Cogeneration and Polygeneration Systems 0128172495, 9780128172490

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Cogeneration and Polygeneration Systems

Cogeneration and Polygeneration Systems

Majid Amidpour Professor of Mechanical Engineering, Department of Energy System Engineering, K.N. Toosi University of Technology, Tehran, Iran

Mohammad Hasan Khoshgoftar Manesh Assistant Professor of Mechanical Engineering, Division of Thermal Sciences & Energy Systems, Department of Mechanical Engineering, University of Qom, Qom, Iran

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-817249-0 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Candice Janco Acquisitions Editor: Graham Nisbet Editorial Project Manager: Andrae Akeh Production Project Manager: Anitha Sivaraj Cover Designer: Greg Harris Typeset by MPS Limited, Chennai, India

Dedication Majid Amidpour dedicated to his wife’s memorial Mohammad Hasan Khoshgoftar Manesh dedicated to his father’s memorial

Contents Dedication..................................................................................................................v Preface .....................................................................................................................xv Acknowledgments ..................................................................................................xxi

CHAPTER 1 Cogeneration and polygeneration ................................ 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9

Introduction ....................................................................................1 Fundamental of cogeneration.........................................................2 Analysis of combined heat and power system ..............................3 Trigeneration ..................................................................................5 Comparison of combined cooling, heating, and power and stand-alone system ..................................................................7 History of cogeneration..................................................................9 Importance of deployment .............................................................9 Polygeneration..............................................................................10 Conclusion ....................................................................................10 References.................................................................................... 12

CHAPTER 2 Main components of cogeneration and polygeneration systems .............................................. 13 2.1 2.2 2.3 2.4 2.5 2.6 2.7

Introduction ..................................................................................13 Steam turbines ..............................................................................14 Gas turbine ...................................................................................15 Combined cycle-based cogeneration plants.................................19 Internal combustion engine..........................................................21 Stirling engines.............................................................................24 Fuel cell ........................................................................................26 References.................................................................................... 26

CHAPTER 3 Applications of cogeneration and polygeneration.......29 3.1 Introduction ..................................................................................29 3.2 Main application...........................................................................30 3.2.1 Industrial............................................................................ 31 3.2.2 Commercial ....................................................................... 32 3.2.3 Institutional ....................................................................... 32 3.3 Prospects for cogeneration in Europe ..........................................32 3.3.1 Fiona Riddoch, COGEN Europe, Belgium ...................... 33 3.3.2 Germany—aiming to double cogeneration by 2020 ........ 33 3.3.3 Spain—Upbeat for combined heat and power ................. 34 3.3.4 Austria—The Green Approach......................................... 35

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3.4 3.5 3.6 3.7

Japan .............................................................................................35 China.............................................................................................36 The United States .........................................................................36 Other countries .............................................................................36 References.................................................................................... 36

CHAPTER 4 Thermodynamic modeling and simulation of cogeneration and polygeneration systems ................ 39 4.1 Introduction ..................................................................................39 4.1.1 The first law of thermodynamics...................................... 40 4.1.2 The second law of thermodynamics................................. 40 4.2 Modeling of CGAM cogeneration plant......................................40 4.3 Thermodynamic modeling of a combined cooling, heating, and power system...........................................................41 4.4 Thermodynamic modeling of a polygeneration system ..............46 4.5 Thermodynamic modeling of a hybrid solar - geothermal cogeneration plant ........................................................................48 References.................................................................................... 54

CHAPTER 5 Exergy and thermoeconomic evaluation of cogeneration and polygeneration systems ................ 55 5.1 Introduction ..................................................................................55 5.2 Definition of exergy .....................................................................56 5.2.1 Dead state.......................................................................... 56 5.2.2 Dead state limited ............................................................. 56 5.2.3 Definition of the environment from the perspective of exergy analysis.................................................................. 57 5.2.4 Exergy ............................................................................... 57 5.2.5 Thermoeconomic............................................................... 57 5.3 Exergy and thermoeconomic modeling .......................................58 5.3.1 Physical exergy ................................................................. 58 5.3.2 Chemical exergy ............................................................... 59 5.3.3 Exergy destruction ............................................................ 60 5.3.4 Exergoeconomic modeling ............................................... 61 5.3.5 Exergy destruction level and exergy cost destruction level concept ..................................................................... 63 5.4 Case studies ..................................................................................65 5.4.1 Exergy and exergoeconomic modeling of CGAM cogeneration plant ............................................................. 65 5.4.2 Exergy and exergoeconomic modeling of a CCHP ......... 71

Contents

5.4.3 Exergy and exergoeconomic modeling of a polygeneration system ...................................................... 71 References.................................................................................... 73

CHAPTER 6 Advanced exergetic evaluation of cogeneration and polygeneration systems .............................................. 75 6.1 Introduction ..................................................................................75 6.2 Advanced exergy-based variables................................................76 6.2.1 Endogenous/exogenous..................................................... 76 6.2.2 Avoidable/unavoidable ..................................................... 76 6.3 Methodology for splitting the variables.......................................77 6.3.1 Unavoidable and avoidable parts...................................... 78 6.3.2 Endogenous and exogenous parts..................................... 79 6.4 Advanced exergy destruction Level representation ....................83 6.5 Application of advanced exergy-based analysis..........................84 6.5.1 CGAM problem ................................................................ 84 6.5.2 Liquefied natural gas cogeneration .................................. 88 References.................................................................................... 93

CHAPTER 7 Total Site integration and cogeneration systems...... 95 7.1 7.2 7.3 7.4 7.5

Introduction ..................................................................................95 Total Site integration....................................................................96 Total Site profiles.........................................................................96 Total Site procedure .....................................................................98 Case studies ................................................................................102 7.5.1 Case 1. A conventional Total Site analysis.................... 102 7.5.2 Case 2. Integration of site utility and thermal power plant................................................................................. 106 References.................................................................................. 113

CHAPTER 8 Desalinated water production in cogeneration and polygeneration systems ............................................ 115 8.1 Introduction ................................................................................115 8.2 Main desalination technologies..................................................116 8.2.1 Multistage flash distillation desalination........................ 116 8.2.2 Multiple-effect distillation desalination.......................... 119 8.2.3 Reverse osmosis desalination ......................................... 122 8.3 Integration with thermal power plants.......................................124 8.4 Integration with of gas turbines .................................................127 8.5 Integration with site utility industrial plants .............................129 References.................................................................................. 135

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CHAPTER 9 Cogeneration and polygeneration targets................ 137 9.1 Introduction ................................................................................137 9.2 Cogeneration issues....................................................................139 9.3 Significant models......................................................................140 9.3.1 Exergetic model ............................................................ 140 9.3.2 T H model ................................................................... 142 9.3.3 Turbine hardware model............................................... 143 9.3.4 Harell method................................................................ 144 9.3.5 Sorin and Hammache method....................................... 145 9.3.6 Medina-Flores and Pico´n-Nu´n˜ez model ....................... 146 9.3.7 Bandyopadhyay model.................................................. 147 9.3.8 Iterative bottom-to-top model....................................... 148 9.3.9 Kapil model................................................................... 149 9.3.10 Actual steam level temperature model ......................... 150 9.3.11 Automated targeting method ........................................ 151 9.3.12 Ren et al. model ............................................................ 151 9.3.13 Other models ................................................................. 154 9.3.14 Software ........................................................................ 155 9.4 Comparison of different methods ..............................................155 9.5 Case study ..................................................................................155 9.6 Conclusion ..................................................................................159 References.................................................................................. 160

CHAPTER 10 R-curve tool............................................................... 163 10.1 Introduction ................................................................................163 10.2 Notation of R-curve ...................................................................165 10.3 R-curve tool................................................................................167 10.3.1 Ideal R-curve or grassroots R-curve............................. 167 10.3.2 Actual R-curve .............................................................. 168 10.4 Developing the extended R-curves ............................................170 10.4.1 Cogeneration targeting.................................................. 170 10.4.2 R-ratio against ED, CD, and BD .................................... 172 10.4.3 Advanced representation of Exergy Destruction Level.............................................................................. 172 10.4.4 The algorithm proposed for advanced analyses ........... 172 10.5 Extended R-curve using in liquefied natural gas cogeneration ...............................................................................174 10.6 Integrating the desalination systems with the help of R-curve .......................................................................................182 10.6.1 Reverse osmosis desalination ....................................... 182 10.6.2 Multieffect distillation desalination system ................. 185

Contents

10.6.3 Integration effect on cogeneration efficiency factor .... 185 10.6.4 Case studies................................................................... 187 References.................................................................................. 193

CHAPTER 11 Environmental impacts consideration...................... 195 11.1 Introduction ................................................................................195 11.2 Life cycle assessment.................................................................196 11.2.1 Stages of life cycle assessment framework .................. 197 11.2.2 Applications of life cycle assessment........................... 197 11.2.3 Benefits of life cycle assessment.................................. 198 11.2.4 Design a life cycle assessment project ......................... 198 11.2.5 Real planning and process management ...................... 198 11.2.6 How is life cycle assessment done? ............................. 199 11.3 Eco-indicator 99 .........................................................................199 11.4 Exergoenvironmental analysis ...................................................201 11.5 Estimation of greenhouse gas emissions ...................................202 11.6 Footprint .....................................................................................203 11.6.1 Carbon footprint............................................................ 203 11.6.2 Emission footprint......................................................... 203 11.6.3 Energy footprint ............................................................ 203 11.6.4 Water footprint.............................................................. 203 11.7 Environmental targeting.............................................................203 11.8 Case studies ................................................................................204 11.8.1 Case 1 ............................................................................ 204 11.8.2 Case 2 ............................................................................ 207 References.................................................................................. 213

CHAPTER 12 Combined heating, cooling, hydrogen, and power production ................................................................. 215 12.1 Introduction ................................................................................215 12.2 System description .....................................................................219 12.3 Modeling and analysis ...............................................................222 12.3.1 Assumptions .................................................................. 222 12.3.2 Modeling and analysis .................................................. 222 12.4 Validation of model ...................................................................231 12.4.1 Performance evaluation ................................................ 232 References.................................................................................. 234

CHAPTER 13 Modern polygeneration systems .............................. 237 13.1 Introduction ................................................................................237 13.1.1 Fuel cell......................................................................... 238 13.1.2 Solar energy .................................................................. 238

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13.2 Fuell cell integration ..................................................................239 13.2.1 Fuel cell 1 thermoelectric generator............................. 239 13.2.2 Fuel cell 1 heat pump/refrigeration .............................. 245 13.3 Fuel cell 1 absorption chillers ...................................................246 13.3.1 Fuel cell—desalination systems ................................... 247 13.3.2 Microbial cell integration ............................................. 248 13.4 Solar energy................................................................................250 13.4.1 General overview .......................................................... 250 13.4.2 Polygeneration with solar energy ................................. 254 13.4.3 Photovoltaic/thermal/CPVT collector driven systems .......................................................................... 264 13.5 Hybrid solar polygeneration systems.........................................267 13.5.1 Integrated solar biomass-driven devices..................... 267 13.5.2 Hybrid solar geothermal ............................................. 271 13.5.3 Hybrid photovoltaic/thermal ocean ............................ 277 13.5.4 Hybrid solar power tower wind turbines.................... 277 13.5.5 Hybrid solar wind/ocean ............................................. 277 13.5.6 Other hybrid models ..................................................... 282 References.................................................................................. 282

CHAPTER 14 Optimization of cogeneration and polygeneration systems...................................................................... 287 14.1 Introduction ................................................................................287 14.2 Optimization problem ................................................................288 14.2.1 System boundaries ........................................................ 288 14.2.2 Objective functions and system criteria ....................... 289 14.2.3 Decision variables ......................................................... 289 14.2.4 Constraints..................................................................... 289 14.3 Optimization techniques.............................................................290 14.3.1 Classical optimization................................................... 290 14.3.2 Numerical optimization techniques .............................. 291 14.3.3 Metaheuristic optimization techniques ......................... 291 14.4 Multiobjective optimization .......................................................291 14.5 Case studies ................................................................................295 14.5.1 Case 1: Solar hybrid cogeneration plant ...................... 295 14.5.2 Case 2: Optimal design of utility systems using targeting strategy........................................................... 304 14.5.3 Grassroots case study.................................................... 306 14.5.4 Optimization results ...................................................... 306

Contents

14.5.5 Case 3: Optimal design of thermoelectric generatorparabolic trough collector-driven polygeneration system............................................................................ 310 14.5.6 Case 4: Biomass solar-driven polygeneration system............................................................................ 316 References.................................................................................. 322

CHAPTER 15 Reliability and availability of cogeneration and polygeneration systems ............................................ 325 15.1 15.2 15.3 15.4

Introduction ................................................................................325 Definitions ..................................................................................327 Reliability modeling of utility system .......................................327 Case studies ................................................................................330 15.4.1 Case 1 ............................................................................ 330 15.4.2 Case 2 ............................................................................ 336 References.................................................................................. 341

CHAPTER 16 Software tools ........................................................... 345 16.1 Introduction ................................................................................345 16.1.1 Power plants .................................................................. 346 16.1.2 Process industries .......................................................... 352 16.1.3 Renewable energy ......................................................... 359 16.1.4 Computer code .............................................................. 361 References.................................................................................. 362 Appendix A............................................................................................................365 Index ......................................................................................................................373

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Preface Cogeneration and polygeneration systems, due to efficient fuel utilization and the generation of several products, are one of the promising and best methods in view of performance, economic, and environmental impacts for the optimal design of energy systems. This book explores the basics, classification, modeling, several analyses and evaluation tools, optimization, and modern polygeneration systems for the audience of mechanical, chemical, process, energy, environmental, and water engineers and researchers. It is targeted at improving operational efficiency through new approaches involving utility system and process integration. New concepts of integrated approaches in the utility system and process analysis as a cogeneration system have developed in the last decade. Researchers should improve such analysis and engineers should understand and practice them for better conceptual design and optimization of energy systems. This book recognizes and discusses issues for both groups ahead of design or in the retrofit of energy systems. This book is especially highlighted in the field of analysis of cogeneration and polygeneration systems in the following topics:

• • • • • • • • •

Thermodynamic modeling and simulation Advanced exergetic analysis Cogeneration and process industry (total site integration) Cogeneration targeting Environmental impacts consideration R-curve tool Reliability and availability analysis Software Renewable and fossil fuel sources and hybrid fuel for polygeneration systems Topics covered include the following chapters: 1. Cogeneration and Polygeneration This chapter is related to introduce the cogeneration system and key elements. The current status of the polygeneration system in the world has been considered with related statistics. 2. Main Components of Cogeneration and Polygeneration System This chapter investigates the main components of cogeneration and polygeneration systems. It refers to prime mover as major equipment in such a system. The most important component of a combined heat and power or combined cooling, heating, and power synchronous production system is the prime mover. This chapter explains the most important types of prime movers.

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3. Applications of Cogeneration and Polygeneration This chapter explains the main applications of cogeneration and polygeneration systems in three categories. In this regard, industrial, commercial, and institutional applications of such systems are investigated. 4. Thermodynamic Modeling and Simulation of Cogeneration and Polygeneration Systems This chapter includes thermodynamic modeling of the different components that are applied in the cogeneration and polygeneration systems. In this regard, four different case studies including most of the components of cogeneration and polygeneration systems are investigated. 5. Exergy and Thermoeconomic Evaluation of Cogeneration and Polygeneration Systems Exergy has become an important tool for the design and evaluation of energy systems. It is also important because of its relevance to the economic aspects. Thermoeconomics uses a combination of the exergy and economic analysis to provide a better evaluation of the design and analysis of a costeffective system. This chapter includes definitions, modeling, relations, and analysis of the exergy and exergoeconomic concepts for cogeneration and polygeneration systems. 6. Advanced Exergetic Evaluation of Cogeneration and Polygeneration Systems This chapter deals with advanced exergy-based modeling and analyses. Such an advanced analysis for exergoeconomic and exergoenvironmental evaluates the interactions among components of the overall system, and the real potential for improving system equipment. Splitting the exergy destruction associated with every single equipment of an energy conversion system into endogenous/exogenous and avoidable/unavoidable parts has been considered. This chapter consists of the definition of advanced exergy-based variables, methodology for splitting variables, advanced graphical representations, and two cogeneration case studies as numerical examples. 7. Total Site Integration and Cogeneration Systems One of the vital uses of cogeneration and polygeneration systems is the chemical and process industries. This chapter provides a general view of basic Total Site analysis that consists of information extraction methods, the formation of Total Site Profiles, Total Site Composite Curves, and the Site Utility Grand Composite Curve. Two case studies are investigated by Total Site Analysis. 8. Desalinated Water Production in Cogeneration and Polygeneration System In order to produce freshwater, one of the best solutions is to produce freshwater in power generation units or power and steam production units, which use low heat to produce freshwater. This chapter shows the state of the art of cogeneration for power and desalinated water production. Different main technologies for producing desalinated water are introduced, and main parameters and equations for modeling of them are investigated. Then,

Preface

9.

10.

11.

12.

integration of desalination processes with stand-alone power generation, gas turbine, and site utility of chemical complex are evaluated, respectively. Cogeneration and Polygeneration Targets Estimation of cogeneration potential before the design of the central utility system for site utility systems is vital to set targets on fuel demand as well as heat and power production. Cogeneration models represent a model for targeting fuel consumption, steam and shaft work production and consumption, cooling demands, and emission prior to the design in the utility system. There are several design considerations for managing utility system operation. Nonetheless, cogeneration targeting is the base of optimization in these systems. There are numerous models for achieving this goal, using existing thermodynamic parameters. In this chapter, the leading works and their advantages and weaknesses are investigated. The main aim is to provide guidance for future research in the field of cogeneration targeting methods. R-Curve Tool A powerful method is introduced for the optimum design and evaluation of a cogeneration and polygeneration system, namely, “R-curve.” First, the R-curve concept and notations are defined. Using R-curve in grassroot and retrofit problem is considered. Then, extended R-Curve tool based on the accurate cogeneration targeting method and using advanced exergetic, exergoeconomic, and exergoenvironmental analysis approaches are introduced. The extended R-Curve for grassroot is investigated for optimal design of LNG cogeneration system. Also, the application of R-Curve for the integration of MED and RO desalination system with cogeneration site utility is discussed. Environmental Impacts Consideration Due to the importance of environmental issues, increasing the production of pollutants, and increasing energy consumption, considering environmental issues in the design and sterilization of simultaneous production systems, this chapter examines environmental analysis. Given the importance of life cycle analysis and environmental impacts, these analyses are first addressed. In this regard, Eco-indicator 99 as a powerful tool for environmental impact assessment is explained. Then, exergoenvironmental modeling based on life cycle assessment is described. It also looks at how greenhouses gases are estimated for cogeneration and polygeneration systems. The brief explanation of footprint analysis is considered. Then, the environment targeting method is introduced. For better understating of environmental consideration, two case studies are considered. Combined Heating, Cooling, Hydrogen, and Power Production The combined production of heating, cooling, hydrogen, and power generation are discussed. First, the main literature review and recent research are investigated and mentioned. Next, the most interesting case study that recently developed a polygeneration system for cooling, heating, power, and

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hydrogen generation is considered and discussed. This system is a geothermal-based polygeneration system including an organic Rankine cycle, ejector refrigeration cycle, an LNG power production system, and a proton exchange membrane electrolyzer system. Thermodynamic, exergy, economic, and exergoeconomic modeling of the considered multigenerational system are described. 13. Modern Polygeneration Systems This chapter investigates and discusses recently developed polygeneration systems. This system is categorized based on energy source driven systems. Due to the importance and high efficiency of fuel cells and its highly effective ability to combine with other equipment, the development of polygeneration systems related to fuel cell driven systems is recently considered to produce power, heat, cooling, freshwater, drying, and so on. Also, renewable energies, especially solar system and the development of relevant technologies and their use as an energy driven in polygeneration systems to reduce fuel consumption and pollutant production, have grown significantly in the recent years. Therefore in the field of modern polygeneration systems, two general classifications are provided based on the use of fuel cells as well as the application of solar systems and other renewables energies. In this regard, first, the different types of combinations of fuel cell systems that can be used as a driver for polygeneration systems are investigated. Then, modern polygeneration systems are studied and discussed based on solar-driven systems. In this regard, different solar collectors, hybridization methods, and combination with other renewable energies are investigated and reported. 14. Optimization of Cogeneration and Polygeneration Systems The optimal design of the cogeneration and polygeneration system is very important from the perspective of various objective functions, including minimizing annual costs, investment costs, pollutant production, environmental impacts, product prices, and maximizing efficiency. Optimal design (grassroots) and optimization of existing systems (retrofit) play a very important role in the cost of product production, overall system performance, environmental impacts, and pollution emissions. In this chapter, the optimization problem definition, requirements, and different optimization techniques are introduced. To better show the application of optimization in the optimal design of cogeneration and polygeneration system, two cogeneration systems and two polygeneration systems are considered as different case studies. 15. Reliability and Availability of Cogeneration and Polygeneration Systems In the design and performance analysis of cogeneration and polygeneration systems, reliability and availability issues are very vital and important. In repairable systems, the Markov method as an analytical technique uses state-space and considers all possible states. But in complex systems a lot of states are dropped from the state-space to capture a simpler

Preface

model of the system; therefore the probabilities of the states are often not accurate. In this chapter, a powerful and simple approach is discussed considering the accurate calculation of the state probabilities as well as the system simplification. To better show the applicability of the present procedure for reliability and availability calculation of cogeneration and polygeneration systems, two cogeneration site utilities are considered as case study. 16. Software Tools Special and professional simulation software for cogeneration and polygeneration systems helps us to have a better understanding of design, evaluation, sensitivity analysis, heat and material balances, sizing, rating, and detailed design of each component in the system. In this chapter, wellknown simulation software for use in modeling, simulation, and analysis of cogeneration and polygeneration systems is introduced. According to the capability and application of each, simulation software in this area is divided into four main divisions. Appendix Appendix includes the calculation of thermodynamic properties for several substances, seawater properties correlations, cost estimation functions, weight estimation functions, and Eco-indicator for some components. Majid Amidpour and Mohammad Hasan Khoshgoftar Manesh September 2020

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Acknowledgments Majid Amidpour warmly registers his appreciation to his professors, Robin Smith and Graham Polly. Mohammad Hasan Khoshgoftar Manesh thanks his father Mohammad and Mother Ashraf for all support in life and encouragement throughout his education and works. He would also like to thank his wife, Ala, for inspiration, love, and support throughout his works. He would also like to warmly register his appreciation to his professor Majid Amidpour for all support and encouragement. In particular, the collaboration of some graduate students is gratefully acknowledged, including Ala Ameryan from the Department of Civil Engineering, Ferdowsi University of Mashhad; Saeed Kabiri from the Department of Energy System Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology; and Seyed Alireza Mousavi Rabeti from the Department of Mechanical Engineering, University of Qom.

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Cogeneration and polygeneration

1

Chapter outline 1.1 Introduction ..................................................................................................... 1 1.2 Fundamental of cogeneration ............................................................................ 2 1.3 Analysis of combined heat and power system ..................................................... 3 1.4 Trigeneration ................................................................................................... 5 1.5 Comparison of combined cooling, heating, and power and stand-alone system ..... 7 1.6 History of cogeneration ..................................................................................... 9 1.7 Importance of deployment ................................................................................. 9 1.8 Polygeneration ............................................................................................... 10 1.9 Conclusion ..................................................................................................... 10 References ............................................................................................................ 12

1.1 Introduction Typical methods to meet the electrical and thermal requirements are the same as the separate production method of the latter, which means that the electricity from the local distribution network and heat generated by burning fuel in boilers or furnaces are produced. Now, if we want to reduce fuel consumption, we should use cogeneration as a combination of power and heat that is needed [1]. “Cogeneration means the production of two or more forms of energy using a primary energy source.” The two common forms of energy are thermal and mechanical, which shaft power energy usually uses to drive an electric generator; thus a more precise definition can be made. Cogeneration is the simultaneous production of electrical energy and useful thermal energy using a primary energy source. The mechanical energy produced is used to drive mechanical equipment such as compressors or generators and the thermal energy produced is used for heating and cooling. It is obtained by using absorption units that act with hot water, steam, or hot gases. Cogeneration began in the late 1880s in Europe and the United States. In the early 20th century, most industrial factories generated their electricity using coal-fired boilers and steam turbine. On the other hand, in many of these factories, hot steam output was used in industrial processes, so in the early 1900s, in the United States, about 58% of the total power generated at the power plants in place was in the form of cogeneration [1,2]. Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00001-X © 2021 Elsevier Inc. All rights reserved.

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When central power plants and reliable electricity networks were built, production and delivery costs were reduced, and as a result, many consumers, such as industrial plants, preferred to buy electricity from these networks and stop their production of electricity [1]. As a result, the use of combined heat and power (CHP) and polygeneration, accounting for 15% of the total US production capacity in 1950, fell to 5% in 1974. Other factors of reducing the use of simultaneous production were regulating electricity generation, a small share of electricity purchasing costs from the network in total current costs of factories, advancement of technologies such as power plant boilers, availability of liquid and gas fuels with the lowest price, and lack of environmental constraints [3].

1.2 Fundamental of cogeneration In a stand-alone power plant process, a large amount of heat is lost from various devices such as cooling systems or exhaust gases. Most of this heat can be recovered and can be used as process heat. So, it is obvious that the total efficiency of a power plant will increase from 80% to 90% by 30% to 50%. The principles and rules governing the production systems are simple. The efficiency of nonsynchronous systems is about 35%
65%, which has recently improved the efficiency of combined cycle power plants by up to 55% (expect the losses that occur for electricity transmission and distribution) [3,4]. Cogeneration reduces these losses by recycling the heat generated, which can be used in the industrial sector, economy, residential sector, or heating and cooling. Therefore cogeneration is the simultaneous generation of heat and power, which is also called CHP [5]. In a cogeneration plant the inlet thermal energy (Qin ) is used for the production of the requirement power (Wnet ) and process heat (Qp ). According to the first law of thermodynamic, we will have some heat losses (Qloss ) [5]. Qin 5 Qloss 1Wnet 1 Qp

(1.1)

The cogeneration units consist of four main components: primary stimulus, generator, heat exchanger, and control system. In the production of an accordion, an initial stimulus (engine or turbine) first uses the chemical energy of the fuel and converts it to the mechanical power of the output axis. Then, the drive axis is generated with a coupler generator and electric power. The sources of waste heat energy, including the heat generated by the exhaust gas from the primary stimulus and the oil used for lubrication, are identified, and by placing the appropriate heat exchangers, heat dissipation in the form of heat at high temperature (heat usable) is recycled. The unique characteristics of CHP systems are achieved by providing the possibility of extraction of waste heat in the process of generating electricity. The principle of a cogeneration plant has been shown in Fig. 1.1 [6].

1.3 Analysis of combined heat and power system

Qp Qin

Cogeneration Wnet

Qloss

FIGURE 1.1 A cogeneration system.

1.3 Analysis of combined heat and power system The fact is that large power plants are destroying a huge amount of energy. The lack of economic justification for recycling and transferring energy from largescale generators to distances dramatically reduces the energy efficiency of these power plants. The best efficiency is related to the thermal generation of the new generation cycle, which has a nominal efficiency of about 60% at the site of production. With the loss of 25% of energy passing through transmission and distribution networks, the efficiency of the place of consumption will not exceed 45%, which in practice is 35%
37%. While the efficiency of small-scale dispersal power plants uses CHP and combined cooling, heating, and power (CCHP) technology, it reaches an impressive 90% in place [7
9]. The efficiency of a conventional thermal power plant can be defined as the portion of the net output power to the inlet heat as follows: ηth 5

Wnet Qin

(1.2)

Another important parameter is the net heat rate that can be defined as the ratio of the inlet heat to the net power output: HR 5


Qin 3600 5 kJ=kWh hth Pnet

(1.3)

The inlet heat to the power plant is the heat that is extracted from the combustion of the fuel. This parameter can be calculated as Eq. (1.4). Qin 5 Qf ηb ðkJ=sÞ

where ηb shows the efficiency of the boiler.

(1.4)

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The electrical efficiency is the other important criterion that indicates the performance of the cogeneration plants. This parameter can be defined as follows: ηconv 5 ηel 5 ηb ηth ηg 5

Pel Qf

(1.5)

where ηb shows the efficiency of the boiler, ηth shows the thermal efficiency of the plant, ηg is the generator efficiency, Pel output power, and Qf shows the heat rate of the fuel. Fuel energy has released in the steam generator component in the plant. The fuel energy can be derived as follows: QF 5 mf LHVðkJ=sÞ

(1.6)

where mf shows the mass flow rate of the fuel and LHV indicates the low heat value of the fuel. These parameters are defined for investigating the performance of the thermal power plants. For the cogeneration plants, some of these relations are transformed and indicated the performance of a CHP plant. These parameters are defined later [8,9]. The energy conversion efficiency in a cogeneration plant is defined as the ratio of the output power to the fuel heat energy. This parameter can be named the electrical efficiency (ηpower Þ. ηpower 5

Wnet Qf

(1.7)

In gas turbine systems or reciprocating engines use fuels to produce power, and recovers exhaust heat. The useful thermal energy of heat usually uses in the form of hot water or steam [8,9]. In steam turbine systems, this process starts with steam generation in the boiler, then the steam generated to start the turbine and turn on the generator is used. A turbine heater can be used to generate heat energy. These types of systems can use various fuels to generate energy, such as natural gas, oil, biomass, and coal [8]. This system includes gas
diesel combustion engines as electric power generators and a recovery boiler for heat recovery to produce hot water or steam. The main advantage of this system is to achieve high-energy efficiency as well as the simultaneous generation of electricity and heat. For this advantage a performance parameter has been defined as the ratio of the output power and the useful heat to the heat rate of the fuel. This parameter is named energy utilization factor. ηCogen 5

ðWnet 1 QP Þ Qf

(1.8)

In the cogeneration plants that run with by the gas turbine and a diesel engine, Qf is a proper parameter to define the input energy to the plant. These plants are opencycle systems. However, in a close-cycle system as steam power plants, the output flue gas from the combustion of the fuel has been used for the production [9].

1.4 Trigeneration

1.4 Trigeneration One of the solutions for optimal consumption and reduction of electric power losses is to bring electricity production points closer to consumers to provide more stable electricity for subscribers, reduce downtime and losses, increase efficiency and economic efficiency, and protect the environment. CCHP, with the help of heat output from the turbine, in addition to generating electrical power, provides the required thermal and refrigeration loads for the project using a dry ice system. In these systems, using an absorber chiller and a vapor recovery generator to heat recover of turbine exhaust can provide the extra heating and cooling generation. Also, it reduces fuel consumption and emissions, minimizes energy consumption and cooling and heating production. It can also generate electricity in fewer hours [9
12]. Fig. 1.2 demonstrates a schematic of trigeneration plant. The CHP and CCHP principle is illustrated in Fig. 1.3. A very operating cycle of trigeneration systems is CCHP. The trigeneration system is a set that can simultaneously produce heating, cooling, and power. The most important feature of the CCHP collection is the use of wasted energy. Waste energy is available in three forms: gas exhaust, steam, and hot water in all industries. Waste heat is the electricity generation of diesel generators and gas turbines in the form of exhaust gas, as well as in diesel generators with a converter, hot water is also produced. Also, in some industries and power plants, there is also a lot of wasteful steam, all of which can be used as heat sources in absorption chillers [12]. In CCHP systems, power generators and absorption chillers are two main parts of the system, which are the two basic components of the division of the CCHP systems [12]. One of the most effective methods for increasing the power of gas and combustion plants during the hot hours of the year is the cooling of the turbine inlet air, which has brought favorable results around the world and within the country (limited). Since the temperature change of the environment is the most important parameter influencing the behavior of the turbines, and given that the increase in

Wnet Qin

Trigeneration

Qheating Qcooling

Eloss

FIGURE 1.2 A trigeneration plant energy balance schematics.

5

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CHAPTER 1 Cogeneration and polygeneration

Public grid

Primary energy

Prime mover

Heat recovery system

Power

Heat

Power demand

Heat demand

CHP

Refrigeration system

Cooling

Cooling demand

CCHP FIGURE 1.3 CHP and CCHP principle. CCHP, Combined cooling, heating, and power; CHP, combined heat and power.

the ambient temperature causes a loss of power and efficiency of gas units, this method can be used to prevent this sudden drop, to be effective [12]. At present, significant cooling power is generated by using these energies. A small amount of cold water produced by an absorption chiller to cool the air entering the turbine and a large portion of this refrigeration or cold-water production is used to cool office and residential space [12]. Therefore using a CCHP system with a suitable solution, the net power can be increased and the cooling and heating capacity can be provided for various uses. In power generation systems, combustion of fossil fuels takes place, and the recovery of waste heat from this fuel is an issue. These reclaimed energies are recovered in a variety of ways. One of the most effective methods for increasing the power of gas and combined cycle power plants during the hot hours of the year is the cooling of the turbine inlet air, which has brought favorable results around the world and within the country. Changing temperature of the environment is the most important parameter influencing the behavior of the turbines, and given the fact that the increase in the ambient temperature results in a sharp drop in the power and efficiency of gas units, this method can be used to prevent this sudden drop, to be effective. At present, using these energies, significant refrigeration capacity and a small amount of cold water produced by an absorption chiller are used by exchangers to cool the air entering the turbine, and a large part of refrigeration or cold-water production is used to cool the office space. Therefore using CCHP systems with a suitable solution, the power generation capacity of the power plant can be increased and the cooling and heating capacity

1.5 Comparison of combined cooling, heating

for different uses can be provided. The main advantages of CCHP systems are classified as follows:

• • • • • • • • •

use of waste energy to improve efficiency; generation of cooling, heating, and power from a single unit; preventing air pollution and emissions of greenhouse gases; reducing energy and thermal energy losses in equipment; useful and effective use of fuel and reduce its consumption; reduce current power costs and increase thermal efficiency; reduce network capacity constraints and improve network utilization from 30% to 60%; very simple installation, commissioning, maintenance, and maintenance; and reduce the space required for cooling and heating systems and power generation [13,14].

1.5 Comparison of combined cooling, heating, and power and stand-alone system The fact is that large power plants are destroying a huge amount of energy. The lack of economic justification for recovering and transferring energy from largescale generators to distances dramatically reduces the energy efficiency of these power plants. The best efficiency is related to thermal generators of the new generation hybrid cycle, which have a nominal efficiency of about 60% at the site of production. With an estimated 25% loss of energy passing through transmission and distribution networks, the efficiency of the place of consumption will not exceed 45%, which in practice is 35%
37%. While the efficiency of small-scale dispersed power plants uses CHP and CCHP technology, it reaches an impressive 90% in place. The schematic of CCHP plant is demonstrated in Fig. 1.4. The unique advantages of CCHP include the following:

• Significant increase in energy efficiency. • Increasing the reliability of electricity. • A good opportunity for private sector investment, given the favorable economic justification of CHP/CCHP plants.

• Fewer costs and much time to build and set up synchronous power plants compared to large power plants.

• Nonpayment of fuel costs for the production of heat and refrigeration. • Saving on initial investment and the lack of specific thermal and refrigerating equipment.

• Provides high-quality electrical energy. • The possibility of selling additional electricity to the global network. • Correction and adjustment of energy sale rates in line with the effective changes of cost components and independent of supportive, economic, and social policies of sovereignty.

7

CHAPTER 1 Cogeneration and polygeneration

9

Pump Dram

5

8p 8

CH4 6

7 Economizer

Evaporator

Air preheater

Combustion chamber

3 4

HRSG

Expander

Air compressor

8

2

1

10

14

12

Absorber chiller 13

11

FIGURE 1.4 CCHP plant. CCHP, Combined cooling, heating, and power.

• Significant reduction of environmental pollutants. • Existence of supportive and encouraging policies by the government from investors to build.

• Because synchronous production technology is commonly used in the form of distributed generation (DG), the special benefits of dispersed generation can also be added to the above. The CCHP system is a set that can simultaneously generate heating, cooling, and power. The most important feature of the CCHP collection is the use of wasted heat. Waste energy is available in three forms: exhaust, steam, and hot water in all power plants and industries. The wasted heat of electricity generates diesel engines and gas turbines in the form of exhaust gas, and in diesel generators, with a converter hot water is also produced. Also, in some industries and power plants, there is also a lot of wasteful waste that all of these reclaimed energy can be used as heat sources in absorption chillers [4,15
17]. In CCHP systems, as we draw a sample in Fig. 1.4, power generators and absorption chillers are two main parts of the system, which are the two most important parts of the CCHP system segmentation.

1.7 Importance of deployment

1.6 History of cogeneration Production began in Europe in the late 1880s. In the early 20th century, most industrial plants produced their electricity using coal-fired boilers and steam turbine generators. On the other hand, in many of these factories, hot steam was used in industrial processes, so that in the early 1900s, in the United States, about 58% of the total power generated at the power plants in the area was in the form of simultaneous production. When central power plants and reliable electricity networks were built, production and delivery costs were reduced, and as a result, many consumers, such as industrial plants, preferred to buy electricity from these networks and stop their production of electricity. As a result, the use of simultaneous production, accounting for 15% of the total US production capacity in 1950, fell to 5% in 1974. Other factors of reducing the use of simultaneous production were regulating electricity generation, a small share of electricity purchasing costs from the network in total current costs of factories, advancement of technologies such as power plant boilers, availability of liquid and gas fuels with the lowest price, and/or lack of environmental constraints [15,18
22].

