PROGRAM
International Conference on Circulating Fluidized Beds and Fluidization Technology - CFB-10
May 1 - 5, 2011
Sunriver Resort Sunriver, Oregon, USA
Conference Chair
Ted M. Knowlton Particulate Solid Research, Inc, USA
Sunday, May 1, 2011
One-Day Seminar on Fluidization 8:00 - 8:30 am 8:30 - 10:15 am 10:15 - 10:30 am 10:30 - 11:15 am 11:15 - 12:00 am
Registration Hydrodynamics (J. Grace) Coffee Break Scaling and Scale-Up (T. Knowlton) Cyclone Design and Operation (T. Knowlton)
12:00 - 1:00 pm
LUNCH
1:00 - 1:45 pm 1:45 - 2:30 pm 2:30 - 2:45 pm 2:45 - 4:15 pm 4:15 - 4:45 pm 4:45 pm
Standpipes/Non-Mechanical Valves (T. Knowlton) Heat Transfer (J. Grace) Coffee Break Reactors and Combustors (J. Werther) Question-and-Answer Period with Instructors Adjourn
Conference Registration 2:00 - 6:00 pm
Activity Program Meeting 5:30 - 6:30 pm
Reception/Dinner 6:30 - 9:30 pm
Special Poster Session/Social Hour 9:30 pm - 11:00 pm
Monday, May 2, 2011 7 - 8:15 am
BREAKFAST
8:15 - 8:30 am
CONFERENCE OPENING PLENARY SESSION Chairman: R. Cocco
8:30 - 9:30 am
P-1: Reflections on Mathematical Models and Simulation of Gas-Particle Flows Sankaran Sundaresan, Princeton University
9:30 - 9:40 am
COFFEE BREAK SESSION 1 Solids Flow and Circulation Co-Chairs: J. Zhu U. Muschelknautz
9:40 - 9:54 am
1-1: Gas Tracer Study in a NonMechanical L-Valve M.M.Yazdanpanah, A. Hoteit, A. Forret Thierry Gauthier IFP Energies Nouvelles Arnaud Delabarre Université Henri Poincaré, France
9:54 - 10:08 am
2-1: Process Decoupling of Plasma Enhanced Synthesis of Chlorinated Polyvinyl Chloride (CPVC) Particles in a Circulating Fluidized Bed W. Lu, T. Cao, Y. Cheng Tsinghua University, China
1-2: Investigation on the Hydrodynamic 2-2 Manufacture of Granular Properties in the External Loop of a Polysilicon from Trichlorosilane in an Circulating Fluidized Bed with a Loop Internally Circulating Fluidized Bed Seal Reactor X. Yao, T. Wang, H. Yang, H. Zhang, Q. Liu J. Lv Tsinghua University, China
10:08 - 10:22 am
SESSION 2 Novel Fluidized Bed Processes Co-Chairs: P. Basu Y. Cheng
C. Wang, T. Wang, Z. Wang Tsinghua University, China
1-3: Hydrodynamics of a Dual Fluidized 2-3 High-Flux Triple Bed Circulating Bed System Which has Internal Mixing Fluidized Bed (TBCFB) Gasifier for Channels Between CFB and BFB Exergy Recuperative IGCC/IGFC Reactor U. Lee, I. Choi, W. Yang, Y. Kim, Y. Choi Korea Institute of Industrial Technology J. Song - SeenTec Co., Ltd. Korea
C. Fushimi, G. Guan, M. Ishizuka Y. Nakamura, A. Tsutsumi The University of Tokyo, Japan Y. Suzuki, National Institute of Industrial Science, Japan E.W.C. Lim, Y. Cheng, C-H. Wang National University of Singapore, Singapore
Monday, May 2, 2011 (continued) 10:22 - 10:36 am
1-4: Particle Flow in L-Valves D. Subbarao University Teknologi PETRONAS, Malaysia
A. Weinert, A. Reichold, P. Bielansky C. Schonberger, B. Schumi Vienna University of Technology Austria
COFFEE BREAK
10:36 - 11:00 am 11:00 - 11:14 am
2-4: Bio-Gasoline from Jatropha Oil: New Applications for the FCC- Process
1-5: A Generalized Flow Diagram for Fluid-Solid Vertical Transport X. Bi University of British Columbia, Canada
2-5 Waste Wood Gasification: Distribution of Nitrogen, Sulphur and Chlorine in a Dual Fluidised Bed Steam Gasifier V. Wilk, C. Aichernig, H. Hofbauer Vienna University of Technology, Austria
11:14 - 11:28 am
1-6: Cold Model Investigations of a 2-6: Removal of Nitrate from Water High Temperature Looping Process in a Using Fluidized Bed Ion Exchange Dual Circulating Fluidized Bed System Column A.R. Bidwe, C. Hawthorne, A. Charitos, M.A.M. Ammar Arab Beddai, V.V. Basava Rao Dominguez, H. Dieter, A. Schuster, G. Osmania University Scheffknecht India University of Stuttgart, Germany
11:28 - 11:42 am
1-7: Hydrodynamics of a Loop Seal Operated in a Circulating Fluidized Bed: Influence of the Operating Parameters on Gas and Solid Flow Patterns
2-7: A Pyrolysis Pilot Unit Integrated to a Circulating Fluidized Bed Boiler Experiences from a Pilot Project
1-8: Effects of Particle Properties on Cluster Characteristics in a 2-D CFB Riser
2-8: Production of Gasoline and Gaseous Olefins by Catalytic Cracking of Pyrolysis Oil
J. Xu and J. Zhu University of Western Ontario, Canada
P. Bielansky, A. Reichhold, A. Weinert Vienna University of Technology Austria
1-9: Flow Field in a Novel Short Residence Time Gas-Solid Separator
2-9: Energetic Optimization of the Lignin Pyrolysis for the Production of Aromatic Hydrocarbons
J. Autio, J. Lehto, Metso Power Oy P. Jokela, UPM J. Alin, Fortum R. Solimene, R. Chirone Istituto di Recerche sulla Combustione - CNR A. Oasmaa, Y. Solantausta, VTT Finland P. Bareschino Universita degli Studi del Sannio P. Salatino Universita degli Studi di Napoli Federico II Italy
11:42 - 11:56 am
11:56 - 12:10 pm
M. Liu, C. Zhou, C. Lu, Z. Wang China University of Petroleum China
M. Franck, B. Lorenz, E-U. Hartge, S. Heinrich, J. Werther Hamburg University of Technology, Germany
Monday, May 2, 2011 (continued) 12:10 - 12:24 pm
1-10: Cold Model Study on 2-10: Studies on Propane Interconnected Fluidized Bed Reactors Dehydrogenation to Propylene in a for Multi-Generation Systems and Gas-Solid-Sold Fluidized Bed Reactor Chemical Looping Processes G.A. Ryabov, O.M. Folomeyev, D.A. Sankin, K.V.Khaneyev, All-Russian Thermal Engineering Institute, Russia
Y. Chu, T. Wu, Y. Li, Z. Nawaz, T. Wang F. Wei Tsinghua University, China
12:30 - 2:00 pm
LUNCH
2:00 - 5:30 pm
FREE TIME SESSION 3 Mathematical Modeling I Co-Chairs: N. Mostoufi F. Johnsson
5:30 - 5:44 pm
5:44 - 5:58 pm
3-1: A Modeling Study of Gas Streaming in a Deep Fluidized Bed of Geldart A Particles
4-1: The Development of a Novel Cu-Mn Oxygen Carrier for the Chemical Looping Gasification of Biomass
S. Karimipour, T. Pugsley University of Saskatchewan Canada
M. Aghabararnejad, J. Chaouki, G.S. Patience Ecole Polytechnique de Montreal Canada
3-2: Effects of Gas Velocity and Solid Hold-Up on the Sub-Grid Behavior of Riser Flows
4-2:CO2 Looping Cycle for CO2 Separation
C.C. Milioli, F. E. Milioli University of São Paulo, Brazil
5:58 - 6:12 pm
SESSION 4 Chemical Looping Co-Chairs: E.-U. Hartge R. Gupta
T. Shimizu, H. T. Takahashi, Narisawa, L. Li, H. Kim Niigata University Japan
3-3: Numerical Simulations of a 4-3: Fluid Dynamic Effects of Ring-Type Circulating Fluidized Bed with a Square Internals in a Dual Circulating Fluidized Cross-Section Bed System T. Li, S. Pannala, C. Guenther National Energy Technology Laboratory S. Pannala Oak Ridge Institute for Science and Education, USA
D.C. Guio Perez, K. Marx, T. Proell H. Hofbauer Vienna University of Technology Austria
Monday, May 2, 2011 (continued) 6:12 - 6:26 pm
3-4: High-Resolution Simulations of 4-4: Design Requirements for Gas-Solids Jet Penetration Into a High- Pressurized Chemical Looping Density Riser Flow Reforming T. Li, C. Guenther National Energy Technology Laboratory, USA
6:26 - 6:40 pm
3-5: Simulation of Particle-Gas Flow in 4-5: The Influence of Carbon Stripper a Cyclone Using URANS Efficiency on CO2 Capture Rate in a Chemical-Looping Combustion A. Karvinen, H. Ahlstedt, Tampere University of Process for Solid Fuels Technology M. Palonen, Metso Power Oy Finland
6:40 - 6:54 pm
K. Marx, T. Proell, H. Hofbauer Vienna Institute of Technology Austria
M. Kramp, A. Thon, E-U. Hartge, S. Heinrich J. Werther Hamburg University of Technology Germany
3-6: Evaluation of a Lagrangian 4-6: Study of Calcination-Carbonation Discrete Phase Modeling Approach for of Calcium Carbonate in Different Application to Industrial Scale Bubbling Fluidizing Mediums for Chemical Fluidized Beds Looping Gasification in Circulating Fluidized Beds S. Cloete, S.T. Johansen, M. Braun, S. Amini, SINTEF Materials and Chemistry, Norway B. Acharya, A. Dutta, P. Basu M. Braun, B. Popoff Dalhousie University Ansys, Germany Canada
6:54 - 7:08 pm
7:08 - 7:22 pm
3-7: Effect of Wall Boundary Conditions 4-7: Understanding Standpipe and Mesh Refinement on Numerical Hydrodynamics Using Electrical Simulation of Pressurized Dense Capacitance Tomography Fluidized Bed for Polymerization C. Qui, R. Joachim Reactor Industrial Tomography Systems, USA P. Fede, O. Simonin, R. Ansart, H. Neau Universite de Toulouse, France I. Ghouila INEOS, France
S.B.R. Karri Particulate Solid Research, Inc., USA
3-8 Fluidized Bed Membrane Reactor for Steam Reforming of Higher Hydrocarbons: Model Sensitivity
4-8: A Practical Model for a Dense-Bed Countercurrent FCC Regenerator
M.A. Rakib, J.R. Grace, C.J, Lim University of British Columbia, Canada
Y. Zhang, C. Lu China University of Petroleum China
7:30 - 9:15 pm
DINNER
9:15 - 11:00 pm
POSTER SESSION and SOCIAL HOUR for Papers Presented in Sessions 1, 2, 3 and 4
Tuesday, May 3, 2011 7 - 8:30 am
Breakfast PLENARY SESSION 2 Chairman: J. Werther
8:30 - 9:30 am
P-2: Electrostatic Phenomena in Fluidized Systems: Present Status of Understanding, and Research Needs Xiaotao Bi, University of British Columbia
9:30 - 9:40 am
COFFEE BREAK SESSION 5 Dynamics of Gas-Solids Flow Co-Chairs: J. Grace B. Formisani
9:40 - 9:54 am
5-1: Design Criteria of Uniflow Cyclones for the Separation of Solid Particles from Gases U. Muschelknautz, P. Pattis, M. Reinalter, M. Kraxner MCI Management Center Innsbruck Austria
9:54 - 10:08 am
SESSION 6 Combustion and Gasification Co-Chairs: W. Nowak A. Luckos
6-1: Experimental Study on the Effects of Gas Permeation Through Flat Membranes on the Hydrodynamics in Fluidized Beds J.F. de Jong, M. van Sint Annaland, J.A.M. Kuipers, Eindhoven University of Technology, The Netherlands
5-2: Erosion in Second Stage Cyclones: 6-2: Experimental Study on Reforming Effects of Cyclone Length and Outlet Activity and Oxygen Transfer of FeGas Velocity Olivine in a Dual Circulating Fluidized Bed System S.B. Reddy Karri, R. Cocco and T.M. Knowlton Particulate Solid Research, Inc., USA S. Koppatz, T. Proell, C. Pfeifer, H. Hofbauer Vienna University of Technology, Austria
10:08 - 10:22 am
5-3: Correlation of the Minimum 6-3: Study of Recarbonation in Spouting Velocity for the Design of Circulating Fluidized Bed Combustion Open-Sided Draft Tube Conical Spouted Beds for the Treatment of Fine I. Hyytiainen, H. Lemmetyinen, Tampere University of Technology Materials M. Olazar, H. Altzibar, G. Lopez, I. Estiati, J. Bilbao, University of the Basque Country Spain
10:22 - 10:36 am
A. Mahlamaki, M. Palonen, M. Varonen, Metso Power Oy Finland
5-4: Hydrodynamics of Conical Spouted 6-4: Coal Ignition Temperature in Beds with High Density Particles Oxygen-Enriched CFB Boiler S. Sari, D. Zaglanmis, M. Koksal, Hacettepe University A. Polat, Middle East Technical University, Turkey
J. Chao, H. Yang, J. Lu, H. Zhang, Q. Liu Y. Wu Tsinghua University China
Tuesday, May 3, 2011 (continued) 10:36 - 11:00 am 11:00 - 11:14 am
COFFEE BREAK 5-5: Experiments and Modeling of Micro-Jet Assisted Fluidization of Nanopowder
6-5: Co-Combustion of Various Biowastes with a High-Sulfur Turkish Lignite in a Circulating Fluidized Bed Combustor with Air Staging
J.R. van Ommen, N. Loojie, Delft University of Technology, The Netherlands A. Atimtay, M. Vario, Middle East Technical D.M. King, A. Weimer, S. Johnson, University University, Turkey of Colorado, USA H. Olgun, U. Kayahan, B. Bay, A. Unlu, R. Pfeffer, University of Arizona, USA TUBITAK-MRC Energy Institute, Turkey B.G.M. van Wachem, Imperial College London, M. C. Celebi, H. Atakul, Istanbul Technical U.K. University, Turkey G. Bardakcioglu, M. Ozcan, GAMA Power Systems Engineering and Contracting, Turkey
11:14 - 11:28 am
11:28 - 11:42 am
5-6: Effect of Gas Bypassing in Deep Beds on Cyclone Dipleg Operation
6-6: Oxy-Combustion of Different Coals in a Circulating Fluidized Bed
A.S. Issangya, S.B. Reddy Karri, T.M. Knowlton, R. Cocco Particulate Solid Research, Inc., USA
M. Kosowska-Golachowska, K. Klos, T. Musial, Czestochowa University of Technology, Poland A. Luckos, Sasol Technology, South Africa
5-7: Fluidization Behavior in a GasSolid Fluidized Bed with Thermally Induced Inter-Particle Forces
6-7: Effects of Secondary Air Injection Upon the Fluidization Characteristics of the Lower Stage in a Two-Stage, Variable-Area Fluidized Bed Riser
J. Shabanian, F. Fotovat, J. Bouffard, J. Chaouki, Ecole Polytechnique de Montreal, Canada
11:42 - 11:56 am
11:56 - 12:10 pm
E.K. Johnson, S.L. Rowan, West Virginia University, USA
5-8: Particle to Gas Heat Transfer in a Circulating Fluidized Bed Riser
6-8: Gas-Solids Hydrodynamics in a CFB with 6 Cyclones and a Pant Leg
Y.T. Makkawi, Aston University, U.K.
L. Cheng, X. Zhou, C. Wang, Z. Wang, Z. Luo, K. Cen, L, Nie, C. Wu, Q, Zhou Zhejiang University, China
5-9: Fast Pyrolysis Process 6-9: The Research of CFB Boiler Intensification: Study of the Gas Phase Operation for Oxygen Enhanced Dried Residence Time Distribution and Lignite Combustion Backmixing in a Downer Reactor W. Muskala, J. Krzywanski, T. Czakiert, M. Huard, F. Berruti, C. Briens, The University W. Nowak, Czestochowa University of Technology, Poland of Western Ontario, Canada
Tuesday, May 3, 2011 (continued) 12:30 - 2:00 pm
LUNCH
2:00 - 3:40 pm
WORKSHOP A Panel Discussion on Energy Chairman: D. Keairns
3:45 - 5:20 pm
WORKSHOP B Chemical Looping Chairman: L. S. Fan
WORKSHOP C WORKSHOP D Instrumentation for Fluid PSRI/NETL Challenge Particle Systems Problem Chairman. J. R. van Ommen
5:30 - 5:44 pm
5:44 - 5:58 pm
SESSION 7 Mathematical Modeling II Co-Chairs: J. Li H. Arastoopour 7-1: DEM-CFD Modeling of a Bubbling Fluidized Bed and a Wurster Coater
SESSION 8 Industrial Operation of Fluidized Beds Co-Chairs: P. Gauville J. de Jong 8-1: Commissioning of a 0.8 MWth CFBC for Oxy-Fuel Combustion
L. Fries, S. Antonyuk, S. Heinrich, S. Palzer Hamburg University of Technology Germany
L, Jia, Y. Tan, D. McCalden, Y. Wu, I He R. Symonds, E.J. Anthony Canmet ENERGY Canada
7-2: Elutriation from Fluidized Beds: Comparison Between Experimental Measurements and 3D Simulation Results
8-2: High Sulfur Lignite Fired Large CFB Boilers-Design and Operating Experience
R. Ansart, H. Neau, O. Simonin, IMFT P. Accart, A. de Ryck, CNRS Universite de Toulouse, France
5:58 - 6:12 pm
M. Lakshminarasimhan, B Ravikumar, A. Lawrence, M. Muthukrishnan, Bharat Heavy Electrical Limited, India
7-3: Fluidized Bed Gasification of Mixed 8-3: Research on Heat Transfer Inside Plastic Wastes: A Material and a the Furnace of Large Scale CFB Boilers Substance Flow Analysis M.L. Mastellone, U. Arena Second University of Naples, Italy
6:12 - 6:26 pm
Co-Chairs: L. Shadle R. Cocco
R. Zhang, H. Yang, H. Zhang, Q. Liu, J. Lu Y. Wu, Tsinghua University, China
7-4: Circulating Fluidized Bed 8-4: Design and Operation of Biomass Combustion-Build-Up and Validation of Circulating Fluidized Bed Boiler with a Three-Dimensional Model High Steam Parameter M. Palonen, V. Yla-Outinen, Metso Power Oy, J. Laine, Tampere University of Technology Finland D. Pallares, A. Larsson, F. Johnsson Chalmers University of Technology Sweden
S. Li, S. Bao, Q. Lu, D. Wang, H. Teng Chinese Academy of Sciences Y. Peng, Z. Liu, B. Hong Changsha Boiler Plant Co., Ltd. China
Tuesday, May 3, 2011 (continued) 6:26 - 6:40 pm
6:40 - 6:56 pm
7-5: 3D CFD Simulation of Combustion 8-5: Operating Experience and Latest in a 150 MWe Circulating Fluidized Bed Developments of Alstom Power's 300 Boiler MWe Class CFB Boilers N. Zhang, B. Lu, W. Wang, J. Li, Chinese Academy of Sciences, China
B. Wilhelm, P. Gauville, I. Abdulally, C. Enault Alstom Power, France
7-6: Comparison Between Measurements and Numerical Simulation of Particle Flow and Combustion at the Duisburg CFBC Plant
8-6: UOP FCC Innovations Developed Using Sophisticated Engineering Tools L. Wolschlag, K. Couch UOP LLC USA
M. Weng, Aixprocess, J. Plackmeyer, Consulting Engineer Germany
6:56 - 7:10 pm
7-7: Hydrodynamics of a Cluster 8-7: Co-Gasification of Biomass and Descending at the Wall of a CFB Riser - Coal in an 8MW Dual Fluidized Bed Numerical Study Steam Gasifier S. Vashisth, J. Grace University of British Columbia Canada
7:10 - 7:24 pm
C. Pfeifer, I. Aigner, H. Hofbauer Vienna University of Technology Austria
7-8: Characteristics of the Solid Volume 8-8: Coal and Biomass Co-Gasification Fraction Fluctuations in a CFB in a Circulating Fluidized Bed Reactor S. Kallio, J. Peltola, V. Taivassalo VTT Technical Research Centre of Finland Finland
A. Czaplicki, M. Sciazko Institute for Chemical Processing of Coal Poland
7:30 - 9:15 pm
DINNER
9:15 - 11:00 pm
POSTER SESSION and SOCIAL HOUR for Papers Presented in Sessions 5, 6, 7 and 8
Wednesday, May 4, 2011 7 - 8:30 am
Breakfast PLENARY SESSION 3 Chairman: L. S. Fan
8:30 - 9:30 am
P-3: Evolution of FCC Technology-Past, Present and Future and the Challenges of Operating a High-Temperature CFB System Ye Mon Chen, Shell Global Services PLENARY SESSION 4 Chairman: L. S. Fan
9:30 - 10:30 am
P-4: Putting Structure Into Fluidized Beds - From Concept to Industrial Applications Fei Wei, Tsinghua University
10:30 - 11:00 am
COFFEE BREAK
11:00 - 11:14 am
SESSION 9 Particle Dynamics Co-Chairs: C. Pfeifer R. Karri
SESSION 10 HT/HP Research
9-1: Sulfur Uptake by Limestone-Based Sorbent Particles in CFBC: The Influence of Attrition/Fragmentation on Sorbent Inventory and Particle Size Distribution
10-1: The Variation of the Bubble Phase Properties of a FCC Fluidized Bed at High Temperature
Co-Chairs: K. Wirth S. Moffatt
R. Girimonte, B. Formisani University of Calabria, Italy
F. Montagnaro, P. Salatino, F. Scala, M. Urciuolo Universita degli Studi di Napoli Federico II, Italy
11:14 - 11:28 am
9-2: Study of Standpipe and Loop Seal 10-2: A Study of Solids and Gas Mixing Behavior in a Circulating Fluidized Bed in a Partitioned Fluidized Bed for Geldart B Particles A.R. Bidwe, A. Charitos, H. Dieter, A. Wei, M. Zieba, G. Scheffknecht University of Stuttgart, Germany
11:28 - 11:42 am
J-H. Moon, Y-J. Seo, S. Kang, S-Y. Lee, Y-C. Park, H-J. Ryu, G-T. Jin Korea Institute of Energy Research, Korea
9-3: Observation of Flow Regime 10-3: Effect of Temperature Field on the Transition in a CFB Riser Using an LDV Coal Devolatilization in a Millisecond Downer Reactor P. Yue, J. Mei, L. Shadle National Energy Technology Laboratory, USA
B. Yan, L. Zhang, Y. Jin, Y. Cheng Tsinghua University, China
Wednesday, May 4, 2011 (continued) 11:42 - 11:56 am
9-4: Bench-Scale Investigation of 10-4: Effect of Bed Temperature, Fuel Limestone Size Evolution in a Fluidized Density and Particle Size on Bed Combustor Hydrodynamic Parameters of 10 MW Fluidized Bed Combustion Power Plant X. Yao, N. Hu, H. Yang Using Riser Waste Tsinghua University, China J.H. Chiu, P. Gauville, S.G. Kang Alstom Power Inc., USA
11:56 - 12:10 am
R..I. Singh Jassar Guru Nanak Dev Engineering College S.K. Mohapatra Thapar University, India
9-5: Catalyst Attrition in the CFB Riser 10-5: Transient Temperature and Char Conversion Profiles in Wood During A. Thon, M. Kramp, E-U. Hartge, S. Heinrich, Devolatilization in a Fluidized Bed J. Werther Combustor Hamburg University of Technology Germany
12:10 - 12:24 am
D.R. Sudhakar, A.K. Kolar Indian Institute of Technology Madras India
9-6: The Relationship Between Fluidization Velocity and Segregation in Two-Component Fluidized Beds: A Preliminary Analysis B. Formisani, R. Girimonte, V. Vivacqua University of Calabria, Italy
12:30 - 2:00 pm
LUNCH
2:00 - 5:30 pm
FREE TIME SESSION 11 Mathematical Modeling III Co-Chairs: R. Cocco T. Shimizu
5:30 - 5:44 pm
11-1: Comparison of Entrainment Rate 12-1: Time-Resolved X-Ray in Acrylonitrile Reactors Using Plant Tomography of a Fluidized Bed of Data and CFD Simulations Geldart A Particles S. Moffatt, S. Ramchandran\ Ascend Performance Materials P. Zhao, K. Williams CPFD-Software, LLC
5:44 - 5:58 pm
SESSION 12 Measurement Techniques Co-Chairs: J. R. van Ommen M. Palonen
R. Mudde, Q. Ricoux, E. Wagner, J.R. van Ommen Delft University of Technology The Netherlands
11-2: Critical Evaluation of Euler-Euler 12-2: A New Approach for Modeling of and Euler-Lagrangian Modelling a Fluidized Bed by CFD-DEM Strategies in a 2-D Gas Fluidized Bed F. Hernandez-Jimenez, A. Acosta-Iborra University Carlos III Madrid Spain J.R. Third, C.R. Muller ETH Zurich Switzerland
S. Karimi, H. Chizari, N. Mostoufi, R. Sotudeh-Gharebagh University of Tehran Iran
Wednesday, May 4, 2011 (continued) 5:58 - 6:12 pm
11-3: Particle-Fluid Flow Simulation of 12-3: Characterization of Fluidization an FCC Regenerator and Mixing of Binary Mixtures Containing Biomass at Low Velocities S. Clark Through Analyzing Local Pressure CPFD Software Fluctuations USA
F. Fotovat, J. Shabanian, J. Chaouki, J. Bergthorson Ecole Polytechnique de Montreal, Canada
6:12 - 6:26 pm
11-4: CFD Simulation of CO2 Sorption in a Circulating Fluidized Bed Using Deactivation Kinetic Model E. Abbasi, H. Arastoopour Illinois Institute of Technology USA
6:26 - 6:40 pm
6:40 - 6:54 pm
F. Wang, Q. Marashdeh, L-S. Fan The Ohio State University, USA
11-5: CFD Modeling of Fluidized Bed Reactor for the Synthesis of Dimethyl Ether
12-5: Description of Pressure Fluctuations in a Circulating Fluidized Bed by Statistical Analysis
R. Kalluri, N. Akunuri, A. Jamal, R. Gupta RTI International USA
R. Coetzer, A. Mostert, A. Luckos Sasol Technology, South Africa
11-6: DEM Study of Fluidized Bed 12-6: Dynamics of Gas-Solids Fluidized Dynamics During Particle Coating in a Beds Through Pressure Fluctuations: Spouted Bed Apparatus A Brief Examination of Methods of Analysis S. Antonyuk, S. Heinrich, A. Ershova Hamburg University of Technology Germany
6:54 - 7:08 pm
12-4: ECVT Imaging of 3-D Flow Structures and Solids Concentration Distributions in a Riser and a Bend of a Gas-Solid Circulating Fluidized Bed
S. Sasic, F. Johnsson Chalmers University, Sweden M-O. Coppens Renssalaer Polytechnic Institute, USA J. van der Shaaf, Eindhoven University of Technology, The Netherlands S. Gheorghiu, Center for Complexity Studies Romania J. R. van Ommen, Delft University of Technology, The Netherlands
12-7: Dynamic Characteristics of Bubbling and Turbulent Fluidization Using a Hurst Analysis Technique H. Azizpour, N. Mostoufi, R. Zarghami R. Sotudeh-Gharebagh University of Tehran, Iran
Wednesday, May 4, 2011 (continued)
7:30 – 10:30 pm
CONFERENCE DINNER at the High Desert Museum
10:45 - 11:45 pm
POSTER SESSION and SOCIAL HOUR for Papers Presented in Sessions 9, 10, 11 and 12
Thursday, May 5, 2011 7 - 8:30 am 8:30 am
Breakfast END OF CONFERENCE
REFLECTIONS ON MATHEMATICAL MODELS AND SIMULATION OF GAS-PARTICLE FLOWS Sankaran Sundaresan Department of Chemical & Biological Engineering Princeton University Princeton, New Jersey 08544 USA
ABSTRACT Examples of complex flow characteristics observed in circulating fluidized beds and turbulent fluidized beds are presented. Different gas-particle modeling and simulation approaches that are being pursued to probe these flow characteristics are summarized. Major advances that are likely to emerge within the next decade are discussed. 1. INTRODUCTION Circulating fluidized beds (CFBs) and turbulent fluidized beds (TFBs) are applied widely in chemical process and energy conversion industries (1). They have been in use in fluid catalytic cracking of gas oil for nearly seven decades, and for lesser duration in many other processes; new processes, such as synthesis of olefins from methanol (2), coal and biomass gasification (3), and CO2 capture by solid sorbents (4, 5), are under development at the present time. Although the long history of use has led to a wealth of design and operational experience with these systems, confidence to design and build commercial plants without significant levels of pilot scale testing at various intermediate scales is still lacking. This is due to an incomplete understanding of the origin and nature of the inherently complex flow structures observed in these devices, and uncertainties as to how they would change upon scale-up. Advanced experimental characterization and rigorous modeling studies are being pursued to unravel the complexities of these flows in both pilot and commercial scale systems. This article presents briefly the author’s perspective on the current status of modeling these flows and the advances that can be expected to emerge in the near future. Section 2 outlines a few illustrative examples of intriguing behavior of CFBs and TFBs, and what one would like to model and understand. This is followed by a brief discussion of why modeling them is difficult. Section 3 attempts to explain why effective fluid-particle drag force model is a critical element in accurate and yet affordable simulations. Section 4 is devoted to advances being made in different modeling approaches. Section 5 touches very briefly on role of gas turbulence. Section 6 outlines some additional data that can benefit modeling efforts. Section 7 provides an outlook of what advances in modeling and simulations can be expected in the next 5-10 years.
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2. SOME FLOW CHARACTERISTICS TO UNDERSTAND AND MODEL In its simplest form, a CFB consists of a riser tube where particles are transported up by co-flowing gas, a device to separate the gas and particles at the top, a standpipe to return the particles to the bottom of the riser and a suitable valve to control the delivery of the particles to the bottom of the riser. The volume fraction of particles in the riser is generally small enough that the particles interact with each other primarily through collisions, while in standpipes it is usually high enough that stress transmission can occur through collisions as well as sustained frictional contact between the particles and between the particles and the wall. More elaborate CFBs would include additional devices such as fluidized beds (e.g., FCC regenerator), leading to more complex flow loops for the particles. Let us briefly review a few flow characteristics that one would like to be able to understand and model. a) In tall CFBs, operating at near atmospheric pressures, the gas pressure can increase appreciably from the top of the standpipe to the bottom resulting in loss of gas volume through compression; to compensate for the adverse effect of this compression, aeration gas is added at a number of elevations along the standpipe. At low aeration levels, stick-slip flow is often observed in the standpipe. Increasing the aeration level enables smoother flow and improved solids circulation rate. However, beyond some threshold aeration level, the flow becomes unstable and the circulation rate becomes very erratic, which is unacceptable (6). How does the onset of this instability depend on the manner in which aeration is administered and the scale of the CFB? b) The flow characteristics in the riser are complex even under stable operating conditions. Risers typically operate in the so-called fast-fluidization regime where there is a denser bottom region, transitioning to a more dilute flow at the top. Furthermore, the time-averaged particle volume fraction and gas and particle mass fluxes manifest significant lateral variations; particle volume fractions generally tend to be high near the riser walls where the mass flux of particles is frequently negative (i.e. downflow) even though the crosssectionally averaged mass flux of particles is positive (7). The particles tend to drag the gas downward in the wall region, and so there can be significant internal recirculation of both particles and gas in the riser. At very high gas velocities, the downflow disappears and one can even get a higher mass flux of particles at the wall region than the core (8). How well can we capture these trends in models and how confident are we in predicting the flow pattern changes that will come about upon scale-up, flow rates or modifications to the flow device? c) Since risers are often used to carry out (catalytic or non-catalytic) chemical reactions involving the gas and particles, one can anticipate that these persistent macro-scale non-uniformities would affect the effective contact between particles and the gas, and hence the conversion and selectivities. How well can we model these effects and propose design choices to maximize conversion and/or selectivity? d) Gas by-passing is a common concern in the operation of turbulent fluidized beds and the beds are extensively baffled to mitigate this problem. Deep beds operating at low (say, near-atmospheric) pressures are particularly
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susceptible to local defluidization and gulf-streaming (9). Can we capture such flows in models and simulations, and predict how they will change upon scale-up and process modifications (such as the introduction of baffles)? When such inhomogeneous flows arise in chemical reactors, they can affect conversions and selectivities; they can also lead to reactant breakthrough to the top of the bed and cause unwanted freeboard reactions. For example, in FCC regenerators, oxygen breakthrough causes CO combustion in the freeboard leading to high temperatures that favor NOx formation (10). Can such problems be detected in simulations in a reliable manner? e) Cyclones play vital functions in CFBs in capturing and returning the particles and minimizing particle emissions. The mass loading of particles in the stream entering the cyclones varies appreciably from stage to stage. The separation efficiency of cyclones is determined by the competition between the swirling flow which aids particle separation and turbulent dispersion which results in re-entrainment of particles into the gas (11, 12). Mass loading of particles affects both the strength of the swirl and turbulent intensity (13-15); how well can we model and simulate these effects? f) In some processes carried out in CFBs and TFBs, liquid is intentionally injected (16) either as a reactant or for coating purposes. In such systems, one can readily expect that in some regions of the bed (e.g., close to where the liquid is injected), the particles will be coated with a liquid and this can lead to agglomeration of the particles. These agglomerates are likely to induce local defluidization and cause secondary flow structures in the CFBs and TFBs (17). How well do we understand these secondary flow structures and their effects on conversions and selectivities (or coating uniformity)? The above list, though incomplete, illustrates some characteristics that one would like to understand and model with confidence, so that the models can then be used as tools to test design options for new plants as well modifications to existing units. More specifically, the models should help us understand the macroscale flow behavior and allow us to perform computational experiments exploring means of manipulating the flow to maximize a desired set of objectives, such as conversion, selectivity and operational stability. What makes modeling difficult? One can readily list a number of reasons, a few of which are described below. a) Circulating fluidized beds typically consist of a number of devices, as mentioned above, and regions with widely different particle volume fractions are encountered in the flow loop. As a result, the manner in which stress is transmitted through the particles changes significantly from location to location. For example, such stress transmission occurs predominantly by collisions in the riser, while in the standpipe and slide (or “L”) valves stresses transmitted through enduring contact become important. As the overall performance involves a complex interaction of various devices in the circulation loop, a good model should account for the effect of stress transmission through particles via collisions as well as enduring contact. b) Meso-scale structures form as a result of the instability of two-phase fluidized flow when the particle volume fraction becomes too small to support sustained force chains (18); the point at which this occurs depends on particle roughness, size (which affects the importance of cohesion) and
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shape. These meso-scale structures take the form of bubble-like voids at high mean particle volume fractions (approximately greater than 0.40) and clusters and streamers at low mean particle volume fractions (approximately less than 0.25) (19). In the intermediate region, a turbulent state exists where the meso-scale structure changes rapidly between bubble-like voids and clusters and streamers. These structures are generally difficult to resolve in computations; yet, they affect the gas-particle interactions (such as the effective fluid-particle drag, the heat and mass transfer rates, and the stress transmission in the particle and fluid phases). c) The particles invariably have a distribution of sizes that evolve naturally through attrition, or develop because of reactions occurring in the fluidized beds. Accounting for the particle size distribution (PSD) is critical to predict accurately the rate of elutriation from turbulent fluidized beds, cyclone efficiency, etc. 3. THE FLUID-PARTICLE DRAG It is easy to understand that one must include gravity (which pulls the particles to the bottom of any device), pressure gradient (which establishes motion of the gas relative to the particles) and fluid-particle drag (which is the principal means by which particles can be suspended against gravity) in any model to capture the flow of fluidized suspensions. The accuracy with which the fluid-particle drag can be determined is critically important in modeling of fluidized suspension flows. A number of empirical constitutive models for the fluid-particle drag in homogeneous suspensions of uniformly sized spherical particles are available in the literature (20); a prominent example is the widely used correlation due to Wen & Yu (21). A practical difficulty comes about when we apply such correlations developed for (nearly) homogeneous suspensions to flows of fluidized gas-particle mixtures. As noted earlier, fluidized suspensions readily form inhomogeneities that span a wide range of length and time scales. As a result of these inhomogeneities, flows in turbulent fluidized beds and risers are invariably multi-dimensional. Furthermore, when the particles and the gas move around from one location to another in a device, inertia should be included in the models. Inertia – especially, the particle phase inertia – is important to capture the formation of flow inhomogeneities such as bubbles, clusters and streamers. As a result of the multi-dimensionality and inclusion of inertia, the models are invariably solved numerically on suitable spatial grids (more on solution methods later). Such computations resolve the flow at scales larger than the grid resolution, but not those occurring at a sub-grid scale. Using extremely fine grid resolution to resolve all the flow structures is often not practical. The challenge in accounting for the effective drag force accurately can be illustrated as follows. Consider a zero-dimensional (0D) model for a turbulent fluidized bed, i.e. the entire bed is simulated using a single numerical grid cell. Such a 0D model ignores all the flow structures present in the bed and reduces to a force balance over a uniformly fluidized bed. If drag force correlations intended for homogeneous suspensions are employed, one readily concludes that the superficial gas velocity must remain well below the terminal settling velocity (vt) of the particles in the bed; however, this is almost never the case and most turbulent fluidized beds operate at velocities in excess of vt. This difference is primarily due to the fact that the inhomogeneities, which were not resolved in this 0D analysis, result in a decrease in
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the fluid-particle drag and it has not been properly accounted for in the analysis. To capture the particle volume fraction in the bed correctly using such a 0D model, one must modify the drag force correlation to reflect the effects of unresolved flow inhomogeneities. If one analyzes the same system as an unsteady flow problem using several numerical grid cells, then some of the flow inhomogeneities will be resolved, and so the modification to the drag force correlation will now be different; as the number of grids increases, the required modification to the drag force correlation decreases. Knowing what inhomogeneous flow structures have not been resolved and how one should account for their effects on the effective fluid-particle interaction (drag) force is a challenge. This issue has been addressed in the literature. O’Brien & Syamlal (22) and Heynderickx et al. (23) corrected the drag coefficient at very low particle volume fractions to account for the consequence of clustering. McKeen & Pugsley (24) used an apparent cluster size in an effective drag coefficient closure. Li and coworkers (25) deduced corrections to the drag coefficient using an Energy Minimization Multi-Scale approach. Filtered models, where the effects of sub-filter scale inhomogeneities on the drag force are modeled by introducing a filter size dependent drag law, are being developed (26, 27); Parmentier et al. (27) have presented an additional advance where the filter size dependent drag force is dynamically corrected in simulation of filtered model simulations. The development of these filtered models is still in the early stage, and many more validation studies are needed to test and refine these models. All the modifications to the drag law that have been described in the literature, which are intended to correct for unresolved structures, are for uniformly sized particles. Drag laws for homogeneous suspensions of particles having a distribution of sizes are described in the literature (28); however, little has been published in the literature on modifying these drag force correlations to correct for unresolved structures. 4. FORM OF THE MODEL FOR GAS-PARTICLE FLOWS The above discussion touched upon numerical computations without making specific reference to the form of the models for gas-particle flows. All the models for gasparticle flows solve the Eulerian form of the continuity and momentum balance equations for the gas phase on a fixed spatial grid, and so the unresolved structures discussed above are obviously relevant. When solving for the particle phase, there are multiple options. 4.1.
Eulerian treatment of the particle phase(s)
In two-fluid models, Eulerian continuity and momentum balance equations are formulated for the particle phase as well, and are solved using the same grids (as for the gas phase). This approach (also referred to as the Eulerian-Eulerian model) has a long history of development and analysis. When multiple types of particles are present, they can be generalized as multi-fluid models, where each particle type is treated as a separate phase, interacting with all the other phases. Two-fluid models have served well in our quest to understand the underlying mechanisms leading to inhomogeneous structures. For example, one can readily find the solution of two-fluid model equations corresponding to the state of uniform fluidization analytically, and examine its linear stability to pinpoint the origin of
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instability leading to bubble-like voids in dense suspensions, as well as clusters and streamers in dilute suspensions (19, 29, 30), and the characteristic length and time scales associated with the dominant instability mode. It has also helped advance simple qualitative arguments explaining why particles segregate towards the walls in riser flows (31). The two-fluid model equation for the particle phase allows for stress transmission through the particle phase. Over the past three decades, researchers have adapted the kinetic theory of dense gases and developed constitutive models for the rheology of assemblies of monodisperse, spherical and inelastic particles interacting through binary collisions (32, 33). Such models, generally referred to as kinetic theory of granular materials, require solution of an additional scalar equation for the kinetic energy per unit mass associated with the fluctuating motion of the particles relative to the local average velocity of the particle phase (a.k.a. granular temperature); the particle phase stress is then expressed in terms of local particle volume fraction, granular temperature and local rate of deformation of the particle phase. The kinetic theory models have also been generalized for mixtures of different types of particles (32-37). These multi-fluid models can take one of two forms: (a) Separate continuity, momentum and granular energy balance equations are formulated for each particle phase, and solved. In this approach, if there are N different particle phases, one has to solve (N+1) continuity equations, d(N+1) momentum balances (where d denotes the number of spatial dimensions involved in the problem) and N granular energy balance equations (34, 35). (b) Continuity equations are formulated for the N different particle species, along with a single momentum balance equation and a single granular energy balance equation for the particle mixture; these are supplemented by algebraic models for the granular temperatures of the different particle species and the “diffusive” flux of each particle species relative to the mixture flow. In this approach, one has to solve (N+1) continuity equations 2d momentum balances, one granular energy balance equation, along with a set of algebraic equations to determine the diffusive fluxes (36, 37). A recent study comparing these two approaches found that both approaches yield similar predictions for binary particle mixtures, with the latter approach requiring less computational time (38); the advantage is likely to be more significant when the number of particle species increases. While the kinetic theory has given us a good handle on particle phase stress resulting from particle streaming and collisions, models for stress in the dense, quasi-static flow regime where the particles make enduring contacts with multiple neighbors and stress is transmitted largely through force chains are by and large phenomenological (39-41). Distribution of particle sizes is handled in the Eulerian modeling approach in several different ways. In one approach, the particles are divided into a number of cuts, each representing a size range and each cut is treated as a separate particle phase. Multifluid models are solved to determine the flow behavior. In the other approach – discrete quadrature method-of moment – the actual PSD is replaced by a sum of
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delta functions placed at judiciously selected particle sizes (quadrature nodes); these quadrature nodes are allowed to change temporally and spatially to capture agglomeration and break-up, change in size by chemical reactions, etc. (42). The number of different particle phases that are needed to capture the effect of PSD will clearly depend on the nature of the PSD; studies addressing how the predictions change as more and more particle phases are used to approximate a given PSD are beginning to appear in the literature (43). The development of software platforms for solving multi-fluid models has progressed appreciably over the past two decades. The open-domain code MFIX developed at NETL (22) and commercial software (e.g., ANSYS Fluent®) are widely used by many research groups around the world to study reacting multiphase flows. Many research groups also employ in-house codes to solve such models (e.g., Neptune in the research group of Olivier Simonin). Application of multi-fluid models to simulate gas-particle flows does raise questions and also poses a number of challenges. Let us consider the two-fluid model where the all the particles are treated as a single particle phase and examine the issues: a) A basic question that one can raise concerns the validity of treatment of the particle phase via a continuum model. In writing such a model, it is presumed that the particles interact with each other rapidly, thus endowing the particle phase with a pressure and a viscosity. In normal fluids, we are able to do this for low Mach number flows as there is a clear separation of scales between the random motion of the molecules that gives rise to pressure and viscosity, and the mean velocity of the fluid phase. It is not at all obvious that such a separation of scales exists for the particle phase in most gas-particle flows where the particles interact via binary collisions. In such situations, the low order moments of the particle velocity distribution function - namely, mass, momentum and fluctuation energy - which are evolved through the particle phase continuity, momentum and granular energy balance equations - may not adequately define the full flow problem. b) High resolution simulations of gas-particle flows via two-fluid models yield fine structures at length scales as small as 10 particle diameters, and it is argued by some that these fine structures are not real features of gas-particle flows and that it is manifestation of the inadequacy of the continuum treatment of particle phase in the two-fluid model. c) The existence of such fine structure raises the issue on the required grid resolution. Resolving all the fine structures contained in the two-fluid model equations requires numerical computations using extremely fine grids. These are simply unaffordable. This necessitates development of filtered two-fluid models where the fine structures are smoothed out and their effects on the resolved flow are modeled. While some progress has been made on the hydrodynamic aspect of filtered two-fluid models, corresponding thermal energy and species balance equations have not yet been developed and validated. d) Handling PSD using multi-fluid models, especially when the PSD is changing due to reactions, break-up, etc., remains a challenge. e) Three-dimensional simulations using two-fluid models of large process units remain expensive (unless one uses filtered models that permit coarse grids); multi-fluid models increase the computational cost significantly.
