Advances in Energy Research, Volume 4

ADVANCES IN ENERGY RESEARCH

ADVANCES IN ENERGY RESEARCH VOLUME 4

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ADVANCES IN ENERGY RESEARCH Series Editor: Morena J. Acosta Advances in Energy Research, Volume 1 2010. ISBN: 978-1-61668-994-0 Advances in Energy Research, Volume 2 2010. ISBN: 978-1-61728-996-5 Advances in Energy Research, Volume 3 2011. ISBN: 978-1-61761-671-6 Advances in Energy Research, Volume 4 2011. ISBN: 978-1-61761-672-3

ADVANCES IN ENERGY RESEARCH

ADVANCES IN ENERGY RESEARCH VOLUME 4

MORENA J. ACOSTA EDITOR

Nova Science Publishers, Inc. New York

Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers‘ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

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Published by Nova Science Publishers, Inc. † New York

CONTENTS Preface Chapter 1

vii Investigation of HVAC System Improvement by HAM Numerical Simulation J.A. Orosa

Chapter 2

Research on Heat and Mass Transfer to Improve HVAC Conditions J.A. Orosa

Chapter 3

Rational Attribution of Environmental Emissions of Cogeneration to Products: Allocating Carbon Dioxide and Other Emissions with Exergy Marc A. Rosen

Chapter 4

A New Technique for Biological Monitoring Using Wildlife Mariko Mochizuki, Makoto Mori, Mutsumi Miura, Ryo Hondo, Takashi Ogawa and Fukiko Ueda

Chapter 5

Second Law Based Methods for Improvement of Energy SystemsEconomics and Environmental Impacts: A Brief Overview Sudipta De

1 19

41 87

97

Chapter 6

Physical Formulation of the Expression of Wind Power Reccab M. Ochieng, Frederick N. Onyango and Andrew O. Oduor

Chapter 7

History and Evolution of Fusion Power Plant Studies: Past, Present, and Future Prospects Laila A. El-Guebaly

117

Regional Impacts of the U.S. Environmental Protection Agency‘s SO2 Policy Vladimir Hlasny

171

Recent Developments in Safety and Environmental Aspects of Fusion Experiments and Power Plants Laila A. El-Guebaly and Lee C. Cadwallader

187

Chapter 8

Chapter 9

109

vi Chapter 10

Chapter 11

Contents Is Nuclear Power a Realistic Alternative to the Use of Fossil Fuels for the Production of Electricity? Jorge Morales Pedraza

231

An Analysis of a Closed Cycle Gas Turbine Using CF4 as the Working Fluid Sundar Narayan

277

Chapter 12

Aviation and Climate Change James E. McCarthy

Chapter 13

Greenhouse Gas Emissions: Perspectives on the Top 20 Emitters and Developed Versus Developing Nations Larry Parker and John Blodgett

293

307

Chapter 14

Global Climate Change: Three Policy Perspectives Larry Parker and John Blodgett

323

Chapter 15

Renewable Energy and Energy Efficiency Tax Incentive Resources Lynn J. Cunningham and Beth A. Roberts

355

Chapter 16

Energy Provisions in the American Recovery and Reinvestment Act of 2009 (P.L. 111-5) Fred Sissine, Anthony Andrews, Peter Folger, Stan Mark Kaplan, Daniel Morgan, Deborah D. Stine and Brent D. Yacobucci

Index

361

385

PREFACE This book presents a comprehensive review of improvments of HVAC conditions; biological monitoring using wildlife; second law based methods for improvement of energy systems; the expression of wind power; history and evolution of fusion power plant studies; EPA's SO2 policy and it's regional impacts; global climate change; and energy previsions in the American Recovery and Reinvestment Act of 2009. Chapter 1 - Nowadays, Spanish public buildings employ, during the spring season, the heating system only if indoor conditions are under certain temperature and relative humidity values. A correct HVAC system design and building construction could let us reduce this energy consumption. In the last years software tools were employed to understand and predict these thermal behaviour but they underestimated the energy consumption because its energy models ignore moisture. Actual HAM tools software could be employed to simulate indoor conditions and phenomena of material and energy transfer thought building envelopes and its effects on indoor conditions. Present paper shows an example of HAM tools application to determine modifications that reduce energy consumption or improve HVAC system in real buildings. Results showed the veracity of this simulation software and that parameters like solar gain or air leakages are so important than thermal inertia. Chapter 2 - This paper shows a research about improvement of indoor conditions controlling heat and mass transfer process. To do it, the first step was to sample real data in different kind of typical Spanish buildings like a set of flats, office buildings, museums and schools located in the area of A Coruña, Spain. Once obtained this data, it was analysed to determine indoor ambience problems and possible solutions related with heat and mass transfer process. In this sense, problems related with comfort conditions, energy saving, health, materials conservation and work risk were found and possible solutions like air renovation, thermal inertia, and moisture buffering were found. Chapter 3- Many from industry, government and academe have struggled with the question of how to allocate emissions for an energy process that has multiple products and multiple inputs, like cogeneration. Present methods are not universally accepted, because they are inconsistent, overly complex, difficult to utilize, and not soundly based. The author proposes that exergy methods can form the basis of rational and meaningful allocation methods for emissions. In this article, methods based on exergy for allocating cogeneration emissions are investigated and compared with other methods. Two illustrations are provided. The rationale for the author‘s view that the exergy-based method is the most meaningful and accurate is discussed, as are problems associated with other methods. An analogy is described

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between allocating carbon dioxide emissions and economic costs for cogeneration. The results indicate that the exergy-based emissions allocations method provides a sensible basis for a meaningful overall approach for emissions trading. It is concluded that the exergy-based method of carbon dioxide emissions allocation for cogeneration is rational, useful and superior to other methods. By permitting these emissions to be allocated more appropriately among commodities generated by cogeneration, the results allow the environmental benefits of technologies that produce multiple products to be better understood and exploited. The results should be of most benefit to designers of energy systems, and to decision and policy makers in companies and government. The author proposes that the exergy-based method be used in allocating cogeneration emissions to help ensure proper decision-making regarding such issues as what effect cogeneration may have on overall carbon dioxide emissions and how emissions should be reduced, how and where cogeneration should be used, and fair ways to establish emissions trading schemes. Chapter 4 - Since data obtained from wildlife are useful for the evaluation of risks to human health, importance of biological monitoring has been pointed out in many studies. However, it is fact that there are many problems on the biological monitoring using wildlife. For example, the outliers were often observed on the data obtained from wildlife. Although the outliers could be excluded by statistical data processing in studies of experimental animals, the outliers may indicate potential contamination of animals in studies of wildlife. In the present study, 80 wild ducks were investigated, and the cadmium (Cd) contents of kidney and that of liver were ND-67.44, ND-21.15 μg/g dry weight respectively. Since the outlier has been observed in several species, such as spot-billed duck, mallard, the authors analyzed those outliers using cadmium standard regression line (CSRL). In the authors previous reports, the CSRL was suggested as a useful index for the understanding of Cd contamination of animals. In conclusion, it was suggested that biological monitoring using the CSRL can make full use of the characteristics of all data, including outliers. Chapter 5 - Second law of thermodynamics is a fundamental law of nature and the concept can be utilized for better performance of energy systems. The entropy generation in any real (i.e., irreversible) process is a measure of the irreversibility of that process. Exergy concept includes the combined effect of a system and environment to measure the maximum possible work potential as the system reaches equilibrium with the environment from its initial state. In this article, a brief overview of different methods based on the principles of second law of thermodynamics in design and analysis of energy systems is discussed. The concept of entropy generation and exergy destruction in real-life processes are combined with economics and the overall impact on the environment during the life-cycle of a system to obtain more useful conclusions. Starting from entropy generation minimization principle (EGMP), exergy analysis, thermoeconomics (exergoeconomics) and exergetic life cycle analysis has been discussed. Some discussions on future trends of application of second law concept are also included. Chapter 6 - This paper touches on a fundamental aspect of wind energy calculation, and goes ahead to formulate three expressions of wind power. The paper attempts to answer the question whether the kinetic energy of a unit mass per second is 1/2, 1/3, or 2/3v3. The answer to this question is of importance for fluid dynamic considerations in general. The classical formulation of wind energy for turbines is based on the definition of the kinetic energy due to the wind impinging on the turbine blades. The expression of wind energy obtained is directly related to half (1/2) of the specific mass multiplied by the cube of wind

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velocity. Usually the assumption used is that the mass is constant. However, by changing this condition, different results arise. The approach by Zekai [1] based first on the basic definition of force and then energy (work) reveals that the same equation is valid but with 1/3 instead of factor 1/2. In his derivation, Zakai [1] has not given any reason as to why a factor 2/3 which can be obtained using his approach is not acceptable. The authors advance arguments to show that three expressions of wind energy are possible through physical formulation. Chapter 7 - This chapter provides a brief history of magnetic confinement fusion power plant conceptual designs, beginning with the early development in 1970, focusing on tokamaks. In addition, the evolution of six more magnetic concepts (stellarator, spherical tori, field-reversed configurations, reversed-field pinches, spheromaks, and tandem mirrors) is highlighted. The key issues encountered are discussed, including the technological obstacles and the elements necessary for economic competitiveness. Extensive R&D programs and international collaboration in all areas of fusion research led to a wealth of information generated and analyzed. As a result, fusion promises to be a major part of the energy mix in the 21st century and beyond. Chapter 8 - This study compares sulfur dioxide concentrations and the resulting health damages across U.S. regions under three alternative policies considered by the U.S. Environmental Protection Agency: emission caps, emission tax and tradable permits. Regional modeling is important because SO2 does not diffuse uniformly across regions, and because the U.S. energy industry is divided geographically by regulatory barriers, and differences in infrastructure, costs and energy demand. Regional concentrations of SO2 are found to vary across competing environmental policies significantly. Hundreds of millions of dollars in damages are at stake for individual states from the EPA‘s policy choice. Emission caps favor southern states, including California, Texas and Florida, where they deliver $840 million lower damages than the other policies. They deliver $390 million higher damages in northern, Great Lakes and New England states. Chapter 9 - Electricity generating plants powered by nuclear fusion have long been envisioned as possessing inherent advantages for the health and safety of the public, the health and safety of plant workers, and good stewardship of the environment while supporting modern society. This chapter discusses the progress and state-of-the-art of these three principal aspects of fusion safety and environment. The fusion safety philosophy and advantages over traditional thermal power plants are described. Fusion workers should be protected commensurately with workers in other comparable industrial activities. The fusion radwaste management strategy must accommodate the new trend of recycling and clearance, avoiding geological disposal. Here, the authors discuss the technical elements as well as the US regulatory approach and policy governing the design of safe and environmentally sound fusion devices. Chapter 10 - It is an undisputed reality that the energy production and their sustained growth constitute indispensable elements to ensure the economic and social progress of any country. For this reason, all type of energy sources available in the country, including nuclear energy, should be included in any study about the energy mix to be prepared in order to ensure its future economic an social development. However, there are certain factors that need to be considered by the competent authorities of a country during the selection of the most economic and convenient energy sources for the generation of electricity. For instance, the use of fossil fuels is a major and growing contributor to the emission of carbon dioxide to

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the atmosphere provoking serious changes in the world climate, while nuclear energy and renewables are almost carbon dioxide free. Considering the different available energy sources that the world can use now to satisfy the foresee increase in energy demand in the coming years, there should be no doubt that, at least for the next decades, there are only a few realistic options available to reduce further the CO2 emissions to the atmosphere as result of the electricity generation. These options are, among others, the following: Increase efficiency in electricity generation and use; Expand use of all available renewable energy sources such as wind, solar, biomass and geothermal, among others; Massive introduction of new advanced technology like the capture carbon dioxide emissions technology at fossil-fueled (especially coal) electric generating plants, with the purpose to permanently sequester the carbon produced by these plants; Increase use of new types of nuclear power reactors that are inherent safe and proliferation risk-free; Increase energy saving. Chapter 11 - When the pressure losses occuring in the Brayton cycle are accounted for, the cycle efficiency depends on the ratio of specific heats of the working fluid. The lower the ratio of specific heats, the higher the cycle efficiency. When tetrafluoromethane (CF4 or Refrigerant-14), a non-toxic, non-flammable, thermally stable, fairly inert gas having a specific heat ratio of 1.1 - 1.14, is used as the working fluid in a closed cycle gas turbine, a 22% increase in the thermal efficiency can be obtained than when air is the working fluid. Other organic gases too could be used in the proposed Closed Organic Brayton (COB) cycle which can achieve a thermal efficiency of about 21 % with a heat source temperature of only 5400C (~1000 deg F). Its capital and operating costs will be competitive with existing small Rankine cycle steam power plants that burn biomass, and have typical gas turbine advantages like small plant footprint and quick startup. Chapter 12 - Aircraft are a significant source of greenhouse gases—compounds that trap the sun‘s heat, with effects on the Earth‘s climate. In the United States, aircraft of all kinds are estimated to emit between 2.6% and 3.4% of the nation‘s total greenhouse gas (GHG) emissions, depending on whether one counts international air travel. The impact of U.S. aviation on climate change is perhaps twice that size when other factors are considered. These include the contribution of aircraft emissions to ozone formation, the water vapor and soot that aircraft emit, and the high altitude location of the bulk of aircraft emissions. Worldwide, aviation is projected to be among the faster-growing GHG sources. If Congress or the Administration decides to regulate aircraft GHG emissions, they face several choices. The Administration could use existing authority under Sections 231 and 211 of the Clean Air Act, administered by the Environmental Protection Agency. EPA has already been petitioned to do so by several states, local governments, and environmental organizations. Congress could address aviation or aviation fuels legislatively, through capand-trade or carbon tax proposals, or could require EPA to set emission standards. Among the legislative options, the cap-and-trade approach (setting an economy-wide limit on GHG emissions and distributing tradable allowances to emitters) has received the most attention. Most cap-and-trade bills, including the House-passed energy and climate bill, H.R. 2454, would include aviation indirectly, through emission caps imposed upstream on their source of fuel—the petroleum refining sector. By capping emissions upstream of air carriers and eventually lowering the cap more than 80%, bills such as these would have several effects: they would provide an incentive for refiners to produce lower-carbon fuels; they would increase the price of fuels, and thus increase the demand for more fuel-efficient

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aircraft; and they might increase the cost of aviation services relative to other means of transport, giving airline passengers and shippers of freight incentives to substitute lower-cost, lower-carbon alternatives. Besides regulating emissions directly or through a cap-and-trade program or carbon tax, there are other tools available to policy makers that can lower aviation‘s GHG emissions. These include implementation of the Next Generation Air Traffic Control System (not expected to be complete until 2025, although some elements that could reduce aircraft emissions may be implemented sooner); research and development of more fuel-efficient aircraft and engines; and perhaps the development of lower-carbon jet fuel. This chapter provides background on aviation emissions and the factors affecting them; it discusses the tools available to control emissions, including existing authority under the Clean Air Act and proposed economy-wide cap-and-trade legislation; and it examines international regulatory developments that may affect U.S. commercial airlines. These include the European Union‘s Emissions Trading Scheme for greenhouse gases (EU-ETS), which is to include the aviation sector beginning in 2012, and discussions under the auspices of the International Civil Aviation Organization (ICAO). Chapter 13 - Using the World Resources Institute (WRI) database on greenhouse gas emissions and related data, this chapter examines two issues. The first issue is the separate treatment of developed and developing nations under the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. This distinction has been a pivotal issue affecting U.S. climate change policy. The second issue is the continuing difficulty of the current approach designed to address climate change through limiting greenhouse gas emissions to a specified percentage of baseline emissions (typically 1990). The data permit examination of alternative approaches, such as focusing on per capita emissions or the greenhouse gas emission intensity (measured as emissions per unit of economic activity). Key findings include: A few countries account for most greenhouse gas emissions: in 2000, the United States led by emitting 19% of the world total, followed by China with 14%; no other country reached 6%; the top seven emitters accounted for 52% of the 185 nations‘ emissions. Land-use effects (e.g., deforestation) on emissions are negligible for most nations, but they cause emissions to rise sharply for certain developing nations, for example, Brazil and Indonesia. While oil- and gas-producing Gulf States have the highest per capita greenhouse gas emissions, in general developed nations rank high in per capita emissions (in 2000, Australia, the United States, and Canada ranked 5, 7, and 9, respectively, in the world), while developing nations tend to rank low (China, India, and Indonesia ranked 98, 156, and 123, respectively). The greenhouse intensity of the economy — the metric by which the George W. Bush Administration addressed climate change — varies substantially among developed countries (the Ukraine emits 667 tons/million international $GDP, while France emits 93 tons/million $GDP, with the United States at 192 tons/million $GDP; developing nations show less variance unless land use is taken into account. The time frame adopted for defining the climate change issue and for taking actions to address greenhouse gas emissions has differential impacts on individual nations, as a result of individual resource endowments (e.g., coal versus natural gas and hydropower) and stage of

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economic development (e.g., conversion of forest land to agriculture occurring before or after the baseline). Differentiating responsibilities between developed and developing nations — as the UNFCCC does — fails to focus efforts on some of the largest emitters. Moreover, many developed countries have not achieved stabilization of their emissions despite the UNFCCC. Given the wide range of situations illustrated by the data, a flexible strategy that allows each country to play to its strengths may be appropriate if diverse countries like the United States and China are ever to reach agreement. Chapter 14 - The 1992 U.N. Framework Convention on Climate Change requires that signatories, including the United States, establish policies for constraining future emission levels of greenhouse gases, including carbon dioxide (CO2). The George H. W. Bush, Clinton, and George W. Bush Administrations each drafted action plans in response to requirements of the convention. These plans have raised significant controversy and debate. This debate intensified following the 1997 Kyoto Agreement, which, had it been ratified by the United States, would have committed the United States to reduce greenhouse gases by 7% over a five-year period (2008-2012) from specified baseline years. Controversy is inherent, in part, because of uncertainties about the likelihood and magnitude of possible future climate change, the consequences for human wellbeing, and the costs and benefits of minimizing or adapting to possible climate change. Controversy also is driven by differences in how competing policy communities view the assumptions underlying approaches to this complex issue. This paper examines three starting points from which a U.S. response to the convention is being framed. These starting points, or policy ―lenses,‖ lead to divergent perceptions of the issue with respect to uncertainty, urgency, costs, and government roles. They also imply differing but overlapping processes and actions for possible implementation, thus shaping recommendations of policy advocates concerning the federal government‘s role in reducing greenhouse gases. A technological lens views environmental problems as the result of inappropriate or misused technologies. The solutions to the problems lie in improving or correcting technology. The implied governmental role would be to provide leadership and incentives for technological development. An economic lens views environmental problems as the result of inappropriate or misleading market signals (prices). The solutions to the problems lie in ensuring that the prices of goods and services reflect their total costs, including environmental damages. The implied governmental role would be to improve the functions of the market to include environmental costs, so the private sector can respond efficiently. An ecological lens views environmental problems as the result of indifference to or disregard for the planet‘s ecosystem on which all life depends. The solutions to the problems lie in developing an understanding of and a respect for that ecosystem, and providing people with mechanisms to express that understanding in their daily choices. The implied governmental role would be to support ecologically based education and values, as well as to promote ―green‖ products and processes, for example through procurement policies, efficiency standards, and regulations. Some initiatives are underway; all the perspectives are relevant in evaluating them and possible further policies. The purpose here is not to suggest that one lens is ―better‖ than

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another, but rather to articulate the implications of the differing perspectives in order to clarify terms of debate among diverse policy communities. Chapter 15 - The following list of authoritative resources is designed to assist in responding to a broad range of constituent questions and concerns about renewable energy and energy efficiency tax incentives. Links are provided for the following: the full text of public laws establishing and extending federal renewable energy and energy efficiency incentives; federal, state, and local incentives resources; incentive resources grouped by technology type (solar, wind, geothermal, and biomass); CRS reports on this topic; and federal grants information resources. The last section of this chapter includes tables displaying popular incentives, the corresponding U.S. Code citations, and current expiration dates of those incentives. This list reflects information that is currently available. Chapter 16 - The American Recovery and Reinvestment Act of 2009 (ARRA, P.L. 111-5) emphasizes jobs, economic recovery, and assistance to those most impacted by the recession. It also stresses investments in technology, transportation, environmental protection, and other infrastructure and proposes strategies to stabilize state and local government budgets. Energy provisions are a featured part of ARRA. More than $45 billion is provided in appropriations for energy programs, mainly for energy efficiency and renewable energy. Most funding must be obligated by the end of FY20 10. ARRA also provides more than $21 billion in energy tax incentives, primarily for energy efficiency and renewable energy. More than $11 billion is provided in grants for state and local governments through three Department of Energy programs. They are the Weatherization Assistance Program, which provides energy efficiency services to low-income households; the State Energy Program, which provides states with discretionary funding that can be used for various energy efficiency and renewable energy purposes; and the new Energy Efficiency and Conservation Block Grant Program, which aims to help reduce energy use and greenhouse gas emissions. The law conditions eligibility for most of the State Energy Program funding on enactment of new building codes and adoption of electric utility rate ―decoupling‖ to encourage energy efficiency. For the Department of Education, about $8.8 billion is provided for ―Other Government Services,‖ which may include renovations of schools and college facilities that meet green building criteria. The Department of Housing and Urban Development ($2 billion),and the Environmental Protection Agency ($1 billion) receive multi-purpose funds that can be used for energy efficiency measures in public housing and state and tribal facilities. New transportation-related grant programs support state and local government and transit agency purchases of alternative fuel and advanced technology vehicles, multi-modal use of transportation electrification, and manufacturers‘ development of facilities for advanced battery production. Nearly $5 billion is provided for ―leadership by example‖ efforts to improve energy efficiency in federal buildings and facilities. The law puts the General Services Administration (GSA) at the forefront of this effort, with $4.5 billion for ―high performance‖ federal facilities. For Department of Defense facilities, ARRA provides $3.7 billion for improvements that have a focus on energy efficiency. ARRA provides $100 million to the Department of Transportation for ―reducing energy consumption or greenhouse gases.‖ The Department of the Interior ($1 billion) and Department of Veterans Affairs ($1 billion) receive multi-purpose funds that can be applied to ―energy efficiency‖ or ―energy projects.‖ Also, GSA receives $300 million for federal purchases of alternative fuel vehicles.

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Nearly $8 billion is provided for energy and other R&D programs, $2.4 billion for energy technology and facility development grants, and $14 billion for electric power transmission grid infrastructure development and energy storage development (including $6 billion for loan guarantees). Also, the $21 billion in tax incentives include $14.1 billion for renewable energy, $2.3 billion for energy efficiency, $2.2 billion for transportation, $1.6 billion for manufacturing, and $1.4 billion for state and local government energy bonds. In response to the weakening value of renewable energy tax credits, caused by the economic recession, ARRA provides a cash grant alternative to both production and investment credits during 2009 and 2010. Chapters 1 - 8 - A version of these chapters was also published in the International Journal of Energy, Environment, and Economics, Volume 18, Issue 1/2, published by Nova Science Publishers. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research. Chapters 9 - 16 - A version of these chapters was also published in the International Journal of Energy, Environment, and Economics, Volume 18, Issue 3/4, published by Nova Science Publishers. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research.

In: Advances in Energy Research. Volume 4 Editor: Morena J. Acosta, pp. 1-18

ISBN: 978-1-61761-672-3 © 2011 Nova Science Publishers, Inc.