1.7 Importance of deployment In 1973 once the massive increase in the price of fuel was followed by the emergence of the energy crisis in most countries of the world, the use of production was accompanied by a growing trend. Due to the reduction of fossil fuel resources and rising prices, these systems, which have more energy efficiency, were very much considered. Today, with remarkable advances in the technology of manufacturing, reciprocating gas engines, heat exchangers, and control systems, the simultaneous production approach is not only economically justifiable but is also an effective way of working energy management has become a country. Several factors have also led the countries of the world to take advantage of the synchronous production method, some of which are:

• increased consumption of electricity, especially growth in peak load; • increasing the price of energy carriers and paying close attention to improving energy efficiency;

• high losses in transmission and distribution networks, low quality of • •

electricity, high drop in frequency and extinguishers, and, in general, the low reliability of the global network in the supply of electricity; the heavy cost of rebuilding old installations; and granting tax deductions and incentive schemes for governments to build DG plants [20,22
24].

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CHAPTER 1 Cogeneration and polygeneration

1.8 Polygeneration Polygeneration allows a team of few to sacrifice clash vectors (cleverness, heating, and cooling) as well as every second gainful product (hydrogen, syngas, biodiesel, fertilizers, drinking water, etc.) by diversifying three or multiple vigor sources. Depths of polygeneration rules will be fueled by renewable sources (geothermal, solar, biomass, ambiance, hydro), as well as fossil fuels (natural gas, coal, hydrogen, etc.). In this formula, advanced encounter technologies, such as provoke cells and greedy ones, are phonied into a banknote, by excluding intention on the carryout strategies implemented for the proper management of polygeneration systems [17
24]. In this ambiance, polygeneration systems fake a lovable and innovative start. Such systems customize second other vitality vectors (power, heating, cooling) and other products (fresh water, syngas, urea, etc.). Their behavior is especially luring if magnified to renewable clash sources. In hard feelings of their capability the start of polygeneration is appeased scarcely second-hand. In authoritativeness, exclusively 10% of the soil power stage is copied by intermediation of polygeneration systems. Exceptions are the countries such as the Netherlands, Finland, and Denmark. In the neighboring countries, this ratio increases up to 30%
50%. Anon, distinguishable European Pact countries are helpful in the prepayment of sensible and talented systems based on polygeneration; exchange for it is recognized as a strategic technology able to reduce greenhouse gas emissions. The alike trademark was exclusive of adoption by the United States, course at stabbing almost the cut corners of energy entertainment, aloft in the industrial sector. Polygeneration plants are expressly and instantaneously fetching rich enough potable water, along with power, heating, and cooling. This is an entirely attractive surrogate, in chary for unfriendly and far-off communities polygeneration systems groundwork in addition to the grant to the diffusion of DG systems. This get-at-able of encode is below in the on touching general concept of put in an district energy systems (DES), which consists of the amalgamation of possibility bantam DG technologies, in place of a limited number of big, remote power plants (centralized production). In DES systems the excitement flies to pieces locally all over to the conclusive owner, and the detrimental end apropos to the transmission losses is avoided. Fig. 1.5 shows the schematic of polygeneration system.

1.9 Conclusion Simultaneous production can now be realized with the help of a wide variety of techniques and technologies. But the main idea is always the same: design and construction of a high-performance integrated system for the production of electrical energy along with the recycling of heat generated in the system. Recycling heat can be used in district heating or industrial processes.

1.9 Conclusion

Electricity

Heating

Cooling

Hot water

Steam

Energy resources

Polygeneration system

Desalinated water

Biodiesel Biogas

Hydrogen

Ethanol/methanol

CO 2

FIGURE 1.5 Polygeneration system.

The advanced technology of small-scale generators, along with the effective recycling of heat production, as well as the minimization of losses due to the transmission and distribution of electricity (because CHP/CCHP systems are generally installed at or near the place of consumption), the overall system efficiency simultaneous production has increased by 90%. In the current era, with the energy crisis and the heavy costs of supplying it, as well as the sharp rise of environmental pollutants, a major challenge has been posed to all countries, taking advantage of the simultaneous production process as a cheap and clean energy production method—it is an effective step to reduce costs and manage the right energy resources. On the other hand, the economic justification for the commissioning of the simultaneous generation plants gives private investors the opportunity for a secure investment, even without the concerns of the energy crisis or the spread of environmental pollutants and merely to earn money, that is safe and productive in the country’s energy industry.

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CHAPTER 1 Cogeneration and polygeneration

References [1] Wilkinson BW, Barnes RW. Cogeneration of electricity and useful heat. CRC Press, 1980. [2] Hu SD. Cogeneration. United States, N. p., 1985. [3] Polimeros G. Energy cogeneration handbook: Criteria for central plant design. Industrial Press, 1981. [4] Kolanowski BF. Small-scale cogeneration handbook. Fairmont Press; 2003. [5] Noyes R. Cogeneration of steam and electric power. Noyes Data Corporation, 1978. [6] Payne FW. Cogeneration sourcebook. Fairmont Press, 1985. [7] Meador R. Cogeneration and district heating: An energy-efficiency partnership. Ann Arbor Science, 1981. [8] Hamed OA, Al-Washmi HA, Al-Otaibi HA. Thermoeconomic analysis of a power/ water cogeneration plant. Energy 2006;31(14):2699
709. [9] Khartchenko NV, Kharchenko VM. Advanced energy systems. CRC Press; 2013. [10] Khoshgoftar Manesh MH, Amidpour M, Khamis Abadi S, Hamedi MH. A new cogeneration targeting procedure for total site utility system. Appl Therm Eng 2013;54 (1):272
80. Available from: https://doi.org/10.1016/j.applthermaleng.2013.01.043. [11] Khoshgoftar Manesh MH, Navid P, Blanco Marigorta AM, Amidpour M, Hamedi MH. New procedure for optimal design and evaluation of cogeneration system based on advanced exergoeconomic and exergoenvironmental analyses. Energy 2013;59:314
33. Available from: https://doi.org/10.1016/j.energy.2013.06.017. [12] Herna´ndez-Santoyo J, Sa´nchez-Cifuentes A. Trigeneration: an alternative for energy savings. Appl Energy 2003;76(1-3):219
27. [13] Liu XJ, Li J, Qu Y, Chen JQ. Overview of modeling of combined cooling heating and power system. Power Syst Clean Energy 2012;7. [14] Sharaf O, Orhan M. An overview of fuel cell technology: fundamentals and applications. Renew Sustain Energy Rev 2014;32:810
53. Available from: https://doi.org/ 10.1016/j.rser.2014.01.012. [15] Boyce MP. Gas turbine engineering handbook. Elsevier; 2011. [16] Light RW. Consensus on pre-commissioning stages for cogeneration and combined cycle power plants. John Wiley & Sons Limited; 2017. [17] Flin D. Cogeneration: a user’s guide, vol. 11. IET; 2010. [18] Payne FW. Cogeneration management reference guide. Prentice Hall; 1997. [19] Payne JH. Cogeneration in the cane sugar industry, vol. 12. Elsevier; 2012. [20] Pehnt M, et al. Micro cogeneration: towards decentralized energy systems. Springer Science & Business Media; 2006. [21] Pilatowsky I, et al. Cogeneration fuel cell—sorption air conditioning systems. Springer; 2011. p. 103
20. [22] Spiewak SA, Weiss L. Cogeneration & small power production manual. The Fairmont Press, Inc.; 1997. [23] Boyce MP. 2nd ed. Handbook for cogeneration and combined cycle power plants, vol. 20. ASME Press; 2010. 10.1115/1.859537. [24] Onovwiona HI, Ugursal VI. Residential cogeneration systems: review of the current technology. Renew Sustain Energy Rev 2006;10(5):389
431. Available from: https://doi.org/10.1016/j.rser.2004.07.005.

CHAPTER

Main components of cogeneration and polygeneration systems

2

Chapter Outline 2.1 Introduction ................................................................................................... 13 2.2 Steam turbines ............................................................................................... 14 2.3 Gas turbine .................................................................................................... 15 2.4 Combined cycle-based cogeneration plants ...................................................... 19 2.5 Internal combustion engine ............................................................................. 21 2.6 Stirling engines .............................................................................................. 24 2.7 Fuel cell ........................................................................................................ 26 References ............................................................................................................ 26

2.1 Introduction Cogeneration and polygeneration is an efficient method of saving energy in which electricity, heating, cooling are produced simultaneously. The heat generated from cogeneration can be used for district heating or in process industries. The simultaneous production process can be based on the use of gas turbines (GTs), steam turbines, or combustion engines, and the primary energy source includes a wide range of fossil fuels, biomass, geothermal energy, or solar energy. This chapter gives an overview of the main components of cogeneration and polygeneration systems. District heating includes a system in which the heat is centrally generated and sold to several customers. This is done using a distribution network that uses hot water or steam as a heat energy carrier. Synchronous production units such as combined heat and power (CHP) and combined cooling, heating, and power (CCHP) can be categorized from different perspectives. These views are the main part of the cycle manager or the prime mover, the order of the generated energy, and unit size [1
7]. In the sequence of energy produced the CHP system is divided into two groups of upper hand cycles and downstream cycles. In the upstream cycle, it first produces power and then generates heat. Nevertheless, in the downstream cycle, the heat is first generated, and then the power is generated [1
7]. The size of the unit is divided into three categories:

Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00002-1 © 2021 Elsevier Inc. All rights reserved.

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CHAPTER 2 Main components of cogeneration

• large scale (1 MW up to hundreds of MW), • small scale (60 kW to 1.5 MW), and • micro-CCHP systems (less than 50 kW).

2.2 Steam turbines The production of heat by a simultaneous production method can be done at power plants equipped with extraction-condensing turbine. In this way, some steam will be removed before it reaches the last stage of the turbine. Concentrated heating can be used by steam extraction from a turbine or for industrial use. Vapor pressure reduction stations are used when not using a steam turbine. In this case, reliable steam will be provided to heat the process. It should be noted that this does not apply to steam generators without a steam turbine. Only a power plant is produced in a conventional power plant, but in one of the extraction-condensing power plants, a portion of the steam is released from the turbine to generate heat [8]. In a back-pressure steam turbine, steam is removed from the end of the turbine at a higher pressure than the condenser pressure. Steam turbines are designed in such a way that the final vapor of their output is high pressure and noncondensing (superheat). Typical booster for turbines with high output pressure is about four times. In practice, these pressures are tailor made for each industrial unit [8]. The exhaust steam from the middle stages of the steam turbine, either extrusively or from the bottom to the bucket, can be used directly as a process steam in industrial applications such as paper and petrochemicals, or a heat exchanger as a hot fluid for heating water and used in district heating systems. In general, steam process applications can include drying, evaporation, and supply of heat for chemical reactors or distillation. A back-pressure steam turbine CHP plant has been shown in Fig. 2.1. Heat generation can be done at power plants equipped with extraction-condensing steam turbines. In conventional steam generators, high-pressure steam is produced in a boiler, which is called live steam. This steam passes through the steam turbine, and after full expansion, it enters a condenser at low pressure. In the heat condenser, steam is transferred to air or water through the cooling tower. Conventional condensing steam turbines have a vacuum back vacuum, which means that the vapor pressure of the condenser is maintained at about 0.1 times with the help of ejectors to extract the maximum possible work from the steam turbine [8]. In the condensation extraction steam turbine turbines, remove some steam from it before reaching the last stage of the steam turbine last stage. Recycled heat from steam extracted from a turbine can be used to heat the building or for industrial use. A typical steam turbine in an industrial plant can have an extraction pressure of 10 and 4 times. A schematic diagram of the cogeneration plant configuration with an extraction-condensing steam turbine, heat exchanger, and district heating system is presented in Fig. 2.2.

2.3 Gas turbine

Steam

Back-pressure turbine

Generator

Process steam Condensate to boiler Heat exchanger

Return Peaking boiler

Heang system

FIGURE 2.1 A back-pressure steam turbine plant.

The cogeneration plants based on extraction-condensing turbines are applicable in a wide range of useful heat-to-power ratios [8]. A schematic of a complete cogeneration plant based on the steam turbines has been shown in Fig. 2.3.

2.3 Gas turbine In a series of CHP production, turbines play a very important role. In this equipment the ambient air is introduced into the compressor section after the passage of the filtration set and then compressed into a certain proportion after passing through the different floors. In the next step, combustion of the compressed air and fuel injected will release energy, and the energy generated by turning the turbine axis provides the mechanical energy necessary for generating the generator. Also to produce mechanical power, the exhaust gases from a high-temperature turbine are transferred to the recovery boiler for later use [9]. A heat recovery steam generator (HRSG) is installed in the direction of these gases to recover energy from hot gases from the GT. Steam generators can also

15

16

CHAPTER 2 Main components of cogeneration

Steam

Back-pressure turbine

G

Process steam

Heang steam

District heang

Exhaust steam

Condenser

Cooling water inlet Cooling water outlet

Supply line

Condensate to boiler

Return line

FIGURE 2.2 An extraction steam turbine flow diagram.

be used to supply the necessary thermal energy in sweeteners. It should be noted that the use of heat power plants in the form of CHP leads to an increase in their thermal efficiency from about 30% to more than 80% [9]. The GT cycle is based on the Brayton cycle. The fuel of the GT can be a natural gas or other high-quality fuel. GT power production varies in 1 MW to hundreds of megawatts [9]. The output power from a system consists of a GT package, and an HRSG can be obtained as following [7,9]:where Wt shows the GT power output, Wc indicates the compressor power demand, mg the gas flow rate, ma the air flow rate, Δht shows the enthalpy difference between the inlet and outlet streams of the GT, and Δhc indicates the enthalpy difference between the inlet and outlet streams of the air compressor (AC) [7,9]. Fig. 2.4 illustrates a GT-based cogeneration plant with an HRSG. Wrev 5 Wt 2 Wc 5 mg Δht 2 ma Δhc

(2.1)

A GT-based cogeneration plant with a supplementary boiler that added to the HRSG and a steam user unit is shown in Fig. 2.5. W5

mg Δht ηit 2 ma Δhc ηic

(2.2)

2.3 Gas turbine

Steam

Steam

Stack HP

LP

Supply line Heang steam Process

Boiler

Pump

Return line

Air Fuel

Exhaust steam Cooling water outlet Cooling water inlet

Condenser HP feed water heater

Steam

Pump

LP feed water heater

Deaerator

Pump

FIGURE 2.3 Flow diagram of a cogeneration plant with the steam turbine as the prime mover.

The mass balance in a GT package can be written as follows:where mf is the fuel mass flow rate that can be calculated:where Qf show the extracted thermal energy from the fuel in the combustion chamber (CC), and LHV is the low heat value of the consumed fuel. mg 5 ma 1 mf mf 5

Qf LHV

(2.3) (2.4)

The process by which the environment and the system can be precisely returned to their original position in a photo path is called the reversible process. Therefore the necessary condition for a reversible process is the quasiconstancy of the process at any given moment. The term “pseudo-terminal” translates into the sense that the image process is at any moment in its thermodynamic equilibrium. It is not difficult to restore a system to its original state. However, restoring the environment to the original state is almost impossible. If the reversible energies and power of the cogeneration plant have been required, Eqs. (2.5) and (2.6) can be useful.where Wc is the power demand of the AC, Qf ;rev shows the reversible thermal energy that can be extracted from the fuel, Wt is the turbine output

17

CHAPTER 2 Main components of cogeneration

Fuel Combuson chamber

Air

Gas turbine

Compressor

Flue gas

HRSG Steam to process

Water inlet

Stack

FIGURE 2.4 A gas turbine
based cogeneration plant with an HRSG. HRSG, Heat recovery steam generator. Fuel Air

Compressor

Combuson chamber

Gas turbine Supply line

Steam Flue gas

HRSG Water inlet

Supplementary boiler

Fuel Air

Storage tank

18

Steam user

Stack Return line

FIGURE 2.5 A gas turbine
based cogeneration plant with a supplementary boiler that added to the HRSG and a steam user unit. HRSG, Heat recovery steam generator.

2.4 Combined cycle-based cogeneration plants

power, and Qu;rev indicates the useful reversible heat that is produced in the cogeneration plant.  Qf 5 Qf ;rev 2 Wc

 1 21 ηic

Qu 5 Qu;rev 1 Wt ð1 2 ηit Þ

(2.5) (2.6)

2.4 Combined cycle-based cogeneration plants The combined cycle power plant comprises several GTs and steam turbines. If the GT is not a hybrid cycle, its exhaust gases, which can withstand temperatures of up to 600 C, enter directly into the air and the remaining energy is wasted. While the combined cycle power plant uses this energy, steam boiler generates steam without any fuel. Therefore by using this method, the efficiency of the cycle increases [10]. Theoretically, the recoverable energy from the exhaust of GTs is about half the energy produced by the GT itself. Therefore the steam turbine power is about half the GT. In some designs the two GTs create the energy needed for a steam turbine, and thus the steam turbines are produced by the turbines of the gas. The combined cycle power plant is a highly efficient, flexible, reliable, cost-effective, and environment-friendly solution for power generation [10
16]. The combined cycle power plant is a combination of steam turbine and GT, in which the generator generates a GT generating electricity, while the heat dissipated from the GT (by combustion products) is used to produce the steam required by the steam turbine. In this way, additional electricity is produced. Combining these two cycles increases the efficiency of the power plant. The electrical efficiency of a simple cycle of a power plant without the use of heat dissipation typically has an efficiency of 25%
40%, while the same power plant with a combined cycle of electrical efficiency is about 60%. As said, these plants are made up of a combination of steam and GTs, depending on the types of turbines, heat recovery boilers (HRBs), and multiple recovery devices [10
16]. With the use of GTs in combinational cycles, the low efficiency can be eliminated, and as a result, it is used to provide the base load, while the other advantages, such as rapid launch and flexibility, in a wide range of used to load [13
16]. If the auxiliary coolers are not used to cool the exhaust liquids from the steam turbine, these units can be used as CHP units. The characteristic of all combined cycle power plants is the recovery of heat from the exhaust gas of GTs. This heat is recycled by boilers and is used to produce vapor for steam turbines. Usually, auxiliary burners that use the exhaust gas of the GT as an intake air are used to

19

20

CHAPTER 2 Main components of cogeneration

boil the boiler to improve the quality of the steam. Combined cycle systems in which liquid from the condenser is used to provide heat are the basis of the production systems along with the combined cycle. Fig. 2.6 demonstrates an open cycle of cogeneration based on a combined cycle system. In addition, Fig. 2.7 shows a combine cycle-based cogeneration plant with a steam user unit. Combined cycle power plants are divided into the following categories in terms of turbine, heat recovery, and burner types [13
16]:

• combined cycle power plants with duct burner, • free combustion plant, • combined cycle power plants with an HRB equipped with water recovery or • •

heating water supply, combined cycle power plants with multiple steam recovery boilers, and combined cycle power plants with GT closed cycle with water heating in the steam cycle.

In the first type of power plants a burner is placed inside the boiler, and it is mostly used in power stations that are supposed to work permanently in the steam section, in which case there should be no dependence on the GT. In the second type of these plants, hot gas is used as combustion products from the GT. This outlet smoke has a high volume and a temperature of about 500 C and is sent to the boiler to convert water to steam to use steam to propel the generator. The use Fuel Air

Compressor

Combuson chamber

Gas turbine Steam Flue gas Extracon steam turbine

HRSG Water inlet

To process

Stack

Cooling water outlet Cooling water inlet

Condenser

FIGURE 2.6 An open cycle of cogeneration based on a combined cycle system.

2.5 Internal combustion engine

Fuel Air

Compressor

Combuson chamber Gas turbine Steam Flue gas Extracon steam turbine

HRSG Water inlet

Supply line

Condensate Stack

Extracon to process Cooling water outlet

Heat exchanger

Load

Pump

Cooling water inlet Condenser

Return line

FIGURE 2.7 A combine cycle-based cogeneration plant with a steam user unit.

of different types of combined cycle cycles is different. The combined cycle is used without a burner to supply base and middle load. In the third type of these plants in the combined cycle, the exhaust gases of a simple GT cycle, including the AC, the CC, and the GT, enter the HRB and are used to produce superheated steam. In low-power hybrid cycles, steam turbine power is about 50% lower than the GT. In the fourth type of steam generating unit with multiple pressures, the temperature of the exhaust gas of the waste heat boiler will be reduced, thereby increasing the plant’s overall efficiency. The simplest of these cycles is the dual cycle, although the triple-cycle cycle is used too. For example, in a dual cycle, a waste HRB has two circuits for steam generation. The first circuit is a highpressure circuit, the steam generated from it enters the turbine input duct, and the second circuit is the pumice pressure circuit, in which the steam generated from the lower pressure classes enters the turbine. In a three-speed proposed compression cycle, another steam is produced by compression between the inlet pressures of two steam turbines. This steam is injected into the GT CC to reduce nitrogen oxide emissions to the standard set. If this method is used, some water will be wasted, which should be compensated on a steady basis.

2.5 Internal combustion engine The diesel engine is an internal combustion engine in which a diesel cycle is used to create motion. The main difference with other engines is the use of combustion

21

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CHAPTER 2 Main components of cogeneration

due to congestion. In this type of combustion, there is no explosion, but the combination of fuel and air is compressed due to high compression without spark ignition, and the main cause of these propulsion engines is unlike petrol engines 100 rpm [17]. They can ignite burning without the need for an electric spark. These engines are used to ignite fuel from high temperatures. In the first instance, the temperature of the CC is very high, and after the temperature rises, the combustion mixture is mixed with air [17]. Two types of heat and oxygen are needed to burn fuel. Oxygen is fed into the cylinder chamber through the inputs of the motor and then compressed by the piston. This compression is so high that it causes very high heat. Then the third factor, the burning, is added to heat and oxygen, which causes the fuel to flare [17]. For example, diesel engines can be categorized according to the number of combustion cycles in each crankcase cycle to two-stroke diesel engines or fourstroke diesel engines, or in terms of horsepower production. Either in terms of the number of cylinders or the shape of the cylinders, which respectively divided into two types of linear motors or V motors. The structure of the diesel engine structure differs only in the system of feeding and regulating the fuel with spark ignition engines. Therefore there are very similar structures between these engines, and the only difference is the following parts that exist in diesel engines and does not exist in other internal combustion engines. Injector pump: The task of adjusting the amount of fuel and supplying the necessary pressure for fuel spraying [17].

• • • •

Injectors: Pump the fuel and gas into the CC. Fuel filters: Separate exhaust and exhaust fuels. Fuel transfer pipes: They must be unstable and resistant to stress. Turbocharger: Increases intake air into the cylinder.

As indicated, diesel engines are divided into two categories of four-stroke and two-stroke engines based on how they operate. However, in both of these engines, four main operations are performed: suction or breathing-compression or pressure-work or explosion and discharge or smoke, but depending on the type of motors, these steps can be taken individually or in combination [17]. All diesel generators, gas generators, and boiling motors generate a certain degree of heat during production, which can be used in a process called “CHP.” CHP, or waste heat recovery, can be used both for heating and cooling large buildings, and for industrial applications. On average, steam engines lose 50% of their thermal energy. With CHP, power plants can reach 80% efficiency. This chapter examines the engineering aspects of CHPs and their current applications in the world [17]. The most common types of waste heat recovery systems are steam and hot water systems. Most motors have a maximum water temperature of 210 F. Other engines may work up to 260 F. Engines must be designed to work at high temperatures. However, the temperature of 210 F is high enough to meet all the

2.5 Internal combustion engine

needs of the devices. Low-pressure water vapor can be made from a water jacket at a temperature between 250 F and 260 F. This temperature (in a properly configured engine) can be generated by using a cooling device in which the steam is generated in a water jacket and then it increases due to the difference in density between water and steam. Before you think about a kennel device, it is best to talk to a professional electrical contractor to determine which water jacket settings are needed [18]. This method is similar to the distributed generation method in gas plants, except the use of reciprocal internal combustion engines instead of a GT. In power plants that use reciprocating motors, heat can be recovered from engine oil, engine-cooling water, or from exhaust gases [18]. The electric efficiency of the return and return motors is between 35% and 42%. Because the advanced engines due to the increased efficiency of the exhaust gases are cooler, the heat recovery can only be in the form of steam and hot water. For example, a 4.2-MW diesel engine can produce 1.5 MW of steam and 3.1 MW of hot water. Given that the total fuel consumption for this engine is about 10 MW, the total output is about 88% [18]. The internal combustion engines have followed two thermodynamic cycles— Otto cycle and diesel cycle. The Otto cycle is a collection of ideal processes that are the basis of the internal combustion engines. Most cycles are used in most public vehicles. It should be noted that gas is used as a fluid in the Otto cycle. Of course, as in the Rankine cycle or the fridge cycle, the fluid does not go through a real cycle in real life, and it is only easier to model the processes that are considered a cycle [19]. In the Otto cycle a mixture of air and fuel in the form of constant pressure is injected into the cylinder (also referred to as suction). After that, the gas is compressed in the form of isentropic and its temperature rises. In the next step, when the piston reaches the highest point, the combustion occurs and leads to a lowering of the piston, and thus the production of the work. In the final stage the temperature and pressure of the gas are decreased in isentropic form. After that, the mixture of fuel and air is again sucked up and the same steps will be repeated [19]. The ideal thermal efficiency of the Otto cycle can be obtained as follows [19]: where k is the isentropic exponent (for air, k 5 1.4) and ε 5 v1 =v2 is the compression ratio. The diesel cycle is very similar to most cycles used in motors. The main difference between this cycle and other cycles is the following: at the beginning of the condensation process, there is no fuel in the cylinder, so the automatic combustion process will not occur in congestion [19]. ηth 5 1 2

1 εk21

(2.7)

Diesel cycle uses compression-based combustion rather than spark ignition. Since the adiabatic density process results in a very high temperature, the combustion process will occur by spraying fuel now of condensation (more will be

23

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CHAPTER 2 Main components of cogeneration

Air

Gas/diesel engine

G

Fuel To stack

Auxiliary boiler Supply to district heang

CHE

CHE

EHE

FIGURE 2.8 Power-generation cycle based on the gas or diesel engine.

discussed later on this process). As a result, diesel and diesel engines do not require spark plugs. In this cycle, the Otto cycle can achieve a higher pressure ratio. This cycle consists of a constant pressure process, a constant volume process, and two isentropic processes. Fig. 2.8 shows the pressure
volume graph of the diesel cycle. The total heat recovery system in a gas/diesel engine cogeneration plant includes three heat exchangers [7]:

• engine exhaust gas heat exchanger • cooling water heat exchanger • lubricating oil heat exchanger Mean effective pressure (MEP) shows the effective power generated, the optimum fuel consumption, and efficiency. MEP is obtained as [7]:where Wnet is the net output power of the engine in kJ and Vdis is engine piston displacement in m3. A cogeneration plant with a gas/diesel engine is demonstrated in Fig. 2.9. MEP 5

Wnet ðkPaÞ Vdis

(2.8)

2.6 Stirling engines The Stirling engine is a heat engine that is very different from the internal combustion engine in your car. This engine was invented by Robert Stirling in 1816 and has the potential to be much more efficient than a gas or diesel engine.

2.6 Stirling engines

Air

Gas/diesel engine

G

Fuel To stack

Auxiliary boiler

LHE

CHE

EHE Fuel

Air Peaking boiler Hot water return line

To stack

Hot water supply line

Hot water storage

Heat user

FIGURE 2.9 A cogeneration plant with a gas/diesel engine as the prime mover.

Today, however, Stirling engines are only used in more specialized applications such as submarines or auxiliary generators for boats or carriages that work with slow sound. Although not successful in the mass market, some highly successful inventors are working on it. This engine uses a Stirling cycle that is unlike the cycle used on internal combustion engines. This engine only moves with the help of upcycling heat. The gases contained within the Stirling engine never comes out of the engine. There is no exhaust system that exhausts gases with high pressure and no explosions occur. Because of these sterling motors, the sound is not very sound or, in short, is very loud. The Stirling cycle uses an external source of heat that can be anything from gasoline to solar energy to heat produced by plant rot. No combustion occurs inside engine cylinders. The key is the Stirling engine; the fixed amount of

25

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CHAPTER 2 Main components of cogeneration

gas inside the engine is reserved. The Stirling cycle involves a series of events that change the gas pressure inside the engine, which makes it work [20
27].

2.7 Fuel cell Fuel cell (FC) series to produce energy with optimum efficiency requires auxiliary equipment called an FC system that optimizes the performance of the FC, including purity of fuel, amount of air and fuel entering the FC series, moisture and water management, temperature control, and, ultimately, control the gases in the system and the FC series. An FC system can be divided into three major parts—a fuel supply (fuel converter and hydrogen storage system); an FC series and a humidity control system, pressure, temperature, and gas discharges; and finally the energy conversion section of the chapter. The joint between the FC and the power consumer is suitable for converting current and voltage to the voltage and current. Depending on the type of FC and its application, these systems are simple or complex, as in the case of power plant FCs, the fuel converter that transforms fossil fuels, biomass, or similar things into pure hydrogen, the complex component of the system fueling. There are five main types of FCs categorized by the kind of used electrolyte, namely, alkaline, phosphoric acid, proton exchange membrane FC (PEMFC), molten carbonate FC, and solid oxide FC [28
30]. FCs generate electricity and heat as a byproduct. Advantages of using a stationary FC over CHP sterling, lack of moving parts, less maintenance, and quieter operation. Extra electricity can be delivered to the network [28
30]. The PEMFC is powered by natural gas or propane and uses a steam reformer to convert methane gas to carbon dioxide and hydrogen, and then hydrogen reacts to oxygen in the FC and generates electricity. A PEMFC-based micro-CHP has a 37% LHV and 33% higher heating value (HHV) electrical efficiency and a 52% LHV and 47% HHV heat recovery efficiency of 40,000 hours or 4000 start/stop cycles equivalent to 10 years [28
30]. In 2013 the life span of FCs was about 60,000 hours. For PEM fueling devices that turn off at night, this is equivalent to an estimated life span between 10 and 15 years. More details of cogeneration and polygeneration based on FC are discussed in Chapter 13, Modern Polygeneration Systems.

References [1] Noyes R. Cogeneration of steam and electric power. Noyes Data Corporation, Editia 29 Energy technology review 1978. [2] Wilkinson BW, Barnes RW. Cogeneration of electricity and useful heat, CRC-Press, 1980.

References

[3] Meador R. Cogeneration and district heating: An energy-efficiency partnership, Ann Arbor Science, 1981. [4] Polimeros G. Energy cogeneration handbook: criteria for central plant design. Industrial Press. 1981. [5] Hu SD. Cogeneration, Reston Publishing, Reston, 1985. [6] Payne FW. Cogeneration sourcebook. Technol Eng 1985;. [7] Khartchenko NV, Kharchenko VM. Advanced energy systems. CRC Press; 2013. [8] Kanoglu M, Dincer I. Performance assessment of cogeneration plants. Energy Convers Manage 2009;50(1):76
81. [9] De Lucia M, Lanfranchi C, Boggio V. Benefits of compressor inlet air cooling for gas turbine cogeneration plants. J Eng Gas Turbines Power 1996;118(3):598
603. [10] Kehlhofer R, et al. Combined-cycle gas & steam turbine power plants. Pennwell Books; 2009. [11] Horlock J. Combined power plants: including combined cycle gas turbine (CCGT) plants. Elsevier; 1992. [12] Rao AD. Combined cycle systems for near-zero emission power generation. Elsevier; 2012. [13] Khoshgoftar Manesh MH, Amidpour M, Khamis Abadi S, Hamedi MH. A new cogeneration targeting procedure for total site utility system. Appl Therm Eng 2013;54 (1):272
80. Available from: https://doi.org/10.1016/j.applthermaleng.2013.01.043. [14] Khoshgoftar Manesh MH, Ghalami H, Amidpour M, Hamedi MH. Optimal coupling of site utility steam network with MED-RO desalination through total site analysis and exergoeconomic optimization. Desalination 2013;316:42
52. Available from: https://doi.org/10.1016/j.desal.2013.01.022. [15] Khoshgoftar Manesh MH, Navid P, Amidpour M, et al. New procedure for optimal design of cogeneration system with considering environmental impacts and total cost. Clean Technol Environ Policy 2013;15:893
919. Available from: https://doi. org/10.1007/s10098-012-0576-0. [16] Khoshgoftar Manesh MH, Navid P, Blanco Marigorta AM, Amidpour M, Hamedi MH. New procedure for optimal design and evaluation of cogeneration system based on advanced exergoeconomic and exergoenvironmental analyses. Energy 2013;59:314
33. Available from: https://doi.org/10.1016/j.energy.2013.06.017. [17] Beuther H, et al. Conversion of synthesis gas to diesel fuel and gasoline. Google patents. 1986. [18] Liu Z, Karim G. Simulation of combustion processes in gas-fuelled diesel engines. Proc Inst Mech Eng, A J Power Energy 1997;211(2):159
69. [19] Cengel YA, Boles MA. Thermodynamics: an engineering approach. Sea 2002;1000:8862. [20] Conroy G, Duffy A, Ayompe LM. Economic, energy and GHG emissions performance evaluation of a WhisperGen Mk IV Stirling engine μ-CHP unit in a domestic dwelling. Energy Convers Manage 2014;81:465
74. Available from: https://doi.org/ 10.1016/j.enconman.2014.02.002. [21] Al Moussawi H, Fardoun F, Louahlia-Gualous H. Review of tri-generation technologies: design evaluation, optimization, decision-making, and selection approach. Energy Convers Manage 2016;120:157
96. Available from: https://doi.org/10.1016/ j.enconman.2016.04.085. [22] Obernberger I, Carlsen H, Biedermann F. State of the art and future developments regarding small scale biomass CHP systems with a special focus on ORC and

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[23]

[24]

[25]

[26] [27]

[28] [29]

[30]

Stirling engine technologies. In: Proceedings of international Nordic bioenergy conference. Jyvaskyla, Finland; 2003. Li T, Tang D, Li Z, Du J, Zhou T, Jia Y. Development and test of a Stirling engine driven by waste gases for the micro-CHP system. Appl Therm Eng 2012;33
34:119
23. Available from: https://doi.org/10.1016/j.applthermaleng.2011.09.020. Conroy G, Duffy A, Ayompe LM. Validated dynamic energy model for a Stirling engine μ-CHP unit using field trial data from a domestic dwelling. Energy Build 2013;62:18
26. Available from: https://doi.org/10.1016/j.enbuild.2013.01.022. Biedermann F, et al. Operating experiences with a small-scale CHP pilot plant based on a 35 kWel hermetic four cylinder Stirling engine for biomass fuels. In: Proceedings of the 11th international Stirling engine conference; 2003. Kong X, et al. Energy efficiency and economic feasibility of CCHP driven by Stirling engine. Energy Convers Manage 2004;45(9
10):1433
42. Obernberger I, et al. Small-scale CHP plant based on a 75 kWel hermetic eight cylinder Stirling engine for biomass fuels
development, technology and operating experiences. In: Second world conference and exhibition on biomass for energy, industry and climate protection; 2004. De Paepe M, D’Herdt P, Mertens D. Micro-CHP systems for residential applications. Energy Convers Manage 2006;47(18
19):3435
46. Steele BC, Heinzel A. Materials for fuel-cell technologies. Materials for sustainable energy: a collection of peer-reviewed research and review articles from Nature Publishing Group. World Scientific; 2011. p. 224
31. Staffell I, Green R. The cost of domestic fuel cell micro-CHP systems. Int J Hydrogen Energy 2013;38(2):1088
102.

CHAPTER

Applications of cogeneration and polygeneration

3

Chapter Outline 3.1 Introduction ................................................................................................... 29 3.2 Main application ............................................................................................ 30 3.2.1 Industrial .....................................................................................31 3.2.2 Commercial ..................................................................................32 3.2.3 Institutional ..................................................................................32 3.3 Prospects for cogeneration in Europe .............................................................. 32 3.3.1 Fiona Riddoch, COGEN Europe, Belgium .........................................33 3.3.2 Germany—aiming to double cogeneration by 2020 ..........................33 3.3.3 Spain—Upbeat for combined heat and power ..................................34 3.3.4 Austria—The Green Approach ........................................................35 3.4 Japan ............................................................................................................. 35 3.5 China ............................................................................................................. 36 3.6 The United States ........................................................................................... 36 3.7 Other countries ............................................................................................... 36 References ............................................................................................................ 36

3.1 Introduction Proper use of energy resources and cost reductions are significant things that over time have not only diminished its importance but also become increasingly important due to the energy crisis and rising fuel carrier prices. In most of the plants, due to the high losses, the average efficiency is 37% and about 15% of the electricity produced is also spent on the losses of the transmission and distribution network and the unit itself, in contrast to the combined heat and power (CHP) systems with a suitable and up-to-date capability to reach yields more than 90%. Therefore, by utilizing these systems, while increasing the efficiency by more than twice, the conventional systems due to local power generation, transmission and distribution network losses are also eliminated and reliability increases. In the usual way, electrical needs of industrial units, commercial, office, and residential buildings of the network and their thermal requirements in the same place are provided by conventional methods, including the use of thermal boilers, but using the production system simultaneously both forms provide energy through a single high-efficiency system [1]. Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00003-3 © 2021 Elsevier Inc. All rights reserved.

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The main advantages of the CHP and combined cooling, heating, and power (CCHP) systems are classified based on economic, environment, and security of supply aspects. In the cogeneration and polygeneration systems, economic profit arises from fuel and power saving. With utilizing and recovery of energy, environmental benefit is achieved by reduction of pollution emission due to reduction of fuel. Furthermore, because of the fuel supplies within the plant, security of supply has been increased consequently.