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f) Boundary conditions for multi-fluid models at solid surfaces are still primitive. g) The continuum hydrodynamic model for the particle phase is obtained by taking the low-order moments of the Boltzmann equation for the particle distribution functions; such an approach usually emphasizes dominant aspects of flow. In some engineering applications, one would like to understand relatively rare events (such as formation of hard particle agglomerates in fluid cokers); two-fluid models are not useful for such inquiries. 4.2.
Lagrangian treatment of the particle phase(s)
Here one formulates and solves Newton’s equations for the motion of particles. Also, the particles are not restricted to be on an Eulerian mesh (e.g., the one used to solve for the gas phase variables). The fluid velocity and pressure gradient at the particle locations, required to solve the particle momentum balance, are readily obtained from the Eulerian (fluid phase) mesh via interpolation. Similarly, the force on the fluid due to the particles can readily be mapped to the Eulerian mesh from the particle locations. Such Lagrangian treatment offers several advantages, while also placing some limitations, as discussed below. At very low volume fractions where inter-particle collisions are rare and unimportant, one can formally ignore collisions and employ a point-particle approximation. Such an approach is used extensively in the literature in studies on particle-turbulence interactions. In the context of CFBs, it is employed in (secondary and tertiary) cyclones. Typically 1-50 million point particles can be tracked in practical simulations and so it is possible to follow all the particles in modestly sized devices only at extremely low particle volume fractions. 4.2.1. Parcel-based approach To circumvent this limitation on the number of particles that can be simulated, parcels of point particles are simulated. Here each test particle being tracked represents a large number of particles having the same characteristics as the test particle (e.g., see Andrews and O’Rourke (44), or Pantakar and Joseph (45). Such a parcel based approach can appreciably expand the range particle volume fractions that can be handled. The approach using parcels of point particles must be modified when the particle volume fraction becomes sufficiently large that interactions between particles via collisions, sustained force chains, cohesion, etc. become important. In the multiphase particle-in-cell (MP-PIC) method, the interaction of other particles with a test particle (parcel) is modeled by a force on the test particle that is proportional to the prevailing particle phase volume fraction gradient at the location of the test particle (46-49). Clearly, even though one tracks point particles, it is recognized that each particle has a finite volume; the particle phase volume fraction and its gradient at the location of the test particle affects both the fluid-particle drag and the effective force due to the interaction between particles. Conceptually, the parcel-based MP-PIC method and the multi-fluid model are equivalent; this has been illustrated recently by direct comparison of the two approaches on a model flow problem (50). Nevertheless, there are clear differences
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in the advantages offered by the two approaches. As noted earlier, the two-fluid model approach is readily amenable to stability and simple macroscopic analyses, which have been useful to develop better understanding of the competing forces leading to complex flow structures. The MP-PIC approach always requires a numerical solution and is not well-suited for simple mathematical analyses. On the other hand, the parcel-based MP-PIC approach does have major attractions: a) Particle size distribution is much more easily handled than in multi-fluid models. b) In dilute flows where the interaction of particles with bounding surfaces occurs mainly by collisions, boundary conditions are easily implemented. c) Changing particle properties and size are easily handled. d) As a large number of parcels are tracked, there is a possibility that rare events can be detected and analyzed. The parcel-based MP-PIC approach [available commercially, CPFD®] has indeed rapidly emerged as very powerful and is being used more and more in industries. Preliminary versions of this approach are available in MFIX as well. It is now being offered as an option in Fluent® as well. In the opinion of this author, parcel-based MP-PIC method will likely emerge over the next decade as a preferred approach for reasons mentioned above. At the same time, it should be noted that this approach has not been tested as extensively as the two-fluid models. There are relatively few interrogations of the properties of solutions obtained by this approach; for example, studies investigating the influence of grid resolution on the solutions for various classes of flow problems – fluidized beds, risers, etc. – are needed. A limited investigation (50) performed recently shows that at fine grid resolution the parcel based approach yields similar microstructure as the two-fluid model, and so it appears that MP-PIC simulation of flows in large devices using coarse grids will need filtered fluid-particle drag force models (and possibly modifications to the effective particle interaction force as well) – as in the case of multi-fluid models. If this is indeed the case, and if so, what filtered fluid-particle drag force model is appropriate for the parcel-based approach, are not clearly understood at the present time. It would also be useful to perform more simulations of classical problems to gain better understanding of the parcel-based approach itself, as well as the underlying flow physics. One example would be simulations of fluidization in a vertical pipe over a wide range of gas velocities and particle fluxes, thus generating a map of average pressure gradient vs. gas velocity at different particle mass fluxes. The general character of such phase diagrams are well known experimentally: choking, multiplicity of states and carrying capacity of the gas have been widely studied experimentally (51, 52). Demonstrating that such complex phase diagrams can be robustly captured would greatly increase the confidence of the method in the minds of the users, than simply testing the method against a few operating conditions. 4.2.2. Discrete Element Method (DEM) for spherical particles One can, in principle, circumvent the need to postulate a phenomenological model for the force on a test particle due to interactions with other particles (in the parcel approach) by directly simulating all the finitely sized particles in a region and their interaction via collisions and enduring contacts. Though these simulations may allow
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for arbitrarily shaped particles, let us focus first on spherical particles, which have been studied the most using granular/molecular dynamics. In assemblies at low particle volume fraction, where the particles interact largely through binary collisions, grains are conveniently modeled as hard particles that experience instantaneous collisions, which are detected using an event-based algorithm. At high volume fractions, where particles tend to make enduring contact, DEM is the preferred approach, where the particles are modeled as soft spheres that can overlap slightly and exert both normal and tangential forces on each other (53). DEM simulations, however, are computationally expensive. In the early 1990’s, DEM simulations were limited to about 103 particles (54, 55); simulations of millions of soft-sphere particles are now feasible using CPUs with higher clock frequency, as well as computer clusters. Also, significant improvements in commercial (e.g. PFC3D (56)), as well as open-source (e.g., LAMMPS (57)) software make DEM simulations more common. Recent applications of DEM-based simulations of particle flows also include a coupling to computational fluid dynamics (CFD) of the fluid phase, e.g., cyclone separators under high mass loads (58), or the DEM-CFD model recently proposed for fluidized bed reactors including heat, and mass transfer, as well as chemical reactions (59). Computations using Graphic Processing Units (GPUs) have become fashionable after software to use these powerful co-processors was published (60). Tailored applications focusing on single-GPU computations have been developed, enabling a roughly 100-fold speed-up compared to conventional single-CPU calculations (61, 62). Thus, the application of DEM-CFD models is poised to grow rapidly in the years ahead. DEM is also widely used as a tool to study rheological behavior of dense particle assemblies (63-65). However, most of these studies have been only for spherical particles. Also, these simulations tend to use simple interaction models (e.g., the linear spring-dashpot model of Cundall and Strack (66)). A comprehensive overview of more sophisticated contact force models, e.g. accounting for rolling and twisting resistance between particles, is given by Luding (67). Rarely do all details of these sophisticated contact forces and torques significantly impact granular flow behavior in most of industrial applications of interest; instead, the effect of particle shape has a more severe impact on the static and dynamic features of a granular assembly (53). 4.2.3. DEM for non-spherical particles The effect of particle shape on flow behavior is currently an active area of research – both from an experimental, as well as modeling point of view. Campbell (68) investigated the flow of prolate spheroidal particles and their effect on granular flow transition; he found that force chain formation, and consequently the stresses in a quasi-static flow situation, depend strongly on particle shape. A specialized algorithm for cylindrical objects has recently been published by Kodam et al. (69, 70). In this latter work the particles are described as true cylinders (as opposed to spherical particles glued together). Also, Kodam et al. provided experimental verification of their approach, as well as a comparison of their simulation with a glued-sphere approach. DEM simulation of non-spherical particles requires significantly more computational resources than a similar simulation of spherical particles. Although various strategies to approximate the true shape of particles exist, they currently cannot compete with the accuracy and details of particle interaction force modeling available for spherical particles. This point is even true for specialized algorithms, e.g., the one used by Kodam et al. (70), as the latter did not include rolling or twisting
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resistance. Thus, there will be always a compromise between particle shape, contact force modeling, as well as time and resources available for the simulation. While the effect of shape on dry granular flow without interaction with the gas phase has been summarized recently (53), there is much less published on fluidized suspensions; more studies would be useful to fully expose the role of particle shape on fluidization. Recently, Liu et al. (71) measured lower fluidization velocities of nonspherical particles compared to sphere packings. Hilton et al. (72) simulated nonspherical Geldart group D particles using a DEM-CFD approach. Rosendahl and Mando (73) recently reviewed the status of models for non-spherical particle motion in gas-solid flows, and highlighted the importance of the alignment of particles with turbulent vortices. The effect of particle shape on fluidization is likely to be a concern in fluidized beds used to gasify biomass, where degassing of the particles may also alter the effective fluid-particle drag considerably. 4.2.4. More on parcel-based methods Even with advances in computational power, DEM simulations will remain prohibitive for large process devices. Therefore, a parcel-based approach will remain the method of choice for large scale problems. As noted earlier, in the parcel-based MPPIC method the particle interaction force is modeled through an empirical particle pressure, while in DEM simulations they are resolved. Researchers have examined if the parcel-based method could be configured in a way that the need for empirical pressure model can be eliminated. Sakai et al. (74) as well as Mohktar et al. (75) assume that the parcel is represented by a sphere with a volume equivalent to the sum of the volumes of the particles making up the parcel. This requires contact detection between parcels, and hence is computationally more expensive than the parcel-based MP-PIC approach discussed earlier. A primitive form of such contact detection has been already used in the work of Patankar and Joseph (45). Bierwisch et al. (76) have shown recently that a parcel-approach with contact detection, when using appropriately scaled interaction parameters, yields simulation results independent of the number of particles making up the parcel. In this approach, one would be performing DEM simulations of the pseudo-particles representing the parcels, where the characteristics of these pseudo-particles are chosen (based on dimensional analysis) such that important features of the flow remain equivalent to the flow of the original particles. Specifically, they show that it is possible to obtain stresses in the quasi-static regime and parcel velocities in all regimes of granular flow, that are independent of the scaling of the system. Analogous scaling can be identified for a linear spring-dashpot model as well (77), whereby properly scaling the spring stiffness, the damping coefficient, as well as cohesive forces, a parcel-based approach can be made to yield the same quasi-static flow behavior, stresses, and particle velocities as the original particle system; however, the parcel-based approach with contact detection overestimates the stresses in the inertial regime (78) where stresses are primarily transmitted through collisions and so a correction is needed. In their latest work, O’Rourke and Snider (46) propose a method to relax the parcel velocities to their local mean value which can be adapted to the parcel-based approach with contact detection to obtain the same particle phase pressure as in the original system of particles (Radl et al. (77) ). Thus, it seems possible to have a discrete particle method based on parcels that can closely approximate the stresses in the original system of particles across different flow regimes. In the opinion of the
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author, this approach holds promise for simulations of flows in standpipes, hoppers, spouted beds, dense phase pneumatic conveying, etc. 5. ROLE OF GAS TURBULENCES IN CFBs AND TFBs In turbulent and fast fluidized beds, where the mass loading of particles is often one or more orders of magnitude larger than that of the gas, gas turbulence has only a secondary effect on the flow in most of the regions (in the opinion of this author). Its effect is more likely to be localized in regions where the particle volume fraction is low (for example, in cyclones where turbulent dispersion of particles lead to loss of separation efficiency) and at the interface separating dense and dilute region where it plays an important role in entrainment of particles into the dilute stream (for example, particle pickup by turbulent eddies in pneumatic conveying, and entrainment of particles into jets). Adequate resolution of gas-phase turbulent fluctuations (e.g., via Large Eddy Simulations, or Direct Numerical Simulations) in industrial-scale devices and jets, especially at high particle volume fractions, does not seem feasible for the foreseeable future. We will continue to rely on sub-grid models for the role of gas turbulence in inducing fluctuations in the particle phase. Such models can readily be included in the granular energy equation of the two-fluid model and in parcel-based models (79, 80). This seems adequate for modeling gasphase stresses in CFB applications, where the mass loading (i.e., the ratio of particle mass flux to gas mass flux) is relatively high. 6. MODEL VERIFICATION AND VALIDATION It is clear from the discussion above that a variety of different models are being applied to study gas-particle flows, and simulations are based on discretized versions of these models. It is important that the models and the simulators based on these models be subjected to careful verification as well as validation with experimental data. What constitutes verification and what is validation have been discussed in some detail by Grace (81). Verification is an essential first step and it can take several different forms depending on the model being tested: a) It is important to demonstrate that simulators based on any model be compared (if possible) with analytically obtainable results for some test problems, even if the problems are highly idealized. For example, in two-fluid models for gasparticle flows, the growth rate of instability modes (starting from a uniformly fluidized state) can be determined readily through linear stability analysis; verifying whether numerical codes can reproduce the analytical results (and if so at what grid resolution and time steps) is a natural test of the fidelity of the code [for example, see ref. (30)]. b) When parcel-based model simulations with collision tracking are formulated, it is important to verify that they yield the expected trends in predictions as one changes parcel size [For example, see ref. (77)]. c) When a filtered two-fluid model is developed by coarse-graining some (say, kinetic theory based) two-fluid model equations, one should verify that simulations of the filtered model yield the same coarse flow structures as the underlying two-fluid model (82).
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d) Models invariably involve approximations; frequently different models tend to capture different aspects of physics more accurately. Yet, there must be situations where the different models agree with each other reasonably well; and so, it makes eminent sense to compare predictions of various models. Such predictions fall in the category of verification and not validation. For example, demonstration that two-fluid model and a parcel-based model yield nearly the same results in simulations of a highly idealized flow problem (50) is a useful verification step as it enhances the credibility, establishes equivalence between models and exposes how ideas from one approach can be adapted for the other. Comparison of CFD-DEM and multi-fluid models is also in the same spirit. e) Comparison of the simulation results obtained at different grid resolutions is also an important verification step. Although this is indeed done in most published articles in an empirical manner (i.e. presenting results obtained at different grid resolutions for one or two test simulations), concrete guidelines on grid resolutions needed to get grid-size independent results are generally not available, with a recent study by Parmentier et al. (27) being a welcome exception. As a result even when grid size independence is demonstrated and the simulation is validated against experimental data in a pilot scale unit, practical challenges regarding grid resolution requirements when applying that simulation approach to large scale devices are not fully appreciated. Good verification studies should strive to bring forward simulation issues at different scales. Even though most of the simulation studies solve the unsteady equations governing the flow, attempts to validate have invariably focused on time-averaged flow characteristics such as axial pressure profile and lateral variation of particle volume fraction and mass flux. Indeed, these quantities arise naturally as the most important ones. Since the flows manifest persistent fluctuations, comparing the power spectra of fluctuations between the models and experiments makes eminent sense (of which differential pressure is the easiest to measure). Since the extent of contact between the gas and the particles is intimately linked to gas dispersion characteristics, they are also important metrics. The challenge problems issued as a part of this CFB-10 conference do indeed focus on validation of models with experimental data on these quantities. A large number of early studies compared time-averaged results obtained from 2D simulations with experimental data; with increasing computing power, more and more 3D simulations are being done. As one would expect, there are quantitative differences between the results obtained with 2D and 3D simulations, and so true validation does require 3D simulations. However, 3D simulations are very expensive and so demonstrating grid independence of solutions is often prohibitive; in the opinion of this author, it is not at all obvious if some of the published simulation results are truly grid independent. This point is particularly clear from the simulation study of turbulent fluidized beds by Parmentier et al. (27) who estimated the grid size needed for nearly grid independent solution of standard two-fluid models used by most researchers; such resolution is often not feasible in commercial scale devices. Based on a recent study (50) comparing the two-fluid model and a parcel based approach, it appears that the grid resolution requirement for the latter approach is also similar. Given this concern (as to whether the computed results are truly grid independent), there is a lingering doubt as to whether favorable comparison of model predictions with experimental data is really indicative of successful validation or a coincidence for the chosen grid resolution. Researchers engaged in simulations
13
certainly understand the need to seek grid independent results, and so the above comment is not intended as a criticism; instead, it is presented as a practical limitation imposed by the size of the problem that one can simulate within the available resources. As clearly demonstrated by Parmentier et al. (27), the grid resolution needed to get a grid-independent solution changes appreciably with particle size. Therefore, experimental data on riser flows and turbulent fluidized beds for particles of different sizes would be valuable. The challenge problem (#3) discussed in this conference considers riser flow data for 59 m (group A) and 802 m (group B) particles. It would be useful to generate riser flow results for several intermediate sizes as well. The challenge problems include turbulent fluidized bed data for a single particle size (~75-80 m, with different fines contents); data for somewhat larger particles would be useful as well. Even if a particular model is validated successfully using pilot scale data (at a certain grid resolution), is there a basis for trusting the simulation results on commercial scale device performance obtained using this model and necessarily coarser grids? This question has been repeatedly posed to the author of this article by researchers in industries (who use the simulation tools to evaluate performance of commercial scale devices). A major concern in scale-up from pilot scale to commercial scale has always been whether the flow characteristics would change qualitatively upon scaleup and lead to serious shortfall in performance. With this in mind, it is suggested that one should compare simulation results obtained at different scales with experimental data. For example, the current challenge problem (#3) considers data obtained in a 30 cm diameter riser; researchers will continue to use these data for many years to further refine their models and simulators. (The data from earlier challenge problems continue to be used for validation studies even today.) It would be useful to collect analogous data on a larger scale unit (say 75 cm diameter riser) for future challenge problems, so that one can evaluate how well the various models and simulation approaches capture both sets of data (30 and 75 cm). A great deal of current research is aimed at incorporating PSD into models and simulations. To better understand the role of PSD and also validate these models, it would be useful to have data for different PSDs (particularly for group B particles in the size range of commercial interest). Risers tend to operate in the fast fluidization regime, where (sometimes) there is a dense phase at the bottom transitioning to a dilute phase at higher elevations. Phase diagrams for riser flows suggest that nearly the same combination of riser gas velocity and particle mass flux can yield different pressure drops across the riser depending on the height of the dense region at the bottom. In such situations it is more sensible to specify one of the fluxes and the pressure gradient and calculate the other flux as an output. Most simulators do not perform such computations and part of the reason for poor validation may be due to this. This suggests that it would be useful to have (at least skeletal) performance data at different riser gas velocities (while fixing the solids flux) and generating results akin to the phase diagram mentioned in section 4.2.1). One would then ask how well models and simulations capture a continuous spectrum of operating conditions – this can help assess if the departure between models and experiments is qualitative or quantitative.
14
In the not too distant future, simulations of the entire CFB loop will become common. It would be of interest to develop good data sets on standpipe operation for models to compare against. The standpipe plays a critical role in stable operation of the CFB loop. Plant operators often seek to increase circulation rate (which is usually tied to productivity of the unit) by improving aeration. Unacceptable failure of the standpipe upon excessive aeration is of practical concern and models and simulations can play a valuable role in understanding this problem and identifying means of improving performance without causing instability. Good data to validate simulations of standpipe flow will help improve confidence in simulations of the full CFB loop. 7. FORWARD LOOK Within the next 5-10 years, significant advances can be expected in simulation of CFBs and TFBs, because of improved computer resources, as well as better modeling approaches. Three-dimensional simulations of full CFB loops will become more common, and these will pave the way for better understanding of global phenomena such as loop instability. Instead of performing simulations at a small number of operating conditions (in individual units such as risers), researchers will map out model predictions over a range of conditions and examine the robustness of trends and quantify uncertainties in the simulation results. At a more fundamental level, coarse-grained drag laws for polydisperse systems will emerge along with better understanding of how they should be constructed for the different simulation approaches (multi-fluid models vs. parcel based method). Parcel based methods (with and without collision detection) will likely emerge as the more preferred approach, and it will be studied in greater detail by academic researchers as well, leading to further improvements in the method. Although not discussed in this article, better understanding of models that one would use for wet systems (such as fluid cokers) where the particles can form agglomerates will also emerge (83). These, in conjunction with flow simulators, will lead to better understanding of secondary flows in such devices. Recent experimental findings, as well as small-scale simulations are a promising starting point to refine our understanding of liquid transport in fluidized beds (84, 85). ACKNOWLEDGEMENTS The author gratefully acknowledges the help of Stefan Radl, William Holloway and Sebastian Chialvo in the preparation of this article. Ted Knowlton’s critique of an initial draft of this manuscript is much appreciated. REFERENCES 1. 2. 3.
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ELECTROSTATIC PHENOMENA IN FLUIDIZATION SYSTEMS: CURRENT STATUS OF UNDERSTANDING AND FUTURE RESEARCH NEEDS Xiaotao T. Bi Fluidization Research Centre, Department of Chemical and Biological Engineering The University of British Columbia, Vancouver, BC, Canada ABSTRACT Electrostatic charging of dielectric powders in gas-solids fluidized beds has received increased attention in recent years, due to its impact on particle agglomeration, dispersion and reactor fouling in polymerization and pharmaceutical processes, and charging and coating uniformity in powder coating processes. This paper provides a critical review on the measurement techniques for characterizing powder charging, distribution of charged powders and the factors influencing the powder electrification in gas-solids fluidization systems. INTRODUCTION The electrification phenomenon in fluidized beds was first observed in late 1950s, and particle charging has been considered as one of the key factors causing particle agglomeration and reactor wall fouling in gas-solids fluidized beds dealing with fine dielectric powders [1]. Electrostatic forces induced by the charges carried by particles can also change the hydrodynamics of gas-solid fluidized beds. Despite these negative effects of electrostatic charging, the mechanisms of charge generation and dissipation are still poorly understood because of the lack of accurate measurement techniques for determining particle charge distributions inside the fluidized beds and the lack of applicable theories for charge separation between similar dielectric materials. Due to the non-homogeneous flow structure in gas-solids fluidized beds, the electric forces between adjacent particles cannot be considered as isotropic in the region close to the gas bubbles. Similarly the electric field of the charged particle bed cannot be treated as a homogeneous medium. Instead, heterogeneous flow structure at the bubble scale has to be considered in order to properly characterize the electrostatic forces and fields associated with charged particles. The reactor performance is impacted by electrostatics as reflected by the particle agglomeration, particle segregation, wall fouling and non-uniform temperature distribution. A proper understanding of charge generation and dissipation
mechanisms at the particle scale or even the molecular scale coupled with the distribution of charged particles at the bubble scale is required to properly predict the impact of electrostatic charges on the reactor hydrodynamics and to develop effective tools for mitigating electrostatic charges. Electrostatic phenomena in industrial fluidized bed polymerization reactors and the mitigation strategies were reviewed comprehensively by Hendrickson [1], while the triboelectric charging of dielectric powders was recently reviewed by Matsusaka et al. [2]. This paper attempts to provide a critical review on measurement techniques, charge generation and spatial distribution in fluidized beds, and the factors determining the degree of particle electrification in gas-solids fluidized beds. MEASUREMENTS OF STATIC CHARGES IN FLUIDIZED BEDS Various techniques have been used to quantify the electrification phenomenon and to measure particle charge density in gas-solids flow systems. The most commonly used method is to capture certain amount of charged particles into a Faraday cup so that the charge density of particles (Q/m) can be obtained. Some methods measure the current or the electric potential from the metal surface of an electrostatic probe either in direct contact with the bed particles (ball probes, collision probes or capacitance probes) or exposed to the electrical field induced by the pass-by particles (induction probes). Particle charge density can also be deduced by analyzing the trajectory of a charged particle subject to an electric or electromagnetic field. Table 1 summarizes the techniques used to measure electrification in gassolids fluidized beds. Direct Methods: Measurements of Charge Density by Faraday Cup and Particle Trajectory Tracking The sign and density of charges on each particle will provide crucial information on the degree of particle charging and the magnitude of electrostatic forces acting on individual particles. Charged particles in the fluidized bed can be removed by a sampling tube or a scooper, then poured into a Faraday cage, as illustrated in Figure 1. Such a sampling method has been applied to measure the particle charge density in the dense bed region [3, 4, 5, 6, 7] and the freeboard region [8] of bubbling/ slugging fluidized beds or CFB risers [9, 10]. Such a method can be made localized by taking samples from different locations of the fluidized bed [5]. Since particles with size distributions have been commonly used in the fluidized bed, it will be important to know the differential charging of particles of different sizes in the bed. To determine the charge density on removed particle samples of different sizes from the fluidized bed, multi-compartment Faraday cup systems were used which separated particles of different charge densities into several Faraday cups arranged either horizontally [11] or vertically [6], as shown in Figure 2.
Table 1. Electrostatics measurement techniques for gas-solids fluidized beds. Researchers Ciborowski and Wlodarski (1962) [12] Boland et al., (1969) [13]; Boland and Geldart (1971) [14] Bafrnec and Bena (1971) [15] Tardos and Pfeffer (1980) [3] Fasso et al. (1982) [8] Wolny and Opalinski (1983) [16]
Probe Ball probe
Signal Potential
Bubbling
Particles Sand, polystyrol, vinyl polyacetate Glass beads
Induction probe
Potential
Bubbling
Glass beads
Ball probe
Potential
Bubbling
Porcelain
Bubbling Bubbling
Glass beads Polystyrene
Q/m; Current Q/m Q/m
Fujino et al. (1985) [4]
Bubbling
Gajewski (1985) [17] Rojo et al. (1986) [18] Wolny and Kazmierczak (1989) [19] Gidaspow et al. (1986) [25] Guardiola et al. (1992; 1996) [20,21] Napier (1994) [22]
Bubbling Bubbling Bubbling
Glass bead, Neobead, PMMA Polyethylene Glass bead Polystyrene
Bubbling
Coal
Faraday cup; Ball probe Faraday cup Faraday cup (Single particle sampling) Ball probe; Faraday cup Multi ring probes Ball probe Tracking of particle trajectory Ball probe
Bubbling
Jiang et al. (1994) [9]
Riser
Coronella and Deng (1997) [23] Mountain et al. (2001) [24] Tucholski and Colver (1998) [10]
Riser
Glass beads, steel Glass beads, sugar FCC, Polyethylene Sand
Bubbling Riser
Polymers Glass beads
Bubbling
Ali et al. (1998; 1999) [11, 5]; Zhao et al. (2003) [6] Servais and Bernot (2000) [26] Park et al. (2002a, 2002b) [27,28] Revel et al. (2003) [7] Murtomaa et al. (2003) [29] Yao et al. (2002) [30] Mehrani et al. (2005; 2007) [31, 32] Chen et al. (2006, 2007) [33, 34, 35] Demirbas et al. (2008) [36] Wang et al. (2008) [37] Moughrabiah et al. (2009) [38]; Liu et al. (2010) [39] Sowinski et al. (2009, 2010) [40, 41] Omar et al. (2010) [42]
Bed Bubbling
Potential; Q/m Current Potential Q/m Current
Ball probe
Potential
Ball probe; Field meter Faraday cup
Q/m (No details) Q/m Potential
Polyamide
Electrostatic field meter Faraday cup Faraday cup; Ball probe; Metal wall Faraday cup
Bubbling
Polyethylene
Ball probe
Potential
Single bubble Slugging Bubbling
Glass beads
Ball probe
Current
Polyethylene Glass beads, lactose, cellulose
Q/m Potential & Q/m
Bubbling Bubbling Bubbling
Glass beads Glass beads, Polyethylene Glass beads
Bubbling
Glass beads
Bubbling Bubbling (P, T) Bubbling
Polyethylene Polyethylene, glass beads Polyethylene
Faraday cup Multi-scan Induction ring probe Ball probe Faraday cup (insitu) Multi induction probes Multi Induction probes Multi ball probes Multi ball probes
Bubbling
Polyethylene
Bubbling
Faraday cup Faraday cup (insitu)
Q/m Q/m Probe current; Wall current. Q/m
Current Q/m Current & Q/m distribution Charge Potential Current distribution Q/m Transient Q/m
Powder in
FB Faraday Cup Grounded Cup Electrometer
Faraday cup Electrometer
+ + ++ + ++++ Insulation
Figure 1. Schematic of a Faraday cup charge density measurement system.
Fluidized bed Plug Powders Metal sampling tube 1 2 3 4 Vertical array of Faraday cup sensors
5 6 7
(a)
(b)
Figure 2. (a) Horizontal and (b) vertical multi-compartment Faraday cups [6, 11]. The charge density of individual particles in the fluidized bed can also be measured using the single particle sampling method [16] or the single particle trajectory method [19]. For single particle sampling, a small vacuum sampling tube (0.5 mm in diameter) is inserted into the bed to remove single polystyrene particles (~ 1 mm in diameter) into a Faraday cup so that the charge density of individual particles can be directly obtained. The particle trajectory method is a non-contact measurement method by which two parallel metal plates are installed in the freeboard region of a transparent fluidized bed. When single particles are ejected into the space between the parallel plate by a single gas nozzle located inside the dense bed right beneath the space of the parallel plates, the trajectory of single particle movement was captured by a high speed video camera which enabled the determination of the
charge density of individual particles by analyzing the trajectory of the particles subjected to an electrical field. The accuracy of this method will be impacted by the interference of the electric field imposed by the charged column walls and the bed particles unless the space between the parallel plates are well isolated. The Faraday cup method can provide the charge density directly, but the measurement accuracy is affected by the additional charging or discharging of samples during the sampling process due to the contact of sampled powders with the sampling tube or device. A novel in-situ Faraday cup fluidized bed method was developed by Mehrani et al., [31, 32] to measure the charge density of entrained fine powders from an electrically isolated copper fluidized bed, which serves as a Faraday cup. As shown in Figure 3, when charged fine particles are elutriated from the fluidized, an equal but opposite charge will be registered by the electrometer connected to the isolated copper fluidized bed. To monitor the change of charge density of entrained fine powders with time, the entrained particles can be captured into a bag filter and weighed by a sensitive balance so that the transient charge density of the entrained fines can be obtained [42]. On the other hand, improvement has been made in minimizing the additional electric charging by discharging bed materials directly into a Faraday cup installed beneath a fluidized bed using a quickopen distributor for the measurement of the average charge density of bed materials at the end of each fluidization test [40, 41], as shown in Figure 4. C o pp e r p ip e
G a s o ut
C op p er c yc lon e
T ef lo n
In n er c o pp e r c olum n
P le x ig las s s ea le d c ha m b er Fine s c olle c te d In a c up
S ca le
O u ter c op p er c olum n
E le ct ro m et e r
D at a ac qu is itio n by c om pu te r
T ef lo n
F lu id iz a tio n g a s
Figure 3. A Faraday cup fluidized bed unit for transient charge measurement of entrained fines [31, 42].
Figure 4. A novel fluidized bed unit with a quick-opening distributor for measurement of powder charging [40, 41]. Indirect Methods: Measurements of Current and Voltage Potential by Electrostatic Probes When particles in the fluidized bed are charged, no matter whether the reactor wall is grounded or not, an electric field will be created in the fluidized bed. By placing a metal probe inside the fluidized bed with the probe connected to an electrometer, a potential against a grounded reference probe (either the reactor wall, the metal distributor or another metal probe) will be registered by the electrometer, as illustrated in Figure 5. The magnitude of the potential against a grounded metal wall or distributor generally increases with fluidization time, and reached a final steady state value, Vs, after a few minutes to an hour, see Figure 5. The final potential is thus believed to reflect the degree of electrification of fluidized particles at steady state when the equilibrium between charging and discharging is reached. An equivalent electric circuit could be used to relate the final potential to the charge behaviour in the fluidized bed [4]. As illustrated in Figure 5 the charged bed particles can be treated as a condenser which captures charges generated in the fluidized bed. The total electrical resistivity of the whole system consists of three parts: bed resistivity accounting for current flow from the bed to the probe, the leakage resistance from the reactor wall and the internal resistance of the electrometer. The final potential is thus related to the resistances by:
Vs I 0 Rt
I 0 Rbed Rwall Rm Rbed Rwall Rbed Rm Rwall Rm
The final potential between the probe and the grounded metal distributor can also be analyzed using a capacitance model, with the final potential being related to the charge in the capacitor by, 4H Vs Q f Dc D p where H is the vertical distance between the probe and the distributor, Dc is the diameter of the distributor and Dp is the diameter of the electrostatic probe. The final potential is then seen to be influenced by the dielectric properties of the bed particles, which depend on the bed voidage, relative humidity etc.
Im Q/m
Rbed
E
C
Rwall
Rm
FB V
Electrometer
Potential
V V s (1 e t / )
Vs
Potential probe Time Figure 5. A typical electrostatic potential probe, its equivalent circuit and measured signals. The other type of electrostatic probes is the collision type current probes, or socalled ball probes. The probe is installed in the fluidized bed and connected to a resistor, with the current measured by an electrometer, see Figure 6. Different from the potential probe which treats the fluidized bed as a charged continuum medium, the current ball probe receives charges both transferred from particles colliding with the probe surface and induced when the particles pass by the probe [3, 27]. For a single bubble passing a current probe in a two-dimensional fluidized bed, Park et al. [27] and Chen et al. [43] proposed a combined charge transfer and induction model to interpret the recorded transient current signal. The net current change is related to the charge transfer from particle-probe collisions while the fluctuations are induced by the passing bubble. Chen et al. [44] further demonstrated that the charge density of the particles in the dense phase surrounding the bubble could be obtained by fitting the model to the measured current signals from a single bubble, for given particle electrical properties and a uniformly charged dense phase. To eliminate probe interference with the motion in the bed and charge transfer due to collisions between particles and the probe, shielded induction probes have also been
used to characterize the electrification of fluidized beds. Boland and Geldart [14] embedded an induction probe onto the Plexiglas wall of a 2-D column, and registered the dynamic response of the probe to the passage of a single injected gas bubble into a fluidized bed of glass ballotini particles. They speculated for the first time that the static electrification is generated by the motion of particles around the gas bubble, particularly in the region of the wake. Single or double ring induction probes have been commonly used to measure particle velocities in pneumatic conveying lines based on probe calibration and cross-correlation of signals from two rings separated by a given distance [45-47] by assuming a uniform particle flow in the pneumatic conveying lines.
Figure 6. A typical collision current probe installed in a 2-D column [27]. Chen et al. [33-35] used multi-induction probes aligned horizontally to measure the charge distribution around rising gas bubbles in a two-dimensional Plexiglas column. Using four induction probes placed flush with the outer surface of the column, signals received from the four probes are shown in Figure 7(a), and used in conjunction with the bubble position captured by a synchronized digital camera to reconstruct the charge density distribution around rising gas bubbles in gas-solids fluidized beds. Figure 7(b) shows the reconstruction results for the induced charges shown in Figure 7(a). It can be seen that the charge inside the air bubble is almost zero, and the charge density increases gradually toward the dense phase region outside the bubble, with the sign of charges in the dense phase remote from the bubble being negative. There is a more negatively charged wake, confirming the postulation based on measurements with a single collision probe [23, 43, 44]. The charge density outside the bubble in the dense phase was approximately –3.6x10-7 C/kg and the charge in the wake was about–6.8 x10-7 C/kg. Murtomaa et al. [29] used a multi-scan induction ring probe to measure the induced potential at different bed height by moving the ring probe vertically. The average particle charge density of the bed under steady state was obtained by signal reconstruction, with the assumption of a uniform charge density on homogeneously distributed particles. As will be discussed later, the non-uniform charge density distribution in the fluidized bed will make this technique difficult to be used for fluidized beds without a priori charge distribution information. The disadvantage of induction probes is that the signals received by those probes
can be interfered by electric fields induced by charged column walls, especially for non-conductive walls such as Plexiglas.
inside bubble
1200
Induced charge, pC
1000
800
600
probe 4 400
line: measured scatter: reconstructed
probe 1
200
0
probe 2 100
50
probe 3 0
-50
-100
Distance from the center of the bubble, mm
(a) 80
3
charge density, pC/mm
60
-2.0 -1.7
40
-1.5
bubble
z, mm
20 probe 1
-1.2
probe 2
probe 3
probe 4
0
-0.90 -0.63 -0.35 -0.075
-20
0
-40 -60 -80 0
10
20
30
40
x, mm
(b) Figure 7. (a) Measured induced charge signals and simulated induced charge signals based on reconstructed charge distribution and (b) reconstructed charge distributions (Hmf = 0.7 m, DB=0.08 m, UB=0.45 m/s, dp=565 μm, ρp=2500 kg/m3, Qair=1.78 m3/s ) [34].
CHARGE GENERATION AND DISTRIBUTION IN FLUIDIZED BEDS Charge Generation and Bipolar Charging Tribo-electrification between dissimilar materials can be characterized by the surface work function, defined as the work/energy needed to pull an electron away from the surface of a material. In general, metal has a lower work function, thus easier to loss electrons when contact with other materials. Work function also closely correlates with the dielectric constant of the material, higher for materials with a higher dielectric constant. When two dissimilar materials are in contact, electrons will flow from the surface of lower work function to that of a higher work function. Thus, in contact with a neutral metal surface, a neutral dielectric particle will extract electrons from the metal surface. On separation, the dielectric particle becomes negatively charged. When a charged particle contacts with a metal surface, however, the net charge exchange will depend on the pre-charge level, collision speed and the collision angle etc. As reviewed in Matsusaka et al. [2], extensive work has been reported on the effect of particle pre-charge level, surface pre-charge level, collision angle and collision speed on the degree of charge transfer and separation. The charge generated by colliding a dielectric particle with a metal surface generally increases with the collision speed and is at the maximum for a head-on collision with the surface. In fluidized beds of dielectric particles, particles are expected to be charged via collision with the reactor walls, either grounded metal wall or poorly conductive plastic walls. Most studies on electrostatic charging of fluidized beds assumed a single polarity of bed materials before 1990s, although bipolar charging was identified as early as in 1950 [48, 49]. Based on the measurement of the charge density of individual particles, it was found that for glass beads of narrow size distributions, particles are approximately evenly divided between positively and negatively charged although the net charge is close to zero. Charging particles through a vibrating chute and then falling them through air between parallel vertical electrode plates to separate particles into eight groups using 8 Faraday pales aligned horizontally beneath the electrode plates with each unit connected to an electrometer, Turner and Balasubramanian [49] confirmed that both positive and negative charges could arise on particle surfaces for three narrowly sized glass bead samples, 45-53 m, 63-75 m and 75-90 m. Cartwright et al. [50] tested polyethylene (PE) powders in a pneumatic conveying system and showed that fine PE powders were charged negatively and coarse powders charged positively. Similar to the tests of Turner and Balasubramanian [49], the ratio of the positively and negatively charged powders strongly depended on the relative humidity, suggesting that the moisture on the particle surface might play an important role during the charging process. Similar bipolar charging phenomenon was also reported by Singh and Hearn [51] and Mazumder et al. [52, 53] for PVC and tonner powders based on the measured charges on individual particles in samples of size distributions using a microprobe system and an electric single particle aerodynamic relaxation time (E-SPART) analyzer, respectively. The first direct evidence of bipolar charging in fluidized beds was given in the paper by Wolny and Kazmierczak [19] who measured the charge density of individual particles using a particle trajectory tracking technique installed in the freeboard of the fluidized bed and reported a probability distribution of charge density, although his
results were not noticed until now. Ali et al. [11] used a horizontal array Faraday cup system (see Figure 2) to measure the charge density of particles charged differently whereby a small sample of charged powders removed from a fluidized bed by a scooper was poured from a height and separation occurred between the majority and the minority particles of opposite charges. The degree of separation is a function of charges on the individual particles and the height at which the sample is poured. Bipolar charging of the fine and coarse fluidized particles was identified in which the fine particles are charged negatively, while coarse particles are charged positively for two polymer samples tested, while the third polymer sample showed the opposite charge signs. Further tests by Zhao et al. [6] using a vertical array Faraday cup system confirmed the bipolar charging behaviour for three polymer powders, all showing negatively charged fines. Mehrani et al. [32] measured the charge density of fines entrained from a copper fluidized bed of glass beads and polyethylene powders. Bipolar charging was identified with fines charged positively for all glass beads samples, but negatively charged fines for polyethylene resin powders at both zero and 60% relative humidity. Further study of Omar et al. [42] using polyethylene resins of different grades from different manufacturers showed consistent results of negatively charged fines. The addition of different fines to the polyethylene powder [32] also showed a strong dependence of the charge level and signs on the relative humidity of the fluidizing gas. The fine Larostat 519 powder was negatively charged at dry nitrogen, but positively charged at 60% relative humidity. The opposite signs reversal phenomenon was observed for fine catalyst powder and silver-coated glass beads. The identification and confirmation of bipolar charging changed the perception of a uniformly charged fluidized bed of particles of singular polarity, and thus opened the door for examining the charge distribution inside the fluidized beds. Charge Distribution Ali et al. [5] studied the charge distribution in fluidized beds of polymer powders with the samples removed from different locations of the fluidized bed by a scooper and poured into a Faraday cup. Their results showed that the charge density inside the bed was quite uniform, except in the near wall region where some fine powders deposited onto the walls. The wall deposit was further analyzed on its mean size and the charge density. It was found that the wall deposit was positively charged in the dense bed region, with the charge density increased with increasing the distance from the distributor [Figure 8(a)]. Above the bed surface, however, the deposit reversed the polarity to negative. In the region where the charge density crosses zero, there was no deposit on the wall. The maximum charge density of the deposit somehow corresponded to the maximum field potential for a uniformly charged bed [Figure 8(b)]. Also, the mean particle size in the deposit of the dense bed was generally larger than that in the freeboard region [Figure 8(c)]. Recently, Sowinski et al. [40, 41] carried out similar tests in a novel fluidized bed unit in which the entrained fine powders were captured into a Faraday cup to have the charge density measured, and the charge density of the bed material, polyethylene powders, was measured by dropping the bed particles into a Faraday cup located beneath the gas distributor right after the fluidizing gas was cut off. The fine powders deposited on the column wall was visually inspected and sampled to have the
charge density and the particle size distribution determined. Their results showed that the entrained fines were highly positively charged, while the bed materials were negatively charged. The particles deposited on the wall were overall highly negatively charged, with most deposit residing in the dense bed region, and minor deposit above the bed surface. Again there was a clean wall region between the dense bed and the freeboard region. Their results are in general consistent with Ali et al. [5], although the deposits at different region were not examined.