Chapter 1

INVESTIGATION OF HVAC SYSTEM IMPROVEMENT BY HAM NUMERICAL SIMULATION J.A. Orosa* Department of Energy and Marine Propulsion, University of A Coruña, Paseo de Ronda 51, 1501. A Coruña, Spain

ABSTRACT Nowadays, Spanish public buildings employ, during the spring season, the heating system only if indoor conditions are under certain temperature and relative humidity values. A correct HVAC system design and building construction could let us reduce this energy consumption. In the last years software tools were employed to understand and predict these thermal behaviour but they underestimated the energy consumption because its energy models ignore moisture. Actual HAM tools software could be employed to simulate indoor conditions and phenomena of material and energy transfer thought building envelopes and its effects on indoor conditions. Present paper shows an example of HAM tools application to determine modifications that reduce energy consumption or improve HVAC system in real buildings. Results showed the veracity of this simulation software and that parameters like solar gain or air leakages are so important than thermal inertia.

NOMENCLATURE Variables Acc C

c c pa H *

Indoor Air Quality acceptable index. Sum of thermal capacity C of each construction. Specific heat capacity of the material (J/kg K). Specific heat capacity of the dry air (J/kg K). Sum of heat loss factor of each construction, ventilation and air leakage.

E-mail address: [email protected], Tel. 034 981 167000 4320, fax. +349811167107. (Corresponding author)

2

J.A. Orosa h

hevap

air enthalpy (kJ/kg). Latent heat of evaporation (J/kg).

ma

Density of moisture flow rate of dry air (kg/m2s).

ml

Density of moisture flow rate of vapour phase (kg/m2s).

PD Psuc Pv Qheat Qloss Qgain

Percentage of dissatisfied (%). Suction pressure (Pa). partial water vapour pressure (Pa). Heat requirement (W). Heat loss (W). Heat gain (W). Density of the air flow rate (m3/m2s).

ra T

t K

w x xa

Temperature (ºC). Time (s). Hydraulic conductivity. Moisture content mass by volume (kg/m3). Space coordinates (m). Water vapour content (kg/kg).

Variables in Greek Letters

p

  a o

Moisture permeability (s). Thermal conductivity (W/mK) Utilisation factor. Density of the material (kg/m3). Density of the dry material (kg/m3).

1. INTRODUCTION A Coruña, located in the north west of Spain, present a mild climate with a high relative humidity as a consequence of winds. Nowadays, Spanish public buildings employ, during the spring season, the heating system only if indoor conditions are under certain temperature and relative humidity values. This HVAC system operation could be improved by some buildings design modifications. In this sense, energy saving methods are focused in the study of heat and mass transfer through buildings envelops [1, 2]. These methods could be employed to reduce or, in some cases substitute, the heating systems. For example, thermal inertia is a parameter that let us reduce energy requirement because in times of abundance (due to solar irradiation…) energy is stored in internal and external building constructions and transferred back into the zone when the indoor temperature decreases [3]. This thermal inertia makes possible to choose a higher design winter temperature or change the working conditions of the

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3

HVAC system like a change from intermittently controlled heat pumps to continuously capacity controlled heat pumps [4-8]. In the last years software tools were employed to understand and predict these thermal behaviour but they underestimated the energy consumption of buildings because the energy models ignore moisture. In this sense, whole building performance can only be realistically evaluated by accounting for the HAM interactions. Through its Energy Conservation in Buildings & Community Systems Program, the International Energy Agency launched Annexes 17 and 41 [9]. It is a working group to address issues surrounding whole building HAM response. The group involved over 50 researchers from 28 institutes and over 20 countries [10]. This Annex 41 tested Building simulation software like H-tools and HAM Tools from Chalmer Institute of Technology to simulate heat and mass transfer through buildings envelops considering heat gains like occupation, solar heat, illumination and air changes and infiltrations between others. Once this software is tested new conclusions could be obtained. In present paper an example of HAM tools application is showed to determine possible modifications to reduce energy consumption or improve HVAC system in real buildings.

2. MATERIALS 2.1. Tiny Tag Data Loggers Temperature and relative humidity were measured using an Innova 1221 data logger equipped with a temperature transducer MM0034, based on thermistor technology, and a humidity transducer MM0037, incorporating a light emitting diode (LED), a light sensitive transistor, a mirror, a cooling element and a thermistor.

2.2. Air Changes One of the components of the measuring apparatus was a multi-gas monitor. The ventilation rate was performed using the concentration decay method, measuring SF6 as tracer gas with a Brüel&Kjaer multi-sampler.

3. METHODS In this sense, two school buildings were sampled during different seasons to relate indoor conditions with weather, heat and moisture balances and HVAC system. ASHRAE Standard 1992 [11] indications and Burch [12, 13] simulations showed that the massiveness of an exterior wall reduces the heating and cooling requirements of buildings, provided the room air temperature floats above the thermostat set point in heating and below the thermostat in the cooling season. The floating temperature occurs more frequently in mild climates and during the spring. Special interest presents the spring season because then the HVAC system could be fully substitute by some passive methods. In consequence, this paper will study thermal inertia effect of two buildings with high and low wall density during the spring season of the mild weather of A Coruña (Spain).

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The week period selected for this study was a weekend and a holiday to identify the appropriate indexes of thermal stability for building envelopes. The focus must consider the solar heat gain and heat storage of building walls under conditions of natural ventilation [14]. During this unoccupied period, occupation and excessive air changes will not interferes the sampled data and an easy environment simulation could be done. After tests the simulations indoor conditions modifications will be proposed for energy saving. In this sense, location of the mass in relation to the insulation has a large effect on the deviation between measured energy use and steady state analysis. In high-density buildings where mass was outside the insulation, the measured energy use closely matched that predicted by steady-state analysis but not when the insulation was outside the mass [11]. In consequence, parameters like thermal inertia, air changes and internal coverings were simulated showing a clear different behaviour in each condition. Conclusions let us understand the passive methods that could let us reach better indoor condition during the first hour of occupation of the morning and evening class periods.

3.1. Schools Two schools are sampled and simulated. One of the areas of the older school was built in 1890, and the other part was built in 1960. The new school was built in 1999. In consequence, the old school presents 0.43 m of Stone and 0.5 cm of concrete in the indoor side of the wall. The wall of the new building consist in layers of insulation, brick, concrete and plaster arranged symmetric respect the middle of the wall and reaching 0.30 m of total thick ness. The classroom sampled, in the old building, is located on the second floor and has a volume of 210 m3, while the new is located on the first floor with a volume of 150 m3. All these buildings present a working period from February to June and an unoccupied period during the weekends and holidays. In those periods classrooms are under natural ventilation and central heating system was not employed. They active period ends in June and, in consequence, it is not interesting for energy saving during summer period. Furthermore, during the winter extreme conditions these schools are not working and, inconsequence heating system will works only when the indoor conditions exceed the thermal comfort during winter and spring.

3.2. Indoor and Outdoor Sampling Conditions The indoor and outdoor humidity and temperature have been monitored simultaneously in the most representative classroom of each school during part of winter and spring seasons. All schools have purely adventitious ventilation. Transducers were hung in the middle of the classrooms. Data has been gathered in Tiny tags data loggers which can store 7,600 readings. Air infiltration was measured by tracer gas method employing SF6 as tracer gas.

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5

3.3. Thermal Comfort and Indoor Air Quality Indexes Local thermal comfort has been evaluated in terms of the parameter PD, Percentage of Dissatisfied Persons, through the equation 1 obtained by Toftum et al (1988) and Simonson et al [15-18].

PD 

100 1  exp( 3.58  0.18(30  T )  0.14(42.5  0.01Pv ))

(1)

An acceptable environment would be that in which less than 15% of the occupants are dissatisfied. The Indoor Air quality has been evaluated through the so-called Acceptable Indoor Air Quality parameter, Acc. The equation 2 was proposed by Fang et al (1988).

Acc  0.033  h  1.622

(2)

3.4. Ham Tools Simulation The mathematical model employed in this simulations is the result of whole building Heat, Air and Moisture (HAM) [19, 20, 21] balance and depends on moisture generated from occupant activities, moisture input or removed by ventilation, and moisture transported and exchanged between indoor air and the envelope [10]. The mathematical model is based in the numerical resolution of the energy and moisture balance through the building. In accordance with the next equations [20], the heat flow presents a conductive and a convective part as we can see in the equation 3 and described in equations 4 and 5.

q  qconductive  qconvective qconductive  

T x

qconvective  ma  c pa  T  hevap

(3)

(4) (5)

The moisture flow transfer was separated in liquid and vapour phases as we can see in the equations 6 and 7.

ml  K 

Psuc x

(6)

The vapour phase was divided in diffusion and convection as we can see in the equation 7.

mv   p 

p  ma  x a x

(7)

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J.A. Orosa

The mass airflow through the structure driven by air pressure differences across the structure is showed in the equation 8.

ma  ra   a

(8)

The finally energy and moisture balance are showed in equations 9 and 10.



 T q  c  o  x t 

 x

m

w t

(9)

(10)

Finally, the numerical model, based on a control volume method present lumped the thermal capacity C in the middle of the total thickness d/2 and, in consequence, the thermal resistances for one half is showed in equations 11, 12 and 13.

R

d /2



Rp 

d /2

Rsuc 

d /2 K suc

p

(11) (12)

(13)

The obtained discretized heat and moisture balance equations are showed in equations 14 and 15.

 (T  T ) (T  Ti )   ( pi 1  pi ) ( pi 1  pi )     i 1 i  i 1   ...   hevap   Ri 1  Ri   Ri 1  Ri  R p ,i 1  R p ,i R p ,i 1  R p ,i   (14) m  c  (T  Ti ) n , ma  0   a pa i 1  n ma  c pa  (T1  Ti 1 ) , ma  0 Ti n 1  Ti n 1  n t C

win 1  win 1  ( p i 1  p i ) ( p i 1  p i )   ( Psuc,i 1  Pisuc,i ) ( Psuc,i 1  p suc,i )          ... t d  R p ,i 1  R p ,i R p ,i 1  R p ,i   Rsuc,i 1  Rsuc,i Rsuc,i 1  Rsuc,i   6.21  10 6  ma  ( pi 1  p i ) n , ma  0   6 n 6.21  10  ma  ( p i  p i 1 ) , ma  0

(15)

Where i is the objective node and i+1 and i-1 are the preceding and following node and n and n+1 de previous and corresponding time steps.

Investigation of HVAC System Improvement by HAM Numerical Simulation

7

To solve these balance equations room models were created from the individual Building Physics Toolbox [22, 23]. Ham –tools library is a Simulink models upgrade version of HTools with the similar structure and specially constructed for thermal system analysis in building physics. The library contains blocks for 1-D calculation of Heat, Air and Moisture transfer thought the building envelope components and ventilated spaces. The library is the part of IBPT-International Building Physics Toolbox, and available for free downloading [24]. This library presents two main blocks; a building envelope construction (walls, windows) and thermal zone (ventilated spaces), which are enclosed by the building envelope. Component models provide detailed calculations of the hydrothermal state of each subcomponent in the structure; according to the surrounding conditions to witch it is exposed. In Figure 1 we can see the principal blocks employed for a building simulation. There we can see a block that represents the different exterior/interior walls, floor, roof and windows components. These constructions are defined respect they physical properties (density of the dry material and open porosity), thermal properties (specific heat capacity of the dry material and thermal conductivity) and moisture properties (sorption isotherm, moisture capacity, water vapour permeability and liquid water conductivity) in accordance with the BESTEST structure. Other parameters are considered in the heat and moisture building balance like, for example, internal gains (convective gains, radioactive gains and moisture gains), air change and heating/cooling system.

Geometry

Geometry Horiz Cat

Construction Zone

Zone S

Radiation

Radiation

Left Node

Geometry

Right Mon

1

Double-pane window Variable solar transmittance IEA Common Excercise

EXTERIOR WALL 1

Constructions

Construction

Horiz Cat

System

One node / CTH 1

Zone

2 T

Geometry

Construction

Zone

Zone

Construction

Gains Radiation

F

ps(T)

Saturation pressure

Horiz Cat

Radiation

3

R

Radiation

FLOOR

Room air / CTH WAVO model

ROOF

Vert Cat simout

System

Zone

Ventilation system AIR IN

Gains

Zone

To Workspace

HEATING/COOLING SYSTEM

In1

Out1

4

Zone out

System

Zone

Ventilation system AIR OUT

Out1

BTweather BESTEST

Internal gains

Figure 1. Matlab blocks for buildings simulations.

Classroom characteristics are defined in the thermal zone block indicating the surface areas, orientations and tilts of each wall. Room volume, solar gain to air and initial temperature is adjusted so. Thermal model of the classroom is based on the WAVO model described by de Witt (2000) [25] and developed by the assumptions that long wave radiation

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J.A. Orosa

is equally distributed over the walls; the room air has the uniform temperature, the surface coefficients for convection and radiation are constant and, finally, that all radioactive heat input is distributed is such a way that all the surfaces except windows absorb the same amount of that energy per unit of surface area. In this paper, wall construction, indoor air changes and internal gains were adjusted in accordance with measured data and simulated for three days of unoccupied period and determine simulations accuracy. Finally, a weather database was done in accordance with meteorological sampled data to be introduced. This database was loaded before simulation adjusting the time step and study period.

3.5. Time Constant The time constant is normally found from a slow cooling down period with a constant low outdoor temperature as (heat capacity)/(heat loss factor) [3]. This method is based on a seasonal steady state energy balance on the building as a whole or on a particular building zone. The thermal inertia is introduced in terms of the utilisation factor that shows the part of energy gains (solar irradiation and others) that can be stored in building construction to be transmitted into the zone when needed, as we can see in equation 16.

Qheat  Qloss  Qgain

(16)

The utilisation factor  is a function of the building periodic time constant and the ratio Qgain/Qloss. The time constant is defined in the standard by the equation 17.



C H

(17)

As [3] recommends, when we want to work in a more precise way, the logarithm of the temperature difference in-outdoors is taken and matched to a straight line by the method of least squares. The time constant is the inverse of the coefficient for the independent variable (time) given by this curve fit. In consequence, after test our simulations with real sampled data; both buildings were simulated under constant weather conditions with the aim to determine building time constants.

4. RESULTS 4.1. Outdoor Conditions Figure 2 shows the outdoor conditions during the unoccupied period.

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9

Outdoor 25

1

Temperature (ºC)

0,8 0,7

15

0,6 0,5

10

0,4 0,3

5

Relative humidity (%)

0,9 20

0,2 0,1 T outdoor

RH outdoor

0

0

17:00 0:00 7:00 14:00 21:00 4:00 11:00 18:00 1:00

8:00 15:00 22:00 5:00 12:00 19:00 2:00

9:00 16:00

Time (hours)

Figure 2. Outdoor sampled temperature and relative humidity.

4.2. Thermal Inertia and Solar Gain: Time Constant Determination Figures 3 and 4 represent the logarithm of the temperature difference between indoor and outdoor temperatures when building is under constant weather conditions. Its linear regression constants will give us the time constant of each building. Old School

2,2

ln(Temperature difference)

2

1,8

1,6

1,4 y = -0,009x + 2,3212 R2 = 0,9947 1,2 Old School

Lineal (Old School)

1 20

40

60

80

Time (hours)

Figure 3. Time constant determination for old school buildings.

100

120

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J.A. Orosa

New School

2,2

ln(Temperature difference)

2

1,8

1,6

1,4 y = -0,0056x + 2,1501 R2 = 0,9364 1,2 New School

Lineal (New School)

1 20

30

40

50

60

70

80

90

100

110

120

Time (hours)

Figure 4. Time constant determination for new school buildings.

Time constant in Old Schools =111 Time constant in New School=178

4.3. Air Changes Sampled air changes were changed from 0.7 during the unoccupied period for the old school and 0.6 in the new school to a lower value of 0.4 air changes with the aim of observant the effect of weather on indoor conditions. Relative humidity 0,80 0,75

Relative humidity (%)

0,70 0,65 0,60 0,55 0,50 0,45 0,40 New School

Covering in New

0,35 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00

Time (hours)

Figure 5. Relative humidity when air changes were reduced in new schools.

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Relative humidity 0,80 0,75

Relative humidity (%)

0,70 0,65 0,60 0,55 0,50 0,45 0,40 Old School

Covering in Old

0,35 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00

Time (hours)

Figure 6. Relative humidity when air changes were reduced in old schools.

5. DISCUSSION As we can see in Figures 2, A Coruña present a mild climate but during all the year exist a certain high relative humidity, nearly 80%, related with some health problems, building maintenance and energy consumption. As was commented previously, simulation and sampled indoor data was compared obtaining a high approach between real and simulated ambiences. Once tested that simulations, the two schools were simulated under a constant weather conditions of 10 ºC and 80% of relative humidity to obtain the time constant, after a linear regression of the logarithm difference temperature respect outdoor ambience. Results of Figures 3 and 4 showed a time constant of 111 for the old and 178 in the new school with an adequate correlation factor in each case. This value shows a higher thermal inertia of the new school than the old. Other simulations were done under different indoor ambience temperatures obtaining the same value. The explanation of this effect is related with heat solar heat gains that the new building experiments respect the old as a consequence of the classroom way and the presence of another nearer buildings that interfere in that heat gain. To understand this solar effect, if we simulate this same process but without this heat gain another time constants were obtained. Once again adequate linear regressions were obtained with correlation values of 0.98 and 1 and time constants of 36.9 and 66.6 for new and old schools respectively. Now the old school present a higher thermal inertia respect the new as a consequence of the purely effect of wall thickness and heat transmission properties.

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J.A. Orosa

Temperature 22,5 22,0

Temperature (ºC)

21,5 21,0 20,5 20,0 19,5 19,0 18,5 18,0 17,5

New School

Covering in New

17,0 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00

Time (hours)

Figure 7. Temperature when air changes were reduced in new schools. Temperature 22,5 22,0 21,5

Temperature (ºC)

21,0 20,5 20,0 19,5 19,0 18,5 18,0 17,5

Old School

Covering in Old"

17,0 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00

Time (hours)

Figure 8. Temperature when air changes were reduced in old schools.

Once obtained these results another change was proposed. For example, old school presents a high air changes during the unoccupied period as a consequence of the air leakages. These air changes reach values upper the 0.5 usually obtained in closed ambiences. When the air changes of the two buildings are changed to 0.4 another curves are showed in the Figures 5 to 14. Indoor air humidity, in the new and old school, experiments a decrease to more adequate values of 60%. This effect is related with the heat and moisture transfer through stonewalls. The indoor air temperature reaches the same maximum values but experiment a slowly decrease in the new building reaching a higher minimum values than under normal conditions of air changes, as we can see in Figures 7 and 8. This thermal effect will be present

Investigation of HVAC System Improvement by HAM Numerical Simulation

13

in the indoor air enthalpy and, in consequence, on the percentage of dissatisfied that reaches slight lower PD maximum values during the night, Figures 13 and 14. The enthalpy conditions indicate that HVAC system is not needed because the indoor air enthalpy under natural ventilation reaches the value of 39kJ/kg, estimated for adequate indoor conditions.

Enthalpy (kJ/kg)

Enthalpy 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 New School Covering in New School 34 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00

Time (hours)

Figure 9. Enthalpy when air changes were reduced in new schools.

Enthalpy (kJ/kg)

Enthalpy 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 Old School Covering in Old School 34 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00

Time (hours)

Figure 10. Enthalpy when air changes were reduced in old schools.

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J.A. Orosa

In resume, we can conclude that a reduction in indoor air changes lead to the new building, where the solar heat gain is more important, to increment its indoor temperature and, in consequence doing an indoor ambience more insensible to outdoor weather change, as we can see in Figure 7 and 8 in the indoor temperature slope after a peak of temperature. PD 22

21

PD (%)

20

19

18

17 New School

Covering in New School

16 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00

Time (hours)

Figure 11. PD when air changes were reduced in new schools.

On the other hand, these buildings were so simulated with a new internal covering as wooden panel. When this permeable covering is employed temperature decay is parallel and slight higher than initial conditions enhancing a reduction on indoor air relative humidity. This increment of indoor air temperature is related with the insulation properties of the wooden panel and let the building reach the highest time constant. PD 22

21

PD (%)

20

19

18

17 Old School

Covering in Old School

16 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00

Time (hours)

Figure 12. PD when air changes were reduced in old schools.

Investigation of HVAC System Improvement by HAM Numerical Simulation

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Acc 0,60

Acceptability

0,50

0,40

0,30

0,20

0,10 New School

Covering in New School

0,00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00 23:00 5:00 11:00 17:00

Time (hours)

Figure 13. PD when air changes were reduced in new schools.

As we have note, the importance of this time constants is based on the fact that the building with the lower time constant reacts faster on weather changes and variation in internal heat gains than the more heavy buildings. Even during a short period of cold weather heat must be supplied in the lightweight building, whereas such periods can be passed without heating in the constructions with higher thermal inertia due to heat stored in the structure from previous warmer periods in accordance with [3]. Furthermore, the amplitude of temperature fluctuation of the inner surfaces of walls made of low time constant buildings under intermittent air-conditioning conditions is 1ºC higher than that of walls of buildings with a higher thermal inertia under continuous air-conditioning conditions in accordance with [14]. Acc 0,60

Acceptability

0,50

0,40

0,30

0,20

0,10 Old School

Covering in Old School

0,00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00

Time (hours)

Figure 14. PD when air changes were reduced in old schools.

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J.A. Orosa

Time constant 200 180

Time constant

160 140 120 100 80 60 40 20

New

Old

0 Without heat gain

Initial conditions

Air renovation reduction

Permeable coverings

Modification

Figure 15. Time constant for each modification.

These effects can be summarised in their study of time constant of each variant. In our case, when indoor air changes are reduced, the time constant for new and old schools is 185 and 112 respectively. These values are similar to that of 325, 164 and 31 for Heavyweight, Massive wood and Lightweight walls showed by [3]. Despite this, exist an internal gain from convective heat source on new building that lead to a higher effect than the wall construction thermal inertia. In consequence, after understand previous results we can conclude that the energy saving and HVAC system design must be done in accordance with its individual characteristics of each building and not only taking into account meteorological data and general procedures.

6. CONCLUSIONS This paper sample and simulate different indoor conditions in school buildings with the aim to determinate the possibility of energy saving and design considerations on building and HVAC systems. The heat-transfer through the night thermal inertia elements is analysed by using 1D time dependent conduction heat transfer equation that is solved numerically by using HAM tools. The model takes into account in a detailed fashion the inertial heat sources and the air changes. These simulations showed that old building presents the lowest thermal inertia than the new as a consequence of a solar heat gain. In consequence, the building with the lower time constant reacts faster on weather changes and variation in internal heat gains than the more heavy buildings. Parameters like air changes and permeable coverings interact in over the time constant. If the air changes is reduced the old building experiment a slowly increment, while the new will experiment a clear internal temperature increment as a consequence of the heat gain. Finally, the presence of permeable internal coverings like wood panel let us increment the time constant as a consequence of the increment of wall insulation,

Investigation of HVAC System Improvement by HAM Numerical Simulation

17

especially in the old school. In consequence, more research is needed to define new design and corrections of HVAC systems taking into account these individual parameters of each building location.

