloro silane 0.0629 0.0212 0.0193 Methylvinyldichlorosilane 0.0526 0.0457 0.0182 Methylvinyl Silicone Rubber 0.0762 0.0348 0.0204 Conductive Silicone Rubber 0.0848 0.0547 0.0208 Methyltrichlorosilane 0.0633 0.0317 0.0188 Methanesiliconic Acid 0.0743 0.0416 0.0166 . . . . .
. . . . . .
677 Water Repellent S~ Methylsilicate Trichlorosilane Vinyl Trichlorosilane SilaneCoupling Agent Peroxide
0.0687 0.0623 0.0649 0.0834
0.0666 0.0537 0.0486 0.0498 ,
0.0219 0.0188 0.0179 0.0198
Along the product chain, the previous product can be used to produce the nearest following one and the proportion of the former put in to production is to be optimized. The objective can be either the whole product structure's economic, environmental and social performance independenti~t or its overall performance. It is supposed that the initial quantity of each organic silicone monomer (dimethyldichlorosilane, trimethylchlorosilane, methyldichlorosilane, methyltrichlorosilane and trichlorosilane) is 1 and the quantities of other products are all zero. If the objective is economic factor, the optimized product structure includes the following four products: hexamethyldisiloxane, conductive silicone rubber, methyltrichlorosilane, and silane coupling agent peroxide. If the objective is environmental factor, the optimized product structure includes the following five products: hexamethyldisiloxane, methyldichlorosilane, conductive silicone rubber, water repellent sodium methylsilicate, and trichlorosilane If the objective is social factor, the optimized product structure includes the following five products: octamethylcyelotetrasiloxane, dimethicone, methyldichlorosilane, methyltrichlorosilane, and silane coupling agent peroxide. 5. CONCLUSIONS In this paper a new LCA model is proposed and an index system is built under this model to evaluate the environmental, economic and social performance of products. Applying this model with process optimization technology, a product structure can be optimized to meet the specified profit target. REFERENCES [1] Qihong Shun, Nianqing Wan, Yuhua Fan, Summary of foreign LCA research, Environment Management, Volume 12 (2000.12), 24-25 [2] Adisa Azapagic, Life cycle assessment and its application to process selection, design and optimization, Chemical Engineering Journal 73 (1999) 1-21 [3] Jun Li, Zhuang Chen, Research on the assessment system for product life cycle, Chong Qin Environmental Siciences, Volume 21 No. 6 (1999.12), 10-12 [4] ISO/DIS 14040, Environmental Management - Life Cycle Assessment - Part 1, Principles and Framework, 1997 [5] J. Fava, R. Dennison, B. Jones, M.A. Curran, B. Vigon, S. Selke, J. Barnum (Eds.), A Technical Framework for Life-Cycle Assessment, SETAC and SETAC Foundation for Environmental Education, Washington, DC, 1991
Process Systems Engineering 2003 B. Chen and A.W. Westerberg (editors) 9 2003 Published by Elsevier Science B.V.
678
Coordination of Multi-Factory Production Planning in the Process Industry Based on Internal-Prices Zhou Wei, Jiang Yong-heng and Jin Yi-hui Dept. Automation, Tsinghua University, Beijing, China, 100084 Abstract A large enterprise may consist of many factories with decision-making authority. The traditional centralized planning method is usually inefficient for this kind of enterprise. A distributed coordination algorithm based on internal-prices is presented in this paper. Within this algorithm, only the factory production planning model needs to be set up, which can be solved by its local information. Through updating the internal-prices in the coordination center, a near optimal solution of the multi-factory production planning is achieved. Numerical testing results show that the internal-prices converge. Keywords 1.
production planning, internal-price, coordination algorithm
INTRODUCTION
The problem discussed in this paper is a practical problem observed in a large process enterprise in China with a coordination center and many factories located in distinct geographical locations. In this enterprise, due to the differences in technology and equipment, intermediates need to be transported among the factories to satisfy the demand of the productions. Every factory has decision-making authority to some extent and makes a production planning by its local knowledge. The coordination center does not learn the local information of every factory in detail, and its responsibility is to coordinate the amount of flow among the factories. Multi-factory planning problem is a large-scale combination problem and NP-Hard, which has been researched widely Ill[21. However, almost all of the methods reported up to now are centralized. For these methods, the coordination center must attain all the local information in each factory, which is impossible for the problem discussed here. So, it is necessary to develop an effective distributed method without accessing local information of each factory. In this paper, a distributed coordination algorithm based on internal-prices is presented. In the approach, the interactions among the factories are modeled by a set of inter-factory material balance constraints as presented in Section 2. Motivated by the real pricing mechanism in the market, these constraints are relaxed by using a set of internal-prices. These internal-prices are Lagrange multipliers associated with these inter-factory constraints and present marginal costs per unit product for the violation of such constraints. So, the overall problem is decomposed into factory-level subproblem, where every factory can optimize its planning only by the local knowledge and internal-prices. Coordination is then achieved by an iterative price updating process as presented in Section 3. With the internal-prices updated, the whole near optimal planning can be achieved. At last, a conclusion is given in Section 4.