Figure 8. Axial distribution of (a) charge density of wall deposit, (b) field intensity of charged bed and (c) volume mean particle size of wall deposit. [5]. Gajewski [17] measured the axial profiles of the average electrostatic current from multiple isolated copper rings embedded inside a glass fluidized bed with polypropylene powders as the bed material. Positive current was measured in the bottom dense bed region, and negative current was found in the upper bed and freeboard region, showing a reversal of current flow in the system. Moughrabiah et al. [38] and Liu et al. [39] also measured the axial distribution of electrostatic current from 8 ball probes located at both the axis and near the wall of the fluidized bed using polyethylene powders. The average currents from those probes were found to be always negative in the lower dense bed region, and positive in the upper and the freeboard region, confirming the current sign reversal phenomenon in the bed. Rojo et al. [18], Servais and Bernot [26] and Wang et al. [37] measured the axial distribution of electrical potentials using potential probes immersed in the fluidized
bed. Wang et al. [37] showed that negative potentials always presented in the lower dense bed region, and positive potentials in the upper bed and freeboard region, appearing to be consistent with the reported current distribution. The new experimental evidence on non-uniform distribution of bipolarly charged powders inside the fluidized beds and the likely induction charging of bed particles from the highly charged wall deposit [5] revealed the complexity of the particle charging and accumulation mechanisms in the fluidized bed. All bulk measurement techniques relying on the assumption of uniform spatial charge distribution would not be able to capture the local characteristics of the charging phenomenon. Localized measurements using sampling tubes in conjunction with cascaded Faraday cups or small current probes and localized current induction tomography in combination with advanced signal analysis and reconstruction methods may provide the opportunity to improve our understanding of local charging behaviour of fluidized beds. FACTORS INFLUENCING POWDER CHARGING Tardos and Pfeffer [3] and Fujino et al. [4] found that the particle charge density was not sensitive to the superficial gas velocity. Revel et al. [7], however, showed that the charge density increased with increasing the superficial gas velocity using polyethylene powders. Note that all those tests were conducted in bubbling fluidized beds. In circulating fluidized risers, both Jiang et al. [9] and Tucholski and Colver [10] showed that the particle charge density was insensitive to the solids circulation rate and the superficial gas velocity. One likely explanation is that the increase in gas velocity and particle circulation rate promotes both charge generation and dissipation via particle-wall collisions, with the net change remaining unchanged. Fujino et al. [4] showed that the relative humidity has little impact on the particle charge density in bubbling fluidized beds. Tardos and Pfeffer [3] showed that the charge density in the bed with a humid gas (RH=36-42%) was almost 50% lower than that at lower humidity (RH=21-25%). Wolny and Kazmierczak [19] also showed that the charge density of a 0.475 mm diameter polystyrene bubbling bed decreased substantially when the relative humidity increased from 30% to 70%. In a charge dissipation test carried out in a packed bed separately, they also demonstrated that the charge dissipation rate at 70% relative humidity was much higher than at 30% relative humidity. The increase in relative humidity was also found to be effective in reducing the particle charge density in circulating fluidized risers [9]. It is also noted that the reduction in average current and final potential measured by static probes has been reported in all tests in the literature, supporting the effectiveness of relative humidity in lowering the particle charge density. In general, such a reduction in bed charge level at high humidities can be attributed to the increased surface conductivity of moisture-coated particles [13]. Also, the increase in the humidity of the fluidizing gas may also lower the resistivity and the break-down potential of the gases, helping the dissipation of particle surface charges. Antistatic agents have been used in the industry for powder charge mitigation. Wolny and Opalinski [16] reported that the addition of 0.1% fine powders of either conductive (active coal and TiO2) or insulating materials (pigment A-extra) was effective in reducing the buildup of particle charge density in a fluidized bed of ~1 mm polystyrene particles. Wolny and Kazmierczak [19] later reported that the addition of fine aluminium and NaCl powders to a bubbling fluidized bed of
polystyrene beads significantly reduced the charge density. It was also observed that all NaCl particles in the bed were negatively charged and that all polystyrene particles were coated with NaCl fines. The effectiveness of fine powders, no matter conductive or not, has also been reported in many studies based on reduced current and potential from electrostatic probes [15, 20, 28]. Servais and Bernot [26] examined the effect of antistatic powders (Larostat 519) on both the axial and radial electrical potential profiles in a fluidized bed of polyethylene and polypropylene powders using a potential probe. The results showed that both the axial and radial electric potential profiles were significantly modified in the presence of antistatic powders. The signs of the potential could even be flipped with the addition of antistatic powders. They also showed that the potential increased with increasing the fines content in polyethylene samples, inferring that higher charges were generated for powders containing more fines, as evidenced by more fines sticking to the column wall. Their results suggest that the addition of fine powders may not only change the bed particle charge density but also altered the spatial distributions of bipolarly changed particles in the fluidized bed. One possible explanation is the lubrication of the fine powders as “spacers” attached to bed particles so that charge dissipation by particle-to-particle contact is enhanced due to the increased particle contacts. The difference between antistatic powders and fine powders is still unclear and requires further investigation in the future. Particle size distribution may also influence the electrostatic behaviour of powders because of the bipolar charging between fine and coarse particles and the effect of the addition of fines on the reduction of charge buildup. The implication is that a proper design of particle size and size distribution may potentially reduce electrostatic charge generation and build-up in fluidized beds. FUTURE RESEARCH NEEDS Understanding the impact of electrostatic charging of particles on gas-solids fluidized bed reactor performances related to particle agglomeration and reactor fouling requires the exploration of electrostatic charge generation and dissipation mechanisms and segregation patterns of charged particles, which, in turn, relies on the development of reliable and accurate measurement techniques. Advanced techniques which can provide transient local particle charge density as well as charge density distribution across a spectrum of particle sizes of the fluidized particles will enable researchers to improve the understanding of the bipolar charging behaviour and segregation of particles of different sizes and charge polarities in the fluidized bed. Resolution of charged particle distribution surrounding gas bubbles in the bubbling beds and particle clusters or particle streamers in the annulus region of risers may provide key information for unlocking particle charging and segregation mechanisms in fluidization systems. ACKNOWLEDGEMENT I am grateful to final supports from NSERC, Mitsubishi Chemicals, Japan Polychem, Nova Chemicals, Chevron-Philip Chemicals and contributions from John Grace, Alissa Park, Poupak Mehrani, Li Yao, Aihua Chen, Flip van Willigen, Kwang-Seok Choi, Wajeeh Moughrabiah, Zhengliang Liu, Amy Yang and Muammar Omar.
NOTATION DB = bubble diameter, m Dc = diameter of gas distributor, m Dp = diameter of electrostatic probe, m dp = mean particle diameter, μm H = vertical distance between probe and distributor, m Hmf = packed bed height, m I = electric current, A m = particle mass, kg Q = electrostatic charges, C Q/m = particle charge density, C/kg Qair = fluidizing air flow rate, m3/s RH = relative humidity of gases Rbed = Electric resistance of bed, Rm = External electric resistance, Rwall = Electric resistance of wall, UB = bubble rise velocity, m V = voltage potential, V Vs = steady state voltage potential, V X = horizontal coordinate, m z = vertical coordinate, m f = dielectric constant ρp = particle density, kg/m3 REFERENCES 1. Hendrickson, G., Electrostatics and gas phase fluidized bed polymerization reactor wall sheeting, Chem. Eng. Sci., 61, 1041 – 1064, 2006. 2. Matsusaka, S., H. Maruyama, T. Matsuyama and M. Ghadiri, Triboelectric charging of powders: A review. Chem. Eng. Sci., 65, 5781–5807, 2010. 3. Tardos, G., and R. Pfeffer, A method to measure electrostatic charge on a granule in a fluidized bed. Chem. Eng. Comm., 4, 665-671, 1980. 4. Fujino, M., S. Ogata and H. Shinohara, The electric potential distribution profile in a naturally charged fluidized bed and its effects. Int. Chem. Eng., 25(1), 149-159, 1985. 5. Ali, F.S., I.I. Inculet and A. Tedoldi, Charging of polymer powder inside a metallic fluidized bed. J. Electrostatics, 45, 199-211, 1999. 6. Zhao, H., G.S.P. Castle, I.I. Inculet, and A.G. Bailey (2003). Bipolar charging of poly-disperse polymer powders in fluidized beds. IEEE Transactions Industry Applications, 39, 612-618. 7. Revel, J., C. Gatumela, J.A. Dodds and J. Taillet, Generation of static electricity during fluidisation of polyethylene and its elimination by air ionisation. Powder Technol., 135– 136, 192– 200, 2003. 8. Fasso, L., B.T. Chao and S.L. Soo, Measurement of electrostatic charges and concentration of particles in the freeboard of a fluidized bed. Powder Technol., 33, 211-221, 1982. 9. Jiang, P.J., J.P. Zhang and L.S. Fan, Electrostatic charge effects on the local solids distribution in the upper dilute region of circulating fluidized beds, CFB?? 10. Tucholski, D. and G.M. Colver, Charging of glass powder in a circulating fluidized bed, IEEE Trans. Ind. Applica. 1906-1912,1998. 11. Ali, F.S., M.A. Ali, R.A. Ali and I.I. Inculet, Minority charge separation in falling particles with bipolar charge. J. Electrostatics, 45, 139-155, 1998. 12. Ciborowski, J. and A. Vlodarski, On electrostatic effects in fluidized beds. Chem. Eng. Sci., 17, 23-32, 1962. 13. Boland, D., Q.A.W. Al-Salim and D. Geldart, Static electrification in fluidised beds, Chem. Eng. Sci., 24, 1389-1390, 1969. 14. Boland, D. and D. Geldart. Electrostatic charging in gas fluidized beds. Powder Technol., 5, 289-297, 1971/2. 15. Bafrnec, M. and J. Bena, Quantitative data on the lowering of electrostatic charge in a fluidized bed. Chem. Eng. Sci., 27, 1177-1181, 1972.
16. Wolny, A. and L. Opalinski, Electric charge neutralization by addition of fines to a fluidized bed composed of coarse dielectric particles. J. Electrostatics, 14, 279289, 1983. 17. Gajewski, A., Investigation of the electrification of polypropylene particles during the fluidization process. J. Electrostatics, 17, 289–298, 1985. 18. Rojo, V., J. Guardiola and A. Vian, A capacitor model to interpret the electric behaviour of fluidized beds. Influence of apparatus geometry. Chem. Eng. Sci., 41, 2171-2181, 1986. 19. Wolny, A. and W. Kazmierczak, Triboelectrification in fluidized bed of polystyrene. Chem. Eng. Sci., 44, 2607-2610, 1989. 20. Guardiola, J., G. Ramos and A. Romero, Electrostatic behaviour in binary dielectric/conductor fluidized beds. Powder Technol., 73, 11-19, 1992. 21. Guardiola, J., V. Rojo and G. Ramos, Influence of particle size, fluidization velocity and relative humidity on fluidized bed electrostatics. J. Electrostatics, 37, 1-20, 1996. 22. Napier, D.H., Generation of static electricity in a fluidized bed and in powder conveying. Proc. 2nd World Congress Particle Technology, 1994. 23. Coronella, C.J. and J.X. Deng, Electrostatic effects in cold-model circulating fluidized beds, 1997. 24. Mountain, J.R., M.K. Mazumder, R.A. Sims, D.L. Wankum, T. Chasser and P.H. Pettit, Triboelectric charging of polymer powders in fluidization and transport processes, IEEE Transactions on Industry Applications, 37(3), 778-784, 2001. 25. Gidaspow, D., D. Wasan, S. Saxena, Y.T. Shih, R. Gupta, A. Mukherjee, Electrostatic desulfurization of coal in fluidized beds and conveyors, AIChE Symp. Ser. 83 (255), 74–85, 1986. 26. Servais, T. and C. Bernot, Measurement of electrostatic effects in a fluidized bed reactor. First European Conference on the Reaction Engineering of Polyolefins, Lyons, July 3–6, 2000. 27. Park, A., H.T. Bi, J.R. Grace and A.H. Chen, “Modeling electrostatic charge transfer in gas-solids fluidized beds,” J. Electrostatics, 55, 135-168, 2002a. 28. Park, A., H.T. Bi and J.R. Grace, “Reduction of electrostatic charges in fluidized beds,” Chem. Eng. Sci., 57, 153-162, 2002b. 29. Murtomaa, M., E. Räsänen, J. Rantanen, A. Bailey, E. Laine, J. Mannermaa, J. Yliruusi, Electrostatic measurements on a miniaturized fluidized bed, J. Electrostatics. 57, 91-106, 2003. 30. Yao, L., H.T. Bi and A.H. Park, “Electrostatic charges in freely bubbling fluidized beds with dielectric particles,” J. of Electrostatics, 56, 183-197, 2002. 31. Mehrani, P., H.T. Bi, and J.R. Grace (2005). Electrostatic charge generation in gas-solid fluidized beds. J. Electrostatics., 63, 165-173. 32. Mehrani, P., X.T. Bi and J.R. Grace, Electrostatic behavior of different fines added to a Faraday cup fluidized bed, J. Electrostatics, 65, 1–10, 2007. 33. Chen, A.H., H.T. Bi and J.R. Grace, Effects of Probe Numbers and Arrangement on the Measurement of Charge Distributions around a Rising Bubble in a TwoDimensional Fluidized Bed, Chem. Eng. Sci., 61, 6499-6510, 2006a. 34. Chen, A.H., F. Kleijn van Willigen, H.T. Bi, J.R. Grace, R. van Ommen, Measurement of charge distribution around a single rising bubble in a twodimensional fluidized bed, AIChE J., 52, 174-184, 2006b. 35. Chen, A.H., H.T. Bi and J.R. Grace, Charge distribution around a rising bubble in a two-dimensional fluidized bed by signal reconstruction, Powder Technology, 177, 113-124, 2007. 36. Demirbas, B., J. Nijenhuis, C.U. Yurteri and J.R. van Ommen, Towards
Monitoring Electrostatics in Gas–Solid Fluidized Beds, Can. J. Chem. Eng., 86, 493-505, 2008. 37. Wang, F., J.D. Wang and Y.R. Yang, Distribution of Electrostatic Potential in a Gas-Solid Fluidized Bed and Measurement of Bed Level. Ind. Eng. Chem. Res., 47, 9517–9526, 2008. 38. Moughrabiah, W., J.R. Grace and X.T. Bi, Effect of pressure and temperature on electrostatics in fluid beds of PE particles, Ind. & Eng. Chem., 48, 320-325, 2009. 39. Liu, Z.L., X.T. Bi, J.R. Grace, Electrostatic charging behaviour of dielectric particles in a pressurized gas-solid fluidized bed. J. Electrostatics, 68, 321-327, 2010. 40. Sowinski, A., Salama, F., Merhani, P., New technique for electrostatic charge measurement in gas–solid fluidized beds. Journal of Electrostatics 67, 568–573, 2009. 41. Sowinski, A., L. Miller, P. Mehrani, Investigation of electrostatic charge distribution in gas–solid fluidized beds. Chem. Eng. Sci., 65, 2771–2781, 2010. 42. Omar, M., K.C. Choi, X.T. Bi, J.R. Grace, Effect of particle size and residence time on charging behaviour of fine polymer powders in fluidized beds. Fluidization XIII, Korea, 2010. 43. Chen, A.H., H.T. Bi and J.R. Grace, “Effect of charge distribution around bubbles on charge induction and transfer to a ball probe in gas-solid fluidized beds,” J. of Electrostatics, 58, 91-115, 2003a. 44. Chen, A.H., H.T. Bi and J.R. Grace, “Specific charges of particles in fluidized beds,” Powder Technology, 133, 237-276, 2003b. 45. Yan, Y., B. Byrne, S. Woodhead and J. Coulthard, Velocity measurement of pneumatically conveyed solids using electrodynamic sensors. Meas. Sci. Technol., 6, 515-537, 1995. 46. Ma, J. and Y. Yan, Design and evaluation of electrostatic sensors for the measurement of velocity of pneumatically conveyed solids. Flow Measurement and Instrumentation. 11, 195-204, 2000. 47. Armour-Chelu, D.I. and S.R. Woodhead, Comparison of the electric charging properties of particulate materials in gas-solids flows in pipelines. J. Electrostatics, 56, 87-101, 2002. 48. Kunkel, W.B., The static electrification of dust particles on dispersion into a cloud. J. Appl. Phys., 21, 820-832, 1950. 49. Turner, G.A. and M. Balasubramanian, The frequency distribution of electrical charges on glass beads, J. Electrostatics, 2, 85-89, 1976. 50. Cartwright, P., S. Singh, A.G. Bailey and L.J. Rose, Electrostatic charging characteristics of polyethylene powder during pneumatic conveying. IEEE Trans. Industry Applicat., IA-21 (2), 541-546, 1985. 51. Singh, S. and G.L. Hearn, Development and application of an electrostatic microprobe. J. Electrostatics, 16, 353-361, 1985. 52. Mazumder, M.K., R.E. Ware, T. Yokoyama, B.J. Rubin and D. Kamp, Measurement of particle and electrostatic charge distributions on tonners using E-SPART analyzer, IEEE Trans. Industry Applicat., 27(4), 611-619, 1991. 53. Mazumder, M.K., S. Banerjee, R.E.Ware, C. Mu, N. Kaya and C.C. Huang, Characterization of tribocharging properties of powder paint, IEEE Trans. Industry Applicant, 30(2), 365-369, 1994.
EVOLUTION OF FCC – PAST PRESENT AND FUTURE – AND THE CHALLENGES OF OPERATING A HIGH-TEMPERATURE CFB SYSTEM Ye-Mon Chen Shell Global Solutions (US) Inc. ABSTRACT The fluid catalytic cracking (FCC) process is one of the most important circulating fluidized bed processes. Although the FCC process has been in commercial operation for over 60 years, the technology continues to evolve in order to meet new challenges, which include processing more difficult feedstock and meeting more stringent environmental regulations. This paper presents selected snap-shots of a few challenges (high temperature erosion, corrosion and emission control) that the FCC process faces today and the new challenges yet to come in the near future. INTRODUCTION The fluid catalytic cracking (FCC) process is one of the most important circulating fluidized bed processes, with more than 400 units in operation worldwide today. The FCC unit is the primary conversion unit in a refinery, which converts, or cracks, low value heavy ends of crude oil into a variety of higher-value, light products, such as gasoline and LPG. The unit consists of a reactor and a regenerator, as shown in Figure 1.
Figure 1: A typical FCCU configuration Historically, the FCC unit and its downstream units, such as the alkylation unit, supply about 50% of the gasoline supply in the US. Although FCC is a mature process commercially deployed for over 60 years, the technology continues to evolve because of following unique capabilities:
The FCC process is the only continuous catalytic process in the refinery business, which can adjust or replace catalyst on the run without a shutdown, The FCC catalyst is relatively robust to handle a wide variety of feedstock, and The FCC process can be operated over a wide range of conditions.
The focus of this paper is NOT on the historical evolution [1, 2, 3, 4] of FCC technology. Instead, the paper focuses on selected snap-shots of issues, such as high temperature erosion, corrosion and emission control, that exemplify the challenges that the FCC process faces today and the new challenges in the future. EXAMPLES OF CHALLENGES THAT FCC FACES TODAY It might sound strange at first that keeping Examples of Today’s Challenges an FCC unit running, without having the unit `fall apart unexpectedly, is in fact the 1. Cyclone reliability biggest challenge today for a process that A. Erosion has been around for over 60 years. To put this in the right perspective, the average B. Corrosion run length between two scheduled 2. Emission Control – NOx reduction maintenance shutdowns for an FCC unit was about 2 years in the 1970’s/80’s, whereas the current average run length is now being stretched to between 4 to 5 years. Considering the fact that an FCC unit, on average, circulates about 50 tons of catalyst per minute between the reactor and the regenerator; keeping an FCC unit running continuously for 4 or 5 years means that the equipment will experience the traffic of over 100 million tons of catalyst without falling apart, which is by no means a small task. On the other hand, the incentive of stretching the FCC run length is also enormous because the average costs of an FCC maintenance shutdown is on the order of 10’s of millions of dollars. Cyclone Reliability The FCC unit relies on reactor and regenerator cyclones to keep the catalyst within the unit while circulating catalyst between the two vessels. Two recent industry surveys reveal the pervasive problems of cyclones used in FCC operation today. Table 1 summarizes the survey results from Grace Davison as presented at their 2002 Dublin FCC conference. The results indicate that catalyst losses from cyclones were the number 1 problem in FCC operation, identified by the participants of the meeting. The 2006 Solomon survey (Figure 2) again revealed that FCC cyclone reliability was the number 1 limitation of FCC unit run length today, with more than 41% of unscheduled shutdowns of FCC units in US caused by cyclone problems. Two common cyclone problems that challenge FCC units today, high-temperature cyclone erosion and corrosion will now be discussed. Cyclone Reliability – Erosion Problem Problem Statement One major contributor to unscheduled FCC unit shutdowns is unexpected cyclone failure due to high temperature erosion. Figures 3a and 3b show two examples of high temperature cyclone erosion problems. Figure 3a shows a cyclone which was eroded
through, from inside out, with holes on the cyclone body. Figure 3b shows multiple cyclones that had severe erosion into the cyclone diplegs such that several diplegs were cut off and fell to the bottom of the vessel. Table 1: Grace Davison 2002 Survey
2006 Solomon FCC Survey Events Determining TAR Timing (Outside of Planned Maintenance)
12%
Rx Cyclones Regen Cyclones Rx Refractory Rg Refractory Rotating Equipment Slide Valves Regulatory Other
13%
14%
28%
3% 2% 12% 16%
Figure 2: 2006 Solomon Survey Chart
(a)
(b)
Figure 3. FCC cyclone damage a) Inside out erosion; b) Diplegs cut off due to erosion What is Happening in the Cyclone? The most pervasive problem is high temperature erosion in the secondary cyclone, particularly in the lower cone and in the transition to the dipleg, which is the focus of the study. There is a fundamental difference between erosion patterns in first and second stage FCC cyclones. Highly-loaded first stage cyclones normally experience little to no cone erosion, whereas the lightly-loaded second stage cyclones can have severe cone erosion. This seems to be counter-intuitive at first. However, the key difference in erosion pattern lies in the differences in the solids flow patterns and vortex formation, as shown in Figure 4. Due to high solids loading and low gas velocity in a typical FCC primary cyclone, the gravitational force plays a key role; as a result, the solids appear to drop (or fall) rapidly down into the cyclone cone and dipleg, as shown in the figure on the left side of Figure 4, taking only one to two full turns before exiting the cyclone bottom. The vortex length in the highly-loaded primary cyclone is much shorter because the high solids loading dampens the formation of a robust vortex. Therefore, the vortex does not “whip” the solids at a high velocity around the cone in the primary cyclone. In a typical FCC second stage cyclone, the solids loading is approximately 1/1000 to 1/10,000 of the loading in the first stage cyclone. Due to the light solids loading and high gas velocity, the vortex is relatively long, energetic and, more importantly, moving asymmetrically about its axis. As the swirling solids in the outer vortex approach the cone in a second stage cyclone, the long, rapidly-rotating vortex accelerates the solids stream and causes it to intensify its rotation (i.e., the solids spin faster similar to the motion of a figure skater pulling inwards).
Figure 4: Schematic Depiction of First and Second-Stage Cyclone Operation The outer vortex in a second-stage cyclone typically takes from four to six turns before exiting the bottom cone, as shown in the figure on the right in Figure 4, and the spinning continues into the top portion of the dipleg below the cone. The concentrated solids stream rotates at a high velocity, and the unstable, continuous movement of the vortex causes the significant erosion observed in the cone and the top of the dipleg of secondstage cyclones. Solution to the Cyclone Erosion Problem Most FCC units in US rely on cyclone vendors to provide cyclones for FCC applications, which will be categorized as “conventional cyclones” in this paper. Shell Global Solutions, on the other hand, has developed a cyclone technology, which is different from conventional cyclone in that it uses a vortex stabilizer. Particulate Solids Research, Inc. (PSRI), an independent, industrial consortium, recently studied and benchmarked different FCC cyclone technologies [5], since high temperature cyclone erosion and cyclone reliability were highlighted as the major concerns of FCC operation for companies in recent surveys. The PSRI cyclone test program was structured to benchmark three different possible solutions to mitigate the damaging erosion occurring in FCC second-stage cyclones: 1. Increasing cyclone length (L/D) of a conventional cyclone 2. Increasing the angle of the cone of a conventional cyclone 3. Adding a vortex stabilizer to a conventional cyclone Air was used as the conveying gas in the test unit. The solids used were equilibrium FCC catalyst with a median (Dp50) particle size of approximately 75 µm. The fines
(material 2
1200
Long Cone
5.1
19.8
46
>2
1200
Vortex Stabilizer
3.1
19.8
46
>4
650
Vortex Stabilizer
5.1
19.8
46
>11
240
Drywall joint compound was also added to the disk to see if the upper surface of the vortex stabilizer would be eroded by the vortex. However, essentially no erosion was measured on the upper surface of the disk. No erosion was found on the supporting rods as well. Commercial Bench-Marking In the 1980’s, Shell had over 30 FCC units within the system, mostly with Conventional Cyclones, which were found to be the number one cause of all FCC unscheduled shutdowns. Shell made a conscious decision in developing improved cyclone technology, using the vortex stabilizer, and started the implementation of the technology in the early 90’s. Figure 9 shows the result of how this improved cyclone technology reduced overall FCC unscheduled down time in Shell refineries. Using 1992 data as the base line, Figure 9 shows that the improved cyclone technology, with the vortex stabilizer, reduced the total unit down time of all FCC units in Shell system by a factor of 10. Cyclone Reliability – Corrosion Problem Problem Statement In recent years, several FCC units have encountered unscheduled shutdowns due to high temperature corrosion failure of the cyclone refractory system. These refractory systems failed unexpectedly in some cases within only 4 to 5 years. Figure 10 (a) [6] shows examples of a failed refractory system which resulted in sheets of refractory
peeling off from the walls of the regenerator cyclones. Figure 10 (b) shows that the fallen refractory sheets were caught above the primary cyclone termination device.
Severity(Incl. nearmisses)[Numberof Events* Duration] 1992=100%
Total Severity of Cyclone Problems 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1992
1993
1994
1995
1996
1997
1998
1999
Figure 9: Severity of cyclone related issues in Shell FCCUs
Regenerator Hexmesh Corrosion
Figure 10. (a) Failed refractory, in sheets, due to Hex Mesh corrosion [6]; (b): fallen refract sheets caught above the primary cyclone termination device
Figure 10: Hex Mesh Refractory Structure
What is Happening to the Cyclone Refractory System? The FCC regenerator cyclones are typically made of SS 304H material for high temperature (~ 1400 ºF) operation. In order to protect cyclones from high temperature erosion damage, as discussed previously, the regenerator cyclone internal surface is commonly lined with SS 304H hex mesh, approximately 2.5 cm deep which is welded on to the cyclone interior and packed with refractory within the hex mesh. The structure of the hex mesh/refractory looks like a honeycomb, as shown in Figure 11. The refractory is a concrete-like material which has high resistance to erosion. Historically, the hex mesh/refractory system has served the FCC industry well in providing protection against high temperature erosion. However, a series of unexpected failures of the hex mesh/refractory system, due to corrosion of the SS304H hexmesh, has surfaced very recently as reported by a number of operating companies as well as FCC licensors [6, 7]. As shown in Table 3 and based on current known information, a common pattern involves the application of a “calcium-rich” refractory in the regenerator section of FCC units and failure appears to be related to a corrosion mechanism that attacks the SS304H hex mesh support system for this refractory. It is more prevalent in complete combustion mode (full burn).
Table 3: Summary of refractory analysis [7]
Refractory Analysis – Sample Summary Effect of exposure to process conditions
Refinery Refinery X Bad
Good
Reported Refractory Type
Location
Instalation Date
SO2 Level (ppm)
S
Na
Volatiles
Ca-rich binder
Dipleg - 1st stage cyclone
2005
2500
1.80
0.41
2.55
1.34
0.21
1.38
-
-
-
1.43
0.62
1.43
Refinery Y
Ca-rich binder
Regen Primary Cyclone
Refinery Z
Ca-rich binder
primary cyclone
Refinery U
Ca-rich binder
1997
Refinery X
Ca-rich binder
Regen cyclone
2002 (original)
2500
1.69
0.27
2.89
Refinery T
Ca-rich binder
Regen Cyclone
2002
900
0.01
0.716
0.33
phos-bonded
Regen Cyclone Dipleg
0.07
0.11
0.27
?
Regen Cyclone
0.16
0.11
0.4
phos-bonded
flue gas expansion joint
1.12
0.07
5.66
Refinery H Refinery Y
900
• Both Ca-rich binder and phos-bonded materials appear to absorb sulfur from the process gas • No clear relation between S, Na and volatile content and “good/bad” rating 124
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Although there are some minor variations, this particular type of corrosion has very specific patterns that are strikingly similar from all reported failures:
The attack of the 304H SS hex mesh was identified primarily as sulfidation/oxidation corrosion of the metal support structure. The corrosion occurs preferentially on the underside of the hexmesh metal lining where it is welded to the base steel, such as at the cyclone wall, whereas little corrosion is observed on the process side of the hexmesh lining where it is exposed to the bulk of flue gas, as shown in Figures 12 and 13 The corrosion on the process side is mild oxidation with a well protected Cr-O layer, shown in Figure 14. The corrosion on the underside is a combination of oxidation, sulfidation and carburization where Cr in SS304H no longer forms a tight formation of Cr-O protection layer. The hexmesh/refractory detached from the wall in sheets, as shown in Figure 10, due to weakening of the corroded hexmesh. The corrosion can be very aggressive. Some units reported that newly installed regenerator cyclones could have total refractory system failure within 4 years.
Figure 11: Refractory on process side vs. underside of the hex mesh lining
Corrosion Characterization General Trends
Process Side Tab
• Process gas itself does not appear corrosive. - Conditions are different within/behind refractory. • Accelerated corrosion seemingly always associated with carburization of base metal.
120
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Figure 12: Assessment of general trends in refractory corrosion characterization [7]
Regenerator Hexmesh Corrosion • SEM/EDS analysis of a corroded hexmesh ligament – Exposed side – oxidation – Creviced region – carburization – Underside – carburization & sulfidation/oxidation
Q&A Session 2010 Cat Cracker Seminar
Figure 13: Possible corrosion mechanism: Process side vs. Underside [6] Possible Solutions The root cause of the high temperature corrosion to the refractory metal support structure is currently being discussed in the FCC industry. Several teams in the industry are actively working on the problem. The common belief is that the hexmesh on the underside is under a sub-environment that is more reducing than the bulk flue gas (more oxidizing), and hence more prone to sulfidation and carburization. However, the exact mechanism of how local reducing, sub-environment is formed is still being debated. Possible solutions are a work in progress at the moment. Emission Control – NOx Reduction Problem Statement The FCC regenerator is a major NOX emission source from the US refineries. Several existing technologies are available to reduce NOX emissions from an FCC regenerator, which include SCR, or selective catalytic reduction. Even though SCR is quite effective, it has several issues:
It is quite expensive, on the order of 10’s of millions of dollars, and even more so, on the order of hundred million dollars, when the CO boiler needs to be replaced due to the increase of back pressure caused by the catalyst bed of SCR. It requires a higher a residual flue gas temperature. Unless a second stage heat recovery system is included (which means additional cost), the unit loses energy efficiency
There is a potential cost-effective solution to this challenge.
What is Happening Inside the Regenerator? Most of the NOX emissions from FCC units comes from nitrogen in the feed. The contribution of direct oxidation of N2 to NOX is negligible, particularly for full combustion FCC regenerators. For partial combustion regenerators, the contribution of direct oxidation of N2 to NOX is relatively small if low-NOX burner technology is applied in the CO boiler/incinerator. A recent study [8] shows that about 50% of the nitrogen in the feed exits the FCC unit on the reactor side and the remaining 50% of nitrogen in the feed exits as coke on the spent catalyst sent to the regenerator. Of the 50% of the feed nitrogen exiting from the reactor, about 10% of the feed nitrogen ends up as ammonia, which is collected in sour water, and the other 40% of the feed nitrogen ends up in various streams of the reactor liquid products. This section focuses on the remaining 50% of the feed nitrogen, which enters the regenerator in the form of coke on the spent catalyst. As the spent catalyst is regenerated and coke is burned off in the regenerator, the nitrogen species on the coke are released into the flue gas. Recent studies [8, 9] further show that only a small fraction, less than 10%, of the feed nitrogen on coke is released in the form of NO X emissions in the flue gas. The majority, or more than 40%, of the feed nitrogen on coke is initially released in the form of NOX or other intermediates, but is eventually converted in-situ to N2 in the regenerator. The recent study [9] of batch regeneration of spent FCC catalyst with oxygen and helium reveals a close interaction between the combustion of carbon and the release of nitrogen in the coke. Figure 15 shows the concentrations of CO, CO2, and O2 in the flue gas as a function of time as coke on the catalyst is burned off in the batch regeneration experiment. The amount of coke on catalyst was not directly measured, but Figure 15 implies that coke on catalyst was removed continuously, converted to CO/CO 2, and became negligible after 26 minutes as both CO and CO2 concentrations fell to negligible levels. For the first 9 minutes, the O2 concentration remained low and the CO concentration was higher than CO2, indicating a reduction environment in this period of the batch regeneration. As O2 broke through the unit around the 10-minute mark and its concentration continued to rise afterwards, coinciding with sharp drop of CO concentration and rise of CO2, the batch regeneration shifted gradually from a reduction environment to an oxidation environment. Figure 16 shows the concentrations of NO, HCN, and N2 in the flue gas as a function of time as coke nitrogen is released in the same batch regeneration experiment. Note that most of the coke nitrogen was released as N2, which peaked around the 13.5-minute mark at about 200 ppm, under a reducing or a slightly oxidizing environment. A fraction of the coke nitrogen was released as HCN, which peaked around the 10.5-minute mark at 35 ppm, under the same environment. Note that the NO concentration was below 20 ppm for the first 14 minutes under a reducing or a slightly oxidizing environment when both coke on catalyst and CO were present. The NO X level increased sharply afterward, and peaked around the 18-minute mark at 190 ppm when the CO concentration fell sharply and the O2 concentration increased beyond 1%, as shown in Figure 15.
Figure 14: CO, CO2 and O2 in the Flue Gas as a Function of Time [9]
Figure 15: NO, HCN and N2 in the Flue Gas as a Function of Time [9] The proposed reaction kinetics [9] for the release of coke nitrogen in the FCC catalyst regeneration process involves initial volatilization of coke nitrogen as HCN, which could be hydrolyzed to another intermediate, NH3. Both intermediates, HCN and NH3, can be oxidized to NO, which can be reduced by the presence of CO or/and coke on catalyst to N2.
Solution to the NOx Emission Problem The FCC regenerator design has a direct impact on the effectiveness of in-situ reduction of NOX to N2, and hence reduction of the final NOX emissions in the flue gas. The new Shell Global Solutions’ low NOX regenerator technology [10] involves an improved process consisting of the strategic design of catalyst and air distributions, as shown in Figure 17, which enables the unit to operate in both full and partial combustion modes with low NOX emission. As shown in Figure 17, the regenerator system 1 includes a single regenerator vessel 10 having an upper end 12 and a lower end 14. The regenerator vessel 10 includes a dilute phase catalyst zone 16 above and a dense phase catalyst zone 18 below with a transition surface 20 between the two. The dense phase catalyst zone 18 further includes a high velocity central region 22, located in the central portion 26 of the dense phase catalyst zone 18, and a low velocity annular region 24, located in the annular portion 28 of the dense phase catalyst zone 18. It is a significant aspect of the new regenerator technology that the high velocity central region 22 and the low velocity annular region 24 are formed within the dense phase catalyst zone 18, without the use of a structural element such as a vertical baffle or a partition. The two fluidization regions are instead formed within the dense phase catalyst zone 18 by the introduction into the dense phase catalyst zone 18 of more than one fluidization gas stream, each of which is directed and controlled in such a manner as to cause the formation of multiple fluidization regions. Introduced into the central portion 26 of the dense phase catalyst zone 18 is a high velocity fluidization gas stream that passes through the fluidization gas distribution ring 32 near the bottom of the regenerator vessel 10. Introduced into the annular portion 28 of the dense phase catalyst zone 18 is a low velocity fluidization gas stream that passes through the fluidization gas distribution ring 38 located within the annular portion 28 near the bottom of the regenerator vessel 10. The controlled introduction of the various fluidization gas streams at the different fluidization gas flow rates along with the directed introduction of the fluidization gas streams to desired locations induces a desired circulation of the FCC catalyst within the dense phase catalyst zone 18, as depicted in Figure 17 by the bold arrows 40 that show the general direction and circulation of the FCC catalyst within the dense phase catalyst zone 18. As shown by the bold arrows 40, catalyst particles in the high velocity central region move in a generally upward direction, and catalyst particles in the low velocity annular region move in a generally downward direction. Catalyst from the bottom end 42 of the low velocity annular region 24 flows into the high velocity central region 22 and most of catalyst from the top end 44 of the high velocity central region 22 flows into the low velocity annular region 24, thereby forming the catalyst circulation within the dense phase catalyst zone 18.
Figure 16: A Schematic Diagram of the Low NOX Regenerator System The regenerator system 1 further includes the introduction of spent catalytic cracking catalyst into the high velocity central region 22 through conduit 50, which can be a riser or a standpipe. Connected to the end of conduit 50 is a spent catalyst distributor 52 that introduces spent FCC catalyst into the high velocity central region 22 in a generally horizontal direction and mixes with catalyst circulating from the bottom end 42 of the low velocity annular region 24. The regenerated catalyst is removed from the low velocity annular region 24 by way of conduit 54, which removes regenerated catalyst from the annular portion 28 of the dense phase catalyst zone 18. One advantage of the new regenerator system is that the induced catalyst circulation pattern distributes partially regenerated spent catalyst to the proximity of the surface 20, which results in reducing NOx emissions from the regenerator. Another advantage of the new regenerator system is that the location and the spent catalyst distributor design induce intimate mixing between catalyst and transport air, thus preventing transport air or entrained hydrocarbon from breaking through the dense bed, and resulting in reduced afterburn. Commercial Experience The new low NOX regenerator technology was implemented as an integrated part of a recent major FCC revamp, as presented in the case study below [10].
The original FCC unit was a large side-by-side unit with a regenerator diameter > 15 m, as shown to the left in Figure 18. The scope of the revamp, as shown to the right in Figure 18, included:
A new stripper,
Catalyst Circulation Enhancement Technology (CCET) [4] at the stripper outlet,
A new, larger air blower,
Additional pairs of regenerator cyclones for handling higher air flow, and
The low NOX regenerator technology, consisting of a new spent catalyst distributor, new regenerator outlets with CCET, new air distributors, and the controlled system.
The unit performance was measured before and after the revamp. Key performance improvements were observed: The unit was able to operate in both full and partial combustion modes with low NO X emission after the revamp. Figure 19 shows the NO X level in partial combustion mode under 4 different air distribution conditions. As shown in the figure, NO X emission is the lowest with Case D, with 40 ppm @ CO concentration of 2.4%. The unit can reach NO X level lower than 40 ppm under either lower CO concentration or full combustion mode with O2 concentration < 1% (not shown in the figure); however, partial combustion with CO concentration in the range of 2 to 3.5% is the preferred mode of operation because of its capability to increase the unit feed rate.
Figure 17: Scope of the FCC Revamp in the Case Study
Stack NOx versus Rgn OH CO 100 90 80
NOx @ 0% O2, ppm
70 60 50 40 30 20 10 0 1.5
2.0
2.5
3.0
3.5
4.0
CO, pct Case A
Case B
Case C
Case D
Figure 18: NOx emission in partial combustion under four cases of air distribution What the Future of FCC Might Look Like Shifts of Product Demands and Feedstock Historically, the FCC unit and its downstream units provide the majority of gasoline supply in the world. However, the landscape of the demand from FCC products is shifting and is region-specific as shown in Figure 20. The demand for propylene from FCC products has increased, and in some regions, the light olefins have become the premier products from FCC. In addition, in many regions the demand for diesel outpaces the demand for gasoline. Figure 21 shows the demand shift in the US. The motor gasoline demand has reached a plateau and the long term projection is a decreasing demand trend. One the other hand, demand for gasoil and diesel is increasing. This shift from motor gasoline to gasoil/diesel follows a similar pattern as was seen in Europe 10 years ago. In parallel, the demand for propylene in the US is also growing so rapidly that the traditional source for propylene steam cracking - cannot keep up with the demand. From the available technologies to bridge the gap, FCC is by far the best.
4.5
Figure 19: Current market demands of FCC products
Figure 20: Road transport fuel demand trends in the United States Given these demand shifts, the refining industry needs a redefinition of the FCC process to enable the following process objectives to be satisfied:
Lower gasoline yield; Higher LCO yield, with better product quality characteristics (lower density and higher cetane number); Higher propylene yield; Flexibility to switch seamlessly between these different production modes and conventional FCC operation depending on the market demand.
There are several new FCC process technologies available that address these expected shifts in market demands. One of them is the Shell MILOS process [11, 12], or Middle Distillates and Lower Olefins Selective process. A relative new direction of FCC is to co-process bio-liquid feedstock. The FCC unit is the single largest contributor, accounting for 30 to 50%, to the overall CO2 footprint of a refinery. Beside the benefit of a potential, inexpensive, alternative feedstock, processing bio-liquid feedstock has the added benefit of a tax credit in a region where CO2 emissions are regulated. Processing bio-feedstock presents totally different challenges, yet to come, in FCC operation such as extremely high oxygen content in the feedstock. MILOS – Process Background Recycling cat-cracked gasoline to the bottom of the riser (where the temperature is typically in the range of 1250-1320 ºF) has been widely practiced in the refining industry with the objective of increasing propylene yields. This has the desired effect of producing more propylene, but with significant penalties of producing large amounts of coke, dry gas, butylenes, LCO+ and it would produce little iso-butane. To confirm this, experiments were conducted in which light and heavy cat cracked gasoline were recycled in the Shell Global Solutions large FCC riser pilot plant. The most favorable condition for the production of propylene was found when light CC gasoline was injected below the VGO feed such that the residence time for cracking pure light CC gasoline was approximately 0.5 to 1 second prior to the VGO injection point. However, it had a large impact on VGO conversion, which dropped at constant coke rate. As a result the net LCO+ flow rate at riser exit increased significantly. Such large increases in LCO+ material were also observed in all other tests. Additionally, injecting light CC gasoline with or above the VGO feed yielded a net decrease in propylene yields due to quenching in that section of the riser reactor. For heavy gasoline, the results were even more disappointing in all cases. The flaw of this option is that it is impossible to achieve optimal conditions for both feeds (both the recycled gasoline and the VGO) in one single riser.
Figure 21: MILOS concept MILOS Process Concept The MILOS concept, shown in Figure 22, consists of adding a separate riser to the FCC unit, in which gasoline or other suitable streams are cracked under process conditions tailored specifically to maximize propylene yields, to maintain or increase the iso-butane yield (which is desirable for the alkylation unit) without producing excessive amounts of dry gas, coke and butylenes. The ideal temperature is relatively low (1050-1150 ºF) in order to minimize thermal reactions. At temperatures lower than these, high gasoline conversions cannot be easily obtained. The catalyst used is the same as in a conventional cat cracker with ZSM-5 added to boost the propylene yield. Typical yields obtained by cracking catalyst cracked gasoline in a MILOS riser are 15.5 %wt propylene, 7.5 %wt dry gas and 5%wt iso-butane. Case Study – Diesel Mode A case study focusing on diesel-mode implementation is presented to demonstrate one aspect of implementing MILOS in existing FCC units. The base-case is a Shell Global Solutions’ designed long residue CCU, processing feed with a Conradson Carbon content of 3.2%wt. Shell’s rigorous heat-balanced Catalytic Cracking Process model was used. This model was tuned to the actual operating conditions of the specific FCC unit, including realistic unit constraints and feed properties. The base-case (Case #1) represents the average operating conditions and average feed properties of the unit over the recent years. Besides this case, the following cases were explored:
Cases #2 and #3: the maximum diesel and the maximum propylene cases based on the conventional FCC-unit without the addition of a separate MILOS riser Case #4: a MILOS riser is added to the FCC unit to achieve maximum diesel and propylene Case #5: a sensitivity case with the same MILOS-FCC unit, maximizing propylene only.
The results of the case studies are presented in Table 4.