REFERENCES [1] Orosa, J. A. and Baaliña A. Passive climate control in Spanish office buildings for long periods of time. Building and Environment 2008; doi:10.1016/j.buildenv.2007.12.001 [2] Orosa, J. A. and Baaliña A. Improving PAQ and comfort conditions in Spanish office buildings with passive climate control. Building and Environment 2008; doi:10.1016/j.buildenv.2008.04.013. [3] Norén, A., Akander, J., Isfält, E. and Söderström, O., The effect of Thermal Inertia on Energy Requirement in a Swedish Building-Results Obtained with Three Calculation Models. International Journal of Low Energy and Sustainable Buildings, 1999.Vol.1. [4] Karlsson, F. and Fahlén, P. Impact of design and thermal inertia on the energy saving potential of capacity controlled heat pump heating systems. International Journal of refrigeration 2008. 31. 1094-1103. [5] Roucoult, J. M., Douzane, O. and Langlet, T. Incorporation of thermal inertia in the aim of installing a natural night time ventilation system in buildings. Energy and Buildings. 1999.29. 129-133. [6] Badescu, V. and Sicre, B. Renewable energy for passive house heating II Model. Energy and Buildings. 2003. 35. 1085-1096. [7] Badescu, V. and Sicre, B. Renewable energy for passive house heating Part I. Building description. Energy and Buildings 35 (2003) 1077–1084. [8] Krüger, E. and Givoni, B. Thermal monitoring and indoor temperature predictions in a passive solar building in an arid environment. Building and Environment. 43. 2008 1792-1804. [9] Andreas Hauer. Harald Mehling. Peter Schossig. Motoi Yamaha. Luisa Cabeza. Viktoria Martin. Fredrik Setterwall. International Energy Agency Implementing Agreement on Energy Conservation through Energy Storage. Annex 17. ―Advanced Thermal Energy Storage through Phase Change Materials and Chemical Reactions – Feasibility Studies and Demonstration projects‖. Final Report. [10] International Energy Agency. http://www.iea.org [11] ASHRAE handbook—fundamentals. Load and Energy Calculations, Energy Estimating Methods. 1993. Chap. 28 [12] Burch, M. D. and Chi, J. MOIST A PC Program for Predicting Heat and Moisture Transfer in building Envelopes. NIST Special Publication 917. NIST United States Department of Commerce Technology Administration. National Institute of Standards and Technology.1997. [13] Burch, D. M., Remmert, W. E, Krintz, D. F.and Barnes, C. S. A Field Study of the Effect of Wall Mass on the Heating and Cooling Loads of Residential Buildings (aka Log Home Report). National Bureau of Standards Washington, D.C. 20234. Proceedings of the Building Thermal Mass Seminar. Knoxville, TN; 6/2-3/82. Oak Ridge National Laboratory

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[14] Ya Feng Thermal design standard for energy efficiency of residential buildings in hot summer/cold winter zones. Energy and Buildings. 2004. 36. 1309-1312. [15] Simonson, C. J., Salonvaara, M. and Ojalen, T. The effect of structures on indoor humidity-possibility to improve comfort and perceived air quality. Indoor Air 2002. 2002.12. 243-251. [16] Simonson, C. J., Salonvaara M. and Ojalen T. Improving indoor climate and comfort with wooden structures. Espoo 2001. Technical Research Centre of Finland. VTT Publications. 2001. 431.200p.+ app 91. [17] Toftum, J., Jorgensen, A. S. and Fanger, P. O. Upper limits for indoor air humidity to avoid uncomfortably humid skin. Energy and Buildings. 1998. 28. 1-13. [18] Toftum, J., Jorgensen, A. S. and Fanger, P. O. Upper limits of air humidity for preventing warm respiratory discomfort. Energy and Buildings 28 (1998) 15-23. [19] Kalagasidis, A. S. BFTools H Building physiscs toolbox block documentation. Department of Building Physics. Chalmer Institute of Technology. Sweeden. 2002. [20] Kalagasidis, A. S., HAM-Tools. International Building Physics Toolbox. Block documentation. [21] Weitzmann, P., Kalagasidis, A. S., Nielsen, T. R., Peuhkuri, R. and Hagentoft, C. Presentation of the international building physics toolbox for simulink. [22] Nielsen, T. R., Peuhkuri, R., Weitzmann, P. and Gudum C. (2002). Modelling Building Physics in Simulink. BYG DTU Sr-02-03. ISSN 1601-8605. [23] Rode, C., Gudum, Weitzmann, P., Peuhkuri, R., Nielsen, T. R., Sasic Kalagasidis, A. and Hagentoft, C-E.: International Building Physics Toolbox-General Report. Department of Building Physics. Chalmer Institute of Technology. Sweden. Report R02: 2002. 4. [24] International Building Physics Toolbox in Simulink. www.ibpt.org. [25] Wit, M.: WAVO. A simulation model for the thermal and hygric performance of a building. Faculteit bouwkunde, Technische Universiteit Eindhoven. 2000

In: Advances in Energy Research. Volume 4 Editor: Morena J. Acosta, pp. 19-39

ISBN: 978-1-61761-672-3 © 2011 Nova Science Publishers, Inc.

Chapter 2

RESEARCH ON HEAT AND MASS TRANSFER TO IMPROVE HVAC CONDITIONS J.A. Orosa* Department of Energy and Marine Propulsion. University of A Coruña, Paseo de Ronda 51,1501. A Coruña, Spain

ABSTRACT This paper shows a research about improvement of indoor conditions controlling heat and mass transfer process. To do it, the first step was to sample real data in different kind of typical Spanish buildings like a set of flats, office buildings, museums and schools located in the area of A Coruña, Spain. Once obtained this data, it was analysed to determine indoor ambience problems and possible solutions related with heat and mass transfer process. In this sense, problems related with comfort conditions, energy saving, health, materials conservation and work risk were found and possible solutions like air renovation, thermal inertia, and moisture buffering were found.

NOMENCLATURE Co Cb Vb o S t Pv H

*

Outdoor CO2 concentrations (ppm) Bedroom CO2 concentrations (ppm) Bedroom volume (dm3) Natural ventilation rate (dm3/s) CO2 source in the room due to its occupancy (dm3/s). Temperature (ºC) Partial water vapour pressure (Pa). Air enthalpy (kJ/kg).

E-mail address: [email protected], Tel. 034 981 167000 4320. (Corresponding author)

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1. INTRODUCTION Located in the northwest coast of Spain, the climate of Coruña, population of 280,000, is mild. However, indoors humidity in the area is relatively high over most of the year due to the effect of the Atlantic Ocean winds. Health authorities claim that the level of incidence of respiratory ailments in the area is higher than in the rest of the country. A possible cause of these interior conditions is the resident‘s habits and outdoors conditions. These climatic characteristics influence the study method of indoor ambiences, design of HVAC and generate problems in different kind of buildings. To control this indoor ambience, a thermostatic system is employed and in summer indoor ambience floats freely and natural ventilation is the more employed method of indoor air renovation. If we analyse all different buildings as a function of their indoor activity we can found different problems to be solved and, in consequence, different objectives. For example, in flats the principal objective is to get the better thermal comfort [1], indoor air quality and reduce health problems like allergies. When we analyse the indoor activity, we can observe that a bad indoor air renovation may cause moisture accumulation, especially during cooking time, which affect the others rooms. These moisture problems may be correlated with fungi growth and its respective allergies problems of occupants. In other cases, this bad indoor air renovation will causes problems like a higher CO2 concentration in bedrooms during the night. In offices buildings and schools the objective of study, during the occupied period, is the local thermal comfort condition, perception of indoor air quality, productivity and energy saving but during the unoccupied period this objectives had not been taken into account and could be solved with new techniques [2]. In schools and museums the principal objective is maintain comfort conditions and relic conservation [3, 4]. Furthermore, at the archive, materials must be stored with less energy consumption as possible. In extreme ambiences thermal comfort is not the most interesting objective and work risk is studies are preferential [5]. Solutions of this problems must began with a clear occupants become aware about how employ the natural ventilation like opening windows or employ of passive methods to energy saving. These passive methods may be correlated with the employ of permeable coverings or solar radiance. Furthermore, this methods may induce to low energy consume or substitute the mechanical HVAC systems. In spite of this, energy saving may be low if we compare with an inadequate operative conditions of heating system. This is why, we must combine these methods with correct residents habits. In the other hand, despite the fact that there are a lot of software that can let us to understand the influence of different variables on real indoor conditions, to quantify energy saving, productivity and health effects and materials conservation on indoor ambiences, we must employ real sampled data. This real data let us take into account typical parameters like real weather data, occupant‘s habits and construction materials and to prove the software veracity. Furthermore, to get a more approximate simulation we must complement these simulations with laboratory tests about building properties and questionnaires involving questions such as the state of the flat, living habits, indoor air quality perception, health and symptoms experienced by the occupants. All this complements let us get the better design software taken into account the real activity and indoor conditions existing on a clear zone of building.

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In order to effectively confirm this claim, a systematic investigation of indoor conditions in the area would be needed. This need prompted the research reported in the present paper, in which data obtained in the Coruña area is presented and analysed in four kinds of typical indoor environments; flats, offices, museums, schools and industrial ambiences.

2. MATERIALS One of the components of the measuring apparatus was a multi-gas monitor. The ventilation rate was measured by a Brüel&Kjaer (Denmark) multi-sampler made up of the following main components: (a) a photo-acoustic infrared detection microprocessorcontrolled gas analyzer; (b) An air multi-sampler with six sampling ports; and (c) application software to remote control the gas analyzer and a personal computer. The apparatus was equipped with a temperature transducer to measure the state of the air at the point of sampling. Temperature and humidity were measured through an Innova 1221 data logger equipped with a MM0034 temperature and MM0037 humidity transducers. Temperature and humidity sensors were adequately located so that a typical air condition of the room could be measured. A Casella AFC124 air suction pump was used in sampling air for microbiota analysis. The sample of air used to be filtered by flowing through a 47mm diameter, 0.45 m pore ALBERT-NCS-045-47-BC cellulose nitrate membrane filter with an ALBERT PF-50-P-02 sterilized polycarbonate filter holder.

3. METHODS 3.1. Temperature and Humidity As previously mentioned, the buildings were randomly chosen so that typical every day life of occupants was not disturbed and typical indoor conditions could be obtained during the measurements. Indoor temperature and humidity have been measured in flats, office buildings, schools, museums and ships in the Coruña area. Measurements used to be taken with a sampling frequency between five to ten minutes and have been referred to the ASHRAE Handbook of Fundamentals [6].

3.2. Ventilation Rate The ventilation rate has been determined by performing sampling in intervals varying between 11 and 14 minutes. It was determined through the concentration of carbon dioxide (CO2) procedure, based on the equation 1.

dCb v0 (C0  Cb )  S  dt Vb

(1)

The natural ventilation rate, o, can be determined from equation (1) given the outdoor and indoor CO2 concentrations and the indoor CO2 production. The minimum ventilation rate,

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min, is the one corresponding to a steady state CO2 concentration with a 1000 ppm CO2 room concentration. The equation 2 results from the equation 1.

vmin 

S (C1000  C0 )

(2)

3.3. Microbiological Load A study of respiratory ailments was carried out during the spring season involving family flats, and with total of 100 individuals. Flats were chosen in such a way that at least one of the occupants have suffered or were suffering of a respiratory ailment at the time of the study. Temperature and relative humidity was measured along with sampling of air to determine the microbiological load through culture and count.

3.4. Thermal Comfort/Indoor Air Quality Local thermal comfort has been evaluated in terms of the parameter PD, Percentage of Dissatisfied Persons, through the equation 3 by Toftum et al (1988) and Simonson et al [7-11].

PD 

100 1  exp( 3.58  0.18(30  t )  0.14(42.5  0.01Pv ))

(3)

An acceptable environment would be that in which less than 15% of the occupants are dissatisfied. The Indoor Air quality has been evaluated through the so called Acceptable Indoor Air Quality parameter, Acc. It has been found elsewhere, Fang et al (1988) [12-14], that this parameter is strongly influenced by the temperature and the relative humidity, being linearly related to the air enthalpy. The equation 4 has been proposed by Fang et al (1988).

Acc  aH  b

(4)

Constants a and b are empirical coefficients. For clean air a=0.033 kg of dry air/kJ and b=1.622. It is interesting to note that Acceptable Indoor Air Quality parameter is a measure of the level of acceptability of air with no known contaminants, as determined by a pertinent authority, and a level of dissatisfied occupants relatively small (lower than 20%). The range of variation of this parameter is 1.

3.5. Questionnaires Data gathering was complemented with questionnaires involving questions such as the state of the flat, living habits, indoor air quality perception, health and symptoms experienced by the occupants. In schools, museums and offices questionnaires includes working habits,

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indoor air quality perception and energy consumption. In ships questionnaires are based in working habits and no fatal accidents happened during last years.

3.6. Climatic Data Outdoors conditions have been obtained by means of weather stations next to the city, to avoid the effect of the buildings on the measured values of the outer conditions. The Environmental Information system of Galicia (SIAM [15, 16]), has as main function facilitating the access to the information on environment and climatology. Another organization that provides the meteorological data is the Forest and Environmental Research Center of Lourizán (Ministry of Environment of the Xunta de Galicia). This center was made up of 42 stations of climatologic observation, distributed by all Galician geography. In 1988, it began the installation of a modern network of automatic stations of meteorological observation, arriving in 2000 to 23 stations that transmit the information in real time.

3.7. Flats The first kind of typical indoor environment is present in Spanish flats where the principal parameters to study are the comfort and health conditions as a function of air conditioning design and residents habits. Measurements involved parameters such as temperature and humidity ratio [17] in addition to ventilation [18, 19], carbon dioxide concentration and microbiological load [20-23] were carried out over 24 hour periods by keeping the household life as regular as possible.

3.8. Museums and Schools The second kind of typical indoor ambience is present in museums and schools where the principal parameters to study are the energy saving [24, 25] and materials preservation [26, 27]. This energy saving is concentrated in understand the outdoor climatic conditions effect on indoor ambience as a consequence of wall thermal inertia, especially during the occupational period. Another question that must be answered is if set point temperatures, usually adopted by curators, are adjusted in accordance with comfort conditions or with relic preservations. With this objective, schools indoor conditions were sampled during two years at different zones. Indoors and outdoors humidity and temperature in some typical classrooms have been monitored in seven schools during winter and summer seasons. In particular, two of them, the oldest and the newest, will be considered. The old school (1) was built in 1890 and the new (2) was built in 1999 as we can see in Figures 1 and 2. The air is only conditioned by a heat water system during the winter season and classrooms are naturally ventilated by windows and infiltrations. The old school building present granite blocks with a supposed higher thermal inertia and the new school building wall structure are showed in Figure 3, with a supposed low thermal inertia. This last wall structure consist in; external coverings, concrete, brick, air barrier, polystyrene, brick, concrete and internal covering.

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Figures 1 and 2. Old and new school building of A Coruña respectively.

1. External coating, 2. Concrete (1 cm.), 3. Brick (8 cm.), 4. Air barrier (3 cm.) 5. Polystyrene(3 cm.), 6. Brick (8 cm.), 7. Concrete (1cm.), 8. Internal coating (plaster, 1 cm.) Figure 3. New school wall structure.

The same thermal inertia effect was studied in new and old museums but, this time, during the full day and night with the aim to understand this effect on paintings and sculpture preservation. The modern museum presents a complex HVAC system that controls temperature and relative humidity with a low margin of error and their wall density construction is low. The old museum presents a higher thermal inertia. Despite of these differences, all museums present three characteristic zones as we can see in Figure 4.

3.9. Office Buildings The third kind of typical indoor ambience is the office buildings. In that kind of buildings the principal objective is to get an adequate PDIAQ and low energy consume [28-36] during the occupied period as a consequence of building materials properties [37-39]. To get this objective, we must understand the HVAC system operation and implement the possibility of replacement this mechanical system with passive methods like permeable internal wall coverings. These offices have the same indoor developed activities between them and a wall structure similar to that showed for new school buildings in Figure 3. The only difference is

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the presence of indoor coverings that can be grouped as permeable, semi-permeable or waterproof in accordance with its theoretical permeability.

1. 2. 3.

Entrance: where the indoor air infiltration is high. Archive; where materials are stored. Exposition zone; where paintings and sculptures are located during the exposition period.

Figure 4. Old museum‘s zones.

With this objective, monitoring of temperature and relative humidity of the air has been carried out during long periods of time in offices located at level of the street in buildings of A Coruña city. Parameters like thermal comfort, indoor air quality and energy saving possibility were calculated in accordance with equations 3 and 4 and compared with international regulations such as ASHRAE [6] and ISO 7730 [40] together with Spanish regulation.

Figure 5. Typical Spanish office buildings.

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3.10. Ships The last kind of typical indoor ambience is the industrial ambience. The worst of this ambiences is present in ships where extreme temperature and relative humidity experiment a frequently change in indoor conditions in short intervals of time. In particular, this change is present especially when workers go from the engine room with a high temperature of 32.5 ºC and a low relative humidity of 25 % to the control engine room with a low temperature of 19.7 ºC and a relative humidity of 41.2%.

Figures 6. Control engine room.

Figures 7. Engine room.

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These temperatures are associated with work risk parameters because when temperature increment from 20ºC to 35-40ºC workers will suffer; psychical disorders (irritability), psychical and physiological disorders (work mistakes) and physiological disorders (cardiaccirculatory system overload). Nowadays, Datasheets [5], OIT [47] and standards [48-50] don‘t show clear information about the engine-room ambience, design conditions and behaviour of marine engineers to prevent work risk. With the aim to obtain work risk indexes and get a real analysis a study was carried out monitoring the air temperature, relative humidity and globe temperature in several locations of a merchant ship that covers the sea-lane Las Palmas-Barcelona. For example, it has been determined the corresponding thermal comfort parameters in the control room [51, 40] and heat stress and sweating indexes in the engine room [52-56] in accordance with the human body thermal balance showed by Fanger [51]. These indexes were compared with ISO indications to propose design corrections.

4. RESULTS 4.1. Climatic Data Coruña is located in the northwest of Spain and presents a mild climate; see Figures 8, and 9. In that figures, we can see a higher relative humidity over most of the year due to the effect of the Atlantic Ocean winds. Relative humidity experiments a mean value of 87.5 % with a maximum mean value of 98% in July and a minimum mean value of 48% in September. The mean outdoor temperature is 13.85 ºC with a maximum mean temperature of 25.6 ºC at August and a min mean temperature of 1ºC at December.

Outdoor Temperature (ºC)

Temperature (ºC)

30 25 20 15 10 5 0 1

2

3

4

5

6

7

Month

Figure 8. Outdoor temperature in A Coruña.

8

9

10

11

12

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Outdoor Relative humidity (%) Relative humidity (%)

110 100 90 80 70 60 50 40 1

2

3

4

5

6 7 Month

8

9

10

11

12

Figure 9. Outdoor relative humidity in A Coruña.

4.2. Flats Figure 10 shows the correlation between the percentage of dissatisfied, calculated in accordance with equation 3, and the fungi concentration obtained as was explained in methods.

400

35

350

30

300

25

250

20

200

15

150

10

100

5

50

0

0 1

2

3

4

5

6

7

8

9

10 11

12

13 14

15 16

17 18

Flat PD

Fungi

Figure 10. Indoor percentage of dissatisfied and fungi concentration.

19 20

21

22 23

24

Fungi( CFU/m 3)

PD (%)

Indoor conditions 40

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4.3. Offices Figures 11 and 12 indoor/outdoor partial vapour pressure difference is represented as internal coverings function like permeable (p), semi permeable (s) and waterproof (i), Summer 200

Partrial vapor pressure difference (Pa)

100 0 P1

P2

SP1

P3

P4

P5

SP2 SP3

SI1

E1

SP4

SI2

SP5

E2

SP6 SP7

E3

SP8

E4

E5

I1

E6

I2

I3

I4

SI1

E5

E4

P2

P5

SI2

P1

P3

P4

-100 -200 -300 -400 -500 -600 -700 -800 Office

Figure 11. Summer indoor/outdoor partial vapour pressure difference. Winter 500

Partial vapor pressure difference (pa)

400 300 200 100 0 SP4 SP1 SP2 SP5 SP3

I1

SP7

I2

SP8

I3

SP6

E1

I4

E6

E2

E3

-100 -200 -300 -400 -500 Office

Figure 12. Winter indoor/outdoor partial vapour pressure difference.

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PDWRC 35 30

PD (%)

25 20 15 10 5 0 p1 p2 p3 p4 p5 p6 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 i1

i2

i3

i4

i5

Office 8:00 Winter

8:00 Summer

Figure 13. Perceived indoor air quality at first hour of occupation.

Figure 13 represents the perceived indoor air quality at first hour of occupation in summer and winter seasons calculated in accordance with the equation 3.

4.4. Schools and Museums Figure 14 shows the daily mean temperature and partial vapour pressure in new and old school buildings and Table 1 shows the mean indoor temperature, relative humidity, perceived indoor air quality and indoor air acceptability in different zones of a new museum. These indexes were calculated in accordance with equations 3 and 4.

4.5. Industrial Ambience: Ships Tables 2 and 3 show the mean, maximum and minimum Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) at the engine room and control engine room. These indexes were obtained with the globe temperature based in Fanger human body balance as NTP indications, respectively. Table 1. Indoor conditions in the new museum. Winter Archive First floor Engraving Summer Archive First floor Engraving

ºC 18.7 21.3 19.0 ºC 18.0 22.1 18.4

RH (%) 58.3 50.8 58.2 RH (%) 75.6 63.7 79.3

PD 6.81 10.84 7.36 PD 8.96 19.91 11.10

Acc 0.39 0.29 0.37 Acc 0.25 0.05 0.18

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Table 2. Indoor temperature. T (ºC) Average Maximum Minimum

Engine room 32.50 38.50 25.40

Control engine room 19.76 27.30 17.40

Table 3. Predicted Mean Vote. PMV Average Maximum Minimum

Engine room 2.15 3.75 1.54

Control engine room 0.52 1.77 -0.90

Table 4. Predicted Percentage of Dissatisfied. PPD Average Maximum Minimum

Engine room 76.61 99.99 53.06

Control engine room 13.54 65.92 5.00

5. DISCUSSION Methods to control these indoor ambiences were grouped in accordance with air renovation, thermal inertia, moisture buffering and awareness.

5.1. Indoor Air Renovation It has been found that the natural ventilation rate was rather poor reaching values significantly lower than the minimum ventilation rate needed. As a result, the CO2 concentrations in flats were so above the levels found in public buildings. When we analyse the humidity ratio we deduce that the bedroom is the zone of the homestead where the humidity is higher, especially during the sleeping time. These values of the indoor humidity ratio are, generally, higher than the outdoors and varied up to a maximum of 9 g/kg. In consequence of this humidity ratio and the observed relative humidity in some of the rooms to attain, values up to 65%. This level could be considered relatively high. Furthermore, as expected should be expected, this ventilation rate tends to promote more uniform humidity and CO2 content in all zones of the flats. The local comfort level, given by the PD and Acc parameters in Figure 10, remains acceptable during the measurement period for most of the observed indoors conditions. When we associate the indoor humidity at temperatures around 21ºC with the fungi development, the Figure 10 is complemented with another curve. From this figure can be observed that fungi load diminishes the order of 3 CFU/m3 in flats when the indoor relative humidity varied up to a maximum of 60% and that a clear relation ship exists between the PD index and fungi development. The general conclusion to be drawn is that ventilation procedures should be modified to keep the relative humidity lower than the maximum recommended of 65% and that, to get this

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objective, natural ventilation and residents‘ habits must be modified. For example, during the cooking or sleeping time indoor air renovations must be effectively used like opening windows. On the other hand, passive methods are not fully operative in this kind of buildings because there are not clear periods of humidification and dehumidification of ambience.