3.2 Main application The CHP systems utilize a prime mover production by heat recovery of gas exhaust by using heat exchangers and other equipment to recover energy. Also, this heat recovery can be used as the energy required for cooling chillers (CCHP) [1]. Today, in many countries, the use of cogeneration and polygeneration schemes has been welcomed for the production of heating, cooling, power, desalinated water, CO2, and hydrogen in various sectors. Some applications of cogeneration and polygeneration are as follows:

• trade centers including shopping malls and shops, commercial parks, sports, and recreational complexes;

• office and residential complexes; • health centers and hospitals; • industries such as steel, petrochemicals, cement, food production, automotive, pharmaceuticals, rubber, and dairy products; and

• workshops and greenhouses. The elimination of transmission losses and the reduction of distribution losses compared to the conventional power plants have been achieved by using cogeneration and polygeneration systems. In traditional methods the electrical power required by a set of major power plants is provided. The average efficiency of these plants, which consume mainly fossil fuels, is about 37.4%. Also, energy losses include transmission, overdistribution, and distribution networks of up to 12.3%, and consumption of about 3% of electricity production for internal use of the plant makes the overall efficiency of the network 32%. This means that a total of 68% of the energy consumed in the various elements of the network is wasted, and from every 100 kW of fuel consumed by the stand-alone power plants on average, only 32 kW of electrical energy reaches to the consumer. However, the energy losses in the country’s gas networks are negligible, and almost the same amount of fuel injected into the gas supply system is finally consumed by the consumer [2 13]. Following are the benefits of using cogeneration and polygeneration [7 13]:

• Heat recovery of stand-alone system to produce a various form of useable energy. • Increasing efficiency.

3.2 Main application

• • • • • • • • • • • • • • •

Reduce the cost of primary energy supplies. Remove post creation costs. New source to earn money by selling surplus energy. The rate of return on capital is economical. Enhance reliability. Reducing concerns about energy price fluctuations. Preventing network capacity increase. Reducing emissions of pollutants. Reduce the need for investment to expand the transmission and distribution network. Combined production lobbies can be divided into five general categories. Recovery from extraction condensing and back-pressure turbines. Recovery from back-pressure turbines. Heat recovery of gas turbines. Recovery of combined cycle. Recovery of reciprocating engines.

CHP and CCHP systems have been designed in various sizes for many different applications. Large-scale units can be on-site or off-site. Off-site units have to be applied close to a steam user to reduce cost of the steam pipeline. In the United States, about 90% of CHP units are used by industry. CHP and polygeneration systems are also applied for small-scale consumers of electricity. Small-scale systems are being designed and built for commercial and industrial applications. Modular CHP and CCHP systems with the range in size from 20 to 650 kW are a compact size and economical to design and manufacturing. It is usually the best option to provide the hot water needs of a building and also for the production of cooling and desalinated water. Consequently, the polygeneration systems are the best choice for buildings that have variable demands for steam, hot water, desalinated water, and cooling such as hotels, hospitals, and restaurants. CHP and CCHP used to ensure a reliable supply of power, heating, cooling, hot water/steam, and drinking water to mains supplies. The main applications of cogeneration and polygeneration are classified into industrial, commercial, and institutional divisions.

3.2.1 Industrial The major applications of cogeneration and trigeneration systems are in the industrial sector. This application has a big market that uses waste heat recovery from the production processes. The industrial process includes chemical plants, natural gas pipelines, petrochemical plants, pulp and paper, refining processes, rubber and plastic, ceramic, metallurgy, and textile. A big potential for cogeneration and polygeneration using the waste heat is existed in industrial sectors to produce heating, cooling, power, desalinated water, and hydrogen. The range of temperature is high and the typical electricity load is about 1 500 MWe.

31

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CHAPTER 3 Applications of cogeneration and polygeneration

Table 3.1 Applications of cogeneration and polygeneration. Application

Temperature level Power capacity

Industrial

Commercial

Institutional

Chemical factories Natural gas pipelines Petrochemicals Pulp and paper Refining Rubber and plastics Textiles Cement Ceramic Dairies High

Data centers Hotels Apartments Laundries Office buildings Refrigerated warehouses Restaurants Supermarkets Green buildings

Hospitals Correctional facilities Schools Universities Wastewater treatment Residential

Low to medium

Low to medium

1 500 MWe

1 kWe 10 MWe

1 kWe 10 MWe

3.2.2 Commercial The other major application of CCHP and CCHP system is for the commercial purpose. Data centers, hotels, apartments, universities, laundries, office buildings, refrigerated warehouses, supermarkets, and green buildings are all commercial sectors that use CHP and CCHP with high reliability and availability. The range of electrical capacity is 1 kWe 10 MWe and the range of temperature is medium to low. Table 3.1 shows the different applications of cogeneration and polygeneration systems.

3.2.3 Institutional Institutional sectors are the other main division that uses cogeneration and polygeneration systems to improve energy efficiency. Hospitals, correctional facilities, schools, universities, and colleges are some samples of institutional division.

3.3 Prospects for cogeneration in Europe The European Union’s (EU’s) 2004 directive on cogeneration should be having a positive effect on CHP development by now, but implementation by member states has been uneven. Here, Fiona Riddoch of COGEN Europe illustrates what can be achieved in three leading countries and describes the CODE project that has been established to monitor the implementation of the directive.

3.3 Prospects for cogeneration in Europe

3.3.1 Fiona Riddoch, COGEN Europe, Belgium Currently, the electricity production in Europe from cogeneration accounts for 11% of total electricity production. Denmark produces 40% of its energy from cogeneration, Finland 35%, and the Netherlands 30%. If the share of electricity production from cogeneration is increased to 18% by 2020, the energy savings could represent around 3% 4% of total gross consumption in the EU. Although there is a large technical potential for cogeneration, the degree of its penetration in the heat and electricity markets is very much influenced by the framework conditions set by the national governments. The growth of cogeneration in the mid-1990s was made possible by the adoption of supportive member state legislation, backed by long-term financial support. The adoption of Directive 2004/8/EC on the promotion of cogeneration opened a new era of active policy-making across Europe. This process, however, is still ongoing and it is too early to determine its effect. The degree of difference between the 27 EU member states becomes clear in their different policy and support approaches. There are encouraging signs among the member states, which have fully implemented the directive. Germany, Spain, and Belgium have all introduced new supporting laws for cogeneration, with a good financial support scheme that brings security to investors. As a result, the prospects for cogeneration in these three countries look good. In some member states, such as Austria, the combination of both the renewable and CO2 reduction target, rather than the energy efficiency target, is driving the promotion of new cogeneration [14,15].

3.3.2 Germany—aiming to double cogeneration by 2020 Germany, with 21 GWe of installed cogeneration capacity, has by far the highest total electricity production from cogeneration in Europe. Historically, the fuel used for firing cogeneration plants was coal, but a gradual switch from coal to natural gas—and investment by the government to develop innovative installations—paved the way to more efficient production. However, the liberalization of the German energy market led to negative consequences for cogeneration, and many plants were forced to close down. In April 2002 the Cogeneration Law came into force, 4 years after the transposition of the 1996 European Directive, which forced member states to open their electricity markets to competition. It provided support for the development of small industrial units and the refurbishment of existing plants, while the number of microcogeneration units and biomass-fired plants also rose. Indeed, the law’s criteria for support eligibility excluded all-new plant over 2 MWe. The 2002 support mechanisms took the form of feed-in premiums; the premiums’ value decreased with time and later the plant was commissioned to lower the premium. Nonetheless, the Cogeneration Law did not bring consistent positive changes and was therefore amended.

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As of January 1, 2009, the bonus is now capacity dependent, and all plants will have to comply with the high-efficiency requirements of the 2004 Cogeneration Directive. The new law also brings an obligation to connect all plants to the grid. All electricity produced is not only supported but also fed into the grid. There is also an investment incentive program for micro-CHP units. The cogeneration sector is also affected by the Renewable Energy Sources Act—giving additional bonuses if cogeneration or innovative technologies are used—and the Energy Tax and Electricity Tax Acts, which exempt cogeneration operators from the payment of the energy tax and for the amount of electricity they supply to the grid. Also, there are other support schemes in the form of investment subsidies or special loans with lower interest rates. There are also support mechanisms for units within the CO2 building improvement programs. The German cogeneration potential study showed that over 50% of Germany’s electricity could be generated in cogeneration plants. The German government has set itself a less ambitious goal—setting a target that the share of cogenerated electricity should reach 25% of electricity generation by 2020 [14,15].

3.3.3 Spain—Upbeat for combined heat and power The introduction of a new regulation for cogeneration (Royal Decrees 616/2007 and 661/2007 transposing the 2004 Cogen Directive) established a favorable financial framework for cogenerators in Spain. During most of the 1990s, cogeneration experienced rapid growth in Spain and the share of cogenerated electricity grew from 3.3% in 1991 to approximately 12% in 2001. However, energy market liberalization in 1999 marked a turning point. Gas prices soared and electricity prices fell, making it difficult for cogenerators to compete on the market dominated by electricity supplied from existing depreciated power plants operating on marginal costs. The primary mechanisms to promote cogeneration in Spain are the different special regimes, whereby cogenerators sell their surplus electricity under relatively stable conditions. But problems remain unresolved. Most cogeneration installed is concentrated in the industrialized regions of Catalonia and Valencia in the northern part of Spain’s Mediterranean coast—and the majority of it is installed in industry. Paper, chemical, food, and ceramics make up approximately 75% of the cogeneration production in the country and the presence of cogeneration in the tertiary sector is still very small (7.5%), representing a substantial opportunity for expansion. As in all southern European countries, and increasingly so in the northern member states, cooling is a growing demand in Spain and trigeneration is being introduced and developed. In 2008 the number of cogeneration plants in Spain is increased by 11 units, taking the total number of plants in the country to 883. This growth indicates a reversal from the tendency of the last few years; but the situation is still far from the “golden era” (1996 2000) when there was an average of 110 new plants installed each year. The total installed capacity in 2008 increased 142 MWe, reaching a total of 6170 MWe, less than the 7000 MWe objective of the Action Plan 2008 12.

3.4 Japan

Historically, cogeneration grows in recession periods. The nature of this current economic slump is special, though the number of cogeneration projects being analyzed currently is huge. In the coming years, cogeneration could play a key role for industry by promoting the overall energy sector, and for this, investments of around h450 million (US$630 million) on new CHP plants are foreseen [16].

3.3.4 Austria—The Green Approach Cogeneration is well developed in Austria and district heating plays an important role. The country has ambitious goals for CO2 emissions reduction. Therefore the government is encouraging an increase of the biomass share, switching from a strategy of promoting natural gas for cogeneration production in the past. The Green Electricity Act, amended in 2006, regulates the Austrian cogeneration power generation. It supports the construction of new cogeneration units with an objective of a 2000 MWe national capacity increase by 2014. The financial support for fossil fuel units consists of a fixed tariff of h0.015/kWh or h0.0125/kWh for lower efficiencies, depending on the plant’s efficiency—for plants above 2 MWe. New cogeneration units running with renewable energy sources benefit from green feed-in tariffs. Small-scale cogeneration units and microcogeneration units running on renewable energy sources are also covered by green feed-in tariffs. Besides the Green Electricity Act, industrial cogeneration (autoproduction) in Austria is currently facing problems due to high operational costs and low electricity prices, following market liberalization. A cogeneration act was adopted in August 2008. The act provides partial financing of operation costs for existing and repowered CHP plants, supplying public district heating systems and also providing investment grants for new fossil-fired plants. Investment subsidies of up to 10% of the required investment sum are also available, decreasing with the total peak capacity of the plant. Both the Austrian Research Promotion Agency and Kommunalkredit can grant subsidies—they are present all over the country, and a budget of h60 million ($83 million) has been provided for this purpose until 2014 [17]. Shortly, the possibility to expand cogeneration in Austria lies mainly in the city areas, where the government is encouraging the growth of cogeneration capacity in combination with district heating. The second targeted sectors are the hospitals and public facilities, especially in areas without district heating. Other plans exist for a biomass plant in connection with the wood industry [18].

3.4 Japan The number of cogeneration and polygeneration plants installed in Japan by 2006 was about 2169 units in industrial applications with 7071 MW as a total capacity and 5190 plants in commercial applications with a total capacity of 1715 MW. Also,

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CHP and CCHP provide 4% of the Japan power production [19,20]. Due to the measures taken by the Japanese government to promote the development of CHP and CCHP systems by low-interest loans, investment subsidies, and special taxation scheme, the total production capacity of CHP and CCHP systems has increased significantly from about 200 kW in 1986 to 9440 MW in March 2010 [19,20].

3.5 China The Chinese government began a series of activities, including direct subsidies and tax exemption to support CCHP projects for energy saving in the early 1990s [21]. So, the capacity of CHP and CCHP increased about 28.15 MW in 1998. Also, due to the government promotion policy for the development of CHP and CCHP, a 120 MW system is installed in Hangzhou and 49.6 MW CCHP system is installed in Jinan [21].

3.6 The United States The installed capacity of the cogeneration and trigeneration in the United States has growth from about 12 GW in 1980 to 45 GW in 1995. Due to the development of CHP and CCHP systems to increase the capacity of 92 GW till 2010, the Combined Heat and Power Association, US Department of Energy, and the Environmental Protection Agency have joined the CHP Challenge. The aim of this project was set to install CHP and CCHP plants in 25% of new design and 10% of the existing buildings in 2010. In this regard, the total installed CCHP power generation capacity in the United States reached about 80 GW in 2004 [20]. The overall capacity of installed CCHP and CHP was included only 8% of the US generation capacity in August 2012. Also, President Administration has been supported to install 40 GW of new, cost-effective CHP, CCHP, and polygeneration by 2020 [20].

3.7 Other countries Different government policies and laws were introduced and applied in various countries, including Iran, Russia, Brazil, Mexico, India, and South Africa, for the development of CHP and CCHP systems [20,22 25].

References [1] Manolas DA, et al. Operation optimization of an industrial cogeneration system by a genetic algorithm. Energy Convers Manage 1997;38(15 17):1625 36. [2] Havelsky´ V. Energetic efficiency of cogeneration systems for combined heat, cold and power production. Int J Refrig 1999;22(6):479 85.

References

[3] Ho J, Chua K, Chou S. Performance study of a microturbine system for cogeneration application. Renew Energy 2004;29(7):1121 33. [4] Liang YW, Hu ZJ, Chen YP. A survey of distributed generation and its application in power system. JPST 2003;12(27):72 7. [5] Najjar YS. Enhancement of performance of gas turbine engines by inlet air cooling and cogeneration system. Appl Therm Eng 1996;16(2):163 73. [6] Tsay MT, Lin WM. Application of evolutionary programming to optimal operational strategy cogeneration system under time-of-use rates. Int J Electr Power Energy Syst 2000;22(5):367 73. [7] Chicco G, Mancarella P. Matrix modelling of small-scale trigeneration systems and application to operational optimization. Energy 2009;34(3):261 73. [8] Mago P, Chamra L, Hueffed A. A review on energy, economical, and environmental benefits of the use of CHP systems for small commercial buildings for the North American climate. Int J Energy Res 2009;33(14):1252 65. [9] Mago P, Fumo N, Chamra LM. Performance analysis of CCHP and CHP systems operating following the thermal and electric load. Int J Energy Res 2009;33 (9):852 64. [10] Oh SD, et al. Optimal planning and economic evaluation of cogeneration system. Energy 2007;32(5):760 71. [11] Sahoo P. Exergoeconomic analysis and optimization of a cogeneration system using evolutionary programming. Appl Therm Eng 2008;28(13):1580 8. [12] Tsatsaronis G, Morosuk T. A general exergy-based method for combining a cost analysis with an environmental impact analysis: part II—Application to a cogeneration system. ASME 2008 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers; 2008. [13] Wu D, Wang R. Combined cooling, heating and power: a review. Prog Energy Combust Sci 2006;32(5 6):459 95. [14] Mahlia T, Chan P. Life cycle cost analysis of fuel cell based cogeneration system for residential application in Malaysia. Renew Sustain Energy Rev 2011;15(1):416 26. [15] Stojkov M, et al. CHP and CCHP systems today. Int J Electr Comput Eng 2011;2 (2):75 9. [16] Mago PJ, Luck R. Evaluation of a base-loaded combined heating and power system with thermal storage for different small building applications. Int J Energy Res 2013;37(2):179 88. [17] Choudhury A, et al. Application of solid oxide fuel cell technology for power generation—a review. Renew Sustain Energy Rev 2013;20:430 42. [18] Moghadam RS, et al. Sizing a solar dish Stirling micro-CHP system for residential application in diverse climatic conditions based on 3E analysis. Energy Convers Manage 2013;75:348 65. [19] Gu Q, et al. Integrated assessment of combined cooling heating and power systems under different design and management options for residential buildings in Shanghai. Energy Build 2012;51:143 52. [20] Jradi M, Riffat S. Tri-generation systems: energy policies, prime movers, cooling technologies, configurations and operation strategies. Renew Sustain Energy Rev 2014;32:396 415. [21] Wu DW, Wang RZ. Combined cooling, heating and power: a review. Prog Energy Combust Sci 2006;32(5):459 95.

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CHAPTER 3 Applications of cogeneration and polygeneration

[22] Silveira JL, Gomes La. Fuel cell cogeneration system: a case of technoeconomic analysis. Renew Sustain Energy Rev 1999;3(2):233 42. [23] Roy-Aikins JEA. Cogeneration in rural development. Energy 1995;20(2):95 104. [24] Szklo AS, Tolmasquim MT. Strategic cogeneration—fresh horizons for the development of cogeneration in Brazil. Appl Energy 2001;69(4):257 68. [25] Ebrahimi M, Keshavarz A. Climate impact on the prime mover size and design of a CCHP system for the residential building. Energy Build 2012;54:283 9.

CHAPTER

Thermodynamic modeling and simulation of cogeneration and polygeneration systems

4

Chapter Outline 4.1 Introduction ................................................................................................... 39 4.1.1 The first law of thermodynamics .....................................................40 4.1.2 The second law of thermodynamics .................................................40 4.2 Modeling of CGAM cogeneration plant ............................................................. 40 4.3 Thermodynamic modeling of a combined cooling, heating, and power system .... 41 4.4 Thermodynamic modeling of a polygeneration system ....................................... 46 4.5 Thermodynamic modeling of a hybrid solar
geothermal cogeneration plant ...... 48 References ............................................................................................................ 54

4.1 Introduction In the thermodynamic modeling, we must first define a control system. After defining the thermodynamic system, everything surrounding it is called the system environment. The interest of engineers and researchers is to find the relationship between the system and its surroundings. In thermodynamic analysis, the system can include a specific part of the substance (CM control mass) or part of the material (volume of CV control). In the control system, while the system is in a thermodynamic process, energy can cross the boundaries of the system. The control mass system is also called a closed system, because no mass can escape from its range. But in the volume control systems (open system), both the energy and the material can cross the boundaries of the system. CV shape and size must be constant [1]. The energy system consists of three parts: kinetic energy, potential energy, and internal energy. Kinetic energy and macroscopic potential are visible. Inner energy is related to turbulence in molecules and is not directly visible. In thermodynamic analysis the energy of the entire system is calculated by acquiring the energy of each individual component.

Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00004-5 © 2021 Elsevier Inc. All rights reserved.

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CHAPTER 4 Thermodynamic modeling and simulation of cogeneration

4.1.1 The first law of thermodynamics The first law of thermodynamics states that energy does not exist and does not disappear but can be transformed from one type to another. Energy in a closed system is obtained from the following equation: E is the total energy of the system, e is the internal energy per unit mass, g gravity acceleration, and two other terms of kinetic and potential energy. When the system changes, the energy change in the system is expressed by the general form of energy balance. Energy generated in the system (reaction) 1 energy lost from the system 2 energy imported into the system 5 energy stored in the system.

4.1.2 The second law of thermodynamics In many reactions around us, the reaction to the move is merely possible. And it cannot be reversed. For example, oxygen and hydrogen generate water quickly, while the reaction photo (electrolysis) is not possible without energy. Another example is adding hot milk to coffee. As soon as it is added to the milk of coffee, the action is unavailable. These events are justified by the second law of thermodynamics. Contrary to the first law, the second law deals with the process. To better understand the second law of thermodynamics, we introduce a new thermodynamic property called entropy. The entropy of the system is used to measure irregularities in molecules and disturbance in the microscopic level. The turmoil in the system reduces the useful energy for doing work. The law states that in an isolated system, entropy can be generated but not eliminated. ΔS 5 Sfinal 2 Sinitial $ 0 for the isolated system In fact, the higher the temperature of the system, the more the movement, and movement of the particles will be greater and the degree of irregularity is higher.

4.2 Modeling of CGAM cogeneration plant A cogeneration plant based on CGAM problem is considered. CGAM system includes a basic gas turbine cycle with regeneration for power generation and a heat recovery steam generator (HRSG) for steam production. The plant produces two products: an electrical power of 30 MW and 14 kg/s of saturated steam at 20 bar [2,3]. The structure of the CGAM cogeneration plant and the temperature profiles of the air preheater and the HRSG are shown in is shown in Fig. 4.1. The thermodynamic relations, input assumptions and output parameters as unknown variables for each component are shown in Table 4.1. The thermodynamic assumptions for modeling and simulation are as follows:

• Air and the combustion gases are considered ideal gases and the specific heats are constant.

4.3 Thermodynamic modeling of a combined

Exhaust gases 7 Feedwater 8

T6

Combustion gases

T7P

Economizer HRSG

8P 1

9

7P

P : Pinch point

T7

T9 T 8P

Process steam

Water

Evaprator

T8 HRSG

6

Air

T5

5 Combustion chamber

1

3

2

Combustion gases

T3

T6

4

Air preheater

Air T2 Fuel Net power Air preheater

Compressor

Gas turbine

FIGURE 4.1 Schematic of CGAM cogeneration plant [2].

• Fuel is assumed to be pure methane. • All the components, except for the combustion chamber, are adiabatic. Table 4.2 indicates the specification of each stream of CGAM cogeneration plant after simulation [1].

4.3 Thermodynamic modeling of a combined cooling, heating, and power system Thermodynamic modeling of a combined cooling, heating, and power system that can be used in different industries is considered. The schematic of the proposed system is shown in Fig. 4.2 that includes gas turbine package, single-pressure HRSG, steam turbine, thermal collectors, heat exchanger for heating, and absorption chiller for cooling. The thermodynamic modeling of the main components of this system are shown in Table 4.3. The steam generated by HRSG drives a steam turbine and it produces extra power. Then the exiting stream from the steam turbine provides the input energy for either heating or cooling part, depend on desired demand. Decreasing the fuel consumption will reduce the emission of the pollutants. In addition, the thermal efficiency of the overall cycle will increase by reduction of the input energy.

41

Table 4.1 Relations, inputs, and outputs of the CGAM cogeneration system. Component

Relations

Inputs

Outputs

Air compressor

_ air ðh2 2 h1 Þ WAC 5 m T1 5 T0 P1 5 P0 P2 5 P18 3 rp;AC 9 < i= 1 h T2 5 T1 1 1 rp;AC ðγair 21Þ=γair 2 1 : ; ηAC

T0 5 298:15K P0 5 1:013 bar rp;AC 5 8:5234 ηAC 5 0:8468 γair 5 1:4 Rair 5 0:287 kJ=ðkg KÞ

_ AC w _ air m T2 ; P2

Combustion chamber

_ 1:cc _ air h3 1 m _ fuel LHVfuel 5 m _ gas h4 1 Q m _ _ fuel LHVfuel ð1 2 ηcc Þ Q1:cc 5 m _ air 1 m _ fuel 5 m _ gas m P4 5 P3 ð1 2 ΔPCC Þ

LHVfuel 5 50; 000 kJ=kg ηCC 5 0:98 ΔPCC 5 0:05 T3 5 914:28K T4 5 1492:63K

_ 1:cc Q p3 p4 _ gas m _ fuel m

APH

_ air cp;air ðT3 2 T2 Þ 5 m _ gas cp;gas ðT5 2 T6 Þ m P3 5 P2 ð1 2 ΔPa;APH Þ P6 5 P5 ð1 2 ΔPg;APH Þ

cp;air 5 1:004 kJ=ðkg KÞ cp;gas 5 1:17 kJ=ðkg KÞ ΔPa;APH 5 0:05 ΔPg;APH 5 0:03

p5 p6 T6

Gas turbine

_ g cp;g ðT4 2 T5 Þ _ GT 5 m w _ net 5 w _ GT 2 w _ AC w

ηGT 5 0:8786 _ net 5 30 MW w

w0GT T5

HRSG

_ g cp;g ðT6 2 T7p Þ 5 m _ steam ðh9 2 h8p Þ m T8p 5 T9 2 ΔTA ΔTpinch 5 T7p 2 T9 . 0 _ steam ðh9 2 h8p Þ T 2m T7 5 6 _ g cp;g m p0 5 p6 ð1 2 ΔTHRSG Þ

ΔTA 5 15K ðh9 2 h8p Þ 5 1956 ðh9 2 h8 Þ 5 2690 kJ=kg ΔPHRSG 5 0:05 kJ=kg _ steam 5 14 kg=s m T8 5 298:15K p8 5 20 bar p9 5 20 bar

T7 p7 T7p T8p T9

APH, Air preheater; HRSG, heat recovery steam generator.

4.3 Thermodynamic modeling of a combined

Table 4.2 Specification of each stream—CGAM cogeneration system [1,3]. Thermodynamic analysis Stream

Material of stream

_ (kg/s) m

T (K)

p (bar)

1 2 3 4 5 6 7 8 10

Air Air Air CG CG CG CG Water CH4

91.28 91.28 91.28 92.92 92.92 92.92 92.92 14.00 1.64

298.1 603.7 850.0 1520.0 1006.2 779.8 426.9 298.1 298.1

1.01 10.13 9.62 9.14 1.10 1.07 1.01 20 12

FIGURE 4.2 Schematic of proposed CCHP system. CCHP, Combined cooling, heating, and power.

Using the solar energy in the solar collector causes less demand for the fuel consumption which leads to decrease the operating costs and the environmental impacts as well. As shown in Table 4.3, thermodynamic modeling of the proposed system is based on related thermodynamic equations, thermodynamic assumptions, and input and output parameters. Thermodynamic equations and assumptions are indicated as thermodynamic relation in Table 4.3. Input parameters are referred to as determinate parameters, and, also, output parameters as unknown variables that are calculated by thermodynamic relations and input parameters. The gas turbine package consists of air compressor, combustion chamber, and gas turbine or expander.

43

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CHAPTER 4 Thermodynamic modeling and simulation of cogeneration

Table 4.3 Relations, inputs, and outputs of the combined cooling, heating, and power system. Component

Relations

Inputs

Outputs

Air compressor

_ air ðh2 2 h1 Þ WAC 5 m T1 5 T0 P1 5 P0 P2 5 P18 3 rp;AC 9 < i= 1 h T2 5 T1 1 1 rp;AC ðγair 21Þ=γair 2 1 : ; ηAC

T0 ; P0 rp;AC cair ; γ air _ air m

WAC T2 ; P2 ηAC

LHVfuel ΔPCC _ air T2 ; P2 ; m _ fuel m _ fg T3 ; P3 ; m T4 Wnet;gc

QCC T3 ; P3 _ fg m ηCC

_ fg ; T4 ; P4 m ΔPSH;fg ΔPSH;s _s TSH ; m T15 ; P15

QSH T5 ; P5 T16 ; P16

_ fg ; T5 ; P5 m ΔPEV;fg ; ΔPEV;s _s m T14 ; P14

QEV T6 ; P6 T15 ; P15

_ fg ; T6 ; P6 m ΔPEC;fg ; ΔPEC;s _s m T13 ; P13

QEC T7 ; P7 T14 ; P14

T7 ; P7 T10 ; P10 _s m

T8 ; P8 T11 ; P11

Combustion chamber

Gas turbine

h1 5 hair@T1 ; h2 5 hair@T2 _ air h2 1 m _ fuel LHVfuel ηCC 2 m _ fg h3 5 0 m _ air 1 m _ fuel 2 m _ fg 5 0 m P3 5 P2 ð1 2 ΔPCC Þ h3 5 hfg@T3 _ WGT 5 m n fg ðh3 2 hh4 Þ io T4 5 T3 1 2 η 1 2 rp;GT ð12γfg Þ=γfg GT

Superheater

Evaporator

Economizer

Deaerator

rp;GT 5 P3 =P P4 P4 5 P0 1 ΔPHRSG;fg Wnet;gc 5 WGT 2 WAC h4 5 hfg@T4 _ fg ðh4 2 h5 Þ 1 m _ s ðh15 2 h16 Þ 5 0 m P5 5 P4 2 ΔPHPSH;fg P16 5 P15 2 ΔPHPSH;s T16 5 TSH _ s ðh16 2 h15 Þ QSH 5 m h5 5 hfg@T5 ; h16 5 hwater@T16 ;P16 _ fg ðh5 2 h6 Þ 1 m _ s ðh14 2 h15 Þ 5 0 m P6 5 P5 2 ΔPEV;fg P15 5 P14 2 ΔPEV;s T15 5 Tsat@P15 _ s ðh15 2 h14 Þ QEV 5 m h6 5 hfg@T6 ; h15 5 hg;water@P15 _ fg ðh6 2 h7 Þ 1 m _ s ðh13 2 h14 Þ 5 0 m P7 5 P6 2 ΔPEC;fg P14 5 P13 2 ΔPEC;s T14 5 Tsat@P14 _ s ðh14 2 h13 Þ QEC 5 m h7 5 hfg@T7 ; h14 5 hf ;water@P14 _ fg ðh7 2 h8 Þ 1 m _ s ðh10 2 h11 Þ 5 0 m P11 5 PDEA T11 5 Tsat@P11 h11 5 hf ;water@P11

WGT ηGT

(Continued)

4.3 Thermodynamic modeling of a combined

Table 4.3 Relations, inputs, and outputs of the combined cooling, heating, and power system. Continued Component

Relations

Inputs

Outputs

Steam turbine

_ s ðh16 2 h17 Þ 2 WST 5 0 m h17 5 h16 2 ηST ðh16 2 h17s Þ P17 5 Pcond

T16 ; P16 ηST Pcond _s m

WST T17 ; P17

Process heat exchanger

_ s ðh18 2 h19 Þ 1 m _ hw ðh26 2 h27 Þ 5 0 m _ s ðh18 2 h19 Þ Qcond 5 m T27 5 T26 1 ΔThw P27 5 P26 2 ΔPcond P19 5 P18 T19 5 Tsat@P19 h19 5 hf ;water@P19 ; h27 5 hwater@T27 ;P27 _ s ðh20 2 h21 Þ 1 m _ chilled ðh24 2 h25 Þ m _ coolant ðh22 2 h23 Þ 5 0 1m _ chilled ðh24 2 h25 Þ Qchilled 5 m _ s ðh20 2 h21 Þ Qabs;s 5 m Qchilled COPabs 5 Qabs;s

T18 ; P18 T26 ; P26 ΔThw

Qcond T19 ; P19 T27 ; P27

Tcoolant Pcoolant Tchilled;in Pchilled;in Tchilled;out dpr;chilled ΔTcoolant dpr;coolant _s m COPabs

_ chilled m _ coolant m Qchilled Qabs;s T22 ; P22 T24 ; P24 T25 ; P25 T23 ; P23 h21 h22 ; h23 h24 ; h25

T9 ; P9 ; v9 PLPP

T10 ; P10 WLPP

T11 ; P11 ; v11 PHPP

T12 ; P12 WHPP

T12 ; P12 dpr;coll ul I F0 τα Ts Φ

T13 ; P13 ASF Qcoll

Condenser (absorption chiller)

Low-pressure pump High-pressure pump Solar flat plate collector

T22 5 Tcoolant ; P22 5 Pcoolant T24 5 Tchilled;in ; P24 5 Pchilled;in T25 5 Tchilled;out P25 5 ð1 2 dpr;chilled Þ 3 P24 T23 5 T22 1 ΔTcoolant P23 5 ð1 2 dpr;coolant Þ 3 P22 h21 5 hf ;water@P21 h22 5 hwater@T22 ;P22 ; h23 5 hwater@T23 ;P23 h24 5 hwater@T24 ;P24 ; h25 5 hwater@T25 ;P25 _ s ðh10 2 h9 Þ WLPP 5 m h10 5 h9 1 v9 ðP10 2 P9 Þ P10 5 PLPP _ s ðh11 2 h12 Þ WHPP 5 m h12 5 h11 1 v11 ðP12 2 P11 Þ P12 5 PHPP _ s ðh13 2 h12 Þ Qcoll 5 m T13 5 TSF P13 5 ð1 2 dpr;coll ÞP12 h13 5 hwater@T  13 ;P13   _ s Cp 0 m _ FR 5 1 2 e 2F ul ASF =ms Cp ul ASF

45

46

CHAPTER 4 Thermodynamic modeling and simulation of cogeneration

The thermodynamic relation, assumptions, and input and output parameters are indicated in Table 4.3. The one pressure HRSG includes superheater, evaporator, and economizer. The energy balance and input parameters are presented in Table 4.1. In addition, thermodynamic modeling for simulation of other components such as absorption chiller, solar flat plate collector, pumps, and steam turbine is based on relations of Table 4.3.

4.4 Thermodynamic modeling of a polygeneration system A polygeneration system that produces power, heat, and desalinated water is investigated. The integration of gas turbine package, HRSG with multieffect distillation system equipped with thermal vapor compression (MED-TVC) and reverse osmosis (RO) desalination is proposed. The schematic of the proposed cycle is demonstrated in Fig. 4.3. The flue gases from gas turbine package enter high-pressure superheater (HPSH) to heat recovery from gas exhausts. Then, it goes into high-pressure evaporator (HPEV) and high-pressure economizers (HPECO2 and HPECO1), respectively, and then enters to deaerator pressure evaporator (DPEV). Finally, flue gases exit from feedwater preheater (FWPH). RO 37 Cooling water discharge

38 Distillate

39 Brine

27 Steam supply

32 Seawater supply

33 Brine 30 Steam return

31

MED-TVC Process HEX

34 Distillate

Valve 2

29

28 35

36

Valve 1

16 26

Desuperheater

12 25

18

17

DE

24

14

HPP

13

22

20

10

11

FWPH

23

19

15

8

9

DPEV

21

HPEC1

6

7

HPEC2

HPEV

HPSH

5

1 4

2 3

FIGURE 4.3 Schematic of proposed polygeneration system [4].

4.4 Thermodynamic modeling of a polygeneration system

Table 4.4 Relations, inputs, and outputs of the polygeneration system: heat and power cycle. Component

Thermodynamic relations

Inputs

Outputs

High-pressure superheater

_ fg ðh5 2 h6 Þ 1 m _ hrsg ðh22 2 h23 Þ 5 0 m P6 5 P5 2 ΔPHPSH;fg P23 5 P22 2 ΔPHPSH;s T23 5 Tsat;HPSH 1 Tsup;HPSH _ hrsg ðh23 2 h22 Þ QHPSH 5 m h6 5 hfg@T6 ; h23 5 hwater@T23 ;P23 _ fg ðh6 2 h7 Þ 1 m _ hrsg ðh21 2 h22 Þ 5 0 m P7 5 P6 2 ΔPHPEV;fg P22 5 P21 2 ΔPHPEV;s T22 5 Tsat@P22 _ hrsg ðh22 2 h21 Þ QHPEV 5 m h7 5 hfg@T7 ; h22 5 hg;water@P22 _ fg ðh7 2 h8 Þ 1 m _ hrsg ðh20 2 h21 Þ 5 0 m P8 5 P7 2 ΔPHPEC2;fg P21 5 P20 2 ΔPHPEC2;s T21 5 Tsat;HPEC2 2 Tsub;HPEC2 _ hrsg ðh21 2 h20 Þ QHPEC2 5 m h8 5 hfg@T8 ; h21 5 hwater@P21 ;T21 _ fg ðh8 2 h9 Þ 1 m _ hrsg ðh19 2 h20 Þ 5 0 m P9 5 P8 2 ΔPHPEC1;fg P20 5 P19 2 ΔPHPEC1;s T20 5 Tsat;HPEC1 2 Tsub;HPEC1 _ hrsg ðh20 2 h19 Þ QHPEC1 5 m h9 5 hfg@T9 ; h20 5 hwater@P20 ;T20 _ fg ðh9 2 h10 Þ 1 m _ de ðh15 2 h16 Þ 5 0 m P10 5 P9 2 ΔPDPEV;fg P16 5 P15 2 ΔPDPEV;s T16 5 Tsat@P19 _ de ðh16 2 h15 Þ QDPEV 5 m h10 5 hfg@T10 ; h16 5 hg;water@P16 _ fg ðh10 2 h11 Þ 1 m _ fw ðh12 2 h13 Þ 5 0 m P11 5 P10 2 ΔPFWPH;fg P13 5 P12 2 ΔPFWPH;s T13 5 Tsat;FWPH 2 Tsub;FWPH _ fw ðh13 2 h12 Þ QFWPH 5 m h11 5 hfg@T11 ; h13 5 hwater@P13 ;T13 _ fw h13 1 m _ de h16 2 ðm _ fw 1 m _ de Þh14 5 0 m P14 5 PDEA T14 5 Tsat@P14 h14 5 hf ;water@P14 s14 5 sf ;water@p14

_ fg ; T5 ; P5 m ΔPHPSH;fg ΔPHPSH;s Tsup;HPSH T22 ; P22

QHPSH T6 ; P6 T23 ; P23 _ hrsg m

_ fg ; T6 ; P6 m ΔPHPEV;fg ΔPHPEV;s T21 ; P21

QHPEV T7 ; P7 T22 ; P22 _ hrsg m

_ fg ; T7 ; P7 m ΔPHPEC2;fg ΔPHPEC2;s T20 ; P20 Tsub;HPEC2

QHPEC2 T8 ; P8 T21 ; P21 _ hrsg m

_ fg ; T8 ; P8 m ΔPHPEC1;fg ΔPHPEC1;s T19 ; P19 Tsub;HPEC1

QHPEC1 T9 ; P9 T20 ; P20 _ hrsg m

_ fg ; T9 ; P9 m ΔPDPEV;fg ΔPDPEV;s T15 ; P15

QDPEV T10 ; P10 T16 ; P16 _ de m

_ fg ; T10 ; P10 m ΔPFWPH;fg ΔPFWPH;s Tsub;FWPH T12 ; P12

QFWPH T13 ; P13 T11 ; P11 _ fw m

T16 ; P16 T13 ; P13

T14 ; P14 ; s14 _ fw mde ; m

High-pressure evaporator

High-pressure economizer 2

High-pressure economizer 1

Evaporator

Feedwater preheater

Deaerator

(Continued)

47

48

CHAPTER 4 Thermodynamic modeling and simulation of cogeneration

Table 4.4 Relations, inputs, and outputs of the polygeneration system: heat and power cycle. Continued Component

Thermodynamic relations

Inputs

Outputs

High-pressure pump

_ fw ðh18 2 h17 Þ WHPP 5 m P18 5 PHPP s18s 5 s17 h18s 5 swater@p18 ;s18s h18s 2 h17 h18 5 h17 1 ηHPP

T17 ; P17 ; s17 PHPP ηHPP

T18 ; P18 WHPP

T18 5 Twater@p18 ;h18 _ desw h25 1 m _ dess h24 2 m _ s h26 5 0 m P26 5 P25 T26 5 Tsat;DES 1 Tsup;DES h26 5 hwater@T26 ;P26

p24 ; T24 p25 ; T25 Tsup;DES

p26 ; T26

Valve 1

h27 5 h26 T27 5 Twater@p27 ;h27

p26 ; T26 ; h26 p27

p27 ; T27 ; h27

Valve 2

h31 5 h30 T31 5 Twater@p31 ;h31

p30 ; T30 ; h30 p31

p31 ; T31 ; h31

Desuperheater

The high-pressure steam from HPSH goes into desuperheater and process heat exchanger (HEX). The steam from valve 1 enters to MED-TVC desalination system to produce desalinated water. The cooling water discharge from MED-TVC desalination system enters to RO unit to produce extra desalinated water. The thermodynamic relations, assumptions, and inputs and output parameters for heat and power cycle are determined in Table 4.4. The heat and thermodynamic relations, input and unknown parameters for MED-TVC are determined in Table 4.5. In addition, modeling of RO desalination is indicated in Table 4.6.