The comparison between the base case FCCU (Case #1) versus the diesel mode FCCU (Case #2) or propylene mode FCCU (Case #3) is straightforward. If we operate the unit in diesel mode (Case #2), the LCO yield is boosted by more than 3%wt (Table 4) and the cetane index is increased by 5 points. However, propylene yield suffers a reduction of 1.2%wt. Valuable LPG (total C3s and C4s) also suffers a reduction of 3.5%wt. On the other hand, if we operate the unit in propylene mode (Case #3), the propylene yield is boosted by 3%wt, and the LPG (total C3s and C4s) is also increased by 9%wt. (The increase in butylenes Cases #3, 4 and 5 is a direct result of ZSM-5 addition). However, the cetane index of LCO suffers a big hit of about 6 points reduction. LCO yield is slightly reduced by 1%wt. The situation above is a typical dilemma faced by FCC that is supplying a diesel and propylene driven market. The diesel mode and propylene mode represent the opposite extreme ends of operation and depending on market forces, the operator swings from one mode to the other. Some operators might choose to operate in the middle of the two modes and not make either maximum propylene or maximum diesel in terms of quantity and quality. However, after revamp with MILOS, the operator can produce more propylene than the standalone FCC propylene mode and more LCO than the standalone FCC diesel mode, with even a higher cetane index, all achieved at the same time (Case 4 in Table 4). Table 4: Diesel revamp case study results Cas e number Case
#1
#2
#3
#4
#5
Base
FCC on ly Di esel mod e
FCC onl y Prop ylen e mode
FCC Di esel mo de + MILOS
FCC Pro pyle ne mode + MIL OS
Typ ical o pera ti ng co ndi ti ons in rece nt ye ars
Case d escrip ti on
Fre sh fe ed rate to FCC riser
t/d
Existing FC C re vamp Existing FCC re vamp Existin g unit op erated Exi sti ng u nit o pe ra te d with a ddi ti on o f wi th a ddi ti on o f to pro duce ma ximum to prod uce maxi mu m MIL OS tech nology. MIL OS tech nol ogy. LCO p ropyl en e FCC i s o per ati ng in FCC i s o pera ti ng in diesel mod e p rop yl en e mo de
100 00
10 000
10 000
10 000
1 000 0
N/A
N/A
N/A
2 50 0
25 00
Base
-19
+5
-30
+5
ZSM-5 a dditive in catal yst in ventory
C % wt
0
0
10
10
10
LCO Ce tane Index
-
2 5.9
31 .0
24.9
3 3.3
2 4.8
% wt
4.3
3.2
4.4
4.7
6.3
%wt
1.1
0.8
1.2
1.9
2.4
% wt
8.2
6.3
12.2
1 3.7
1 7.1
Propylene
%wt
5.2
4.0
8.2
9.8
1 1.9
Prop ane
% wt
3.1
2.3
4.0
4.0
5.1
%wt
1 0.2
8.6
13.7
1 6.0
1 7.8
i-Bu ta ne
% wt
3.2
2.8
3.8
5.5
5.6
n-Bu ta ne
% wt
1.2
0.9
1.5
1.7
2.0
Total C4 Ole fins i-Bu tylene s
% wt % wt
5.8 1.3
4.9 1.1
8.4 2.9
8.9 2.9
1 0.2 3.5
Li gh t cat cra ck gasol in e (C5 - 142 de gC ) % wt He avy cat crack g asolin e ( 142 - 22 1 d eg C) % wt
3 4.7 1 1.2
32 .8 12 .4
30.3 9.4
1 4.4 1 2.0
1 6.1 1 0.8
LCO (221 - 370 degC)
%wt
1 5.8
18 .9
14.7
1 9.3
1 5.2
HC O (3 70 - 4 25 de gC)
% wt
3.4
4.4
3.4
4.5
3.5
SO (4 25 d egC+)
% wt
4.5
6.7
4.4
7.5
4.5
Co ke
% wt
7.6
6.8
7.5
7.9
8.8
wt
2.9
2.4
2.7
1.4
1.8
Recycl e of ligh t cat crack gaso lin e to MILOS t/d FCC riser ou tlet temp erature
o
Ove rall yie ld C2 mi nus Ethyle ne Total C 3
Total C4
Gas oline /C ycle Oil ratio
It is clear from the results above that a revamp with MILOS (Case #4) on a conventional FCC unit brings the benefits of maximising propylene make, maximising LCO make and increasing LCO Cetane quality, all at the same time. This is achieved by allowing the FCC to operate in diesel mode to achieve the desired high LCO yield and high LCO Cetane. Directing the cooler MILOS spent cat (cooler relative to regen temperature) to the FCC riser also plays an important role in improving the LCO yield and quality. On the other hand, the MILOS riser is focusing on maximising propylene by cracking recycled light cat-cracked gasoline. With the same FCC and MILOS unit, we have studied a sensitivity case to see if the propylene make can be boosted further. In this study, the conventional FCCU is operated towards maximum propylene make instead of operated in the diesel mode (Table 4). It is clear that with this MILOS-FCC configuration (Case #5), the propylene yield can be further boosted if the operator is ready to accept the same level of LCO yield and quality as they are getting during the conventional FCC propylene mode operation. The propylene yield can be increased to almost 1.5 times compared to the standalone FCC propylene mode operation. MILOS vs. other process technologies The MILOS process has significant advantages compared to technologies licensed by Shell’s competitors (Tables 5 and 6). Most importantly, the operational flexibility offered by MILOS is a key advantage. As MILOS is integrated in an FCCU, it can even be reverted on the run to regular FCC operation if this is required. DCC and PetroFCC do not have this flexibility. A DCC/PetroFCC implementation requires many more significant changes to an existing FCC unit. Overall, the revamp of a conventional FCC unit to a Diesel MILOS-FCC is a very attractive option to refiners, especially for those units located in Europe or other regions where both diesel and propylene demand are expected to grow rapidly. The operating flexibility provided by MILOS helps set a refinery up for long term success with changing market environments. CONCLUSIONS The FCC process is one of the most important circulating fluidized bed processes. Through a few examples, some current challenges of high temperature erosion, corrosion and NOx emission in operating a high temperature CFB process like FCC have been highlighted. Although the FCC process has been in commercial operation for over 60 years, the technology continues to evolve. A new FCC technology, MILOS, for producing more light olefins and diesel in light of the market demand shift has been proposed and the new trend of co-processing bio-feedstock has been discussed. These new applications will present new challenges to the operation of FCC.
Table 5: Typical features of MILOS relative to Deep Catalytic Cracking technology
Table 6: Typical features of MILOS relative to PetroFCC technology
REFERENCES 1. Fluid Catalytic Cracking hits 50 year mark on the run, A.D. Reichle, OGJ, May 18, 1992, P. 41. 2. Evolutionary design changes mark FCC process, J. R. Murphy, OGJ, May 18, 1992, P. 49. 3. FCC is far from being a mature technology, A.A. Avidan, OGJ, May 18, 1992, P. 59. 4. “Fluid Catalytic Cracking”, in Handbook for fluidization and fluid-particle systems, Y. Chen, Wen-Ching Yang, ed. (2003) 5.
“Keeping FCC units on track, winning the operation race with an innovative cyclone technology”, Y. Chen, et al, 2010 NPRA meeting
6. 2010 NPRA FCC seminar 7. 2010 NPRA Q&A 8. Rosser, F.S., et al, “ Integrated view to understanding the FCC NOx puzzle “, 2004 AIChE Annual meeting. 9. Stevenson, S. A. et al., “Model of NOx emission from laboratory regeneration of spent fluid catalytic cracking catalyst”, Ind. Eng. Chem. Research, 2005, 44, 2966-2974. 10. Y. Chen and D. Brosten “A New Technology for Reducing NO X Emission from FCC Regenerators”, 2008 NPRA meeting 11. W. Mo, F. H. H. Khouw and G. A. Hadjigeorge, US2006/0231461A1 – “Method and Apparatus for making a Middle Distillate Product and Lower Olefins from a Hydrocarbon Feedstock”; US2006/0178546A1; US2006/0191820A1. 12. M. Nieskens, “MILOS – Shell’s ultimate flexible FCC technology in delivering diesel/propylene”, NPRA Annual Meeting, AM-08-54, March 9-11, 2008.
PUTTING STRUCTURES INTO FLUIDIZED BEDS - FROM CONCEPT TO INDUSTRIAL APPLICATIONS Qiang Zhang, Weizhong Qian, Guohua Luo, Yao Wang, Fei Wei* Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China Corresponding author. Fax: +86-10-62772051; E-mail:
[email protected] ABSTRACT Structures of particles, particle agglomerates, distributors, and internals have significantly influence on hydrodynamics and transfer behaviors of the dense gas-solid fluidized bed. For nanomaterial production, the particle surface and their agglomerated structures directly influence the fluidization behaviors; while for coal to chemical process, the distributors, internals play an important role in regime transient, and hydrodynamics. Carbon nanotubes mass production, coal to chemicals process. and fuel production were employed as examples to describe the concept of putting structures into fluidized bed, and then to put these structures into industrial applications. The asymmetric surface of solids plays a very important role in material functions, such as its reactivity, catalytic performance, adsorption, and transfer behaviors, which were rapidly developed with the growth of nanotechnology and information technology. Recently, nano-structured granular materials, which are consisted of various flakes, tubes, and rods, possess unique mechanical, electrical, optical, electrochemical, and thermal properties. Novel advanced functional nanomaterials for energy conversion and storage, environmental, catalysis, materials engineering, biology, drug, sensors, devices, information technology are highly concerned. Those particles usually belong to group C particles according Geldart classification, which were extremely fine powders and therefore the most cohesive particles. The fluidization of Group C particles was difficult to achieve, which may require the application of some external forces, such as mechanical agitation. Recently, with the fast development of nanotechnology, nanomaterials with tunable structures and extraordinary properties were widely investigated.
In fluidized bed, the particles become fluid-like dense flow. Thus, the heat transfer between fluid and solid phase is over thousand times of that on porous catalysts, which is an important step for high-efficiency solid processing. For fluidization
1
behavior, the symmetric spherical particles and cylinder fluidized beds always the first and simplest choice. While for the academic researches and industrial applications, it is quite important to let materials or catalysts well fluidize, and to control their fluidized states as well as transfer behaviors. Structured internals in a fluidized bed reactor have significantly influences on regime transition. Recently, they were also widely used in a fluid catalytic cracking (FCC) stripper. To get high yield of intermediate products, plug flow fluidized bed with high efficient heat transfer were highly required. Structured distributors and internals can also play an important role in control of the backmixing and heat transfer of the fluidized bed. When the fluidized bed reactor is coupled with special structures for various functions, excellent heat and mass transfer properties as well as the multifunctional properties were provided by the particles. This significantly enlarges fluidized bed reactor applications for chemical or material production, or even device manufacture. When putting those structures into fluidized bed, a series of scientific questions were proposed: 1) Can those structures be smoothly fluidized? 2) Can we use fluidized bed to produce structures in large scale? 3) Can we use structure to improve the efficiency of fluidized bed process? To answer these questions, we briefly review our recent progress on putting structures into fluidized bed reactor, and demonstrate several examples in mass production of carbon nanotubes (CNTs), high efficient catalytic reaction for coal to chemicals and particle sensors for detection of fluidization state. FLUIDIZATION SCIENCE: FLUIDIZATION
NANOPARTICLE AGGLOMERATION AND ITS
Nanomaterials with morphological features on the nanoscale (smaller than 100 nm in at least one dimension) have become the focus of science because of many interesting properties that make them attractive for various applications. Interactive forces between nanosized particles significantly increase with decreasing particle size, so nanoparticles coalesce easier than micron-sized particles. The properties of the primary particles determine the properties of the agglomerates that control the behavior of the two-phase flow in a fluidized bed. For instance, as shown in Fig. 1, the SiO2 aerogels with a size distribution of 7 to 16 nm form agglomerates, called multi-stage agglomerates (MSA), by several steps with different bonding mechanisms.[2] The cohesive force between nanoparticles and the gravity force on the agglomerates are effectively diminished by the porous MSA structure, thus, even with a fairly high bed expansion, these SiO2 agglomerates still form bubbleless fluidized beds that have a texture much closer to the particulate fluidization of a liquid–solids system and which obeys the Richardson–Zaki relation, as opposed to the aggregative bubbling regime in many gas–solids systems. In the region of Ug =
2
0.01–0.08 m/s, the average hydrodynamic diameters of the fluidized complex agglomerates are 230–331 μm. The structure of the agglomerates as well as their size, apparent weight and the interactive forces between them significantly influence the hydrodynamic behavior of agglomerating fluidization. Except nanoparticles, 1D nanomaterials, such as CNTs which consist of graphene sheet wrapped around to form cylindrical tubes, were selected as model to demonstrate their fluidization behavior. The dependences of bed expansion and pressure drop on gas velocity in a CNT nano-agglomerate fluidize bed is shown in Fig. 2.[3] A smooth and highly expanded fluidization was achieved, but a strong hysteresis exists in the CNT fluidization curve. On the defluidization branch, similar to Geldart-A particles, particulate fluidization, ABF, turbulent, and fast fluidizations can be successively observed. However, Uc and Use of the CNTs were relatively lower than that of Geldart-A particles, due to the weak interaction among the CNT agglomerates and their highly porous structure. This indicated that good fluidization behavior can be achieved only with strong turbulence of the fluidized bed, because of strong interaction between agglomerated CNTs.
4
Increasing gas velocity Decreasing gas velocity
H/H0
3
2
1 U 0.0
mf
Umb=0.038 =0.017
Uc=0.11
0.1
Ut=0.205
0.2
Ug (m/s)
Fig. 1. Nanoparticle agglomeration behavior on Geldart classification.
Fig. 2. Dependence of bed expansion and pressure drop on gas velocity in a CNT nano-agglomerate fluidized bed.[3]
Fig. 3. (a) Design of prototype PMS. (b) A PMS prototype.[4]
3
To identify a particle trace in a fluidized bed, an intelligent particle spy capable of detecting, transferring, and storing data, is proposed under the name of Particle Measurement Sensor (PMS) (Fig. 3).[4] The mobile PMS can be appended to the measurement system to sense movement information, including acceleration, velocity, distance between particles, and so on. A prototype 60-mm-dia PMS was tested and served as a particle spy in a fluidized bed delivering the in situ acceleration information it detects. With increasing superficial gas velocity in the fluidized bed, the acceleration felt by PMS was observed to increase. The variance of the signals, which reflect the fluctuation, increased at first and then reached a maximum at the gas velocity (Uc), which marks the transition from bubbling to turbulent fluidization. The probability density distribution peak can be divided into the emulsion phase peak and the bubble phase peak. The average acceleration of emulsion and bubble phase increased, while the variance of both phases reached a maximum at Uc, at the same time. However, the difference between the variances of two phases reached the maximum at Uc. The solids mixing behavior of nanoparticle in a fluidized bed were also investigated.[5] The axial and radial solids dispersion coefficients of nanoparticles were two orders of magnitude lower than those in fluid catalytic cracking (FCC) catalyst systems. The axial solids dispersion coefficient increased with increasing superficial gas velocities, and ranged between 9.1×10−4 and 2.6×10−3 m2/s. There was a step increase in the axial solids dispersion coefficient between the particulate fluidization regime and bubbling and turbulent fluidization regimes. As the superficial gas velocity increased, the radial solids dispersion coefficient increased gradually, from 1.2×10−4 to 4.5×10−4 m2/s. The much smaller Da and Dr, compared to regular fluidized systems, is mainly due to the reduced density difference between the fluidized particles and fluidizing medium. PARTICULOGICAL DESIGN: FLUIDIZED BED
NANOSTRUCTURE PRODUCTION IN A
The fluidization technique has been an efficient route for materials production. However, how to produce tunable nanostuctured materials in a fluidized bed is still an open area. We selected CNTs as a typical advanced nanostructured materials to describe their mass production via fluidization bed chemical vapor deposition (CVD).[1, 6] CNTs possess extremely high tensile strength, high modulus, large aspect ratio, low density, good chemical and environmental stability, and high thermal and electrical conductivity. It is a new type of carbon nanomaterials with high performance that are in demand for different potential applications, including both the large-volume applications (such as supercapacitor or battery electrodes, battery electronic additives, conductive, high-strength composites, etc.) and limited-volume applications (such as electronic devices, etc). Recently, CNTs have been used as fillers in advanced battery electronic additives, supercapacitor or battery electrodes,
4
and light high-strength composites at a scale of hundreds of tons. Mass production of CNTs with desired structure at a low cost is the first step.
Fig. 4. The pilot plant facility for MWCNT production.[1] Among various ways to synthesis CNTs, the CVD method has the advantages of mild operation, low cost, and controllable process, and is the most promising method for the mass production of CNTs. In CVD methods, the carbon source is deposited with the assistance of a catalyst at temperatures lower than 1200 oC. Tubular CNTs are deposited at the catalyst site. Fe, Co, Ni particles, always show high activity for CNT growth. With sustainable growth of CNTs, the as-grown tubes will cluster into agglomerated or aligned CNTs. The CNT aggregates were nanocarbon products in which CNTs are randomly entangled with each other, while the CNT arrays were nanocarbon products in which CNTs are nearly parallel to each other. The agglomerated CNTs are 3-dimensional network structure composed with large amounts of CNTs. They are very easy to be synthesized for the reason that CNTs are
5
prone to entangle with each other during the growth on powder catalysts.[7] If the wall number of CNTs decreased and became double/single-walled, then the as-obtained CNTs will be very flexible. S/DWCNTs were confined grown in the porous catalyst.[8] S/DWCNT growth needs not only a good dispersion of the active metal components on the catalyst support and a suitable large BET surface area, but also a proper catalyst structure. Layered double hydroxides (LDHs), also known as hydrotalcite-like materials, which are a class of Fig. 5. CNT-array double helices grown on two-dimensional nanostructured anionic clays Fe/Mg/Al LDH flakes. a) As-obtained Fe/Mg/Al whose structure is based on LDH flakes; b) a large number of CNT-array brucite (Mg(OH)2)-like layers, double helices; c) dextrorotatory CNT-array can be used as a novel double helices grown on LDH flakes; d,e) catalysts for SWCNT growth calcined LDH flakes and middle section of (c); with a huge BET surface area of 1289 m2/g.[9] Based on catalyst concept design, multiphase flow of CNTs, and process scale up rules, A pilot plant for producing high quality and purity MWCNTs was designed (Fig. 4).[1] Based on the fluidized bed technologies developed, we realized a commercial production of MWCNTs with a capacity of 560 tons per year, and SWNCTs with a capacity of 8 tons per year in the middle of 2009. The aligned structures of CNTs were always obtained via a bottom-up self-assemble process during thermal/floating CVD. The synchronous growth of a CNT forest induced by stress was found.[10] Based on CNT growth and agglomeration mechanism, various effective strategies for VACNT mass production have been proposed. It is commonly reported that aligned CNTs can be synthesized on a flat surface. However, the surface area of the flat substrate is often limited and its mobility is poor. Only 1 g/h VACNT arrays can be obtained with flat silica as substrate. If a substrate with a larger surface area is used, such as spheres, more VACNT
6
arrays can be produced. We have recently large scale synthesized VACNT arrays on spheres,[11] quartz fibers[12] and flakes.[13] Left or right handed CNT arrays were twisted into a double helix on a LDH flake through a self-organization process during growth by chemical vapor deposition (Fig. 5).[13a] The wall number and diameter of CNT in the double helix can be tuned by chemical precursor mediated process. [13b] To avoid the damage caused by the collisions among CNT arrays during the transport or fluidization process, a strategy of intercalating VACNTs into layered compounds and directly constructing a layered hybrid nanocomposite composed of alternate CNT films and inorganic sheets was proposed (Fig. 6).[14] This is a successful way for the mass production of VACNT arrays in a fluidized bed reactor. A 3.0 kg/hr VACNT array productivity was realized in a fluidized bed reactor with a diameter 500 mm. [15] The CNTs in the as-grown arrays were with good alignment, and can be easily purified by facile acid treatment.
Fig. 6. Illustration of the formation of hybrid composites by intercalating vertically aligned CNT films into layered inorganic compounds, showing stacked layers in the original vermiculite (left panel), catalyst particles adhering to the surface of the layers after impregnation (middle), and aligned CNTs between the layers after the CNT growth process (right); d) SEM image showing an enlarged view of a single interlayer with aligned CNTs and an interlayer distance of 20mm. e) SEM image showing CNT growth on both sides of a vermiculite layer. f) Transmission electron microscopy image of a multiwalled CNT.
7
For the horizontally aligned CNT formation, the gas flow, which makes the CNTs grow in a way similar with a flying kite, is efficient for super long (20 cm) CNT growth at a fast rate (80-90 μm/s).[16] The growth rate of CNT is very high, and the weight space velocity can reach 108 gCNT/gcath, which is millions of times of that of agglomerated CNTs on porous catalyst. The synthesis of HACNTs on movable or free substrates in a fluidized bed to obtain long CNTs is still an important issue. Based on particuological concept design, the agglomerated CNTs and aligned CNTs have been successfully mass produced in ton scale via fluidized bed process, and are available in the market. This will greatly promote the bulk applications of CNTs, and provide a sustainable route for the development of the novel advanced materials. INDUSTRIAL APPLICATION: MULTI-FUNCTIONAL ADVANCED CATALYTICAL FLUIDIZATION TECHNIQUE
STRUCTURE
FOR
The last level of structure in fluidized bed is in macro-scale of fluidized bed, such as the distributors, internals or bluffs in a fluidized bed reactor. Chemical looping concept for high efficient fossil fuel conversion by a family of configurations with different reactors or regions of fluidized bed have also been explored. [17] Many applications of internals in fluidized bed have been employed for acrylonitrile, aniline, vinyl acetate and vinyl chloride monomer synthesis,[18] and fluid menthol to propylene. The influence of internals on hydrodynamics and mixing in fluidized bed and related reactor performance will also be presented. Due to the worldwide crude oil shortage and the rapidly increasing demands for light olefins, the methanol to olefins (MTO) process and dimethyl ether to olefins (DTO) process were selected as the alternative ways for the production of light olefins. Because of the strongly exothermicity of MTO/DTO reactions and fast deactivation of SAPO-34 zeolite catalyst, a fluidized bed was the preferred reactor for industrial application.[20] Consequently, the MTO/DTO catalysts needed to be prepared for facile fluidization. Herein, a hierarchical SAPO-34 zeolite with high crystallinity and excellent hydrothermal stability was directly synthesized in the nanoscale confined environment provided by the natural layered material kaolin (Fig. 7). [19] The hierarchical sample showed a much higher conversion than the conventional zeolite. The overall olefin selectivity was as much as 96.7%, which was 4–7% higher than that of the conventional zeolite. The presence of the mesopore structure shortened the diffusion path, thus, the primary products could easily diffuse out of the zeolites and secondary reactions were avoided. Therefore, higher propylene selectivity and overall olefin selectivity were obtained. This expected to be a facile and economically
8
feasible way to prepare more effective catalyst for fluidized MTO/DTO process. A fluidized bed methanol to propylene (FMTP) industrial technology was developed. The industrial practice of fluidized bed for FMTP process at Huainan with a 470 hours of continuous operation at full capacity were carried out, and high yield of propylene was reached (Fig. 7c).
Fig. 7. a) As synthesized hierarchical SAPO-34 zeolite; b)DME conversions and overall olefin selectivities (Conversion on hierarchical (■) and conventional (□) SAPO-34; selectivity on hierarchical (▲) and conventional (Δ) SAPO-34.[19] c) 300kT/a multistage fluidized bed FMTP unit Polyvinylchloride (PVC) is second largest general plastics in the world, the very low space velocity makes gas-phase catalytic hydro-chlorination of C2H2 on HgCl2/activated carbon (AC) catalyst only can be carried out in packed bed at high conversion. However, if a multistage fluidized bed (MSFB) is applied into this process, a high conversion of C2H2 at 130-140 °C at a high space velocity of C2H2, while maintaining high selectivity to vinyl chloride monomer (VCM), could be obtained by efficiently inhibiting the back-mixing of gases between stages. The new catalyst with a coconut-shell-type AC as the support shows much higher mechanical strength for stable fluidization with a proper pore structure for the dispersion of HgCl2 and a high thermal stability, as compared to a catalyst with coal-based AC as the support in
9
conventional packed bed reactors. The catalyst lifetime estimated by simulation and a rapid sublimation experiment fits in well with the data from the pilot plant test. These results suggest that the combination of a MSFB and the new catalyst described here is a novel technology for producing VCM on a large scale at low cost. The scale-up of MSFB through a series of small hot model, 3000 ton/a pilot-plant and 100 kT/a industrial plant was demonstrated and reactor modeling of the scale up of the MSFB was carried out, as shown in Fig. 8.
Fig. 8. Top: Internal structured fluidized bed reaction; Bottom: Multistage fluidized bed reactor for VCM production scale-up process: Left: small hot model test unit; Middle: 3 kT/a pilot plant; Right: 100kT/a multistage fluidized bed CVM unit The flow structure of gas-solid can significantly regulated the flow directions. The gas-solid downward flow (downer) reactor can reduce axial gas and solids backmixing by 30 times in comparison with upward flow riser reactor. Downer is therefore acknowledged as a novel multiphase flow reactor with great potential in high-severity operated processes, such as the short contact time reactions with the intermediates as the desired products.[21] Compared with the riser reactor with the same feeds and catalysts, the LPG and propylene yield increased by 8.15 and 4.30 wt%, respectively. The gasoline octane number likewise reached 94.8 with 28 wt% olefin content. However, dry gas is significantly suppressed, and the coke has little
10
change in yield even with the increased catalyst to oil ratio. With some gasoline recycling, the LPG and propylene yield increased by 11.45 and 5.06 wt %, respectively. The olefin content in gasoline significantly decreased to 22 wt %; the high octane number (95.4) is maintained. The computational fluid dynamics (CFD) coupled with a 6-lump kinetic model is also applied to simulate the FCC process for the industrial trials. The yield of propylene and butylene and the temperature profile along the axis direction demonstrated consistency between the simulation results and the experimental data. The axial solid mixing mechanisms in gas-solids cocurrent upflow and downflow circulating fluidized bed systems revealed that among the many influencing factors, flow direction has the most profound influence on the axial solids mixing. When the flow is in the direction of gravity (downflow in the downer), axial solids dispersion is very small and the flow pattern approaches plug flow; when the flow is against gravity (upflow in the riser), axial solids dispersion is significantly larger and the flow pattern deviates significantly from plug flow. Solid mixing is found to be mainly due to the dispersion of dispersed particles in the downer; however, in the riser, both the dispersion of dispersed particles and particle clusters were co-existed in the riser.. In both the riser and the downer, the dispersion of the dispersed particles is very small, indicating that dispersed particles pass through the system in a near plug flow pattern. Dispersion due to particle clusters in the riser, on the other hand, is very significant, contributing to the large axial solids backmixing and the bimodal solids residence time distribution in the riser. CONCLUSIONS Development of fluidization technique has spanned several decades, attesting to the importance of this technique in chemical engineering, thermal engineering, and metallurgy. When nanostructured granular materials were used for fluidization, some kinds of nanoparticles (such as SiO2, carbon nanotubes) are agglomerated into multi-stage agglomerates for stable fluidization. The structure of the agglomerates as well as their size, apparent weight and the interactive forces between them significantly influence the hydrodynamic behavior of agglomerating fluidization. The concept of a particle spy was tested in the form of an encapsulated prototype PMS was proposed to in situ detect the phase structure of fluidized bed. The axial and radial solids dispersion coefficients were both two orders of magnitude lower than those in FCC systems. Based on the scientific understanding of nanomaterials fluidization, the fluidization process was successfully employed for nanomaterial (both agglomerated and aligned CNT) production. Multi-functional nanostructures, such as hierarchical SAPO-34 zeolite, were employed as advanced catlaysts for catalytic fluidized bed route for methanol to olefin process. Novel fluidized bed reactor, such as the novel riser-downer coupling reactor for the FCC process and a two-stage fluidized-bed for gas-phase catalytic hydrochlorination of acetylene, were
11
also proposed. More efforts should be pain on new scientific challenges for nanomaterials fluidization and novel industrial process for nanomaterial production and (nano)structure enhanced fluidization technology for advanced materials, novel chemical production, and energy conversion. REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
F. Wei, et al., Powder Technol 2008, 183, 10-20. Y. Wang, et al., Powder Technol 2002, 124, 152-159. H. Yu, et al., AIChE J 2006, 52, 4110-4123. Q. Zhang, et al., Particuology 2009, 7, 175-182. a) C. Huang, et al., Powder Technol 2006, 161, 48-52; b) C. Huang, et al., Powder Technol 2008, 182, 334-341. Y. Wang, et al., Chem Phys Lett 2002, 364, 568-572. Y. Hao, et al., Carbon 2003, 41, 2855-2863. Y. Liu, et al., Carbon 2008, 46, 1860-1868. a) M. Q. Zhao, et al., Carbon 2010, 48, 3260-3270; b) M. Q. Zhao, et al., Adv Funct Mater 2010, 20, 677-685. a) Q. Zhang, et al., J Phys Chem C 2007, 111, 14638-14643; b) J. Q. Huang, et al., Nanotechnology 2008, 19, 435602; c) J. Q. Huang, et al., Nanoscale 2010, 2, 1401-1404. Q. Zhang, et al., Carbon 2008, 46, 1152-1158. a) Q. Zhang, et al., Mater Chem Phys 2008, 107, 317-321; b) K. Zhou, et al., Nanoscale Res Lett 2010, 5, 1555-1560. a) Q. Zhang, et al., Angew Chem Int Ed 2010, 49, 3642-3645; b) M. Q. Zhao, et al., J Am Chem Soc 2010, 132, 14739-14741. Q. Zhang, et al., Adv Mater 2009, 21, 2876-2880. a) Q. Zhang, et al., Carbon 2009, 47, 2600-2610; b) Q. Zhang, et al., Carbon 2010, 48, 1196-1209. a) Q. Wen, et al., Adv Mater 2010, 22, 1867-1871; b) Q. Wen, et al., Chem Mater 2010, 22, 1294-1296. L. S. Fan, Chemical Looping Technology for Fossil Energy Conversions, John Wiley, 2010. X. B. Wei, et al., Ind Eng Chem Res 2009, 48, 128-133. J. Zhu, et al., Chem Commun 2009, 3282-3284. H. Q. Zhou, et al., Appl Catal A-gen 2008, 348, 135-141. a) R. S. Deng, et al., Ind Eng Chem Res 2005, 44, 1446-1453; b) H. Liu, et al., Ind Eng Chem Res 2005, 44, 733-741; c) F. Liu, et al., Ind Eng Chem Res 2008, 47, 8582-8587; d) Y. Cheng, et al., Powder Technol 2008, 183, 364-384.
12
A MODELING STUDY OF GAS STREAMING IN A DEEP FLUIDIZED BED OF GELDART GROUP A PARTICLES Shayan Karimipour, Todd Pugsley1 Department of Chemical Engineering, The University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, Canada S7N 5A9 1
Corresponding author (
[email protected]), Tel.: (+1-306) 966-4761, Fax: (+1-306) 966-5205
ABSTRACT Gas streaming has been modeled in a deep fluidized bed of 5 m depth and 0.3 m inside diameter. The model results suggest that the lower pressure drop of the stream zone compared to the remainder of the bed is the reason for severe streaming flow in deep beds. The effects of different parameters such as bed depth, gas velocity and particle size on the severity of the streaming flow are also evaluated with the model. INTRODUCTION Several studies in the past decade have demonstrated that in sufficiently deep fluidized beds (i.e. beds approaching a depth of 1 m or greater) of Geldart Group A particles (1), gas bypassing may occur by increasing the superficial gas velocity (2-6). When this phenomenon occurs, the fluidizing gas bypasses the bed in the form of streams of gas, leaving a large fraction of the bed unfluidized or poorly fluidized. The concept of gas streaming was first reported in the literature by Wells (2). He performed several experiments in large scale units with up to 2.5 m diameter and 5 m bed depth and observed streaming flow under conditions that were expected to lead to operation in the bubbling regime. He attributed the streaming phenomenon to gas compression, caused by the pressure head of the deep solids bed over the distributor. Karri et al. (3) investigated the formation of streaming flow in a column of 0.3 m inner diameter and 4.9 m height. They found that for all combinations of operating conditions investigated, the addition of a sufficient amount of fines to the bed of Geldart Group A particles was able to delay the streaming. In another work, Issangya et al. (4) used several pressure transducers mounted at various radial positions to detect the presence of streaming flow. Recently, Karimipour and Pugsley (5) have performed a systematic study on the streaming flow in deep beds of FCC particles. They discussed the effects of streaming flow on the pressure fluctuations time series measured in the fluidized bed for different combinations of bed depth, gas velocity, particle size, and distributor design. They concluded that streaming flow does not appear suddenly, but emerges gradually in the bed by increasing the bed depth. They found that although changing parameters such as superficial gas velocity and/or fines content can
1
reduce the severity of the streaming flow, streaming is the dominant phase for deep fluidized beds operating at gas velocities where a fully bubbling bed regime would normally be anticipated. The only mathematical model to predict the onset of gas streaming is that of Wells (2). Wells (2) concluded that when the ratio of the density at minimum fluidization to the density of the emulsion phase becomes less than a critical value for a given bed depth, streaming occurs. The model of Wells (2) was tremendously valuable for improving the understanding of streaming, but it was not a direct function of operating conditions such as bed depth and gas velocity. The objective of the present work is to develop a simple phenomenological model for the streaming flow and to use the model to evaluate the effect of bed depth, gas velocity, and particle size. MODEL DEVELOPMENT Based on our finding from a separate experimental campaign (e.g. 5), the deep fluidized bed is divided into two adjacent regions in which the smaller region is occupied by the stream flow and the other region is assumed to be at minimum fluidization conditions. It is assumed that by increasing the superficial velocity the gas in excess of that required for minimum fluidization is directed into the stream zone. Also based on our experimental observations, the cross section of the gas stream is assumed to be circular and its diameter to be less than one fourth of the bed diameter. The stream therefore forms a vertical cylinder of constant diameter along the fluidized bed. A small lateral zone above the distributor is reported to be better fluidized (2) and gas and particles from other parts of the distributor find their way towards the stream and move upward through the stream. As such, particles can be assumed to move upward only in the stream and after discharging at the surface of the bed slowly return to the bottom through the non-streaming region. Similar to the acceleration zone of a circulating fluidized bed riser (6, 7), the stream can be modeled by a force balance over a single particle inside the stream:
ρ pV p
2
⎞ 1 ⎛u = ρ g ⎜ st − υ p ⎟ Ap CD − ( ρ p − ρ g )V p g ⎟ 2 ⎜⎝ ε g dt ⎠
dυ p
(1)
Assuming the particles as spheres of constant diameter, and incorporating Eq. 2 from the derivative theory, the force balance equation can be re-written as Eq. 3:
dυ p dt
=υp
dυ p
(2)
dz 2
⎞ 3ρ g CD ⎛ ust g = (ρ p − ρg ) ⎜⎜ − υ p ⎟⎟ − dz 4d p ρ pυ p ⎝ ε g ⎠ ρ pυ p
dυ p
(3)
We have estimated the drag coefficient, CD, in Eq. 3 based on the correlation of Mostoufi and Chaouki (8). The porosity in these equations is calculated from the solids mass balance equation as follows: Gp = ρ p (1 − ε g )υ p (4) The initial value of the particle velocity at the bottom of the stream is obtained from the solids mass balance. Thus, Eq. 3 will be solved subject to the following initial condition:
υp
z =0
=
Gp
(5)
ρ p (1 − ε mf )
2
Once the axial profile of particle velocity in the stream is determined from Eq. 3, the corresponding solids holdup can be calculated from ε p = 1− ε g (6) The axial profile of the pressure drop along the stream can be determined from the momentum balance over the stream. The momentum balance could be expressed as follows:
dp ⎛ dp ⎞ ⎛ dp ⎞ ⎛ dp ⎞ = ⎜ ⎟ +⎜ ⎟ +⎜ ⎟ dz ⎝ dz ⎠ head ⎝ dz ⎠ acceleration ⎝ dz ⎠ friction
(7)
where
⎛ dp ⎞ = ρ pε p g + ρ g ε g g ⎜ ⎟ ⎝ dz ⎠ head
(8)
dυ p u d ⎛ ust ⎛ dp ⎞ = ρ pε pυ p + ρ g ε g st ⎜ ⎜ ⎟ dz ε g dz ⎜⎝ ε g ⎝ dz ⎠ acceleration
⎞ ⎟⎟ ⎠
(9)
The pressure drop caused by friction includes two sources, i.e., gas-wall and particle-wall frictions:
⎛ dp ⎞ ⎛ dp ⎞ ⎛ dp ⎞ =⎜ ⎟ +⎜ ⎟ ⎜ ⎟ ⎝ dz ⎠ friction ⎝ dz ⎠ gas − wall ⎝ dz ⎠ particl − wall
(10)
These pressure losses are defined by the Fanning equation as
ust2 1 ⎛ dp ⎞ ρ = f g g ⎜ ⎟ εg 2d st ⎝ dz ⎠ gas − wall 1 ⎛ dp ⎞ ρ pε pυ p2 = fp ⎜ ⎟ 2d st ⎝ dz ⎠ particle − wall
(11) (12)
Since gas-wall and particle-wall frictions form a minor portion of the overall pressure drop, type of the friction factor does not have a major effect on the results. Here, the gas-wall friction factor, fg, has been calculated from the Blasius formula (9)
fp =
0.316 , Reg0.25
Reg ≤ 105
(13)
and the particle-wall friction factor has been estimated using the correlation of Kanno and Saito (10)
fp =
0.057 1/ 2 ( gd st ) 2υ p
(14)
The wall in our case corresponds to the “wall” of the cylindrical stream in the bed. In order to solve these equations, the solid circulation rate (Gp) is needed as an input. Since the system is not a real circulating fluidized bed, a pseudo-circulating rate may be calculated from the correlations proposed for the internally circulating fluidized bed. An internally circulating fluidized bed resembles the current case in that both of the systems involve flow of gas and solids between a fluidized bed at minimum fluidization conditions and a dilute bed (a riser in an internally circulating fluidized bed and a stream in the current case). The net rate of the particle exchange between two zones along the fluidized bed is considered to be trivial. The correlation of Jeon et al. (11) has been used for this purpose:
⎛u ΔPor = 5.327 × 10 ⎜ st ⎜u ⎝ mf 3
⎞ ⎟⎟ ⎠
0.795
⎛ dp ⎞ ⎜ ⎟ ⎝ d or ⎠
0.728
(15)
3
Gp = Cdis
Sor 2 ρ p (1 − ε mf )ΔPor Sst
(16)
In the above equations, the orifice refers to that point at the bottom of the bed that allows for the exchange of gas and particles between the stream and non-streaming zones. For the pressure drop through the none-streaming zone which is considered to be at minimum fluidization conditions, the pressure drop is assumed to be due to the mass of the particle bed:
dp = ρ p g (1 − ε mf ) dz
(17)
RESULTS AND DISCUSSIONS The model predictions of pressure drop along the fluidized bed for a bed depth of 5 m are provided in Fig. 1. As can be seen in the figure, the model predicts a lower pressure drop immediately above the distributor for the non-stream zone compared to the case of the stream zone. Therefore, streams do not form in this region. However, the stream pressure drop decreases dramatically with increasing distance from the distributor, which makes the streams a preferable pathway for the gas. The higher pressure drop of the stream immediately above the distributor is due to the much higher flow of gas and particles in the stream compared to the non-stream zone. Similar trend of pressure drop has been reported for the bottom of FCC risers (7, 8). As illustrated in the figure, as the upper surface of the bed is approached, the difference between the pressure drop of the streaming and non-streaming zones decreases. The result of this would be that preferential flow of gas through the stream would be diminished, allowing gas to diffuse into other parts of the bed and provide more uniform fluidization at upper regions. This is consistent with visual observations from experiments, which showed improved fluidization at the upper regions of the bed. 6
Axial Position (m)
5
Non-Stream Zone Stream Zone
4 3 2 1 0 0
5000
10000
15000
20000
25000
30000
35000
Pressure Drop (Pa) Figure 1. Axial profile of the pressure drop in the fluidized bed, Bed depth = 5 m, Superficial gas velocity = 0.2 m/s, Particle diameter = 84 microns
4
Effect of Bed Depth Fig. 2 illustrates the differences between the pressure drops of stream and non-stream pathways at the bottom of the fluidized bed for different bed depths. As can be seen, the difference in the pressure drops of the two zones, which is considered to be the motivation for the formation and stability of the streams, increases with increasing bed depth. Experimentally we found that the onset of streaming flow occurred gradually in the fluidized bed as bed depth was increased. According to the model results, this can be attributed to the gradual increase of the difference in pressure drop between the streaming and non-streaming zones. This difference is probably low enough in shallow beds that the gas is able to fluidize all of the cross section and prevents the formation or permanence of streaming flow. Effect of Gas Velocity
Difference between the pressure drop of Stream and Non-Stream zones at the bottom of the bed (Pa)
Fig. 3 provides the axial profile of the pressure drop in the fluidized bed for different superficial gas velocities. As Fig. 3 illustrates, two changes occur in the fluidized bed by increasing the gas velocity. Firstly, the difference between the pressure drops of the streaming and non-streaming zones decreases and secondly, the region expands above the distributor where streaming is not preferred or present. The positive influence of increasing the gas velocity on diminishing the streaming flow has been emphasized in all of the previous experimental works in the literature (2-6). As the figure indicates, at gas velocities higher than 1 m/s streaming flow is not preferred anywhere in the fluidized bed and uniform fluidization would be possible throughout the bed. 20000
16000
12000
8000
4000
0 0
1
2
3
Bed Depth (m)
4
5
6
Figure 2. Difference between the pressure drop of Stream and Non-Stream zones at the bottom of the bed for different bed depths, Superficial gas velocity = 0.2 m/s, Particle diameter = 84 microns
5
Axial Position (m)
6 5
Non-Stream Zone Stream Zone, U0 = 0.2 (m/s)
4
Stream Zone, U0 = 0.4 (m/s) Stream Zone, U0 = 0.6 (m/s)
3
Stream Zone, U0 = 0.8 (m/s) Stream Zone, U0 = 1 (m/s) Stream Zone, U0 = 1.2 (m/s)
2 1 0 0
10000
20000
30000
40000
50000
Pressure Drop (Pa) Figure 3. Axial profile of the pressure drop in the fluidized bed for different superficial gas velocities, Bed depth = 5 m, Particle diameter = 84 microns Effect of Particle Size Fig. 4 illustrates the axial profile of the pressure drop in the fluidized bed for different particle sizes and a constant particle density of 1400 kg/m3. As can be seen, the pressure drop in the stream increases by increasing the particle size. Thus, its preference as an alternative pathway with lower pressure drop for gas decreases gradually. According to the literature, streaming flow has only been reported for Geldart Group A particles; it does not appear to exist for coarser Geldart B particles (2-6). Thus, as the model predicts, the fluidized bed of these particles display uniform fluidization. The results show that the model is able to predict this directional effect of increasing particle size. Effect of Stream Size The effect of the size of stream zone (i.e. stream diameter) on the axial profile of pressure drop has also been investigated (results not shown due to space constraints). Our model predicts that decreasing the stream size from 1/4 to 1/8 of the bed diameter reduces the preference of streaming as an alternative pathway for gas flow. CONCLUSIONS In the present work, gas streaming flow has been modeled in a deep fluidized bed of 5 m bed depth and 0.3 m diameter. The trend of the model predictions have been qualitatively compared and validated with the experimental findings. The model is based on the assumption that the stream already exists in the bed. The initiation of streaming flow has been discussed in our previous work (6). According to that work, the potential for streaming always exists in a fluidized bed. The results of the present work suggest that what causes a severe streaming flow with increasing bed depth is probably the gradual increase of the difference between pressure drop of two zones: that smaller portion of the bed where streaming becomes preferred and the remainder of the bed at minimum fluidization. Our model results show that increasing the bed
6
depth favors the streaming flow, while increasing the gas velocity increases the uniformity of the bed and decreases the streaming severity. Streaming flow was found to be less severe for larger particle sizes. All of these findings are in conformity with experimental investigations reported previously in the literature. 6
Non-Stream Zone Stream Zone, Particle Ave. Diam. = 42 microns
Axial Position (m)
5
Stream Zone, Particle Ave. Diam. = 84 microns Stream Zone, Particle Ave. Diam. = 168 microns
4
Stream Zone, Particle Ave. Diam. = 252 microns
3 2 1 0 0
5000
10000
15000
20000
25000
30000
35000
Pressure Drop (Pa) Figure 4. Axial profile of the pressure drop in the fluidized bed for different particle sizes, Bed depth = 5 m, Superficial gas velocity = 0.2 m/s NOTATION Ap Ar
cross-sectional area of particle (m2) Archimedes number ( d 3p ρ g ( ρ p − ρ g ) g / μ 2 )
Cdis CD dp dst D f fp fg g Gp p ∆Por Reg Sor Sst t umf ust vp
gas discharge coefficient effective drag coefficient particle diameter (m) stream diameter (m) fluidized bed diameter (m) drag coefficient correction factor solid-wall friction factor gas-wall friction factor acceleration of gravity (m/s2) solids flux (kg/m2s) pressure (Pa) orifice pressure drop (Pa) gas Reynolds number (D U0 ρg/μg) orifices cross sectional area (m2) stream cross sectional area (m2) time (s) minimum fluidization velocity (m/s) gas velocity in stream (m/s) particle velocity (m/s)
7
Vp z
particle volume (m3) fluidized bed height above distributor (m)
Greek Letters εg gas voidage gas voidage εp εmf voidage at minimum fluidization ρg gas density (kg/m3) ρp particle density (kg/m3) μ gas viscosity (Pa∙s) REFERENCES [1] D. Geldart, Types of gas fluidization, Powder Technology 7 (1973) 285-292 [2] J. Wells, Streaming flow in large scale fluidization, Paper presented at the AIChE annual meeting, Particle Technology Forum, Reno, Nevada, USA, 2001. [3] S. B. R. Karri, A. S. Issangya, M. Knowlton, Gas bypassing in deep fluidized beds, In Fluidization XI, U. Arena, R. Chirone, M. Miccio, P. Salatino, eds., Ischia (Naples), Italy, 9-14 May 2004. [4] A. Issangya, T. Knowlton, S. B. R. Karri, Detection of gas bypassing due to jet streaming in deep fluidized beds of group A particles, In Fluidization XII, F. Berruti, X. Bi, T. Pugsley, eds., Vancouver, British Columbia, Canada, 13-17 May 2007. [5] S. Karimipour, T. Pugsley, Study of gas streaming in a deep fluidized bed containing Geldart’ Group A particles, Chemical Engineering Science 65 (2010) 3508–3517. [6] T. S. Pugsley, F. Berruti A predictive hydrodynamic model for circulating fluidized bed risers, Powder Technology 89 (1996) 57-69 [7] Sh. Karimipour, N. Mostoufi, R. Sotudeh-Gharebagh, Modeling the hydrodynamics of downers by cluster-based approach, Ind. Eng. Chem. Res. 45 (2006) 7204-7209 [8] N. Mostoufi, J. Chaouki, Prediction of effective drag coefficient in fluidized beds, Chemical Engineering Science 54 (1999) 851-858 [9] R. W. Fox, A. T. McDonald, P. J. Pritchard, Introduction to fluid mechanics, 6th ed.; Wiley: New York, 2003 [10] H. Kanno, S. Saito, Pneumatic conveying of solid through straight pipes, J. Chem. Eng. Jpn. 2 (1969) 211-217 [11] J. H. Jeon, S. D. Kima, S. J. Kim, Y. Kang, Solid circulation and gas bypassing characteristics in a square internally circulating fluidized bed with draft tube, Chemical Engineering and Processing 47 (2008) 2351-2360
8
EFFECTS OF PARTICLE PROPERTIES ON CLUSTER CHARACTERISTICS IN A 2-D CFB RISER Jing Xu and Jesse Zhu* Particle Technology Research Centre, Department of Chemical & Biochemical Engineering, The University of Western Ontario, London, Ontario, Canada N6A 5B9 *Corresponding author. Tel.: +1 519 661 3807; fax: +1 519 850 2441; email address:
[email protected] (J. Zhu)
ABSTRACT The characteristic of particle clusters was studied in a 2-D circulating fluidized bed riser by using a digital image system and optical fiber probes. A new parameter, cluster number fraction, was proposed to characterize the clusters. The results indicated the smaller and lighter particles have higher potential to aggregate, while the effects of particle sphericity were less significant than those of particle density and size.