5.2. Moisture Buffering Capacity To analyse the possibility of passive methods to energy saving [41-46], a statistical analysis of the real indoor conditions data was carry out. From this study, it was observed, in the worker‘s zone, a trend to present higher values of temperature and relative humidity, reaching in many cases values that exceed those fixed by the ISO 7730. Through the statistical analysis of One-Way Anova, the difference between internal coverings permeability effect on indoor conditions was observed and showed in Figure 11. There permeable coverings represented with the letter (p), improve partial indoor vapour pressure. For example, in summer, these permeable coverings show a great tendency to reduce the partial vapour pressure excess while in winter indoor partial vapour pressure is clear higher than the outdoor one. The opposite effect is observed with waterproof coverings represented by the letter (i). As a consequence of this effect, when we compare the existing conditions in an office that has waterproof (plastic) cover to the values obtained in standard offices, a trend to a higher relative humidity during the summer and lower during the winter was observed. These impermeable internal coverings effects worsen the comfort condition and the energy saving possibility. At the same time, it was accomplished the study of the comfort conditions starting from the clean air equations 3 and 4 and applying the combined temperature and relative humidity model. From Figure 13 it was observed that, during the winter, the indoor conditions are close to those of thermal neutrality in higher percentage than in the spring and summer. What is more, when the temperature is increased the acceptability of the air is close to zero.

5.3. Thermal Inertia Schools From Figure 14 we can see that in new school buildings mean indoor air temperature is about 20ºC with a relative humidity of 55% and in old schools the mean indoor temperature and relative humidity is about 19 ºC and 62% respectively. During the weekend indoor conditions change and reach, in new schools, a mean indoor temperature of 19 ºC and a relative humidity of 55% and, in old schools, the mean temperature is about 17 ºC and relative humidity is 62%. Outdoor conditions stay about 12 ºC and at a relative humidity of 85%. From these mean values we can conclude that indoors relative humidity is higher than in new and that indoor temperature is 1 ºC low in old schools. The mean temperature and relative humidity are the statistical indexes that show the wall isolation effect is the mean indoor conditions of temperature and relative humidity. For example, in Figure 14 we can observe that the mean indoor dry bulb temperature in new school is one degree centigrade higher than in the old and seven degrees higher than the outdoor during the winter season. If now we observe the relative humidity value, we can say that is 6% higher in the old than in new schools. This effect is consistent with the same indoor

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air enthalpy for a 1ºC lower and, in consequence, the same indoor air acceptability. In conclusion, this higher relative humidity in old schools, as a consequence of wall infiltrations, let the corresponding energy saving on HVAC system. 22

Temperature (ºC)

20

18

16

14

12

10 Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

Day Indoor old school

Indoor new school

Outdoor

Figure 14. Daily Indoor/Outdoor mean temperature in new and old schools.

During the unoccupied period of weekend, new schools present a low decrement of temperature as a consequence of a good wall isolation that let a higher mean indoor temperature for the first hours of occupation of the next Monday and reducing the energy consume peak in that moment. On the other hand, the old school present a fast decrement of indoors temperature as a consequence of infiltrations. When we analyse the partial vapour pressure during the weekend, a tendency to values closer to outdoor air is observed. In this case, indoor air renovations by windows and doors infiltrations are present again. The indexes that show the thermal inertia effect are the minimum and maximum values observed during the occupied and unoccupied periods. From sampled data the maximum temperature achieved during the occupied period was in the old school and the minimum value during the unoccupied periods. In conclusion, schools present an ambience whose thermal isolation is so interesting than thermal inertia because the air renovations by infiltrations are so high that meddle the wall structure temperature absorption.

Museums Nearly the same effects as in schools were obtained when we compare new and old museums. Now it is interesting to maintain the indoor ambience conditions during the occupied and unoccupied periods and, in certain zones, the indoor air renovations must be very low letting work wall structure materials. For example, results showed that old museums present, at the archive, a stable situation with daily variations of temperature of 1ºC as a consequence of the high walls thermal inertia, which is interesting to get a better materials conservancy.

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Vapor pressure (Pa)

1350

1300

1250

1200

1150

1100 Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

Day Indo o r o ld scho o l

Indo o r new scho o l

Outdo o r

Figure 15. Daily Indoor/Outdoor mean partial vapour pressure in new and old schools.

It was obtained that the summer difference between archive and engraving room, respect the first floor, reaches a mean difference of 4 ºC. This is a consequence of the high influence of outdoor conditions on first floor by the door and the lower thermal insulation respects the other zones of the building. From this, could be concluded that this zone‘s thermal inertia and isolation effect can be interrupted by an inadequate indoor air renovation. In new museums, the archive presents again better indoor conditions than the first floor and engraving room. In winter, fluctuations are upper 3 ºC as a consequence of a low thermal inertia and that the HVAC system can not get the set point temperature. In summer this fluctuations are lower. In particular, at the archive and engraving room, relative humidity is extremely higher in summer with values that reach 74% and 78%, respectively. These values are extremely higher than 65%, which was indicated to reduce the risk of mould and microorganisms. This could be a consequence of an insufficient mechanical dehumidification process. At the first floor, we can say that the conditions during the summer time present the worst percentage of dissatisfied with a value of 20% respect a limit of 15%, see Table 1. Acceptability is in all zones under a value of 1 but the better indoor acceptability was in the first floor with a value near 0. This could be correlated with a higher indoor air renovation by infiltrations. As we can see, a possible solution to get a better indoor ambience could have ground into combined the effect of thermal inertia and passive methods, like internal permeable coverings, to solve this with higher energy saving. This effect acts especially when the indoor air renovations are low and this is the cause of old museums interest in materials conservancy.

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5.4. Awareness Ships show different thermal environments that must be studied with greater depth. From Table 2 we can observe that the engine room presents air conditions out of any recommendations from standards. The control engine room shows limit conditions of thermal comfort with temperature values too low, as a physical hazard. As the outdoor air conditions are suitable, an increase of renovations with outdoor air can be proposed in both cases. Using the obtained relationship between time and globe temperature for this engine room we can affirm that the worker must be in the engine room for 17 minutes and must have a rest at the control room for at least 10 minutes in order to get the suitable heat release. One more time, results will be useful for marine engineers training education in work risk prevention. Moreover, this real data must be taken into account for future standards revisions to obtain a better engine room design. Four controls that must be chosen are suggested: 1. Drinking water. Sources of drinking water must be available close to work locations and workers must be informed about the necessity of drinking frequently. 2. Acclimatization. OIT indicates that acclimatization is an effective method to reduce the heat stress index. Workers starting new or going back to work require an exposure time for achieving acclimatization. The control room can be employed as acclimatization room reducing the sweating index. 3. Metabolic heat. Adjusting length and frequency of breaks and work periods, and work rates may be reduced the metabolic heat release. If it is possible works must be scheduled in time of less heat. Work periods into engine room must not be higher than seventeen minutes and, after it, worker must be about ten minutes in the control room. The OIT indicates that workers must be kept under constant watch by a trained colleague for detecting any symptom of heat strain but don‘t show the supervision interval values.

6. CONCLUSIONS The following general conclusions can be drawn form the reported investigation: Ventilation rate was considered rather poor in flats and, inconsequence, the level of fungi and mesophiles presence found in some of the flats was relatively high. In offices internal coverings effect on indoor air conditions are real solutions in mild climates. Furthermore, an adequate employ of this methods can let to implement or substitute the HVAC system getting better comfort conditions and perceived indoors air quality, especially during the first hour of occupation. The higher thermal inertia of old museum archive walls get better materials conservation by indoor air temperature control. Otherwise, Indoor air renovation was low and this is why acceptability index was the worst. Schools don‘t present the better ambience to employ the thermal inertia effect as indoor air control method because the air renovation is excessive. Preventive and corrective methods are proposed in industrial ambiences to reduce fatigue risk. It has been possible to define a design correction involving an increase in the renovations

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with outdoor and preventive corrections such as limiting the time that a person can work without heat stress, in accordance with ISO standards and training.

REFERENCES [1] Rodriguez, E., Baaliña, A., Vazquez, A., Castellanos, L., Santaballa, J. A. and Infante, C. R. Indoor air quality evaluation using carbon dioxide levels in bedrooms in La Coruña (Spain). The 8th International Conference on Indoor Air Quality and Climate. Edinburgh. 1999; 5: 335-345. [2] Hameury, S. Moisture buffering capacity of heavy timber structures directly exposed to an indoor climate: a numerical study. Building and Environment 40 (10) (2005) 14001412. [3] Lee, S.C. and Chang, M. Indoor and outdoor air quality investigation at schools in Hong Kong. Chemosphere. 41 (2000) 109-113. [4] Chicora Foundation, 1994. Managing the museum environment (http://palimpsest.stanford.edu/byorg/chicora/chicenv.html). [5] International Hazard Datasheets on Occupation. Ship-Engineer (Machinist). HDOEDIT (© ILO/CIS, 1999) program. Approved by DG [6] ASHRAE (1988) ASHRAE HANDBOOK. HVAC FUNDAMENTALS. Atlanta. American Society of Heating, Refrigeration and Air Conditioning Engineers. 215-260. [7] Simonson, C.J., Salonvaara, M. and Ojalen T. The effect of structures on indoor humidity-possibility to improve comfort and perceived air quality. Indoor Air 2002. 12 (2002) 243-251. [8] Simonson, C. J., Salonvaara, M. and Ojalen, T. Improving indoor climate and comfort with wooden structures. Espoo 2001.Technical Research Centre of Finland, VTT Publications 431.200p.+ app 91 p, 2001. [9] Orosa, J. A., Baaliña, A. Passive climate control in Spanish office buildings for long periods of time. Orosa JA, Baaliña A. Passive climate control in Spanish office buildings for long periods of time. Building and Environment (2008). doi:10.1016/ j.buildenv.2007.12.001 [10] Toftum, J., Jorgensen, A. S. and Fanger, P.O. Upper limits for indoor air humidity to avoid uncomfortably humid skin. Energy and buildings 28 (1998) 1-13. [11] Toftum, J., Jorgensen, A. S. and Fanger, P. O. Upper limits of air humidity for preventing warm respiratory discomfort. Energy and buildings 28 (1998) 15-23. [12] Fang, L., Clausen, G. and Fanger, P. O. Impact of Temperature and Humidity on the Perception of Indoor Air Quality. Indoor Air. 8 (1998) 80-90. [13] Fang, L., Clausen, G. and Fanger, P. O. Impact of Temperature and Humidity on Perception of Indoor Air Quality During Immediate and Longer Whole-Body Exposures. Indoor Air. 8 (1998) 276-284. [14] Fang, L. P. Wargocki, T Witterseh, Clausen, G. and Fanger, P. O. 1999. ―Field study on the impact of temperature, humidity and ventilation on perceived air quality‖. The 8th International Conference on Indoor Air Quality and Climate. Edinburgh, Scotland Vol 2 Pag. 107. 8–13, 1999.

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[15] MeteoGalicia. Anuario climatolóxico de Galicia 2002. Consellería de Medio Ambiente. Xunta de Galicia. 2002. ISBN: 84-453-3520-0, 2002. [16] MeteoGalicia. Anuario climatolóxico de Galicia 2002. Consellería de Medio Ambiente. Xunta de Galicia. ISBN: 84-453-3520-0, 2002. [17] Hens, H. Indoor climate in student rooms: measured values. IEAEXCO energy conservation in buildings and community systems annex 41 ‗‗moist-eng‘‘ Glasgow meeting. [18] Reindl, D. T. Estimating Ventilation Rates Using Dynamic CO2 Measurements. Proceedings of the ISIAQ Fifth International Conference on Healthy Builidings'97 3 (1997) 507-512. [19] Wang, S and Jin, W. CO2-Based Occupancy Detection for On-Line Outdoor Air Flow Control. Indoor Built Environment. 7 (1998) 163-181. [20] Jovanovic, S., Felder-Kennel, A., Gabrio, T., Kouros, B., Link, B., Maisner, V., Piechotowski, I., Schick, K., Schrimpf, M., Weidner, U., Zöllner, I. And Schwenk, M. Indoor fungi levels in homes of children with and without allergy history. International Journal of Hygiene and environmental Health. 207 (2004) 369-378. [21] Liao, C. M, Luo, W. C, Chen, S. C., Chen, J. W, Liang HM.Temporal/seasonal variation of size-dependent airborne fungi indoor/outdoor relationship for a wind-induced naturally ventilated airspace. Atmospheric environment.38 (2004) 4415-4419. [22] Hargreaves, M., Parappukkaran, S., Morawska, L., Hitchins, J., He, C. and Gilbert, D. A pilot investigation into associations between indoor airborne fungal and non-biological particle concentrations in residential houses in Brisbane, Australia. The Science of the Total Environment. 312 (2003) 89-101. [23] Editorial. Housing characteristics and mite allergen levels: to humidity and beyond. Clinical and experimental allergy. 31 (2001) 803-805. [24] Conceicao, E. Z. E., Lúcio, M. M. J. R. Thermal study of school buildings in winter conditions. Building and Environment. 43 (2008) 82–792. [25] Rachel Beckera, Itamar Goldbergera and Monica Paciukb. Improving energy performance of school buildings while ensuring indoor air quality ventilation. Building and Environment. 42 (2007) 3261–3276. [26] Stolow, N. Conservation and exhibitions: packing, transport, storage, and environmental considerations. London, Butterworths, 1987. [27] Padfield, T. and Klenz, P., 2004. ―How to design museums with a naturally stable climate‖. Annual General Meeting of the International Institute for Conservation. [28] Simonson Carey, J. and Salonvaara Mikael, H.. Mass transfer between indoor air and a porous building envelope: Part I- Field measurements. Proceedings of Healthy Buildings 2000, Vol. 3. [29] Simonson Carey J., Tuomo Ojanen. Moisture performance of buildings envelopes with no plastic vapour retarders in cold climates. Proceedings of Healthy Buildings 2000, Vol. 3. [30] Padfield, T. The role of absorbent building materials in moderating changes of relative humidity. Ph.D. thesis The Technical University of Denmark Department of Structural Engineering and Materials, October 1998. [31] Hameury, S. and Lundstrom. T. Contribution of indoor exposed massive wood to a good indoor climate: in situ measurement campaign. Energy and Buildings 36 (2004) 281292.

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[32] Simonson, Carey J., Salonvaara Mikael and Ojalen, Tuomo. Improving Indoor Climate and Comfort with wooden Structures. Espoo 2001.Technical Research Centre of Finland, VTT Publications 431.200p.+ app 91 p, 2001. [33] Salonvaara Mikael, H. and Simonson, Carey J. Mass transfer between indoor air and a porous building envelope: Part II- Validation and numerical studies. Proceedings of Healthy Buildings 2000, Vol. 3. [34] Trechsel Heinz R. (Editor). Moisture control in buildings. ASTM Manual series. February 1994. [35] Meininghaus, R., Knudsen, H. N. and Gunnarsen, L. Impact of sorption and diffusion on indoor air pollution. Proceedings of indoor air quality 99, 1999. [36] Orosa, J. A. and Baaliña, A. Improving PAQ and comfort conditions in Spanish office buildings with passive climate control, Building and Environment (2008). doi:10.1016/j.buildenv.2008.04.013. [37] Osanyintola, O. F. and Simonson, C. J. Moisture buffering capacity of hygroscopic building materials: Experimental facilities and energy impact. Energy and Buildings 38 (2006) 1270-1282. [38] Plathner, P., Littler, J. and Stephen, R. Dynamic water vapour sorption: measurement and modelling. Proceedings of indoor air quality 99, 1999. [39] Kirchner, S., Badey, J. R., Knudsen, H. N., Meininghaus, R., Quenard, D., Saarela, K., Sallee, H. and Saarinen, A. Sorption capacities and diffusion coefficients of indoor surface materials exposed to VOCS: proposal of new test procedures. Proceedings of indoor air quality 99. 1999. [40] International Standard ISO 7730-2005. Ergonomics of the thermal environment. Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria, 2005. [41] L. Pérez-Lombard, J. Ortiz and C., Pout. A review on building energy consumption information, Energy and buildings 40 (2008) 394-398. [42] Taylor, P., Fuller, R. J. and Luther, M. B. Energy use and thermal comfort in a rammed earth office building, Energy and buildings.2007. doi:10.1016/j.enbuild.2007.05.013. [43] Omer, A. M. Renewable building energy systems and passive human comfort solutions. Renewable and sustainable Energy Reviews, 12 (2008) 1562-1587. [44] Cardinale, N., Micucci, M. and Ruggiero, F. Analysis of energy saving using natural ventilation in a traditional Italian building, Energy and buildings, 35 (2003) 153-159. [45] Makaka, G., Meyer, E. L. and McPherson, M. Thermal behaviour and ventilation efficiency of a low cost passive solar energy efficient house, Renewable energy. 33. (2008) 1959-1973. [46] Yang, L., Lam, J. C. and Tsang, C.L. energy performance of buildings envelopes in different climate zone in China, Applied Energy 85 (2008) 800-817. [47] Accident prevention on board ship at sea and in port. An ILO code of practice (ISBN 922-109450-2), Ginebra, 1996. [48] ISO 7547 Ships and marine technology. Air-conditioning and ventilation of accommodation spaces. Design conditions and basis of calculations, 2002. [49] Shipbuilding. Engine-room ventilation in diesel-engined ships. Design requirements and basis of calculations, 1998.

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[50] International Standards for the Assessment of the Risk of Thermal Strain on Clothed Workers in Hot Environments. K. C. PARSONS. Ann. occup. Hyg., Vol. 43, No. 5, pp. 297±308, 1999. British Occupational Hygiene Society [51] Fanger, P. O. Thermal Comfort. Danish Technical Press. Copenhagen. 1970. [52] NTP 18 (Heat stress evaluation of sever exposures) [53] NTP 350 (Heat stress evaluation required sweating index) [54] ISO 7243. Hot environments. Estimation of the heat stress on working man, based on the WBGT-index (wet bulb globe temperature), 1989. [55] ISO 7933. Ergonomics of the thermal environment. Analytical determination and interpretation of heat stress using calculation of the predicted heat strain, 2004. [56] O‘Connor, P. J. and O‘Connor. N.. Work-related maritime fatalities. Accident Analysis & Prevention 38 (2006) 737-741.

In: Advances in Energy Research. Volume 4 Editor: Morena J. Acosta, pp. 41-85

ISBN: 978-1-61761-672-3 © 2011 Nova Science Publishers, Inc.

Chapter 3

RATIONAL ATTRIBUTION OF ENVIRONMENTAL EMISSIONS OF COGENERATION TO PRODUCTS: ALLOCATING CARBON DIOXIDE AND OTHER EMISSIONS WITH EXERGY Marc A. Rosen* Faculty of Engineering and Applied Science University of Ontario Institute of Technology 2000 Simcoe Street North, Oshawa, Ontario, L1H 7K4, Canada

ABSTRACT Many from industry, government and academe have struggled with the question of how to allocate emissions for an energy process that has multiple products and multiple inputs, like cogeneration. Present methods are not universally accepted, because they are inconsistent, overly complex, difficult to utilize, and not soundly based. The author proposes that exergy methods can form the basis of rational and meaningful allocation methods for emissions. In this article, methods based on exergy for allocating cogeneration emissions are investigated and compared with other methods. Two illustrations are provided. The rationale for the author‘s view that the exergy-based method is the most meaningful and accurate is discussed, as are problems associated with other methods. An analogy is described between allocating carbon dioxide emissions and economic costs for cogeneration. The results indicate that the exergy-based emissions allocations method provides a sensible basis for a meaningful overall approach for emissions trading. It is concluded that the exergy-based method of carbon dioxide emissions allocation for cogeneration is rational, useful and superior to other methods. By permitting these emissions to be allocated more appropriately among commodities generated by cogeneration, the results allow the environmental benefits of technologies that produce multiple products to be better understood and exploited. The results should be of most benefit to designers of energy systems, and to decision and policy makers in companies and government. The author proposes that the exergy-based method be used in allocating cogeneration emissions to help ensure proper decision-making regarding *

E-mail address: [email protected], Tel: 905/721-8668. (Corresponding author)

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Marc A. Rosen such issues as what effect cogeneration may have on overall carbon dioxide emissions and how emissions should be reduced, how and where cogeneration should be used, and fair ways to establish emissions trading schemes.

1. INTRODUCTION The potential benefits of cogeneration (i.e., combined heat and power) are significant (Klein, 1999a, b, c, d, 2001a, b). But many companies, government agencies and researchers have struggled with the question of how to allocate emissions for an energy system that has multiple products and multiple inputs. Some work has been done for cogeneration, e.g., several attempts have been made to determine how to allocate emissions among the products of cogeneration systems (Strickland and Nyboer, 2002a, 2002b; Upton, 2001; Phylipsen et al., 1998). However, the results are not universally accepted and, in the view of the author, are often not based on sound reasoning. In addition, the results are often conflicting. Further, the methods developed often are overly complex, thus rendering it difficult to use them and to convince decision and policy makers of their potential benefits. For cogeneration, for instance, existing methods of allocating emissions among outputs include ―efficiency methods,‖ ―work potential methods‖ and ―heat content methods,‖ but results obtained with each are generally different and not based on clear reasoning. The challenge becomes even more difficult for more complex systems, such as those involving trigeneration (i.e., simultaneous production of electrical, heating and cooling services). The author feels that much research is needed in this area, and that the direct use of exergy methods can form the basis of sound and meaningful allocation methods for emissions. In this article, we investigate rational methods, based on exergy, for allocating emissions for complex energy systems having multiple inputs and products, like cogeneration. This method is compared with the other allocation methods. Throughout, the results are present in as simple a manner as possible. Consequently, this article focuses on carbon dioxide because it is the primary greenhouse gas, but it is extendable to CO2 equivalent emissions in terms of greenhouse gas potential. Calculations are made for example systems of how much output power is lost and thermal energy gained, when heat is captured or extracted for cogneration purposes. An overall approach with regards to energy efficiency and emissions trading is discussed.