4.5 Thermodynamic modeling of a hybrid solar
geothermal cogeneration plant A solar
geothermal hybrid cogeneration plant is proposed to produce power and heat. In this regard, the solar-steam Rankine cycle is integrated into the geothermal section and the organic Rankine cycle (ORC). The schematic diagram of the plan is shown in Fig. 4.4. The temperature and pressure of the boiler and the geothermal source are assumed to be 150 C and 10 C, respectively. This thermal source is coupled with a downstream heat exchanger. In order to minimize geothermal source cooling and reduce problems such as the formation of silica, it is assumed that the salt is released at a minimum temperature of 72 C [8]. R134a has been selected as the

4.5 Thermodynamic modeling of a hybrid

49

Table 4.5 Relations, inputs, and outputs of the polygeneration system: multieffect distillation system equipped with thermal vapor compression (MED-TVC). Component

Relations

Inputs

Outputs

MED-TVC

_D _D m m Performance:PR 5 ; RR 5 _s _F m m

n _D m Ts xsw TTD RR Te ðnÞ Tsw;in ; psw;in pmotive2steam

SA _s m _F m _ sw m _ cwd m xB PR

Mass balance ith effect:FXF 5 BXB ; FXF 5 Be XBe Energy balance ith effectDc ΔhDc 5 DhD 1 BhB 2 FhF Boiling-point elevation:  BPED 5 TD 2 TDsat Area: Dc ΔhDc 5 Ae Ue TDprev 2 Te sat 23

Ue 5 10 ½1939:1 1 1:40562ðTDprev sat 2 0:0207525ðTDprev 2273:15Þ2 sat 1 0:0023186ðTDprev 2273:15Þ3  sat

[5]

2 273:15Þ

Terminal temperature difference:TTDe 5 Tc 2 Te

Mass balance ith flashbox:Dbd 1 Dfb 5 Dinbd 1 Dc Energy balance ith flashbox:Dbd hDbd 1 Dfb hDfb 5 Dinbd hDin 1 Dc hDc bd out in _ feedheater:Qfh 5 Dc ðhinDc 2 hout Dc Þ 5 mF ðhm _FÞ _ F 2 hm T in_ 2 T out _F mF  m  Qfh 5 Afh Ufh  in ln TDc;sat 2 T out = T Dc;sat 2 T _ _F m mF Ufh 5 1023 ½1617:5 1 0:1537ðTDc;sat 2 273:15Þ 1 0:1825ðTDc;sat 2273:15Þ2 2 0:00008026ðTDc;sat 2273:15Þ3  n X _D5 m DðiÞ i51

_ s 5 Dc ð1Þ m _ F 5 Fð1Þ m _ B 5 BðnÞ m

P

Specific area:SA 5

Ae 1

P _D m

Afh 1 Ac

TVC: pmotive2steam Er 5 psuction Cr 5

pdischarge psuction

Mr 5

_ motive2steam m _ suction m

Mr 5 2 1:9342 1 2:1525Cr 1

113:49 2 0:52Cr 2 [6] Er

2

14; 735:96 31:85Cr 2 1 0:047Cr 3 Er Er 2

1

900; 786 495:6Cr 10:02Cr 2 2 1 3 2 Er Er Er

_s5 m

_ s;1st effect m 1 1 Mr

50

CHAPTER 4 Thermodynamic modeling and simulation of cogeneration

19

18

Solar field Pump 20 21

Pump

14

15

Superheater

Evaporator

Economizer

16

Steam cycle

Condenser Steam turbine

13 17

Pump

11 12

Pump 9

18

8

10

4

7

Evaporator

Geothermal brine outlet

ORC turbine

Binary cycle (R134a)

24

Geothermal brine from production well 5

Condenser

Process 2

6

1

Pump

23

Recuperator

22 3

FIGURE 4.4 Schematic of hybrid solar
geothermal plant cogeneration plant.

working fluid of the ORC. This fluid receives the geothermal heating element by a heat exchanger and reaches an approximate temperature of 150 C. The expedition temperature of the geothermal cycle is assumed to be approximately 150 C. Using the flow control controller, the R134a discharge is determined in such a way that the output temperature of the boiler from the converter reaches 72 C. In order to increase the efficiency of the downstream cycle, a recuperator is used at the turbine output [8].

4.5 Thermodynamic modeling of a hybrid

Table 4.6 Relations, inputs, and outputs of the polygeneration system: reverse osmosis (RO). Component

Thermodynamic relations

RO

RR 5

_D m _F m

RR 5 RR9T525 3

Jw Jw jT525

_ F 5m _ D 1m _B m _ F 5m _ cwd;MED m _ F ðpFeed 2 p37 Þ 3 100 _ RO 5 m W ρ 3 ηpump Jw 5

 Dw Cw Vw  ðpF 2 pD Þ 2 ðπF 2 πD Þ RTe½K

πi 5

385 3 sali 3 Ti 0:14507ð1000 2 10sali Þ

[7]

Inputs

Outputs

_F m T37 ; p37 pFeed T R e 5 2 3 1026 ðmÞ Vw 5 18ðm3 =molÞ Cw 5 ρ MWw k:Boltzmann ηRO2pump

_ D; m _B m RR _ RO W p38 ; T38 p39 ; T39 x39

T:average temp: of RO R:universal gas constant e:membrane thickness Vw :water molar volume Cw :water concentration k 3 T½K Dw 5 3πF μw ds μw 5 4:23 3 1025 1 ½0:157ðTF 164:993Þ2 291:29621 ds 5 0:076 MWw ds :stocks diameter MW:molcular weight salF salB 5 1 2 RR h39 5

h37 2 RR 3 h38 1 2 RR

In the proposed hybrid power plant, the linear parabolic collectors with the lubricant oil (terminal VP1) have been used as the heat transfer fluid interface. The solar part of the storage enclosure has no thermal energy, and the exhaust temperature of the particle collectors used is 395 C. In order to circulate the flow of fluid with controlled discharge, a pump is used to allow the fluid to reach the desired outlet temperature at the collector output. On the other hand, a series of heat exchangers have been used to transfer the direct heat of solar energy to the steam’s Rankine cycle [8]. Economizers, evaporators, and superheaters are used to heat transfer from solar collector to the Rankine cycle. Water as a working fluid enters the

51

52

CHAPTER 4 Thermodynamic modeling and simulation of cogeneration

Table 4.7 Relations, inputs, and outputs of the equipment. Component

Relations

Inputs

Outputs

ORC pump

WORC;Pump 5 mORC;cycle ðh2 2 h1 Þ P2 5 P1 1 ΔpORC; Pump h2 5 ðh2s 2 h1 ÞηORC;Pump 1 h1

WORC;Pump h2 P2

Coupling pump

WCouplingpump 5 m11 ðh11 2 h9 Þ P11 5 P9 1 ΔpCP h11 5 ðh11sη 2 h9 Þ 1 h9

P1 ; ΔpORC;Pump ηORC;Pump h1 P9 ; ΔpCP ηCP h9 m11 P25 ; ΔpSP ηSP h25

CP

WCp h11 P11

Steam pump

WSteampump 5 mSC ðh13 2 h25 Þ P13 5 P25 1 ΔpSP h13 5 ðh13sη2 h25 Þ 1 h25

HTF pump

WHTFpump 5 mHTF ðh18 2 h24 Þ P18 5 P24 1 ΔpHTFP h18 5 ðh18sη 2 h24 Þ 1 h24

P24 ; ΔpHTFP ηHTFP h24

WHTFP h18 P18

ORC turbine

WORCturbine  5 mORCcycle ðh4 2 h5 Þ P5 5 P6 = 1 1 ΔpORC;Recuperator h5 5 h4 2 ðh4 2 h5s ÞηORC;Turbine P6 5 PORC;Condenser

WORC;Turbine h4 PORC;Condenser ΔpORC;Recuperator ηORC;Turbine

mORCcycle h5 P5

Steam turbine

WSteamturbine 5 mSC ðh16 2 h17 2 h24 Þ h17 5 h16 2 ðh16 2 h17s 2 h24 ÞηST P17 5 PSteam;Condenser

ORC recuperator

h3 2 h2 5 h6 2 h5 P6 5 PORC;Condenser P2 P3 5 ð1 2 ΔpORC;Recuperator Þ

mSC h17 P17 m24 h6 P6 P3

ORC condenser

mORCcycle ðh6 2 h1 Þ 5 mCW ðh23 2 h22 Þ T23 5 T22 1 ΔTCW P23 5 P22 ð1 2 ΔPCW Þ h1 5 h@x50;P5PORC;Condenser h22 5 h@T5T22 ;P5P22 h23 5 h@T5T23 ;P5P23

Steam condenser

mSC ðh17 2 h25 Þ 5 m11 ðh12 2 h11 Þ T12 5 150ð CÞ; P12 5 10ðbar Þ h25 5 h@x50;P5PSteam condenser h12 5 h@T5T12 ;P5P12

WST h16 ηST PSteam;Condenser h2 h3 h5 PORC;Condenser ΔpORC;Recuperator P2 mORC;cycle h6 h22 h1 T22 ; P22 ΔTCW ΔPCW PORC;Condenser mSC h17 h11 T12 ; P12 PSteamcondenser

WSP h13 P13

SP

HTFP

h1 mCW h23 T23 ; P23

h25 h12 m11

(Continued)

4.5 Thermodynamic modeling of a hybrid

Table 4.7 Relations, inputs, and outputs of the equipment. Continued Component

Relations

Inputs

Outputs

ORC evaporator

mORCcycle ðh4 2 h3 Þ 5 mBrine ðh8 2 h9 Þ mBrine 5 100 kg=s h4 5 h@T5T4 ;P5P4 h8 5 h@T5T8 ;P5P8 h9 5 h@T5T9 ;P5P9 mSC ðh14 5 h13 Þ 5 mHTF ðh21 2 h24 Þ h14 5 h@x50;P5P14

mORCcycle h4 mBrine

h3 h8 h9

mSC h13 mHTF h21 mSC h14 mHTF h20 mSC h15 mHTF h19 h18 Rf etaopt fshad Ms Acoll DNI mHTF

h14 h24

Steam economizer

Steam evaporator

mSC ðh15 2 h14 Þ 5 mHTF ðh20 2 h21 Þ h15 5 h@x51;P5P15

Steam superheater

mSC ðh16 2 h15 Þ 5 mHTF ðh19 2 h20 Þ h16 5 h@T5T16 ;P5P16

Solar field

Qu 5 Rf Qcoll 2 Qloss;abs 2 Qloss;pipe Qu 5 mHTF ðh19 2 h18 Þ Qcoll 5 ηopt fshad Ms Acoll DNI h19 5 h@T5T19 ;P5P19

h15 h21

h16 h20

Qcoll Qloss;abs Qloss;pipe Qu h19

ORC, Organic Rankine cycle; HTF, heat transfer fluid.

turbine after heat absorption and is finally poured into the converters through the pump after the heat dissipation in the condenser. Equipment used in this cycle is high-pressure equipment. Under the design conditions the superconducting steam enters a turbine at a constant temperature and leaves the turbine [8]. In order to integrate the two cycles, the geothermal source is used as a heat transfer fluid interface. Due to reheat, the low-temperature jet of the output of the geothermal cycle is directed to the condenser, and by using a recurring cycle, this brine is mixed with the original salt. With this, only the heat source of the solar cycle can be if the energy source of the downstream cycle is geothermal energy, and the thermal energy obtained from the high-cycle condenser is handed. The pump and flow controller are used to adjust the temperature of the solar water outlet from the condenser at 150 C [8]. Thermodynamic modeling of different components of the proposed cycle is shown in Table 4.7.

53

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CHAPTER 4 Thermodynamic modeling and simulation of cogeneration

References [1] Bejan A, Tsatsaronis G, Moran MJ. Thermal design and optimization. Wiley; 1995. [2] Lazzaretto A, Toffolo A. Energy, economy and environment as objectives in multicriterion optimization of thermal systems design. Energy 2004;29:1139
57. [3] Tsatsaronis G, Morosuk T. A general exergy-based method for combining a cost analysis with an environmental impact analysis: part I—theoretical development. In: ASME 2008 international mechanical engineering congress and exposition. 2008. [4] Vazini Modabber H, Khoshgoftar Manesh MH. Exergetic, exergoeconomic and exergoenvironmental multi-objective genetic algorithm optimization of Qeshm power and water cogeneration plant. Gas Process 2019;7(2):1
28. [5] Mistry KH, Antar M, Lienhard V JH. An improved model for multiple effect distillation. Desalin Water Treat 2012;51:1
15. [6] Hassan AS, Darwish MA. Performance of thermal vapor compression. Desalination 2014;335(1):41
6. [7] Al-Zahrani A, et al. Thermodynamic analysis of a reverse osmosis desalination unit with energy recovery system. Procedia Eng 2012;33:404
14. [8] Bonyadi N, Johnson E, Baker D. Technoeconomic and exergy analysis of a solar geothermal hybrid electric power plant using a novel combined cycle. Energy Convers Manage 2018;156:542
54.

CHAPTER

Exergy and thermoeconomic evaluation of cogeneration and polygeneration systems

5

Chapter outline 5.1 Introduction ................................................................................................... 55 5.2 Definition of exergy ........................................................................................ 56 5.2.1 Dead state ....................................................................................56 5.2.2 Dead state limited .........................................................................56 5.2.3 Definition of the environment from the perspective of exergy analysis 57 5.2.4 Exergy ..........................................................................................57 5.2.5 Thermoeconomic ...........................................................................57 5.3 Exergy and thermoeconomic modeling ............................................................. 58 5.3.1 Physical exergy .............................................................................58 5.3.2 Chemical exergy ............................................................................59 5.3.3 Exergy destruction .........................................................................60 5.3.4 Exergoeconomic modeling ..............................................................61 5.3.5 Exergy destruction level and exergy cost destruction level concept .....63 5.4 Case studies .................................................................................................. 65 5.4.1 Exergy and exergoeconomic modeling of CGAM cogeneration plant ....65 5.4.2 Exergy and exergoeconomic modeling of a CCHP .............................71 5.4.3 Exergy and exergoeconomic modeling of a polygeneration system ......71 References ............................................................................................................ 73

5.1 Introduction Exergy analysis, based on the first and second law of thermodynamics, has been considered since the beginning of the last century, and since 1930, this analysis has been expanded further. With the onset of the energy crisis in 1970 the exergy analysis was at the head of the thermodynamic research of the last three decades. According to the second law of thermodynamics in each real process, the generated entropy is proportional to the loss of exergy and its destruction during the process. By definition, exergy is the maximum useful work that comes from a certain amount of available energy or from the flow of materials. In the analysis of exergy the main purpose is determining the location and amount of production Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00005-7 © 2021 Elsevier Inc. All rights reserved.

55

56

CHAPTER 5 Exergy and thermoeconomic evaluation

of irreversibility during the various processes of thermodynamic cycle and the factors affecting the production of this irreversibility [1,2]. In this way, in addition to evaluating the efficiency of various components of the thermodynamic cycle, ways to increase cycle efficiency are also identified. In the exergy analysis, we try to obtain the greatest amount of work produced in the cycle by simultaneously applying the first and second rules of thermodynamics and using the surrounding environment as the reference state. Exergy analysis consists of two basic steps. The first step is to identify and investigate the undesirable thermodynamic processes of the system based on the determination of exergy losses. Exergy losses are obtained by writing the exergy equilibrium in different parts of the system. The second step is to determine the most possible corrections possible in the system based on the concepts of avoidable exergy waste and exergy losses. The smallest amount of exergy in the system during a process that cannot be ruled out by existing techniques and economic considerations is called the inevitable exergy waste. By definition, avoidable exergy losses determine the greatest potential for process optimization. Therefore a system is rapidly analyzed by exergy analysis, as well as the undesirable thermodynamic processes and the most possible corrections [1].

5.2 Definition of exergy If the matter reaches the dead state during a return process from its original condition, the maximum returnable work is obtained, which is called exergy or the ability to perform the work of the substance [1].

5.2.1 Dead state The dead state refers to the state of matter in which the material is in a thermal, mechanical, and chemical environment. In the dead state the material temperature is equal to the ambient temperature, and the pressure of the material in relation to atmospheric pressure and the material velocity relative to the environment is zero, and its potential energy is also zero [1].

5.2.2 Dead state limited A dead-end state is said to be in a state in which the material is in a thermal and mechanical equilibrium with a potential velocity and potential energy of zero. However, chemical balance between matter and environment may not exist [1].

5.2 Definition of exergy

5.2.3 Definition of the environment from the perspective of exergy analysis From the perspective of exergy analysis the large object environment is located in a complete thermodynamic equilibrium, in which there is no gradient of pressure, temperature, and chemical potential, and it is impossible to obtain the work of interacting its various components with each other. Any system that is out of the environment has at least one different parameter to the environment, and the interaction of that system and the environment is possible to obtain work. Therefore the environment is a reference body that is used to evaluate the potential of different systems [1].

5.2.4 Exergy Exergy is the maximum useful work of theory (axial or electric) that can be obtained from a thermal system when it is transmitted to a thermodynamic equilibrium and interacts only with its surroundings. Or equivalently, it can be said that exergy is the minimum theoretical (electric or axial) theory needed to determine a certain amount of matter in a state that is in equilibrium with the environment in a definite state. Therefore it can be interpreted that exergy represents the degree of deviation of the state of a system relative to the environment.

5.2.5 Thermoeconomic In order to evaluate and optimize the energy systems, it is necessary to compare the annual amounts of investment costs, fuel costs, and operating and maintenance costs. These cost components may significantly change the lifetime of energy system. Therefore in evaluating and optimizing these systems, annualized levels should be used for all cost components. The following sections illustrate the method of generating revenue requirements, known as total revenue requirement (TRR), based on the method developed by Electric Power Research Institute (EPRI) in 1993. This method calculates all the costs associated with a project and also includes a minimum return on the required capital. On the basis of the total investment cost and the assumptions for the economic, financial, operational, and commercial inputs, the total income requirement is calculated annually. At the end, uneven amounts of annual costs related to investment, performance (other than fuel costs), and maintenance and fuel costs of the system are calculated. In fact, by doing so, these values are converted into a series of constant equilibrium payments [1]. The economic analysis can be based on the current dollar price, taking into account the effect of inflation, or on the basis of the fixed dollar price of 2 and without regard to inflation. In general, studies that are related to time intervals are better than the current dollar, and in contrast to studies that take longer periods (e.g., 20
40 years to come), it is better to use fixed dollar.

57

58

CHAPTER 5 Exergy and thermoeconomic evaluation

Exergy analysis provides the required information for the complete assessment of the design and operation of an energy system from a thermodynamic point of view. However, we still need to know how much exergy damage to the components of the system will cost to the exploiter. Finding an insight about this cost is very useful in optimizing a system in terms of economic. In an analysis the thermoeconomic evaluation and optimization of the cost ratio associated with each flow of matter or energy is used to compute the thermoeconomic variables of the system components. Thermoeconomic variables deal with the cost of investment and the costs associated with thermodynamic inefficiencies based on logical decision criteria, changes in system structure, or parameter values that are specified. Therefore it can be said that minimizing the cost for a heating system involves finding the best balance between the cost of investment costs and the costs associated with exergy destruction.

5.3 Exergy and thermoeconomic modeling 5.3.1 Physical exergy In the absence of nuclear, magnetic, electrical, and surface tension effects, the total exergy of an Esys system can be divided into four parts [1]: PH Esys 5 Esys 1 EKN 1 EPT 1 ECH

(5.1)

The “sys” substrate distinguishes the total and physical exergies from other exergy values by including the transitions that are related to the flow of the material. The total exergy per unit of mass of the esys is as follows [1]: KN esys 5 ePH 1 ePT 1 eCH sys 1 e

(5.2)

The physical exergy for a thermodynamic system is calculated by the following equation: PH Esys 5 ðU 2 U0 Þ 1 p0 ðV 2 V0 Þ 2 T0 ðS 2 S0 Þ

(5.3)

where S, U, and V represent, respectively, the internal energy, volume, and entropy of the system. Subfield 0 indicates the same mode at the temperature T0 and the pressure P0 of the environment. This equation used for exergy analysis of nonflowing system or closed system. The physical exergy rate associated with the mass flow for open system is represented as the following [1]:

 PH E_ ms 5 H_ 2 H_ 0 2 T0 S_ 2 S_0

(5.4)

_ respectively, indicate the enthalpy and entropy rates. The subwhere H_ and S, script “0” also represents the values of these properties at a temperature T0 and a pressure P0 of the environment.

5.3 Exergy and thermoeconomic modeling

Kinetic exergy and potential are equal to the same kinetic energy and potential: 1 EKN 5 mV 2 2

(5.5)

EPT 5 mgz

(5.6)

In these relationships, V and z indicate the speed and height relative to a reference coordinate in the environment (V0 5 0 and z0 5 0), respectively. Relationships (5.5) and (5.6) can be applied both to systems and to the flow of materials.

5.3.2 Chemical exergy Chemical exergy is the maximum amount of useful work that can be achieved when a system is placed at a temperature of T0 and pressure P0 to achieve a chemical equilibrium with the environment. Therefore in order to calculate chemical exergy, not only must the temperature and pressure be specific, but also the chemical composition of the environment should be determined. Since our natural environment is not in equilibrium, it is necessary to model an exergy reference environment. Using the standard chemical exergy table for materials that are in the environment under standard conditions (Pref 5 1.013 bar, Tref 5 298.15K), it facilitates the calculation of exergy values [1]. Excess chemical exergy levels exist in tables for different environments. The effect of minor variations in the values of T0 and P0 on chemical exergy values of reference materials in practical applications can be considered. The chemical exergy is an ideal mixture of ideal gas N as follows [1,3,4]: eCH M;ig 5

N X k51

xk eCH k 1 RT0

N X

xk Lnxk

(5.7)

k51

If the temperature of the environment is T0, eCH is the chemical standard k molar mass of the kth chemical material, and xk is the mole fraction of k in the system at T0 [1] (Table 5.1). To calculate the chemical exergy of some fuels, Szargut et al. introduced values ex of the exergy as φ 5 LHVf . For most hydrocarbons, this factor is near one [1], for instance, for methane and hydrogen the factor is φCH1 5 1:06 and φH2 5 0:985,
respectively [3]. Szargut et al. [5] provided the correlation for liquid fuels Cα Hβ Nγ Oδ , based on their atomic compositions as follows [3]:   β δ γ β φ 5 1:041 1 0:1728 1 0:0432 1 0:2169 1 2 2:062 α α α α

(5.8)

The chemical exergy of seawater streams (molar basis) in kJ/kmol is given as follow [6,7]: 0 0 exCH sw 5 ns ðμs 2 μs Þ 2 nw ðμw 2 μw Þ

where ns is moles number of salt in seawater and nw is that of water.

(5.9)

59

60

CHAPTER 5 Exergy and thermoeconomic evaluation

Table 5.1 Standard chemical values for selected substances at T0 5 298.15K and P0 5 1 atm [3]. Component

eCH k

Component

eCH k

Component

eCH k

Ag (s) Al (s) Ar (s) As (s) Au (s) B (s) Ba (s) Bi (s) Br2 (l) C (s, graphite) Ca (s) Cd (sa) Cl2 (g) Co (sa) Cr (s) Cs (s) Cu (s) D2 (g) F2 (g) Fe (sa) H2 (g)

70.20 888.40 11.69 494.60 15.40 628.80 747.40 274.50 101.20 410.26 712.40 293.20 123.60 265.00 544.30 404.40 134.20 263.80 466.30 376.40 236.10

Kr (g) Li (s) Mg (s) Mn (sa) Mo (s) N2 (g) Na (s) Ne (g) Ni (s) O2 (g) P (s, red) Pb (s) Rb (s) S (s, rhombic) Sb (s) Se (s, black) Si (s) Sn (s, white) Sn (s) Ti (s) N2

34.36 393.00 633.80 482.30 730.30 0.72 336.60 27.19 232.70 3.97 863.60 232.80 388.60 609.60 435.80 346.50 854.60 544.80 730.20 906.90 0.72

V (s) W (s) Xe (g) Zn (s) V (s) W (s) Xe (g) Zn (s) He (g) Hg (l) I2 (s) K (s) Kr (g) Ne (g) CO2 H2O U (s) Ar (g) He (g) O2

721.10 827.50 40.33 339.20 721.10 827.50 40.33 339.20 30.37 115.90 174.70 366.60 34.36 27.19 19.87 9.49 1190.70 11.69 30.37 3.97

Moreover, μs is molar chemical potential of salt in seawater in kJ/kmol, and μw is that of water. The superscript zero indicates the global dead state so that μ0 5 f ðP0 ; T0 ; salinity0 Þ and salinity0 5 salinityfeed . The chemical exergy of seawater streams (mass basis) can be obtained in kJ/kg [6,7]: 

0 exCH sw 5 mfs ðμ s 2 μs Þ 2 mfw ðμ



w

2 μ0w Þ

(5.10)

where mfs is mass fraction of salt in seawater and nw is that of water.  Moreover, μ s is chemical potential of salt in seawater in kJ/kg at restricted  dead state condition and μ w is that of water.  The superscript  indicates the restricted dead state so that μ 5 f ðP0 ; T0 ; salinityith stream Þ.

5.3.3 Exergy destruction The rate of exergy destruction in the kth component of a system is determined by the following relationship:

5.3 Exergy and thermoeconomic modeling

E_ D;k 5 E_ F;k 2 E_ P;k 2 E_ L;k

(5.11)

In this equation, E_ D;k shows the exergy destruction rate in the component, E_ F;k shows the rate of exergy associated with the fuel of component, E_ P;k shows the rate of exergy associated with the product of component, and E_ L;k shows the loss of exergy in the component [1]. For all of the components, we can introduce E_ D;k as the exergy destruction rate in each component and ηex;k as the exergy efficiency for each component. E_ D;k 5 E_ F;k 2 E_ P;k

(5.12)

E_ P;k ηex;k 5 E_ F;k

(5.13)

The exergy destruction rate of each component can be compared to the rate of the total exergy destruction overall system.

5.3.4 Exergoeconomic modeling For the whole system an exergoeconomic balance is as follows: CP;tot 5 CF;tot 1 Zk;tot

(5.14)

The exergoeconomic balance for each component is as follows: :

:

:

:

CP;k 5 CF;k 2 CL;k 1 Z k

(5.15)

The CF;k is cost rates associated with the fuel and CP;k is related to the product. So, for a component receiving a heat transfer and generating power, we would write [6]: X

:

e

:

:

Ce;k 1 Cw;k 5 Cq;k 1

X i

:

:

Ci;k 1 Zk

(5.16)

To solve for the unknown variables, it is necessary to develop a system of equations applying Eq. (5.16) to each component, and in some cases, we need to apply some additional equations, to fit the number of unknown variables with the number of equations. All costs due to owning and operating a plant depend on the type of financing, the required capital, the expected life of components, and so on. The annualized (levelized) cost method is used to estimate the capital cost of the system components in this study. The amortization cost for a particular plant component may be written as [3,8]: PW 5 Ci 2 Sn PWFði; nÞ

(5.17)

 :
C $=year 5 PW 3 CRFði; nÞ

(5.18)

The present worth of a component is converted to an annualized cost using the capital recovery factor CRFði; nÞ. Dividing the levelized cost by 8000 annual operating hours, we obtain the following capital cost for the kth component of the plant:

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CHAPTER 5 Exergy and thermoeconomic evaluation

:

:

Zk 5

Φk C k ð3600 3 8000Þ

(5.19)

ið11iÞn ð11iÞn 2 1

(5.20)

ð1 1 in Þ ð1 1 r Þ

(5.21)

CRF 5

11i5

The maintenance cost is taken into consideration through the factor Φk for each component. The results from exergy analysis provide a base for an exergoeconomic analysis, an exergy-aided method to determine appropriate costs. A general exergy balance for any component may be formulated utilizing the first and second laws of thermodynamics. A cost balance, on the other hand, expresses that the cost rate associated with the product of the system (CP); the cost rate equals the total rate of expenditures made to generate the product, namely, the fuel cost rate (CF). Average costs per exergy unit of fuel for each component is as follow: cF;k 5

C_ F;k E_ F;k

(5.22)

Also, average costs per exergy unit of product for each component is: cP;k 5

C_ P;k E_ P;k

(5.23)

With considering that the fuel is fixed, we can introduce the cost of exergy destruction by: C_ D;k 5 cF;k E_ D;k

(5.24)

If the product is fixed, we can introduce the cost of exergy destruction by: C_ D;k 5 cP;k E_ D;k

(5.25)

The relative cost difference rk for the kth component is introduced by: rk 5

cP;k 2 cF;k cF;k

(5.26)

The exergoeconomic factor, fk , defined for: fk 5

Z_k
 Z_k 1 cF;k E_ D;k 1 cF;k E_ L;k

(5.27)

Bejan et al. [9] proposed the following procedure to the improvement of energy systems based on exergy and thermoeconomic analysis: 1. Rank the equipment in descending order of sum ðZ_k 1 C_ D;k Þ 2. Consider design modifications to the equipment for which the value of this sum is high.

5.3 Exergy and thermoeconomic modeling

3. The components with a high relative cost difference are considered for improvements when the sum of ðZ_k 1 C_ D;k Þ high. 4. Determination of the major cost source by fk : a. If the fk value is high, try to decrease the capital investment for the selected component by reducing the component efficiency. b. If the fk value is low, try to increase the capital investment for the selected component by increasing the component efficiency. 5. Remove any subprocesses that increase the exergy destruction or exergy loss without affecting the decreasing of capital investment or fuel costs for other components. 6. Pay attention to improve the component with relatively low exergy efficiency, high exergy destruction, or loss ratio by increasing exergetic efficiency.

5.3.5 Exergy destruction level and exergy cost destruction level concept Due to the importance of cost of destruction and to achieve a deep understanding of plant performance, two parameters have been introduced by Khoshgoftar Manesh and Amidpour [2] and developed by Khoshgoftar Manesh and Rosen [10]: exergy destruction level (EDL) and exergy cost destruction level (ECDL). These definitions allow applicable representations to be developed of the performance of each component of a system. The target value (TV) is defined as an exergetic criteria that indicates the power target or exergy production target for each component. This promotes more importance for the exergetic parameter in the components during evaluation [10]. EDL and ECDL are introduced for component j as follows: EDLj 5

ECDLj 5

ED; j TVj CD; j TVj

(5.28)

(5.29)

The parameter EDL determines the exergy destruction per unit TV (e.g., 1 MW), while ECDL demonstrates the cost of the exergy destruction per unit TV. For instance, in a combustor TVj 5 EP;Combustor , so EDL indicates the exergy destruction per 1 MW exergy rate produced in the combustor, and ECDL demonstrates how much of the exergy cost is related to exergy destruction rate in the combustor per 1 MW exergy production. The values EDL and ECDL for the heat recovery boiler, all heat exchangers and the feed water heaters are calculated. For the turbines the TV is power generation; hence EDL and ECDL for turbines show how much exergy and exergy cost are destroyed per 1 MW power generation. For the power consumer components such as compressor, pumps, and blowers the TV is based on the power uses in this component. So, EDL and ECDL are varied relative to the power that is used in

63

CHAPTER 5 Exergy and thermoeconomic evaluation

Exergy loss 1

Exergy destrucon

4 3

Exergy loss cost Exergy destrucon cost

4 3

2

TV1

1- Component 1 2- Component 2 3- Component 3 4- Component 4

ECDL ($/hr MW)

1

1- Component 1 2- Component 2 3- Component 3 4- Component 4

EDL (MW/MW)

64

2

TV2

TV3

TV (MW)

TV4

TV1

TV2

TV3

TV4

TV (MW)

FIGURE 5.1 EDL
ECDL representation [10]. ECDL, Exergy cost destruction level; EDL, exergy destruction level.

the compressor, pump, and blower and EDL and ECDL, respectively, determine the exergy and exergy cost destruction per 1 MW shaft power consumed in the compressor, pump, or blower. EDL and ECDL parameters for the condenser in Rankine cycle indicate how much exergy and exergy cost are destroyed per 1 MW power generation by the steam turbine. EDL and ECDL are defined based on the TV of each component. The EDL representation is demonstrated by plotting EDL against TV for each equipment. Therefore ECDL is generated by plotting ECDL versus the TV. The area below EDL illustrates the value of exergy destruction. Also, the area below ECDL indicates the exergy cost destruction of each component. EDL and ECDL are based on exergy and exergoeconomic concepts and calculations consequently. ECDL is applied for measurement of exergy cost destruction per unit TV and has two significant applications. EDL
ECDL representation is demonstrated in Fig. 5.1, where the horizontal axis shows the TV for a component. On the left side of graph the vertical axis is EDL and the shaded areas determine exergy destruction rates or exergy loss rates (in MW). On the right side the vertical axis is ECDL and the shaded areas show exergy destruction cost rate or exergy loss cost rate [10]. A comparison and evaluation of EDL with ECDL indicates where exergy destruction is more important. For instance, when EDL for an equipment is high and ECDL is very high, the exergy destruction of component contributes significantly to increasing the product cost. So, it is likely worth carefully considering modifications to this component. But when EDL is high and ECDL is low, the exergy destruction cost contributes less to increasing product cost and so it has relatively less importance. The general procedure for analysis and evaluation of thermal plants has been shown in Fig. 5.2.

5.4 Case studies

FIGURE 5.2 Proposed algorithm for the evaluation of thermal plants [10].

5.4 Case studies In this section the exergetic and exergoeconomic modeling of three case studies have been considered.

5.4.1 Exergy and exergoeconomic modeling of CGAM cogeneration plant Thermodynamic modeling of CGAM cogeneration plant has been discussed in Section 4.2. Also, schematic of the CGAM problem has been shown in Figure 4.1. Exergy calculation of each stream of considered system [11,12] has been determined in Table 5.2. It indicates material, physical, and chemical exergy of each stream. Also, Table 5.3 determines the exergetic product, fuel, and efficiency for each component. Furthermore, the economic model is demonstrated in Table 5.4, and fixed parameters regarding purchases component costs are indicated in Table 5.5. In addition, Table 5.6 shows the cost rates of fuel and products based on exergoeconomic modeling.

65

Table 5.2 Exergy calculation of each stream in CGAM problem. Stream

Material of stream

1 2 3 4 5 6 7 8 9 10

Air Air Air Gas Gas Gas Gas Water Steam Net power

11

Compressor power

12

CH4

Exergy modeling h i ei 5 cp;a Ti 2 T0 2 T0 ln TT0i 1 Ra T0 ln pp0i ði 5 1; 2; 3Þ   T0 2 T0 lnðTi =T0 Þi 1 Rg T0 lnðpi =p0 Þ ei 5 cp;g h Ti 2P j5O2 ; N2 ; CO2 ; H2 O 1 Rg T0 xji ln xji  =xj0  cp;g 5 1:17 kJ=ðkg KÞ;

γg 5 1:33;

Rg 5 0:29 kJ=ðkg KÞ;

ði 5 4; 5; 6; 7Þ

e9 2 e8 5 h9 2 h8 2 T0 ðs9 2 s8 Þ e9 2 e8p 5 h9 2 h8p 2 T0 ðs9 2 s8p Þ : : _ s ðe9 2 e8 Þ E9 2 E8 5 m _ W net _ AC W Specific exergy of the fuel: ef 5 51; 850 kJ=kg Specific energy of the fuel: hf 5 LHV 5 50; 000 kJ=kg : : E12 5 mfuel efuel

Table 5.3 Exergetic product, fuel, and efficiency for each component (CGAM problem). Component AC CC APH GT HRSG Overall plant


 Exergy of product E_ p E_ 2 2 E_ 1 E_ 4 E_ 3 2 E_ 2 _ net 1 W _ AC W _E 9 2 E_ 8 _ net 1 m _ s ðe9 2 e8 Þ W


 Exergy of fuel E_ f _ AC W _E 3 1 E_ 12 E_ 5 2 E_ 6 E_ 4 2 E_ 5 E_ 6 2 E_ 7 _ a e1 1 m _ fuel efuel m


 Exergy efficiency ηex 5 E_ p =E_ f _ AC ðE_ 2 2 E_ 1 Þ=W _E 4 =ðE_ 3 1 E_ 12 Þ ðE_ 3 2 E_ 2 Þ=ðE_ 5 2 E_ 6 Þ _ net 1 W _ AC Þ=ðE_ 4 2 E_ 5 Þ ðW _ _ ðE 9 2 E 8 Þ=ðE_ 6 2 E_ 7 Þ
 _ net 1 m _ s ðe9 2 e8 Þ =ðm _ a e1 1 m _ fuel efuel Þ W

AC, air compressor; CC, combustion chamber; APH, air pre heater; HRSG, heat recovery steam geneator; GT, Gas Turbine.