INTRODUCTION The analysis of the micro structure of the gas-solid two phases is considered complex and remains unclear. The micro flow structure is usually identified as the behavior of clusters, which are defined as dense clouds of particles having significantly more particles per unit volume than the surrounding dilute regions (1). Since the particles in CFB tend to aggregate and form clusters, which flow quite differently from a single particle, the gas-solid flow in CFBs is often characterized by the existence of particle aggregates or clusters (1, 2). Most of the researchers (1-5) used the visualization technique and intrusive probe to obtain the micro flow behaviors. However, the visualization technique they used was restricted to dilute flow. In addition, almost all of the former studies appear to conduct the investigation within one type of particles, and very few references can be found clarifying the effects of particle properties on the cluster characteristics. Since previous studies (6, 7) have revealed that a strong dependence of the particle properties on the solids concentration and particle velocity, the particle properties, including particle density, size and sphericity may play an non-negligible role in affecting the cluster properties. In this study, various types of particles with typical different density, size or sphericity were taken into consideration. Moreover, very few studies have been carried out to investigate the cluster properties by combining the visualization with intrusive probes. In this study, we applied high-speed video camera and optic fiber probes in a 2-D circulating fluidized bed, to connect the two different measurements and make both function better. This study also realized the visualization technique to be effectively used under high solids concentration (Gs is up to 200 kg/m2s).
EXPERIMENTAL APPARATUS All experiments were carried out in a rectangular circulating fluidized bed which is illustrated schematically in Figure 1. The riser is a rectangular column with 7.6 m height and 19 mm × 114 mm (0.75 in × 4.5 in) cross-section. The visualization system was self-designed and set up. To eliminate the entrance and exit effects, the system was mounted focusing on the upper fully developed region, where Z = 5m. The system consists of a light source, a high-speed video camera and programs for digital image analysis (Figure 2). The speed of the highspeed video camera is up to 16,000 fps. A MATLAB program was used to allow the images to be analyzed frame by frame. The probe chosen to measure the local solids concentration in this study was optical fiber probe, which is capable of measuring the solids concentration with a reliable pre-calibration (1, 8, 9). The solids concentrations were measured at 9 lateral positions and 6 axial levels along the entire riser. The instantaneous solids concentration was analyzed by a selfdeveloped FORTRAN program which introduces sensitivity analysis to identify the clusters from solid concentration signals.
Images Monitor
Face Wall
Diffuser Panel
Video Camera
Images Analysis
Lamp CFB Riser
Figure 1 Circulating fluidized bed unit and schematic of riser cross section
Side Wall
Figure 2 Sketch of Visualization system
The properties of the particles used in this study are listed in Table 1. The materials were selected in order to investigate the effects of particle size, density and sphericity, respectively. In this study, the solids concentration measurement was conducted under the operating conditions of Ug = 3 ~ 8 m/s, and Gs = 50 ~ 200 kg/m2·s. The high-speed videos were recorded simultaneously under the corresponding operating conditions at 2000 fps.
Table 1 Properties of particles used Particles
FCC
Glassbeads #1
Glassbeads #2
Glassbeads #3
Sand
Particle Sauter mean diameter, μm
67
76
134
288
138
1877
2453
2403
2498
2467
1125
1434
1421
1475
1453
Sphericity, -
0.95
~1
~1
~1
0.65~0.75
Particle terminal velocity, m/s
0.26
0.42
1.19
3.73
N/A
Particle density, 3 kg/m Bulk density, kg/m
3
RESULTS AND DISCUSSION Observation and Description of Clusters Generally, the solids flowing in the riser were observed to distribute dense in wall regions and dilute in the center. The particles were moving faster in the column center and slower towards the wall. Nearly no appearance of solids buildup or fallingdown was observed on either of the face walls. Cluster Forms Figure 3 shows an image sequence with a solid cluster moving upwards in the center region of the riser. The frames (window size of 46.6 mm × 13.5 mm) were taken at a recording rate of 2000 fps and an exposure time of 500 μs. The movement and development of the cluster can be recognized as the darker structure which is visualized directly within the observed area. The sequence of images clearly show that a U-shape cluster is formed with a round nose facing downward and a core on the “nose tip”, where it is much darker than the peripheral area. Initially, the cluster is moving as a longish core with blurred boundary. Continuously, the core is stretched to be longer and crotched. Since the cluster is moving slower, the dispersed particles passing by are blocked and adhere to the cluster. Therefore, the U-shape outline is clearer and the core is darker and bigger with the aggregating of particles. In this study, the U-shape cluster is the most common form of clusters observed in the riser center. As observed, the clusters exist as U-shape, longish strand and other cluster structures. The pictures of the clusters shown in Figure 4 are for different particles under several typical operating conditions. As mentioned above, the U-shape is the most common structure viewed in the riser center. Some of them have a core with particular high solids concentration at the nose tip, while the others don’t. The opening of the U-shape cluster faces upward or downward with a size range from 5 cm to 30 cm, most are smaller than 20 cm. The longish strand cluster is the cluster form also often observed in the core region of the riser. The size of the strand cluster is approximately 10 mm in width and 10 cm to 50 cm in length. The motion of this form of cluster is slower than other particles in the vicinity. Occasionally, some clusters in small size (1-2 cm), and in shapes of circle, short stripe (as shown in Figure 4(c)) or crescent are observed. They are always seen to flow as fast as the adjacent non-aggregative region. In addition, the shape of these types of clusters is nearly constant during the motion in the observation window. Besides the forms of individual clusters aforementioned, clusters in different structures may adhere
together to form floc-like clusters. The most common combination seen is between the U-shape and the strand clusters, typically shown in Figure 4(d), (e) and (f).
Figure 3 Sequence of 12 images taken in the core region with a U-shape cluster
(a)
(b)
(c)
(d)
(e)
(f)
Figure 4 Cluster structures observed in the riser center under different operating conditions: (a) Glassbead #3, Gs = 100 kg/m2·s, Ug = 5 m/s; (b) Glassbead #3, Gs = 100 kg/m2·s, Ug = 8 m/s; (c) Sand, Gs = 100 kg/m2·s, Ug = 5 m/s; (d) Glassbead #1, Gs = 100 kg/m2·s, Ug = 5 m/s; (e) Glassbead #1, Gs = 150 kg/m2·s, Ug = 5 m/s; (f) FCC, Gs = 100 kg/m2·s, Ug = 5 m/s
Effects of Particle Properties on Cluster Forms Five different types of particles were employed in this study. The effects of particle density, size and sphericity on the clusters behaviors were compared. All the images in Figure 4 are in the size of 256 pixel ×880 pixel with a scale of 1.99×10 -4 m/pixel. In Figure 4(a) and (b), Glassbead #3 with a mean size of 288 μm is seen to form clusters typically in U or strand shape and with a large cluster size. The Sand particles having particle diameter 134 μm and approximate 0.7 sphericity are observed to have the flow structure indistinguishable from the Glassbead #3, while small size clusters can be found occasionally. With further decreasing of particle size to 76 μm for Glassbead #1, the form of clusters turns to be more complicated and interconnective. It is seen that the clusters are smaller in size than that of the larger particles, and tend to connect together with each other. Therefore, the individual cluster is difficult to be identified in small size particle flow. Compared with the flow of Glassbead #1 (2453 kg/m3), the structure of clusters is more intricate and irregular when lighter FCC particles (1877 kg/m3) were used, as shown in Figure 4(f). The
area with darker color, which is occupied by the clusters, is obviously larger than the area in lighter color. In other words, - a great amount of particles are seen moving in aggregating form and few particles are left to move individually. The size and form of individual cluster is unlikely to be identified since the interconnection among the highly populated clusters is very intense. The observation indicates that the smaller and the lighter particles have higher potential to aggregate than the larger and heavier particles, whereas, the effect of particle sphericity cannot be distinguished here. Characterizing Clusters with Optical Fiber Probe To identify clusters with reflective probes applied, a set of criterion must be satisfied. Based on the previous studies, it is suggested that (3, 10): (1) the solids concentration inside the cluster must be n-times the standard deviation of the sampled signal over the local time-mean solids concentration; (2) the number of consecutive samples above the critical solids concentration, Ns, must be set to determine the minimum time interval for the perturbation caused by a cluster; (3) the sampling volume must be greater than one to two orders of particle diameter. Furthermore, it is found in this study that the sampling frequency of solids concentration signal is another sensitive criterion to establish the cluster properties. The sampling frequency in this study is placed very high, at 100 kHz, which allowed high sensitivity to the solids concentration. 1.0
Ns = 30
Large size cluster fraction
FCC, y/Y = 0 FCC, y/Y = 0.5 FCC, y/Y = 0.98 GB #1, y/Y = 0 GB #2, y/Y = 0 GB #3, y/Y = 0 Sand, y/Y = 0
Ug = 5.5 m/s
0.8
2
Gs = 100 kg/m s 0.6
0.4
0.2
0.0 0
50
100
150
200
250
300
350
400
Ns
Figure 5 Cluster number fraction vs. Ns
Subjecting to the difference of sampling frequency among various studies, the optimal values for both n and Ns are required to be determined individually. The sensitivity analysis, firstly introduced by Manyele et al. (3), is adopted in this study to optimize the value for n. Consequently, n=2 is finalized as optimal critical solids concentration. Then, such method is further improved to identify Ns. A new parameter, cluster number fraction, Flc, is proposed for the first time to characterize clusters. Flc is defined as Flc Nlc N c , where Nc is the total number of perturbations with the solids concentration higher than the critical value over the time series studied; while Nlc is the number of perturbations with Ns consecutive samples above the critical solids concentration. Considering the cluster number fraction as a sample parameter for the sensitivity analysis, an increase/decrease of Ns would lower/raise the fraction of clusters with existence time longer than Ns sampling time interval.
Some of the perturbations of solids concentration are very small and with short time intervals, therefore, they are classified as part of the dispersed particulate phase. Since such small perturbations are numerous, there tends to be a decrease of Flc with the increase of Ns. As shown in Figure 5, there is a sharp change at Ns = 30, which is the critical value that demarcates the particulate phase and the clusters. In addition, single critical Ns is also applicable to different types of particles and multiple lateral positions under specific operating condition. Cluster Number Fraction (Flc) It is found that Flc can not only be used to determine the optimal value for Ns, but also characterizes the effects of particle properties on the aggregates. Since Ns is the number of consecutive samples, with a sampling frequency, it can be easily transfromed to the minimum time interval, Tc, set for the perturbation caused by a cluster. Since the clusters are irregular in shape and vary both laterally and axially within a riser, the accurate definition of the cluster size is impossible. Therefore, the vertical length is usually used to identify the size of clusters. Since the time interval of the solids concentration perturbation measured by the optical fiber probe represents the traveling time of a cluster passing through a probe tip, it is feasible to calculate the cluster vertical length with dvl Vc Tc , where Vc is the vertical velocity of cluster (11). With the evaluation of the cluster velocity, it is possible to obtain the size of clusters in the riser. To some extent, the cluster number fraction reflects the distribution of cluster size. Figure 6 plots Flc against minimum time interval to elucidate the cluster characteristics of different particles. Figure 6(a) interprets the fractions of cluster number at different lateral positions. The Flc at the riser center region (y/Y = 0), middle region (y/Y = 0.5) and wall region (y/Y =0.98) are seen decrease with the increase of minimum time interval. In other words, with increasing Tc set for a cluster, the number of clusters with the transit time longer than the criterion decreases. It can also be seen that the decreasing curves of y/Y =0 and y/Y = 0.5 are almost overlapping and cross approximately at Tc = 1.1×10-3 µs. Below this value, the cluster number fraction at y/Y =0 is larger than that at y/Y = 0.5, while smaller when beyond the point. The curve for the wall region with y/Y = 0.98 is obviously above the other two, meaning that the population of clusters in the wall region is larger than that in the center or middle region. Due to the wall friction, the particle velocity is low on the wall, which increases the tendency of forming clusters and the probability of existence for clusters. Figure 6(b) compares Flc of particles with different mean size. It is found that under identical Tc, the Flc of smaller size particles is greater than that of larger particles. It shows that the finer particles are prone to aggregate and incline to form clusters. It is agreed well with the observations in the section 3.2. The finer particles (GB #1 or FCC particles) were seen to form numerous clusters interconnecting with each other, while only several huge size clusters were observed occasionally in the coarse particles (GB #3 or Sand).
Large size cluster fraction, Flc
1.0
(a)
1.0
0.8
0.8
0.6
0.6
(b) Ug = 5.5 m/s 2
Gs = 100 kg/m s Z = 5.33 m
GB #1 (dp = 76m), y/Y = 0
FCC, y/Y = 0 FCC, y/Y = 0.5 FCC, y/Y = 0.98
0.4
0.2
GB #2 (dp = 138m), y/Y = 0
0.4
GB #3 (dp = 288m), y/Y =0 0.2
-3
Tc=1.110 s
0.0
0.0 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1.0
(c)
-3
Tc=0.5510 s
Large size cluster fraction, Flc
4.0
0.0
0.6
0.6
3
1.5
2.5
3.0
3.5
4.0
(d)
GB #2 ( s 1), y/Y = 0
0.4
Sand ( s 0.7), y/Y = 0
3
GB #1 (p = 2453kg/m ), y/Y = 0 0.2
2.0
-3
0.8
FCC (p = 1877kg/m ), y/Y = 0
1.0
Tc=0.4510 s
0.8
0.4
0.5
1.0
0.2
0.0
0.0 0.0
0.5
1.0
1.5
2.0
2.5
3.0
Minimum time interval, T c, ( s)
3.5
4.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Minimum time interval, T c, ( s)
Figure 6 Cluster number fraction for different particles.
For the particles with different density, the curves of Flc against Tc cross approximately at Tc = 0.55×10-3 µs, as the dotted line shown in Figure 6(c). Under the given small time criterion, the fraction of clusters number for FCC particles is larger than that of GB#1. With the increase of Tc, the clusters for heavier particles (GB #1) are greater in fraction than for lighter particles (FCC) when Tc passes over the marked dotted line. It indicates that the lighter particles tend to form smaller clusters than the heavier particles. It is matched with the observations by high-speed films in section 3.2. The curves for particles with different sphericities are also observed in Figure 6(d) when Tc = 0.45×10-3 µs. Same interpretation can also be applied to the different sphericities particles: the more spherical particles incline to aggregate larger size clusters than the irregular particles when Tc is beyond the value of the intersection point. Below the marked line, the two curves are nearly overlapping, indicating that the clusters fraction in each cut for both types of particles are almost the same. In other words, the sphericity of particles has only minor effects on the small size clusters but more obvious effects on the larger size clusters.
CONCLUSION The particle aggregation characteristics were studied in a narrow rectangular CFB riser with a 19 mm × 114 mm cross-section and a 7.6m length. The study was conducted by operating FCC, glassbeads and sand particles under various operating conditions. Forms of clusters were observed by employing a visualization system with a high-speed video camera. The lighter and smaller particles tended to
aggregate and form interconnected clusters, while the effects of particle sphericity on the form of clusters are less evident. The aggregation characteristics were also studied with the optical fiber probe. A new parameter, the cluster number fraction, was originally defined. The results obtained by analyzing the instantaneous solids concentration agreed well with the observations and also indicate that the low particle density and small particle size contribute to form clusters. It shows that the particle sphericity plays a limited role in affecting the particle aggregation.
NOTATION dp Flc Tc Vc y Nc Z Φs
Sauter mean diameter of particle, m Cluster number fraction Minimum time interval for cluster, s Cluster vertical velocity, m/s Lateral coordinates Number of perturbations higher than critical solids concentration Height from riser bottom, m Particle sphericity
dvl Gs Ug Y Nlc Ns ρp
Cluster vertical length, m Solids circulation rate, kg/m2·s Superficial gas velocity, m/s Half of riser width, m Number of clusters Number of consecutive samples above critical solids concentration Density of particle, kg/m3
REFERENCES 1. Bi, H.T., J.X. Zhu, Y. Jin, and Z.Q. Yu. Forms of particle aggregations in CFBs. in Proceedings of the Sixth Chinese Conference on Fluidization. 1993. Wuhan, China. 2. Soong, C.H., K. Tuzla, and J.C. Chen. Experimental determination of cluster size and velocity in circulating fluidized bed. in Fluidization VIII. 1995. New York: Engineering Foundation. 3. Manyele, S.V., J.H. Pärssinen, and J.-X. Zhu, Characterizing particle aggregates in a high-density and high-flux CFB riser. Chemical Engineering Journal, 2002. 88: p. 151-161. 4. Li, H., Y. Xia, Y. Tung, and M. Kwauk, Micro-visualization fo clusters in a fast fluidized bed. Powder Technology, 1991. 66: p. 231-235. 5. Hatano, H. and N. Kido, Microscope visualization of solid particles in circulating fluidized beds. Powder Technology, 1994. 78: p. 115-119. 6. Mastellone, M.L., U. Arena, The effect of particle size and density on solids distribution along the riser of a circulating fluidized bed. Chemical Engineering Science, 1999. 54: p. 5383-5391. 7. Xu, J. and J. Zhu, Effects of particle properties on flow structure in a rectangular circulating fluidized bed: solids concentration distribution and flow development. Submitted to Chemical Engineering Science, 2011. 8. Zhang, H., P.M. Johnston, J.-X. Zhu, H.I. de Lasa, and M.A. Bergougnou, A novel calibration procedure for a fiber optic solids concentration probe. Powder Technology, 1998. 100: p. 260-272. 9. Liu, J., J.R. Grace, and X. Bi, Novel multifunctional optical-fiber probe I: Development and validation. AIChE Journal, 2003. 49: p. 1405-1420. 10.Soong, C.H., K. Tuzla, and J.C. Chen. Identification of particle clusters in circulating fluidized bed. in Circulating Fluidized Bed Technology IV. 1994. New York: AIChE. 11.Li, H., Q. Zhu, H. Liu, and Y. Zhou, The cluster size distribution and motion behavior in a fast fluidized bed. Powder Technology, 1995. 84: p. 241-246.
OXY-COMBUSTION OF DIFFERENT COALS IN A CIRCULATING FLUIDIZED BED Monika Kosowska-Golachowska1, Adam Luckos2, Konrad Klos1, Tomasz Musial1 1 Czestochowa University of Technology Institute of Thermal Machinery Armii Krajowej 19C, 42-201 Czestochowa, Poland e-mail:
[email protected] 2
Sasol Technology R&D 1 Klasie Havenga Road, Sasolburg PO Box 1, Sasolburg 1947, South Africa e-mail:
[email protected] ABSTRACT Combustion of three Polish and one South African bituminous coal particles in air versus O2/CO2 mixtures with oxygen concentrations in the range from 21% to 60% vol. was conducted at temperature of 850°C in a 12 kW bench-scale CFB combustor. Combustion in air was proceeded at ~50˚C higher centre temperatures and was slightly shorter in time compared to combustion in O2/CO2 mixture with 21% vol. O2. Larger heat capacity of CO2 compared to that of N2 also retards the ignition of volatiles in O2/CO2 mixtures with 21% O2. However, when the concentration of oxygen in O2/CO2 mixtures is larger than 30%, the ignition time decreases and surface and centre temperatures increase significantly with increasing O2 content. INTRODUCTION Nowadays, greenhouse gases emissions from coal-fired systems, particularly CO2, become more and more important. Oxy-fuel combustion is one of the promising options for power generation with carbon dioxide capture. This technology can reduce significantly emissions of NOx and improve the thermal efficiency of the combustion process through the reduction of flue gas volume. In the oxy-fuel combustion, coal particles are burnt in a mixture of pure oxygen and recycled flue gas. Because nitrogen is eliminated from the oxidizing gas, the flue gas leaving the combustion chamber is highly enriched in CO2 which means that the combustion process takes place in an O2/CO2 environment. Partial recycling of flue gas helps to control the flame temperature in the combustion chamber. Extensive studies in both pilot-plant and lab scales have pointed out the pronounced influence of gas composition (air versus O2/CO2) on coal combustion performance. The heat transfer and temperature distribution in a furnace are greatly affected by the large specific heat capacity of CO2. Coal ignition is delayed in O2/CO2 in comparison to in O2/N2 with the same O2 concentration. To match the flame/particle temperature in air, a large amount of O2 in CO2, typically around 30%, is required. Coal conversion rate, char properties, and reactivity are also affected by the replacement of air with an O2/CO2 mixture, Zhang et al. (1). Buhre et al. (2) and Toftegaard et al. (3)
summarized the literature on oxy-fuel combustion of pulverized coal and discussed a number of operational concerns, including ignition, heat transfer, environmental issues and flame stability. Oxy-fuel combustion has now been well studied for pulverized coal combustion, but to date has received relatively little attention for oxyfuel circulating fluidized bed combustion (CFBC), Jia et al. (4). Work in this field has been conducted by: Foster Wheeler Energia Oy and VTT (5), ALSTOM (6), CANMET (4) and Czestochowa University of Technology (7). In the present work, oxy-fuel CFB combustion tests were conducted in a 12-kW bench-scale CFB combustor. The main objective of this study is to investigate the combustion behaviour of three Polish and one South African bituminous coal particles, in air and O2/CO2 mixtures, in terms of particle temperature profiles, ignition time, devolatilization time and the total combustion time. EXPERIMENTAL Oxy-CFB Combustor Oxy-fuel combustion tests were conducted in a 12-kW bench-scale CFB combustor shown schematically in Figure 1. The bench-scale CFBC consists of a combustion chamber (1), a cyclone (2) a downcomer (3) and a loop seal (4). The electricallyheated rectangular combustion chamber (riser), 680×75×35 mm, is the main component of the unit. The front wall of the riser is made of transparent quartz through which the combustion process can be directly observed.
Fig. 1. Schematic diagram of the experimental apparatus for oxy-CFB combustion 1-combustion chamber, 2-cyclone, 3-downcomer, 4-loop seal, 5-coal particle, 6-insulation, 7-drain valve, 8-preheater, 9-card, 10-computer, 11-temperature measurement and control system, 12-gas cylinders, 13-air compressor, 14-pressure regulators, 15-rotameters, 16-valves, 17-mixer, 18-gas analyser, 19-ventilation duct, T1–T3-S-type thermocouples
Particles of silica sand between 100 and 400 μm, with d50=210 μm (d50, median sand particles diameter, represents the size at which 50%, of the sand particles, by weight, are smaller than the specified diameter), constitute the inert bed (see Figure 2 for the particle size distribution). Total mass of circulating solids is 0.3 kg. The gases to make up gas mixtures are supplied from cylinders (12) to a mixer (17) and then transferred via a preheater (8) directly into the combustion chamber. Flow rates of gases are controlled by valves (16) and measured by rotameters (15). During combustion tests, the superficial gas velocity was kept at a constant level of about 5 m/s. The temperature was held at 850°C by means of microprocessor thermoregulators (11). S-type thermocouples (T1–T3) measured the temperature at three different levels inside the combustion chamber with an accuracy of ±2°C. A single coal particle (5) was introduced into the combustion chamber and positioned stationary in the bed. To measure the temperatures in the centre and at the surface of the coal particle a special stand was constructed. It provides a support for two S-type thermocouples. The tip of the first thermocouple was located inside the particle, while the second thermocouple measured the surface temperature and served as a basket in which the coal particle was places. The thermocouples were connected via a card (9) to a computer (10) in order to record the temperature measurements. Ignition time and devolatilization time were measured by stopwatch with an accuracy of 0.1s. The intraparticle temperature, the surface temperature, ignition time and devolatilization time were measured simultaneously. The experiments were carried out in air (base case) and mixtures of O2/CO2 with oxygen concentrations in the range from 21% to 60% vol. Video and digital cameras were used to record the progress of combustion. a)
b)
Cumulative mass fraction, %
100 90 80 70 60
50 40 30
d50 = 210 μm ρs = 2620 kg/m3
20 10 0 0
100
200
300
400
500
600
700
dp, μm
Fig. 2. The bed material (silica sand): a) particle size distribution, b) SEM picture Coals Tested Particles of three Polish and one South African bituminous coal were used in this study. Table 1 shows proximate, ultimate and petrographic analyses of these coals. Spherical 10-mm particles were produced from coal lumps through mechanical grinding.
Table 1. Proximate, ultimate and petrographic analyses of the coals tested Coal Proximate analysis (%, air-dried basis) Volatile Matter Moisture Ash Fixed carbon Calorific value (HHV), MJ/kg Ultimate analysis (%, dry, ash-free basis) Carbon Hydrogen Nitrogen Sulphur Oxygen (by difference) Petrographic analysis, % Vitrinite Liptinite Inertinite Mineral matter
A Polish
B Polish
C Polish
D South African
30.9 2.7 2.4 64.0 32.38
30.9 4.3 8.2 56.6 31.31
28.9 10.1 11.1 49.9 24.66
23.7 3.8 25.0 47.5 22.44
83.73 4.59 1.34 0.33 10.01
82.97 5.14 1.28 0.68 9.93
75.94 4.59 1.48 2,17 15.82
81.90 3.44 2.44 2.71 9.51
52 9 39 0
88 2 6 4
59 12 21 8
14 0 68 18
RESULTS AND DISCUSSION Results of proximate and petrographic analyses (Table 1) reveal that ash and inertinite (maceral that is less reactive than vitrinite) contents in the South African are much higher than those in Polish coals. Therefore, it can be expected that the combustion behaviour of these coals may differ significantly. Thus, the main objective of our study was to investigate the combustion behaviour of these coals, in air and O2/CO2 mixtures, in terms of particle temperature profiles, ignition time, devolatilization time and the total combustion time. Figure 3 shows temperatures measured at the surface and in the centre of C and D coal particles burned at 850˚C in air and in O2/CO2 mixture with 21% vol. O2. In both cases, after an initial delay, the centre temperature exceeds the surface temperature and stays approximately 100˚C during the course of combustion. Lower surface temperatures can be explained by intensive heat transfer between burning coal particles and bed material. Combustion in air proceeded at ~50˚C higher centre temperatures and was slightly shorter in time compared to combustion in O 2/CO2 mixture with 21% vol. O2. b) coal D 1400
1200
1200
Temperature, C
Temperature, C
a) coal C 1400
1000 800 600 air - surface
400
air - centre 21%O2 + 79%CO2 - surface
200
1000 800 600 air - surface
400
air - centre 21%O2 + 79%CO2 - surface
200
21%O2 + 79%CO2 - centre
0
21%O2 + 79%CO2 - centre
0 0
200
400
600
800
Time, s
1000
1200
1400
0
200
400
600
800
1000
1200
1400
Time, s
Fig. 3. Temperature profiles for coal C (a) and coal D (b) combusted in CFB in air and 21%O2+79%CO2
As expected, the total combustion time, in both air and O2/CO2 mixture, for coal D was much longer (~90%) than that for more reactive coal C. Figure 4 illustrates the effect of oxygen content in O2/CO2 mixtures on surface and centre temperatures measured for coals C and D. For both coals the trends are similar; these temperatures increase significantly with increasing O2 concentration. In the case of coal D, the maximum difference between the centre and surface temperatures is larger than for coal C. b) coal D 1400
1200
1200
1000
1000
Temperature, C
Temperature, C
a) coal C 1400
800 600 30%O2 + 70%CO2 - surface
400
30%O2 + 70%CO2 - centre 60%O2 + 40%CO2 - surface
200
800 600 30%O2 + 70%CO2 - surface
400
30%O2 + 70%CO2 - centre 60%O2 + 40%CO2 - surface
200
60%O2 + 40%CO2 - centre
60%O2 + 40%CO2 - centre
0
0
0
100
200
300
400
0
500
100
200
300
400
500
600
700
800
900
1000
Time, s
Time, s
Fig. 4. Temperature profiles for coal C (a) and coal D (b) particles combusted in CFB in various mixtures of O2/CO2 Figure 5 shows the effect of gas composition on the ignition time of volatiles. Ignition time was characterized by the time required to achieve a visible flame. Larger heat capacity of CO2 compared to that of N2 retards the ignition of volatiles in O2/CO2 mixtures with 21% O2. However, when the concentration of oxygen in O2/CO2 mixtures is larger than 30%, the ignition time decreases with increasing O2 content. The trends are consistent with those found in the literature, Toftegaard et al. (3) and Molina and Shaddix (8). 30
Air 21%O2 + 79%CO2 30%O2 + 70%CO2 40%O2 + 60%CO2 50%O2 + 50%CO2 60%O2 + 40%CO2
Ignition time, s
25
20
24 19
16
15
13 11
10 5
11
9 5
6
8 6
4
3
2
5
4
9 7
6
5
3
10
4
1
0 Coal A
Coal B
Coal C
Coal D
Fig. 5. Ignition time for bituminous coal particles combusted in various atmospheres The influence of oxygen concentration in O2/CO2 mixtures on the devolatilization time is shown in Figure 6. Devolatilization time was the duration of the visible flame
(from ignition of volatile matter to the end of combustion of volatile matter). The devolatilization time decreases with increasing O2 concentration for coals A, B and C and increases for coal D. Different devolatilization behaviour in the case of coal D can be associated with differences in its internal structure (porosity and pore size) and lower reactivity.
Devolatilization time, s
65
Coal A
60
Coal B
55
Coal C Coal D
50 45 40 35 30 25 10
20
30
40
50
60
70
Oxygen concentration, %vol
Fig. 6. Devolatilization time for coal particles combusted in mixtures of O2/CO2 Figure 7 shows the influence of O2 concentration in O2/CO2 mixtures on the total combustion time. For all coals tested the total combustion time decreases with increasing oxygen concentration. The largest combustion times have been recorded for coal D. They are approximately 100% higher compared to those for coal C. Figure 8 shows images of A coal particle at different stages of combustion.
Total combustion time, s
1500
Coal A Coal B
1300
Coal C
1100
Coal D
900 700 500 300 100 10
20
30
40
50
60
70
Oxygen concentration, %vol
Fig. 7. Total combustion time for coal particles combusted in mixtures of O2/CO2 CONCLUSIONS Oxy-fuel tests were conducted for 10-mm three Polish and one South African bituminous coal particles in a 12 kW bench-scale CFB combustor at temperature of 850°C. The experiments were carried out in air (base case) and mixtures of O2/CO2 with oxygen concentrations in the range from 21% to 60% vol. Results of proximate and petrographic analyses reveal that ash and inertinite (maceral that is less
reactive than vitrinite) contents in the South African are much higher than those in Polish coals. Therefore the combustion behaviour of these coals was different in air and O2/CO2 mixtures. Combustion in air proceeded at ~50˚C higher centre temperatures and was slightly shorter in time compared to combustion in O 2/CO2 mixture with 21% vol. O2. As expected, the total combustion time, in both air and O2/CO2 mixture, for South African coal was much longer (~90%) than that for more reactive Polish coal C. Larger heat capacity of CO2 compared to that of N2 also retards the ignition of volatiles in O2/CO2 mixtures with 21% O2. These trends are consistent with those found in the literature. However, when the concentration of oxygen in O2/CO2 mixtures is larger than 30%, the ignition time decreases with increasing O2 content. Surface and centre temperatures increase significantly with increasing O2 concentration. The devolatilization time decreases with increasing O2 concentration for Polish coals A, B and C and increases for South African coal D. Different devolatilization behaviour in the case of coal D can be associated with differences in its internal structure (porosity and pore size) and lower reactivity. The total combustion time decreases with increasing oxygen concentration for all coals tested. air
21%O2 + 79%CO2
40%O2 + 60%CO2
60%O2 + 40%CO2
Heating and drying
Ignition of volatile matter
Combustion of volatile matter
Combustion of char
Fig. 8. Visualisation of A coal particle in CFBC in air and O2/CO2 mixtures
This paper included fundamental research and it is only the first step in studying and modeling of the oxy-fuel combustion process. The next step will be combustion of a portion of coal not only single coal particles. It will allow us to answer a question how a specific coal influences operation of oxy-CFB combustor like temperature profile in combustion chamber, real particles distribution of fluidized bed (fragmentation and erosion processes) and emission of pollutants. Next research will also answer a question how much oxygen in mixture of O2/CO2 is needed to have similar conditions like in the air combustion. Concentration of oxygen in O2/CO2 mixture will differ for various types of coal. It is very important for design and operation of oxy-fuel CFB units. ACKNOWLEDGMENT This work was supported by the Polish Ministry of Science and Higher Education from sources for science in the years 2009-2010 under Research Project No. N N513 396336. The support is gratefully acknowledged. NOTATION dp d50 ρ
– particle diameter, μm – median particle diameter, μm – density, kg/m3
REFERENCES 1.
2. 3. 4. 5.
6. 7. 8.
Zhang L., Binner E., Chen L., Qiao Y., Li C.Z., Bhattacharya S., Ninomiya Y.: Experimental Investigation of the Combustion of Bituminous Coal in Air and O2/CO2 Mixtures: 1. Particle Imaging of the Combustion of Coal and Char. Energy Fuels 24, p.4803-4811, 2010. Buhre B.J.P., Elliott L.K., Sheng C.D., Gupta R.P., Wall T.F.: Oxy-fuel combustion technology for coal-fired power generation. Progress in Energy and Combustion Science 31, p.283-307, 2005. Toftegaard M.B., Brix J., Jensen P.A., Glarborg P., Jensen A.D.: Oxy-fuel combustion of solid fuels. Progr. in En. and Comb. Scien 36, p.581-625, 2010. Jia L., Tan Y., Anthony E.J.: Emissions of SO2 and NOx during Oxy-Fuel CFB Combustion Tests in a Mini-Circulating Fluidized Bed Combustion Reactor. Energy Fuels 24, p.910-915, 2010. Kuivalainen R., Eriksson T., Hotta A., Sacristan A.S.B., Jubitero J.M., Ballesteros J.C., Lupion M., Cortes V., Anthony B., Jia L., McCalden D., Tan Y., He I., Wu Y., Symonds R.: Development and Demonstration of Oxy-fuel CFB Technology. Proc. of the 35th International Technical Conference on Clean Coal & Fuel Systems, Florida, USA, 2010. Fiveland W.: Advanced Combustion Technology: Oxy-Firing to Enable CO2 Capture. 2nd Workshop Intern. Oxy-Combustion Res. Network, USA, 2007. Czakiert T., Sztekler K., Karski S., Markiewicz D., Nowak W.: Oxy-fuel circulating fluidized bed combustion in a small pilot-scale test rig. Fuel Processing Technology 91, p.1617-1623, 2010. Molina A., Shaddix C.R.: Ignition and devolatilization of pulverized bituminous coal particles during oxygen/carbon dioxide coal combustion. Proc. Combust. Inst. 31(2), p.1905-12, 2007.
FLUIDIZED BED MEMBRANE REACTOR FOR STEAM REFORMING OF HIGHER HYDROCARBONS: MODEL SENSITIVITY M.A. Rakib*, J.R. Grace and C.J. Lim University of British Columbia, Vancouver, Canada V6T 1Z3 * Corresponding author (
[email protected]), currently with SABIC T&I, Riyadh 11551, Saudi Arabia ABSTRACT A fluidized bed membrane reactor (FBMR) was built and operated at temperatures Qd ,req , Qd ®b =
R.T P
å Fi ,d £ Qd ,req , Qb®d = Qd ,req -
i =1 NC i =1
R.T P
NC
R.T P
åF
i,d
i =1
R.T P
- Qd ,req , NC
åF i =1
i,d
,
Qb®d = 0
(9)
Qd ®b = 0
(10)
This way of maintaining the flow in the dense phase is consistent with CFD predictions for small particles (13). Thus, the mole balance equation for the separation side can be written as:
dFH 2, p dL
(
= a e b Qm H ,b + (1 - e b )Qm H , d 2 2
)
(11)
where α, (≤1), the overall permeation effectiveness factor, is an adjustable parameter to fit the simulated hydrogen permeation yields to the experimental results.
3
For the ith species in Bubble Phase:
dFi, b dh
= fb r p A
NR
åg ij R j,b + kiq abe b A(Ci, d - Ci,b ) - ae b Qmi,b j =1
dQbd dQdb Ci , b + Ci , d dh dh
with i = C7H16, C3H8, CH4, H2O, CO, CO2, and H2
(12)
th
A similar mole balance is written for the i species in the dense phase. The flux of all components other than hydrogen through the membranes is zero. To predict the reactor offgas composition, it is also necessary to account for catalytic reaction in the freeboard. An amount of catalyst equivalent to 0.8 mm of static bed depth was assumed to be distributed uniformly in the freeboard region, based on least squares error minimization with respect to the experimental concentrations of methane, CO2, and H2 in the reformer off-gas for all of the experimental runs. The freeboard was then modeled as a single-phase dilute suspension, with
dFi , fb dh fb
NR
= f fb r p Aå g ij R j , fb
(13)
j =1
The following quantities are calculated to assess the reactor performance: Permeate hydrogen yield =
molar flow of pure H2 extracted via membranes
(14)
molar flow of hydrocarbon in feed stream
Total hydrogen yield = molar flow of pure H2 extracted via membranes + molar flow of H2 in retentate stream (15) molar flow of hydrocarbon in feed stream
Carbon oxides yield = Methane yield =
total molar flow of CO and CO in retentate stream 2 molar flow of carbon (in hydrocarbon) in feed stream
molar flow of methane in retentate stream molar flow of carbon (in hydrocarbon) in feed stream
(16) (17)
RESULTS AND DISCUSSION Temperature profiles for the experiments, greatly affected by the heater arrangement, are shown in each plot below. The model was used (8) to simulate previous experimental results (5,6), and good agreement was achieved with α as the only adjustable parameter. Fitting of experimental data to the model for hydrogen permeation through the membranes gave a = 0.248 as a correction to a membrane permeation equation provided by the suppliers of the membrane panels. The decline in permeation flux relative to that in tests in a permeation test rig without particles was likely due to formation of a thin coating of catalyst fines on the membrane foils. Figure 2 plots the superficial gas velocities for propane steam reforming with six membrane panels. Four factors caused the variations in superficial velocity: (1) Intermittent abrupt variations of the superficial gas velocity due to changes in cross-sectional area in the spaces between adjacent membrane panels. (2) The superficial gas velocity is affected by the temperature variations. (3) The steam reforming reactions lead to a net increase in molar flow. This caused steep increases in U near the FBMR entrance, where propane conversion is completed. Subsequent methanation (reverse reactions from equations (2) and (4)) can result in the opposite trend.
4
Superficial velocity also varies owing to hydrogen removal via the membranes.
Superficial Gas Velocity (m/s)
Local Temp. (oC)
(4)
570 520 470 420
0.08
0.06
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
Height above Distributor (m)
Figure 2: Gas superficial velocities for propane steam reforming.
The sensitivity of the reactor model was tested (8) to understand the relative importance of the various phenomena inside the FBMR, as well as the effect of uncertainties in estimating parameters in the model. The bulk mass transfer was found to be negligible compared to the other three components of the mole balance equations. Similar observations apply to steam methane reforming in an FBMR (14). The kinetic rate constants for all reactions included were first varied upwards and downwards by a factor of 10 compared with those based on the literature values. Some variations in performance occurred near the reactor entrance, affected mainly by the propane steam reforming kinetics. However, over most of the height, there was very little difference in the local yields of methane, carbon oxides or hydrogen. To test the importance of hydrodynamics and interphase mass transfer, Figure 3 shows the reactor performance with the interphase mass transfer coefficient increased and decreased a factor of 10 relative to those from the Sit and Grace (9) correlation. The higher coefficient results in almost immediate transfer of propane from the bubbles to the dense phase, whereas, slower mass transfer retains more propane in the bubbles, delaying its conversion, Since methane is an intermediate component, it appears more slowly in the reactor, and its overall conversion is also delayed. With delayed transfer of hydrogen from the dense phase, where it is produced, to the bubble phase, where negligible hydrogen is produced, the net removal of hydrogen via membranes is reduced. While the effects of tenfold upward and downward changes in the interphase mass transfer coefficient are discernible, these effects are not very significant. Hence, interphase mass transfer, while not a negligible factor, plays a secondary role with respect to overall reaction. Since the bed hydrodynamics mostly enter the model through the interphase mass transfer, one may also conclude that accurate portrayal of bed hydrodynamics is of secondary importance for this process and for the operating conditions investigated. To explore the effect of permeation capacity variation of the membranes, the membrane permeation effectiveness factor was set at a = 0.15, 0.248 ( fitted value), and 0.35. As shown by Figure 4, the FBMR performance depends strongly on the hydrogen permeation through the membranes.