2. BACKGROUND Many governments have launched initiatives involving air issues and the energy sector. For instance, Environment Canada has pursued the Ozone Annex, the NOx/VOC Plan and acid rain initiatives, the Strategic Options Process for air toxics, and the National Plan for Climate Change. Environment Canada is interested in emissions trading, and its implications for air quality issues across Canada. Also, methods for allocating emissions are discussed in relevant publications and correspondences (e.g., letters between the National Council for Air and Stream Improvement and World Resources Institute (Upton, 2001)). Much work on assessing the performance of cogeneration systems and their emissions has been carried out in Europe. For instance, the European Parliament (2004) issued a directive in February 2004 on the promotion of cogeneration based on a useful heat demand

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in the internal energy market. Also, the European Committee for Standardization (CEN) and the European Committee for Electrotechnical Standardization (CENELEC) published in 2004 a workshop agreement manual for the determination of combined heat and power (CEN/CENELEC, 2004). Although these two documents have been criticized (Verbruggen, 2007a, 2007b), they are widely accepted for primary energy savings for cogeneration. They provide indicators for carbon dioxide emissions allocation from cogeneration systems. However, these documents ignore exergy, and thus suffer from the same difficulties as mentioned earlier of allocation methods. With a move towards output-based standards for emission guidelines, various measures on emissions trading, and comparative evaluations of air pollution and greenhouse gases (GHGs) for all energy sources, better information is needed on estimating the equivalence between heat, cooling and electricity. Both shaft mechanical power and electricity are much more valuable than most forms of industrial heat, but this depends on the temperature and quality of the steam or hot water extracted from the process. The normal definition of efficiency (fuel utilization) does not capture this relationship. The concept of comparing emission mass per unit energy output (in kg/MWh, for example) of various emissions must consider the tradeoff between power and heat for cogeneration and district energy, if suitable comparisons are to be made. This has been done in an approximate fashion for the 1992 CCME Gas Turbine emission guideline. More definitive work is needed to address this for energy products such as high- and low-pressure steam, and hot and cold water. Emissions trading will require a shared allocation of credits/allowances for these among several industrial and commercial energy producers and users. Such estimations can also be valuable for the conversion of emission factors from kg/MWh, into $/tonne externalities and $/MWh of reduction measures. Emissions trading of air pollution and GHGs will require a shared allocation of credits/allowances for these among several industrial and commercial energy producers and users. More definitive work on the quality of energy of energy systems is needed to address this for products such as high and low pressure steam, and hot and cold water.

2.1. Cogeneration Cogeneration usually refers to the simultaneous production of two energy forms (electricity, and heat in the form of steam and/or hot water) from one energy source (normally a fossil fuel). Cogeneration has been used, particularly by industry, for approximately a century. A cogenerator can be a utility, an industry, a government, or any other party. Cogeneration systems are often extensions of thermal electricity-generation systems. In thermal electrical generating stations, the energy content of a resource (normally a fossil fuel) is converted to heat (in the form of steam or hot gases) which is then converted to mechanical energy (in the form of a rotating shaft), which in turn is converted to electricity. A portion (normally 20 to 45%) of the heat is converted to electricity, and the remainder is rejected to the environment as waste. Cogeneration systems are similar to thermal electricity-generation systems, except that a percentage of the generated heat is delivered as a product, normally as steam or hot water, and the quantities of electricity and waste heat produced are reduced. Overall cogeneration efficiencies based on both the electrical and thermal energy products of over 80% are

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achievable. Other advantages generally reported from cogenerating thermal and electrical energy rather than generating the same products in separate processes include:   

reduced energy consumption, reduced environmental emissions (due to reduced energy consumption and the use of modern technologies in large, central installations), and more economic, safe and reliable operation.

Most thermal systems for large-scale electricity generation are based on steam and/or gas turbine cycles, and can be modified relatively straightforwardly for cogeneration. Two main categories of heat demands can normally be satisfied through cogeneration: (i) residential, commercial and institutional processes, which require large quantities of heat at relatively low temperatures (e.g., for air and water heating); and (ii) industrial processes, which require heat at a wide range of temperatures (e.g., for drying, heating and boiling in, for instance, chemical processing, manufacturing, metal processing, mining and agriculture). The use of a central heat supply to meet residential, commercial and institutional heat demands is often referred to as district heating. As well as satisfying heat demands, cogenerated heat can drive chillers; this application can be particularly beneficial in locations where the annual peak electrical demand is associated with the summer cooling load. Many general descriptions and studies of cogeneration systems have been reported (MacRae, 1992; Rogner, 1993; FVB/Eltec, 1993; MacLaren, 1988; Henneforth and Todd, 1988; Acres, 1987; Horlock, 1987; Rosen, 1993, 1994, 1998; Rosen et al., 1997; Hart and Rosen, 1994; Rosen and Le, 1994; Sherwood and Rosen, 1996; Simpson and Rosen, 1996). Cogeneration systems are in use throughout the world (e.g., thousands are listed by the Association of Energy Engineers), and the basic technology is proven. Numerous examples exist of large cogeneration systems. The size and type of a cogeneration system are normally selected to match as optimally as possible the thermal and electrical demands. Many matching schemes can be used. Systems can be designed to satisfy the electrical or thermal base-loads, or to follow the electrical or thermal loads. Storage systems for electricity (e.g., batteries) or heat (e.g., hot water or steam tanks) are often used to overcome periods when demands and supplies for either electricity or heat are not coincident. Cogeneration systems are sometimes used to supply only the peak portions of the electrical or thermal demands.

2.2. Exergy The thermodynamic analysis tool exergy analysis is central to this article. The exergy of an energy form or a substance is a measure of its usefulness or quality. Exergy is based on the first and second laws of thermodynamics, and combines the principles of conservation of energy and non-conservation of entropy. Exergy is defined as the maximum amount of work which can be produced by a system or a flow of matter or energy as it comes to equilibrium with a reference environment. Exergy is a measure of the potential of the system or flow to

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cause change, as a consequence of not being completely in stable equilibrium relative to the reference environment. Unlike energy, exergy is not subject to a conservation law (except for ideal, or reversible, processes). Rather exergy is consumed or destroyed, due to irreversibilities in any real process. The exergy consumption during a process is proportional to the entropy created due to irreversibilities associated with the process. Exergy and exergy analysis are discussed further elsewhere (Dincer and Rosen, 2007; Sato, 2005; Szargut, 2005; Hevert and Hevert, 1980; Kotas, 1995; Moran, 1989; Moran and Sciubba, 1994, 2004; Moran and Shapiro, 2007; Szargut et al., 1988; Szargut, 1980; Edgerton, 1992; Rosen, 1999). As a simple illustration, consider an adiabatic system containing fuel and air at ambient conditions. The fuel and air react to form a mixture of hot combustion gases. During the combustion process, the energy in the system remains fixed because it is adiabatic. But the exergy content declines as combustion proceeds due to the irreversibilities associated with the conversion of the high-quality energy of fuel to the lower quality energy of combustion gases. The different behaviours of energy and exergy during this process are illustrated qualitatively in Figure 1.

Figure 1. Qualitative comparison of the energy and exergy changes during fuel combustion.

2.2.1. Exergy Analysis Exergy analysis is a methodology that uses the conservation of mass and conservation of energy principles together with the second law of thermodynamics for the analysis, design and improvement of energy and other systems. The exergy method is useful for improving the efficiency of energy-resource use, for it quantifies the locations, types and magnitudes of wastes and losses. In general, more meaningful efficiencies are evaluated with exergy analysis rather than energy analysis, since exergy efficiencies are always a measure of the approach to the ideal. Therefore, exergy analysis identifies accurately the margin available to design more efficient energy systems by reducing inefficiencies. Many engineers and scientists suggest that thermodynamic performance is best evaluated using exergy analysis because it provides more insights and is more useful in efficiency-improvement efforts than energy analysis. Many applications of exergy analysis to processes and systems have been reported (Dincer and Rosen, 2007; Cownden et al., 2001; Rosen, 1992, 1996, 2000; Rosen

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and Dincer, 2002; Rosen and Horazak, 1995). Some of those have included investigations by the author of cogeneration using exergy methods (Dincer and Rosen, 2007; Rosen, 1990; Rosen and Le, 1996).

Figure 2. The electrical generating station considered. The external inputs are coal and air, and the output is stack gas and solid waste for unit A. The external outputs for unit E are electricity and waste heat. Electricity is input to units G and J, and cooling water enters and exits unit F.

For exergy analysis, the characteristics of the reference environment must be specified completely. This is commonly done by specifying the temperature, pressure and chemical composition of the reference environment. The results of exergy analyses, consequently, are relative to the specified reference environment, which in most applications is modelled after the actual local environment. The exergy of a system is zero when it is in equilibrium with the reference environment. The tie between exergy and the environment has implications regarding environmental impact has been investigated previously (Dincer and Rosen, 2007; Sciubba, 1999, 2004; Ayres et al., 1998; Connelly and Koshland, 1997; Creyts and Carey, 1997; Berthiaume et al., 2001; Crane et al., 1992; Rosen and Dincer, 1997, 1999, 2001; Gunnewiek and Rosen, 1998; Daniel and Rosen, 2002).

2.2.2. Illustrative Applications The use of exergy methods to analyze a device so as to permit its performance to be better understood and its efficiency improved is demonstrated for three illustrative applications.

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The first application considered is electricity generation using a coal-fired steam power plant. The plant considered is the coal-fired Nanticoke generating station, which has been operating since 1981 in Ontario, Canada. Each of the eight units in the station has a net electrical output of 505 MW. A single unit of the electrical generating station is illustrated in Figure 2, and consists of four main sections (Rosen, 2000): a) Steam Generators: Pulverized-coal-fired natural-circulation steam generators combust coal to produce primary and reheat steam. Air is supplied to the furnace by motor-driven forced-draft fans, and regenerative air pre-heaters are used. The flue gas passes through an electrostatic precipitator and exits the plant via multi-flued chimneys. b) Turbine Generators and Transformers: The steam produced passes through a turbine generator, which is connected to a transformer. Each turbine generator has one single-flow high-pressure cylinder, one double-flow intermediate-pressure cylinder and two double-flow low-pressure cylinders. Steam exhausted from the high-pressure cylinder is reheated in the steam generator. Several steam extractions from the turbines preheat feed water in low- and high-pressure heat exchangers and one spray-type open de-aerating heat exchanger. The low-pressure turbines exhaust to the condenser. c) Condensers: Cooling water condenses the steam exhausted from the turbines. d) Preheating Heat Exchangers and Pumps: The temperature and pressure of the condensed steam are increased in a series of pumps and heat exchangers.

(a)

(b)

Figure 3. Overall energy and exergy balances for the station, represented by a rectangle. Widths of flow lines are proportional to the relative magnitudes of the represented quantities. CW denotes cooling water. a) Exergy balance showing flow rates (positive values) and consumption rate (negative value, denoted by hatched region) of exergy (in MW). b) Energy balance showing flow rates of energy (in MW).

Exergy and energy analyses of the station have been performed (Rosen, 2000). Overall balances of exergy and energy for the station are illustrated in Figure 3, and the main findings, which improve understanding of the thermodynamic behaviour of the plant and help identify areas having significant efficiency-improvement potential, follow:

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





For the overall plant, the energy efficiency (ratio of net electrical energy output to coal energy input), was found to be 37%, and the corresponding exergy efficiency 36%. In the steam generators, the energy and exergy efficiencies were evaluated, considering the increase in energy or exergy of the water as the product. The steam generators appear significantly more efficient on an energy basis (95%) than on an exergy basis (50%). Physically, this discrepancy implies that, although most of the input energy is transferred to the preheated water, the energy is degraded as it is transferred. Most of the exergy losses in the steam generators are associated with internal consumptions (mainly due to combustion and heat transfer). In the condensers, a large quantity of energy enters (about 775 MW for each unit), of which close to 100% is rejected; and a small quantity of exergy enters (about 54 MW for each unit), of which about 25% is rejected and 75% internally consumed. In other plant devices, energy losses were found to be very small (about 10 MW total), and exergy losses were found to be moderately small (about 150 MW total). The exergy losses are almost completely associated with internal consumptions.

The second application considered is an electrical resistance space heater which produces heat at a temperature suitable for keeping a room at a comfortable temperature. The energy efficiency of electric resistance space heating is often quoted to exceed 99%. The implication clearly is that the maximum possible energy efficiency for electric resistance heating is 100%, corresponding to the most efficient device possible. This understanding is erroneous, however, as energy analysis ignores the fact that in this process high-quality energy (electricity) is used to produce a relatively low-quality product (warm air). Exergy analysis recognizes this difference in energy qualities, and indicates the exergy of the heat delivered to the room to be about 5% of the exergy entering the heater. Thus, the efficiency, based on exergy, of electric resistance space heating is found to be about 5%. We therefore obtain useful information from the exergy results. Since thermodynamically ideal space heating has an exergy efficiency of 100%, the same space heating can in theory be achieved using as little as 5% of the electricity used in conventional electric resistance space heating. In practical terms, space heating can be achieved with much less electricity input using a high-efficiency heat pump, using 20% of the electricity that electric resistance heating would require, for a heat pump with a ―coefficient of performance‖ of 5. The final application considered is a thermal energy storage (TES), which receives thermal energy and holds the energy until it is required. TESs can store energy at temperatures above or below the environment temperature, and come in many types (e.g., tanks, aquifers, ponds, caverns). The evaluation of a thermal energy storage system requires a measure of performance that is rational, meaningful and practical. The conventional energy storage efficiency is an inadequate measure. A more perceptive basis for comparison is needed if the true usefulness of a thermal storage is to be assessed, and so permit maximization of its economic benefit. Efficiencies based on ratios of exergy do provide rational measures of performance, since they can measure the approach of the performance of a system to the ideal. That the energy efficiency is an inappropriate measure of thermal storage performance can best be appreciated through a simple example. Consider a perfectly insulated thermal storage containing 1000 kg of water, initially at 40C. The ambient temperature is 20C, and

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the specific heat of water is taken to be constant at 4.2 kJ/kg K. A quantity of 4200 kJ of heat is transferred to the storage through a heat exchanger from an external body of 100 kg of water cooling from 100C to 90C. This heat addition raises the storage temperature 1.0C, to 41C. After a period of storage, 4200 kJ of heat are recovered from the storage through a heat exchanger which delivers it to an external body of 100 kg of water, raising the temperature of that water from 20C to 30C. The storage is returned to its initial state at 40C. For this storage cycle the energy efficiency, the ratio of the heat recovered from the storage to the heat injected, is 4200 kJ/4200 kJ = 1, or 100%. But the recovered heat is at only 30C, and of little use, having been degraded even though the storage energy efficiency was 100%. The exergy recovered in this example is evaluated as 70 kJ, and the exergy supplied as 856 kJ. Thus the exergy efficiency, the ratio of the thermal exergy recovered from storage to that injected, is 70/856 = 0.082, or 8.2%, a much more meaningful expression of the achieved performance of the TES. Consequently, a device that appears to be ideal on an energy basis is correctly shown to be far from ideal on an exergy basis, clearly demonstrating the benefits of using exergy analysis for evaluating TESs.

2.3. Existing Allocation Methods for Energy-System Emissions Limited work has been done in general on methods for allocating the emissions from energy systems that produce multiple products. Some methods have been developed for allocating carbon dioxide emissions from cogeneration systems to the electrical and thermal energy products. The need for these methods is premised on the fact that when the owner of the cogeneration plant, the thermal energy user and the electrical energy user are not the same, a method for allocating the emissions is needed to ensure each party is credited with their appropriate share of the emissions from the system. In addition, having a meaningful allocation method allows the sources of carbon dioxide and other emissions to be better understood and, where appropriate, reduced. Strickland and Nyboer (2002a, 2002b) list seven methods of calculating the fuel allocation to the thermal and electrical energy products of a cogeneration system. Allocations in those methods are based on product energy contents, product exergy contents, product economic values, incremental fuel consumption to electrical production, incremental fuel consumption to the heat production, shared emission savings between heat and electricity, and agreement. In their work, Strickland and Nyboer (2002a, 2002b) adapt the calculational methods introduced earlier by Phylipsen et al. (1998). In the methods used, the fuel allocation is multiplied by the appropriate carbon dioxide emission factor to evaluate the share of emissions allocated to each product. Others have also investigated methods for allocating greenhouse gas emissions associated with manufacturing and other industries. Such investigations have been carried out by the World Resources Institute, Washington, DC and the National Council for Air and Stream Improvement, Inc., Corvallis, OR, as evidenced by correspondences between these organizations (Upton, 2001). In general, the allocation methods discussed by Upton (2001) are variations on those discussed by Strickland and Nyboer (2002a, 2002b) and Phylipsen et al. (1998).

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3. SIMPLIFIED SELECTED METHODS FOR ALLOCATING CARBON DIOXIDE EMISSIONS FOR COGENERATION The allocation methods presented by Strickland and Nyboer (2002a, 2002b) based on work by Phylipsen et al. (1998) are further adapted and simplified so that they present the fractions, rather than the total, carbon dioxide emissions allocated to each product. The categorizations follow for convenience those used by Strickland and Nyboer (2002a, 2002b). Of course, emissions can be allocated according to other methods than those described in this section.

3.1. Allocation Based on Energy Content of Products The allocations are evaluated in proportion to the energy contents of the products, as follows: fE = E/(E + Q) fH = Q/(E + Q) where fE and fQ denote respectively the fractions of the emissions allocated to the electrical and thermal products, and E and Q denote respectively the net outputs of electrical energy and thermal energy from the cogeneration system. The term Q can represent an actual transfer of thermal energy, or the net thermal energy transferred via a material flow in and out of a heat exchange device. Although this allocation method is straightforward and simple, it ignores the quality of energy and focuses only on the quantities involved. Consequently, it can be argued that it underestimates the share of the emissions allocated to the electrical product.

3.2. Allocation Based on Exergy Content of Products The allocations are evaluated in proportion to the exergy contents of the products, as follows: fE = ExE/(ExE + ExQ) fQ = ExQ/(ExE + ExQ) where ExE and ExQ denote respectively the net outputs of electrical exergy and thermal exergy from the cogeneration system. It is noted that electrical energy and electrical exergy are equivalent, so that ExE = E. In this allocation method, one can treat the thermal product in two ways: simply thermal energy and thermal energy transfer via moving materials. Both of these ways of treating thermal products are described below. For simple thermal energy, the corresponding thermal exergy can be written as ExQ = Qτ where τ denotes the exergetic temperature factor and can be evaluated as

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τ = 1 – To/T Here, T denotes the temperature at which heat Q crosses the system boundary, and To denotes the temperature of the reference environment. For a reference environment at a temperature of 300 K (27°C), the value of the exergetic temperature factor τ is 0.25 for heat transfers at 400 K, 0.5 for heat transfers at 600 K, and 0.75 for heat transfers at 1200 K. If, on the other hand, the thermal energy is delivered via a material flow into and out of a heat exchange device, then the term ExQ is evaluated as the difference between the exergy of the incoming flow and the outgoing flow. The flowing commodity could be steam, hot water, cold water, etc., and the factors that must be taken into account in determining the corresponding exergy flow rates include mass flow rate, temperature, pressure and sometimes vapour fraction. An additional point regarding the exergy-based allocation method is that the choice of reference environment for determining exergy quantities is important, and can affect the results. Normal practice involves selecting a reference environment that is similar to the actual environment. But other reference environments can be used. For example, Upton (2001), in an exercise to allocate emissions, evaluates exergy values using a referenceenvironment temperature of 100°C, which is well in excess of the actual environment annual mean temperature (perhaps 10°C to 20°C, depending on location). He uses this value because it is related to the process, in that he considers thermal energy below this temperature to be non-useful. Although this choice of a reference-environment temperature is permitted when using exergy methods, it is important to note that care must be exercised to ensure that a consistent reference environment is used throughout an analysis. It is noted that the type of exergy-based allocation method presented by Strickland and Nyboer (2002a, 2002b) is not general, in that it assumes that the thermal product of cogeneration can be modelled only as pure thermal energy, rather than also considering the transfer of heat via flowing materials. This allocation method accounts for the quality and quantity of the commodities involved. Consequently, compared to the energy allocation method, the exergy method avoids underestimating the share of the emissions allocated to the electrical product, and allocates a lower portion of the emissions to the thermal product.

3.3. Allocation Based on Economic Value of Products The allocations are evaluated in proportion to the economic values of the products as follows: fE = cEE/(cEE + cQQ) fQ = cEQ/(cEE + cQQ) where cE and cQ denote respectively the unit economic values of the electrical product and the thermal product of the cogeneration system. Two important points are noted: 

The unit economic values presented here are on an energy basis (i.e., the economic value of a type of energy commodity per unit quantity of energy of that commodity),

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

but the unit economic values based on other quantities—such as exergy—could also be used. The economic values can be determined using several economic measures in several ways. For instance, they can reflect as the costs to produce the commodities, or their prices, or some other economic measure for them.

This method is sometimes considered to have advantages to owners of cogeneration systems that sell electrical and thermal products separately. It is not necessary to know the numerical values of both unit economic values, cE and cQ, when applying this method of emission allocation. Rather, it is the ratio of the unit economic values that is important. This phenomenon can be seen by modifying the expressions for fE and fQ for this method of allocation as follows: fE = E/[E + Q(cE/cQ)-1] fQ = Q/[E(cE/cQ) + Q] Here, cE/cQ denotes ratio of the unit economic value for electricity to that for thermal energy. Since the unit economic value for electricity normally exceeds that for thermal energy, it is normally found that cE/cQ > 1.

3.4. Allocation Based on Incremental Fuel Consumption to Electrical Production The emissions allocations are evaluated here by dividing the total fuel consumed in the cogeneration among the electrical and thermal products, while considering electricity generation to be a by-product of the thermal energy production process. Then, the emissions are allocated in proportion to the fuel division. Two steps are used to divide the fuel consumption. First, the fuel consumption attributed to thermal energy production is evaluated as the hypothetical amount of fuel that would be consumed by an independent device for providing the same thermal energy as the cogeneration system (e.g., a reference steam boiler if the thermal energy is in the form of steam). That is, FQ = Q/ηb where FQ denotes the fuel consumption attributed to the production of thermal energy Q, and ηb denotes the energy efficiency of the independent device for providing the same thermal energy as the cogeneration system (e.g., a reference steam boiler). Second, the fuel consumption attributed to electricity generation, FE, is evaluated by subtracting this hypothetical amount of fuel from the total primary fuel energy consumed by the cogeneration system, F. That is, F E = F – FQ Then, the emission allocation fractions fE and fQ are determined as the ratios FE/F and FQ/F, respectively. That is,

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fQ = Q/(Fηb) f E = 1 – fQ This allocation method is consistent with the ―Fuel Charged to Power‖ (FCP) method used by many cogeneration consulting firms.

3.5. Allocation Based on Incremental Fuel Consumption to Thermal Energy Production This emissions allocation method is similar to the previous one, except the emissions allocations are evaluated here by dividing the total fuel consumed in the cogeneration among the electrical and thermal products, while considering thermal energy production to be a byproduct of the electricity generation process. Again, the emissions are then allocated in proportion to the fuel division, using the following two steps to divide the fuel consumption. First, the fuel consumption attributed to electricity generation is evaluated as the hypothetical amount of fuel that would be consumed by an independent device for providing the same electrical energy as the cogeneration system (e.g., a reference power plant). That is, FE = E/ηpp where ηpp denotes the energy efficiency of the independent device for providing the same electrical energy as the cogeneration system (e.g., a reference power plant). Second, the fuel consumption attributed to thermal energy production, FQ, is evaluated by subtracting this hypothetical amount of fuel from the total primary fuel energy consumed by the cogeneration system. That is, F Q = F – FE Again, the emission allocation fractions fE and fQ are then determined as the ratios FE/F and FQ/F, respectively, as follows: fE = E/(Fηpp) f Q = 1 – fE

3.6. Allocation Based on Shared Emission Savings between Electrical and Thermal Energy The allocations are evaluated for each product in proportion to the hypothetical fuel that would be used to produce that product independently, relative to the total hypothetical fuel that would be used to produce both products independently. Using the terms previously defined, the hypothetical fuel consumption attributed to an independent process for thermal energy production (e.g., a reference steam boiler if the thermal energy is in the form of steam) is evaluated as FQ = Q/ηb

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and the hypothetical fuel consumption attributed to an independent process for electricity generation is evaluated as FE = E/ηpp Then, the emission allocation fractions fE and fQ are determined as the ratios FE/F and FQ/F, respectively, as follows: fE = (E/ηpp)/(E/ηpp + Q/ηb) fQ = (Q/ηb)/(E/ηpp + Q/ηb) This allocation method therefore shares the emissions among the products in a particular format. This method somewhat extends the concepts used in the previous two emissions allocation methods, but is more of a compromise in terms of treating one or the other product as the primary one.