Table 5.4 Purchased equipment cost of CGAM problem. Component AC CC GT APH HRSG


 ΔTLM 5 ððT5 2 T3 Þ 2 ðT0 2 T2 ÞÞ= ln ðT5 2 T3 Þ=ðT0 2 T2 Þ



 ΔTLMTD;Economizer 5 ðT7p 2 T8p Þ 2 ðT7 2 T8 Þ = ln ðT7p 2 T8p Þ=ðT7 2 T8 Þ

Purchased cost [11]



 _ a = c11 2 ηAC p2 =p1 ln p2 =p1 ZAC 5 c11 m

 _ a = c22 2 ðp4 =p3 Þ ½1 1 EXPðc23 T4 2 c24 Þ ZCC 5 c21 m


 _ g = c32 2 ηGT ln p4 =p5 ½1 1 EXPðc33 T4 2 c34 Þ ZGT 5 c31 m

 0:6 ZAPH 5 c41 mg ðh5 2h6 Þ =ðU2ΔTLM Þ

0:8 _ PH =ðΔTLMÞ 0:8 1 Q _ EVA =ðΔTLMÞ _ s 1 c53 m _ 1:2 1 c52 m ZAPH 5 c51 Q PH EVA g _ PH 5 m _ s ðh8p 2 h8 Þ Q _ EVA 5 m _ s ðh9 2 h8p Þ Q



 ΔTLMTD;Evaprator 5 ðT6 2 T9 Þ 2 ðT7p 2 T8p Þ = ln ðT6 2 T9 Þ=ðT7p 2 T8p Þ

5.4 Case studies

Table 5.5 Fixed variables for economic model of CGAM problem. Component

Fix variables [11]

AC CC

c11 5 39:5 $=kg=s c12 5 0:9 c21 5 25:6 ð$=kg=sÞ c22 5 0:995 c24 5 26:4 c23 5 0:018 ðk21 Þ

APH GT

c41 5 2290 ð$=m1:2 Þ U 5 0:018 kW=ðm2 kÞ c31 5 266:3 $=ðkg=sÞ c32 5 0:92 c34 5 54:4 c33 5 0:036 ðk21 Þ

HRSG

c51 5 3650 $=ðkW=kÞ0:8

c52 5 11; 820 $=ðkg=sÞ c53 5 658 $=ðkg=sÞ1:2

Table 5.6 Exergetic cost rate of fuels and product for CGAM problem. _ pÞ Exergetic cost rate of product ðC

_ fÞ Exergetic cost rate of fuel ðC

_1 _2 2C C _ C4

_W C AC _ 12 _3 1C C

GT

_2 _3 2C C _W C GT

_6 _5 2C C _5 _4 2C C

HRSG

_8 _9 2C C

_7 _6 2C C

Component AC CC APH

Table 5.7 Exergetic product, fuel, and efficiency for each component (CCHP). Component Air compressor Combustion chamber Gas turbine Gas turbine pack Superheater Evaporator Economizer Deaerator HRSG pack Steam turbine

E_ F

E_ P

WAC E_ 2 1 E_ fuel

E_ 2 2 E_ 1 E_ 3

E_ 3 2 E_ 4 E_ 1 1 E_ fuel

WGT

E_ 4 2 E_ 5 E_ 5 2 E_ 6

Wnet;gc E_ 16 2 E_ 15 E_ 15 2 E_ 14

E_ 6 2 E_ 7 E_ 7 1 E_ 8

E_ 14 2 E_ 13 E_ 11 2 E_ 10

E_ 4 2 E_ 8 E_ 16 2 E_ 17

E_ 11 1 E_ 10 2 E_ 16 2 E_ 13 WST E_ 27 2 E_ 26

Condenser (absorption chiller)

E_ 18 2 E_ 19 E_ 20 2 E_ 21 1 E_ 22 2 E_ 23

High-pressure pump

WHPP

E_ 24 2 E_ 25 E_ 12 2 E_ 11

Low-pressure pump

WLPP
 Qcoll 1 2 ðT0 =Tr Þ

E_ 10 2 E_ 9 E_ 13 2 E_ 12

Condenser

Collector

69

Table 5.8 Purchase equipment cost for the CCHP case. Component Air compressor Combustion chamber Gas turbine HRSG Steam turbine Condenser Deaerator

Purchase/cost ($)



 44:71ma Urp; AC Ulnðrp; AC ÞU 1= 0:95 2 ηAC [4]
 28:98ma = 0:995 2 ðpout =pin Þ Uð1 1 eð0:015ðTout 21540ÞÞ Þ [4]

  479:34 mfg = 0:93 2 ηGT Ulnðrp;GT ÞUð1 1 eð0:036 Tin 254:4Þ Þ [13] h
0:8
0:8
0:8 i 1 21276mw 1 1184:4m1:2 6570 QEC =ΔTEC 1 QEV =ΔTEV 1 QSH =ΔTSH fg [13]

ðηST =0:85Þ 3 10ð2:625911:4398 1773msteam [14]
0:8 [15] 6570 QDEA =ΔTDEA

Pump

0:71 3540WPump [14]

Collector

235ASF



log10ðWST Þ20:1776ðlog10ðWS TÞÞ2 Þ

[14]

5.4 Case studies

5.4.2 Exergy and exergoeconomic modeling of a CCHP As discussed in Section 4.3 and Figure 4.2, a combined cooling, heating, and power (CCHP) system is considered that includes a gas turbine package, single pressure HRSG, steam turbine, thermal collectors, heat exchanger for heating, and absorption chiller for cooling. Table 5.7 represents the exergetic fuel and product model for each component. In addition, Table 5.8 determines purchased model for each component. The exergetic cost rates for fuel and product models are similar to Table 5.7; however, the exergetic term changes to cost rate parameters.

5.4.3 Exergy and exergoeconomic modeling of a polygeneration system A polygeneration system that produces power, heat, and fresh water is considered for exergy and exergoeconomic modeling. Thermodynamic modeling of this system has been introduced in Section 4.4 and schematic of considered case is shown Figure 4.3. The exergetic fuel, product, and efficiency models are determined in Table 5.9. The economic model based on purchased cost for each component is determined in Table 5.10. The exergetic cost rates for fuel and product of the polygeneration system are indicated in Table 5.11. Table 5.9 Fuel and product exergy streams of the equipment. 



Component

ExF

ExP

Air compressor

WAC _ 3 _ 2 1 Ex Ex

_ 1 _ 2 2 Ex Ex _Ex4

_ 5 _ 4 2 Ex Ex _ 6 _Ex5 2 Ex

_ GT W _ 22 _Ex23 2 Ex

_ 7 _ 6 2 Ex Ex _ 8 _ 7 2 Ex Ex

_ 21 _ 22 2 Ex Ex _ 20 _ 21 2 Ex Ex

_ 9 _ 8 2 Ex Ex _ 10 _Ex9 2 Ex

_ 19 _ 20 2 Ex Ex _ 15 _Ex16 2 Ex

_ 11 _ 10 2 Ex Ex _ 16 _Ex13 1 Ex

_ 12 _ 13 2 Ex Ex _Ex14

_ 11 _ 5 2 Ex Ex _ HPP W

_ 25 2 Ex _ 12 _ 23 1 Ex Ex _ 17 _ 18 2 Ex Ex

_ 25 _ 24 1 Ex Ex _ 29 _Ex28 2 Ex

_ 26 Ex _ 35 _ 36 2 Ex Ex

_ 26 Ex _ 30 Ex

_ 27 Ex _ 31 Ex

_ 32 _ 27 1 Ex Ex _ RO2pump _ 37 1 W Ex

_ 30 1 Ex _ 37 1 Ex _ 30 _ 34 1 Ex Ex _ 38 Ex

Combustion chamber Gas turbine High-pressure superheater High-pressure evaporator High-pressure economizer 2 High-pressure economizer 1 Deaerator pressure evaporator Feed water preheater Deaerator HRSG pack High-pressure pump Desuperheater Process heat exchanger Valve 1 Valve 2 MED
TVC RO

MED-TVC, multi effect distillation with thermal vapour compression; RO, Reverse Osmosis

71

Table 5.10 Purchase equipment cost of the equipment in ($). Component Air compressor Combustion chamber Gas turbine HRSG Deaerator Pump Valve Desuperheater MED TVC RO

MED, multi-effect distillation

Purchase cost



 44:71ma Urp; AC Ulnðrp; AC ÞU 1= 0:95 2 ηAC [4]
 28:98ma = 0:995 2 ðpout =pin Þ Uð1 1 eð0:015ðTout 21540ÞÞ Þ [4]

  479:34 mfg = 0:93 2 ηGT Ulnðrp;GT ÞUð1 1 eð0:036 Tin 254:4Þ Þ [13] h
0:8
0:8
0:8 i 1 21276mw 1 1184:4m1:2 6570 QEC =ΔTEC 1 QEV =ΔTEV 1 QSH =ΔTSH fg [13]
0:8 [15] 6570 QDEA =ΔTDEA 0:71 3540WPump [14]


 _ 3 Ti =Pi 0:05 3 Pe 20:75 [16] 8:07 3 0:989 3 m _ 1060 3 ðm=ρÞ [16] P P P PECeffects 1 PECfeed2heaters 1 PECflash2boxes 1 PECcondenser
0:6 [15] PECHX 5 12; 000 Area=100
 _ 3 Ti =Pi 0:05 3 Pe 20:75 [16] 2 3 8:07 3 0:989 3 m PECmembrane 1 PECpretreat 1 PECRO2pump 1 PECRO2valve PECmembrane 5 NO:membranes 3 PECone2membrane PECone2membrane 5 7846
 _ RO2feed =ρÞ 3 24 3 3600 0:8 PECpretreat 5 996 3 ξ 1 ðm [16,17] ξ 1 5 1:399: inflation factor PECRO2pump 5 393000ξ 1 1 701:19 3 14:5 3 PRO2feed
 _ 3 Ti =Pi 0:05 3 Pe 20:75 PECRO2valve 5 8:07 3 0:989 3 m

References

Table 5.11 Exergetic cost rates for fuel and product of the polygeneration system. Component Air compressor Combustion chamber Gas turbine High-pressure superheater High-pressure evaporator High-pressure economizer 2 High-pressure economizer 1 Deaerator pressure evaporator Feed water preheater Deaerator HRSG pack High-pressure pump Desuperheater Process heat exchanger Valve 1 Valve 2 MED
TVC RO

_F C

_P C

_ W;AC C _3 _2 1C C

_1 _2 2C C _ C4

_5 _4 2C C _6 _ C5 2 C

_ W;GT C _ 22 _ 235 2 C C

_7 _6 2C C _8 _ C7 2 C

_ 21 _ 22 2 C C _ 20 _ C21 2 C

_9 _8 2C C _ 10 _9 2C C

_ 19 _ 20 2 C C _ 15 _ 16 2 C C

_ 11 _ 10 2 C C _ 16 _ 13 1 C C

_ 12 _ 13 2 C C _ 14 C

_ 11 _5 2C C _ Cw;HPP

_ 25 2 C _ 12 _ 23 1 C C _ _ C18 2 C17

_ 25 _ 24 1 C C _ 29 _ C28 2 C

_ 26 C _ 35 _ 36 2 C C

_ 26 C _ 30 C

_ 27 C _ 31 C

_ 32 _ 27 1 C C _ w;RO2Pump _ 37 1 C C

_ 30 1 C _ 37 1 C _ 30 _ 34 1 C C _ 38 C

References [1] Bejan A, et al. Thermal design and optimization. John Wiley & Sons; 1996. [2] Khoshgoftar Manesh M.H., Amidpour M. New graphical methodology for energy integration in nuclear steam power plant. 2008. Available from: https://doi.org/10.1115/ ICONE16-48432. [3] Dincer I, Rosen MA. Exergy: energy, environment and sustainable development. Elsevier Science; 2007. [4] Dincer I, Rosen MA, Ahmadi P. Optimization of energy systems. Wiley; 2017. [5] Szargut J, Morris DR, Steward FR. Exergy analysis of thermal, chemical, and metallurgical processes. Hemisphere; 1988. [6] Dincer I, Rosen MA. Chapter 3
chemical exergy. In: Dincer I, Rosen MA, editors. Exergy. 2nd ed. Elsevier; 2013. p. 31
49. [7] Sharqawy MH, Zubair SM, Lienhard JH. Second law analysis of reverse osmosis desalination plants: an alternative design using pressure retarded osmosis. Energy 2011;36 (11):6617
26. [8] Kwak HY, Kim DJ, Jeon JS. Exergetic and thermoeconomic analyses of power plants. Energy 2003;28(4):343
60.

73

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CHAPTER 5 Exergy and thermoeconomic evaluation

[9] Bejan A, Tsatsaronis G, Moran MJ. Thermal design and optimization. Wiley; 1995. [10] Manesh MHK, Rosen MA. Combined cycle and steam gas-fired power plant analysis through exergoeconomic and extended combined pinch and exergy methods. J Energy Eng 2018;144(2):04018010. [11] Toffolo A, Lazzaretto A. Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design. Energy 2002;27(6):549
67. [12] Valero A, et al. CGAM problem: definition and conventional solution. Energy 1994;19(3):279
86. [13] Ghaebi H, Saidi MH, Ahmadi P. Exergoeconomic optimization of a trigeneration system for heating, cooling and power production purpose based on TRR method and using evolutionary algorithm. Appl Therm Eng 2012;36:113
25. [14] Boyaghchi FA, Heidarnejad P. Thermoeconomic assessment and multi objective optimization of a solar micro CCHP based on organic Rankine cycle for domestic application. Energy Convers Manage 2015;97:224
34. [15] Cavalcanti EJC. Exergoeconomic and exergoenvironmental analyses of an integrated solar combined cycle system. Renew Sustain Energy Rev 2017;67:507
19. [16] El-Sayed YM. The thermoeconomics of energy conversions. Elsevier Science; 2013. [17] Park C, et al. Stochastic cost estimation approach for full-scale reverse osmosis desalination plants. J Membr Sci 2010;364(1):52
64.

CHAPTER

Advanced exergetic evaluation of cogeneration and polygeneration systems

6

Chapter outline 6.1 Introduction ................................................................................................... 75 6.2 Advanced exergy-based variables .................................................................... 76 6.2.1 Endogenous/exogenous ..................................................................76 6.2.2 Avoidable/unavoidable ...................................................................76 6.3 Methodology for splitting the variables ............................................................ 77 6.3.1 Unavoidable and avoidable parts ....................................................78 6.3.2 Endogenous and exogenous parts ...................................................79 6.4 Advanced exergy destruction level representation ............................................ 83 6.5 Application of advanced exergy-based analysis ................................................ 84 6.5.1 CGAM problem .............................................................................84 6.5.2 Liquefied natural gas cogeneration .................................................88 References ............................................................................................................ 93

6.1 Introduction The performance improvement and optimal design of energy conversion systems from the viewpoints of thermodynamics, economics, and environmental impacts need particular and deep attention to: 1. the real thermodynamic inefficiencies and causes of exergy destruction and losses, 2. the costs associated with components and effects of exergy destruction, and 3. the environmental impact related to components and thermodynamic inefficiency. The exergy destruction determines a major inefficiency and must be minimized when the overall system efficiency should be maximized. In the design of an energy systems, however, the exergy destruction within an equipment determines not only a thermodynamic inefficiency but, in general, also an opportunity to reduce the investment cost and also the environmental impacts related to the equipment being considered and, thus, with the overall system. In the conventional exergy-based analysis the interconnecting effects among the system components have not been considered. Also, the real potential for improving the system cannot be evaluated [1
3].

Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00006-9 © 2021 Elsevier Inc. All rights reserved.

75

76

CHAPTER 6 Advanced exergetic evaluation of cogeneration

Advanced exergy-based methods are responsible for (1) determination of connection effects among plant elements (splitting to endogenous/exogenous parts) and (2) explore the real potential for improvement (avoidable/unavoidable values). This approach has been introduced and applied by Morosuk and Tsatsaronis [1,2].

6.2 Advanced exergy-based variables The interactions among different components of the system can be predicted by the splitting exergy destruction, investment cost associated with such component, and cost of exergy destruction within each system component into endogenous/ exogenous and avoidable/unavoidable parts that called advanced exergy and exergoeconomic analysis [1,2].

6.2.1 Endogenous/exogenous Endogenous exergy destruction is the part of a total exergy destruction obtained for a component when all other equipment operate ideally. The exogenous part is the difference between the value of exergy destruction or investment costs in the real system and the endogenous part: EN EX E_ D;k 5 E_ D;k 1 E_ D;k

(6.1)

EN EX Z_k 5 Z_k 1 Z_k

(6.2)

6.2.2 Avoidable/unavoidable UN The unavoidable exergy destruction introduces as (E_ D;k ). It cannot decrease more because of technological limitations. The difference between unavoidable exergy AV destruction for a component is the avoidable exergy destruction (E_ D;k ) that should have particular attention to improvement [2]: UN AV E_ D;k 5 E_ D;k 1 E_ D;k

(6.3)

UN (Z_D;k )

The unavoidable investment cost for a component can be predicated by assuming the minimum values accordingly as follows: UN AV Z_ 5 Z_ 1 Z_

(6.4)

k k k The combination of endogenous/exogenous and avoidable/unavoidable parts gives us an opportunity to calculate the avoidable endogenous part of a parameter AV;EN AV;EN used in an advanced exergy-based analysis (E_ D;k and Z_D;k ). This parameter is more important to show the maximum potential of improvement [4]. The following variables should be used in an advanced evaluation:

AV;EN

• avoidable endogenous (E_D;k destruction and

AV;EX ) and avoidable exogenous (E_ D;k ) exergy

6.3 Methodology for splitting the variables

UN;EN

• unavoidable endogenous (E_ D;k exergy destruction.

UN;EX

) and Unavoidable exogenous (E_ D;k

)

Endogenous/avoidable exergetic cost of destruction is calculated by: AV;EN AV;EN C_ D;k 5 cF;k UE_ D;k

(6.5)

and endogenous/avoidable exergetic environmental impacts related to exergy destruction is calculated by: AV;EN AV;EN B_D;k 5 bF;k UE_ D;k

(6.6)

The splitting exergy destruction for advanced exergy-based analysis is shown in Fig. 6.1. Table 6.1 shows the concept of combination of endogenous
exogenous and avoidable
unavoidable exergy destruction.

6.3 Methodology for splitting the variables In this section, approaches to splitting exergy destruction to avoidable/unavoidable exogenous/endogenous parts are discussed.

The kth component works in real condition and the rest work in ideal condition

kth Component

Exergy, exergoeconomic, exergoinvironment analysis

The kth component works in real condition and the rest work in unavoidable condition

All of components work in unavoidable conditions

Ė

,Ċ

E Ċ

Ė

=Ė

−Ė

Ċ

=Ċ

−Ċ

Ḃ

=Ḃ

−Ḃ

Ḃ

Ė

Ė

=Ė

−Ė

Ċ

=Ċ

−Ċ

Ḃ

=Ḃ

−Ḃ

Ė

=Ė

−Ė

Ė

=Ė

−Ė

Ċ

=Ċ

−Ċ

Ċ

=Ċ

−Ċ

=Ḃ

−Ḃ

=Ḃ

−Ḃ

Ċ Ḃ

Ė

Ḃ

,

Ċ Ḃ

,B

FIGURE 6.1 Schematic of advanced exergy-based analysis.

Ė

=Ė

−Ė

Ċ

=Ċ

−Ċ

Ḃ

=Ḃ

−Ḃ

,

Ḃ

,

,

77

78

CHAPTER 6 Advanced exergetic evaluation of cogeneration

Table 6.1 The concepts of endogenous
exogenous and avoidable
unavoidable exergy destruction [4]. Parameter

Concept

AV;EN E_ D;k

Can be reduced through an improvement of the efficiency of the kth component

AV;EX E_ D;k

Can be decreased by a optimization of the overall system or by improving the efficiency of the remaining components Due to technological limitation, it cannot be reduced

UN;EN E_ D;k UN;EX E_ D;k

Cannot be reduced because of technical and or process limitations in other components of the overall system for the given structure

Table 6.2 Assumption for unavoidable condition. Component

Unavoidable condition

Turbomachinery

ηUN 5 max; ED;K 5 0

Heat exchangers

UN ΔTpinch 5 min; ΔPUN 5 min

Combustion chamber

UN Tcg 5 max Adiabatic combustion temperature at minimum excess air

6.3.1 Unavoidable and avoidable parts There are two approaches for splitting the total exergy destruction within each system component into the unavoidable and avoidable parts [1]. In the first method, each component of the system should be evaluated in isolation. The mass flow rate of the working fluid through each equipment keeps the same as for the real operating conditions. To calculate the unavoidable entropy generation for each system component, we assume based on Table 6.2. The value of “max” or “min” for unavoidable condition depends on the best technology limitation. To set for different equipment sizes, unavoidable exergy destruction per unit exergy of the product is calculated. zUN D;k 5


UN E_ D;k E_ P;k

(6.7)

The unavoidable exergy destruction for the kth component is calculated by:


E_ D;k E_ P;k

UN ,


Real   E_ D;k Real zUN , z D;k D;k E_ P;k


UN UN Real E_ D;k E_ D;k 5 E_ P;k E_ P;k

(6.8)

(6.9)

According to the second method, a process similar to the real one is made but with only unavoidable irreversibilities in each equipment.  In this way the product of the overall system keeps constant E_ P;k 5 constant . However, the mass flow rate of the working fluid through each equipment is different from the real condition.

6.3 Methodology for splitting the variables

So, two options are considered: 1. The unavoidable exergy destruction is equal to the exergy destruction within the same component calculated by this method.  UN 2. The unavoidable ratio E_ D;k =E_ P;k is predicted from this calculation and the unavoidable exergy destruction. The unavoidable capital investment cost and unavoidable construction-ofcomponent-related environmental impact can be calculated using same equations UN Real Z_k 5 E_ P;k

UN Real Y_ k 5 E_ P;k



UN




Z_k E_ P;k




Y_ k E_ P;k

UN (6.10) UN (6.11)

_ _ The value of zUN is calculated by practical applications. The k 5 Z k =E P;k  UN _ _ term Z k =EP;k is indicated by arbitrary choosing a set of thermodynamic parameters for this equipment that lead to a very inefficient condition and by estimating the capital investment costs and construction component-related environmental impact.   _ _ UN is calculated by the combination of materials and The term zUN Y;k 5 Y k =E P;k manufacturing methods that result in a minimum for this term. The unavoidable and avoidable investment cost is obtained from understanding the relationship between the investment cost and the exergy destruction (or exergetic efficiency) of an equipment. The operating and maintenance costs are assumed to be constant and independent of the selection of the design point for the component being considered. As shown in Fig. 6.2, the shaded area shown the range of variation of the investment cost. The investment cost per unit of product exergy increases with reducing with increasing the efficiency or reducing exergy destruction. Technological constraints will only allow a minimum exergy destruction or maximum exergy efficiency to be attained in the component as unavoidable exergy destruction for the component. The figure also illustrates that the investment cost per unit of product exergy reduces as the exergy destruction [4,5].

6.3.2 Endogenous and exogenous parts The endogenous part of the exergy destruction within a component is independent of the exergy destruction in the remaining components. It is defined as the exergy destruction occurring within the kth component operating with its current efficiency when all other components operate in an ideal way. They are three approaches to calculate endogenous/exogenous parts.

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CHAPTER 6 Advanced exergetic evaluation of cogeneration

FIGURE 6.2 Expected relationship between investment cost and exergy destruction for the kth component of an energy system [5].

I

II

III

FIGURE 6.3 General case: step-by-step connection of elements.

6.3.2.1 Simple approach The total system consists of three components—I, II, and III (Fig. 6.3). In this simple illustration the product of one component is the fuel of the next component with the fuel of component I, being the fuel of the total system and the product from component II, E_ F;I , being the total product of the system E_ P;tot . In this analysis the total product of the system is kept constant [4]. For this case, there are no exergy losses at the level of the overall system. The exergy balance for this simple illustration can, therefore, be written as: E_ F;tot 5 E_ P;tot 1

X k

E_ D;k 1 E_ L;tot

(6.12)

6.3 Methodology for splitting the variables

Hence, the exergy destruction for each component is defined as follows: E_ D;III 5 E_ P;III

1 ηEx;III

! 2 1 5 E_ P;tor

1 ηEx;III

! 21

(6.13)

It is clear that the exergy destruction within the component III is only dependent on the entropy generation within the component itself, where 0 , ηEx;III , 1. Hence, the exergy destruction within this component is the endogenous exergy EN destruction of the component E_ D;III 5 E_ D;III . For component IIwhere 0 , ηEx;II , 1 and E_ D;III 5 0 or ηEx;III 5 1 Hence, the exergy destruction within this component II is the endogenous EN exergy destruction of the component E_ D;II 5 E_ D;II . !

E_ D;II 5 E_ P;tot ηEx;III

1 ηEx;II

21

(6.14)

For component I ! E_ D;II 5 E_ P;tot ηEx;III ηEx;II

1 ηEx;I

21

(6.15)

where 0 , ηEx;I , 1 and E_ D;III 5 E_ D;II 5 0 or ηEx;III 5 ηEx;II 5 1. So, the exergy destruction within this component I is the endogenous exergy EN destruction of the component E_ D;I 5 E_ D;I .

6.3.2.2 Thermodynamic approach The first step in the procedure of calculating the endogenous and exogenous parts of exergy destruction, capital investment cost, and construction-of-componentrelated environmental impact is to describe the theoretical operation conditions for the energy conversion system being considered. Since not all components could be considered at ideal operating conditions (E_ D;K 5 0 and ηEx;K 5 1), the so-called theoretical operating conditions should be used. _ _ th th ED;K 5 0 and ηth Ex;K 5 1, (where this is possible) and ED;K 5 min and ηEx;k 5 1 in all other cases. For the different component types the theoretical conditions are determined as Table 6.2. The most difficult problem is estimating the theoretical operating conditions for a combustion chamber. As indicated in Table 6.3, some assumptions are made for a theoretical combustion chamber.

6.3.2.3 Engineering approach Due to requirement only when a theoretical cycle cannot be developed for a real system, Solang Kelly proposed engineering approach [4]. For the ideal system (superscript I) E_ D;k 5 0

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CHAPTER 6 Advanced exergetic evaluation of cogeneration

Table 6.3 Theoretical condition for different component in thermal systems. Component

Theoretical condition

Turbomachinery

ηth 5 100%; E_ D;K 5 0

Heat exchangers Combustion chamber

ΔTpinch 5 0; ΔP 5 0; E_ D;K 5 min ΔPCC 5 0 real eth cg;out 5 ecg;out mth air mth fuel

5

mreal air mrel fuel

th th th E_ air;in 1 E_ fuel;in 5 E_ cg;out

_ _ th _ th eair;in m air;in 1 efuel;in E fuel;in 5 ecg;out mcg;out th

Then I I I E_ F;tot 2 E_ L;tot 5 E_ P;tot

(6.16)

If just one component in the system is real, then 

   I I I I I EN E_ F;tot 1 ΔE_ F;tot 2 E_ L;tot 1 ΔE_ L;tot 5 E_ P;tot 1 E_ D;k

(6.17)

For the real energy-conversion system (superscript R) 

   I R I R I EN E_ F;tot 1 ΔE_ F;tot 2 E_ L;tot 1 ΔE_ L;tot 5 E_ P;tot 1 E_ D;k 1 E_ D;others

(6.18)

R R where ΔE_ F;tot and ΔE_ L;tot indicate the increases in the exergy of fuel and loss respectively as a result of the exergy destructions in all overall system. As the other components within the system approach ideal operation, E_ D;others tends to zero, and their respective exergy efficiencies, ηEx;k , approaches 100%.

lim

E_ D;others -0

EN E_ D;k 5 E_ D;k

(6.19)

I R I R I Hence, by plotting ðE_ F;tot 1 ΔE_ F;tot Þ 2 ðE_ L;tot 1 ΔE_ L;tot Þ 2 E_ P;tot versus E_ D;others , the value of E_ D;k at ηEx;k can be obtained at the intercept where E_ D;others 5 0. The endogenous part is a function of the exergetic efficiency. So, the exergetic efficiency of the component must be remained constant, while E_ D;others is being modified. Straight lines are obtained when E_ D;others is changed, as demonstrated in Fig. 6.4. The dotted line extension of the straight line shows the values obtained if it were possible to decrease the exergy destruction in all components with the exception of the kth component to zero. For some components such as a combustion chamber and a throttling valve, it is impossible to achieve ideal operations, because it is difficult to define an ideal process associated with such components. In addition, in some systems, it may be impossible for all the components to operate at ideal conditions and still maintain the required system product output.

6.4 Advanced exergy destruction Level representation

FIGURE 6.4 Schematic of the engineering method [4].

6.4 Advanced exergy destruction Level representation Due to importance of exergy destruction cost and better understanding of energy conversion system, the parameters have been defined as exergy destruction level (EDL), exergy cost destruction level (ECDL) by Khoshgoftar Manesh and Amidpour [6] and extended by Khoshgoftar Manesh and Rosen [7]. Also, in the other study by Khoshgoftar Manesh et al. proposed environmental impact of EDL (EEDL). Furthermore, new graphical representation has been developed to reveal the performance of each component in overall system. In each component, target value (TV) is an exergetic criterion that can indicate shaft work target or the exergy of product. Overally, EDL, ECDL, and EEDL are defined for each equipment (i) as follows: EDLi 5

ED;i TVi

(6.20)

ECDLi 5

CD;i TVi

(6.21)

EEDLi 5

BD;i TVi

(6.22)

The value of EDL determines how much exergy is destruct per 1 MW TV and ECDL value shows how much exergy cost is destructed per 1 MW TV. For example, in combustor TVi 5 EP;Combustor , so EDL shows how much exergy destruct per 1 MW exergy that it produces in combustor. Also, ECDL demonstrates that how much exergy cost related to destruction in combustor per 1 MW exergy production. In addition, the proposed parameter EEDL represents that how much environmental impact of exergy associated to destruction in combustor per 1 MW

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CHAPTER 6 Advanced exergetic evaluation of cogeneration

exergy production. EDL, ECDL, and EEDL for boiler, heat recovery steam generator (HRSG), and feed water heaters are calculated like combustor. In all expanders such as steam or gas turbines, TV is shaft work which is produced. Therefore EDL, ECDL, and EEDL for turbines represents how much exergy and exergy cost are destructed per 1 MW shaft work produced [7]. For pump and compressor the TV is based on shaft work that is consumed in these components. Accordingly, EDL, ECDL, and EEDL is vary with shaft work that is consumed in pump or compressor and EDL, ECDL, and EELD show how much exergy and exergy cost are destructed per 1 MW shaft power that is consumed in compressor or pump. EDL, ECDL, and EEDL in condenser shows how much exergy and exergy cost are destructed per 1 MW shaft work that is produced in steam turbine [6]. In advanced exergy-based analysis, EDLUN, ECDLUN, and EEDLUN are defined UN UN for unavoidable exergy destruction (E_ ), exergy destruction cost (C_ ), and destrucD;k

D;k

UN tion environmental impact (B_D;k ), respectively. Also, EDLEN, ECDLEN, and EEDLEN EN EN are defined for endogenous exergy destruction (E_ D;k ), exergy destruction cost (C_ D;k ), EN and destruction environmental impact (B_D;k ), accordingly.

6.5 Application of advanced exergy-based analysis 6.5.1 CGAM problem The conventional exergoeconomic analysis was done to the cogeneration system [4,5] and the results are shown in Table 6.4. All assumptions were taken from references based on Refs. [4,5]. Assumption for unavoidable exergy destruction per product exergy in each component for CGAM (C. Frangopoulos, G. Tsatsaronis, A. Valero, M. Spakovsky) is indicated in Table 6.5. In addition, endogenous curve equations for selected components for CGAM problem by engineering approach have been indicated in Table 6.6. Based on the engineering approach, the exergy efficiency of the HRSG will have to keep constant during the process of determining its endogenous value. So, E_ D;HRSG will have to be constant as well. In such a case the engineering approach cannot be applied and a different approach should be used. Splitting of the exergy destruction based on two parts (endogenous/exogenous or avoidable/unavoidable) in the components of the CGAM power system is shown in Table 6.7. Also, Table 6.8 demonstrates the splitting of the exergy destruction based on avoidableendogenous, avoidable-exogenous, unavoidable-endogenous, and unavoidableexogenous parts for each component of the CGAM power system. The cost rate of exergy destruction associated with each exergy destruction category is listed in Table 6.9 based on avoidable/unavoidable or endogenous/ exogenous and based on combination splitting of advanced parts in Table 6.10.

6.5 Application of advanced exergy-based analysis

Table 6.4 Results of the conventional exergoeconomic analysis for the CGAM cogeneration system [4]. c F, k (MW)

c

Component

P, k (MW)

rk (%)

fk (%)

_ D;k C (US$/h)

Z_ k (US$/h)

_ D;k 1 Z_ k C (US$/h)

AC APH CC GT HRSG

0.0076 0.0070 0.0038 0.0070 0.0070

0.0088 0.0086 0.0058 0.0075 0.0110

14.87 23.97 50.32 8.17 58.02

49.75 14.78 1.04 38.58 9.13

57.56 70.68 420.66 76.67 168.59

56.99 12.26 4.40 48.16 16.93

114.55 82.94 425.06 124.83 185.52

AC, Air compressor; APH, air preheater; CC, combustion chamber; GT, gas turbine; HRSG, heat recovery steam generator.

Table 6.5 Assumption for unavoidable exergy destruction per product exergy in each component [4]. Component (k)



AC

0.054

APH CC

0.0164 0.367

GT

0.021

HRSG

0.345

E_ D;k =E_ P;k

UN

Assumptions Purchased equipment cost of the compressor becomes infinite when isentropic efficiency is 90% A minimum temperature of 10K was assumed

• • • •

Temperature of the fuel 811K fuel Temperature of the air 1000K 5 1360K for air High outlet temperature 1773K/2998K Adiabatic combustion

Purchased equipment cost of the expander becomes infinite when isentropic efficiency is 92% A minimum temperature of 10K was assumed

AC, Air compressor; APH, air preheater; CC, combustion chamber; GT, gas turbine; HRSG, heat recovery steam generator.

Table 6.6 Endogenous curve equations for selected components. Component

Endogenous curve equation

AC APH CC Expander HRSG

y 5 1:027x 1 1:3184 y 5 1:0083x 1 1:6047 y 5 2:718x 1 16:364 y 5 0:9957x 1 2:77 y5x

AC, Air compressor; APH, air preheater; CC, combustion chamber; HRSG, heat recovery steam generator.

The splitting of the investment costs of components for CGAM problem is shown in Table 6.11. In addition, the combination splitting of the investment costs of components for CGAM problem based on advanced exergoeconomic analysis is indicated in Table 6.12.

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CHAPTER 6 Advanced exergetic evaluation of cogeneration

Table 6.7 Splitting of the exergy destruction in the components of the CGAM power system. Component

E_ D (MW)

EN E_ D;k (MW)

EN E_ D;k (%)

EX E_ D;k (MW)

EX E_ D;k (%)

AV E_ D;k (MW)

AV E_ D;k (%)

UN E_ D;k (MW)

UN E_ D;k (%)

AC APH

57.56 70.68

36.21 60.68

62.91 85.85

21.35 10.00

37.09 14.15

15.96 65.01

27.73 91.98

41.60 5.67

72.27 8.02

CC Expander

420.66 76.67

225.63 69.45

53.64 90.58

195.03 7.22

46.36 9.42

195.10 35.44

46.38 46.22

225.56 41.24

53.62 53.79

HRSG

168.59

168.59

100.00

0.00

0.00

58.27

34.56

110.32

65.44

AC, Air compressor; APH, air preheater; CC, combustion chamber; HRSG, heat recovery steam generator.

Table 6.8 Combination splitting of the exergy destruction in the components of the CGAM power system. Component

E_ D (MW)

E_ D;k (MW)

AV;EN

AV;EN E_ D;k (%)

UN;EN E_ D;k (MW)

UN;EN E_ D;k (%)

AV;EX E_ D;k (MW)

AV;EX E_ D;k (%)

UN;EX E_ D;k (MW)

UN;EX E_ D;k (%)

AC

57.56

7.62

13.24

28.59

49.67

8.34

14.49

13.01

22.60

APH

70.68

55.73

78.85

4.95

7.00

9.28

13.13

0.72

1.02

CC

420.66

147.71

35.11

77.91

18.52

47.39

11.27

147.65

35.10

Expander

76.67

32.61

42.53

36.84

48.05

2.82

3.68

4.40

5.74

HRSG

168.59

110.32

65.44

110.32

65.44

0.00

0.00

0.00

0.00

AC, Air compressor; APH, air preheater; CC, combustion chamber; HRSG, heat recovery steam generator.