5
Local Temp. ( o C)
570 520 470
C)
570
o
420
Local Temp. (
520 470 420
0.7
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
0.5 0.3 0.7
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
0.7
a = 0.15 a = 0.2484 a = 0.35
0.5 0.3 0.7
0.3 0.1
a = 0.15 a = 0.2484 a = 0.35
0.5
Methane Yield
Methane Yield
0.5
0.9
Carbon Oxides Yield
Carbon Oxides Yield
0.9
1.00
0.50
0.1
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
0.25
Yield
Propane Conversion
0.3
0.75
7 5
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
3 1 10
a = 0.15 a = 0.2484 a = 0.35
3 1
10 8
Yield
8
6
2
6 4
0.1 x Mass Transfer 1 x Mass Transfer 10 x Mass Transfer
2
Total H
Total H
2
Yield
Permeate H
Yield
5
Permeate H
7
2
2
0.00
4
a = 0.15 a = 0.2484 a = 0.35
2 0
0 0.0
0.5
1.0
1.5
2.0
Height above Distributor (m)
Figure 3: FBMR performance with variations in interphase mass transfer coefficient
2.5
0.0
0.5
1.0
1.5
2.0
2.5
Height above Distributor (m)
Figure 4: FBMR performance with variations in permeation effectiveness factor.
CONCLUSIONS A fluidized bed membrane reactor is modeled to simulate its performance for producing hydrogen from propane. Model sensitivity studies show that the chemical kinetics are fast enough at all temperatures tested for their role to be insignificant in determining the FBMR performance. The interphase diffusional mass transfer rate is somewhat more significant in affecting reactor performance, but again plays a secondary role. From these results, it is evident that the FBMR performance is primarily controlled by chemical equilibrium and by the rate of hydrogen permeation through the membranes. Hence the model is sensitive to accurately characterizing
6
the chemical equilibrium and hydrogen permeation, but insensitive to the chemical kinetics, interphase mass transfer and hydrodynamics, at least for the temperature range of interest (450-550°C). ACKNOWLEDGEMENT Financial support from the Canada Foundation for Innovation and the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged. M.A.R. thanks NSERC for a two-year doctoral scholarship. NOTATION
ab A
AP C i ,b
Specific surface area of gas bubbles (m2/m3) Cross-sectional area of bed (m2) Membrane permeation area per unit length of membrane (m2/m) Molar concentration of species i in bubble phase (mol/m3)
C i ,d
Molar concentration of species i in dense phase (mol/m3)
E H2
Activation energy for permeation (J/mol)
Fi ,b
Molar flow rate of species i in bubble phase (mol/s)
Fi,d
Molar flow rate of species i in dense phase (mol/s)
Fi , fb
Molar flow rate of species i in freeboard (mol/s)
h
h fb
Vertical coordinate measured from distributor (m) Vertical co-ordinate from dense catalyst bed surface (m)
k iq
Interphase mass transfer component for species i (m/s)
m, n NC, NR P
Stoichiometric constants (-) Number of components, reactions (-) Pressure (Pa) Partial pressure of species i (bar)
Pi PH 2 ,b , PH 2 ,d Partial pressure of hydrogen in bubble, dense phase (atm)
PH 2 , p
Partial pressure of hydrogen on permeate side (atm)
PM 0
Pre-exponential factor for permeation (mole/(m.min.atm0.5))
Qbd Qdb
Cross-flow from bubble to dense phase per unit length (m3/(m.s)) Cross-flow from dense to bubble phase per unit length (m3/(m.s))
Qd ,req
Flow requirement for dense phase to prevent de-fluidization (m3/s)
Qmi ,j
Membrane permeation rate of species i for j phase (mol/(m.s))
R Rj
Universal gas constant (J/mol/K) Rate of jth reaction (mol/kg catalyst/s) Superficial gas velocity (m/s) Thickness of hydrogen selective membranes (m)
U
dH eb fb , f d f fb 2
Volume fraction of catalyst bed occupied by bubble phase (-) Bed volume fraction occupied by particles in bubble, dense phase (-) Volume fraction of freeboard occupied by solid particles (-)
7
g ij rp
Stoichiometric coefficient of component i in j th reaction
DH
Heat of reaction (kJ/mol)
Density of catalyst particles (kg/m3)
Subscripts b, d fb i in j m
j
Bubble, dense phase Freeboard Species i At reactor inlet Reaction j Membrane side Phase j
REFERENCES 1. Rostrup-Nielsen, J., Catalytic steam reforming. In Catalysis Science and Technology, Andersen, J.R.; Boudart, M., Eds. Springer-Verlag: 1984; pp 1-117 2. Adris, A.M.; Lim, C.J.; Grace, J.R., The fluidized bed membrane reactor system: A pilot scale experimental study. Chem. Eng. Sci. 1994, 49, 5833-5843. 3. Boyd, T.; Grace, J.; Lim, C.J.; Adris, A.M., Hydrogen from an internally circulating fluidized bed membrane reactor. Int. J. Chem. React. Eng. 2005, 3, A58. 4. Patil, C.S.; Annaland, M.V.S.; Kuipers, J.A.M., Fluidised bed membrane reactor for ultrapure hydrogen production via methane steam reforming: Experimental demonstration and model validation. Chem. Eng. Sci. 2007, 62, 29893007. 5. Rakib, M.A.; Grace, J.R.; Lim, C.J.; Elnashaie, S.S.E.H., Steam reforming of heptane in a fluidized bed membrane reactor. J. Power Sources 2010, 195, 57495760. 6. Rakib, M.A.; Grace, J.R.; Lim, C.J.; Elnashaie, S.S.E.H.; Ghiasi, B., Steam reforming of propane in a fluidized bed membrane reactor for hydrogen production. Int. J. Hydrogen Energy 2010, 35, 6276-6290. 7. Grace, J.R., Fluidized-bed hydrodynamics. In Handbook of Multiphase Systems, Hetsroni, G., Ed. Hemisphere: Washington, 1982; pp 8-5 - 8-64. 8. Rakib, M.A.; Grace, J.R.; Lim, C.J.; Elnashaie, S.S.E.H., Modeling of a fluidized bed membrane reactor for hydrogen production by steam reforming of hydrocarbons. Ind. Eng. Chem. Res., 2011, In Press. 9. Sit, S.P.; Grace, J.R., Effect of bubble interaction on interphase mass transfer in gas fluidized beds. Chem. Eng. Sci. 1981, 36, 327-335. 10. Wilke, C. R., Diffusion properties of multicomponent gases. Chem. Eng. Prog. 1950, 46, 95-104. 11. Darton, R.C.; Lanauze, R.D.; Davidson, J.F.; Harrison, D., Bubble growth due to coalescence in fluidized-beds. Trans. IChemE 1977, 55, 274-280. 12. Sieverts, A.; Zapf, G., The solubility of deuterium and hydrogen in solid palladium. Zeitschrift für Physikalische Chemie 1935, 174, 359-364. 13. Li, T.W.; Mahecha-Botero, A.; Grace, J.R., Computational fluid dynamic investigation of change of volumetric flow in fluidized bed reactors. Ind. Eng. Chem. Res. 2010, 49, 6780-6789. 14. Adris, A.M.; Lim, C.J.; Grace, J.R., The fluidized-bed membrane reactor for steam methane reforming: Model verification and parametric study. Chem. Eng. Sci. 1997, 52, 1609-1622.
8
CRITICAL EVALUATION OF EULER-EULER AND EULERLAGRANGIAN MODELLING STRATEGIES IN A 2-D GAS FLUIDIZED BED F. Hernández-Jiméneza, J.R. Thirdb, A. Acosta-Iborraa, C.R. Müllerb a Universidad Carlos III of Madrid, Department of Thermal and Fluid Engineering, ISE research group. Av. de la Universidad, 30, 28911, Leganés, Madrid, Spain . b ETH Zürich, Institute of Energy Technology, Laboratory of Energy Science and Engineering, Leonhardstrasse 27, 8092 Zürich, Switzerland. Abstract Two-phase granular systems are commonly encountered in industry, and fluidized beds are particularly important due to their excellent heat and mass transfer characteristics. Here, we critically evaluate the differences between two modelling strategies, Euler-Euler and Euler-Lagrangian models. Euler-Euler simulations were performed using MFIX and an in-house code was used for Euler-Lagrangian simulations. A 2D bed of width, height and transverse thickness of respectively, 0.2 m, 0.5 m and 0.01 m, served as a test case. The settled bed height was H 0 = 0.2 m. Particles of density ρ = 1000 kg/m³ and diameter dp = 1.2 mm were fluidized with air. The drag-law proposed by Benyahia et al. (10) was used in both models. Comparison between the simulation results was based on both instantaneous and time-averaged properties. A particular focus of this study was the influence of the coefficients of restitution and friction on the simulation results. INTRODUCTION Fluidized beds have various applications in industry, such as fluid catalytic cracking (FCC), gasification and combustion of coal, and Fischer−Tropsch synthesis (Kunii and Levenspiel (1)). Despite the fact that fluidized beds have been used in industry since the 1920s and good progress has been made in numerical simulations using two-fluid (Gidaspow (2)) or discrete element models (Tsuji et al. (3)), some aspects of fluidized bed hydrodynamics, such as bubble splitting, are still far from fully understood. Numerical modelling of fluidized beds has advanced significantly over the last two decades, the most popular modelling approaches being the Euler-Euler and EulerLagrangian models. The Euler-Lagrangian approach combines an Eulerian description of the fluid-phase with a Lagrangian particle simulation, in which the trajectory of each particle is calculated based on Newton's second Law. The gassolids interaction is computed through semi-empirical closure models (Deen et al. (4)). Although very promising, the Euler-Lagrangian approach is very computationally expensive and is, therefore, currently unable to simulate the large number of particles encountered in medium- or large-scale fluidized beds. In the Euler-Euler approach (Gidaspow (2), Wachem and Almstedt (5)) the particulates and the fluid phase are treated as inter-penetrating continua (two-fluid model). As in
the case of the Euler-Lagrangian approach, two-fluid simulations of fluidized beds require closure relationships for the gas-solids interaction. However, since the particle motion is not modelled in detail, the two-fluid model also requires closure relationships for the particle-particle interactions. These closure relationships may be empirical in nature or may be derived from theoretical relations that are linked to the kinetic theory of granular gases (Gidaspow (2)). The aim of this work is to compare the Euler-Euler and Euler-Lagrangian approaches for a specific test case, consisting of a two-dimensional (2D) gas fluidized bed. In addition, the effect of parameters such as the inter-particle and particle-wall coefficients of friction, and the coefficient of restitution, will be studied for both models. DEM APPROACH A Discrete Element Model (DEM) has been constructed based on the work of Tsuji et al (3), which combines the discrete element model of Cundall and Strack (6) to simulate the particulate phase, with the volume-averaged Navier-Stokes equations for the fluid phase, as derived by Anderson and Jackson (7). For each particle, the linear and angular momenta are governed by Newton’s second law:
d vs V p d s =−V p ∇ p vg− vs F c Ip =Tp dt 1− g dt s , V p , vg , , F c , Tp and I p are the mass, linear and angular where m p , vs , mp
velocities of the particle, the particle volume, the velocity of the gas phase, the interphase momentum exchange coefficient, the force and torque resulting from the collision of the particles, and the moment of inertia of the particle, respectively. To model the collision between contacting particles the soft-sphere approach was used, in which the particles are allowed to overlap by a small amount, δ. For the fluid the volume-averaged continuity and Navier-Stokes equations are given by Anderson and Jackson (7):
∂ g g ∇ · g g vg =0 ∂t
∂ g g vg ∇ · g g vg ²=− g ∇ p−∇ · g g − F p g g g ∂t is the viscous stress tensor and F p is the rate of exchange of
here, g momentum between the particulate and the fluid phases. The fluid was assumed to be Newtonian. The rate of momentum exchange between the particulate and fluid phases was calculated by adding up the fluid forces acting on the N p individual particles in a fluid cell of volume V cell : Np
F p=
Vp V cell
∑ vg− vs n=1
1−g
TWO-FLUID MODEL APPROACH The two-fluid model, based on the conservation equations of mass, momentum and granular temperature, was solved using the MFIX code (Multifluid Flow with Interphase eXchanges) (Syamlal et al (8), Benyahia et al (9)). The kinetic theory of
granular gases was used for the closure of the solids pressure stress terms. The governing equations can be summarized as follows. Mass conservation of the gas (g) and solid (s) phases:
∂ g g ∇ · g g vg =0 ∂t
∂ s s ∇ · s s vs =0 ∂t
Momentum conservation of the gas and solids phases:
∂ g g vg ∇ · g g vg =−g ∇ p∇ · g g g g −K gs vg −vs ∂t ∂ g g vg ∇ · s s vs =−s ∇ p−∇ p s∇ · s s s g K gs vg −vs ∂t where g , s , g , s , vg , vg correspond to gas and solids volume fraction, gas and solids density and gas and solids velocity respectively, p is pressure, g , s the g is the acceleration due to the stress tensors for gas and solids respectively, K gravity and gs is the gas-solids momentum exchange coefficient. The balance equation for the granular temperature, Θ, is given by:
3 ∂ ∇ · s s vs =− p s I s :∇ vs ∇ · k ∇ −−3K gs 2 ∂t s s where − p s I s : ∇ vs is the generation of Θ by the solids stress tensor, ∇ ·k ∇ is the diffusion of Θ energy, is the collisional dissipation of energy and 3K gs is the transfer of kinetic energy between phases. A second order accurate scheme (Superbee) was used to discretize the convective derivatives in the balance equations. NUMERICAL SIMULATIONS The gas-fluidized bed studied was of 0.2 m width, 0.01 m transverse thickness and 0.5 m height, filled with spherical particles of density ρ = 1000 kg/m³ and diameter d p = 1.2 mm. The static bed height was H0 = 0.2 m and the gas inlet velocity was U = 0.6 m/s, corresponding to U/Umf = 2 . Several cases were studied to evaluate the effect of the properties of the particles and walls. Table 1 summarizes the cases studied in this work. The parameters that are varied are the inter-particle and particle-wall coefficients of friction, and the restitution coefficient. Case 1 is taken to be the base case incorporating commonly used parameters. The inlet has been modelled as a homogeneous velocity inlet and the outlet as a constant pressure outlet for both models. The computational domain for the two-fluid model simulations comprised 57 × 141 × 8 cells in the x- (width), y- (height) and z- (thickness) directions, respectively. This creates a mesh with a 3.5 mm cell size, which is below 10 particle diameters and ensures grid-independent results. A partial slip boundary condition was applied at the walls of the fluidized bed, with a partial slip coefficient of Ф=0.6. The fluid computational domain for the DEM model comprised 58 × 148 × 3 cells in the x-, y- and z- directions. The fluidized bed contained 265650 particles. Interactions between particles are modelled using a damped Hertzian spring with an E-modulus of 1.2×106 N/m2. Both models use the drag law proposed by Benyahia et al. (10). For the time-averaged results, 40 seconds are employed for the EulerEuler model and 28 seconds for the Euler-Lagrangian model.
Model
Two-fluid model
DEM
Parameter
Case 1 Case 2 Case 3 Case 4
Restitution coefficient
0.9
0.9
0.9
0.5
Coefficient of friction between particles
0.57
0.1
0.57
0.57
Walls boundary conditions
Partial slip
Partial slip
Free slip
Partial slip
Restitution coefficient
0.9
0.9
0.9
0.5
Coefficient of friction between particles
0.57
0.1
0.57
0.57
Friction between particles and walls
0.57
0.1
0
0.57
Table 1: Simulation parameters for the two-fluid and DEM simulations. RESULTS DISCUSSION Figure 1 shows instantaneous snapshots of the solids volume fraction for case 1 simulated using the two models. Both snapshots were taken after the transient fluidization that occurs during start-up. The snapshots show the characteristic pattern of 2-D beds: small and narrow bubbles appearing in the bottom of the bed, and bigger and less numerous circular bubbles reaching the bed surface. Here bubbles are located where the solids volume fraction reaches a value close to zero. The solids volume fractions presented have been averaged over the entire bed thickness.
Figure 1. Instantaneous snapshot of the bed showing αs: a) two-fluid model; b) DEM. Figures 2 and 3 show the solids volume fraction averaged over the width and thickness of the bed, as a function of time, for the two fluid model and DEM respectively. Both models show the creation of small, slow-moving bubbles close to the distributor and the coalescence and eruption of faster bubbles at distances around y = 0.1 m above the distributor. Figure 4a and 4b show the power spectra obtained from the data presented in Figures 2 and 3 at two different heights, y = 0.005 m (close the distributor) and y = 0.217 m (close to the top of the bed). For both models the maxima in the power spectra occur at higher frequencies at y = 0.005 m than at y = 0.217 m. This is expected because bubbles coalesce as they rise through the bed, leading to a reduction in the number of bubbles that cross a horizontal section.
It should be noted, however, that the frequency depicted in Figure 4 is a 'bubble coherence frequency' because several bubbles may cross a horizontal section at any instant of time. Therefore, the frequencies of Figure 4 cannot be interpreted as a single bubble frequency unless the size of the bubble is comparable to the bed width, i.e. near the bed surface. The bubble coherence frequency near the distributor defines the principal frequency of bubble formation. This frequency of bubble formation is qualitatively similar in both models, namely ~ 6 Hz. The principal frequencies at y = 0.217 m, i.e. the frequency of bubble eruption, are also similar for both simulation strategies. In particular, Figure 4 shows that the peak of the power spectrum at y = 0.217 m occurs at ~ 2.5 Hz, which is in agreement with the bed oscillation frequency due to bubble eruption given by Baskakov et al. (11), f = g/ H o /=2.23 Hz .
s
Figure 2. XZ-averaged αs, two-fluid model. Case 1.
s
Figure 3. XZ-averaged αs, DEM. Case 1. The average solids volume fraction in an x-z plane located at y = 0.22 m is shown in Figure 5 for the two fluid model and DEM. This y position is close to the freeboard of the bed. Figure 5a reveals that the amplitude of the fluctuations in the solids volume fraction is smaller in the two-fluid simulations when compared with the DEM results. This is expected since the two-fluid approach tends to smear the distinction between the bubble and particulate phase. For the DEM a sharper, and more realistic, transition between the bubble and particulate phase is modeled. Figure 5b plots the dominant frequencies, extracted as the peak-frequency from the power spectra, as a function of vertical position, y. In both simulation strategies, the
profiles of peak-frequencies are in good agreement. In particular, high frequencies (around 6 Hz) are observed near the distributor and there is a transition zone in 0.05 m < y < 0.1 m. Near the freeboard both simulations show a region where the frequency stabilizes due to big bubbles passing at a frequency around 2.5Hz. Figures 2 and 3 reinforce this observation: both figures indicate a large number of slow-moving bubbles close to the distributor and a smaller number of faster bubbles after the transition zone.
Figure 4. Power spectra of XZ-averaged αs, a) two-fluid model, b) DEM. y = 0.005 m (solid line); y = 0.217 m (dash line). Case 1.
Figure 5. a) XZ-averaged αs at a height of 0.22 m b) Vertical profile of peak frequency for XZ-averaged αs: two-fluid model (solid line); DEM (dash line). Case 1. The effect of the wall friction is demonstrated in Figures 6a and 6b. Here, the solids velocity and solids volume fraction, averaged with respect to time and transversal thickness, are presented at a height y = 0.01 m for both simulation strategies. In case 1, both models predict very similar magnitudes for the solids velocity, however the bed hydrodynamics are substantially different. In the two-fluid model there are two preferential bubble paths at a distance of ~ 0.05 m away from the lateral walls (Figure 6b). On the other hand in the DEM there is only one path in the middle of the bed. For case 3, which employs a free slip condition at the walls, the time-averaged velocities within the bed are an order of magnitude greater than those obtained for case 1. Furthermore, there are substantial discrepancies between the two-fluid and DEM results obtained for case 3: the two-fluid model predicts velocities that are approximately twice those predicted by the DEM and also predicts higher solids volume fractions, i.e. smaller bed expansion.
Finally, Figure 7 compares the solids velocity in both models for cases 1, 2 and 4. For the two-fluid model only small changes in the profile of the solids velocity can be observed for the case that the coefficients of friction and restitution are reduced. However, for the DEM the coefficient of friction plays an important role. Reducing the coefficient of friction in the DEM from 0.57 to 0.1 leads to a substantial increase in the time-averaged solids velocities, as seen in Figure 7b. Furthermore, it is observed that for the two-fluid model reducing the coefficient of restitution decreases the gradient along x-direction in the solids velocity profile; only very small variations were observed in the DEM results.
Figure 6. Time averaged values of a) solids vertical velocity and b) αs at a height of 0.1 m: two-fluid model case 1 (solid line); DEM case 1 (dash line); two-fluid model case 3 (dot line); DEM case 3 (dash-dot line).
Figure 7. Time averaged values of solids vertical velocity at a height of 0.1 m, a) two-fluid model b) DEM: case 1 (solid line); case 2 (dash line); case 4 (dot line). CONCLUSIONS DEM and two-fluid model simulations of 2D bubbling fluidized beds have been compared in this work. For the base case, in which the coefficient of friction was set to 0.57, both simulation strategies yield time-averaged velocities with similar magnitudes, however the agreement of the characteristics of the velocity profiles is disappointing, especially for the case using zero friction for the particle-wall contact. The two-fluid model predicts that the highest velocities within the bed are located at a distance of ~ 0.05 m away from the side wall, whereas the DEM predicts that the highest velocities are located at the centre of the bed. For both simulation techniques, the time-averaged solids volume fractions show minima that are coincident with the maxima in the velocity profiles. This is consistent with the
hypothesis that bubbles preferentially pass through these locations. The behaviour of bubbles has been examined by averaging the solids volume fraction over horizontal cross sections of the bed. Both the two-fluid and DEM simulations predict a coherence bubble frequency of 6 Hz close to the distributor and a frequency of 2.5 Hz close to the surface of the bed. Furthermore, the influence of the coefficients of friction and restitution on the simulation results has been investigated. The time-averaged solids velocity and solids volume fraction profiles suggest that, within the range examined here, the behaviour of the bed, using two-fluid and DEM models, is relatively insensitive to the particle-particle coefficient of friction and, for the DEM results, to the coefficient of restitution. However, setting the particle-wall coefficient of friction to zero was found to have a pronounced effect on the particle motion within the bed. Under these conditions both models were found to give time-averaged solids velocities an order of magnitude larger than those predicted for simulations with particle-wall friction. Nevertheless, further work is required to establish the causes of the discrepancies between the DEM and two-fluid models highlighted here. Acknowledgement This work has been co-funded by the Spanish Government (Project DPI2009 -10518) and the Autonomous Community of Madrid (Project S2009/ENE-1660). References 1. D. Kunii, O. Levenspiel, Fluidization Engineering: Butterworth-Heinemann: Newton, MA, 1991. 2. D. Gidaspow, Multiphase flow and Fluidization: Continuum and kinetic theory descriptions; Academic Press: San Diego, CA. 1994. 3. Y. Tsuji, T. Kawaguchi, T. Yanaka, Discrete particle simulations of 2-dimensional fluidized-beds. Powder tech. 77 (1993) 79-87. 4. N.G. Deen M. van Sint Annaland, M.A. van der Hoef, J.A.M. Kuipers, Review of discrete particle modelling of fluidized beds. Chem. Eng. Sci. 62 (2007) 28-44. 5. B.G.M. van Wachem, A.E. Almstedt, Methods for multiphase computational fluid dynamics. Chem. Eng J. 96 (2003) 81-98. 6. P.A. Cundall, C.D.L. Strack, A discrete numerical-model for granular assemblies. Geotechnique 29 (1979) 47-65. 7. T.B Anderson, R. Jackson, A fluid mechanical description of fluidized beds. Ind. Eng. Chem. Fund 6 (1967) 527-539. 8. M. Syamlal, W. Rogers, T.J. O'Brien, MFIX Documentation: Theory guide, U.S. department of Energy (DOE), Morgantown Energy Technology Center, Morgantown, West Virginia, 1993. 9. S. Benyahia, M. Syamlal, T.J. O'Brien, Summary of MFIX equations 2005-4, 2007. 10. S. Benyahia, M. Syamlal, T.J. O'Brien, Extension of Hill-Koch-Ladd drag correlation over all ranges of Reynolds number and solids volume fraction. Powder Tech. 162 (2006) 166-174. 11. A. P. Baskakov, V. G. Tuponogov, N. F. Filippovski. A study of pressure fluctuations in a bubbling fluidized bed. Powder Tech. 45 (1986) 113-117.
DESCRIPTION OF PRESSURE FLUCTUATIONS IN A CIRCULATING FLUIDIZED BED BY STATISTICAL ANALYSIS Roelof L.J. Coetzer, Andre Mostert and Adam Luckos Sasol Technology, Research and Development 1 Klasie Havenga Road, Sasolburg, 1947 South Africa ABSTRACT In this paper we evaluate different methods for statistically analyzing the variability in pressure fluctuations measured at three locations in an 80-mm-ID, 5-m-tall CFB model operated with natural rutile particles and air at ambient conditions. The methods evaluated are the Shannon entropy, Fischer information matrix together with kernel density estimation, and an estimation of the magnitude of the pressure amplitudes. The accuracy of the different methods is estimated by the bootstrap method. We illustrate how informative statistics from these methods can be used to quantify the effect of the process variables on fluidization at different bed locations. Depending on the interest of the experimenter, the method and statistic can be selected which explains fluidization operation most accurately. INTRODUCTION The continuous monitoring of gas-solid fluidized-bed reactors is an important issue in the industrial practice because of the complex dynamical behaviour characterizing these systems. Failures and difficulties experienced in the operation of fluidized-bed reactors are usually attributed to an un-sufficient understanding of the physics of gassolid fluidization (1). In particular, our knowledge on systems with irregularly shaped particles with wide size distributions operated at higher gas velocities (in the turbulent regime and in the fast fluidization regime), elevated temperatures and pressures is still relatively poor. In the last three decades, several techniques have been developed to describe the dynamic phenomena that take place within the bed. Among these techniques, the pressure fluctuation measurements are the most popular owing to their low costs and direct relation to the bed dynamics (2). Pressure measurements sampled at frequencies 20–1000 Hz can be used to describe important fluidized-bed characteristics such as the quality of fluidization, size and frequency of bubbles, transition from bubbling to turbulent regime and minimum fluidization velocity.
In our previous studies, several important process parameters such as critical velocities, distributions of solid concentration and pressure fluctuations were measured in a CFB cold model (3–6). The variability in the pressure fluctuations were previously evaluated by using the standard deviation and the coefficient of variation (7). In this paper, we extend the analysis of the data by applying Shannon entropy and Fisher information matrix (8). Our analysis should establish the relationship between two entities, (1) process variables, and (2) pressure fluctuations at different levels in the riser. This relationship will provide a basis for controlling the operation of a CFB reactor. TEST APPARATUS AND PROCEDURE Measurements of pressure fluctuations were carried out in the riser of the 80-mm, 5-m tall CFB cold model made of transparent PVC. Data acquisition units recorded the signals (sampled at 200 Hz) from three pressure transducers located at the bottom (0.2 m above the distributor), in the middle (at 2.46 m), and at the top of the riser (at 4.47 m). All tests were conducted with air at ambient conditions. At each stable condition signals were collected over a period 40 s, an interval producing 8192 (i.e. 213) pressure readings. The solid material used was natural rutile (TiO2). Its particles fall into group B of Geldart’s classification. They are sub-rounded, fine (80–165 µm) and dense (4085 kg/m3). A more detailed description of the apparatus and test procedure can be found in an earlier paper on the subject (4). RESULTS The concentration of solids in the riser adopts a ‘C’ shape, which becomes less pronounced as the solid-circulation rate, Gs, at a given superficial fluidizing velocity, U, decreases (4–6). Concomitant with the decrease is a shift in solids concentration at each point in the riser to lower values, and move to greater solids concentrations at the top of the riser than at the bottom. A higher suspension density at the top of the riser—a consequence of the rebounding of particles from the plate closing the top of the riser— is a phenomenon that is well known in small-scale ( 0.2 (Ergun, 20; Gidaspow, 9) 2 αg d pφp d pφ p ~ ρg αsα g u g − u s − 2.65 3 β = C Ds αg for α s ≤ 0.2 (Wen and Yu, 21; Gidaspow, 9) 4 d pφ p K3 =
(
(
)
(
C Ds
)
(
)
)
( )
24 0 ,687 for R e 1 + 0.15 R e p p = for 0.44
R e p < 1000
Rep = R e p ≥ 1000
u~ g − u s d p ρ g α g µg
(Rowe, 22)
—————————————————————————————————————
3
SIMULATIONS The simulations were performed for a solid phase derived form a high Stokes number monodisperse particulate typical of low density risers (520 µm diameter, 2620 kg/m3 density), and for solid phase average volume fractions of 0.015, 0.03, 0.05, 0.07 and 0.09. Accelerating flows were generated and the results were analysed for increasing gas velocities from about 3 to about 9 m/s. A 2x2 cm wide and 8 cm tall vertical hexahedral domain was considered, applying a 1x1x1 mm uniform hexahedral numerical mesh. The flow entered the domain through the bottom and exited at the top. The density and viscosity of the gas phase were, respectively, 1.1614 kg/m3 and 1.82 x 10-5 N.s/m2. A solid phase volume fraction at maximum packing of 0.38 was applied following Gidaspow and Ettehadieh (23), and a restitution coefficient of 0.9 was taken following Agrawal et al. (2). Table 2. Solid phase’s effective stresses and effective drag. ————————————————————————————————————— = ρs α su su s
α s u su s ~ ~ τ se = ρ s α s ( u~ s u s − u su s ) = ρ s α s α
αu αu − s s⋅ s s αs αs
M sI = − β (u s − u~ g ) = − β (u s − u~ g )
M sI = − βe u s − u~ g
s
α su s ⋅ α su s − αs ~ β us − u g βe = u s − u~ g
(
)
————————————————————————————————————— The driving force factor ( ψ ) was set to 1.5. This value allowed the simulations to go along a suitable range of gas axial velocities in a reasonable computing time. Initial conditions for the accelerating runs were obtained by running previous simulations applying ψ = 1 , departing from uniform quiescent suspensions with fixed uniform solid volume fractions. A time step of 5x10-5 s was applied which is suitable for solid phase highly resolved simulations. The lower characteristic time scale of clusters of the order of 10-2 s (24). Also, for the present 520 µm particulate size the smaller clusters on the flow are expected not to be larger than 5.2 mm (following 2). Therefore, regarding the solid phase, both the spatial and temporal meshes which were applied are suitable for highly resolved simulations. The convergence criterion for the numerical procedure was a rms of 1x10-5. The simulations were carried out using the software Cfx (25). RESULTS The effects of the domain average solid volume fraction and gas phase axial velocity over the flow effective hydrodynamics were evaluated. The greyscale plots of solid phase fraction in Figure 1 show that the topology of the flow considerably changes by changing the concerning parameters. By increasing the average solid fraction, for a particular gas velocity, larger clusters are formed. By increasing the gas velocity, for a particular average solid fraction, the clusters become stretched in the axial direction. Figure 2 shows plots of the effective stresses of the solid phase. Even though the results are very scattered, it is possible to observe that higher solid fractions give rise to higher stresses. The effective shear stresses seem not to change with gas velocity, while the normal stresses show considerable variations (e.g. drawn line in Fig. 2 (b)).
4
0.015
0.05
0.09
0.015
∼ 3 m/s
0.05
0.09
0.015
0.05
∼ 6 m/s
0.09
∼ 9 m/s
> 0.25
0
Figure 1. Solid volume fraction in an axial section of the domain for αs = 0.015,
v g ≅ 3, 6, 9 m/s.
0.05, 0.09, and
|τxx,se|, |τyy,se|, |τzz,se| (N/m )
(b) 2
2
|τxy,se|, |τxz,se|, |τyz,se| (N/m )
(a) 1
1E-3
1E-6
3
4
5
6
7
8
9
< vg > m/s
10
0.1
1E-3
3
4
5
6
7
8
9
< vg > (m/s)
Figure 2. Effective shear (a) and normal (b) stresses of the solid phase as a function of v g , for αs = 0.015 (S); 0.03 (z); 0.05 (Â); 0.07 (U) and 0.09 ({). Figure 3 shows the behavior of the slip velocity and the effective drag coefficient. As seen, the higher the solid fraction, the lower the slip velocity, and the higher the effective drag coefficient. Both the parameters resulted little affected by the gas velocity, except for the slip velocity at higher solid fractions, where an oscillating behavior is also observed. This is possibly due to the formation of larger clusters in comparison to the size of the domain (see Fig. 1). Those oscillations are expected to disappear at sufficiently enlarged domains, that would always hold a considerable number of clusters throughout the whole range of gas velocities in an accelerating run. This issue, of course, requires verification. Figure 4 brings some of the predictions compared to empirical data of Luo (26). This author performed experiments in a riser column with the same conditions applied in the current simulations. From the measurements, Luo determined effective shear stresses and effective drag coefficients for various average solid fractions. A few of those solid fractions, for a gas velocity close to 5 m/s, fall in the ranges considered in
5
the present simulations. As seen in Figure 4, Luo’s results for those cases compare reasonably well with the present predictions, which is fine considering that Luo’s results apply to regions close to the column wall, while the predictions are volume averaged over a free slip walls domain. (a)
(b)
1000
βe (kg/m s)
4.5
750
3
< vg - vs > (m/s)
5.0
4.0 3.5
500 250
3.0 5
6
7
8
0
9
5
6
7
8
9
< vg > (m/s)
< vg > (m/s)
Figure 3. Slip velocity (a) and effective drag coefficient (b) as a function of
v g , for
αs = 0.015 (S); 0.03 (z); 0.05 (Â); 0.07 (U) and 0.09 ({). (b)
900
2
|τxy,se|, |τxz,se|, |τyz,se| (N/m )
(a)
3
βe (kg/m s)
1
0.01
1E-4 0.00
0.02
0.04
0.06
0.08
600
300
0 0.00
0.10
< αs >
0.02
0.04
0.06
0.08
0.10
< as >
Figure 4. Effective shear stresses of the solid phase (a) and the effective drag coefficient (b) as a function of αs , for v g ≅ 5 m/s; (X) empirical, Luo (26). CONCLUSION Two-fluid SGS was developed to investigate the sub-grid behavior of riser flows for a solid phase derived from a high Stokes number monodisperse particulate. Accelerated flow simulations were performed for a range of average solid fractions and gas velocities typical of risers. The effects of those parameters over the flow topology, the effective hydrodynamics of the solid phase and the effective drag were analyzed. The effects of both the gas velocity and the solid hold-up were found to be significant. A comparison was made of predictions against a few empirical data, and a reasonable agreement was found.
6
ACKNOWLEDGEMENTS This work was supported by The State of São Paulo Research Foundation (FAPESP), The National Council for Scientific and Technological Development (CNPq), and The Coordination for the Improvement of Higher Level Personnel (CAPES). NOTATION CD
drag coefficient (nd)
P
pressure (Nm-2)
dp
particle diameter (m)
∇P*
additional pressure gradient (Nm-3)
D e g
strain rate tensor (s-1) restitution coefficient (nd) gravity acceleration (ms-2) radial distribution function (nd) unit tensor (nd) interface drag force (Nm-3)
Re p
particle Reynolds number (nd) time (s) velocity vector (ms-1) Cartesian velocities (ms-1) SGS domain volume (m3) Cartesian coordinates (m)
g0 I M
t u u , v, w
V x , y, z
Greek letters α β Θ
λ
µ
ρ
volume fraction (nd) friction coefficient (kgm-3s-1) granular temperature (m2s-2)
τ τe φp ψ
bulk viscosity (Nsm-2) dynamic viscosity (Nsm-2)
density (kgm-3) viscous stress tensor (Nm-2) effective stress tensor (Nm-2) particle sphericity (nd) driving force factor (nd)
Subscripts e g I
meso-scale or effective gas phase interface
max
s
x , y, z
maximum solid phase Cartesian directions
Others
~
LSS filtered (resolved)
...
volume average,
f
=
1 V
~ αf Favre average, f = α
∫ f dV V
REFERENCES 1. Sundaresan, S. Modeling the hydrodynamics of multiphase flow reactors: current status and challenges. AIChE J. Vol. 46-6, pp. 1102-1105 (2000). 2. Agrawal, K., Loezos, P. N., Syamlal, M. and Sundaresan, S. The role of meso-scale structures in rapid gas-solid flows. J. Fluid Mech. Vol. 445, pp. 151-185 (2001). 3. Andrews IV, A. T., Loezos, P. N. and Sundaresan, S. Coarse-grid simulation of gas-particle flows in vertical risers. Ind. Eng. Chem. Res. Vol. 44-16, pp. 6022-6037 (2005). 4. van der Hoef, M. A., Ye, M., van Sint Annaland, M., Andrews IV, A. T., Sundaresan, S. and Kuipers, J. A. M., Multiscale modeling of gas-fluidized beds, Adv. Chem. Eng. Vol. 31, pp. 65-149 (2006). 5. Igci, Y., Andrews IV, A. T., Sundaresan, S., Pannala, S. and O’Brien, T. Filtered two-fluid models for fluidized gas-particle suspensions. AIChE J. Vol. 54-6, pp.
7
1431-1448 (2008). 6. Bagnold, R. A. Experiments on a gravity-free dispersion of large solid spheres in a Newtonian fluid under shear. Proc. R. Soc. Vol. A225, pp. 49-63 (1954). 7. Jenkins, J. T. and Savage, S. B. A Theory for the rapid flow of identical, smooth, nearly elastic spherical particles. J. Fluid Mech. Vol. 130, pp. 187-202 (1983). 8. Lun, C. K. K., Savage, S. B., Jeffrey, D. J. and Chepurniy, N. Kinetic theories for granular flows: inelastic particles in Couette flow and singly inelastic particles in a general flow field. J. Fluid Mech. Vol. 140, pp. 223-256 (1984). 9. Gidaspow, D. Multiphase flow and fluidization. San Diego. Academic Press (1994). 10. Goldhirsch, I., Tan, M. -L. and Zanetti, G. A molecular dynamical study of granular fluids I: the unforced granular gas in two dimensions. J. Sci. Comput. Vol. 8-1, pp. 1-40 (1993). 11. Tan, M. -L. and Goldhirsch, I. Intercluster interactions in rapid granular shear flows. Phys. Fluids Vol. 9-4, pp. 856-869 (1997). 12. Benyahia, S., On the effect of subgrid drag closures, Ind. Eng. Chem. Res., Vol. 49, pp. 5122-5131 (2010). 13. Anderson, T. B. and Jackson, R. Fluid mechanical description of fluidized beds. Equations of motion. Ind. Eng. Chem. Fund. Vol. 6, pp. 527-539 (1967). 14. Ishii, M. Thermo-fluid dynamic theory of two-phase flow. Paris. Eyrolles (1975). 15. Drew, D. A. Averaged field equations for two-phase media. Stud. Appl. Math. Vol. 1-2, pp.133-136 (1971). 16. Enwald, H., Peirano, E. and Almstedt, A. -E. Eulerian two-phase flow theory applied to fluidization. Int. J. Multiphase Flow. Vol. 22, pp. 21-66 (1996). 17. Fede, P. and Simonin, O. Numerical study of the subgrid fluid turbulence effects on the statistics of heavy colliding particles. Phys. Fluids Vol. 48, pp. 045103 (2006). 18. Syamlal, M, Rogers, W. and O´Brien, T. MFIX documentation theory guide. West Virginia. U.S. Department of Energy (1993). 19. Lun, C. K. K. and Savage, S. B. The effects of the impact velocity dependent coefficient of restitution on stresses developed by sheared granular materials. Acta Mech. Vol. 63, pp. 15-44 (1986). 20. Ergun, S. Fluid flow through packed columns. Chem. Eng. Prog. Vol. 48-2, pp. 89-94 (1952). 21. Wen, C. Y. and Yu, Y. U. Mechanics of fluidization. Chem. Eng. Prog. S. Ser. Vol. 62, pp. 100-111 (1966). 22. Rowe, P. N. Drag forces in a hydraulic model of a fluidized bed. Part II. Trans. Inst. Chem Eng. Vol. 39, pp. 175-180 (1961). 23. Gidaspow, D. and Ettehadieh, B. Fluidization in two-dimensional beds with a jet. Part II. Hydrodynamic modeling. Ind. Eng. Chem. Fund. Vol. 22, pp. 193-201 (1983). 24. Sharma, A. K., Tuzla, K., Matsen, J. and Chen, J. C. Parametric effects of particle size and gas velocity on cluster characteristics in fast fluidized beds. Powder Technol. Vol. 111, pp. 114-122 (2000). 25. Cfx, Discretization and solution theory. In Solver theory manual. Release 10. Waterloo. Ansys Canada (2005). 26. Luo, K. M., Experimental gas-solid vertical transport, PhD Thesis, Illinois Institute of Technology, Chicago, Illinois, (1987).
8
A GENERALIZED FLOW REGIME DIAGRAM FOR FLUIDSOLID VERTICAL TRANSPORT Xiaotao T. Bi Fluidization Research Centre Department of Chemical and Biological Engineering The University of British Columbia, Vancouver, Canada ABSTRACT An ideal generalized flow regime diagram was proposed for fluid-solids vertical transport systems with no bottom and top restrictions. Such an ideal flow regime diagram was further extended to shed light onto the understanding of the flow regimes and instabilities encountered in bottom- restricted bubbling and circulating fluidized bed systems. INTRODUCTION Flow patterns and flow regimes in gas-solids two-phase fluidization and vertical flow systems have attracted a great attention in the multiphase research community since the 1940s. A number of flow regime maps have been proposed to distinguish different unique flow patterns. Although it has been commonly agreed that there exist distinct flow patterns in gas-solids fluidized beds and vertical transport lines, such as the bubbling and slugging fluidization and dilute phase transport based on extensive research from 1940s to 1960s. Controversies still exist on the existence of turbulent fluidization, which was first proposed in late 1960s, fast fluidization, which was first proposed in late 1970s. The transition from pneumatic conveying to fast fluidization or dense suspension upflow is still not well defined, as reflected in the debates on the definition of choking in Fludization X in Beijing and CFB-7 in Naragra Falls. Further work on this topic is warranted in order to develop a generalized flow regime diagram for the flow pattern identification. In this work, attempt was made to elucidate the flow patterns in free or non-restricted gas-solids vertical flow systems in hope that such an analysis will shed some light on the understanding of the bottomrestricted fluidized bed systems and the dense suspension upflow system in which the solids feeding system is coupled with the flow in the riser. FLOW PATTERNS IN FREE GAS-SOLIDS VERTICAL FLOW SYSTEMS In gas-solids vertical flow systems with gas flowing upward, particles can travel up or down, giving rise to two possible flow modes: co-current upflow and counter-current flow. The termination of counter-current flow occurs when solids can no longer fall downward (i.e. at the flooding point) and the gas-solids co-current upflow ceases when the gas velocity is lower than particle terminal settling velocity.