679
2.
PROBLEM FORMULATION
The enterprise discussed here is a large process enterprise with a coordination center and many factories located in distinct geographical locations. As the enterprise is enormous, it is impossible and unnecessary for the coordination center to get all the local information in each factory. So, the whole enterprise planning cannot be achieved by common centralized optimal methods. To solve this problem, a coordination method based on inner-prices is presented. By giving an intemal-price to every intermediate transported among the factories, the independent factory model can be set up and solved only by its local problem according to the intemal-prices. Then, the optimal solutions of all the factory planning are submitted to the coordination center. After updated, the internal-prices are given the factories again. Through some iterations, a near optimal enterprise planning can be attained.
2.1 Formulation of Individual Factory Problems The enterprise considered is as follows: the enterprise consists of a fixed set of process factories located in distinct geographical locations; the products of a factory are provided to the customers as well as supplied to the other factory as intermediates internally; a time horizon divided into a number of time periods is also given with known demands at the end of each time period. Every factory has a limited production capacity and a limitless stock holding capability. The stock level can not be lower the safety stock level. There is a fixed production cost and variable production cost for a product. Variable costs are proportional to the production amount. Transportation costs are proportional to the transported amount of the intermediates, and the transportation time is ignored. The goal in the every factory is to determine the production planning over the full horizon to minimizing production, inventory and transportation costs. The transportation cost of intermediates is included in the cost of the factory sending them. Our model benefits from the work of McDonald and Karimi TM. The model of any factory n in the enterprise can be presented as follows:
(Rn) Min L.: ~ Z v , . . X ..... + Z Z p .....c ..... + Z Zh,,. I ..... + Z ~~po,,.6, ..... t6IkI pJ4 t
t~l pa~t t
+ E ZZz t~l\l e'u m
t
t~l\l nM t
t~I\l t~
t
(1)
.... e : ....
s.t. I .... t
=1
.... ,-1-[-Yi.n,t-Jt-Zpt,rm,.n,t-Z m~
I,.,--I L ,
,
t,n,l
>0
X .... , < M S ....,
C,,., t =
~ flr,,,.X,,,.., J': fl,.,,., s O
pp
t,n,m.t
-din,
V i ~ I \ I RM , .
(2)
m
Vi ~ I \ I RM
Vi~IklRM
Vi ~ I \ I FP
(3) (4)
(5)
680 Vi ~ 1 Ip
(6)
Vi ~ I \ I ~
(7)
C .... , = ~ p,.~,..,, m'
X ....,Ri,.. t < Hi,., ' X , ., , , C ,,. ,, , I , ,. ,, , P,[.,m., PP >0 l,m,n,l ,
6 , ,. ,, e {0,1}
'
(8)
Constraints (2) describe the material balance in factory n. Constraints (3) indicate that the inventory level cannot be lower than the safety target. Constraints (4) ensure the fixed production cost to be incurred whenever producing. Constraints (5) represent the consumption of raw or intermediate materials using the bills of material. Constraints (6) force all material shipped to factory n to be consumed there in the same time period. Constraints (7) enforce the production capacity limitations of factory n. 6,,..~., is binary variable, which is 1 if factory n produce product i in the time period t, and 0 otherwise.
2.2 Formulation of the Overall Problem The goal of the enterprise is to determine the amount of intermediates transported among the factories to minimize the overall cost, i.e. the sum of individuals' objectives. (P)
Min L = ~ L,,
(9)
n
s.t.
P,[.,ma= Pp,,.,m,'
(10)
Vi ~ 1 le
The inter-factory constraints (10) indicate that the amount of intermediate i transported promised by factory n should be equal to the requirement of factory m. 3.
SOLUTION METHODOLOGY
For the whole enterprise model (P), constraints (10) are relaxed by the internal-prices, i.e. Lagrange multipliers 2, . . . . . t"
J
"- ZLn
+ E Z Z Z ~ , I ,
n
1
n
m
.... t(Pt,rn,m,t-
(11)
Ppt,n,m,t )
l
Collecting all the terms related to factory n in (11), the modified model for factory n is described as follows:
(R,~
L.
(12) i
m'
t
i
m
t
s.t. (2),(3),(4),(5),(6),(7),(8) It can be found that all the variables in (R'n) are local, so (R'n) can be solved by the local information. The internal-prices can be updated by:
681 Ak§ = A,k +akg(A, k)
(13)
Here, k is the iteration index, and g(2) is subgradient attached to ~. and specifies the violation degree of constraints (10): g(2 . . . . . . 6t k
(14)
t) = et,r,m,t - P pl,m,ll,l
is the step size at the kth iteration which is given by: Q
a k=~
j_jk
g(~ )~g(~ )
,0