3.7. Allocation by Agreement Allocation of CO2 emissions to each product of cogeneration can be determined purely based on an agreement between the various parties involved in a project.

4. ALLOCATING CARBON DIOXIDE EMISSIONS FOR COGENERATION The author explains in this section his view that the most rational and meaningful method of allocating carbon dioxide emissions for cogeneration processes is based on the exergy content of products. To support this contention, the different emissions allocation methods discussed for cogeneration processes in the previous section are examined and compared in this section. Before discussing and comparing the different emissions allocation methods, however, it is useful to understand the basic intentions and considerations in allocating emissions.

4.1. Objective in Allocating Emissions for Multi-product Production Processes The general objective when allocating a type of emission for a multi-product production process is to allocate the emission to each product according to the actual emission that is in fact attributable to that product, accounting for all thermodynamic losses, when it is produced in the multi-product production process. Usually the emission allocation breakdown is directly proportional to the breakdown of fuel use that is attributable to each product, when it is produced in the multi-product production process.

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4.2. Basic Considerations in Allocating CO2 Emissions for Cogeneration Following this description and considering carbon dioxide emissions for a cogeneration processes, the total CO2 emissions C for a multi-product production process can be expressed as C = C E + CQ where CE and CQ denote respectively the CO2 emissions associated with the electrical and thermal energy products, when they are produced in the cogeneration process. We can also express the total CO2 emissions as C = Fφ where F denotes the total fuel use in the process and φ a CO2 emission coefficient for the fuel. The terms F and φ must be on consistent bases (e.g., if F is in energy units, then φ must be the CO2 emission per unit fuel energy consumed). The total CO2 emissions C can also be written as C = (FE + FQ)φ where FE and FQ denote respectively the fuel consumption associated with the electrical and thermal energy products, when they are produced in the cogeneration process. Clearly, FE + FQ = F. Furthermore, we can write C = (fE + fQ)Fφ where fE = FE/F and fQ = FQ/F Clearly, the fractions of fuel consumption associated with the electrical and thermal energy products relate as follows: fE + fQ = 1.

4.3. Energy-Based Considerations in Allocating CO2 Emissions for Cogeneration Using an energy basis, the fuel consumption associated with generating the electricity in the cogeneration process can be expressed as FE = E/ηE where ηE denotes the energy efficiency of generating the electrical energy product within a cogeneration process. Correspondingly, the fuel consumption associated with producing the thermal energy in the cogeneration process can be expressed as FQ = Q/ηQ

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where ηQ denotes the energy efficiency of producing the thermal energy product within a cogeneration process. Combining the above equations, we can write the following expressions for the fractions fE and fQ: fE = (E/ηE)/(E/ηE + Q/ηQ) and fQ = (Q/ηQ)/(E/ηE + Q/ηQ)

4.4. Exergy-Based Considerations in Allocating CO2 Emissions for Cogeneration Alternatively, we can use an exergy basis rather than an energy basis in establishing the above equations. Then, the fuel exergy consumption, ExFE, associated with generating the electrical exergy, ExE, in the cogeneration process can be expressed as ExFE = ExE/ψE where ψE denotes the exergy efficiency of generating the electrical energy product within the cogeneration process. Correspondingly, the fuel exergy consumption, ExFQ, associated with producing the thermal exergy, ExQ, in the cogeneration process can be expressed as ExFQ = ExQ/ψQ where ψQ denotes the exergy efficiency of producing the thermal energy product within the cogeneration process. Combining the above equations, we can write the following expressions for the fractions fE and fQ, using exergy terms: fE = (ExE/ψE)/(ExE/ψE + ExQ/ψQ) and fQ /ψQ= (ExQ/ψQ)/(ExE/ψE + ExQ)

4.5. Advantages of Allocating Cogeneration CO2 Emissions Using Exergy over Energy 4.5.1. Trade-off between Thermal and Electrical Products of Cogeneration When an electrical generation process is modified so that it becomes a cogeneration process, some of the electrical product is sacrificed for a gain in thermal output. When considering energy quantities, it is usually seen that the thermal energy gain is often very great, even for a small decrease in electrical energy output. In addition, there is often no dependence on the temperature at which the thermal energy is delivered. When considering exergy quantities, however, the trade-off between electrical and thermal exergy products is more balanced. That is, a small decrease in electrical exergy output usually leads to a relatively small and similar in magnitude increase in thermal exergy output, while a large decrease in electrical exergy output usually leads to a correspondingly large increase in

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thermal exergy output. Furthermore, the increase in thermal exergy is directly dependent on the temperature at which the thermal energy is delivered; generally, the greater is the temperature the greater is the thermal exergy. Table 1. Overall Energy and Exergy Efficiencies for an Electricity Generating Station Converted to Various Types of Cogeneration1. Operating mode Electricity generation only Low-temperature cogeneration2 Intermediate-temperature cogeneration2 High-temperature cogeneration2

Temperature of product thermal energy (°C) – 36 243 383

Energy efficiency (%) 37 69 60 55

Exergy efficiency (%) 37 39 37 35

1. Based on data in Rosen (1990). 2. For cogeneration cases, 50% of the resulting process heat is assumed to be useful product.

For example, a previous study (Rosen, 1990) of the effects of modifying a coal-fired electrical generating station for cogeneration showed that the overall variation in exergy efficiency is relatively small, while the corresponding variation for the energy efficiency is large. These results are illustrated in Table 1, where the exergy efficiencies are seen to vary between 35 and 39%, while the energy efficiencies vary between 37 and 69%. An interesting observation can be drawn from Table 1. The exergy results demonstrate that the benefits of cogeneration are not really due to the shift from electricity generation to heat production, since there is a balanced trade-off between the exergy of the two product commodities, and the overall exergy efficiency remains relatively fixed. Rather, the benefits of cogeneration are due to the fact that the heat produced offsets the need for a separate heat production process that uses additional fuel and—on an exergy basis—is inefficient. The energy results present an entirely different perspective, one that is skewed due to the fact that energy analysis values electrical and thermal energy equally.

4.5.2. Implications for CO2 Emissions Allocations The observation that a decrease in electrical exergy output of a cogeneration plant usually leads to a relatively similar magnitude increase in thermal exergy output, but that a decrease in electrical energy output of a cogeneration plant usually leads to a dissimilar magnitude increase in thermal energy output, suggests the following: 



The exergy efficiency of generating the electrical product within the cogeneration process, ψE, is similar to the exergy efficiency of generating the thermal product within the cogeneration process, ψQ. The energy efficiency of generating the electrical product within the cogeneration process, ηE, is not similar to the energy efficiency of generating the thermal product within the cogeneration process, ηQ.

As a consequence of the above two bullets, it can be seen from the analyses presented earlier that

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Marc A. Rosen  

the allocation method based on exergy contents of the products (see section 3) most closely approximates the allocation expressions presented in sections 4.1 through 4.3. the other emissions allocation methods in section 3 are significantly inaccurate relative to the objective of allocating emissions fairly and accurately.

These two bullets are discussed further in the next section, where the different emission allocation methods from section 3 are compared, bearing in mind the information presented in this section. As a consequence of the analysis presented in this section, it can be seen that the exergybased allocation method provides a rational means to determine the more productive modifications for a plant, when the objective is to reduce CO2 emissions. In applying the exergy-based method for allocating CO2 emissions, there is a need to know the exergy contents of the various commodities that may be encountered in cogeneration. Such information is presented in section 5.

4.5.3. Other Advantages of Basing CO2 Emission Allocations for Cogeneration on Exergy Another advantage of the method of allocating CO2 emissions for cogeneration processes based on the exergy content of the products is that the allocation method is generalizable to any number and type of products. For instance, the exergy-based method can accommodate:   

cogeneration processes with multiple electricity and heat outputs, trigeneration processes (i.e., cogeneration processes in which, in addition to electricity and heat outputs, cooling capacity is also a product), and other processes producing two or more products (e.g., a fuel production process to produce hydrogen which also yields pure oxygen as a product or by-product, or a chemical process yielding two different chemical commodities).

Most of the other allocation methods described in section 3 are much less flexible.

4.6. Comparison of CO2 Emission Allocation Methods for Cogeneration Based on the results obtained in this section, the author proposes that the most rational and meaningful method of allocating carbon dioxide emissions for cogeneration processes is to do so based on the exergy content of products. To justify this view, the different emissions allocation methods discussed for cogeneration processes in section 3 are compared. In particular, the problems inherent in the other CO2 emission allocation methods for cogeneration processes are discussed. The allocation method based on energy contents (section 3.1) leads to inaccurate breakdowns of the carbon dioxide emissions, essentially because such a method presumes that the energy efficiency of generating the electrical product within a cogeneration process, ηE, is approximately similar to the energy efficiency of generating the thermal product within the cogeneration process, ηQ. As discussed earlier (section 4.5), this presumption is not valid, as values for ηE and ηQ can vary widely. The allocation method based on shared emission savings between electrical and thermal energy (section 3.6) leads to inaccurate breakdowns of the carbon dioxide emissions,

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essentially because such a method presumes that the energy efficiency of generating the electrical product within a cogeneration process, ηE, is approximately similar to the energy efficiency of generating the electrical product via a separate process, ηpp. This presumption is not valid, as values for ηE and ηpp normally vary widely. Similarly, this allocation method presumes that the energy efficiency of generating the thermal product within a cogeneration process, ηQ, is approximately similar to the energy efficiency of generating the thermal product via a separate process, ηb, again a presumption that is not valid, as values for ηQ and ηb normally vary widely. It makes sense that these efficiencies vary since one of the main reasons to consider cogeneration is that it allows one to generate two products simultaneously with a higher efficiency than would be the case if each product were produced in a separate and independent process. It is noted that one could determine the shared-emissions allocations (section 3.6) based on exergy, rather than energy. Doing so would in fact overcome many of the problems associated with the shared-emissions allocation method based on energy. This observation is attributable to the fact that the exergy-based efficiencies for electricity generation in the part of a cogeneration system responsible for electricity generation and in a pure electricity generation process are similar (i.e., ψE ≈ ψpp), while the exergy-based efficiencies for thermal energy production in the part of a cogeneration system responsible for thermal energy production and in a pure thermal energy production process are similar (i.e., ψQ ≈ ψb). Thus, the shared-emissions allocation method based on exergy reduces approximately to the allocation method based on the exergy contents of the products. The allocation methods based on incremental fuel consumption to either electrical production (section 3.4) or to thermal energy production (section 3.5) both lead to inaccurate breakdowns of the carbon dioxide emissions. The reasons are similar and follow below: 



In essence, the allocation method based on incremental fuel consumption to electrical production presumes erroneously that the energy efficiency of generating the thermal product within a cogeneration process, ηQ, is approximately similar to the energy efficiency of generating the thermal product via a separate process, ηb. Still worse, the method then presumes that the value of the energy efficiency for generating the electrical product within the cogeneration process, ηE, can simply be selected so that the overall emissions total correctly. The ensuing values of ηE can as a result vary radically and for the most part arbitrarily. Similarly, the allocation method based on incremental fuel consumption to thermal energy production essentially presumes erroneously that the energy efficiency of generating the electrical product within a cogeneration process, ηE, is approximately similar to the energy efficiency of generating the electrical product via a separate process, ηpp. Further, the method then presumes that the value of the energy efficiency for generating the thermal product within the cogeneration process, ηQ, can simply be selected so that the overall emissions total correctly. As for the values of ηE in the preceding bullet, the ensuing values of ηQ can as a result vary radically and for the most part arbitrarily.

In general, the effect of the incremental-based allocations is that they arbitrarily underestimate the emissions from one of the products of a cogeneration process at the expense of the other. Both incremental-based allocations methods are thus unfair, since we

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seek the true and fair distribution of emissions among products—based on the efficiency of production for each within the cogeneration process. It is noted that one could determine the incremental-based allocations (sections 3.4 and 3.5) based on exergy. However, this determination is not carried out here since the incremental allocation method is itself somewhat arbitrary and therefore not rational.

Figure 4. Comparison of thermal energy and exergy at various temperatures (using data in Table 2).

A common problem shared by the two incremental-based allocation methods (sections 3.4 and 3.5) and the shared-emissions allocation method (section 3.6) is that they introduce independent devices for providing thermal energy (e.g., a reference steam boiler) and electrical energy (e.g., a reference power plant). The results obtained using these allocation methods are dependent on the energy efficiencies of these independent devices (ηb for the reference steam boiler and ηpp for the reference power plant). But, the values of ηb and ηpp can vary relatively widely depending on the specific devices chosen (e.g., high- versus mediumversus low-efficiency models), and these variations cause the emissions allocations evaluated with these methods to vary over correspondingly wide ranges.

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The allocation method based on economic values of products (section 3.3) leads to inaccurate breakdowns of the carbon dioxide emissions because that method allows economic parameters to skew the allocations. The proper allocations of carbon dioxide emissions for a cogeneration process should be based entirely on principles of thermodynamics or physics. Economic parameters such as costs and prices vary with time and location, but proper emissions allocations do not as they are dependent on characteristics of the technology involved. If one nevertheless chooses to modify the appropriate emissions allocations by penalizing certain products in terms of their emissions, through economic or other means, then it must be recognized that the resulting emissions allocations deviate arbitrarily from the appropriate emissions allocations. Similarly, the allocation method based an agreement between the various stakeholders in a project (section 3.7) leads to inaccurate breakdowns of the carbon dioxide emissions because that method allows arbitrary factors that generally are not based entirely on principles of thermodynamics or physics to affect the allocations. If one nevertheless chooses to modify the appropriate emissions allocations by penalizing certain products in terms of their emissions, through factors such as agreements between various stakeholders, then it must be recognized that the resulting emissions allocations almost certainly deviate arbitrarily from the appropriate emissions allocations. In summary, it is pointed out that all of the allocation methods described in section 3, except the exergy-based one, assign some arbitrary and/or subjective values to the differences between the product commodities. We need, instead, a rigorous scientific method, to help get the correct allocation and to remove the arbitrariness, and the exergy approach provides such a method. If, after determining the exergy-based allocations of CO2 emissions, we nevertheless choose to allocate emissions differently—for economic, political or other reasons—we can do so, but at least we do so knowing the appropriate unbiased allocation.

5. EXERGY VALUES FOR TYPICAL COGENERATION COMMODITIES When allocating carbon dioxide emissions based on the exergy contents of the products in a cogeneration process, it is necessary to know the exergy values associated with electrical and thermal energy. The situation for electrical energy is straightforward, as the energy and exergy contents of electricity are equivalent. For thermal energy, however, the energy and exergy contents generally differ, and the differences in some cases can be quite significant. Values of the energy and exergy associated with thermal energy, when it is treated purely as heat, are presented in Table 2 and illustrated in Figure 4. That table and figure consider heat (i.e., thermal energy transferred at temperatures above the environment temperature) and cold (i.e., thermal energy transferred at temperatures below the environment temperature), for various temperature categories. The ratio of exergy to energy is also shown in Table 2 and illustrated in Figure 5. Some interesting observations can be made: 



For heat, the ratio of exergy to energy varies from zero when the thermal energy is transferred at the environment temperature to unity as the temperature of heat transfer approaches infinity. For cold, the values of exergy rate are negative, implying that although heat is taken out of a system to make it colder, the exergy associated with the thermal energy is

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Marc A. Rosen input to the system to make it colder. That is the flows of energy and exergy in such instances are in opposite directions. This observation implies what is intuitively understood when dealing with systems at below-environment temperatures: cold is the useful commodity. Also for cold, the magnitude of the ratio of exergy to energy varies from zero when the thermal energy is transferred at the environment temperature to greater than unity as the temperature of heat transfer approaches absolute zero. For very cold systems, therefore, the exergy transfer can be larger than the energy transfer.



Table 2. Comparison of Quality of Various Types of Thermal Energy1 Thermal energy type

Temperature category2

Heat

Low Medium

High

3

Cold

Moderate

Very low Cryogenic

Energy rate

Temperature (K) 293

20

(kW) 1000

323 373

50 100

473

(C)

Exergy rate

Exergy-toenergy ratio

(kW) 0

0.000

1000 1000

93 215

0.093 0.215

200

1000

381

0.381

573 773

300 500

1000 1000

489 621

0.489 0.621

1273

1000

1000

770

0.770

1773

1500

1000

835

0.835

2273

2000

1000

871

0.871

283

10

1000

35

0.035

273

0

1000

73

0.073

263 243

10

1000 1000

114

0.114

30

206

0.206

223

50

1000

314

0.314

173 123

100

694

0.694

150

1000 1000

1382

1.382

73

200

1000

3014

3.014

23

250

1000

11,740

11.74

1. Reference-environment temperature To = 20C = 293 K. 2. The breakdown of temperature categories used here is arbitrary. 3. Cold is taken to be a transfer of thermal energy at below environmental temperatures.

Thermal energy is often transferred via a medium, and in cogeneration systems the medium of choice is often water. Values of the energy and exergy of water in various forms are presented in Table 3. That table considers water conveying heating capacity (e.g., superheated steam, dry saturated steam, hot water), and conveying cooling capacity (e.g., cold water). The ratio of exergy to energy is also shown in Table 3. Similar observations as for Table 2 can be made, in that the magnitudes of energy and exergy flows differ and, for cold commodities, the flows of energy and exergy are in opposite directions.

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Figure 5. Ratio of thermal exergy to thermal energy at various temperatures (using data in Table 2).

Table 3. Comparison of Quality of Water in Various Conditions1 Thermal category of water Hot

Condition of water Superheated steam

Dry saturated steam

Liquid hot water

Cold

Liquid cold water

40

Specific energy (kJ/kg) 3822

Specific exergy (kJ/kg) 1677

Ratio of exergy to energy 0.439

500 300 200

40 40 15.54

3361 2919 2709

1372 1146 912

0.408 0.393 0.337

Medium Low High

150 100 100

4.758 1.014 1.014

2663 2592 335

747 525 39.9

0.281 0.203 0.119

Medium Low Moderate

50 30 10

0.126 0.0425 0.0123

125 41.8 42

6.93 0.78 0.778

0.0553 0.0187 0.0185

5 0

0.00872 0.00611

63 84

1.524 3.021

0.0274 0.0360

Temp. (C)

Pressure (bar)

High

700

Medium Low High

Temp. category2

1. Reference-environment temperature and pressure are To = 20C = 293 K and po = 1 bar, respectively. 2. The breakdown of temperature categories used here is arbitrary.

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• Ultra-low-temperature cryogenic cool (≈3 K) • Electricity, work • Hydrocarbon fuels • Very high-temperature heat (≈2000 K) • High-pressure steam, cryogenic cool (≈200 K) • Hot water, normal refrigeration cool • Space heating, space cooling Figure 6. Quality of energy based on exergy (in descending order).

An overall qualitative comparison of the energy quality of a range of energy forms, where exergy is used as the measure of quality, is shown in Figure 6.

6. ANALOGY BETWEEN ALLOCATING CARBON DIOXIDE EMISSIONS AND ECONOMIC COSTS FOR COGENERATION Many researchers have investigated the relations between economics and thermodynamics. In particular, a growing field of study linking exergy and economics, called thermoeconomics or exergoeconomics, has evolved (Dincer and Rosen, 2007). One of the objectives of exergoeconomics, when it is applied to cogeneration processes, is determining the appropriate allocations of costs associated with the system with the coproducts. The types of costs considered include both fixed capital costs as well as operating costs such as fuel costs. A good understanding the proper allocations of costs is important because it allows individual product prices to be established that cover the costs of producing the products and allow for a margin or profit. In addition, such an understanding identifies when the product prices are such that the products are being sold below cost. One of the outcomes of many exergy and economic studies is that the most appropriate way to allocate costs among the products of a cogeneration system may be to do so based on the exergy contents of the products. Other cost allocation methods, particularly those based on energy, are inadequate in that they divide costs in ways that radically differ from market prices. Clearly, then, there appears to be an analogy between the exergy-based method proposed here for allocating carbon dioxide emissions for a cogeneration system, and the exergy-based methods for allocating costs. This topic is beyond the scope of the present article. Nevertheless, it would almost certainly be worthwhile to investigate further this analogy, as it may provide insights that allow the allocation of costs or carbon dioxide emissions to be more appropriately carried out and better understood.

7. ILLUSTRATIVE EXAMPLES The different methods for allocating carbon dioxide emissions for cogeneration plants that are described in section 3 and examined and discussed in subsequent sections are

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illustrated for three example cases in this section. In the first two cases, the different methods of allocating carbon dioxide emissions are illustrated for actual cogeneration plants. In the third case, a hypothetical cogeneration scenario is compared to equivalent separate processes for producing thermal and electrical energy, and the implications of the different allocation methods are discussed. In addition, the third case is used to illustrate how the results could be used to determine carbon dioxide emission credits that might be used in emissions trading. The examples are intended to demonstrate the types of values that can be obtained using the different methods, and to illustrate the attributes of the different methods.

7.1. Illustrations of CO2 Emissions Allocations for Two Actual Cogeneration Plants 7.1.1. Descriptions of the Examples The two example cases considered are  

the University of Toronto Cogeneration System, and the Cornwall Cogeneration and District Energy System.

Data for these systems have been drawn mainly from information sheets by Wiggin (1997) and Consumers Gas (1995) that have been compiled by Klein (1999c). Table 4. Technical Parameters for the Cogeneration Systems Considered. Parameter

University of Toronto Cogeneration System1

Cornwall Cogeneration and District Energy System2

Engine type

Gas turbine

Two reciprocating engines

Heat use

Heating of campus using 6 km steam tunnel system 30,000 lb/hr of 200 psi steam

Municipal district heating using 4.5 km hot-water distribution network 7 MW via 120C and 1585 kPa steam None

Heat quantity and type (base load) Supplemental firing Environmental controls Installation date

Heat recovery steam generator can be supplementary-fired to 90,000 lb/hr steam at 200 psi Water injection to control nitrogen oxide emissions (to 42 ppm) 1993

Engines use lean-burn technology 1995

1. Source is Consumers Gas (1995). 2. Source is Wiggin (1997).

The basic technical parameters for the two cogeneration systems considered, including data on the cogeneration engine and the cogenerated heat, are summarized in Table 4. Some general thermodynamic parameters for the cogeneration systems considered that are specified in the literature (Wiggin, 1997; Consumers Gas, 1995) are presented in the top part of Table 5.

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Emission-allocation fraction

7.1.2. Energy and Exergy Values Energy and exergy data for the cogeneration systems considered are presented in the bottom two parts of Table 5. Those data include efficiencies and flow rates for products and inputs, and are based on data in the literature (Wiggin, 1997; Consumers Gas, 1995) and evaluations by the present author. It can be seen that the energy and exergy flow rates for the fuel are similar, as are the energy and exergy flow rates for the electrical products. However, the product thermal energy and thermal exergy rates differ markedly for both processes, as do the energy and exergy efficiencies. 1.2 1 0.8 0.6 0.4 0.2 0 0

1

2

3

4

5

6

Electricity to heat cost ratio Heat (U. Toronto) Electricity (Cornwall)

Heat (Cornwall) Electricity (U. Toronto)

Figure 7. Carbon dioxide emissions allocations based on economic values of products for two cases.