Table 6.9 The splitting of the cost of exergy destruction in selected components of the CGAM. Component

_ EN C D;k (MW)

_ EN C D;k (%)

_ EX C D;k (MW)

_ EX C D;k (%)

_ AV C D;k (MW)

_ AV C D;k (%)

_ UN C D;k (MW)

_ UN C D;k (%)

AC APH CC GT HRSG

36.21 60.68 225.63 69.45 168.59

62.91 85.85 53.64 90.58 100.00

21.35 10.00 195.03 7.22 0.00

37.09 14.15 46.36 9.42 0.00

15.96 65.01 195.10 35.44 58.27

27.73 91.98 46.38 46.22 34.56

41.60 5.67 225.56 41.24 110.32

72.27 8.02 53.62 53.79 65.44

AC, Air compressor; APH, air preheater; CC, combustion chamber; HRSG, heat recovery steam generator.

Table 6.13 demonstrates the values of both the conventional exergoeconomic parameters and the advanced exergoeconomic parameters for each component. AV;EN AV;EN With considering the sum of Z_k 1 C_ D;k and Z_k 1 C_ D;k the combustion chamber should be improved first, then the HRSG and the expander, respectively. There is difference between conventional and advanced exergy and exergoeconomic analysis. For example, the conventional exergoeconomic analysis determines that the air preheater should be improved last, whereas the advanced exergoeconomic analysis indicates that the air compressor should be improved last. Advanced exergy-based analysis gives us the additional information by the splitting exergy destruction of the components.

Table 6.10 The combination splitting of the cost of exergy destruction in selected components of the CGAM. Component

_ AV;EN C D;k (MW)

_ AV;EN C D;k (%)

_ AV;UN C D;k (MW)

_ AV;UN C D;k (%)

_ AV;EX C D;k (MW)

_ AV;EX C D;k (%)

_ UN;EX C D;k (MW)

_ UN;EX C D;k (%)

AC APH CC GT HRSG

7.62 55.73 147.71 32.61 58.27

13.24 78.85 35.11 42.53 34.56

28.59 4.95 77.91 36.84 110.32

49.67 7.00 18.52 48.05 65.44

8.34 9.28 47.39 2.82 0.00

14.49 13.13 11.27 3.68 0.00

13.01 0.72 147.65 4.40 0.00

22.60 1.02 35.10 5.74 0.00

AC, Air compressor; APH, air preheater; CC, combustion chamber; GT, gas turbine; HRSG, heat recovery steam generator.

Table 6.11 The splitting of the investment costs of selected components in the CGAM. Component

Z_ D (US$/h)

Z_ D;k (US$/h)

EN

EN Z_ D;k (%)

EX Z_ D;k (US$/h)

Z_ D;k (%)

EX

AV Z_ D;k (US$/h)

Z_ D;k (%)

AV

UN Z_ D;k (US$/h)

Z_ D;k (%)

UN

AC

56.99

39.93

70.06

17.06

29.94

46.92

82.33

10.07

17.67

APH

12.26

7.02

57.26

5.24

42.74

4.83

39.40

7.43

60.60

CC

4.41

2.37

53.74

2.04

46.26

2.55

57.82

1.86

42.18

GT

48.16

43.29

89.89

4.87

10.11

32.17

66.80

15.99

33.20

HRSG

16.93

16.93

100.00

0.00

0.00

3.89

22.98

13.03

76.96

AC, Air compressor; APH, air preheater; CC, combustion chamber; GT, gas turbine; HRSG, heat recovery steam generator.

Table 6.12 The combination splitting of the investment costs of selected components in the CGAM. Component

Z_ D (US$/h)

AV;EN Z_ D;k (US$/h)

AV;EN Z_ D;k (%)

Z_ D;k (US$/h)

UN;EN

UN;EN Z_ D;k (%)

Z_ D;k (US$/h)

AV;EX

AV;EX Z_ D;k (%)

Z_ D;k (US$/h)

UN;EX

UN;EX Z_ D;k (%)

AC

56.99

33.90

59.48

6.03

10.58

13.02

22.85

4.03

7.07

APH

12.26

4.38

35.73

2.64

21.53

0.44

3.59

4.79

39.07

CC

4.41

1.23

27.89

1.14

25.85

1.32

29.93

0.72

16.33

GT

48.16

29.57

61.40

13.73

28.51

2.60

5.40

2.27

4.71

HRSG

16.93

13.03

76.96

3.89

22.98

0.00

0.00

0.00

0.00

AC, Air compressor; APH, air preheater; CC, combustion chamber; GT, gas turbine; HRSG, heat recovery steam generator.

Table 6.13 Comparison of results between the conventional exergoeconomic analysis and the advanced exergoeconomic analysis [4]. Conventional exergoeconomic analysis

Advanced exergoeconomic analysis

Component

_ D;k Z_ k 1 C

ηEx

rk

fk

AV;EN _ AV;EN 1C Z_ k D;k

ηAV;EN Ex;k

fkAV;EN

AC APH CC GT HRSG

114.55 82.94 425.06 124.83 185.52

93.05 83.04 66.76 95.22 65.48

14.87 23.97 50.32 8.17 58.02

49.75 14.78 1.04 38.58 9.13

41.52 60.11 148.94 62.18 71.3

99.02 86.13 85.12 97.91 84.59

81.65 7.29 0.83 47.55 18.28

AC, Air compressor; APH, air preheater; CC, combustion chamber; GT, gas turbine; HRSG, heat recovery steam generator.

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CHAPTER 6 Advanced exergetic evaluation of cogeneration

Of the three components considered to be improved first, the expander has the lowest potential for improvement, whereas the HRSG has the highest as determined by the ηAV;EN values. The low fkAV;EN value in the combustion chamber Ex;k reveals that the avoidable endogenous cost associated with this component is due to the high cost of exergy destruction.

6.5.2 Liquefied natural gas cogeneration Iran liquefied natural gas (LNG) plant consisted of two LNG trains in its initial phase. The LNG cogeneration plant schematic is shown in Fig. 6.5. The plant design was conceived to match the power demand in the worst condition when the temperature was particularly high. So, the gas turbines were designed to operate at 48 C and the plant was designed for normal operation at 43 C that produced 703.6 MW. However, the plant was fitted to operate at 48 C with accepting lower efficiency and reduced generation capacity [8]. Steam and power maximum requirements in Iran LNG project are illustrated in Table 6.14 [3]. Conventional and advanced exergy, exergoeconomic and exergoenvironmental analysis have been performed for the Iran LNG cogeneration plant by Khoshgoftar Manesh et al. [3]. Table 6.15 shows some data obtained from the conventional exergetic, exergoeconomic, and exergoenvironmental analysis. In addition, Tables 6.6
6.8 demonstrates some data obtained from the advanced exergetic, exergoeconomic, and exergoenvironmental analysis, respectively[3].

FIGURE 6.5 Optimum configuration of Iran LNG’s steam and power-generation plant [3]. LNG, Liquefied natural gas.

6.5 Application of advanced exergy-based analysis

Table 6.14 Data parameters for liquefied natural gas case [3]. Parameters

VHP

HP

Pressure (bara) Saturation temperature ( C) Mass flow requirements (t/h)

101.9 312.38 60.3

10.84 183.42 400

HP, High pressure; VHP, very high pressure.

Table 6.15 Results obtained from the conventional exergetic, exergoeconomic, and exergoenvironmental analysis [3]. Exergetic analysis

Exergoeconomic analysis

Exergoenvironmental analysis

Component

ED (MW)

ε (%)

Z ($/h)

CD ($/h)

Z 1 CD ($/h)

Y (Pts/h)

BD (Pts/h)

Y 1 BD (Pts/h)

AC CC GT HRSG ST CT

34.1 397.7 272.2 20.7 5.8 43.2

94.37 82.85 80.94 97.61 81.76 80.73

213.48 304.92 686.22 272.04 22.78 728.12

8536.5 30909 25579 2113.7 600.27 4460.4

8749.98 31213.92 26265.22 2385.74 623.05 5188.52

7.37 0.88 8.82 4.65 0.82 4.62

9575.53 34906.9 28839.1 2383.14 672.83 4999.6

9582.9 34907.78 28847.92 2387.79 673.65 5004.22

AC, Air compressor; CC, combustion chamber; CT, condensing turbine; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

Table 6.16 Splitting the exergy destruction within the kth component of the cogeneration system [3]. Unavoidable/ avoidable

No.

Component

1

AC

2

CC

3

GT

4

HRSG

5

ST

6

CT

Endogenous/ exogenous

EDUN (MW)

EDAV (MW)

EDEN (MW)

EDEX (MW)

8.2 (24%) 157.4 (39%) 211.5 (77%) 4.0 (19%) 3.4 (59%) 26.7 (61%)

25.7 (75%) 240.2 (60%) 60.7 (22%) 16.6 (80%) 2.3 (40%) 16.5 (38%)

8 (23%) 340.8 (85%) 272.7 (100%) 11 (53%) 1 (18%) 8.3 (19%)

25.9 (76%) 56.8 (14%) 0 (0%) 9.6 (46%) 4.7 (81%) 34.8 (80%)

Splitting EDReal ðMWÞ EDUN

EDAV

EDUN;EN

EDUN;EX

EDAV;EN

EDAV;EX

2.0

6.3

6.1

19.6

134.9

22.5

206.0

34.3

211.5

0.0

60.8

0.0

2.2

1.9

8.9

7.8

0.6

2.8

0.4

1.9

5.1

21.6

3.2

13.3

AC, Air compressor; CC, combustion chamber; CT, condensing turbine; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

89

Table 6.17 Splitting the capital investment cost within the kth component of the cogeneration system [3]. Unavoidable/avoidable

Splitting ZReal($/h)

Endogenous/exogenous Z

AV

No.

Component

Z

1

AC

7.1 (13%)

46.2 (87%)

2 3

CC GT

4 5 6

HRSG ST CT

7.8 (10%) 26.2 (15%) 18 (26%) 3.4 (15%) 42.6 (15%)

68.3 (90%) 145.3 (85%) 49.9 (74%) 19.3 (85%) 241.7 (85%)

($/h)

Z

($/h)

EN

Z

($/h)

21.75 (41%) 42.5 (56%) 118.6 (69%) 39.6 (58%) 11.4 (50%) 142.1 (50%)

Avoidable/endogenous

AV

Z

Z

Z

Z

ZAV,

CAV;EN D ($/h)

Z AV;EN 1 CAV;EN D ($/h)

31.6 (59%)

2.9

4.2

18.9

27.4

300.8

319.7

33.6 (44%) 52.9 (31%)

4.4 18.1

3.5 8.1

38.2 100.6

30.2 44.8

2390.2 2306.5

2428.4 2407.1

28.3 (42%) 11.4 (50%) 142.1 (50%)

10.5 1.7 21.3

7.5 1.7 21.3

29.2 9.7 120.8

20.8 9.7 120.8

113.3 132.8 987.2

142.5 142.5 1108.0

UN,

UN

UN

EX

Z

($/h)

EN

UN,

EX

AV,

EN

EX

AC, Air compressor; CC, combustion chamber; CT, condensing turbine; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

6.5 Application of advanced exergy-based analysis

The results from the conventional exergetic analysis show that the most important components from the thermodynamic viewpoint are the combustion chamber, gas turbine, HRSG, and air compressor. If the analysis is based only on the value of the component-related cost rate (Z_k ), then we have two major candidates for improvement: gas turbine and combustion chamber. All other components have much lower values of Z_k . Completely different results are obtained from the exergoeconomic analysis of _ the LNG plant. The economic impact associated  with the exergy destruction C D;k _ _ is the largest contributor to the sum Z k 1 C D;k for all components. In this view, we have two major candidates for improvement: combustion chamber and gas turbine (Table 6.15). Based on advanced exergy destruction and capital investment cost (Table 6.16 and 6.17), we have two major options for improvement: combustion chamber

FIGURE 6.6 (A) EDL—avoidable/unavoidable representation, (B) ECDL—avoidable/unavoidable representation, and (C) EEDL—avoidable/unavoidable representation [3]. ECDL, Exergy cost destruction level; EDL, exergy destruction level; EEDL, environmental impact of exergy destruction level.

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and gas turbine. Table 6.16 shows splitting the exergy destruction within the kth component of the cogeneration system and Table 6.17 indicates splitting the capital investment cost within the kth component of the cogeneration system. EDL/ECDL/EEDL representations of unavoidable and endogenous of LNG cogeneration are shown in Figs. 6.6 and 6.7. As indicated in Fig. 6.6, combustion chamber has most EDLAV and compressor has most ECDLAV/EEDLAV, so it means that rate of exergy, exergy cost, and exergy environmental destruction is high for production 1 MW TV in view of unavoidable exergy. In addition, HRSG have lowest EDLAV/ECDLAV/EEDLAV rather than other components. It demonstrates that the rate of exergy, exergy cost, and exergy environmental in view of

FIGURE 6.7 (A) EDL—endogenous/exogenous representation, (B) ECDL—endogenous/exogenous representation, and (C) EEDL—endogenous/exogenous representation [3]. ECDL, Exergy cost destruction level; EDL, exergy destruction level; EEDL, environmental impact of exergy destruction level.

References

unavoidable exergy is low. As indicated in Fig. 6.6, both back pressure steam turbine and condensate turbine has most EDLEN/ECDLEN/EEDLEN; so it indicates that the rate of exergy, exergy cost, and exergy environmental destruction is high for production 1 MW TV in view of endogenous exergy. Also, HRSG have lowest EDLEN/ECDLEN/EEDLEN rather than other components, so it indicates that the rate of exergy, exergy cost, and exergy environmental in view of endogenous exergy is low.

References [1] Tsatsaronis G, Morosuk T. A general exergy-based method for combining a cost analysis with an environmental impact analysis: Part I—Theoretical development. In: ASME 2008 International Mechanical Engineering Congress and Exposition. 2008. [2] Tsatsaronis G, Morosuk T. A general exergy-based method for combining a cost analysis with an environmental impact analysis: Part II—Application to a cogeneration system. In: ASME 2008 International Mechanical Engineering Congress and Exposition. 2008. [3] Khoshgoftar Manesh MH, Navid P, Blanco Marigorta AM, Amidpour M, Hamedia MH. New procedure for optimal design and evaluation of cogeneration system based on advanced exergoeconomic and exergoenvironmental analyses. Energy 2013;59:314
33. [4] Kelly S. Energy systems improvement based on endogenous and exogenous exergy destruction. 2008. [5] Park M-H. On avoidable and unavoidable exergy destructions and investment costs in thermal systems. Energy Convers Manage 2002;43:1259
70. [6] Khoshgoftar Manesh MH, Amidpour M. New graphical methodology for energy integration in nuclear steam power plant. 2008. [7] Manesh MHK, Rosen MA. Combined cycle and steam gas-fired power plant analysis through exergoeconomic and extended combined pinch and exergy methods. J Energy Eng 2018;144(2):04018010. [8] Khoshgoftar Manesh MHMV, Amidpour M. Power plant optimization study in Iran LNG project through MINLP method. Chem Eng Trans 2009;18:935
40.

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7

Chapter outline 7.1 7.2 7.3 7.4 7.5

Introduction ................................................................................................... 95 Total Site integration ...................................................................................... 96 Total Site profiles ........................................................................................... 96 Total Site procedure ....................................................................................... 98 Case studies ................................................................................................102 7.5.1 Case 1. A conventional Total Site analysis .....................................102 7.5.2 Case 2. Integration of site utility and thermal power plant ..............106 References ..........................................................................................................113

7.1 Introduction Total Site targets of industrial systems were implemented by Dhole and Linnhoff based on the definitions of site heat source and heat sink profiles and targets for fuel consumption, emission production, cooling, and cogeneration potential [1]. Raissi introduced Total Site integration as analytical tools and systematic way that assist one understand the interactions between fuel consumption, heat recovery, and cogeneration potential, in Total Site [2]. This approach was used in integration problems, including site expansions, retrofit, and grassroots. With the introduction of power cogeneration targets, Klemeˇs et al. made further developments in the field [3]. The idea was extended to industrial sites initially to refining processes and petrochemical processes. In general, such processes function as parts of industrial plants. On these Total Sites, heat integration is achieved by a series of energy sinks and sources, usually steam at multiple pressure levels, plus hot water, cool and chilled water. The processes are supplied by a central distribution system supplying heat and power. Matsuda et al. applied Total Site methodology to different large industrial process plants in Japan to improve the performance of each plant [4]. In this regard, wide energy-saving and promising options for improvement projects are being proposed and accomplished. A transshipment model for multiperiod operation to find optimum steam pressure levels of Total Site utility also was proposed first by Shang and Kokossis as the targeting stage [5] for the optimal design of utility steam and power generation systems. Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00007-0 © 2021 Elsevier Inc. All rights reserved.

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Khoshgoftar Manesh et al. developed Total Site analysis (TSA) to integration of a central site utility of petrochemical complex based on new cogeneration targeting and exergoeconomic analysis. TSA is promising approach to optimal design of cogeneration and polygeneration system.

7.2 Total Site integration The TSA consists of a collection of data about the cooling and heating demands for site. Thermodynamic analysis of these requirements is based on pinch technology tools such as composite curves (CCs) and determination of the special targets. The targets can be minimum fuel consumption and maximum power production as cogeneration potential. An industrial plant usually includes a number of different process units. These units need different utilities [6]. Main utilities are as follows: Heating demands: It consists of steam in different pressure levels or hot oil in furnaces. Cooling demands. It is supplied by using ambient air, cooling water, refrigeration, or cryogenic system. Power demands. Different mechanical equipment such as pump, blower, and compressors and, also, electrical devices in industrial sites require power. Water demands. It requires different processes and also to produce steam. Schematic of a Total Site is shown in Fig. 7.1. In addition, Fig. 7.2 demonstrates the complex Total Site that integrated with renewable energy.

7.3 Total Site profiles A grand CC (GCC) is built by plotting variations in enthalpy verso temperature, and values of hot and cold utilities are investigated. This schematic shows the effects of the utility services and thus an optimum integrated design for both process and utility applications [3]. The GCC representation indicates at every temperature level, the potential of heat generation in a process. Utilities demand and grand composite curves are indicated in (Fig. 7.3). A heat sink profile located above and below the pinch is introduced as a heat source (Fig. 7.3). The site sink profile is constructed by the heat sink data associated with all processes, and the cold CC is built by the heat source data related to available processes (Fig. 7.3). The Total Site profiles (TSPs) indicate a heat surplus and heat deficit simultaneously for all the processes on the site. The GCC representation indicates at every temperature level the potential of heat generation in a process. A heat sink located profile above below the pinch is introduced as a heat source. The site sink profile is constructed by the heat sink data associated with all processes,

7.3 Total Site profiles

Steam turbine network

Boiler

W,st

Fuel

Power

Fuel

Wgt Air

COND

HRSG

Gas turbine

High pressure

Med pressure Low pressure

Fuel

Fuel

Process A

Process B

Process C Cooling water

Refrigeration

Total Site FIGURE 7.1 Schematic of a Total Site [3].

Renewables

Nuclear

Fossil fuels

Steam turbine

Gas turbine

Wind

Sun

Heat pump

Fossil fuel

Biofuel Electricity Steam Hot water Cooling utility

Fossil fuel Fossil fuel

Fossil fuel

Biofuel

FIGURE 7.2 Complex Total Site integrated [7].

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CHAPTER 7 Total Site integration and cogeneration systems

T

T

T

H

H

H

Plant

Plant

Plant

Steam

Steam

Cooling

Power

Fuel Utilities Steam

T

Steam

T

T

H Plant l

H

H Plant V

Cooling

Plant VI

FIGURE 7.3 Utilities demand and grand composite curves [1].

and the cold CC is built by the heat source data related to available processes. The TSP indicates surplus and deficit heat for all the processes on the Total Site [3]. TSP helps the designer with promising information about the generation of the different steam levels. It can be used to predict the steam generation, steam used, and power production at each level. The process steam generation at each steam level precisely satisfies some of the heating demands of the processes. The site source and sink profiles are plotted by integrating the heat source and heat sink data from considered processes. The site profiles are associated with the hot and cold CCs for the individual processes. Fig. 7.4 demonstrates the construction of TSP through original GCC. In addition, a combination of hot and cold streams in TSP is shown in Fig. 7.5. Fig. 7.6 demonstrates the location and variation of steam main in TSP. Using the TSP representation, cogeneration potential and fuel consumption can be predicated without need to simulation. The fuel and cooling requirements are calculated from the TSP. As shown in Fig. 7.7, the shaded area in the TSP is related to the potential of power production by using steam turbine.

7.4 Total Site procedure TSP as an efficient and straightforward analytical tool can be applied to calculate fuel consumption, heat recovery, the potential of power production, and fuel saving for a Total Site. To determine the different potential of steam and power production in the Total Site, site utility grand composite curve (SUGCC) is defined using site CCs.

7.4 Total Site procedure

T

Heat sink T

Heat source Site source profile

Process I H

Site sink profile

T H

H 0

Heat sink

Total Site profile Heat source

Process II H

FIGURE 7.4 Total Site profile generation with GCC [3]. GCC, Grand composite curve.

The designer can easily evaluate the best promising choices at the targeting level for both utilities and processes. The data preparation for Total Site approach includes the following steps [3]: Step 1: Stream data extraction and preparation. Step 2: Setting ΔTmin . Step 3: Construct the initial GCC for each process. Step 4: Generate modified GCC based on elimination of process to process heat transfer. Step 5: Compute heat demand that is satisfied by Total Site. Step 6: Add the other process data. Step 7: Reputation of the procedure for the remaining processes on the Total Site. The targeting phase consists of the following steps: Step1: Generation of site profiles. This is constructed by the sink/source profiles that determine the heat sources and sinks for the Total Site and the location of utilities to satisfy heating and cooling demands. These can be drawn on a temperature/enthalpy plot or a Carnot factor/enthalpy plot. Step 2: Generation of Total Site CCs. This indicates the maximum heat recovery and also minimization of steam used in the system. Furthermore, the potential for power generation is determined. Also, the site pinch is determined.

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CHAPTER 7 Total Site integration and cogeneration systems

T(°C)

Composite hot stream

Heat sources

Heat sink

H(MW)

Total Site profile Composite cold stream

T(°c)

H(MW)

FIGURE 7.5 Combining hot and cold streams in Total Site profile.

T

Steam generated by the processes

Steam generated by the processes

HP HP

MP

LP

MP

Steam demand by the processes

LP

H

Steam demand by the processes

FIGURE 7.6 Schematic of steam generation and used in Total Site profiles.

Step 3: Construction of the site utility curve. Based on the Total Site CC and getting utility loads, and potential of power generation, the site utility curve is made up. The targeting approaches are necessary for a flexible design of site utility and cogeneration systems integrated with process industries (Fig. 7.8).

7.4 Total Site procedure

Heat in

Carnot factor

ηC

VHP

VHP

VHP

VLP/C W

VLP/CW

Total Site pinch

VLP/C W

Heat out Enthalpy

H

FIGURE 7.7 Total Site profiles and potential of cogeneration [3].

Site fuel

η

c

Total Site pinch

W

Cooling

Power H

Site composite curve

Steam Site infrastructure (steam system,etc)

Process power

Process fuel FIGURE 7.8 Targeting procedure [1].

Net site power

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CHAPTER 7 Total Site integration and cogeneration systems

7.5 Case studies In this section, in order to better understand the use of TSA and integration, two examples are given showing how to use this tool.

7.5.1 Case 1. A conventional Total Site analysis The first case study is related to TSA of a conventional site utility of process industry. The selected steam levels and cooling mains for considered sites are determined in Table 7.1 [8]. The high-pressure (HP) steam levels are considered to be at 86 bar. These steam levels and cooling demands are then added to the site profile representation and are demonstrated at their real saturated temperatures. The TSPs for the considered steam mains are shown in Fig. 7.9. Table 7.1 Steam levels and cooling mains data for the Total Site [8]. Steam levels

Saturated temperature ( C)

Pressure (bar)

HP MP LP Cooling water

300 200 160 20

86 15.5 6.2 1

HP, High-pressure; LP, low-pressure; MP, medium-pressure.

VHP steam from boiler (2–299.5 kW)

350.0

HP

300.0

Steam generated

Temperature (°C)

102

250.0

MP

200.0 MP LP LP

150.0

100.0

Steam used 50.0 CW 0.0 –2000.0

–1500.0

–1000.0

–500.0

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

Enthalpy (kW)

FIGURE 7.9 Steam profiles with steam boilers providing VHP steam [8]. VHP, Very high pressure.

7.5 Case studies

A horizontal line in TSP indicating the steam level temperature takes the site profile, the shift in temperature of the considered process stream section, and the actual temperatures of the steam levels means that heat transfer will be performed at the minimum approach temperature for that process [8]. The amount of steam demand that to be supplied by the different steam levels equals the total heat to be provided to the heat sinks in the site profiles. This value is 2299.5 kW that supplied from steam boilers. The potential of steam generation, or satisfied by cooling demand, equals the heat source section. As shown in Fig. 7.9 and Table 7.2, this value is 1948.5 kW. The value of steam used and steam generation at each steam level to satisfy the heat sinks and heat sources of the site processes, and the value of steam generation from the heat sources of the processes are achieved from the TSPs as indicated in Table 7.2. The potential of steam heat recovery to achieve the fuel saving and reduction of emission pollutions is shown in Fig. 7.10. Table 7.2 Steam used and steam generated across the Total Site [8]. Steam main

Steam used (kW)

Steam generated (kW)

HP MP LP Cooling water

849 250.5 1200 0

0 405.9 769.1 773.5

HP, High-pressure; LP, low-pressure; MP, medium-pressure.

350.0

HP

300.0

Temperature (°C)

Steam generated 250.0

MP

200.0

MP LP LP

150.0

Steam used 100.0

50.0 CW

0.0 –2000.0

–1500.0

–1000.0

–500.0

0.0

500.0

1000.0

1500.0

2000.0

Enthalpy (kW)

FIGURE 7.10 Total Site profiles with potential steam heat recovery [8].

2500.0

3000.0

103

CHAPTER 7 Total Site integration and cogeneration systems

350.0

VHP steam from boiler (1–124.5 kW) HP

300.0

Temperature (°C)

104

250.0

MP

MP

200.0 LP

150.0

100.0

50.0 CW

0.0 –2000.0

–1500.0

–1000.0

–500.0

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

Enthalpy (kW)

FIGURE 7.11 Steam profiles with steam boilers providing VHP steam [8]. VHP, Very high pressure.

Fig. 7.11 demonstrates maximum shifting between the source and sink profiles by moving sink profiles toward the source profiles. The maximum amount of heat recovery is obtained by maximum overlap between the sink and source profiles. In this regard the very high pressure (VHP) steam generation in boilers and total fuel consumption have been decreased from 2299.5 to 1124.5 kW, and the value of maximum heat recovery by the steam system is 1175 kW. The final analytical tool for TSA is the SUGCC, which demonstrates the potential heat and power (cogeneration) production that is available in the Total Site utility. The SUGCC can be derived from the TSPs and Total Site composite curves. Fig. 7.12 shows the SUGCC for maximum heat recovery in the Total Site. As shown in Fig. 7.9, the value of steam that is supplied by boilers in maximum heat recovery is 1124.5 kW. It is assumed that the boilers are generating steam at a pressure of 86 bar and a saturated steam temperature of 300 C. A horizontal line indicates that the steam at 300 C is then constructed from 0 to 1124.5 kW enthalpy values. The value of HP steam demand is 849 kW, and this value is going back toward the original HP steam main at an enthalpy value of 275.5 kW. Then, a vertical line is moving down to this amount of enthalpy at the temperature of 200 C for medium-pressure (MP) level [7]. As we know from TSPs, the MP steam generation at the MP main is

7.5 Case studies

350.0

Steam supplied by boiler (1124.5 kW) 300.0

HP use (849 KW)

Temperature (°C)

250.0

MP generation (405.9 kW)

200.0

MP use 150.0

250.5 kW

LP generation 1200 kW

LP use

769.1 kW

100.0

50.0

CW use 0.0 0.0

200.0

773.5 kW

400.0

600.0

800.0

1000.0

1200.0

1400.0

Enthalpy (kW)

FIGURE 7.12 SUGCC with maximum heat recovery [8]. SUGCC, Site utility grand composite curve.

405.9 kW, and a horizontal line is plotted from the present point to an enthalpy value of 681.4 kW. The value of MP steam used is 250.5 kW in heat sink of Total Site, and again the line is going back to amount of enthalpy of 430.9 kW. As the same way, a vertical line is moved down to the low-pressure (LP) steam main. The LP steam generation by site processes is 769.1 kW, and a horizontal line is plotted from the moved down point at 430.9 1200 kW. The amount of LP steam used by the site processes is 1200 kW, and the horizontal line is going back to the 0 kW [8]. As shown in Fig. 7.13, there is an enclosed area between the HP and LP steam mains. This indicates the amount of power cogeneration that can be achieved by appreciating steam turbines. Also, Fig. 7.13 reveals that the only 849 kW of total steam generation in boiler is being used by the processes, and the amount of the 275.5 kW of steam is being passed to the MP steam level. This amount of steam has cogeneration potential to power generation by adding appropriate steam turbine between this enclosed area. At the MP steam level, only 250.5 kW is supplied as heat sink because site process generated 405.9 kW of MP steam. The amount of 155.4 kW of MP steam passed to the LP steam main. At the LP steam main the amount of 1200 kW steam is as heat sink of the site processes. The 275.5 kW of excess HP steam, 155.4 kW of the excess MP steam, and the 769.1 kW of LP steam generation supplied LP steam main requirements.

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CHAPTER 7 Total Site integration and cogeneration systems

FIGURE 7.13 Process flow diagram of a 315 MW gas-fired steam power plant [9].

7.5.2 Case 2. Integration of site utility and thermal power plant Integration of a 315 MW gas-fired steam power plant to a site utility of a process plant was studied [9,10]. A process of diagram of considered power plant is demonstrated in Fig. 7.13. Table 7.3 determines the property value of each stream at full-load condition. The base power plant efficiency is 38.5%. The net power should be 315 MW, and the fuel mass flow rate can be changed in the integrated plant. In the integrated scenario, some of the steam extracted from the steam turbines directly provides the process steam of site utility, and the fuel consumption is increased, respectively. Fig. 7.14 demonstrates the TSPs of a considered process site. Also, Fig. 7.15 demonstrates the central process site utility for the base case. Table 7.4 determines the steam demand requirements in base process site utility. As shown in Table 7.5, steam used and generation in base site utility are determined. Furthermore, SUGCC of base process site utility is shown in Fig. 7.16. The SUGCC of the site utility for the Total Site in the base case is demonstrated in Fig. 7.16. The steam demands of the Total Site are supplied by bleed steam that extracted from the power plant. In this regard, streams of 103, 104, and 107 as bleed steam are extracted from steam turbines and entering towered the same steam mains in central process site utility. So, three feedwater heaters are

7.5 Case studies

Table 7.3 Property values at various state points in a 315 MW plant at fullload conditions [9]. Parameter

T

P

m

Stream/unit 146 148 147 70 144 143 Air 121 101 111 10 122 123 102 112 124 103 113 104 114 125 105 115 106 116 126 127 107 117 128 108 118 Fuel Stack gas 40 151 115 213



Bar 185.50 44.13 47.49 0.15 1.01 1.01 1.01 19.21 0.67 0.67 1.01 17.44 15.75 1.61 1.61 14.41 3.44 3.44 6.67 6.67 13.55 11.52 11.52 18.36 18.36 209.80 208.70 29.48 29.48 207.70 48.68 47.26 1.04 1.01 7.00 193.79 11.52 209.80

kg/s 279.25 255.25 255.25 175.04 8141.00 8141.00 3471.61 220.90 12.34 12.99 0.65 233.90 233.90 9.25 28.93 233.90 9.64 19.69 10.05 10.05 233.90 9.85 279.90 8.21 36.21 274.30 274.30 11.84 28.00 274.30 16.16 16.16 16.77 367.10 16.06 0.68 279.90 274.30

C 560.00 560.00 356.90 53.60 51.94 40.29 36.00 54.69 88.70 88.70 36.00 87.14 111.80 158.50 92.14 136.60 233.10 116.80 307.60 141.60 161.30 374.10 186.10 436.50 195.70 190.70 209.70 502.90 214.70 235.60 358.90 240.60 36.00 134.70 309.10 363.10 186.10 190.70

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CHAPTER 7 Total Site integration and cogeneration systems

300

250

Temperature (°C)

108

200

150

100

50

0

–400

–300

–200

–100

0 100 Heat load (MW)

200

300

400

FIGURE 7.14 Total Site profile (base case) [9].

Process steam generator

VHP 101 bar Process steam generator

HP 20.6 bar

Power plant steam generator Process steam generator

Process steam demand

MP 4.1 bar

Power plant steam generator Process steam generator

Process steam demand

Process steam demand

LP 2.7 bar

Power plant steam generator

Process steam demand

FIGURE 7.15 Schematic of site utility process plant (base case).

eliminated. Fig. 7.17 shows process flow diagram of integrated steam power plant. The steam generation and use in integrated system are indicated in Table 7.6. The TSPs for the integrated condition are shown in Fig. 7.18. Fig. 7.19 represents the SUGCC of integrated system. The potential of steam and power production as cogeneration in integrated system is shown. The fuel

7.5 Case studies

Table 7.4 Properties of steam requirements of base process site utility [9]. Parameter

VHP

HP

MP

LP

Pressure (bar) Saturation temperature ( C) Net heat load (MW)

101 312 110.8

20.6 214 21.4

4.1 144.5 9.3

2.7 130 73.6

HP, High-pressure; LP, low-pressure; MP, medium-pressure; VHP, very high pressure.

Table 7.5 Steam generation and use (base case) [9]. Steam level

Process steam generation (MW)

Process steam demand (MW)

VHP HP MP LP

0 120 47.7 15.4

110.8 141.4 57 89

HP, High-pressure; LP, low-pressure; MP, medium-pressure; VHP, very high pressure.

100.3 kg/s

TVHP = 529

O

Saturation temprature ( C)

300

46.21 kg/s

14.35 MW

250

T HP = 357 200

36.66 kg/s

10.28 MW

32.56 kg/s

2.27 MW

150

T MP = 170

100 0

50

100

150

200

Heat load (MW)

FIGURE 7.16 The SUGCC of base process site utility [9]. SUGCC, Site utility grand composite curve.

250

109

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CHAPTER 7 Total Site integration and cogeneration systems

FIGURE 7.17 Process flow diagram of integrated steam power plant [9].

Table 7.6 Steam generation and use in integrated system [9]. Steam level

Process steam generation (MW)

Process steam demand (MW)

VHP HP MP LP

0 150.10 72.34 38.34

110.8 141.4 57 89

HP, High-pressure; LP, low-pressure; MP, medium-pressure; VHP, very high pressure.

consumption demands and boiler load in integrated central site utility are indicated to be reduced rather than the base case. In this regard the site utility power generation is reduced, and the VHP steam requirements of the integrated site utility are reduced by 65%. Fig. 7.20 shows the schematic of integrated process site utility system. In addition, extended SUGCC is shown in Fig. 7.21. The left side of this representation indicated conventional SUGCC, and the right side is illustrated T S diagram that shows the actual state of the central site utility.

7.5 Case studies

FIGURE 7.18 Site profiles of integrated site utility [9].

i=1(VHP, 101 bar)

110.8 MW

S a t u ra t io n t e m p ra t u r e ( C)

300

O

250

i =2(HP, 20.6 bar)

–8.70 MW

200

i =3(MP, 4.1 bar)

150

–10.34

i=4(LP, 2.7 bar)

50.66 MW

100 0

20

40

60

80

100

120

140

Heat load (MW) FIGURE 7.19 SUGCC of integrated system [9]. SUGCC, Site utility grand composite curve.

111

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CHAPTER 7 Total Site integration and cogeneration systems

Process steam generator

Process steam generator

VHP 101 bar

Qnet,i =Qi DEM –QiGEG =110.8 MW

HP 20.6 bar

Qnet,i =Qi DEM –QiGEG = – 8.70 MW

MP 4.1 bar

Qnet,i =Qi DEM –QiGEG = –15.34 MW

LP 2.7 bar

Qnet,i =Qi DEM –QiGEG = 50.66 MW

Process steam demand

Power plant steam generator Process steam generator

Process steam demand

Power plant steam generator Process steam generator

Process steam demand

Power plant steam generator

Process steam demand

FIGURE 7.20 Schematic of site utility process plant (integrated case) [9].

Temprature (°C) 550 4

500

450

400

Boiler

3

350

101 bar 300

VHP_Hp Turbine 250

20.6 bar 2 200

HP_MP Turbine

1 150

4.1 bar

MP_LP Turbine 2.7 bar 100 –6

–4

Heat load (10 MW)

–2

0

2

4

6

8

Enthropy (kJ/kgk)

FIGURE 7.21 The modified SUGCC of steam network (integrated plant) [9]. SUGCC, Site utility grand composite curve.

References

References [1] Dhole VR, Linnhoff B. Total site targets for fuel, co-generation, emissions, and cooling. Comput Chem Eng 1993;17:S101 9. [2] Raissi K, Total site integration. In Process integration. Manchester: The University of Manchester, Thesis (Ph.D. p. 206. [3] Klemeˇs J, et al. Targeting and design methodology for reduction of fuel, power and CO2 on total sites. Appl Therm Eng 1997;17(8):993 1003. [4] Matsuda K, et al. Applying heat integration total site based pinch technology to a large industrial area in Japan to further improve performance of highly efficient process plants. Energy 2009;34(10):1687 92. [5] Shang Z, Kokossis A. A transhipment model for the optimisation of steam levels of total site utility system for multiperiod operation. Comput Chem Eng 2004;28 (9):1673 88. [6] Klemeˇs JJ, et al. Process integration and intensification. De Gruyter; 2014. [7] Perry S, Klemeˇs J, Bulatov I. Integrating waste and renewable energy to reduce the carbon footprint of locally integrated energy sectors. Energy 2008;33(10):1489 97. [8] Perry S. 6 - Total site methodology. In: Klemeˇs JJ, editor. Handbook of process integration (PI). Woodhead Publishing; 2013. p. 201 25. [9] Khoshgoftar Manesh MH, et al. Exergoeconomic and exergoenvironmental evaluation of the coupling of a gas fired steam power plant with a total site utility system. Energy Convers Manage 2014;77:469 83. [10] Abadi SK, et al. Integration of a gas fired steam power plant with a total site utility using a new cogeneration targeting procedure. Chin J Chem Eng 2014;22 (4):455 68.