If we feed solids from the middle section into a vertical tube with open top and bottom in which gas is flowing from bottom to top, both co-current upward flow in the upper section above the feeding point and counter-current flow in the lower section below the feeding point are possible depending on the gas velocity and the solids feeding rate. At a gas velocity lower than the particle terminal velocity, all feed particles will fall downward at a low feed rate, forming a counter-current flow in the lower section of the tube and a single-phase gas flow in the upper section, as shown in Figure 1. However, with the increase in solids feed rate, flooding will be reached when particles discharge rate from the bottom end of the tube becomes smaller than the solids feed rate. As a result, solids start to build up upward into the upper section, forming a dense fluidized bed in the upper section. This flooding phenomenon is in analogy to the flooding in gas-liquid counter-flow systems. 1400
II
V
Solids feed rate, kg/m2s
1200
1000 Saturation 800
IV 600
400 Flooding
I 200
Ut
III
Use
0 0
2
4
6
8
10
12
Superficial gas velocity, m/s
Figure 1. A flow regime diagram for non-restricted vertical transport lines. FCC particles in ambient air: mean particle size, 60 μm; particle density, 1800 kg/m3. Let us now consider the case when the gas velocity in the tube is higher than the particle terminal settling velocity. At a low solids feed rate all fed particles are carried upward giving a co-current upward flow in the upper section, and a single-phase gas flow in the lower section, shown in Figure 1. When the solids feed rate is increased to such an extent that the solids feed rate exceeds the saturation particle carrying capacity of the gas, excess amount of particles will fall downward and leave the tube from the bottom, forming a counter-current flow in the lower section as well as a cocurrent upward flow in the upper section. If the solids feed rate is further increased to such an extent that the downflowing particle rate exceeds the flooding rate which corresponds to the maximum discharge rate from the bottom end of the column, a
dense suspension starts to build up above the solids feed point, forming a co-current dense suspension upflow in the upper section and a flooded counter-current flow in the lower section. A flow regime diagram for a given vertical tube, gas and particle properties can be constructed based on the flooding velocity and the gas velocity corresponding to the saturation carrying capacity, estimated from two correlations: Equation (1) from Papa and Zenz [1], which was modified from the Sherwood equation originally developed to predict flooding in packed towers, is selected to predict flooding point:
⎡ U g ⎛ ρ g ⎞1 / 2 ⎤ ⎜⎜ ⎟⎟ ⎥ ⎢ ⎢⎣ gD ⎝ ρ D ⎠ ⎥⎦
2/3
1 ⎛G /ρ ⎞ + 1/ 3 ⎜ s p ⎟ 2 ⎜⎝ gD ⎟⎠
2/3
1/ 3
⎛ 1 ⎞ =⎜ ⎟ ⎝ 2 tan θ ⎠
(1)
where Ug is the superficial gas velocity, Gs is the solids flux, D the column diameter, θ is the angle of internal friction and is typically around 70 degrees for round-shaped particles. Equation (2) developed by Bi and Fan [2] based on experimental data in CFB risers is selected to predict the saturation carrying capacity:
U CA / gd p = 21.6 Ar 0.105 (Gs / ρ gU CA ) 0.542
(2)
Figure 1 shows such a flow regime diagram for a gas-solids vertical transport line with an upward gas flow. It is seen that there exist five unique flow regimes in the tube, as summarized in Table 1. Table 1. Flow regimes and corresponding flow patters in a vertical tube with open ends. Regime Ug Gs Upper section Lower section I P1 >PR while in laboratory research units PS = PR = P2. Some of the empirical correlations proposed to relate solid flux to gas velocity are summarized in Table 1. Table 1: Correlations reported in the literature for sold flux through L-Valve Reference Correlation Geldart and Jones (2) Gs u
Dt
= 3354
u mf
− 2965
Smolders and Baeyens (6)
⎛ u Gs = 79600⎜ ⎜u Dt ⎝ mf
Daous , Al-Zahrani (10)
Gs u = 1.08 − 5450 Dt Dp
Chan et l.(12)
⎡ ⎛ u ⎞⎤ Gs ⎟⎥ = 0.0002 ⎢ln⎜ Dt ⎢⎣ ⎜⎝ D p ⎟⎠⎥⎦
2
⎞ 0 .6 ⎟ Dp ⎟ ⎠
8.9
Yang and Knowlton (9) presented a model to estimate solid flow rate in a Lvalve assuming no slip between flowing gas and particles; the net gas flow was assumed to include external aeration introduced along with the gas flowing through the solids in the standpipe; they adopted the equation of Jones and Davidson (10) (developed for particle discharge through an orifice from fluidized beds) for solids flow rate through an active flow area which increased with L-valve pressure drop; however, the correlation proposed by them reflects the universally observed need of a threshold aeration rate to initiate flow of solids. Daos and Al-Zahrani (11) and Tong and Zheng (12) considered gas-particle slip velocity to be described by Ergun’s equation for flow through packed beds. Tong and Zheng (12) considered mechanics of particulate media flow in modeling gas and particle flows in a L-valve; they noted that external aeration rate can split into two streams – one flowing out horizontally through the bend and the other through the vertical stand pipe – depending on the relative resistance in each section for gas flow. Asymmetry in the introduction of gas flow and two phase flow through a three dimensional 90o bend in a L-valve makes particle flow visualization difficult. In an interesting piece of effort, Chan et al. (13) investigated particle motion by positron emission particle tracking technique to identify particle flow structure and observed solid flow to be stable for u/umf less than 6. They observed that maximum solid flow rate in a hopper fed laboratory L-valve is limited by the hopper discharge pipe diameter. Agarwal (14) investigated particle flow structure in a 2-dimensional L-valve made of 0.6 cm thick Perspex sheets with a cross sectional size of (13.8 cm x 2 cm). Effect of standpipe height (i150 to 100 cm), length of horizontal section (i30 to 50 cm) and particle size on solid discharge rate as a function of aeration rate were investigated. A typical solid flow trajectories are shown in Fig 1. For gas flow rates below threshold aeration, solids were stagnant; at and around the threshold aeration rate first movement of solids was observed near the 900 bend at the upper edge of the L-valve; coordinates of points farthest from Y-axis upto which solids velocity is zero (y) were noted for each (x) coordinate; these points were plotted as shown in Fig. 1.
cms
Zone of Stagnant Solids
Zone of Solid flow
cms
Fig.1 Solid flow trajectories in a 2-D L-valve (Agarwal(14))
At an aeration rate of 422 cc/s with a low solid flow rate of 10 kg/hr, particles movement was restricted to a rather narrow region very near the inside of 900 turn and along the top edge of the horizontal section over a stagnant layer; at the exit, particles rolled out over the slope dictated by angle of repose; cross sectional area of particle down flow in the stand pipe increased with distance away from the 90o bend. At 750 Kg/hr, the stagnant region decreased in size and at 1700 Kg/hr it decreased further but still a substantial portion of the particles in the bend remained stagnant. Similar solid flow profiles were observed at different aeration point locations with the other particles. A simple equation for solid flow rate as a function of the affecting parameters is needed for rational design of L-valves. In the present work, an attempt is made to develop equations for a L-valve operating with PS = PR = P2 considering that a threshold aeration rate is needed to initiate solid flow and solids flow rate increases with fluid drag on particles while their movement is resisted by the drag due to wall / stationary particles on moving solids. THE MODEL The aeration rate Q provided to a stand alone L-valve containing particles in the standpipe and horizontal pipe in a packed bed form gets split into an up flow stream QU (through the standpipe) and downflow stream QD (through the horizontal pipe) depending on their relative packed bed resistance for gas flow. At a threshold aeration rate QTh, solids flow rate is initiated through a “small throat” at upper wall 90o bend of the horizontal pipe as the gas drag force overcomes the friction between particles to wall/particles to push the particles to the edge where they roll over by gravity. Particles in the standpipe descend by gravity to the extent of solid flow through the horizontal section. As the particles descend in a section of the standpipe against the up flow aeration rate of QU, the gas to particle relative velocity in that section will be at the incipient fluidization condition as the bed voidage is around 0.5. The “small throat” diameter needs to be greater than 5xDp to 10xDp to overcome arching tendency. Further increase in external aeration through the L-valve increases the gas drag force on particles to increase solid flow rate and throat area near 90o bend near the upper wall. Due to the gas-particle slip velocity and increased sold flow rate, most of the external aeration ends up in the down flow stream assisting solid flow rate. Together, the solid flow rate depends on aeration rate Q above the threshold aeration rate QTh, standpipe diameter D, height of the standpipe above the aeration point HU, length of downstream solid flow path HD through which solids flow out, particle diameter Dp, particle density !p, gas density !g and gas viscosity . Based on this hypothesis, equations are developed in the following sections. Model for Threshold Aeration Rate QTh: Threshold aeration rate is the aeration rate at which the drag by gas can just overcome the friction between particles and particles/wall to let the solids flow along with the gas. Aeration flow introduced into the standpipe at a pressure P1 gets split into upward flow through the standpipe and downward/horizontal flow through the horizontal section depending on the resistance in each section. As both sections are filled with particles in a packed bed form, assuming laminar flow and exit pressure P2 to be same, from Ergun’s equation, the upflow and downflow components can be expressed as
QU = QD =
πDt2
ε3
πDt2
ε3
D p2 (P1 − P2 )
4 150 ( 1 − ε )2
μHU
1
μH D
2
D p2 (P1 − P2 )
4 150 ( 1 − ε )2
From ratio of these two flows
QU H D = QD H U
3
and the two components add upto the aeration rate Q
QU + QD = Q
4
From equations 3 and 4, flow in the horizontal section can be obtained as
⎛ H ⎞ Q D ⎜⎜1 + D ⎟⎟ = Q ⎝ HU ⎠
5
From equations 2 and 5
D p2 (P1 − P2 ) ⎛ H U + H D ⎜⎜ Q= 4 150 ( 1 − ε ) 2 μH D ⎝ HU
πDt2
ε3
⎞ ⎟⎟ ⎠
6
At the point of particle flow initiation, particles flow through a critical throat area (fU (Dt2/4)). Relative velocity between gas and particles in the moving section of the standpipe will be around umf. Hence, pressure drop for gas upflow in that section can be expressed as
P1 − P2 = H U (ρ p − ρ g )(1 − ε )g
7
With this approximation, threshold gas velocity can be obtained as
D p2 (ρ p − ρ g )g (H U + H D ) QTh = fU 4 150 ( 1 − ε ) HD μ ( HU + H D ) πDt2 = fU umf = fU X HD 4
πDt2
ε3
8
with X defined as
X =
πDt2 4
u mf
HU + H D HD
9
Threshold Aeration Model Validation: Experimental observations on QTh reported in the literature are compared with parameter X, defined by equation 9 in Fig. 2. The correlation is reasonably good and an average value for the factor fU at the initiation of particle flow is estimated to be 0.07.
QTh = 0.07
πDt2 4
umf
HU + H D HD
10
350 300
QTh, mL/s
250 Knowlton
200
Arena 150
Geldart Zheng
100
QTh=0.07 X 50 0 0
1000
2000
3000
4000
5000
6000
Parameter X
Fig.2 Correlation of data on threshold aeration rate reported in literature with parameter X (Eq.9) Model for Particle Flow Rate: Let the interstitial gas velocity be u and solids velocity be v in the horizontal pipe through which solids flow out of L-valve. The flow of solids due to fluid drag is resisted by the friction at the non moving particles/pipe walls.
πD 2 H D 6(1 − ε ) πD p2 Np FD = c Dm ρ g (u − v )2 3 4 πD p 4
=
f πDH D ρ p (1 − ε )ε
= Friction at the wall
Fluid drag on particles
v2 2
11
For aeration rates Q greater than the threshold QTh, area (D2/4) of solid flow increases with gas flow upto (Dt2/4) where the solid flow rate will be Wmax.
W D 2 − DTh2 = Wmax Dt2
≈
D2 Dt2
12
Assuming laminar flow conditions, the drag coefficient and friction factor can be taken as
c Dm
∝
μg
D p ρ g (u − v )
; f
∝
μb Dρ b v
13
Rearranging equations 11 with 12 and 13
u
2 ⎛ k μ b D p ⎞⎟ ⎜ = 1+ v ⎜ 1 − ε μ D2 ⎟ g ⎝ ⎠
14
where k is a constant. Combining equation 14 with 12
(Q − QTh )
⎛ D2 W ⎞ ρ p (1 − ε ) = ⎜⎜1 + kv p2 max ⎟⎟W Dt W ⎠ ε ⎝
15
where kv is a constant incorporating gas viscosity, moving bed viscosity, bed voidage, k and other constants.
Yang and Knowlton (8) suggested that maximum flow rate of solids may be estimated using the equations proposed by Jones and Davidson (10) developed for particle discharge rate from fluidized beds through an orifice
= 0.56 ρ p (1 − ε )g 0.5 H U0.5 ( Dt − D p ) 2
Wmax
16
However, particles move as a packed bed in a section of the horizontal pipe. Hence, maximum flow rate of solids may be dictated by the diameter of the horizontal pipe and may be estimated by the equation of Beverloo et al. (15) given as
= 0.58ρ p (1 − ε )g 0.5 ( D t − 1.5D p ) 2.5
Wmax
17
Validation of the Model for Particle Flow Rate: Data of Knowlton and coworkers (1), Zeng et al. (4) and Arena et al. (7) on solid flow rate as function of aeration rate are compared with the present model equation 15 in Fig.3 with and kv assigned values of 0.4 and 2640. Table 2 summarizes the details of the properties of the experimental systems. The estimates of maximum solid flow rate by the equation 16 of Jones and Davidson (10) and equation 17 of Beverloo et al.(15) are included in Table 2. Experimentally observed maximum solid flow rates are in the range suggested by the Beverloo equation. Table 2. Details of the systems used for the evaluation of the equation 15 Reference Knowlton 1, (1) Knowlton 2, (1) Knowlton 3, (1) Zheng, (4) Arena 1, (7) Arena 2, (7) Arena 3, (7) Arena 4, (7)
D cm 7.62 5.08 7.62 4.7 2.7 2.7 2.7 2.7
HS cm 854 854 854 295 270 270 270 270
HD cm 45.72 45.72 45.72 32.6 31.7 31.7 31.7 31.7
Dp cm 0.02609 0.02609 0.02609 0.0605 0.0073 0.0156 0.0341 0.017
!p g/cm3 2.611 2.611 2.611 1.392 2.55 2.55 2.55 4.46
W(1+[kvDp2Wmax)/(Dt2W)], g/s
10000
1000 Knowlton 1 Knowlton 2 Knowlton 3
100
Zheng Arena 1 Arena 2
10
Arena 3 Arena 4 Parity line
1 1
10
100
1000
10000
(Q‐QTh)!p((1‐ε)/ε), g/s
Fig.3. Comparison of equation 15 with the literature data.
Wmax. g/s Eq.16 Eq.17 38820 3798 17253 1378 38820 3798 4629 605 2678 277 2678 277 2678 277 4683 485
Data of Knowlton and coworkers (1) Zeng et al. (4) and Arena et al.(7) are resasonably well represnted by the equation 15 as shown in Fig.3.. Using equations 15 and 17, maximum solid flow rate and corresponding maximum aeration rate Qmax upto which the L-valve can be operated can be estimated. CONCLUSIONS 1). A model for solid flow rate in a stand alone L-valve as a function of external aeration rate is developed considering that a minimum threshold aeration rate QTh is necessary to initiate solids flow rate in L-valves. 2). Threshold aeration rate increases with increase in L-valve diameter and particle minimum fluidization velocity. The model equation 10 gives a reasonable description to the experimental observations reported in the literature as shown in Fig.2. 3). Equation 15 with kv assigned a value of 2640 could correlate the data of Knowlton and coworkers (1), Zeng et al. (4) and Arena et al. (7) on solid flow rate as a function of external aeration rate as shown in Fig.3.
⎛ D p2 Wmax ⎞ ρ p (1 − ε ) ⎟W (Q − QTh ) = ⎜1 + 2640 2 ⎜ ⎟ D W ε t ⎝ ⎠
18
ACKNOWLEDGEMENT The author is thankful to University Teknologi PETRONAS for the encouragement and support for carrying out this work. NOTATION cDm D Dp Dt f fU g Gs HD HU Hs P1 P2 Q QD QTh Qu
Gas-particle drag coefficient Diameter of throat through which solids flow (cm) Diameter of particles (cm) L-valve diameter (cm) Friction factor for flow of solids Fraction of standpipe particles set in motion Acceleration due to gravity (cm/s2) Particle mass flux, (kg/m2/s) Length of down flow section (cm) Height of upflow section (cm) Standpipe height (cm) Aeration point pressure (g/cm/s2) Discharge point pressure (g/cm/s2) Aeration flow rate (cm3/s) Downward aeration flow through Horizontal section (cm3/s) Threshold Aeration Rate (cm3/s) Upward aeration flow through standpipe (cm3/s)
u’, v’ u umf W Wmax
Actual gas and particle velocities (cm/s) Superficial gas velocity (cm/s) Minimum fluidization air velocity (cm/s) Solids flow rate (gm/s) Maximum solid flow Rate (gm/s)
Greek Symbols ,!g !b !P b
Bed voidage Gas density (g/cm3) Density of moving bed, Particle (g/cm3) Gas viscosity (g/cm/sec) Viscosity of moving bed (g/cm/sec)
REFERENCES 1. 2. 3.
4.
5.
6. 7. 8. 9.
10. 11. 12. 13.
14. 15.
T. M. Knowlton, I. Hirsan, L-valves characterized for solids flow, Hydrocarbon process, 3(1978) 149-156 D. Geldart, P. Jones, The behavior of the L-valves with granular powders, Powder Technol, 67 (1991) 163-174. M.Ozawa, S.Tobita, T Mii, Y.Tomoyasu, T.Takebayashi, K Suziki, Flow pattern and flow behaviour of solid particles in L-valve, CFB Technology III, (Eds) P.Basu, M. Horio and M.Hasatani, Pergamon, Oxford (1991), 615-620. Q.Zheng, Z.Ma, A.B.Wang, Experimental study of the flow pattern and flow behaviour of gas-solid two phase flow in L-valve, in:A.A.Avidan, (ed). CFB Technology IV, Hidden Valley, USA (1993) 246-252 M. Rhodes, H. Cheng, Operation of an L-Valve in a circulating bed of fine solids, in:A.A.Avidan, (ed). CFB Technology IV, Hidden Valley, USA (1993) 240-245. K.Smolders and J.Baeyens, “ The operation of L-Valves to control standpipe flow” Advanced Powder Technology, vol. 6 (1995) 163-176 U. Arena, A. Cammarota, “L-valve behavior with solids of different size and density”, Powder Technol, 98 (1998) 231-240. W.Yang, T. M. Knowlton, L-valve equations, Powder Technol, 77 (1993) 4954 T. M. Knowlton, “Standpipes and return systems”, in: J.R.Grace, A.A.Avidan and T.M.Knowlton (Eds.), Circulating Fluidized Beds, Chap 7, Blackie Academic & Professional, an imprint of Chapman & Hall, London, 1997, 214260. D.R.M. Jones, J.F. Davidson, The flow of particles from a fluidized bed through an orifice, Rheologica Acta 4 (1965) 180– 192. M. A. Daous, A. A. Al-Zahrani, “Modeling solids and gas flow through an Lvalve”, Powder Technol, 99 (1998) 86-89. H. Tong, Q. Zheng, “Hydrodynamic modeling of the L-valve”, Powder Technol, 129 (2003) 8-14. C.W.Chan, Jonathan Seville, Xianfeng Fan, Jan Baeyens, “Particle motion in L-valve as observed by positron emission particle tracking”, Powder Technology 193 (2009) 137-149. Amit Agarwal, “Solid flow in a 2 D Lvalve”, M.Tech Thesis IIT Delhi (2004). W.A. Beverloo, H.A.Leniger, J, van de Velde, The flow of granular solids through orifices, Chem.Eng.Sci., 15 (1961)260-269.
PROCESS DECOUPLING OF PLASMA ENHANCED SYNTHESIS OF CHLORINATED POLYVINYL CHLORIDE (CPVC) PARTICLES IN A CIRCULATING FLUIDIZED BED Wei Lu, Tengfei Cao, Yi Cheng* Department of Chemical Engineering, Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Tsinghua University Beijing 100084, P.R. China PH: +86-10-62794468; FAX: +86-10-62772051; e-mail:
[email protected] ABSTRACT Plasma enhanced synthesis of CPVC particles in a gas-solid plasma circulating fluidized bed reactor (PCFBR) is proposed as a novel CPVC synthesizing method. The chlorination process is decoupled into a fast initiation step in a plasma riser and a slow chlorination process in the accompanying bed. The CPVC product has good properties in terms of chlorine content and microstructure. INTRODUCTION Chlorinated polyvinyl chloride (CPVC) is produced by chlorination of polyvinyl chloride (PVC) particles. In recent years, CPVC has received much attention as a kind of high-performance thermoplastic which gives better performance in heat stability, mechanical properties and flame retardant ability because of the increase of polarity compared with PVC. Therefore, CPVC can be widely used in hot and cold water pipes, industrial liquid handling and other commercial applications (1). For the rapid development of the chlor-alkali industry in China, a large amount of excess chlorine has caused a serious problem, i.e., how to balance the noxious chlorine gas or the chlorine ion as in HCl from a sustainability viewpoint. PVC is a major solution to this problem, CPVC provides a further valuable route to immobilize chlorine. By further chlorinating the PVC particles to CPVC, more chlorine gas can be fixed into the solid products. It is known that the chlorine content of PVC is about 56.7 wt%. Most commercial CPVC resins have a chlorine content ranging from 63 wt% to 69 wt%, and even to 74 wt% by special treatments. For example, about 400 kg of chlorine is consumed by chlorinating 1,000 kg of PVC to CPVC with a 69 wt%
chlorine content. Accordingly, the CPVC industry not only consumes the excess chlorine, but also converts PVC into another high-value material. Among the reported approaches to synthesizing CPVC, the aqueous suspension method is the main production technology at the commercial scale (2-3). However, a gas-solid method is acknowledged as a cleaner process, considering the ease to control and to separate the CPVC products from waste gases, etc. The main challenge of this method is to improve the gas-solid contact efficiency and find an effective initiator. In general the approach is carried out in fluidized beds, utilizing UV light as the initiator. But, the UV light is too easily shielded by PVC particles and the capability of initiation is weakened. So, finding a more effective and cleaner initiator for the gas-solid chlorination method is of great importance. It is reported that the typical mechanism of PVC chlorination is a series of free radical reactions (4). It was proposed that cold plasma could be an effective initiator instead of UV. Cold plasma can be comprised of many kinds of ions, radicals, UV light, etc. Researchers have shown that cold plasma can be widely applied in the surface treatment of polymer materials (5-6). The mechanism showed that plasma could activate the surface of polymers and reactive gas simultaneously. However, the chlorine absorbed on the surface of a PVC particle must migrate into the core of the particle to chlorinate the bulk of the particle, which is assumed to be a slow diffusion-like process (7). Therefore, the chlorination process can be decoupled into two steps: the first step is the initiation of chlorination by plasma, and the second step is the chlorine diffusion from the surface to the inside of particles in a chlorine atmosphere. In this work, we proposed a novel CPVC synthesizing method in the Plasma Circulating Fluidized Bed Reactor (PCFBR). EXPERIMENTS First, a fixed-bed DBD reactor was employed to examine the feasibility of the CPVC synthesizing method. PVC particles were chlorinated with/without plasma at 100 C. During chlorination, the plasma power source was turned on for 1 minute and then turned off for 10 minutes to simulate the process decoupling method. The power density of the plasma was 8.1 W/cm3 at atmospheric pressure, and the working frequency was 13.8 KHz. A PCFBR was designed to synthesize CPVC as shown in Figure 1. The riser was assembled by two 0.5 m long straight coaxial quartz tubes with a gap of 4 mm, and the thickness of quartz tubes was ~2 mm. An iron bar was inserted into the inner tube as one electrode while some copper was wound outside the outer tube as the other.
One electrode was connected to the DBD power source (CTP-2000K, Nanjing Suman Electronics Co., Ltd), the other electrode was grounded. The plasma was generated by means of double–dielectric barrier discharge method inside the gap of two tubes. The accompanying bed was made of quartz, the same as the riser. The particle circulation rate was controlled by the flow rate of carrier gas. The chlorine gas had a purity of 99.999%, and the other gases including Ar and N2 had a purity of 99.9%. The waste gas from the reaction unit was treated by an alkali solution. Waste gas
Alkali Solution
Electrodes Plasma generator MFC Cl2 Thermostat
Ar Cl2 N2
Figure 1 Schematic drawing of the plasma circulating fluidized bed reactor (PCFBR) Approximately 70 g of PVC particles (SG-5 provided by Xinjiang Tianye Co. Ltd.) with ~150
m diameter were added to the accompanying bed. The whole apparatus was
purged with N2 gas in order to exhaust the oxygen in the system and fluidize the particles. The accompanying bed was heated up from room temperature to 70 oC, and then the reactant gas, e.g., the mixture of chlorine and N2, was injected into the accompanying bed from the bottom nozzle. The gas flows were fixed at 1 SLM Cl2 and 1 SLM N2. At the same time, a mixture of chlorine and Ar was injected into the riser from the bottom nozzle with two different flow rates of 0.3/2.0 SLM and 0/2.5 SLM. The Cl2/Ar plasma was generated inside the gap between the two tubes by the DBD method. The power density and frequency were 1.02 W/cm3, 12.7 KHz (Cl2/Ar=0.3/2.0) and 1.24 W/cm3, 13.6 KHz (Cl2/Ar=0/2.5, respectively). The particle circulation rate in both situations was ~0.2 g/s. After treating particles at 70 C for 20 minutes, the temperature was raised to 100 C for 40 minutes. After that, the particles were chlorinated for different times. At the end of the experiments, the plasma generator, chlorine flow and heating apparatus were turned off but N2 and Ar were kept flowing for several minutes to purge the remaining chlorine in the apparatus and that absorbed on the particles. Then the particles were exposed in the fume hood for
a day or more so that chlorine gas volatilized thoroughly. The CPVC products were characterized by several methods. The chlorine content was analyzed by oxygen combustion and electrification method. Each product was characterized for at least 3 times to get an average and the error was found to be less than 0.5%. The distribution of chlorine in CPVC particles was measured by SEM and EDS (JSM-6460LA and EDS were made by the Oxford Instrument Company). The microstructure of CPVC products was characterized by Raman spectrum analysis (RM2000 made in Renishaw, UK.). In the measurements, the laser was Ar-514, with 20X objective lens, 5 m diameter of facula, 4.7 mw and 30 s scanning time. RESULTS AND DISCUSSION Chlorination of PVC particles without a plasma initiator was carried out in the first series of experiments in the fixed bed reactor. The chlorine content of the CPVC particles rose to 69.7 wt% after 12 hours (see Figure 2). It proved that the chlorination process could take place by heating PVC particles at high temperature without any other initiator. However, it is not an effective method due to the low reaction rate. Then an atmospheric pressure DBD plasma was employed as the initiator in the second experimental series. It can be clearly seen that the chlorination process was accelerated significantly by the cold plasma and the chlorine content rose to 67 wt% in only 3 h. Considering that the working time of plasma was only 5 min/h under the intermittent operation, this proved that plasma was an efficient initiator in PVC chlorination.
68
64
60
o Heated PVC particles at 100 C
56
for chlorination o CPVC at 100 C with plasma as initiator
0
3
6
9
12
Chlorine Content /%
Chlorine content /wt%
72 the ratio of Ar/Cl2=2.5/0,
66
1.24 W/cm , 13.6KHz
63
1.02 W/cm , 12.7KHz
3
the ratio of Ar/Cl2=2.0/0.3, 3
60
57
15
Chlorination time /h
Figure 2 Chlorination curves of PVC particles with/without plasma as the initiator at 100 C
0
1
2
3
4
5
Chlorination Time /h
Figure 3 The chlorine content of CPVC by mass as a function of reaction time in PCFBR
The results obtained from fixed-bed DBD reactor proved that the cold plasma was
able to initiate the chlorination effectively. At the same time, the method of process decoupling was feasible and could be operated in a CFB reactor. That was, PVC particles were treated fast in the riser by cold plasma and the chlorination was initiated, then the chlorine slowly diffused into the core of the particles in the accompanying bed with a relatively long residence time. Particles were circulated in the PCFBR several times to be chlorinated sufficiently. The following discussion is based on the experiments in the PCFBR. The chlorine content of CPVC is shown as a function of reaction time in Figure 3. One series of the results shows the chlorine content increasing up to 65.0% when pure Ar was used as the carrier gas with plasma in the riser. These results revealed that PVC can be chlorinated in the PCFBR. The total residence time of particles in the plasma zone was only about 22.2 s during the 4-hr experiments. In fact, with appropriate optimization of the PCFBR operation, a successful single-run of the chlorination process would be ~1.5 hr, or even much less. Figure 3 also indicates that under the same power input of plasma, the composition of carrier gas in the riser is very important, because it provides the atmosphere in which plasma is generated, and the reactive species are different in different plasmas. So it is possible to adjust the property of plasma by changing the ratio of Cl2/Ar. At the same time, the mechanism of chlorination is changed. For example, chlorination can occur in the riser when Cl2 is introduced, which is different from the case of pure Ar as carrier gas. It was noticed that the chlorine content increases linearly as a function of reaction time in Figure 3 for the case of Cl2/Ar=2.0/0.3. This might indicate that when introducing chlorine gas with Ar to generate plasma, not only are the PVC particles activated, but also the surface of the PVC particles is chlorinated rapidly in the plasma zone. However, the chlorine content increased slowly when the chlorination time was lengthened. Another fact is that the discharging of Cl/Ar plasma was weaker than pure Ar plasma so that the initiation effect became weaker. Obviously, work is required for a deep understanding of the complex mechanism and for guiding this optimization of the process design. CPVC characterization SEM and EDS can give a semi-quantitative result by comparing two samples under the same conditions. The chlorine content shown in Figure 4 is the mass fraction of chlorine. The total amount of chlorine and carbon elements (hydrogen is not included because EDS cannot discern light elements). So this value is larger than the actual chlorine content of CPVC. Considering that the analysis error of the instrument is less than 5 %, in principle, the results of EDS shown in Figure 4 indicate that the CPVC synthesized in this work has a much higher chlorine content than PVC raw
material. In addition, the scanning area was the interior of the CPVC particles, which proved that chlorination occurred inside the PVC particles, not only on the surface.
Figure 4 Chlorine distribution in the particle of plasma synthesized CPVC (left), and PVC raw material (right) The microstructure of CPVC was analyzed by Raman spectral analysis. In Figure 5, five kinds of CPVCs were analyzed, including four samples of CPVC synthesized using PCFBR with Ar plasma as the initiator and one sample of commercial CPVC using the aqueous suspension method. The PVC SG-5 raw material was also analyzed for comparison. In the spectra, the characteristic peak of CPVC is at 300-500 cm-1.
25000
Intensity
20000 Commercial CPVC
15000
PECFB 4h
10000
PECFB 3h PECFB 2h
5000
PECFB 1h PVC SG-5
0
0
750
1500
2250
3000
-1
Wavenumber cm
Figure 5 Raman spectra of PVC or CPVCs
3750
In the chlorination process, the increase of the amount of -CCl- can be seen clearly. At the same time, there is essentially no -C=C- group in all of the samples, which has a signal at 1650 cm-1. This proves that almost all the CPVC samples have a fine microstructure. But with it is still evident that there is a very weak -C=C- signal in the spectrum of an every CPVC sample and no signal in the spectrum of PVC, which reveals the influence of Elimination-Addition mechanism. To sum-up, it was shown that chlorination occurred in the bulk of the PVC particles and that the microstructure of plasma synthesized CPVC was comparative to the commercial product by the Raman analysis. CONCLUSION In this article, a novel CPVC synthesizing method was proposed that employed plasma as the initiator and which operated in a circulating fluidized bed. The chlorination process consisted of two steps: a fast initiation step and a slow chlorination process. In the chlorination process, the residence time of particles in the plasma zone was only about 22 seconds in the total 4-h reaction time, which indicated that plasma had high initiation efficiency. In the experiments so far, the chlorine content of CPVC product reached 65.0%. Characterizations by SEM and Raman spectrum show that the bulk of the particles were chlorinated. Moreover, the particle product had a uniform chlorine distribution inside the particles and a fine microstructure. The objective of this work was to propose a novel CPVC production method with preliminary demonstration of feasibility. This would also open a specific, potential area for CFB applications. Still, there is lots of work to do in the future, for example, to investigate the operational performances of PCFBR, plasma-related design and optimization, influence of PVC particle properties on the chlorination process, etc. ACKNOWLEDGEMENT Financial support from the National Science and Technology Key Supporting Project (No.2009BAC64B09) and the Program for New Century Excellent Talents in University are acknowledged. REFERENCES 1. Liu, H., and Zhang, X.M. (2008). “Review on chlorinated polyvinyl chloride.” Polyvinyl Chloride, 36 (11), 9. 2. Alan, O. J., and Robert, V. G., Process for chlorination of PVC in water without use of swelling agents: US, 4412898 [P], 1983 3. Wakabayashi, T., Kobayashi, Y., and Tujii, I., Process for the proparation of
chlorinated polyvinyl chloride resin: US, 3534013 [P], 1970 4. Lukas, R., Svetly, J., and Kolinsky, M. (1981). “Structure of chlorinated poly (vinyl chloride) X. Conclusions on the chlorination mechanisms.” Journal of Polymer Science: Polymer Chemistry Edition, 19, 295. 5. Arpagaus, C., Sonnenfeld, A., and Von Rohr, P. R., (2005). “A downer reactor for short-time plasma surface modification of polymer powders.” Chemical Engineer Technology, 28 (1), 87. 6. Arpagaus, C., Rossi, A., and Von Rohr, P. R., (2005). “Short-time plasma surface modification of HDPE powder in a plasma downer reactor – process, wettability improvement and ageing effects.” Applied Surface Science, 252, 1581. 7. Wachi, S., Morikawa, H., and Inoue, H. (1988). “Conversion distribution in diffusion-governed chlorination of poly (vinyl chloride).” AICHE Journal, 34 (10), 1683.
A PRACTICAL MODEL FOR A DENSE-BED COUNTERCURRENT FCC REGENERATOR Yongmin Zhang, Chunxi Lu State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, 102249, P. R. China Abstract In this study, a new practical countercurrent regenerator model for in-situ FCC operation optimization was proposed. A three-zone-and-two-phase gas model and a new two-CSTR-with-interchange model were used to give better descriptions on the gas and solids flow patterns, addressing the region-dependent mass transfer rates and the freeboard effect on catalyst regeneration. The model coupled mass and heat balances, hydrodynamics and reaction kinetics. The modeled results are in reasonable agreement with the commercial data from an industrial FCC regenerator under both partial and full CO combustion modes. INTRODUCTION A regenerator is an indispensable part of a FCC unit, acting as a fluidized-bed reactor to burn the coke deposited in the spent catalyst and recover its cracking activity. An ideal FCC regenerator requires very low levels of carbon content in regenerated catalyst (CCR) (0.05~0.1 wt%) with minimized air consumption and maximized coke burning intensity (CBI) (usually defined as weight of coke burned for a given catalyst inventory and a given period). A practical regenerator model based on sound understanding of its intrinsic hydrodynamics, mixing and reaction kinetics is undoubtedly valuable to optimization of its design and operation. There have been several published studies on modeling dense-bed FCC regenerators (1-7). However, they all failed to describe the gas and solids flow patterns properly in the three zones (grid zone, dense-bed zone and freeboard zone) of a regenerator simultaneously, resulting in modeled results divergent largely from experimental facts and low reliability and predictability. Some of them (1-5) used the simple Orcutt fluidized-bed model (8) to model gas flow in the dense bed, which falsely modeled the reactant gas concentration in the emulsion phase to be a constant level. Otherwise, only Lu (5) and De Lasa et al. (7) considered the large amount of particles entrained in the freeboard and the associated reactions. However, Lu (5) improperly modeled the solid flow in the freeboard with a multiple-CSTR-in-series model, which over-predicted the freeboard reaction. The freeboard model of De Lasa et al. (7) was a particle-trajectory based model, which was too complex to use in engineering practice. The goal of this study is to establish a modified model for a countercurrent regenerator. This model has a modified hydrodynamic model that provides better
-1-
descriptions for gas and solid flows in both dense bed and freeboard. Otherwise, its structure still remains simple enough to be a practical engineering tool. MODEL SCHEME y O , out u2 At2 2
freeboard
freeboard
CCf
y O , z=H u1At1 2
K be
bubble void
Fs,df
Fs,df
dense bed
f
Fs0
emulsion
dense bed CCd
kj
jet
emulsion
y O , z=0 u1At1
Fs0
2
(a)
(b)
Fig. 1 Gas and solid flow patterns in the countercurrent FCC regenerator model: (a) gas flow pattern; (b) solid flow pattern
A countercurrent regenerator is usually the preferred choice in a FCC unit for its better performance, where catalysts are usually injected to the top of its dense bed by a specially designed catalyst distributor, and withdrawn through the bed bottom. Figure 1 illustrates the hydrodynamic models describing the gas and solid flow patterns in this study. For the gas flow in the dense bed, a simple two-phase bubbling-bed model proposed by Chavarie and Grace (9) is used. This is a two-phase model with a “stagnant” emulsion phase, i.e. gas in the emulsion phase coming only from mass transfer from the bubble phase and without axial dispersion. Different from the Orcutt model (8), there is an axial gradient for the reactant gas concentration in the emulsion phase in agreement with experimental facts. Axially, two zones were partitioned in the dense bed to address the different gas transfer rates between emulsion and voids in the bubbling zone and jets in the jet zone. In the freeboard, the gas phase becomes a continuous phase, where interphase mass transfer becomes less important than in the dense bed. Gas flows in the jets, voids and freeboard were all modeled as plug flow without back-mixing. For an irreversible first order reaction A B with negligible volume change, mole balances on A in the bubble phase and emulsion phase yield, respectively, u0
dC Ab + kbeab db (C Ab - C Ae ) + kr f sbC Ab = 0 , dz
(1)
kbeabdb (C Ae - C Ab ) = kr fseC Ae .
(2)
-2-
For the solids flow, a two-CSTR-with-interchange model shown in Fig. 1(b) was adopted. A distinct difference in this model lies in its different d t2 manipulation on solid flow in the freeboard. In a typical fluidized bed, particles in the freeboard come mainly from bubble eruptions on the bed surface. Particle concentration freeboard and upward flux decrease exponentially with increasing distance from the bed surface. Only a negligibly small fraction of particle leaves from the freeboard top and is Cs Fs0 C CHs u1At1 QH O captured by cyclones. This demonstrates that most solid inventory in freeboard exists within a small-height zone near d t1 the bed surface, i.e. the so called splash zone. In this zone, bubbling violent mixing due to strong gas flow turbulence and large zone solids exchange rate between the dense bed and the jet zone freeboard can be expected. Therefore, solids flow in F s0 CCr freeboard was modeled as a separate CSTR reactor with u1At1 solid exchange with the dense bed in this model. Physically, Fig. 2 Geometry model for freeboard in this model is to provide particles with additional an FCC regenerator with time to burn coke with negligible interphase mass transfer an expanded freeboard resistance. 2
Other simplifications are assumed to facilitate the modeling. First, the hydrogen content of the coke is assumed to combust instantly near the bed surface due to the much higher combustion rate of hydrogen (usually an order faster than carbon combustion) (10). Second, the structure of the FCC regenerator is simplified as showed in Fig. 2. The bottom bed section is always assumed to have the same height as the dense bed, Hf, whereas the expanded top section is assumed to be a cylinder of diameter dt2 and height Ht-Hf. MODEL SETUP Kinetic Model Due to the simplification for hydrogen combustion, only carbon combustion needs to be considered in this model. Carbon combustion can be described by .
C+
æ b ö÷ æ 1 ö÷ 2b +1 O2 çç ÷ CO2 + çç ÷ CO ÷ ç 2 (b +1) è b +1ø èç b +1ø÷
(3)
where is the ratio of CO2 to CO released. is affected by many factors including catalyst type, feedstock, temperature, contents of oxygen and CO promoter etc. In this model, is simply determined as the ratio of CO2 and CO concentrations in the flue gas of the modeled regenerator. This also simplifies the complex homogeneous and heterogeneous CO combustion procedures in actual conditions. The carbon combustion rate is estimated by (11)
-3-
æ 1.422´105 ö÷ ÷. kC = 2.967 exp çççè RT ø÷÷
(4)
Hydrodynamics Model Two important parameters in the grid zone, jet length and jet diameter, are determined by Lu‘s correlations (5). Lj 141.85d or (
p d p 0.273 g uor d or 0.654 uor2 0.408 , ) ( ) ( ) gd or g d or g
D j 0.388d or (
uor2 0.332 . ) gd or
(5)
(6)
The average bed density is also determined based on the measured industrial data as expressed by Eqs. (7) and (8). The derivative in Eq. (8) is derived from a correlation of Cai et al. (12) rB =rB,exp +
¶rB (u1 - u1e ) ¶u1
(7)
3n (rp - rB,exp ) ¶rB ¶ éêërp (1- e)ùúû = =u1 ¶u1 ¶u1
(8)
The dense bed height and the axial particle concentration profile are determined based on Zhang et al. (13), which considered the solid mass balance of the whole regenerator. The solid fraction in the freeboard is expressed as fs =f s* + ( f s0 -fs* ) exp (-azf ) ,
(9)
where fs* is the saturated solids fraction, determined by measured cyclone inlet concentration in this study, fs0 is the initial solid fraction at the bed surface and determined by 0.3 u1 umf 1 mf At1 . (10) fs0 At2ub
Here, ub is void rise velocity determined by the ratio of superficial gas velocity and bubble fraction in the dense bed, i.e. u1/ b; the exponent coefficient is determined by 0.7/u2 according to Zhang et al. (13). Based on mass balance in the regenerator,
B H f At1 p At2
H t Hf
fs dzf M s ,
0
(11)
the dense bed height Hf can be determined. Gas transfer coefficient between jet and emulsion is estimated by Lu’s correlation (7), -0.504
æ d u r ÷öæ u 2 ÷ö k j = 0.48 ççç or or g,j ÷÷ççç or ÷÷ èç Lj ÷øèç gLj ÷ø
-4-
æ Lj çç çè d
0.905
ö÷ ÷÷ ÷ or ø
0.068
æ ö çç d or uor rg,j ÷÷ ÷÷ çç u è ø g,j
.
(12)
Bubble-emulsion gas transfer coefficient is estimated by De Groot’ s correlation (14), K be = kbe ab =
u1 , 0.67 H f0.5 d t10.25
(13)
which omits the need to know the average bubble size, a very difficult parameter to estimate in large-scale industrial fluidized beds. Mass and Heat Balances To determine the profiles of gas components, carbon content in the catalyst and temperature in the regenerator, the oxygen balance, carbon balance and heat balance are needed in the model. Due to page limit here, these procedures are only briefly introduced in the following text. During the regeneration process, changes of gas compositions, carbon content and temperature are interrelated. Their values need to be solved together. Oxygen balance in the dense bed is based on Chavarie and Grace (14) with consideration of different mass transfer rates in the grid and bubbling zones. In the freeboard, interphase mass transfer is neglected, with reaction kinetics as the controlling factor. With oxygen concentration, concentrations of CO2 and CO are readily known according to the reaction formula shown in Eq. (3). The profile of carbon content is determined according to the solids raw data flow model and the consumption of data initialization oxygen. In this model, the carbon contents in the dense bed and Td0 Tf0 freeboard are constant due to the hydrodynamics completely mixed assumption. With higher mass transfer rate, the carbon CCd0 CCf0 content in the freeboard is a little gas component balance lower than in the dense bed. The heat balance needs to consider the carbon balance heat input from combustion of carbon CCd0 ≠ CCd1 CCd1 CCf1 and hydrogen, heat to heat up the CCf0 ≠ CCf1 influent air and spent catalyst, heat heat balance loss to atmosphere from the outside Td0 ≠ Td1 Td1 Tf1 shell, and heat removed from catalyst T ≠T f0
coolers.
f1
output
Solving Algorithm
Fig. 3
Flow chart of model program
This model is programmed in Matlab language using a modularized scheme and solved by an iterative method as shown in Fig. 3. There are seven modules and two iteration loops. To establish a model for optimizing the operation of a specified FCC regenerator, a calibration procedure is required to determine key unit-dependent parameters based on existing industrial data. Then, basic operating data can be
-5-
changed to see their effects on the performance of the regenerator and to determine optimized operating parameters.
MODEL VALIDATIONS AND DISCUSSION Table 1 A comparison of the modeled results and industrial data Items
Partial mode
Full mode
Catalyst inventory, ton
185
160
Superficial gas velocity in the dense bed, m/s
0.85
0.93
Superficial gas velocity in the freeboard, m/s
0.48
0.52
Items for comparison
Model
Bed height of dense bed, m Bed density, kg/m
Exp.
7.91
3
Model
Exp.
8.05
278
276
221
220
10.9
12
14.9
14
Dense bed temperature, °C
660
662
689
690
Freeboard temperature, °C
669
670
696
699
Carbon content of the spent catalyst, (wt) %
1.49
Carbon content of the regenerated catalyst, (wt) %
0.18
0.15
0.038
0.05
CBI, kg/(h.ton (cat.))