Table 5. Specified and Evaluated Thermodynamic Parameters for the Cogeneration Systems Considered1 Parameter General Thermodynamic Parameters Fuel type Fuel input rate (kg/s) Thermal-product type Thermal-product temperature (C) Thermal-product absolute pressure3 (bar) Thermal-product flow rate (kg/s) Energy Parameters Fuel energy input rate (MW) Electrical energy generation rate (MW) Product thermal energy rate (MW) Energy efficiency4 (%)

University of Toronto Cogeneration Plant

Cornwall Cogeneration and District Energy System

Natural gas2 0.3949 Steam (dry saturated) 197.6 14.8 3.78

Natural gas2 0.2660 Hot water 120 16.85 15.12

19.75 6 10.393 83

13.3 5 7 90

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Table 5. Continued Parameter Exergy Parameters Fuel exergy input rate5 (MW) Electrical exergy generation rate (MW) Product thermal exergy rate (MW) Exergy efficiency (%)

University of Toronto Cogeneration Plant

Cornwall Cogeneration and District Energy System

20.35 6 3.654 46.0

13.70 5 1.134 44.8

1. Reference-environment temperature and pressure are 10C and 1 bar, respectively. 2. Natural gas is modelled as methane in calculations. 3. Pressure data is assumed to be gauge. 4. Energy efficiencies provided in sources are assumed to be based on lower heating value. 5. Ratio of chemical exergy to lower heating value for methane is evaluated as 1.03 based on data in Moran and Shapiro (2007).

Table 6. Allocation of Emissions for University of Toronto Cogeneration Plant1. Emission-allocation method Based on exergy content of products Based on energy content of products Allocation of incremental fuel consumption to electrical production2 Allocation of incremental fuel consumption to heat production3 Based on a shared emission savings between electricity and heat2,3 Based on economic value of products4

Emission allocation (%) To electrical product To thermal product 62.1 37.9 36.6 63.4 41.5

58.5

86.8

13.2

59.8

40.2 1

5/[5 + 7(cE/cQ) ]  100%

7/(5cE/cQ + 7)  100%

1. Reference-environment temperature and pressure are To = 10C and po = 1 bar, respectively. 2. An efficiency of 90% is assumed for the boiler that would have been used in the production of the same amount of heat as produced by the cogeneration system. 3. An efficiency of 35% is assumed for the power plant that would have been used in the production of the same amount of electricity as produced by the cogeneration system. 4. The parameter cE/cQ denotes the ratio of the economic value of the electricity produced to the economic value of the thermal energy produced.

7.1.3. Results and Discussion The results of applying the methods for allocating CO2 of emissions for the University of Toronto cogeneration plant are presented in Table 6 and Figure 8, and for the Cornwall Cogeneration and District Energy System are presented in Table 7 and Figure 9. For both example cases, it is clear that the allocations of CO2 emissions vary markedly, depending on the allocation method used. The author contends, as discussed throughout this article, that the exergy-based allocations are the most appropriate. Thus, using the other emissions allocation methods can be very misleading, since the resulting emissions may deviate widely from those obtained using the exergy-based method. Some of the problems with the other allocation methods are illustrated in Tables 6 and 7 and Figures 8 and 9. Some examples follow:

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Marc A. Rosen 



In one case (where the allocation is based on incremental fuel consumption to heat production, for the Cornwall system), the absurd situation exists in which the allocations of emissions to the thermal product are evaluated to be negative and the allocations to the electrical product to exceed 100%. This result is simply a consequence of the flaws in that allocation method and its use of an energy efficiency ηpp for an independent device for providing the same electrical energy as the cogeneration system (e.g., a reference power plant). Here, a value of ηpp = 35% is used. If, instead, the value of ηpp is selected such that ηpp = 38%, then the allocations of carbon dioxide emissions to both products would be positive and less than 100%. For the allocation method based on economic value of products, the results depend on the value of the ratio of the economic value of the electricity produced cE to the economic value of the thermal energy produced cQ. The value of this ratio, even considering only the present time, varies with location. In Tables 6 and 7, therefore, the values of the emission allocations for this method are left variable. In Figures 8 and 9, a range of cE/cQ values are considered.

So as appreciate the wide range of possible emissions allocations possible when using the method based on economic value of products, the emissions allocations values are plotted in Figure 7 for a wide range of cE/cQ values, for both the University of Toronto cogeneration plant and the Cornwall Cogeneration and District Energy System. At a cost ratio of 1 (i.e., cE = cQ), electrical and thermal energy have the same economic value, while electricity is the more valuable commodity when cE/cQ > 1 and heat is more valuable when cE/cQ < 1. It is observed in Figure 7 that all emissions are attributable to heat for a value ratio cE/cQ = 0. As the value ratio increases, more emissions are shifted from heat to electricity. As the ratio value approaches infinity, the emissions approach being entirely attributable to electricity. Table 7. Allocation of Emissions for the Cornwall Cogeneration and District Heating System1 Emission-allocation method Based on exergy content of products Based on energy content of products Allocation of incremental fuel consumption to electrical production2 Allocation of incremental fuel consumption to heat production3 Based on a shared emission savings between electricity and heat2,3 Based on economic value of products4

Emission allocation (%) To electrical product To thermal product 81.5 18.5 41.7 58.3 41.5

58.5

107.4

7.4

64.7

35.3 1

6/[6 + 10.4 (cE/cQ) ]  100%

10.4/(6cE/cQ + 10.4)  100%

1. Reference-environment temperature and pressure are To = 10C and po = 1 bar, respectively. 2. An efficiency of 90% is assumed for the boiler that would have been used in the production of the same amount of heat as produced by the cogeneration system. 3. An efficiency of 35% is assumed for the power plant that would have been used in the production of the same amount of electricity as produced by the cogeneration system. 4. The parameter cE/cQ denotes the ratio of the economic value of the electricity produced to the economic value of the thermal energy produced.

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Figure 8. Allocation of emissions for University of Toronto cogeneration plant (based on data in Table 6).

7.2. Illustrative Comparison of CO2 Emissions Allocations for a Cogeneration Plant and Equivalent Independent Plants 7.2.1. Description of Scenario In this section, a hypothetical cogeneration scenario is compared to equivalent separate processes for producing the same thermal and electrical energy, and the implications of the different allocation methods are discussed. The main characteristics of the processes being compared are as follows: 



The hypothetical cogeneration system produces 4 MW of electrical power and 4 MW of thermal power from a fuel energy input rate of 10 MW. The energy efficiency is 80%. The separate processes consist of (i) an electricity generation system which produces 4 MW of electrical power from a fuel energy input rate of 10 MW, and (ii) a heating system which produces 4 MW of thermal power from a fuel energy input rate of 5

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Marc A. Rosen MW. The energy efficiency of the overall (combined) process is 53% (i.e., 8/15  100%).

In both cases, the input fuel is natural gas. The carbon dioxide emissions for natural gas are taken to be 50 kg CO2/GJ natural gas.

Figure 9. Allocation of emissions for Cornwall cogeneration and district heating system (based on data in Table 7).

The 4 MW of thermal energy produced in each case is taken to be made up of 2 MW of steam and 2 MW of hot water. For simplicity, the state of the steam is taken to be the same as for the steam produced in the University of Toronto Cogeneration Plant described in the previous section (dry saturated steam at a pressure of 200 psi gauge), while the state of the hot water is taken to be the same as for the hot water produced in the Cornwall Cogeneration and District Energy System described in the previous section (hot water at a temperature of 120°C and a pressure of 1585 kPa gauge).

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Table 8. Specified and Evaluated Thermodynamic Parameters for the Cogeneration and Independent Processes Considered1. Parameter General Thermodynamic Parameters Fuel type Fuel energy input rate (MW)

Heating

Independent processes Electricity Overall generation (combined)

Cogeneration

Natural gas

Natural gas

Natural gas

Natural gas

5

10

15

10

–

Thermal-product temperature (C)

Steam (dry sat.) Hot water 197.6 (steam) 120 (hot water)

Thermal-product absolute pressure (bar)

14.8 (steam) 16.85 (hot water)

–

Thermal-product flow rate (kg/s) Energy Parameters Fuel energy input rate (MW) Electrical energy generation rate (MW) Product thermal energy rate (MW) Steam Hot water Total Energy efficiency2 (%) Exergy Parameters Fuel exergy input rate3 (MW) Electrical exergy generation rate (MW) Product thermal exergy rate (MW) Steam Hot water Total Exergy efficiency (%)

0.727 (steam) 4.32 (hot water)

–

Steam (dry sat.) Hot water 197.6 (steam) 120 (hot water) 14.8 (steam) 16.85 (hot water) 0.727 (steam) 4.32 (hot water)

Steam (dry sat.) Hot water 197.6 (steam) 120 (hot water) 14.8 (steam) 16.85 (hot water) 0.727 (steam) 4.32 (hot water)

Thermal-product type(s)

–

5

10

15

10

–

4

4

4

2 2 4 80

– – – 40

2 2 4 53.3

2 2 4 80

5.15

10.3

15.45

10.3

–

4

4

4

0.777 0.323 1.100 21.4

– – – 38.8

0.777 0.323 1.100 33.0

0.777 0.323 1.100 49.5

1. Reference-environment temperature and pressure are 10C and 1 bar, respectively. 2. Energy efficiencies provided in sources are assumed to be based on lower heating value. 3. Ratio of chemical exergy to lower heating value for methane is evaluated as 1.03 based on data in Moran and Shapiro (2007).

7.2.2. Energy and Exergy Values Energy and exergy data for the cogeneration and independent processes for heating and electricity generation are presented in Table 8. Those data include efficiencies and flow rates for products and inputs, and are based on evaluations by the present author. The results in Table 8 demonstrate that the energy and exergy flow rates for the fuel are similar, as are the energy and exergy flow rates for the electrical products. However, the

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product thermal energy and thermal exergy rates differ markedly for both processes, as do the energy and exergy efficiencies. Table 9. Allocation (in %) of Emissions for the Cogeneration and Independent Processes Considered1. Process Emission-allocation method Independent Heating Electricity generation Overall (combined) Cogeneration

Based on exergy content of products Based on energy content of products Allocation of incremental fuel consumption to electrical production2 Allocation of incremental fuel consumption to heat production3 Assuming a reference power plant efficiency of ηpp = 35% Assuming a reference power plant efficiency of ηpp = 40% Based on a shared emission savings between electricity and heat2,3 Assuming a reference power plant efficiency of ηpp = 35% Assuming a reference power plant efficiency of ηpp = 40% Based on economic value of products4 Assuming an electrical-to-thermal cost ratio of cE/cQ = 1.5 Assuming an electrical-to-thermal cost ratio of cE/cQ = 1.8 Assuming an electrical-to-thermal cost ratio of cE/cQ = 2.1

Emission allocation (%) To electrical To thermal product product 0 100 66.7 78.4 50.0

100 0 33.3 21.6 50.0

55.6

44.4

114.3

–14.3

100

0

72.0

28.0

69.2

30.8

60.0

40.0

64.3

35.7

67.7

32.3

1. Reference-environment temperature and pressure are To = 10C and po = 1 bar, respectively. 2. An efficiency of 90% is assumed for the boiler that would have been used in the production of the same amount of heat as produced by the cogeneration system. 3. Efficiencies of 35% and 40% are considered for the power plant that would have been used in the production of the same amount of electricity as produced by the cogeneration system. 4. The parameter cE/cQ denotes the ratio of the economic value of the electricity produced to the economic value of the thermal energy produced.

Two particular results are observed in Table 8 regarding efficiency:  

Cogeneration is much more efficient (on energy or exergy bases) than the independent processes for producing the same thermal and electrical products. The exergy efficiencies are lower than the energy efficiencies, reflecting the fact that the thermal energy products are both of lower usefulness (or quality) than electricity. Of the two thermal products, the exergy values indicate that the usefulness of the steam is greater than that for the hot water

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Table 10. Allocation (in g CO2/s) of Emissions for the Cogeneration and Independent Processes Considered1. Process Emission-allocation method Independent Heating Electricity generation Overall (combined) Cogeneration

Based on exergy content of products Based on energy content of products Allocation of incremental fuel consumption to electrical production2 Allocation of incremental fuel consumption to heat production3 Assuming a reference power plant efficiency of ηpp = 35% Assuming a reference power plant efficiency of ηpp = 40% Based on a shared emission savings between electricity and heat2,3 Assuming a reference power plant efficiency of ηpp = 35% Assuming a reference power plant efficiency of ηpp = 40% Based on economic value of products4 Assuming an electrical-to-thermal cost ratio of cE/cQ = 1.5 Assuming an electrical-to-thermal cost ratio of cE/cQ = 1.8 Assuming an electrical-to-thermal cost ratio of cE/cQ = 2.1

Emission allocation (g CO2/s) To electrical To thermal product product

Total

0

250

250

500

0

500

500

250

750

392 250

108 250

500 500

278

222

500

572

–72

500

500

0

500

360

140

500

346

154

500

300

200

500

322

178

500

339

161

500

1. Reference-environment temperature and pressure are To = 10C and po = 1 bar, respectively. 2. An efficiency of 90% is assumed for the boiler that would have been used in the production of the same amount of heat as produced by the cogeneration system. 3. Efficiencies of 35% and 40% are considered for the power plant that would have been used in the production of the same amount of electricity as produced by the cogeneration system. 4. The parameter cE/cQ denotes the ratio of the economic value of the electricity produced to the economic value of the thermal energy produced

7.2.3. Results and Discussion The results of applying the methods for allocating CO2 of emissions for the cogeneration and independent processes are presented in Tables 9 and 10 and Figures 10 and 11. Percentage breakdowns are shown in Table 9 and Figure 10, while absolute emissions rates are shown in Table 10 and Figure 11. For the independent processes, there exists no ambiguity regarding the allocations of emissions to the thermal and electrical products. For the cogeneration process, the appropriate method to allocate emissions is not clear, so the allocations methods discussed earlier are

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applied. It is observed that the allocations of CO2 emissions vary markedly, depending on the allocation method used. The author contends, as discussed throughout this article, that the exergy-based allocations are the most appropriate. Thus, using the other emissions allocation methods can be very misleading, since the resulting emissions may deviate widely from those obtained using the exergy-based method.

Figure 10. Allocation of emissions (in %) for independent and cogeneration processes considered (based on data in Table 9 and a reference power plant efficiency of 40%).

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75

Some of the problems with the other allocation methods are illustrated in Tables 9 and 10 and Figures 10 and 11. Some examples follow: 

In one case (where the allocation is based on incremental fuel consumption to heat production), the absurd situation exists in which the allocations of emissions to the thermal product are evaluated to be negative and the allocations to the electrical product to exceed 100%. This result is simply a consequence of the flaws in that allocation method and its use of an energy efficiency ηpp for an independent device for providing the same electrical energy as the cogeneration system (e.g., a reference power plant). Here, a value of ηpp = 35% is used. For comparison, an alternate value of ηpp is selected (40%); then the allocations of carbon dioxide emissions to both products do not exceed 100%.

Figure 11. Allocation of emissions (in g CO2/s) for independent and cogeneration processes considered (based on data in Table 10 and a reference power plant efficiency of 40%).

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Marc A. Rosen 

For the allocation method based on economic value of products, the results depend on the value of the ratio of the economic value of the electricity produced cE to the economic value of the thermal energy produced cQ. The value of this ratio, even considering only the present time, varies with location. Here, for illustration only, economic parameter values from a recent U.S. report (Harrell, 2002) are used. With these values, the cost of electricity is approximated as US$0.060/kWh, and the cost of steam as US$0.0334. The electrical-to-thermal cost ratio is then cE/cQ = 1.8. In Tables 9 and 10, therefore, this value is used. Also, values of cE/cQ of 1.5 and 2.1 are used to indicate the sensitivity of the allocations to the cost ratio.

7.3. Illustration of Use of Results to Determine CO2 Emissions Credits for Trading Purposes from Switching to Cogeneration from Equivalent Independent Plants In this section, the case illustrated in section 7.2 is used to illustrate how the results could be used to determine carbon dioxide emission credits that might be used in emissions trading. Here, we consider two energy users, one of electricity and one of thermal energy. The types of thermal energy used are the same as those described in section 7.2. The decrease in CO2 emissions attributable to the energy users are evaluated, when each switches from obtaining the energy required (electricity or thermal energy) from a producer of the just the required energy to a supplier using cogeneration. The decrease in CO2 emissions for each energy user is its CO2 emissions credits. The characteristics of the cogeneration plant and the independent electrical power plant and heating plant are as in section 7.2.

7.3.1. CO2 Emissions Credits when an Electricity User Switches to Cogeneration In this situation, we consider an electrical consumer who normally obtains electricity from a power plant. We wish to determine the decrease in CO2 emissions attributable to that consumer (i.e., the CO2 emissions credit for the consumer) if the consumer switches to obtaining electricity from a supplier that uses a cogeneration plant. We consider a multi-step calculation procedure. For simplicity, we consider a unit energy use by the consumer of 1 GJ of electricity. Parameter values that characterize the technologies considered are drawn from section 7.2 The CO2 emissions attributable to the consumer when obtaining electricity from a power plant can be evaluated as the product of the fuel use in the power plant and the carbon dioxide emissions factor for the fuel. That is, CO2 emissions = (Fuel use in power plant)(Fuel CO2 emissions factor) = (Electricity use/Power plant efficiency)(Fuel CO2 emissions factor) = (1 GJ electricity/0.40 GJ electricity/GJ fuel)(50 kg CO2 /GJ fuel) = 125.0 kg CO2 /GJ electricity

The total CO2 emissions of the cogeneration plant (to produce 1 GJ of electricity as well as thermal energy) can be evaluated as the product of the total fuel use in the cogeneration plant and the carbon dioxide emissions factor for the fuel. That is,

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77

CO2 emissions = (Fuel use in cogeneration plant)(Fuel CO2 emissions factor) = (Electricity use/Cogeneration efficiency for elec.)(Fuel CO2 emissions factor) = (1 GJ electricity/0.40 GJ electricity/GJ fuel)(50 kg CO2 /GJ fuel) = 125.0 kg CO2 /GJ electricity

Figure 12. Carbon dioxide emissions for electricity generation via power plant and cogeneration, and carbon dioxide emissions credit.

Of the total CO2 emissions of the cogeneration plant (to produce 1 GJ of electricity as well as thermal energy), the CO2 emissions attributable to the consumer when obtaining electricity from a cogeneration plant can be evaluated as the fraction of the total CO2 emissions of the cogeneration plant attributed to electricity production. In this article, we have argued that the division of CO2 emissions for cogeneration among electrical and thermal products should be based on the exergy contents of the products. For the present case, it was shown in section 7.2 that 78.4% of the total CO2 emissions for the cogeneration plant should be attributed to the electrical product based on exergy (see Table 9). Thus, the CO2 emissions attributable to the consumer when obtaining electricity from a cogeneration plant can be evaluated as follows: CO2 emissions for user = (Total CO2 emissions of cogeneration plant)(Fraction for elec.) = (125.0 kg CO2 /GJ electricity)(0.784) = 98.0 kg CO2 /GJ electricity Finally, the CO2 emissions credit for switching to cogeneration, evaluated as the decrease in CO2 emissions attributable to the electricity user, can be evaluated as the difference between the CO2 emissions attributable to the consumer when obtaining electricity from a power plant and from a cogeneration plant. That is, CO2 emissions credit =(CO2 emissions for elec. from power plant attributed to consumer) – (CO2 emissions for elec. from cogen. attributed to consumer) = (125 kg CO2 /GJ electricity) – (98.0 kg CO2 /GJ electricity) = 27.0 kg CO2 /GJ electricity

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The results are presented in Table 11 and illustrated in Figure 12, and demonstrate the manner in which they can assist in evaluating CO2 emissions credits for electricity users, for trading and other purposes. Table 11. Carbon Dioxide Emissions and Emissions Credit for Switching to Cogeneration for Electricity and Heat Users*. CO2 emissions (kg) For User of Electricity (1 GJ) CO2 emissions for user of electricity from power plant CO2 emissions for user of electricity from cogeneration plant CO2 emissions credit for user of electricity for switching from power plant to cogeneration plant For User of Heat (1 GJ) CO2 emissions for user of heat from heating plant CO2 emissions for user of heat from cogeneration plant CO2 emissions credit for user of heat for switching from heating plant to cogeneration plant

125.0 98.0 27.0

62.5 27.0 35.5

* Allocation of emissions from cogeneration to electrical and heat products is determined based on exergy contents of products.

7.3.2. CO2 Emissions Credits when a Heat User Switches to Cogeneration In this situation, we consider a heat consumer who normally obtains heat from a heating plant. We wish to determine the decrease in CO2 emissions attributable to that consumer (i.e., the CO2 emissions credit for the consumer) if the consumer switches to obtaining heat from a supplier that uses a cogeneration plant. We consider a multi-step calculation procedure. For simplicity, we consider a unit energy use by the consumer of 1 GJ of thermal energy. The thermal energy used by the consumer is of the type described in section 7.2. Parameter values that characterize the technologies considered are drawn from section 7.2

Figure 13. CO2 emissions for heat production via heating plant and cogeneration, and CO2 emissions credit.

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79

The CO2 emissions attributable to the consumer when obtaining heat from a heating plant can be evaluated as the product of the fuel use in the heating plant and the carbon dioxide emissions factor for the fuel. That is, CO2 emissions = (Fuel use in heating plant)(Fuel CO2 emissions factor) = (Heat use/Heating plant efficiency)(Fuel CO2 emissions factor) = (1 GJ heat/0.80 GJ heat/GJ fuel)(50 kg CO2 /GJ fuel) = 62.5 kg CO2 /GJ heat The total CO2 emissions of the cogeneration plant (to produce 1 GJ of heat as well as electrical energy) can be evaluated as the product of the total fuel use in the cogeneration plant and the carbon dioxide emissions factor for the fuel. That is, CO2 emissions = (Fuel use in cogeneration plant)(Fuel CO2 emissions factor) = (Electricity use/Cogeneration efficiency for heat)(Fuel CO2 emissions factor) = (1 GJ heat/0.40 GJ heat/GJ fuel)(50 kg CO2 /GJ fuel) = 125.0 kg CO2 /GJ heat Of the total CO2 emissions of the cogeneration plant (to produce 1 GJ of heat as well as electrical energy), the CO2 emissions attributable to the consumer when obtaining heat from a cogeneration plant can be evaluated as the fraction of the total CO2 emissions of the cogeneration plant attributed to heat production. In this article, we have argued that the division of CO2 emissions for cogeneration among electrical and thermal products should be based on the exergy contents of the products. For the present case, it was shown in section 7.2 that 21.6% of the total CO2 emissions for the cogeneration plant should be attributed to the thermal product based on exergy (see Table 9). Thus, the CO2 emissions attributable to the consumer when obtaining heat from a cogeneration plant can be evaluated as follows: CO2 emissions for user = (Total CO2 emissions of cogeneration plant)(Fraction for heat) = (125.0 kg CO2 /GJ heat)(0.216) = 27.0 kg CO2 /GJ heat Finally, the CO2 emissions credit for switching to cogeneration, evaluated as the decrease in CO2 emissions attributable to the heat user, can be evaluated as the difference between the CO2 emissions attributable to the consumer when obtaining heat from a power plant and from a cogeneration plant. That is, CO2 emissions credit =(CO2 emissions for heat from heating plant attributed to consumer) – (CO2 emissions for heat from cogeneration attributed to consumer) = (62.5 kg CO2 /GJ heat) – (27.0 kg CO2 /GJ heat) = 35.5 kg CO2 /GJ heat The results are presented in Table 11 and illustrated in Figure 13, and demonstrate the manner in which they can assist in evaluating CO2 emissions credits for thermal energy users, for trading and other purposes.