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Desalinated water production in cogeneration and polygeneration systems

8

Chapter outline 8.1 Introduction .................................................................................................115 8.2 Main desalination technologies .....................................................................116 8.2.1 Multistage flash distillation desalination .......................................116 8.2.2 Multiple-effect distillation desalination .........................................119 8.2.3 Reverse osmosis desalination .......................................................122 8.3 Integration with thermal power plants ............................................................124 8.4 Integration with of gas turbines .....................................................................127 8.5 Integration with site utility industrial plants ...................................................129 References ..........................................................................................................135

8.1 Introduction Cogeneration plants are widely used for production of water and power simultaneously in the coastal region. It is more efficient and economic than standalone power plants and desalination plants. Bearing in mind the shortage of other water resources and the increasing requirements for fresh water, the application of the other resources such as saline water resources, that is, seas’ salty water resource is considered. Desalination process is one of the most important and major technologies to product fresh water in especially in the costal seas and arid areas. The desalination system uses a lot of energy. The main commercial desalination processes include multiple effect distillation (MED), multistage flash (MSF), and reverse osmosis (RO). In addition, the integration between them as hybrid desalination systems is considered. The dual-purpose power water production plants as cogeneration system make use of energy gained from power plants in form of low-pressure steam to supply heat source to MSF and MED desalination processes. Also, the power generation in power cycle can be used in the RO desalination. In this chapter, main desalination systems consisting MSF, MED, and RO are investigated. Comparison of different main desalination processes is indicated in Table 8.1.

Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00008-2 © 2021 Elsevier Inc. All rights reserved.

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CHAPTER 8 Desalinated water production in cogeneration

Table 8.1 Comparison of different main desalination technologies [1]. Process Size of membrane pore Temperature of feed Driving force parameter Main form of energy demand Energy source

Quality of desalinated water

MSF, MVC, MED, MEDTVC

RO

MEDRO, MSFRO



0.13.5 nm

0.20.6 μm

60
C120
C

,45
C

40
C80
C

Concentration and temperature Thermal

Pressure and concentration Mechanical

Concentration and temperature Thermal and mechanical

Low-pressure steam, from low-grade heat Very high TDS ,20 ppm

Electrical

Low-pressure steam, from low-grade heat and electrical High with TDS 20500 ppm

Potable water TDS ,500 ppm

MSF, Multistage flash; MVC, mechanical vapor compression; RO, reverse osmosis; TDS, total dissolved solids; TVC, thermal vapor compression.

There are three general categories to modify single-purpose power generation to cogeneration and polygeneration systems that can produce power, water, and process steam. In the advanced energy systems the simultaneous production of hydrogen, CO2, and cooling is considered more interesting products. The following commercial integration with desalination technologies is investigated:

• integration with thermal power plants • integration with of gas turbines or gas engines • integration with site utility industrial plants

8.2 Main desalination technologies 8.2.1 Multistage flash distillation desalination MSF distillation is a desalination system that desalinated the seawater by flashing the water into steam in a series of flash boxes of what are especially countercurrent heat exchangers. Fig. 8.1 shows the MSF desalination process. The flashing process is performed from stage to stage. The vapor production condenses on each of the preheater. The distillate is then gathered and carried into the next stage and a product is removed from the last stage [3]. Depending on the range of temperatures, the overall evaporation in total stages is up to approximately 85% of the water flowing in the system. The maximum allowable temperatures are 110
C120
C [4].

8.2 Main desalination technologies

Deaerator

Cooling seawater Aeration steam

Condensor tube

Distillate trays Intake seawater

Heating steam

Distillate product

Brine blowdowm

Brine pool Condensate

Demister

Feed seawater Brine recycle

Feed brine

Heat input section

Chemical

Heat recovery section

Heat rejection section

Fead pretreatment

FIGURE 8.1 Schematic of MSF desalination process [2]. MSF, Multistage flash.

The cycle consists essentially of a steam source, a water/steam heat exchanger (brine heater), pumping devices, and parts of flashing stages The brine seawater flows into the tubes of condenser by pump from the end of the rejection part to the left of the section. Due to setting of heat balance, the seawater is rejected into the sea partially before the recovery stage. Another section is combined with recycle brine, moved toward the final stage associated with recovery section, and warmed up in the condenser component by heat exchange with the distillate vapor. In the brine heater the seawater that is preheated is heated up further and enters the first flash chamber with low pressure (LP) and the highest temperature available [2,5]. Partly upon reaching the next stage, the brine flashes into vapor and on the condenser tubes condenses. The condensed vapor collects and enters the distillate tray across the stages. The brine is split into a recycle stream and a blow downstream, which is mixed with the inlet water and flow into the heat recovery part. The mathematical model for MSF simulation and analysis based on mass and energy balances is presented. The assumptions considered to the mathematical model consist the following [2,5]:

• The process operates in the steady-state condition. • Heat losses of the system to the environment are zero. • The thermophysical properties for brine, feed seawater, and distillate product • •

are the only functions of composition and temperature. For recovery and rejection area the fouling resistance is the same and constant. The desalinated water production is salt-free.

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CHAPTER 8 Desalinated water production in cogeneration

• Heating of mixing is zero. • No condensate subcooling exits the heater to the brine. The mathematical equations are based on a mass and energy balances, which are indicated next [2,5,6]. Flash chamber mass balances are: Bi21 5 Bi 1 Di

(8.1)

The salt balance regarding each stage is obtained by xbi 5

Bi21 xbi21 Bi

(8.2)

For distillate tray the mass balance is as follows: N X

ðD i Þ 5

i51

N21 X

ðDi 1 DN Þ

(8.3)

i51

Overall energy balance on the stage is as follows: WR Cpi ðTFi 2 TFi11 Þ 5

N 21 N X   X   Di CpDi21 TDi21 2 Tref 2 Di CpDi TDi 2 Tref i51   i51 1 Bj21 CpBi21 TBi21 2 Tref 2 Bi CpBi ðTBi 2 Tref Þ

(8.4)

The heat transfer relation is as follows: WR Cpi ðTFi 2 TFi11 Þ 5 Ui Ai LMTDi

(8.5)

where Ui is computed in terms of WR, TFi, TFi11, TDi, ID, OD, and fi. Energy balance associated with brine heater is calculated as: WR CpRH ðTB0 2 TF1 Þ 5 Ws Ls

(8.6)

Heat transfer relation in the brine heater is: BðTB0 2 TF1 Þ 5 UH AH LMTD

(8.7)

Mass balance equation related to splitters is: BD 5 BNS 2 R

(8.8)

CW 5 Mf 2 FMU

(8.9)

Mass balance for makeup mixers is: WR 5 R 1 FMU

(8.10)

RXBNS 1 FMU XMU 5 WR XR

(8.11)

Plant gain output ratio that shows the performance of desalination processed is defined as: GOR 5

DN Ms

(8.12)

8.2 Main desalination technologies

Table 8.2 Equations related to main parameters of multistage flash (MSF) desalination system. Parameter

Equations

Heat transfer area of stage 1

A1 5

Heat transfer area of stages 2 to N

Ai 5

Total heat transfer area of stages

M s Ls Ue1 ðTs 2 T1 Þ

ðDi21 1 D0i21 ÞLi21 Uei ΔT

Atot 5

n P

Ai

i51

ðDN 1 D0N ÞLN

Condenser heat transfer area

Ac 5

Overall heat transfer coefficients in the evaporator

Ue 5 1:9695 1 1:2057 3 1022 3 Tb 8:5989 3 1025 3 Tb2 1 2:5651 3 1027 3 Tb3

Overall heat transfer coefficients in the condenser

UC 5 1:7194 1 3:2063 3 1023 3 TV 1 1:5971 3 1025 3 TV2 2 1:9918 3 1027 3 TV3

Condenser logarithmic mean temperature difference Temperature difference of all stages

LMTDc 5

Temperature of stage 1 or top brine temperature Temperature of stages 2 to N Seawater temperature Vapor temperature Vapor temperature formed by flash process Boiling point elevation (BPE)

T3 5 Ts 2 ΔT

Nonequilibrium allowance (NEA)

ΔT 5

Uc LMTDc

ðTf 2 Tcw Þ

ðln ððTN 2 Tcw Þ=ðTN 2 Tf ÞÞÞ

T3 2 T4 N

Ti11 5 Ti 1 ΔT Tf 5 TN 1 N 3 ΔT Tvi21 5 Ti 2 BPEi T 0i 5 Ti 1 NEAi ðTÞ BPE 5 Ax 1 Bx 2 1 Cx 3 A 5 8:325 3 1022 1 1:883 3 1024 T 1 4:02 3 1026 T 2 B 5 2 7:625 3 1024 1 9:02 3 1025 T 2 5:2 3 1027 T 2 C 5 1:522 3 1024 2 3 3 1026 T 2 3 3 1028 T 2 iÞ NEAi 5 0:33 ðTi21 2T Tv

0:55

i

BPE, Boiling point elevation; NEA, nonequilibrium allowance.

Table 8.2 shows some relations to calculate main parameters for MSF desalination system.

8.2.2 Multiple-effect distillation desalination Multiple-effect distillation (MED) desalination is a process that includes multiple effects. The feed water is heated in tubes with steam in each effect, usually by spraying saline water on them. Portion of the water evaporates, and afterward the steam enters the tubes of the next effect. In this regard, more water is heated and evaporated subsequently.

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CHAPTER 8 Desalinated water production in cogeneration

Each effect mainly reuses the energy from the prior stage, with consecutively lower pressures and temperatures after each one [7]. There are various types of arrangements, such as forward-feed and backward-feed. Besides, this steam uses to preheat inlet brine water between stages. Due to low energy demand, multieffect distillation system is equipped with thermal vapor compression (MEDTVC) and it is especially more interested than other thermal desalination processes [8]. MEDTVC is well known as attractive with high performance ratio, easy operation, and low maintenance necessity. MEDTVC has attractive characteristics that make it exceedingly competitive to other well-known technologies for desalination of saline water [8]. Fig. 8.2 illustrates the MEDTVC desalination system. In addition, the schematic of TVC system is demonstrated in Fig. 8.3. This system is parallelcrossfeed and consists of evaporators, boxes for flashing, ejector with steam jet, and

FIGURE 8.2 MEDTVC desalination system [5]. TVC, Thermal vapor compression.

Nozzle

Suction chamber

Throat

Diffuser

Motive steam

Discharge (compressed) steam Entrained (suction) vaper

FIGURE 8.3 Schematic of TVC. TVC, Thermal vapor compression.

8.2 Main desalination technologies

condenser. After the last step, the condenser is placed, which preheats the feed water using the heat released from the condenser and condenses the vapor formed in the last effect. Initially, seawater enters the condenser and the latent heat of vapor is absorbed in the last effect and the flash box so that the temperature is warmed up from Tcw to Tf. Then, a portion of the heated seawater is removed from the system by cooling water Indeed, the task of cooling water is to remove surplus heat from the system. The inlet feed water is sprayed into the tubes of the first effect and is heated by the vapor flow. Heating vapor is the combination of the high-pressure motive steam generated by boiler and low-pressure steam sucked from the last effect. Due to the heat transfer between the sprayed water and steam, a small part of vapor steam as heating steam is made by boiling in the effect and flows into inside tubes of the next effect. The steam enters the flash box after the condensing and, because the flash box works at a lower temperature than water evaporation, a small portion of the water evaporates by flashing process. The remaining condensed steam is accumulated in the flash box as desalinated water, and this process is repeated from effect by effect, subsequently. Also, the remaining seawater in each effect, which has a more salinity than the inlet feed brine water, enters the next effect and is removed from the system in the final effect. The governing mathematical relations for mass, energy balance, and heat transfer equations are provided by El-Dessouky and Ettouney [6]. The following assumptions were considered for the MEDTVC system:

• • • • • •

Vapor produced in each effect is salt-free. Thermal losses from to environment is assumed to be zero Salinity of last rejection is 70,000 ppm. The same heat transfer area for evaporators 2 to N is considered. Parallel feed configuration with same flow rate is supplied to all effects: F1 5 F2, . . . ,FN 5 Mf/N). It is assumed that initially all effects of temperature difference are constant as T1 and TN, respectively. T1 2 TN N 21

(8.13)

T1 5 Ts 2 ΔT

(8.14)

ΔT 5

Ti11 5 Ti 2 ΔT;

i 5 2; . . .; N

(8.15)

It is important to note that Eq. 8.13 is an initial guess for computation, and at the end of the calculations, the temperature difference of all the effects will be

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CHAPTER 8 Desalinated water production in cogeneration

different. The salt and water mass balances for the former effect and the effects of 2 to N are as follows: B 1 5 F 2 D1 Bi 5 F 1 Bi21 2 Di ; xi 5 xi 5

(8.16)

i 5 2; . . . ; N

F xf B1

F Bi21 xf 1 xi21 2 Di ; Bi Bi

(8.17) (8.18)

i 5 2; . . . ; N

(8.19)

The first effect balance of energy is as: D1 5

  1 Ms Ls 2 FCp T1 2 Tf L1 Tf 5 TN 2 ΔTc

(8.20) (8.21)

The vapor is generated by boiling and flashing mechanisms in the effect 2 to N. In these effects the brine that removes from each effect enters the next effect. Due to reduction of pressure, a small quantity of vapor is made. Other small value of vapor is made in the flash box in consequence of the flashing of distillate condensed in the prior effect. The main parameters of MEDTVC desalination are calculated as determined in Table 8.3.

8.2.3 Reverse osmosis desalination RO is membrane-based technique that is reliable state-of-the-art system for seawater and brackish water desalination. In the RO system, by entering feed through a water-permeable membrane, a pressurized brine solution separated desalinated water from the dissolved salts. No phase change or heating occurred and only mechanical wok is needed. The major work requirement is for the pressurization of the brine feed water. The operating pressures from 250 to 400 psi are related to brackish water desalination and from 800 to 1000 psi to seawater desalination. Fig. 8.4 represents the overall schematic of RO desalination system. The simple mathematical model based on the mechanism of preferential sorption capillary flow as suggested by Sourirajan is used for the RO plant [9,10]. The RO modeling is in accordance with the following assumptions:

• The brine side and the feed have a uniform bulk concentration. • The removed salinity at the membrane wall cause in a more uniform concentration.

• In each stage the permeability of water is constant and independent from pressure.

• The coefficient of mass transfer is constant.

8.2 Main desalination technologies

Table 8.3 Equations related to main parameters of MEDthermal vapor compression. Descriptions

Equations

Temperature difference at each effect Effect 2 temperature (temperature of top brine) Effects 2 to N temperature Flow rate of brine at first effect Flow rate of effects 2 to N First effect salinity

ΔT 5

T1 2 TN N21

T3 5 Ts 2 ΔT Ti 5 Ti 1 ΔT B1 5 F 2 D1 Bi 5 F 1 Bi21 2 Di xi 5

F B1 xf

Salinity of effects 2 to N

xi 5

F Bi

Energy balance of first stage

D1 5

Feed water temperature The mass flow rate of flash box vapor is made Mass flow rate of each effect

Tf 5 TN 2 ΔTc   T 2 T0 D0i 5 Di21 Cp vi21Li i    Di 5 L1i Di21 1 D0i21 Li21 2 FCpðTi 2 Tf Þ 2 Bi21 CpΔT

Mass flow rate of cooling water

Mcw 5

Heat load of evaporator at first stage Heat load of evaporator at stage 2 to N Heat load of condenser

A1 5

Compression ratio (Cr)

Cr 5

Expansion ratio (Er)

Er 5 PN ðAi 1 Ac Þ a 5 i51Dtot

The specific heat transfer area Total desalinated water

Ai 5 Ac 5

xf 1

Bi21 Bi xi21

2 Di

½Ms Ls 2 FCpðT1 2 Tf Þ

1 L1

ðDN 1 D0N 2 Mev ÞLs Cp ðTf 2 Tcw Þ

2 Mf

Ms Ls Ue1 ðTs 2 T1 Þ

ðDi21 1 D0i21 ÞLi21 Uei ΔT

ðDN 1 D0N ÞLN Uc LMTDe Phs Pev Pm Pev

Dtot 5

N P

ðDi Þ

i51

Total brine Gain output ratio (GOR)

Mb 5 B n GOR 5

Dtot Mm

The basis of RO desalination work is to overcome the mechanical pressure difference over the osmotic pressure difference. If two fluids with different concentrations are located on either side of a semiconductor membrane. This concentration difference causes a pressure difference on both sides of the membrane, which is called the osmotic pressure difference, and it is directed from the fluid with a lower concentration to the fluid with a higher concentration [11]. In RO tap water the purpose is to create a flow against the natural flow. This means that it is desirable for the flow to move from the fluid with higher concentration to the

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CHAPTER 8 Desalinated water production in cogeneration

RO pump

Feed brine water

Membranes

Distillate

Pretreatment Valve

Brine discharge

FIGURE 8.4 Schematic of RO desalination system. RO, Reverse osmosis.

fluid with lower concentration, which is the same as tap water. Therefore a pressure difference must be applied to the opposite of the natural pressure difference on both sides of the membrane to overcome it. The pump is mechanically used to create this pressure difference. Since RO desalination does not require input steam, the performance ratio parameter is not defined for this type of desalination and instead the recovery ratio parameter is used as a parameter to evaluate the performance quality of this type. The desalination plants are used, which are equal to the ratio of the freshwater flow rate produced to the saline water flow rate of this desalination plant. The power of the RO water-desalination pump is one of the important parameters of the output of this equipment because this equipment does not consume heat and the power consumption of its pump is also significant. The osmotic pressure on each side of the membrane can be calculated using salinity and temperature. The main parameters for modeling of RO desalination system are indicated in Table 8.4.

8.3 Integration with thermal power plants The power plant can be combined with desalination system to produce simultaneously power and freshwater. In this section the integration of Qom combined cycle (CC) power plant with MED, MSF, RO, MEDRO, and MSFRO is shown. The considered plant evaluated in this section is Qom CC power plant. The plant consists of four gas turbines and two steam turbines with overall power output of 691 MW. The integration of this plant with different main desalination systems has been studied by Khoshgoftar Manesh et al. based on energetic [12], exergetic, and exergoeconomic analyses [13]. Fig. 8.5 demonstrates a simplified process flow diagram of the system0. Also, specification of each steam in the Qom CC plant at full load condition is indicated in Table 8.5. Fig. 8.6 shows the integrated CC with MSFRO desalination systems. The results of freshwater production capacity in integrated plant are demonstrated in Table 8.6.

8.3 Integration with thermal power plants

Table 8.4 Equations related to main parameters of reverse osmosis (RO) system. Description

Equations

Mass balance of overall system Recovery ratio

_ F 5m _ D 1m _B m _D m RR 5 _ mF w RR 5 RRjT525 3 JwJjT525 _ F ðpFeed 2 p37 Þ 3 100 m _ W RO 5 ρ 3 ηpump  Cw Vw ðpF 2 pD Þ 2 ðπF 2 πD Þ Jw 5 DwRTe Vw: water molar volume Cw: water concentration T: average temp. of RO R: universal gas constant e: membrane thickness

Work requirement for RO The specific flow rate of the solvent (water) through the membrane can

The coefficient of diffusion of the H2O in the membrane Stocks diameter Enthalpy of output brine discharge of RO Salinity of output brine stream of RO Osmotic pressure (kPa)

Dw 5

k3T 3πF μw ds ;

k: Boltzmann constant

ds 5 0:076MWw ; MW: molcular weight hF 2 RR 3 hD 1 2 RR F salB 5 1 sal 2 RR 385 3 sali 3 Ti πi 5 0:14507ð1000 2 10 sali Þ

hBD 5

μw 5 4:23 3 1025 1 ½0:157ðTF 164:993Þ2 291:29621

Dynamic viscosity

Air inlet LP outlet

GT

AC CC

HP outlet

Cooled water inlet

Fuel

LTE Stack gas

IPB

HPE2

Pump

IPS1

HPE3

HPB1

HPS3

HRSG

Pump inlet ST inlet

ST

ST outlet

Air outlet Air-cooled condensor

Air inlet

FIGURE 8.5 Schematic of Qom CC power plant [12]. CC, Combined cycle.

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CHAPTER 8 Desalinated water production in cogeneration

Table 8.5 Specification of each steam in the Qom CC plant at full load condition. Stream

T (
C)

P (bar)

_ (kg/s) m

H (kJ/kg)

(Inlet air to compressor) (Air out) (GT inlet) (GT out) (Stack gas) (ST in) (ST out) (Condenser out) (Pump out) HRSG HP HRSG LP (Fuel)

18.6 295 1165.35 547.95 163.45 462.5 68.42 65.31 66.87 301.65 160.2 69.5

0.86 11.66 11.31 0.96 0.9 78.2 0.345 0.287 4.2 85.3 5.72 23.01

387.5 386.9 393.5 394.2 1643.25 180.2 233.9 65.98 66.58 184.23 45.36 7.58

57.37 394.23 1539.84 796.34 377.77 3310.5 2418.65 272.56 275.61 2769.35 2749.16 55857.58

GT, Gas turbine; HP, high pressure; HRSG, heat recovery steam generator; LP, low pressure; ST, steam turbine.

Air inlet LP outlet

GT

AC

HP outlet

Cooled water inlet

CC

Supply seawater

Disalinated water

Fuel

LTE Stack gas

IPB

HPE2

Pump

IPS1

HPE3

HPS3

HPB1

HRSG

Pump inlet

Brine blowdown

ST inlet

Heating steam

Extraction

Vacuam steam Air outlet

Disalinated water

Air-cooled condensor

MSF desalination unit

ST

ST outlet

Condensate to DA

126

Brine blowdown

Air inlet

Brine discharge

FIGURE 8.6 Integration of combined cycle with MSFRO. MSF, Multistage flash; RO, reverse osmosis.

8.4 Integration with of gas turbines

Table 8.6 Net power, desalinated water production, and fuel consumption of different cases. Cases

Net power (MW)

Desalinated water production (m3/h)

Fuel consumption (kg/s)

CC CC 1 MED CC 1 MSF CC 1 RO CC 1 MEDRO CC 1 MSFRO

691 619 595 684 585 502

0 277.8 1056.7 73 500 606

30.5 30.5 30.5 30.5 30.5 30.5

CC, Combined cycle; MSF, multistage flash; RO, reverse osmosis.

Table 8.7 Power cost and desalinated water price related to each case. Case

Power cost ($/kWh)

Desalinate water rate ($/m3)

Base case CC 1 MED CC 1 MSF CC 1 RO CC 1 MEDRO CC 1 MSFRO

0.047 0.039 0.039 0.046 0.0112 0.0132

0 0.65 0.75 0.18 0.38 0.46

CC, Combined cycle; MSF, multistage flash; RO, reverse osmosis.

As shown, the most freshwater production is related to CC 1 MSF and then MSFRO, respectively. In the all cases, fuel consumption is same as base CC plant. In addition, net power production is associated with each case as determined in Table 8.6. Power cost and desalinated water price related to each case are indicated in Table 8.7. The CCMED and CCMSF have the lowest price of electricity and CCMSFRO has highest value of electricity price. As shown in Table 8.7, CCRO has lowest cost of desalinated water and CCMSF has highest freshwater cost.

8.4 Integration with of gas turbines One of the efficient methods for thermal recovery of exhaust gases from gas turbines is the integration of gas turbines with desalination units. In this section the thermal recovery of gas turbine that used in gas stations through heat recovery

127

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CHAPTER 8 Desalinated water production in cogeneration

steam generator (HRSG) to produce steam and generation of additional power and fresh water is investigated. The Nadoshan pipeline gas station in Iran is considered as a case study. The technical specifications of the considered station are indicated in Table 8.8. Fig. 8.7 demonstrates the schematic of proposed power-water cogeneration system through heat recovery of gas turbine. A well-known MAPNA 25 MW gas turbine in the compressor gas turbine is used to drive one gas compressor gas Table 8.8 Specifications of Nadoshan cogeneration plant [14]. Parameter

Unit

Value

Ambient average temperature Average relative humidity Site level Mass flow of process gas Gas suction pressure Gas discharge pressure Feed inlet temperature Outlet temperature of station (Max.) Feed water temperature Flue gases of HRSG Energy efficiency




22.9 34 1660 4033 953 1305 44 50 39 136 34.10

C % M t/h Psig Psig
C
C
C
C %

HRSG, Heat recovery steam generator.

FIGURE 8.7 Integration of gas turbine turbo compressor with HRSG and desalination systems [14]. HRSG, Heat recovery steam generator.

8.5 Integration with site utility industrial plants

Table 8.9 Water cost, net power, and payback period of integrated cogeneration system. Case

Water cost ($/m3)

Net power (MW)

Payback (year)

MSF MED MSFRO MEDRO

1.141 1.100 1.148 0.903

4.108 7.809 3.646 7.471

3.26 2.56 3.54 2.79

MSF, Multistage flash; RO, reverse osmosis; TVC, thermal vapor compression.

stations. A HRSG is applied to recovery of exhaust gases and produce steam. Then, high-pressure (HP) steam enters back-pressure steam turbine and generates additional power. The low-grade heat of low-pressure steam enters thermal desalination system. Integrations of GTHRSG with MSF, MED, MEDRO, and MSFRO are considered. Results indicate that hybrid integrated (MEDRO and MSFRO) cases are more valuable than a MSF/MED system. Table 8.9 determines desalinated water cost, net power, and payback period of each selected integrated cases. As determined, MEDRO case is the best selection from a point of view of lowest water cost.

8.5 Integration with site utility industrial plants An industrial or chemical site usually includes several processes, which use heat and power to operate. In this case a site utility system can be converted to polygeneration system by integrating desalination system to site utility. So, steam at different pressures, power, and desalinated water can be produced simultaneously. Thermal desalination used low-grade heat to produce freshwater from brine water sources. Khoshgoftar Manesh et al. integrated different desalination systems with site utility of chemical process [5]. The base central utility case [5] is demonstrated in Fig. 8.8. The base case includes four steam levels, with four back-pressure steam turbines between very HP (VHP)HP headers. In addition, two turbines include between HPmedium pressure (MP) and MPLP, respectively. Total site utility requirements are indicated in Table 8.10. The main parameters of site related to each period are determined in Table 8.11. The site profile of base total site is demonstrated in Fig. 8.9 based on each processes and utility requirements. The site utility grand composite curve (SUGCC) demonstrated the horizontal partition between the source and sink. Steam requirement at VHP, HP, MP, and LP steam mains is 110.8, 21.4, 9.3, and 73.6 MW, respectively. Shaft power production

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CHAPTER 8 Desalinated water production in cogeneration

FIGURE 8.8 Base site utility case [5].

Table 8.10 Total site utility demands [5]. Parameter

Units

Summer

Base

Power requirements Steam load (VHP) Steam load (HP) Steam load (MP) Steam load (LP) Overall steam requirements Overall return of condensate Pump 1 Pump 2 Pump 3 Pump 4 Cooling demand

MW MW MW MW MW MW % MW MW MW MW MW

68 110.82 21.4 9.34 73.6 215.17 80 5.0 1.1 2.0 0.6 300

62 116.36 30.61 16.67 88.8 252.17 80 5.2 1.3 2.2 0.6 200

HP, High pressure; LP, low pressure; MP, medium pressure; VHP, very high pressure.

8.5 Integration with site utility industrial plants

Table 8.11 Site specification per period [5]. Period Year fraction Ambient temperature Relative humidity Power cost

%
C % Peak ($/kWh) Off-peak ($/kWh) h $/kg $/kg $/t

Peak hours per day Fuel oil cost Natural gas cost Feed water cost

Summer

Base

33 25 60 0.08 0.05 12 0.19 0.22 0.05

67 10 60 0.07 0.05 7 0.19 0.22 0.05

350

VHP

T (°C)

300

250

HP 200

MP

150

LP

100

50

–500

–400

–300

–200

–100

0

0

100

200

300

400

500

Enthalpy (MW)

FIGURE 8.9 Total site profiles of base case [5].

potential is shown in Fig. 8.10 as areas in the SUGCC with VHPHP, HPMP, and MPLP power generation potential of 79.8, 58.4, and 49.1 MW, respectively. The total site profiles with considering the combination of desalination with total site are represented in Fig. 8.11.

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CHAPTER 8 Desalinated water production in cogeneration

FIGURE 8.10 SUGCC-base case site utility [5]. SUGCC, Site utility grand composite curve.

As indicated in Fig. 8.11, 43.17 MW heat load is included as low-grade heat at the temperature more than 115
C. Due to using low-grade heat below 115
C that is shifted by 43.17; the cooling demand for the total site reduced to 43.17 MW, consequently. The main values that considered for design of MEDTVC, MSF, and RO desalination systems are indicated in Tables 8.12 and 8.13, respectively. Also, Table 8.14 shows the techno-economic evaluation of base cogeneration system and different proposed polygeneration systems. The gain output ratio (GOR), power demand for desalination system, desalinated water production flow rate, desalination cost, and cold utility requirement are indicated in Table 8.14. As indicated in Table 8.14, the MEDTVC case has the lowest desalination power demand and the highest GOR and MEDRO has the highest freshwater production and the highest cost of desalination. Furthermore, Table 8.15 reveals the price of desalinated water generation for different desalination systems. As indicated, by considering same energy cost in the central utility and thermal recovery of low-grade heat for freshwater production, the lowest freshwater cost is associated with MEDTVC case with payback of 5.7 years. The polygeneration MSFRO has highest freshwater cost, power cost, and the payback.

8.5 Integration with site utility industrial plants

FIGURE 8.11 Site profile associated with Integration of base case with desalination processes [5].

It should be noted that these results are based on the assumptions used for the cost and performance parameters as well as the consideration of the costs incurred. By changing the assumptions as well as the cost of fuel prices, other results as well as other interpretations for the scenarios are obtained.

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CHAPTER 8 Desalinated water production in cogeneration

Table 8.12 Main parameters assumptions of MEDthermal vapor compression (TVC) and multistage flash (MSF) thermal desalination systems [5]. Parameter

Unit

MSF

MEDTVC

Input steam temperature Temperature top brine Final stage temperature Seawater temperature Number of effects Compression ratio of ejector Final effect salinity (reject stream) Seawater salinity (feed) Tube diameter Tube length




115 110 40 25 21  70,000 36,000 19.05 5

70 67 40 25 6 3 70,000 36,000 19.05 5

C C
C
C   ppm ppm mm m


Table 8.13 Main parameters assumptions of reverse osmosis desalination system [5]. Number of stages



3

Permeability of pure water Permeability of solute Mas transfer coefficient of salt Factor of dissociation Maximum allowance pressure of membrane ΔPdrop Seawater temperature

m/(s atm) m/s m/s  bar bar
C

8.33 3 1028 3.51 3 1028 2.7 3 1025 0.9 69 0.7 25

Table 8.14 Techno-economic evaluation of base case and different integrated scenarios [5].

Case

GOR

Power demand (MW)

Base MSF MEDTVC MSFRO MEDRO

 8.4 9.3 8.4 9.3

 1.94 0.94 5.01 4.02

Cold demand (MW)

Fresh water (kg/s)

Desalination cost (M$)

Base

Summer

 134.4 148.8 249.4 263.5

 15.2 16.5 26.1 27.4

368 324.83 324.83 324.83 324.83

368 324.83 324.83 324.83 324.83

GOR, Gain output ratio; MSF, multistage flash; RO, reverse osmosis; TVC, thermal vapor compression.

References

Table 8.15 Freshwater production price and simple payback for different scenarios. Case

Power cost (M$/year)

Desalinated water cost ($/m3)

Overall utility price (M$/year)

Pay back (year)

Base MSF MEDTVC MSFRO MEDRO

23.77 24.75 24.25 26.31 25.81

 0.77 0.65 0.84 0.76

94.06 94.03 94.03 94.03 94.03

 6.9 5.7 7.3 6.2

MSF, Multistage flash; RO, reverse osmosis; TVC, thermal vapor compression.

References [1] Khoshgoftar Manesh MH, et al. Optimal coupling of site utility steam network with MED-RO desalination through total site analysis and exergoeconomic optimization. Desalination 2013;316:4252. [2] Abdel-Jabbar NM, et al. Simulation of large capacity MSF brine circulation plants. Desalination 2007;204(1):50114. [3] Ghaffour N, Missimer TM, Amy GL. Technical review and evaluation of the economics of water desalination: current and future challenges for better water supply sustainability. Desalination 2013;309:197207. [4] Panagopoulos A, Haralambous K-J, Loizidou M. Desalination brine disposal methods and treatment technologies  a review. Sci Total Environ 2019;693:133545. [5] Khoshgoftar Manesh MH, et al. A new targeting method for combined heat, power and desalinated water production in total site. Desalination 2012;307:5160. [6] El-Dessouky HT, Ettouney HM. Chapter 6  Multi-stage flash desalination. In: ElDessouky HT, Ettouney HM, editors. Fundamentals of salt water desalination. Amsterdam: Elsevier Science B.V.; 2002. pp. 271407. [7] Panagopoulos A. Process simulation and techno-economic assessment of a zero liquid discharge/multi-effect desalination/thermal vapor compression (ZLD/MED/TVC) system. Int J Energy Res 2020;44(1):47395. [8] Wazeer I, Al-Mutaz I. Current status and future directions of MED-TVC desalination technology. Desalin Water Treat 2014;55. [9] Kimura S, Sourirajan S. Analysis of data in reverse osmosis with porous cellulose acetate membranes used. AIChE J 1967;13(3):497503. [10] Helal AM, et al. Optimal design of hybrid RO/MSF desalination plants Part I: Modeling and algorithms. Desalination 2003;154(1):4366. [11] Kim J, et al. A comprehensive review of energy consumption of seawater reverse osmosis desalination plants. Appl Energy 2019;254:113652. [12] Khoshgoftar Manesh MH, et al. Thermodynamic evaluation of a combined-cycle power plant with MSF and MED desalination. J Water Reuse Desalin 2020. [13] Khoshgoftar Manesh MH, et al. Exergoeconomic modeling and evaluation of a combined-cycle plant with MSF and MED desalination. J Water Reuse Desalin 2020. [14] Khoshgoftar Manesh MH, et al. Exergoeconomic evaluation of desalinated water production in pipeline gas station. In: Aroussi A, Benyahia F, editors. Proceedings of the third gas processing symposium. Oxford: Elsevier; 2012. pp. 191198.

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Cogeneration and polygeneration targets

9

Chapter Outline 9.1 Introduction .................................................................................................137 9.2 Cogeneration issues .....................................................................................139 9.3 Significant models ........................................................................................140 9.3.1 Exergetic model ........................................................................140 9.3.2 T
H model ...............................................................................142 9.3.3 Turbine hardware model .............................................................143 9.3.4 Harell method ...........................................................................144 9.3.5 Sorin and Hammache method ....................................................145 9.3.6 Medina-Flores and Pico´n-Nu´n˜ez model .......................................146 9.3.7 Bandyopadhyay model ...............................................................147 9.3.8 Iterative bottom-to-top model .....................................................148 9.3.9 Kapil model ..............................................................................149 9.3.10 Actual steam level temperature model ........................................150 9.3.11 Automated targeting method ......................................................151 9.3.12 Ren et al. model .......................................................................151 9.3.13 Other models ............................................................................154 9.3.14 Software ...................................................................................155 9.4 Comparison of different methods ...................................................................155 9.5 Case study ...................................................................................................155 9.6 Conclusion ...................................................................................................159 References ..........................................................................................................160

9.1 Introduction Global energy consumption is increasing as a result of increasing in world population and expanding economies that have rising energy demands. Fossil fuels are still and have been for centuries the main source of energy in most societies [1]. The low price of natural gas often makes the dispatch of natural gas units more interested than other fossil fuels power plants [2]. However, along with these, rapid industrialization has increased emissions and caused many environmental problems. Increasing energy efficiency and reducing fuel consumption have been objectives of researchers since the oil crises of the 1970s. One of the tools for this aim Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00009-4 © 2021 Elsevier Inc. All rights reserved.

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is process integration that can help designers reduce resource consumption or harmful emissions. This tool can be extended for heat recovery, mass transfer, and supply chain systems [3]. The need for steam at different pressures and temperatures is required for industrial heating and nonheating processes. To produce steam in the required conditions, the designer must decide to supply the steam in the desired conditions and then distribute it at different levels [4]. In addition to heat and power consumption, some processes produce steam, mostly at medium or low grades. So, it can be economical to use product steam in other processes to reduce costs and fuel consumption. Total Sites are the best approach for such applications at this time. Total Sites are facilities that merge different processes, used by and connected through a central process utility system. A schematic of an industrial Total Site is shown in Fig. 9.1. The utility system uses fuel, generates electricity or shaft power, and distributes steam. It is clear that the power and steam produced in utility system must match the demands, so the requirements of the processes must be identified. “Grand composite curves” (GCCs) demonstrate the cooling and heating demands of each single process. Each of the GCCs shows and suggests suitable different steam mains and its heat loads [5]. Based on pinch technology and its application to GCCs, we are able to target before design. One of the principle and vital tasks for the design of the cogeneration and polygeneration to produce different steam levels, cooling, and power systems is targeting fuel consumption, fuel-related emissions, shaft work production, and cooling requirements in advance of design [6]. Targets for these options help the engineer and designer to identify the most promising modifications in either

FIGURE 9.1 Schematic of an industrial Total Site.

9.2 Cogeneration issues

processes and/or cogeneration system as site utility prior to basic design straightforwardly. Numerous studies have been performed for improving shaft work targeting and computation of cogeneration potential before the basic design of central process utility system. The objective of the present study is to review critically recent advancements in these approaches and models.