102.1
105.7
112.8
106.7
O2
0.89
0.8
3.31
3.1
CO
1.61
1.6
0.31
0.3
CO2
16.88
16.8
15.8
15.4
Freeboard density, kg/m
3
Components of flue gas (dry), v%
1.74
Industrial data from a FCC unit in Luoyang Petrochemical Corporation, Sinopec were used to compare with the modeled results. This FCC unit has a coaxial reactor-regenerator layout, processing 1.4 million tons of atmospheric residue per year. A single-stage countercurrent regenerator is used to regenerate the spent catalyst. The regenerator was first operated in the full CO combustion mode with a CO promoter. Later, in order to increase the processing capacity and decrease the main air flow rate, the regenerator was revamped to partial CO combustion mode with reduced air flow rate and without CO promoter. An advantage of this model is that only one fitting parameter, i.e. the interchanging solids flux between the dense bed and the freeboard, Fs,df, is used, which was determined based on the difference of temperature in the dense bed and freeboard. With a same Fs,df, both regeneration modes are modeled. The modeled results are compared with industrial data in Table 1. The main modeled hydrodynamic and performance results are in reasonable agreement with the industrial data, demonstrating the feasibility of this model. With this model, the axial profiles of voidage, temperature, gas components and carbon content can be predicted, as shown in Fig. 4 for a typical partial CO combustion mode. It can be seen that most of the solids inventory in the freeboard is
-6-
concentrated within a ~2 m high from the bed surface, where solids mixing is vigorous and a large solids exchange flux exists between the dense bed and freeboard. Therefore, there is only a small temperature increase in the freeboard, as seen in Fig. 4(b). Due to the different mass transfer rates, oxygen concentration decreases much sharply in the grid zone than in the bubbling zone. In the grid zone, the difference of oxygen concentration in the emulsion and dilute phases is much lower than in the bubbling zone. Due to higher mass transfer rate, carbon burns more efficiently in the freeboard, as indicated by the lower carbon content shown in Fig. 4 (d). 1.2
700
o
dense bed
1.0 0.9
freeboard 0.8 0.7
0
5
T
680
Temperature, C
Voidage, -
1.1
10
Height, m
15
660 640 620 600
20
freeboard
dense bed
0
5
10
Height, m
(a) 0.20
dense bed
yCO2
12
Carbon content, w%
16
Mole content, %
20
(b)
20
yH2O yb,O2
8
freeboard
4
ye,O2 0
15
0
CC
0.18
0.16
dense bed
0.14
freeboard
yCO 5
10
Height, m
15
20
0.12
0
5
10
Height, m
(c)
15
20
(d)
Fig. 4 Predicted profiles of (a) voidage, (b) temperature, (c) gas composition, and (d) carbon content under partial CO combustion mode
CONCLUSION In this study, a modified countercurrent FCC regenerator model is proposed based on modified gas and solids flow patterns. The gas flow pattern in dense bed employs the “two-phase bubbling bed model” proposed by Chavarie and Grace (8), which can predict gas concentration profiles in better agreement with experimental facts. The modification in solids flow patterns focuses on the solids flow in freeboard, which was modeled as another CSTR exchanging solid with the dense bed. The model was applied to an industrial FCC regenerator operated under both full and partial CO
-7-
combustion modes with agreeable modeled results obtainable with industrial data for both modes.
NOTATION At bed area, m2 C gas concentration, carbon content, CC diameter, m d bed diameters, m dt solid volume fraction, fs Fs,df interchange solid rate, kg/m2.s bubble-emulsion mass transfer kbe coefficient, m/s bubble-emulsion mass transfer Kbe coefficient, 1/s jet-emulsion mass transfer kj coefficient, kg/m2.s reaction constant, 1/s kf R gas law constant, kj/(kmol.K) T temperature, K dense bed height, m Hf jet length, m Lj M mass, kg u superficial gas velocity, m/s y concentration, z height, m
b
coefficient, 1/m interphase area per volume of bubble, m2/m3 CO2/CO, b bubble fraction, ε void fraction, density, kg/m3; Subscripts b bubble/bed e emulsion d dense bed s solid f freeboard g gas j jet mf minimum fluidization p particle or orifice 0 initial 1(2) dense bed (freeboard) * saturated
REFERENCES 1. Ford, F. D., Reinmen, R. C., Vasalos, I. A., and Fahrig, R. J., 1977. Chem. Eng. Prog. 73 (4): 92. 2. De Lasa, H. I., Errazu, A, Barreiro, E. And Solioz, S., 1981. Can. J. Chem. Eng. 59: 549-553. 3. Faltsi-Saravelou, O., Vasalos, I. A. And Dimogiorgas, G., 1991. Comp. Chem. Eng. 15: 647-656. 4. Filho, R. M., Batista, L. M. F. L. and Fusco, M., 1996. Chem. Eng. Sci. 51: 1807-1816. 5. Lu, C., 1996. Doctoral dissertation, China University of Petroleum, Beijing, China. (in Chinese) 6. Lee L. S., Yu, S. W., Cheng, C. T. and Pan, W. Y., 1989. Chem. Eng. J. 40: 71-82. 7. De Lasa, H. I. and Grace, J. R. 1979. AIChE J. 25: 984-991. 8. Orcutt, J. C., Davidson, J. F. and Pigford, R. L., 1962. Chem. Eng. Prog. Symp. Ser. 58: 1–15. 9. Chavarie, C. and Grace, J. R., 1975. Ind. Eng. Chem. Fundam. 14: 75-91. 10. Wang, G., Lin S., and Yang G., 1986. Ind. Eng. Chem. Proc. Des. Dev. 25: 626. 11. Dong, X. and Hao, X., 2006. Petroleum Refinery Engineering. 36 (6): 7-9. (in Chinese) 12. Cai, P., Jin, Y., Yu, Z. Q., and Wang, Z. W., 1990. AIChE. J. 36: 955-956. 13. Zhang Y. M., Lu C. X., Shi M. X., 2008. Chem. Eng. Tech. 31, 1735-1742.
-8-
INVESTIGATION ON THE HYDRODYNAMIC PROPERTIES IN THE EXTERNAL LOOP OF CIRCULATING FLUIDIZED BED WITH A LOOP SEAL Xuan Yao, Tao Wang, Hairui Yang, Hai Zhang, Qing Liu, Junfu Lv, Guangxi Yue Key laboratory for Thermal Science and Power Engineering of ministry of Education, Tsinghua University, Haidian District, Beijing 100084, China T: 86-1-62773384; F:86-10-62781743;E:
[email protected] ABSTRACT The pressure balance and mass balance are influenced by the characteristics of different components in the loop of a circulating fluidized bed (CFB). Experiments were conducted in a 4.3 m high cold laboratory CFB test rig with a loop seal. With a fixed bed inventory and superficial gas velocity, the pressure drop of the loop seal decreased with increasing aeration, thus causing an increase in the solid circulation flux (Gs). Correspondingly, the pressure drop in the riser became higher with increasing Gs; the pressure drop of the cyclone had a non-linear relationship with Gs, and the transition point was determined in the experiment. Using the laser fiber and gas tracer method, hydrodynamic characteristics in the standpipe were directly measured. It was found that the pressure gradient, voidage, and solid height in the standpipe were affected by the pressure balance in the whole loop. By adjusting the gas flow rate and direction in the standpipe, the gas-solid slip velocity and pressure gradient changed correspondingly. Therefore, the standpipe could maintain the pressure balance and realize self-equilibrium of the loop by absorbing the pressure drop variations of other parts in the system. INTRODUCTION Circulating fluidized bed (CFB)normally consists of the riser, cyclone, standpipe and solid recycling valve. The standpipe and recycling valve can overcome the high pressure difference and recycle particles collected by cyclone to the bottom of the riser. The standpipe often has the function to prevent gas bypassing. Non-mechanical valves, such as loop seal, L valve, are commonly used in industrial CFB boilers as they can work under the harsh conditions of high temperature and pressure. The solid circulating flux is commonly controlled by changing the aeration rate in the valves. Though a number of studies have been conducted on gas-solid flow in the riser and cyclone [1-5], there are few studies on the hydrodynamic properties in the external loop of CFB system, especially for the influence of riser operation conditions on the performance of the recycle valve and standpipe. Basu [1] built a model to describe the CFB external loop pressure drop and analyzed the influence of aeration rate,
1
riser velocity and Gs on the standpipe pressure gradient. However, the gas flow direction and vodiage in the standpipe were not measured directly. Monazam[2] studied the influence of bed inventory, riser velocity and aeration rate on GS and confirmed that the performance of the loop seal and standpipe can’t be studied ignoring the influence of other components in the whole system. Standpipe is a special part of the CFB system. It has the function to absorb the pressure variation of other components in the system and the gas bypassing can be prevented by designing the standpipe correctly. However, there is very little literatures on the performance of the standpipe in the CFB system. The flow state in the standpipe is still in controversy and there is little evidence to validate the different viewpoints [1,6]. In this paper, experiments was conduced in a 4.5 m high CFB test rig with a loop seal to study the influence of operating conditions on the performance of hydrodynamic properties in the external loop, including the loop seal and standpipe. Laser fiber and gas tracer methods were applied to measure the flow behavior in the standpipe directly, including the voidage and gas flow direction. EXPERIMENTAL Experimental test rig Experiments were conducted in a CFB cold test rig as shown in Fig.1. The rig consisted of a distributor, a riser, a cyclone, a standpipe and a loop seal. The riser had a cross-section area of 0.1x0.1 m2 and a height of 4.5m. The cyclone was of high separation efficiency. The standpipe had a height of 3.0m and a diameter of 0.08m and connected the riser with a loop seal [7]. The dimension of the loop seal was shown in Fig.2. 20 pressure taps were installed at different heights along the solid circulation loop to measure the pressure drop online. The fluidizing gas rate and loop seal aeration rate, Q, were measured by gas flow meters. Two methods were used to measure the solids circulation flux, GS. One was based on the time for the recirculating solids to accumulate to a certain height in the standpipe after a sudden close of a butterfly valve installed in the standpipe. The other method used a self-designed measuring device placed under the
2
Fig. 1 Schematic diagram of experimental test rig
cyclone. All experiments were carried out at ambient temperature and atmospheric pressure. The bed material was quartz sand, its physical properties are listed in Table 1. Real density 2625 kg/m3
Table 1 Physical properties of the bed material Bulk Minimum Minimum Size range voidage voidage fluidization velocity 0.50 0.58 0.09 m/s 200-500μm
Sauter diameter 360μm
Flow behavior measurement in the standpipe and loop seal To study the gas flow behavior in the standpipe, high purity CO2 gas was used as the tracer. As shown in Fig. 2, CO2 was injected into the system at Point A, and CO2 concentration at Tap 1, 2 and 3 were simultaneously measured by a CO2 detecting system with 3 channels, each equipped with a sampling probe, a CO2 sensor (GSS-C20) and a vacuum pump. A laser fiber was used to measure the solid volumetric fraction at Taps 2 and 3. Signals from the CO2 sensors and laser fiber were recorded online through a data acquisition system.
Fig. 2 Measurement system of flow behavior in loop seal and standpipe
In the first test, it found that CO2 concentrations at different locations in the same cross section were nearly the same, so the gas and solids in the standpipe were regarded as well mixed horizontally. Shown in Fig.2, in the control volume enclosed by the dot lines and solid walls, the gas balance can be expressed as following equations when CO2 is injected with a certain volumetric flow rate q at Point A. According to Eqn.1and 2, QV and QH can be calculated with known Q, q, C1 and C3.
QV + QH = Q + q QV × C3 + QH × C1 = q
(1) (2)
DISCUSSION Riser pressure drop The solid circulation flux GS of the system has significant influence on the mass balance in the CFB reactor, and directly determines the performance of the system. As shown in Fig. 3, GS increases with the aeration rate, Q, in the loop seal. After Q increases to a critical value, GS no long changes with aeration rate and maintains at
3
the maximum rate about 45kg/m2s. GS can be estimated by the particle suspension density in the upper region of the riser. GS = ρ s(1 − ε ) (uriser − ut) .Therefore, GS can be used characterize the suspension density in the upper riser. Fig. 4 shows the solids hold up distribution in the riser at Uriser=5.0 m/s. With increasing Gs, particles hold up in the riser becomes denser. Former studies have shown that, with increasing Gs at a certain riser fluidizing velocity, the flow in the riser will transition from dilute phase pneumatic conveying to the fast fluidization state [8]. In the fast fluidized bed, the voidage in the dense zone and upper dilute zone are held stably, while the height of dense zone ascends gradually and the voidage in the transition zone increases. 50 2
4
Riser hieght (m)
bubbling transintion point
30
2
Gs (kg/m s)
40
20 10 0 5.0
5.5
6.0
6.5
7.0
7.5
3
Gs=15.7 Kg/m s ,△P=515 Pa 2 Gs=27.7 Kg/m s ,△P=950 Pa 2 Gs=38.1 Kg/m s ,△P=1500 Pa 2 Gs=39.8 Kg/m s ,△P=2542 Pa 2 Gs=45.6 Kg/m s ,△P=3670 Pa 2 Gs=45.7 Kg/m s ,△P=5020 Pa
2 Uriser=5.0 m/s
1
0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 Solid hold up 1-ε
8.0
3
Areation rate (m /h)
Fig. 3 Variation of GS with Q of loop seal Fig. 4
solid hold up distribution in riser
The pressure drop of the riser △Pr is approximately equal to solids gravity pressure drop, which is determined by solids inventory in the riser. Fig. 5 shows the relationship of △Pr and Gs. △Pr and Gs represent proportional relationship, but after the △Pr is larger than 3.0KPa, Gs maintains at about 45kg/m3s. According to Figs 4 and 5, when Gs reaches the critical value 45kg/m2s, solid hold up of upper and bottom region of the riser remains unchanged. The increase of solids inventory in the riser only causes height of dense phase zone increase. Overall solids hold up distribution fit the S type curve of fast fluidization, and the saturated carrying rate under 5.0m/s in the paper is about 45 kg./m2s. Cyclone characteristic The pressure drop or resistance of the cyclone △Pcyc has a square relationship with the entering gas velocity. However, △Pcyc is also influenced by solids concentration CS. CS is defined as the solids mass in one cubic meter of fluidizing gas. It can be calculated with GS by CS = GS AS / (Ar uriser). In this paper, besides the traditional steady method, a transient method is used to
4
study the characteristic of the cyclone pressure drop. At a specific Uriser, after a steady state with a large Gs was maintained for a few minutes, the loop seal aeration was suddenly cut off, the bed inventory was then elutriated out of the riser and accumulated in the standpipe. The flow regimes in the riser changed from fast fluidization to dilute convey regime with decreasing Gs, until the riser was empty of particles. This method is simple and has better reproducibility than traditional steady method. Fig. 6 shows the characteristic of the cyclone at different Uriser. The comparison between the steady method and transient method further verifies the reliability of the transient method. 1000
6000 5000
800
△Pcyc (Pa)
△ Pr (Pa)
4000 3000 2000 1000 0
Uriser 6.0 m/s 5.0 m/s 4.0 m/s
steady transient method method
600
400
200 0
10
20
30
40
0
50
2
3
4
5
Cs(kg/m )
Gs (kg/m s)
Fig. 5
1
3
2
Fig. 6
Variations of △Pr with Gs
character of cyclone △Pcyc
Flow behavior of loop seal and standpipe It’s generally believed that all the aeration gas passes through the loop seal into the riser. However, industrial operation has verified that the gas-bypassing phenomenon in standpipe can’t be ignored. As shown in Fig. 7, the actual gas flow rate across the loop seal is not equal to the aeration rate. With a small aeration rate,Q, QH through the loop seal is close to the aeration rate, account for more than 95% percent of Q. However, with increasing Q, more gas goes up through the standpipe, QH/Q rapidly decreases. A loop seal can be divided into a horizontal part and a vertical part based on its physical structure, as shown in Fig.2. The gas-solid flow in the horizontal part belongs to the transport state, the resistance of which is mainly caused by the friction among particles and the wall. With increasing aeration rate, the pressure drop of the horizontal section (P17-P18) increases due to higher GS. The flow in the vertical section is in the bubbling bed regime. With higher aeration rate, as QH/Q decrease, the ejecting height of particle decreases rapidly due to less QH. This results in the reduction of △Plsl, as shown in Fig. 7.
5
Gas flow ratio (%)
120 110 100 90 80 70 60 2000 50
Voidage ε △P/L (kPa/m) Height (cm)
Therefore, the loop seal characteristics are closely related to the flow state in the
QH/Q
loopseal
0 5.0
solid height
40 12 0 8
standpipe pressure gradient
4 0
voidage
0.52
vertical hozirontal 5.5
0.3 U (m/s)
△P (Pa)
500
80
0.56
1500 1000
120
6.0
6.5
3
Areation rate (m /h)
7.0
7.5
0.2 0.1 0.0 5.0
Fig. 7 the performance of loop seal (Uriser=5.0 m/s)
Usl Ug Us
5.5
6.0
6.5
7.0
Areation rate (m 3/h)
7.5
Fig. 8 flow behavior of standpipe (Uriser=5.0 m/s) standpipe, which affects the actual gas passing through the loop seal. In this paper, the pressure drop gradient, voidage and gas flow were directly measured. As shown in Fig. 8, when Gs increases with increasing aeration rate, Q, at fixed Uriser and bed inventory, IV, the mass of solid and thereby solid height in the standpipe decrease because more solids accumulate in the riser. At the same time, the pressure gradient in the standpipe increases. This is a special feature of transient packed bed flow [9], which is related to the pressure gradient and gas-solid slip velocity USl. According to the experimental measurement of voidage by laser fiber and gas flow rate measured by gas tracer, the slip velocity USl can be calculated by the equation USl=GS/ρp(1-ε)+Ug/ε[9]. With increasing aeration rate, particle velocity, US, increases due to higher GS. At the same time, the upward gas flow rate in the standpipe, QV, also increases. Therefore, the slip velocity will increase with increasing aeration rate. Although the solid height decreases, the solid seal can provide a pressure head because of the increasing pressure gradient and slip velocity USl. With increasing aeration rate, the upward gas flow, QV, keeps on increasing and the voidage gradually approaches to the minimum fluidization voidage. With very high aeration rates, upward flowing bubbles can be visually observed and the flow reaches the bubbling state. Pressure balance of the CFB external loop As shown in Fig. 9, at fixed riser fluidizing velocity, more bed material accumulates in the riser with increasing aeration rate. As a result, △Pr increases. At the same
6
△Pr (Pa)
time, both the pressure drop gradient 6000 and total pressure drop, △Psp, in the standpipe increase. Under stable 5000 operation condition, the pressure balance of the CFB system can be 4000 expressed as △Pr + △Plsl + △Pcyc = △Psp △Psp. Whenever pressure drop of 3000 any other parts changes, the pressure gradient △P/L and Usl in the standpipe 2000 △Plsl △Pr change correspondingly to rebalance 1000 the system. Therefore, the standpipe △Pcyc has the ability to keep pressure 0 balance in the CFB by adjusting inner 5.0 5.5 6.0 6.5 7.0 7.5 3 Areation Rate (m /h) flow behavior. Its characteristic is greatly affected by the operation Fig. 9 Variations of pressure drops around conditions of the riser and other parts, the CFB loop and should be studied in the external loop of CFB system [10]. CONCLUSION The flow behavior of a CFB external loop, including loop seal and standpipe is schematically studied in this paper. It found that with a fixed bed inventory and fluidizing velocity, the pressure drop of the riser increases with solid circulation flux because more particles accumulate in the riser. When Gs reach a critical value, solids suspension distribution can be described by the S type curve of the fast fluidization. The pressure drop of a cyclone depends on the riser gas velocity and solids concentration. The cyclone pressure drop first decreases and then increases with suspension solid concentration. The actual gas flow ratio QH/Q through the loop seal decreases with increasing aeration rate, while the upward flow gas rate in the standpipe increases. Voidage in the standpipe increases and the flow state gradually transitions to the minimum fluidizing state from packed bed flow. The flow behavior in the standpipe adjusts to conditions in the whole CFB system. The standpipe maintains the pressure balance by changing the slip velocity and pressure gradient to provide the required pressure head with lower solids height. Because the CFB system used in this paper is an inventory constrained system, the results, especially for the transition points, may be different under different bed inventories. At same time, the solids properties also have an influence on the results. These factors will be studied in the future studies.
7
ACKNOWLEDGMENT Financial support of this work by High Technology R&D (863) (2009AA05Z302) is acknowledged. NOTATION Ar CS IV QH q Uriser US ρs P0~P19
section area of riser, m2 solids concentration, kg/m3 system bed inventory, kg gas flow cross loop seal, m3/h gas tracer(CO2) flow rate,m3/h riser fluidizing velocity, m/s actual particle velocity m/s particle density kg/m3 Number of pressure taps
△Plsl
pressure drop of loop seal, △Pr P17-P19 Pa pressure drop of standpipe, △Psp/L P17-P14,Pa
△Psp
As GS Q QV Ug USl Ut ε △Pcyc
section area of standpipe, m2 solid circulation flux, kg/m2s loop seal aeration, m3/h gas flow cross standpipe, m3/h superficial gas velocity m/s gas –solid slip velocity m/s actual solid velocity m/s voidage pressure drop of cyclone, P13-P14, Pa pressure drop of riser,P0-P13, Pa pressure drop gradient, (P16-P15)/0.1, Pa /m
REFERENCES 1. Basu P; Cheng L. An analysis of loop seal operations in a circulating fluidized bed. Chemical Engineering Research and Design,2000,78(7) :991-998 2. Monazam E R; Shadle L J ,et al. Impact of the circulating fluidized bed riser on the performance of a loopseal non-mechanical valve. Industrial & Engineering Chemistry Research,2007,46(6):1843-1850 3. Wang Qing,Sun Jian. A overall fluid flow process dynamic model of circulating loop for CFB boiler. Proceedings of the CSEE,1999,19(12):31-35 4. Hu Nan,Wang Wei,et al. Study on gas-solid s flow properties in the 38 m/54 m riser of circulating fluidized bed. Proceedings of the CSEE,2009,29(26):7-12 5. Lim K S, Zhu Jinxu, et al. Hydrodynamics of gas-solid fluidization. International Journal Multiphase Flow, 1995,21(1):141-193 6. Kim, S. W; Kim, S. D. Effects of particle properties on solids recycle in loop-seal of a circulating fluidized bed, Powder Technology, 2002,124(1) :76-84. 7. Yao, X; Yang, Set al; Experiment study of solids circulating rate's effect on the pressure loop in circulating fluidized bed. Proceeding of CSEE. 2010, 30(20), 1-6 8. Yerushalmi J, Cankurt N T.Further studies of the regimes of fluidization. Powder Technology,1979, 24(2): 187-205 9. Ergun S. Fluid flow through packed columns. Chemical Engineering Progress,1952, 48 (2): 89-94. 10. Yang, S; Yang, H. R et al. Impact of operating conditions on the performance of the external loop in a CFB reactor. Chemical Engineering and Processing: Process Intensification. 2009, 48(4), 921-926.
8
COAL IGNITION TEMPERATURE IN AN OXYGEN-ENRICHED CFB BOILER Junnan Chao, Hairui Yang, Junfu Lv, Hai Zhang, Qing Liu, Yuxin Wu Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Thermal Engineering, Tsinghua University, Beijing, 100084, China T: 86-1-62773384; F:86-10-62781743;E:
[email protected] ABSTRACT The oxygen-enriched Circulating fluidized bed (CFB) combustion technology is a new method to reduce CO2 emissions. The coal ignition temperature, TiF, in an oxygen-enriched CFB boiler is an important parameter for designing the startup burner and for choosing the operating strategy during the startup process. The combustion of five types of coal under four different atmospheres (air, O2 27 %, O2 40%, O2 53%, CO2 as balance gas) was measured in a laboratory scale fluidized bed (FB) with an under-bed preheat system. Using thermocouples and a Gas Analyzer, the changes in bed temperature and the concentration of the different components, such as O2, CO2 and CO, in flue gas were directly measured to determine TiF. It was found that TiF decreased with increasing O2 concentration. The differences between the ignition temperatures determined in air and with 27 % O2 were not significant. At lower bed temperatures, for two coal types with higher volatiles, a two stage-ignition for volatiles and char was observed under a high O2 concentration. The time delay between the two stages decreased and finally merged into one with increasing bed temperature. Similar results were obtained in air. The coal with the higher volatile content had a lower ignition temperature in an oxygen-enriched CFB. Comparison of the ignition temperatures obtained by different methods and the feed temperatures in industrial CFB boilers showd that the measured result in a fluidized bed can be used as a reference for oxygen-enriched CFB boilers. INTRODUCTION As one of the “green house” gases, CO2 was considered as the one of the main reasons for causing climate changes. Oxygen-enriched combustion in a PC boiler is considered one potential technology to reduce CO2 emissions. Many researchers have focused on this area. By recycling the flue gas, the CO2 concentration in the exhaust gas can reach more than 90%, which is convenient for the application of Carbon Capture and Storage (CCS) technology.
An oxygen-enriched CFB is superior being studied as a new technology researchers. Recycling of the flue desulphurization sorbent, which
to a traditional CFB, Yuru Mao et al (1), and is to reduce CO2 emissions by more and more gas increases the contact of the SO2 and decreases the conversion rate of the
SO2 ,Zhongyang Luo et al,(2).The emissions of NOX is much lower because of the ‘time gap’ in the formation of nitrogen species and the lack of thermal NOx,Omasz Czakiert et al,(3). However, there are still many promblems to be solved for oxygen-enriched CFB, such as the coal ignition temperature and coal combustion characteristics in the CFB boiler. The Coal ignition temperature in CFB boilers, Ti, defined as the lowest bed temperature required for stable coal combustion, is an important parameter for burner design and automatic control during the startup process. When the coal is fed into a furnace at a temperature lower than Ti, the coal will not burn and the furnace temperature will decrease even more. Once the fuel concentration and temperature in the furnace reach critical conditions, the mixture will flash and the furnace temperature will suddenly increase, which leads to overheating. Feeding the coal in the furnace at temperatures higher than Ti is safer but consumes time and oil. The coal ignition temperature is influenced by the type of the reactor, the way of heating and particle size in the conventional CFB boiler, Hairui YANG et al,(4).The ignition characteristics of the coal can be described by an ignition index, which is defined as:
Fi =
ΔT (t1 + t2 )
where ΔT is the difference between the initial furnace temperature and the maximum temperature, t1 is the time to reach the lowest temperature, and t2 is the time to reach the maximum temperature, Hairui YANG et al,(4). In this work, ignition temperatures of five different types of coal under four different atmospheres (air, O2 27 %, O2 40%, O2 53%, CO2 as balance gas) were measured in a laboratory scale bubbling fluidized bed (FB) with an under-bed preheat system. By using thermocouples and a Gas Analyzer, bed temperature, O2 concentration and Fi were obtained to determine the factors influencing the coal ignition temperature and the combustion characteristics. EXPERIMENTAL SETUP The ignition temperature was measured in a laboratory-scale fluidized bed reactor with an inner diameter of 65 mm, which was heated with an under-bed preheat system. The sketch of the experimental setup is shown in Figure 1. Quartz sand was used as bed material. The size of the bed particles was in the range of 0.275mm-0.3mm.The height of the bed was 40mm. CO2 and O2 were mainly used as the component gases in this experiment to obtain three kinds of inlet gases with different concentration of O2. The component gases were supplied by gas cylinders. The simulated gas flowed through the pre-heater directly into the FB. The superficial gas velocity in the combustion chamber was about three times Umf. Since the operating conditions greatly influence the coal ignition characteristics, standard
operating conditions as a baseline were specified in Table 1.
Fig.1. Schematic drawing of the experimental setup Table 1 The standard conditions of coal particle ignition tests Fluidized gas O2/ CO2
No. 1 2 3 4 5
U/ Umf.
Coal particles (g)
Bed material
Bed height(mm)
3
2.0
Quartz sand
40
Table 2 Proximate analyses of different coals proximate analyses [wt%] Coal Type Vad Fc ad Aad Mt Long Yan 4.72 4.30 58.02 32.96 Lu An 2.12 10.31 61.03 26.54 Fu Gu 3.96 30.19 44.31 21.53 Yan Zhou 0.00 35.51 55.67 8.82 Xiao Long Tan
9.88
49.07
36.10
Bed material size(µm) 275-300
LHV,ar (MJ/kg) 19.07 25.05 22.58 21.38
4.95
12.43
Table 3 Ultimate analyses of different coals No. 1 2 3 4 5
Coal Type Long Yan Lu An Fu Gu Yan Zhou Xiao Long Tan
Cad 52.30 62.13 56.58 55.38 36.72
Had 1.04 2.67 3.61 2.04 1.87
Oad 0.83 7.33 14.28 6.42 12.59
Nad 0.71 1.18 1.12 1.12 1.01
Sad 1.12 0.15 0.42 0.50 1.66
Five different types of coal were used as fuel. Tables 2 and 3 list the proximate and ultimate analyses of each fuel. The size of the coal sample was in the size rang of 1-2mm and the mass in each batch experiment was about 2g. The temperature of the dense bed was measured by thermocouples during the experiment. A Gas Analyzer was used to measure the changing of concentration of different components. Procedure 1. After the furnace was electrically heated to the selected furnace temperature, Tb, the coal particles were injected into the furnace. 2. The furnace temperature was then recorded by the data acquisition system. Ignition of the coal particles was evaluated by observing the flame and sparks through a mirror. If the coal particles ignited, the furnace temperature was reduced to a lower temperature Ti+1 and steps 1 and 2 were repeated until the coal particles did not ignite. 3. If the coal particles did not ignite, TiF, was assumed to be half way between Ti and Ti-1 and the process was repeated. The process was terminated when |Ti - Ti-1 | 0.8). Contact forces between particles are calculated according to a viscoelastic contact model based on the Kelvin-Voigt law with a constant restitution coefficient (Cundall and Strack (7)). The normal and tangential contact forces are defined as follows: r r Fc(,ijn ) = (kc ,ij ,n ⋅ sij ,n + ηij ,n ⋅ s&ij ,n ) ⋅ nij , (3)
r (kc ,ij , s ⋅ sija, s + ηij , s ⋅ s&ij , s ) ⋅ tij r ( ij ) Fc , s = min , r ( μij ⋅ Fc(,ijn ) ) ⋅ tij
(4)
where kc,ij,n and kc,ij,s are the contact stiffness in normal and shear direction, μij is the dynamic friction coefficient. The overlap in normal and shear direction is sij,n and sij,s. ηi,j,n and ηi,j,s are the normal and shear damping given as:
⎡
⎛ π ⎞⎤ ⎟⎥ . ⎝ ln e ⎠ ⎦
η = 4m* ⋅ kc / ⎢1 + ⎜ ⎣
(5)
m* is equivalent mass of contact partners. kc is the contact stiffness. The coefficient of restitution, e, describes the energy dissipation during impact and can be found as a ratio of rebound velocity to impact velocity of the particle, Antonyuk et al. (8). The hydrodynamics of the gas phase considered as continuum is calculated using volume-averaged Navier-Stokes equations (6)-(7).
∂ (ερ g ) + ∇ (ερ g ug ) = 0 , ∂t ∂ (ερ g u g ) + ∇ (ερ g u g u g ) = −ε ∇pg − ∇ (ε τ g ) − S p + ερ g g . ∂t
(6) (7)
Simulations were performed using the commercial simulators EDEM and Fluent. Simulation Parameters The geometry of fluidization chamber (2 in Fig. 1) is discretized in mesh cells (Fig. 5). The mesh consists of 76.725 tet/hybrid cells with an interval size of 0.008 and minimum volume of 4 mm3. The air with the temperature of 25 °C, density of 1.18 kg/m3 and kinematic viscosity of 15.7·10-6 m2/s was calculated. To describe the effects of turbulent fluctuations of velocities on the pressure drop of the empty apparatus a k-ε model (with the turbulence intensity of 5%) was applied for the calculation which showed good results for the spout beds in CFD simulations of Gryczka et al. (9). The parameters of the DEM model are given in Table 3.
Table 3 Properties of the particles in DEM model. Parameter uncoated coated diameter in µm
800
820
density in kg/m
190
300
stiffness in N/mm
10
10
shear modulus in MPa
6.25
6.25
restitution coefficient
0.6
0.4
friction coefficient
0.8
0.8
rolling friction coefficient
0.01
0.01
number of the particles
150.000
150.000
3
Gas inlet
Fig. 5 Mesh of CFD simulation.
The DPM simulations were performed for the dry uncoated aerogel particles and compared with the case of coated particles. The coated particles are significantly heavier than dry uncoated aerogel. Moreover, with the wetting the energy adsorption during impact increases that results in the decreasing the coefficient of restitution (Antonyuk et al. (10)). SIMULATION RESULTS First simulations were performed for the empty apparatus without the solid particles. The inlet velocity of the gas was varied in the range of 1-2 m/s. The calculated pressure drop increases with increasing inlet velocity (Fig. 6). The calculated pressure drop predicted well with the experimental measurements that were carried out for the full spout bed apparatus included its cylindrical chamber (Fig. 7). Therefore the predicted values of the pressure drop are smaller than experimental obtained pressure drops. The gas velocity reaches its maximum in the narrow vertical inlet splits (Fig. 8). This velocity in these zones increases linearly with increasing the inlet velocity (Fig. 6). 15
150
10 100 5 50
0
0 0
1
2
inlet velocity in m/s
3
200
pressure drop [Pa]
pressure velocity
max. velcity [m/s]
pressure drop [ Pa]
200
Experiment 150
CFD 100 50 0 0
20
60
40 3
volume flow [m /h]
Fig. 6 Calculated pressure drop and Fig. 7 Comparison of the calculated maximum gas velocity in the empty pressure drop depending on the apparatus versus the inlet gas velocity. volume flow of the inlet gas. Fig. 8 shows the time-averaged flow profiles. The flow starts with a relatively high velocity at the bottom and becomes wider and slower with the height. Due to turbulence, two vortices are arisen that generate the secondary flow moving from the top to down in the near-wall region.
On top of the T-shaped bottom a stagnant air region takes place. The movement and drying of the particles will be reduced in this area. The particles can sink on the bottom, as shown in Fig. 9 (DPM simulation, after real fluidization time of 1.1 s). During coating experiments that leads to sticking of the particles in this region. To overcome this problem, the nozzle can be placed above this zone and the T-shaped element must be produced as knee-shaped. Fig. 10 shows the instantaneous particle positions in the apparatus. As expected the maximum particle velocity is reached in the spout region. Here the inlet gas accelerates the particles and picks up nearly vertically according to primary flow. The gas velocity is decreased gradual over the apparatus height and leads to decreasing the particle velocity. The particles deviate from vertical air flow and moves downward along the walls. The maximum bed height depends on the gas velocity, particle mass and restitution coefficient. With increasing of the particle mass, the necessary bed height decreases. The Fig. 11 compares the calculated bed heights.
particles in sagnant zone
stagnant zone
Fig. 8 The plot of time-averaged fluid Fig. 9 The particle deposition on the velocities in the empty apparatus at the inlet middle profile in the stagnant zone velocity of 2 m/s. of the gas (see Fig. 8). Maximum bed height in mm .
300
3
ρ = 190 kg/m
200
y
100
x z
3
ρ = 300 kg/m 0 0.2
0.6
1
1.4
Time t in s
Fig. 10 Instantaneous particle positions and velocity distributions inside the spout bed apparatus (particle density = 190 kg/m3, inlet gas velocity = 1.3 m/s).
Fig. 11 Influence the particle density on the maximum bed height in the spout bed apparatus (inlet gas velocity = 1.3 m/s).
The wet coated aerogel particles showed a lower translation and rotation velocities in comparison with dry and light aerogels (Table 4). Therefore, during the coating process, the inlet gas flow and the velocity must be increased in order to keep constant particle dynamics and to avoid sticking and agglomeration. The obtained distributions of the average particle velocity and impact velocity in spouted bed can be described with a lognormal distribution function, as it shown in Fig. 12. The average particle-particle impact velocity (Fig. 12 right) is about 7 times smaller than the absolute particle velocity at the same simulation time. The mean impact velocity by particle-wall impact is at the average 15 % higher than that by impact of particles. Table 4 Time-averaged motion parameters. parameter
uncoated particles
coated particles
maximum bed height in mm
165
130
mean/maximum particle velocity in m/s
0.36/1.62
0.28/1.20
average particle rotation in 1/s
275
226
P(v)
30
2
0.6
p(v) 0.4
1
P(v imp) [-]
0.8
p (v) [1/(m/s)]
0.8
P (v) [-]
1
20
impact velostiy:
0.6 0.4
P(vp-p) _V
P(v p-w p-w)
p(vp-p) _V
p(v p-wp-w) 10
0.2
0.2 0
0
0 0
0.5
1
0 0
1.5
p(v imp) [1/(m/s)]
3
1
0.1
0.2
0.3
0.4
impact velocity v imp [m/s]
velocity [m/s]
Fig. 12 Distribution function P and its density p for: (left) average absolute particle velocity and (right) relative impact velocity in the spout bed apparatus. (particle density of 300 kg/m3; p-p - particle-particle impacts, p-w - particle-wall impacts). 6
1500
ρ = 300 kg/m
Impact force in mN
Collision rate [1/s].
2000
3
1000
500 ρ = 190 kg/m
3
0 0.3
0.6
0.9
Time t in s
1.2
1.5
Fc,n,breakage = 310 ± 100 mN
4
Fc,n,max
2
Fc,t,max
0 0.2
0.6
1
1.4
Time t in s
Fig. 13 (left) Collision rate of the particles during the fluidization time, (right) maximal values of the normal and tangential impact forces (particle density of 300 kg/m3).
Fig. 13 shows the calculated particle-particle collision rates. The fluidized bed of wetted particles shows smaller height and porosity and so higher collision rates than for dry aerogels. The particle collides with another particle almost ten times more frequently than with a wall. The calculated forces acting on the particles during collisions in the bed (Fig. 13 right) are significantly smaller than breakage range of the aerogels (Table 2). This confirms the experimental fact that no breakage occurs during fluidization of aerogels. The small magnitude of the force can be explained by relative small particle impact velocities in the presented apparatus (Fig. 12). CONCLUSIONS The process of coating silica aerogels with pH sensitive polymers was performed successfully in the experimental spouted bed apparatus. To produce a closed Eudragit® layer and to avert the shrinking and breakage of the aerogel the particles can be coated with PEG 2000 as protection material. The DPM simulations showed a high gas velocity in the bottom part of the apparatus and its gradual decrease over the apparatus. The increasing mass and energy dissipation at the contact during the coating decreases the bed height, particle velocities and increases the collisions rate. The average impact velocity in spouted bed can be described with a lognormal distribution function. No breakage of the aerogels was obtained because the impact forces acting in the fluidized bed are significantly smaller than the measured breakage force of aerogel particles. REFERENCES 1. Antonyuk, S., Heinrich,S., Alnaief, M. and I. Smirnova: Application of a novel spouted bed process for the drying and coating of silica aerogel microspheres for advanced drug delivery, 17th International Drying Symposium, Magdeburg, 2010. 2. Mörl, L., Heinrich, S., Peglow, M., The Macro Scale I: Processing for Granulation, in Handbook of Powder Technology, Vol. 11, Elsevier Science, (2005), 21-188. 3. Alnaief, M., Smirnova I.: In situ production of spherical aerogel microparticles, J. of Supercritical Fluids 55 (2010) 3, 1118-1123. 4. Antonyuk, S., Palis, S., Heinrich, S.: Breakage behaviour of agglomerates and crystals by static loading and impact, Powder Technology 206 (2011) 88-98. 5. van Buijtenen, M.S., Deen, N.G., Heinrich, S., Antonyuk, S. and J.A.M. Kuipers: A discrete element study of wet particle-particle interaction during granulation in a spout fluidized bed, Can. J. Chem. Eng., (2009), Vol. 9999, 1-10. 6. Fries L., Antonyuk, S., Heinrich, S., Palzer, S.: DEM-CFD modelling of a fluidized bed spray granulator, Chemical Engineering Science (2011), in Press. 7. Cundall, P.A., Strack, O.D.L., A discrete numerical model for granular assemblies. Geotechnique 29 (1979), 47-65. 8. Antonyuk, S., Heinrich, S., Tomas, J., Deen, N.G., van Buijtenen, M.S. and J.A.M. Kuipers: Energy absorption during compression and impact of dry elastic-plastic spherical granules, Granular Matter 12 (2010) (1), 15-47. 9. Gryczka, O., Heinrich, S., Deen, N. van Sint Annaland, M., Kuipers, J.A.M. and L. Mörl: CFD-modeling of a prismatic spouted bed with two adjustable gas inlets, Can. J. Chem. Eng. 87 ( 2009), 318-328. 10. Antonyuk, S., Heinrich, S., Deen, N.G. and J.A.M. Kuipers: Influence of liquid layers on energy absorption during particle impact, Particuology 7 (2009), 245-259.
FLUIDIZED BED GASIFICATION OF MIXED PLASTIC WASTES: A MATERIAL AND A SUBSTANCE FLOW ANALYSIS Maria Laura Mastellone*,** and Umberto Arena*,** * Department of Environmental Sciences - Second University of Naples, Via A. Vivaldi, 43 - 81100 Caserta, ITALY ** AMRA s.c. a r.l., Via Nuova Agnano, 11 - 80125 Napoli, ITALY ABSTRACT Gasification as a reliable and convenient waste-to-energy process for the economic analysis of mixed-plastic waste (MPW) was investigated. To this end a pilot scale bubbling fluidized bed air gasifier was fired with two commercially available MPWs to obtain syngas composition and characterization of the bed material, cyclone collected fines and purge material from the scrubber. These results were then processed by means of Material and Substance Flow Analyses to evaluate the main process performance parameters for the two MPWs tested. INTRODUCTION Pervasive use of plastics as packaging materials makes this the most important fraction of municipal solid waste to be considered to reach a gradually larger intensity of separate collection (6). The sorting process of this fraction after a household separate collection generally produces a high percentage of residues, together with some completely recyclable streams and a not negligible fraction of a non homogeneous plastic scrap, called mixed plastic waste (MPW). This latter stream contains several types of plastic polymers that often are together with a not negligible amount of ferrous and non-ferrous metals. Due to its heterogeneity MPW can be utilized to substitute virgin materials only for a limited number of goods. On the other hand, its high calorific value makes thermal treatment an environmental sustainable and economic attractive alternative (9,1). The study investigates the possibility to utilize the gasification as a reliable and convenient waste-to-energy process for the economic valorisation of mixedplastic waste. To this end a pilot scale bubbling fluidized bed air gasifier, having a thermal capacity of 500kJ/s, was fired with two commercially available MPWs. The results have been combined with an environmental assessment tool, the Material Flow Analysis, which is named Substance Flow Analysis when it is referred to a specific chemical. MFA/SFA is a systematic assessment of the flows and stocks of materials and elements within a system defined in space and time. It connects the sources, the pathways, and the intermediate and final sinks of each species in a specific process (7). In this study MFA/SFA was applied to a system boundary that includes the BFB gasifier and the cleaning system for ash separation (cyclone and wet scrubber). The BFB gasifier was further divided into two sections: the first corresponds to the dense bed and splashing zone; the second to the freeboard. THE PILOT SCALE FLUIDIZED BED GASIFIER The utilized pilot scale BFB gasifier has the characteristics schematically listed in Table 1. An olivine - a magnesium-iron silicate, (Mg,Fe2)SiO4 - was selected as material for the fluidized bed on the basis of results of previous investigations carried out on the same pilot-scale BFBG [Arena et al., 2010a] that indicated olivine as an interesting candidate to act as a bed catalyst for the tar cracking
reactions in waste-derived fuel gasification, even taking into account its low cost and excellent resistance to attrition in the fluidized bed reactor. The main characteristics of the utilized olivine are reported in Table 2. Table 1. Main design and operating features of the pilot scale bubbling fluidized bed gasifier. ID: 0.381m; total height: 5.90m; Geometrical parameters reactive zone height: 4.64m 100 kg/h Feedstock capacity 145 kg Typical bed amount over-bed air-cooled screw Feeding system feeder 700-950°C Bed temperatures 0.3 –1m/s Fluidizing velocities cyclone, scrubber, flare Flue gas treatments Table 2. Characteristics of the olivine particles utilized as bed material in the pilot scale bubbling fluidized bed gasifier. Mg-Fe silicate Mineral Chemical composition, % 39-42 SiO2 48-50 MgO 8-10.5 Fe2O3