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7.3.3. CO2 Emissions Credits for Other Cases The procedures illustrated in the previous two subsections can be formalized for variations of the cases considered here, as well as for various other cases. Some of the other situations that could be considered include on- and off-site plants, different fuels, different thermal energy requirements and trigeneration systems.

8. CONCLUSIONS The exergy-based method of carbon dioxide emissions allocation allows for rational and meaningful allocations of such emissions for cogeneration, and is superior to other methods. By permitting carbon dioxide emissions to be allocated more appropriately among the different commodities generated in cogeneration, the results allow the environmental benefits of technologies that produce multiple products to be better understood and exploited. These results should therefore allow the more beneficial among competing technologies to be identified in a rational and meaningful manner. The results also indicate that the exergy-based emissions allocations method provides a sensible basis for a meaningful overall approach for emissions trading. Indirectly, due to the analogy between cost and emissions allocations, the results may also lead to economic benefits, as the results should permit the costs associated with cogeneration technologies to be more appropriately allocated among the different commodities generated. The results consequently indicate that the exergy-based method should be used in allocating carbon dioxide emissions for cogeneration devices. Using the exergy-based method would help ensure proper decision-making regarding issues such as    

how emissions should be reduced in a given device, how and where cogeneration technologies should be used, what effect introducing cogeneration will have on overall carbon dioxide emissions, and a fair way to establish detailed schemes for emissions trading.

The results presented in this article should be of benefit to designers of energy systems, and to decision and policy makers in companies and government. If the results are used appropriately, they should allow benefits to accrue to society through the selection and design of better energy technologies, based on environmental considerations. The results may also be useful as input to the regulations developed in Europe for defining and assessing the benefits of cogeneration (European Parliament, 2004; CEN/CENELEC, 2004). In particular, the incorporation of exergy into these regulations would likely improve them markedly, both in general and in particular for allocating emissions. To advance this approach, research is recommended in a number of related areas. First, the work should be expanded from cogeneration to trigeneration (where the products are electricity, heat and cold), and systems that generate multiple products of other types. Also, the exergy-based allocation method needs to be applied with more detailed process (e.g. hourly data for a plant over a year, rather than annual average data). Third, detailed methodologies for emissions trading should be developed that utilize overall exergy-based

Rational Attribution of Environmental Emissions of Cogeneration to Products

81

emissions allocations approach. Finally, the analogy between allocating carbon dioxide emissions and economic costs for cogeneration should be investigated further, with the aim of discovering insights that allow the allocation of costs or carbon dioxide emissions to be more appropriately carried out and better understood.

ACKNOWLEDGMENTS The support provided by the Oil, Gas and Energy Branch of Environment Canada is gratefully acknowledged. The author is especially grateful for the assistance provided by Manfred Klein, formerly at that branch of Environment Canada and now affiliated with the Gas Turbine Laboratory, Institute for Aerospace Research, National Research Council Canada.

NOMENCLATURE cE cQ C CE CQ E ExE ExFE ExFQ ExQ fE fQ F FE FQ Q T To

unit economic value of electrical product of cogeneration unit economic value of thermal product of cogeneration total CO2 emissions from cogeneration CO2 emissions associated with electrical energy produced via cogeneration CO2 emissions associated with thermal energy product produced via cogeneration net output of electrical energy from cogeneration net output of electrical exergy from cogeneration fuel exergy consumption associated with generating electricity via cogeneration fuel exergy consumption associated with producing thermal exergy via cogeneration net output of thermal exergy from cogeneration fraction of cogeneration emissions allocated to electrical product fraction of cogeneration emissions allocated to thermal product total primary fuel energy consumed by cogeneration system fuel consumption attributed to electricity generation fuel consumption attributed to production of thermal energy net output of thermal energy from cogeneration temperature temperature of reference environment

GREEK SYMBOLS φ ηb ηE ηQ

CO2 emission coefficient for a fuel energy efficiency of independent device (e.g., boiler) for thermal energy energy efficiency of generating electrical energy via cogeneration energy efficiency of producing thermal energy via cogeneration

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Marc A. Rosen ηpp ψE ψQ τ

energy efficiency of independent device (e.g., power plant) for electrical energy exergy efficiency of generating electricity via cogeneration exergy efficiency of producing thermal energy product via cogeneration exergetic temperature factor

REFERENCES [1] Acres International Limited (1987). Cogeneration Potential in Ontario and Barriers to its Development, Report for Ontario Ministry of Energy, Toronto. [2] Ayres, R. U., Ayres, L. W. and Martinas, K. (1998). Exergy, waste accounting, and lifecycle analysis. Energy 23, 355-363. [3] Berthiaume, R., Bouchard, C. and Rosen, M. A. (2001). Exergetic evaluation of the renewability of a biofuel. Exergy, An Int. J. 1, 256-268. [4] CEN/CENELEC (2004). Workshop Agreement Manual for Determination of Combined Heat and Power (CHP), CWA 45547, European Committee for Standardization (CEN) and European Committee for Electrotechnical Standardization (CENELEC), Brussels. [5] Consumers Gas (1995, estimated). Cogeneration at the University of Toronto. Information sheet. [6] Connelly, L. and Koshland, C. P. (1997). Two aspects of consumption: using an exergybased measure of degradation to advance the theory and implementation of industrial ecology. Resources, Conservation and Recycling 19, 199-217. [7] Cownden, R., Nahon, M. and Rosen, M. A. (2001). Exergy analysis of a fuel cell power system for transportation applications. Exergy, An Int. J. 1, 112-121. [8] Crane, P., Scott, D. S. and Rosen, M. A. (1992). Comparison of exergy of emissions from two energy conversion technologies, considering potential for environmental impact. Int. J. Hydrogen Energy 17, 345-350. [9] Creyts, J. C. and Carey, V. P. (1997). Use of extended exergy analysis as a tool for assessment of the environmental impact of industrial processes. Proc. ASME Advanced Energy Systems Division, ed. M.L. Ramalingam, ASME, New York, AES-Vol. 37, 129137. [10] Daniel, J. J. and Rosen, M. A. (2002). Exergy-based environmental assessment of life cycle emissions for various automobiles and fuels. Proc. CSME Forum 2002, 21-24 May, Kingston, Ontario, Sec. 20, Paper 4, pp. 1-14. [11] Dincer, I. and Rosen, M. A. (2007). Exergy: Energy, Environment and Sustainable Development, Elsevier, Oxford, UK. [12] Edgerton, R. H. (1982). Available Energy and Environmental Economics, D.C. Heath, Toronto. [13] European Parliament (2004). Directive 2004/8/EC of the European Parliament and of the Council of 11 February 2004 on the Promotion of Cogeneration Based on a Useful Heat Demand in the Internal Energy Market and Amending Directive 92/42/EEC, Official Journal of the European Union (L52) 47, 50-60. [14] FVB/Eltec (1993). Potential Heat Production from Existing and Future Electric Generating Plants, Report No. 9102-G-806 for Canadian Electrical Association.

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[15] Gunnewiek, L. H. and Rosen, M. A. (1998). Relation between the exergy of waste emissions and measures of environmental impact. Int. J. Environment and Pollution 10, 261-272. [16] Harrell, G. (2002). Steam System Survey Guide, Report ORNL/TM-2001/263 for Oak Ridge National Laboratory. [17] Hart, D. R. and Rosen, M. A. (1994). Environmental and health benefits of utility-based cogeneration in Ontario, Canada. Energy and Environment 5, 363-378. [18] Henneforth, J. C. and Todd, D. M. (1988, August). Cogeneration on a large scale. Mechanical Engineering 110, 51-53. [19] Hevert, H. W. and Hevert, S. C. (1980). Second law analysis: an alternative indicator of system efficiency, Energy-The International Journal 5, 865-873. [20] Horlock, J. H. (1987). Some practical CHP schemes. Chapter 5 of Cogeneration: Combined Heat and Power (CHP), Pergamon, Oxford, England, pp. 158-174. [21] Klein, M. (1999a). The need for standards to promote low emission, high efficiency gas turbine facilities. Paper 99-GT-405, presented at the ASME International Gas Turbine and Aeroengine Congress and Exhibition, Indianapolis, Indiana, 7-10 June, pp. 1-10. [22] Klein, M. (1999b). Full fuel cycle emissions estimations. Paper 99-IAGT-402, presented at the 13th Symposium on Industrial Applications of Gas Turbines, Banff, Alberta, 13-15 October, pp. 1-22. [23] Klein, M. (1999c, estimated). Brief Descriptions of Commercial and Institutional CHP Systems in Canada. Report by Oil, Gas and Energy Branch, Environment Canada. [24] Klein, M. (1999d). Canadian Gas Turbine Cogeneration Plants. Report by Oil, Gas and Energy Branch, Environment Canada. [25] Klein, M. (2001a). High efficiency combined heat and power facilities: benefits and barriers. Proceedings of the Canadian Technology Development Conference, Toronto, pp. 1-20. [26] Klein, M. (2001b). Cogeneration and district energy in Canada, Cogeneration and OnSite Power Production, March-April, Vol. 2, No. 2. [27] Kotas, T.J. (1995). The Exergy Method of Thermal Plant Analysis, reprint edition, Krieger, Malabar, Florida. [28] MacLaren Engineers Inc. (1988). Cogeneration Sourcebook, Report for Ontario Ministry of Energy, Toronto. [29] MacRae, K.M. (1992). Realizing the Benefits of Community Integrated Energy Systems, Canadian Energy Research Institute, Calgary, Alberta. [30] Moran, M.J. (1989). Availability Analysis: A Guide to Efficient Energy Use, Revised Ed., ASME, New York. [31] Moran, M.J. and Sciubba, E. (1994). Exergy analysis: principles and practice. J. Engineering for Gas Turbines and Power 116, 285-290. [32] Moran, M.J. and Shapiro, H.N. (2007). Fundamentals of Engineering Thermodynamics, 6th ed., Wiley, New York. [33] Phylipsen, G.J.M., Blok, K. and Worrell, E. (1998). Handbook on International Comparisons of Energy Efficiency in the Manufacturing Industry. Department of Science, Technology and Society, Utrecht University, the Netherlands. [34] Rogner, H.-H. (1993). Clean energy services without pain: district energy systems. Energy Studies Review 5, 114-120.

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[35] Rosen, M.A. (1990). Comparison based on energy and exergy analyses of the potential cogeneration efficiencies for fuel cells and other electricity generation devices. Int. J. Hydrogen Energy 15, 267-274. [36] Rosen, M.A. (1992). Evaluation of energy utilization efficiency in Canada using energy and exergy analyses. Energy-The International Journal 17, 339-350. [37] Rosen, M.A. (1993). Energy utilization efficiency in a macrosystem (Ontario): evaluation and improvement through cogeneration. Proc. Int. Symp. CO2 Fixation and Efficient Utilization of Energy, 29 Nov.-1 Dec., Tokyo, pp. 17-26. [38] Rosen, M.A. (1994). Assessment of various scenarios for utility-based cogeneration in Ontario. Energy-The International Journal 19, 1143-1149. [39] Rosen, M.A. (1996). Thermodynamic investigation and comparison of selected production processes for hydrogen and hydrogen-derived fuels. Energy-The International Journal 21, 1079-1094. [40] Rosen, M.A. (1998). Reductions in energy use and environmental emissions achievable with utility-based cogeneration: simplified illustrations for Ontario. Applied Energy 61, 163-174. [41] Rosen, M.A. (1999). Second law analysis: approaches and implications. Int. J. Energy Research 23, 415-429. [42] Rosen, M.A. (2000). Thermodynamic comparison of coal-fired and nuclear electrical generating stations. Trans. Can. Soc. Mech. Eng. 24 (1B), 273-283. [43] Rosen, M.A. and Dincer, I. (1997). On exergy and environmental impact. Int. J. Energy Research 21, 643-654. [44] Rosen, M.A. and Dincer, I. (1999). Exergy analysis of waste emissions. Int. J. Energy Research 23, 1153-1163. [45] Rosen, M.A. and Dincer, I. (2001). Exergy as the confluence of energy, environment and sustainable development. Exergy, An Int. J. 1, 3-13. [46] Rosen, M.A. and Dincer, I. (2002). Energy and exergy analyses of thermal energy storage systems. Chapter 10 of Thermal Energy Storage Systems and Applications, Wiley, London, pp. 411-510. [47] Rosen, M.A. and Horazak, D.A. (1995). Energy and exergy analyses of PFBC power plants. Chapter 11 of Pressurized Fluidized Bed Combustion, ed. M. Alvarez Cuenca and E.J. Anthony, Chapman and Hall, London, England, pp. 419-448. [48] Rosen, M.A. and Le, M. (1994). Assessment of the potential cumulative benefits of applying utility-based cogeneration in Ontario. Energy Studies Review 6, 154-163. [49] Rosen, M.A. and Le, M. (1996). Efficiency analysis of a process design integrating cogeneration and district energy. Proc. ASME Advanced Energy Systems Division, AESVol. 36, ed. A.B. Duncan, J. Fiszdon, D. O‘Neal and K. Den Braven, ASME, New York, pp. 473-480. [50] Rosen, M.A., Le, M.N. and Dimitriu, J. (1997). Efficiency improvement for large energy systems through utility-based cogeneration and district energy: investigation for Ontario, Canada. World Energy System: An International Journal 1, 56-63. [51] Sato, N. (2005). Chemical Energy and Exergy: An Introduction to Chemical Thermodynamics for Engineers, Elsevier, Oxford, UK. [52] Sciubba, E. (1999). Exergy as a direct measure of environmental impact. Proc. ASME Advanced Energy Systems Division, ed. S.M. Aceves et al., AES-Vol. 39, 573-581.

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[53] Sciubba, E. (2004). From engineering economics to extended exergy accounting: A possible path from monetary to resource-based costing. Journal of Industrial Ecology 8(4), 19-40. [54] Sherwood, M. and Rosen, M.A. (1996). Efficiency comparison of several alternatives for cogeneration-based district heating and cooling. Proc. Symp. on Thermal and Fluids Engineering at the CSME Forum, 7-9 May, Hamilton, Ontario, pp. 293-298. [55] Simpson, J. and Rosen, M.A. (1996). District energy and cogeneration systems: modelling, simulation and analysis. Proc. Symp. on Thermal and Fluids Engineering at the CSME Forum, 7-9 May, Hamilton, Ontario, pp. 275-282. [56] Strickland, C. and Nyboer, J. (2002a). Cogeneration Potential in Canada: Phase 2. Report for Natural Resources Canada, by MK Jaccard and Associates. [57] Strickland, C. and Nyboer, J. (2002b). A Review of Existing Cogeneration Facilities in Canada. Report by Canadian Industrial Energy End-Use Data and Analysis Center, Simon Fraser University. [58] Szargut, J. (1980). International progress in second law analysis, Energy 5, 709-718. [59] Szargut, J. (2005). Exergy Method: Technical and Ecological Applications, WIT Press, Southampton, UK. [60] Szargut, J., Morris, D.R. and Steward, F.R. (1988). Exergy Analysis of Thermal, Chemical and Metallurgical Processes, Hemisphere, New York. [61] Upton, B. (2001). Letter of 16 November 2001 to P. Bhatia, World Resources Institute, Washington, DC, from B. Upton, Senior Research Engineer, National Council for Air and Stream Improvement, Inc., West Coast Regional Center, Corvallis, OR. [62] Verbruggen, A. (2007a). What's needed next to refine the EU directive on cogeneration regulation. The Electricity Journal 20(2), 63-70. [63] Verbruggen, A. (2007b). Quantifying combined heat and power (CHP) activity, Int. J. Energy Tech. and Policy 5(1), 17-35. [64] Wiggin, M. (1997, estimated). Cornwall, Ontario District Heating System. Information sheet, CANMET Energy Technology Centre, Natural Resources Canada.

In: Advances in Energy Research. Volume 4 Editor: Morena J. Acosta, pp. 87-95

ISBN: 978-1-61761-672-3 © 2011 Nova Science Publishers, Inc.

Chapter 4

A NEW TECHNIQUE FOR BIOLOGICAL MONITORING USING WILDLIFE Mariko Mochizuki1, Makoto Mori2, Mutsumi Miura1, Ryo Hondo3, Takashi Ogawa1 and Fukiko Ueda3, 1

Department of Applied Science, School of Veterinary Nursing and Technology, Nippon Veterinary and Life Science University, 1-7-1 Kyounan, Musashino, Tokyo 180-8602, Japan 2 Department of Applied Biological Chemistry, Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan 3 Laboratory of Veterinary Public Health, School of Veterinary Medicine, Nippon Veterinary and Life Science University, 1-7-1 Kyounan, Musashino, Tokyo 180-8602, Japan

ABSTRACT Since data obtained from wildlife are useful for the evaluation of risks to human health, importance of biological monitoring has been pointed out in many studies. However, it is fact that there are many problems on the biological monitoring using wildlife. For example, the outliers were often observed on the data obtained from wildlife. Although the outliers could be excluded by statistical data processing in studies of experimental animals, the outliers may indicate potential contamination of animals in studies of wildlife. In the present study, 80 wild ducks were investigated, and the cadmium (Cd) contents of kidney and that of liver were ND-67.44, ND-21.15 μg/g dry weight respectively. Since the outlier has been observed in several species, such as spotbilled duck, mallard, we analyzed those outliers using cadmium standard regression line (CSRL). In our previous reports, the CSRL was suggested as a useful index for the understanding of Cd contamination of animals. In conclusion, it was suggested that biological monitoring using the CSRL can make full use of the characteristics of all data, including outliers.

Keywords: Biological Monitoring, Cadmium, ICP-AES, Wildlife. 

E-mail address: [email protected], Tel:+81-422-31-4151, Fax: +81-422-33-2094. (Corresponding author)

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INTRODUCTION Data obtained from wildlife are useful for the evaluation of risks to human health, including those to the next generation. In this study, various environmental pollutants were investigated using samples from wildlife. We have previously investigated the degree of contamination with toxic elements, such as cadmium (Cd), chromium (Cr), molybdenum (Mo), thallium (Tl) and vanadium (V), in kidney and liver samples obtained from wild birds (Mochizuki et al., 1998, Mochizuki et al., 1999, Mochizuki et al., 2002 a,b, Mochizuki et al., 2005, Ueda et al., 1998). Studies that use wildlife are prone to problems (Krimsky, 2000), although the importance of biological monitoring has been pointed out in many studies that have investigated environmental pollution (Colborn et al.,1996). It is known that the levels of several elements, such as Cd, nickel (Ni) and selenium (Se), increase and decrease depending on the age of the animal (Elinder et al.,1981, Sakurai,1996), and detailed analysis of the age of wildlife specimens is usually difficult. Another problem is that it is difficult to draw inferences from the degree of pollution of the habitat of wildlife such as migratory birds. Table 1. The wild birds used in the present study. The sample from wood duck was obtained from a zoo in Japan.

Carnivores are frequently used for studies involving wildlife because they are positioned at the top of the food chain. The greater scaup (Aythya marila) is generally classified as a carnivorous bird that eats animals such as shellfish. However, it is known that this bird also uses feed of vegetable origin, depending on the environments in which it rests during migration. The often narrow classification of feeding habits is a daunting problem for studies

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of wildlife. To obtain a detailed understanding of the actual migratory flight path and the degree of contamination of the environment inhabited by migratory species is even more difficult. Thus, the data obtained from wildlife are usually distributed over a wide range, and outliers are often obtained. We investigated these problems using the data obtained in our studies of wildlife, and suggest the possibility of a new index for biological monitoring. This study is the same in content in our previous study in a book published by NOVA Science Puclisher (Mochizuki et al.,2009).

MATERIALS AND METHODS Samples from Wild Birds A total of 80 wild ducks were used in the present study (Table 1). Most of ducks were captured between 1993 and 1995, for another National legal investigative project being conducted by the Japanese Ministry of the Environment. Other birds were supplied through the Gyotoku Bird Observatory. The samples were put into Pyrex tubes, dried, weighted, and digested. Cd contents of organs were analyzed using an inductively coupled plasma emission spectrometry (ICP-AES, Spectro A.I./Germany). The condition of instrument, sample preparation methods were also described in our previous reports in detail (Mochizuki et al., 1999, 2002 a,b,c, 2005 ).

Statistic Analysis The data were analyzed using Lotus 2001(Lotus Development Corporation), Excel 2003 (Microsoft Corporation) and JMP 7 (SAS Institute Japan). Table 2. The Cd content (μg/g dry wt.) in kidney and liver of 80 wild birds. ND: not detectable data, a); ND was replaced by zero, b); the results except ND.

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RESULTS AND DISCUSSION The Cd content of kidney and liver samples was not detection (ND)-67.44 and ND-21.15 μg/g dry weight (wt.), respectively. The data obtained from dabbling and diving ducks had a wide distribution (Table 2). Previous studies of biological monitoring using wildlife have performed comparisons using mean values, and the comparisons have been made among different species. It is difficult to compare data from different species because the distributions of the data varied greatly with species, as shown in Figure 1 a, b. The data obtained in our studies show that biological monitoring using the comparison only of mean values is not appropriate.

(a)

(b) Figure 1. The distribution of Cd content (μg/g dry wt.) in kidney (Figure 1 –a ) and liver (Figure 1-b) of each species. Dabbling ducks, a: Spot-billed duck, b: Eurasian wigeon, c: Northern pintail, d: Mallard, e: Common teal, f: Northern shoveler, g: Gadwall, h: Gargancy, i: Wood duck, Diving ducks, j: Greater scaup, k: Tufted duck, l: Common pochard, m: Common scoter, □: Mean value, ●: Median.

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The presence of outliers was observed in our study, as shown in Table 2. The outliers may be excluded by statistical data processing in studies that use experimental animals. However, outliers should be not excluded in studies of wildlife because the outliers may indicate potential contamination of the animals. Various problems in the measurement of Cd contamination of animal specimens have been solved using a new index, the cadmium standard regression line (CSRL) (Mochizuki et al., 2008, 2009a, b). The degree of oil contamination in diving ducks was analyzed using CSRL in a previous study (Mochizuki et al., 2009b). In that study, acute Cd poisoning of oiled diving ducks was clearly demonstrated, although the degree of Cd contamination was not made clear by the comparison of mean vales.

Figure 2. The relationship between the Cd contents of kidney and liver based on 101 data points from different wildlife (original data, Mochizuki et al., 2008 a). Filled triangles: land birds and waterfowl; empty triangles: seabirds; empty squares: terrestrial mammals; filled squares: marine mammals.

The 101 points obtained from previous publications that reported Cd levels in wildlife as arithmetic means were plotted on a graph and a straight line (the CSRL) was obtained, as shown in Figure 2. After logarithmic transformation, the parameters of the CSRL were: log10(Y)=0.900 log10(X)-0.580, R2 =0.944, p