9.2 Cogeneration issues Prediction of cogeneration potential before the basic design of central process utility system plays a vital role in setting targets on the fuel requirements and steam flow rate as well as heat and power generation and optimization [7]. Several researchers investigated cogeneration and shaft work targeting. All of them use the site utility GCC (SUGCC). This curve shows the net steam production and usage loads in processes and steam production in boilers on temperature
enthalpy based on source and sink profiles. The utility system layout, which is combined with the SUGCC, is shown in Fig. 9.2. Each zone is indicated by one single back pressure turbine that can generate electrical power. This task is the basis of cogeneration.

FIGURE 9.2 Utility system layout on an SUGCC. SUGCC, Site utility grand composite curve.

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CHAPTER 9 Cogeneration and polygeneration targets

The first question is “How much power can be produced?” The answer is obtained by shaft work targeting. Utility systems may face a different scenario. Several models estimate power and flow rate in each main using thermodynamic parameter. According to the first law of thermodynamics, the power produced by a steam turbine without heat loss can be expressed as follows: _ out 2 hin Þ W_ 5 mðh

(9.1)

where m_ is the mass flow rate of steam through turbine, and hout and hin are the outlet and inlet specific enthalpies for the steam turbine, respectively. Calculating specific enthalpy values requires temperatures of the steam mains, which are not often available at the targeting stage. So, some experimental models use the saturation temperature of the mains to estimate the shaft work targeting. As a result, the turbine power is usually expressed as follows: _ Tsat ; Psat Þ W_ 5 f ðm;

(9.2)

where m_ denotes the mass flow rate of steam, and Tsat and Psat are the saturation temperature and pressure. In this study the leading works in the area and their advantages and weaknesses are investigated.

9.3 Significant models A number of models and analysis approaches have been proposed for the early estimation of shaft work. The most important of these are described in this section.

9.3.1 Exergetic model Pinch analysis based on basic process information is used for the design of lowtemperature processes to minimize shaft work targets, which has been introduced by Linnhoff and Dhole [8]. The procedure is mainly based on combination of pinch analysis with exergy methodology. The procedure provides shaft work values from refrigeration heat loads [8]. This method was expanded later in 1992 [5] to target for fuel, cogeneration, emissions, and cooling ahead of design in utility system. The authors considered the use of the site source and sink profiles (SSSPs) in adjustment targets for the process Total Site. The SSSP tunes the heat load targets for generation and use of steam at various levels. The very highpressure (VHP) load adjusts the boiler size. So, the steam demand at the VHP level can be obtained based on demands of processes and using turbine simulation. Hence, the fuel requirement is estimated from the layout of the fuel profile on the horizontal axis in temperature-specific enthalpy diagram (Fig. 9.3). Shaft work targets can be adjusted by drawing the specific enthalpy segment of the steam at the input and exit of the steam turbine versus the Carnot factor (see Fig. 9.4). Between the two-shaded areas, profiles [between VHP and

9.3 Significant models

FIGURE 9.3 Total Site target construction for exergetic method.

FIGURE 9.4 Turbine details: (A) shaded area between VHP and HP steam levels (shaft work production) and (B) shaft work calculation between VHP and HP steam levels. HP, Highpressure; VHP, very high-pressure.

141

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CHAPTER 9 Cogeneration and polygeneration targets

low-pressure (LP) steam levels] in Fig. 9.4 represent exergy change that is easier to computation in accordance with the following equation [5]: ΔEx 5 QVHP Uηc;VHP 2 QHP Uηc;HP

(9.3)

where Q and ηc are the heat load and Carnot factor, respectively. As a result the actual power is computed as follows: W act 5 ηex UΔEx

(9.4)

Mavromatis and Kokossis [9] point out that, because the exergetic efficiency does not consider the input and output conditions, and the load of expansion. The accuracy compared with simulation is about 30%. Also, this method has not considered the degree of superheat (DSH) for steam mains.

9.3.2 T
H model Raissi [10] used the approximation to estimate the shaft work. This model employs the expression of a specific steam heat load (q): q 5 hst 2 hsat

(9.5)

where hst is the outlet steam
specific enthalpy of the turbine, and hsat is the specific enthalpy of same saturated water. So, the steam mass flow rate, m_ s , based on _ can be calculated as follows: heat load demand, Q, m_ s 5

Q_ q

(9.6)

The Salisbury approximation indicates that the shaft power can be predicted in accordance with the difference between the saturation temperatures: sat W 5 εðTinsat 2 Tout Þ

(9.7)

where ε is a constant. The total produced power, E, is obtained by the following equation: E 5 m_ s w

(9.8)

By combining Eqs. (9.7) and (9.8), the following equation is derived: E5

ε sat sat _ ðT 2 Tout ÞQ q in

(9.9)

On the SUGCC diagram (see Fig. 9.2) the power generation is obtained by the rectangle shading area (i.e., the expansion zone that is indicated by a single back pressure steam turbine) and a conversion factor CF, identified as ε/q: E 5 CFUAREA

(9.10)

A powerful graphical tool provides to understand the interconnections between fuel demand, heat recovery, cooling demand, and power generation.

9.3 Significant models

This approach has two disadvantages. First, it can be indicated that the power generation does not precisely change linearly with the saturation temperature difference between the inlet and discharge pressures. Second, the VHP and cooling targets cannot be accurately visualized on the SUGCC [6]. As can be seen in Eq. (9.10), the DSH cannot be effectively addressed with the T
H model at each level. Also, the T
H model assumes the turbine efficiency is constant. Due to these deficiencies, significant errors are incurred when estimating cogeneration potential using the T
H model. Further, it does not work to take into account the operating conditions and sizes of the plants and revels arbitrary even in samples of constant loads.

9.3.3 Turbine hardware model The turbine hardware model (THM) and related approaches to evaluate considered design selections prior to design were introduced by Mavromatis and Kokossis [9]. The THM depends on the Willans line, which supplies a linear correlation between the steam flow rate (M) and the power output (W) of the turbine, as follows: M5


 1 W 1C n

(9.11)

where C is a constant. The Willans line can be defined as follows: W 5 nM 2 W loss

The turbine internal losses W rate n can be written as follows:

loss

(9.12)

and the inverse of the incremental steam flow

 61 Δh is M max 2 A 5B
 61 A n5 Δh is 2 max 5B M

W loss 5 0:2W max 5

(9.13) (9.14)

where Δh is is the isentropic-specific enthalpy change, and A and B are regression parameters that vary with turbine size. As a result, the steam turbine operation can be identified, once its capacity is indicated in terms of the maximum steam inlet M max . The Willans expression is derived as follows:

 61 A 1 max M2 M W5 Δh is 2 max 5B M 6

(9.15)

Note that, by calculation of output shaft work, it is possible to determine the steam turbine isentropic efficiency. Also, the THM applies an iterative methodology based on specific heat demands to compute the flow rates through the steam turbines.

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CHAPTER 9 Cogeneration and polygeneration targets

9.3.4 Harell method Harell [11] introduced the extractable power and efficiency of header to make the cogeneration potential prior to basic and detailed design. This approach is based on the application of a Mollier representation, as demonstrated in Fig. 9.5. The power generation of the steam turbine is defined as follows: _ is ðhin 2 hout;is Þ W_ 5 mη

(9.16)

where W_ indicates the power generation from the steam turbine, m_ is the mass flow rate of steam passing through the turbine, ηis the isentropic efficiency, hin the specific enthalpy of the steam at the inlet temperature and pressure of the turbine, and hout;is the isentropic-specific enthalpy at the outlet pressure of the turbine. The Mollier graph is determined in Fig. 9.5. Then, the specific power generated by a turbine can be defined as follows: _ header ðhin 2 hout;header Þ W_ 5 mη

(9.17)

where ηheader is the overall system efficiency can be computed by the following equation: Δh ηheader 5

real

Δhheader

(9.18)

To simplify Eq. (9.17), extractable energy e, which is defined as follows: e 5 ηh

FIGURE 9.5 Demonstration of Mollier.

(9.19)

9.3 Significant models

where η denotes steam turbine efficiency, and h is the specific enthalpy at the considered state. In the next step the shaft power production is as follows: _ in 2 eout;header Þ W_ 5 mðe

(9.20)

Eq. (9.20) is used to predict the cogeneration potential. However, the efficiency must suppose assumed to be a constant in the method, causing some deviations from real conditions.

9.3.5 Sorin and Hammache method An overall exergy balance for a steam turbine cycle can be expressed as follows [6]: Q_ in θH 5 W_ 1 Q_ out θL 1 I_

(9.21)

where W_ denotes the actual shaft work rate produced by the turbine, Q_ in is the heat provided by the boiler, Q_ out the turbine outlet load, θH the Carnot factor for the heat demand, θL the Carnot factor for the outlet heat, and I_ the irreversibility rate. The Carnot factor is calculated as follows: θ512


 T0 T

(9.22)

where T is considered as the average temperature for the providing heat ðTH Þ or the outlet heat ðTL Þ. Fig. 9.6 shows T
s diagram for steam turbine expansion process.

FIGURE 9.6 T
s Diagram for steam turbine.

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CHAPTER 9 Cogeneration and polygeneration targets

For simplicity the expansion process is assumed ideal so that I_ 5 0. Then, by combining Eqs. (9.21) and (9.22), the following expression is obtained for the ideal shaft work rate, W_ id : ðθH 2 θL;is Þ W_ id 5 Q_ out;id ð1 2 θH Þ

(9.23)

The actual work rate produced is expressed as follows: W_ 5 ηis W_ id

(9.24)

Fig. 9.6 shows the average temperatures for inlet and outlet steam turbines and these are defined as follows: TH 5

h1 2 h3 s1 2 s3

(9.25)

TL;is 5

h20 2 h3 s1 2 s3

(9.26)

where h and s are the specific enthalpy and specific entropy, respectively. The isentropic efficiency is as follows: ηis 5

h1 2 h2 h1 2 h20

(9.27)

At the final step, by the SUGCC graphical tool [10], a new approach is introduced to predict the fuel, cooling requirement, and shaft power targets. The energy balance computes the mass flow; and with considering constant isentropic efficiency, the power generation is predicted. The great weakness of this method is that there is no logical justification for assuming that all steam turbines operate in accordance with the Rankine cycle. Another disadvantage of this method is that Sorin and Hammache method uses saturated temperature. Therefore this hypothesis lowers the calculation accuracy.

9.3.6 Medina-Flores and Pico´n-Nu´n˜ez model _ of a single extraction, back pressure steam The power generation as a form of (W) turbine is calculated by the following equation: _ is Δhis Þ W_ 5 m_ ðhin 2 hout Þ 5 mðη

(9.28)

where m_ is the steam mass flow, hin and hout are the specific enthalpies at the inlet and outlet, respectively, and Δhis is the difference of isentropic specific enthalpy. MedinaFlores and Pico´n-Nu´n˜ez [12] applies curves achieved by Peterson and Mann that represents the variation of isentropic efficiency that depends on the shaft work generation to pass out turbines. All the graphs are correlated by the following equation: W_ 5 α 1 β W_ ηis

(9.29)

9.3 Significant models

Combining Eqs. (9.27) and (9.28) yields: _ is 2 αÞ ðmΔh W_ 5 β

(9.30)

where α and β are parameters are indicated as a correlation of inlet pressure: α 5 0:1854 1 0:0433Pin

(9.31)

β 5 1:2057 1 0:0057Pin

(9.32)

The term of Δhis from Eq. (9.30) is calculated by using the following equation: Δhis 5

ΔTsat ð1854 2 1931qin Þ

(9.33)

where ΔTsat is the difference saturation temperature of inlet and outlet. The qin parameter is the specific enthalpy obtained by the HP saturated liquid to lead the superheated. The heat load that the turbine delivers to the process is calculated by the following equations: qout 5 qin 1 Cp ΔTsat 2 w_

(9.34)

Cp 5 3:38 1 0:006123Tsat

(9.35)

where w denotes the specific power output, and Tsat is the saturation temperature that related to the turbine inlet pressure. To calculate the shaft work generation, _ an iterative approach is required. W, The advantage of this method rather than THM is that it derives one correlation in the range of steam turbine sizes. In addition, it can estimate the part load and performance of single and multiple pass steam turbines under variation of the operating.

9.3.7 Bandyopadhyay model The methodology introduced by Bandyopadhyay et al. [13] is linear, single and uses a rigorous balance of energy at each steam main. This approach is associated with the Salisbury approximation. With considering the enthalpies of the saturated water at Pin and Pout as hin;sat and hout;sat , respectively, the specific power generation of the turbine can be expressed using the Salisbury approximation as follows: w_ 5 hin 2 hout 5 hin;sat 2 hout;sat

(9.36)

At the initial step, different steam level
specific heat loads are assumed. The steam mass flow is as follows: m_ load 5

Q_ laod qheader

(9.37)

where the steam main
specific heat load qheader defined as follows: qheader 5 hheader 2 hheader;sat

(9.38)

147

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CHAPTER 9 Cogeneration and polygeneration targets

with considering steam flow calculation, an equation of energy balance associated with each steam mains be solved. The computation repeated till convergence is obtained and then cogeneration potential is calculated. Despite its linearity and simplicity, this model has challenges, particularly a lack of adequate accuracy. This is in part because the model does not consider the DSH in steam mains. In addition, the boiler temperature must be known before the model can be employed. Abdul Aziz et al. [14] proposed a framework based on pinch technology for site planning with considering low CO2 emissions based on Bandyopadhyay model. CO2 pinch analysis is performed in the last step to gain the minimum CO2 production as target.

9.3.8 Iterative bottom-to-top model The iterative bottom-to-top model (IBTM) is a methodology according to a simple steam turbine model and utilizes a fixed isentropic efficiency to computation of the power generation of the steam turbines demonstrated in the SUGCC. The cogeneration targeting methodology proposed by Ghannadzadeh et al. [7] for an obtained site utility systems is presented in Fig. 9.7.

FIGURE 9.7 IBTM methodology for estimating temperatures of steam mains [7]. IBTM, Iterative bottom-to-top model.

9.3 Significant models

FIGURE 9.8 Construction of SUGCC based on IBTM. IBTM, Iterative bottom-to-top model; SUGCC, site utility grand composite curve.

In this method the steam levels are indicated by i, starting from the lowest pressure steam level. Temperature intervals are identified by j index beginning from lowest [i.e., j 5 1 is for medium-pressure (MP) steam level to LP steam level], and one steam turbine is added at each expansion zone and is also specified by j. The SUGCC is demonstrated in Fig. 9.8. After calculating temperatures for all headers, the mass flow rate in each header can be determined: m_ load 5

Q_ load qheader

(9.39)

where qheader is the specific heat load at each header. The mass flow rate of steam expanding through each turbine can be calculated by applying mass load balances to the steam mains. Then, the shaft power produced through the steam turbine can be determined as follows: 2 hACTUAL Þ W_ j 5 m_ j ðhACTUAL i11 i

(9.40)

where the mechanical efficiency is considered to be 100% as assumption. Note that the isentropic efficiency is assumed constant in this method. However, iterative approach removes the requirements for simulation.

9.3.9 Kapil model Kapil et al. [15] proposed an approach to predict cogeneration potential through an integration of bottom-up and top-down methodology. The method includes the

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CHAPTER 9 Cogeneration and polygeneration targets

superheat degree for each steam level and calculates the minimum demand flow rate from a steam production unit and the mains of superheat at each steam level accordance with heat loads determined through site profiles for the heat source and sink. The cogeneration potential of the system is dependent on the expansion efficiency. Kapil et al. use the correlation of Varbanov et al. to calculate the isentropic efficiency [16]: W_ is;max 2 A W_ max Uηmax 5 W_ max 5 B W_ is;max

(9.41)

where A and B are regression parameters that related to the steam turbine and depend on the saturation temperature. In this procedure, the properties of steam are determined for the given specific entropy and pressure at the lower steam level. If the superheat degree related to low-pressure steam level is less than demand, the operating condition of the VHP steam main is updated and then repeats until acceptable conditions of superheated for the LP steam main are achieved. In this procedure, first, the temperature of the lowest steam main pressure is applied to computation the flow rate of steam mass for the expansion zone between the lowest and the next highest pressure main. This methodology is consecutively iterated till the expansion zone for the highest steam pressure level is indicated. m_ load 5

Q_ laod ðhheader 2 hheader:sat Þ

(9.42)

Finally, the highest mass flow rate is calculated from the flow rates at the lower levels.

9.3.10 Actual steam level temperature model Khoshgoftar Manesh et al. [4] proposed an accurate model based on actual temperatures of steam levels. This method utilizes the relationship of the enthalpy, entropy, and isentropic efficiency to determine targeting shaft power generation in the Total Site and steam boiler heat duty and temperature with high accuracy [4]. The procedures outlined by the algorithm can be summarized in the following steps: 1. Create initial predictions of the superheat temperature of boiler. 2. Create initial predictions for mass flow rates related to each zone by considering isentropic expansions at each main as follows: m_ 5

Q_ load ðhi 2 hi:sat Þ

(9.43)

where Q_ load denotes given level net heat load, hi is the steam level isentropicspecific enthalpy at a considered main, and hi;sat is the specific enthalpy of saturated liquid at the given main.

9.3 Significant models

3. Correct the efficiency using the thermodynamic model of Varbanov: W_ is:max 2 A W_ max Uηmax 5 W_ max 5 B W_ is:max

(9.44)

4. For given steam mains, correct hi and m_ net;i . 5. Repeat the steps iteratively until they achieve the stopping criteria from the second iteration: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi z X ðmz 2mz;new Þ2 # ε

(9.45)

i51

6. When the first iteration of the procedure terminates, check the LP temperature of superheat. If it falls below the allowed minimum, the superheat temperature of boiler is increased and the steps are iterated until the desired amount of superheat in the LP steam main is met. The new procedure considers the DSH temperatures and operates with actual temperatures rather than saturated temperatures to accurately predict the cogeneration potential. Also, it can estimate the efficiency of each expansion zone. Moreover, by adding step 7, the potential of desalinated water production can be predicted. 7. Evaluation of desalinated water production through low-grade heat by SUGCC.

9.3.11 Automated targeting method Ng et al. proposed steam cascade analysis (SCA) for targeting cogeneration potential. Algebraic targeting methodology for single and multiple sources is developed. This method is based on the steam cascade table. In this regard, automated targeting method (ATM) based on SCA is developed [17]. Furthermore, the SCA method is integrated with optimization framework based on ATM targeting procedure. Fig. 9.9 shows methodology for targeting of single and multiple sources.

9.3.12 Ren et al. model Ren et al. proposed a procedure for Total Site utility cogeneration potential targeting with a commercial software. In this method [18], Aspen Plus software is used to predict the temperatures of steam levels, steam flow rates, and shaft power generated by steam turbines. As shown in Fig. 9.10, first in step 1, the initial VHP steam temperature of the main should be guessed that can be achieved by adding at least 100 C DSH to the saturated temperature of the VHP steam level. The VHP steam temperature main is indicated as a variable parameter in Aspen Plus. By running simulation the value of TVHP will be adjusted by simulator till the specification of TLP is achieved. Also, the temperatures of VHP, HP, MP, and LP steam levels can be achieved simultaneously.

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FIGURE 9.9 Overview of SCA algebraic targeting technique [17]. SCA, Steam cascade analysis.

In the simulation step, steams of HP, MP and LP generation steam levels come from the process. The steams of HP, MP, and LP steam demand mains are used for the process. The steams mass flow rates of these steam mains can be calculated in the “calculator” function of Aspen Plus based on heat loads. The following formula is defined in the “calculator” of Aspen Plus: mi 5

Qi hi 2 href

(9.46)

9.3 Significant models

FIGURE 9.10 Overview of Ren et al. procedure [18].

where hi is the steam main enthalpy i at its pressure and temperature, and href is the enthalpy of the condensate boiler feedwater. When the mass flow rates of each steam levels are calculated, the mass flow rate of VHP steam level is determined through mass balance. A simple simulation flow sheet based and comprehensive database in the simulator have been performed for calculation. This method does not need to iterative procedure.

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So, the calculation algorithm is much easier. It is shown that the calculation procedure proposed in this chapter easy to use with high accuracy.

9.3.13 Other models Other models use the basis of the previously cited models and try to improve them. Manninen et al. [19] used combined pinch and exergy representation (CPER) for thermodynamic analysis and mathematical optimization of power plants. CPER is a graphical demonstration of the heat exchanger network in a power plant. It is associated with exergy composite curves and exergy GCCs, which are applied to performance evaluation, including shaft work and heat transfer. Mare´chal and Kalitventzeff [20] used the isentropic enthalpy difference through the turbine and isentropic efficiency to calculate steam turbine shaft work. However, this method is useful for problems for which steam flow rates are known through each steam turbine. Shang and Kokossis [21] applied a THM to optimize steam mains of a Total Site central utility system. They used this model for synthesis and design of flexible site utility systems [22]. Varbanov et al. [16] proposed an improved THM by assumption of variations in turbine efficiency with variation of load. This model takes into account the effect of back pressure. Varbanov considered three factors as affecting steam turbine performance: (1) maximum power load as turbine size, (2) pressure drop across the turbine, and (3) current load. This model revealed better results than the THM but still did not have adequate accuracy. Mohan and El-Halwagi [23] introduced the extractable power concept. It was applied as a basis for making a linear algebraic cogeneration targeting systematic procedure for effective usage of biomass in combined CHP systems by pinch technology. Aguilar et al. [24,25] adopted Willans’ Line concept to represent part-load turbine performance for single-stage units. This method considers Willans’ Line with turbine size. The procedure applied in this research represents a linear formulation for determining turbine performance for a given inlet steam property (temperature, pressure) and exhaust pressure, even for cases where equipment size and load are variables. Aguilar et al. also present an iterative procedure to determine steam header conditions before optimization. This procedure stimulates the zone between steam levels with the available steam turbine of the biggest size. This method guarantees dryness in the exhaust of the condensing turbine and determines the temperature of the lowest pressure header. Sun et al. proposed a heat recovery and power targeting method for utility systems. This method has been introduced to evaluate and improve systematically site utility system with considering heat recovery, distribution, and cogeneration [26]. In this work a practical graphical method based on modified site composite curves allowing for boiler feedwater preheating and steam superheating in steam production, and steam desuperheating for heating. This model has been developed with considering practical limits such as superheat of steam mains and dryness of turbine outlet [26].

9.5 Case study

Khatita et al. proposed a procedure to use waste heat to generate power through the organic Rankine cycle [27].

9.3.14 Software STAR is a software package for the design of site utility and cogeneration systems. The interactions between the processes on the site and the steam systems—steam turbines, gas turbines (with auxiliary firing options), boiler house, local fired heaters, and cooling systems—are all analyzed using STAR. A given utility system configuration incorporates important degrees of freedom for optimization. Multiple boilers with different efficiencies and different fuels, multiple back pressure steam turbines, condensing turbines, gas turbine heat recovery steam generators, and letdown valves provide optional heat flow paths that can all be exploited for significant cost reductions. STAR has a utility system optimization facility that allows existing utility systems to be optimized. In the field of cogeneration targeting, Kundra [28] presented an application of STAR for cogeneration targeting by a stepwise procedure that starts with bringing steam turbines into action from bottom to top. Unlike the existing shaft work targeting models, this methodology takes into account the DSH.

9.4 Comparison of different methods There are a number of issues that must be considered in cogeneration targeting models as shown in Table 9.1. The first is DSH, as it has an important role in the delivery of steam at appropriate temperatures and pressures as well as shaft work. This parameter can be improved the calculation of shaft power and to achieve accurate solution. The second issue is efficiency of expansion zones. Methods that use constant efficiency have usually less accuracy, mainly because of the variation of inlet and outlet conditions and turbine load during the estimation of turbine shaft work. The other issues include calculation in part-load condition, using extraction turbine model instead of simple turbine and accuracy of model. Table 9.1 compares the targeting models listed earlier.

9.5 Case study A utility system is employed for demonstration of the IBTM, Kapil, and Khoshgoftar Manesh methods. This system comprises four steam levels: VHP, HP, MP, and LP. Table 9.2 presents the pressures and net utility requirements at each mainstream. Boiler water supply and the condensate returns are assumed to be at 105 C. The SUGCC can be obtained from data in Table 9.2 and is shown in Fig. 9.11. By applying various cogeneration shaft work targeting methods, each method can demonstrate its ability to estimate the output work in each zone.

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Table 9.1 Comparison of different targeting cogeneration models.

Model Linnho et al. (exergy model) Raissi et al. (T
H model) Mavromatis et al. (THM) Harell et al. (extractable power model) Sorin et al. (SHM model) Medina-Flores et al. model Bandyopadhyay et al. model Ghannadzadeh et al. (IBTM) Kapil et al. model Khoshgoftar Manesh et al. model

Considering DSH

Variation efficiency

Calculation in part-load condition

Extraction turbine

Accuracy Very low Very low

Yes

Yes

Yes

Yes

Low Low

Low Yes

Medium Medium

Yes Yes Yes

Medium Yes Yes

Yes

High Highest

DSH, Degree of superheat; IBTM, iterative bottom-to-top model; SHM, Sorin and Hammache method; THM, turbine hardware model.

Table 9.2 Steam mains parameters. Parameter

Unit

Value

VHP HP MP LP VHP steam demand HP steam demand MP steam demand LP steam demand Condensate return temperature Degree of superheat temperature

bar (abs) bar (abs) bar (abs) bar (abs) MW MW MW MW  C  C

120 50 14 3 0 50 40 85 105 40

HP, High-pressure; LP, low-pressure; MP, medium-pressure; VHP, very high-pressure.

9.5 Case study

FIGURE 9.11 SUGCC for case study. SUCCG, Site utility grand composite curve.

FIGURE 9.12 Cogeneration potential obtained by IBTM. IBTM, Iterative bottom-to-top model.

Figs. 9.12
9.14 show the cogeneration potential obtained by the IBTM, Kapil, and Khoshgoftar Manesh methods, respectively. Furthermore, for the purposes of comparison between the three models, Fig. 9.15 presents the shaft work targeting results.

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FIGURE 9.13 Cogeneration potential obtained by the Kapil method.

FIGURE 9.14 Cogeneration potential obtained by the Khoshgoftar Manesh method.

9.6 Conclusion

FIGURE 9.15 Shaft work targeting result.

9.6 Conclusion Cogeneration models represent a leap forward in the targeting for fuel, shaft work, emissions, and cooling ahead of the design of utility system. There are several design considerations for managing utility system operation. Nonetheless, optimizing these systems is perhaps the most important task. The cost
benefit associated with using optimization methods for utility systems is an important motivation and could result in resource conservation and emissions reductions. The present study comprehensively reviewed cogeneration targeting methods that are the first step in utility system optimization. It is determined that, over the approximately two decades from Linnhoff and Dhole (1991) to Khoshgoftar Manesh (2013), the simulation of utility systems for estimating cogeneration has improved notably. This is mainly due to the development of better assumptions; the inclusion of superheat temperature, variable efficiencies, and DSH; applying better relations for thermodynamic parameters; and enhanced modeling of components and considering more accurate values of constants in them. This review can helps researchers to better understand the challenges associated with cogeneration targeting models. The models considered have both advantages and disadvantages. It is evident that improvements that consider better practical problems could render utility systems competitive in the future. Further, the advantages of these models can be used in modeling tools to improve their simulation abilities. Because of the importance of utility systems in supplying energy for several industries within Total Sites, cogeneration targeting continues to develop along with other branches of process integration.

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References [1] Riaz A, Zahedi G, Klemeˇs JJ. A review of cleaner production methods for the manufacture of methanol. J Cleaner Prod 2013;57:19
37. [2] Administration EI. International Energy Outlook 2013 with projections to 2040. U.S. Government Printing Office; 2013. [3] Klemeˇs JJ, Varbanov PS, Kravanja Z. Recent developments in process integration. Chem Eng Res Des 2013;91(10):2037
53. [4] Khoshgoftar Manesh MH, et al. A new cogeneration targeting procedure for total site utility system. Appl Therm Eng 2013;54(1):272
80. [5] Dhole VR, Linnhoff B. Total site targets for fuel, co-generation, emissions, and cooling. Comput Chem Eng 1993;17:S101
9. [6] Sorin M, Hammache A. A new thermodynamic model for shaftwork targeting on total sites. Appl Therm Eng 2005;25(7):961
72. [7] Ghannadzadeh A, Perry S, Smith R. Cogeneration targeting for site utility systems. Appl Therm Eng 2012;43:60
6. [8] Linnhoff B, Dhole VR. Shaftwork targets for low-temperature process design. Chem Eng Sci 1992;47(8):2081
91. [9] Mavromatis SP, Kokossis AC. Conceptual optimisation of utility networks for operational variations—I. Targets and level optimisation. Chem Eng Sci 1998;53 (8):1585
608. [10] Raissi K. Total Site integration. Chemical engineering. Manchester: University of Manchester Institute of Science and Technology (UMIST); 1994. [11] Harell D. Resource conservation and allocation via process integration. Chemical engineering. Texas A&M University; 2004. [12] Medina-Flores JM, Pico´n-Nu´n˜ez M. Modelling the power production of single and multiple extraction steam turbines. Chem Eng Sci 2010;65(9):2811
20. [13] Bandyopadhyay Santanu, James Varghese, Vikas Bansal Targeting for cogeneration potential through total site integration. Applied Thermal Engineering 2010;30(1): 1–16. doi:https://doi.org/10.1016/j.applthermaleng.2009.03.007. [14] Abdul Aziz E, et al. An integrated pinch analysis framework for low CO2 emissions industrial site planning. J Cleaner Prod 2017;146:125
38. [15] Kapil A, et al. Site-wide low-grade heat recovery with a new cogeneration targeting method. Chem Eng Res Des 2012;90(5):677
89. [16] Varbanov PS, Doyle S, Smith R. Modelling and optimization of utility systems. Chem Eng Res Des 2004;82(5):561
78. [17] Ng RTL, et al. Targeting for cogeneration potential and steam allocation for steam distribution network. Appl Therm Eng 2017;113:1610
21. [18] Ren X-Y, et al. Targeting the cogeneration potential for Total Site utility systems. J Cleaner Prod 2018;170:625
35. [19] Manninen J, Zhu XX. Thermodynamic analysis and mathematical optimisation of power plants. Comput Chem Eng 1998;22:S537
44. [20] Mare´chal F, Kalitventzeff B. Targeting the optimal integration of steam networks: mathematical tools and methodology. Comput Chem Eng 1999;23:S133
6. [21] Shang Z, Kokossis A. A transhipment model for the optimisation of steam levels of total site utility system for multiperiod operation. Comput Chem Eng 2004;28 (9):1673
88.

References

[22] Shang Z, Kokossis A. A systematic approach to the synthesis and design of flexible site utility systems. Chem Eng Sci 2005;60(16):4431
51. [23] Mohan T, El-Halwagi MM. An algebraic targeting approach for effective utilization of biomass in combined heat and power systems through process integration. Clean Technol Environ Policy 2007;9(1):13
25. [24] Aguilar O, et al. Design and optimization of flexible utility systems subject to variable conditions: Part 1: Modelling framework. Chem Eng Res Des 2007;85 (8):1136
48. [25] Aguilar O, et al. Design and optimization of flexible utility systems subject to variable conditions: Part 2: Methodology and applications. Chem Eng Res Des 2007;85 (8):1149
68. [26] Sun L, Doyle S, Smith R. Heat recovery and power targeting in utility systems. Energy 2015;84:196
206. [27] Khatita MA, et al. Power generation using waste heat recovery by organic Rankine cycle in oil and gas sector in Egypt: a case study. Energy 2014;64:462
72. [28] Kundra V. To develop a systematic methodology for the implementation of R-curve analysis and its use in site utility design and retrofit. MSc Dissertation. Manchester: University of Manchester; 2005.

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R-curve tool

10

Chapter Outline 10.1 Introduction ...............................................................................................163 10.2 Notation of R-curve .....................................................................................165 10.3 R-curve tool ...............................................................................................167 10.3.1 Ideal R-curve or grassroots R-curve ...........................................167 10.3.2 Actual R-curve ........................................................................168 10.4 Developing the extended R-curves ...............................................................170 10.4.1 Cogeneration targeting .............................................................170 10.4.2 R-ratio against ED, CD, and BD ..................................................172 10.4.3 Advanced representation of Exergy Destruction Level .................172 10.4.4 The algorithm proposed for advanced analyses ...........................172 10.5 Extended R-curve using in liquefied natural gas cogeneration .......................174 10.6 Integrating the desalination systems with the help of R-curve .......................182 10.6.1 Reverse osmosis desalination ...................................................182 10.6.2 Multieffect distillation desalination system ................................185 10.6.3 Integration effect on cogeneration efficiency factor ....................185 10.6.4 Case studies ...........................................................................187 References ..........................................................................................................193

10.1 Introduction In the majority of industrial sites, energy is one of the main factors on the operational costs of the site. Therefore, in order to maximize the profitability of a site, methods and strategies for reducing energy costs must be evaluated. On the other hand, decreasing the level of energy consumption can have other positive impacts such as reducing gas emissions and improving the possibility of protecting the environment. Dhole and Linnhoff [1] were the first to propose the study of a graphical method for analyzing a Total Site, and that method was later expanded upon by Raissi [2]. In these studies a number of graphical tools were devised based on a temperature
enthalpy diagram with the objective of identifying cogeneration and fuel-saving targets. It should be noted that the top-down method first evaluates the utility system and then investigates changes in processes, while the conventional bottom-up method goes the opposite direction [3]. Fig. 10.1A depicts the energy transformation. Fig. 10.1B demonstrates the conventional Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00010-0 © 2021 Elsevier Inc. All rights reserved.

163

FIGURE 10.1 (A) Energy transformation [3]; (B) conventional R-curve [3]; (C) steam turbine network [3]; (D) estimation of heat demand, fuel consumption, and power generation [3]; and (E) ideal R-curve.

10.2 Notation of R-curve

R-curve. Also, the relating steam turbine network is shown in Fig. 10.1C. Identification and estimation of heat demand, fuel consumption, and power generation are presented in Fig. 10.1D. In addition, the ideal R-curve with R-pinch is determined in Fig. 10.1E. On the other hand, the notion of the R-curve was proposed by Kenney [4] as an analysis tool for evaluating the cogeneration potential of a given industrial site. Nevertheless, since the original R-curve was proposed based on a utility system with a straightforward configuration, and it did not account for the capacity and efficiency of the available equipment, it is not well suited for application in complex systems. In order to resolve these issues, a number of new R-curves, such as the retrofit R-curve and the grassroots R-curve, have been proposed [3]. In addition, various chemical processes generally need steam with different values for pressure and temperature for realizing heating and nonheating objectives. There are two main ways for providing the steam needed for a process with the suitable characteristics. The first one is to provide the steam in the extreme condition, followed by reducing it to different levels needed for a specific process. The second method is to produce steams in various conditions in separate boilers. Choosing either of these two methods is the discretion of the designer. A large number of industrial processes operate under “Total Sites” conditions, where a central utility system is responsible for serving and linking them. This central utility system employs indirect heat integration in order to meet the individual process unit’s demands for specific heat and power levels. However, through a holistic approach focused on the entire site, better savings in energy costs and capital costs can be achieved. This strategy of Total Site integration is capable of optimizing each individual process as well as the utility system based on the conditions of the entire site [5]. In order to design utility systems, it is highly important to first account for fuel consumption and shaft work production, and then the design can be considered. This chapter presents an R-curve tool for the performance of evaluation and optimal design for the cogeneration in process industries. Also, the extended Rcurve involves the expansion of the R-curve notion by providing estimations for parameters, including costs, the environmental effects, and the exergoeconomic and exergoenvironmental components. It should be noted that the procedure introduced can be utilized for synthesizing the cogeneration and polygeneration systems in retrofit and grassroots problems.

10.2 Notation of R-curve In general, the energy of a fuel in an industrial site will be converted into power as well as heat, and a portion of that energy will be lost during the conversion. Accordingly, the cogeneration efficiency, denoted by ηcogen , indicates the efficiency of using the fuel, and it can be described as the ratio of the usable portion

165

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of the energy to the total fuel consumption. Therefore the cogeneration efficiency can be expressed as follows: ηcogen 5

W 1 Qheat Qfuel

(10.1)

Moreover, the ratio of the power to the heat, expressed in the following equation, indicates the operating conditions of a specific site [3]: R5

W Qheat

(10.2)

It should be noted that by changing the level of power generation, while keeping the steam heat demand constant, different values for the R-ratio and their respective cogeneration efficiencies can be obtained. Moreover, the R-curves can be created for these R-ratios, as depicted in Fig. 10.1C. These curves can show the association between the maximum ηcogen and the ratio of power to heat, that is, R. Moreover, the R-curves can be used for obtaining the optimized configuration according to the desired value of the R-ratio for a simple utility system. However, there are two main limitations with regard to these R-curves. The first limitation has to do with the fact that the R-curve is obtained for a simple utility system, so it is not well suited for use in a complex system containing a number of different levels of steam distribution with sophisticated configuration of the steam turbine. The second limitation involves the fact that the R-curve considers constant isentropic efficiencies for the steam turbines, leading to unrealistic outcomes. Considering these limitations, the R-curves are rarely used for practical applications. Thus in order to mitigate the abovementioned limitations of the R-curve, a number of novel methods and models can be employed for the steam turbine. In this case, plotting the R-curve can be a suitable strategy for any type of complex system since it can provide relatively reliable results. The levels of steam generation and consumption in a site can act as a basis for obtaining Qheat , which is the net steam heat demand. The net steam heat demand contains the heat demand of steam heaters and the stem demands of the process (e.g., steam needed for stripping and reforming). In this case the process’s steam is considered as a source of heat; however, in some cases, it is solely considered as mass. Nonetheless, in broader terms, a desuperheating system can be utilized in order to maintain the temperature of the steam mains relatively constant. The cogeneration potential is shown in Fig. 10.1D as a shaded area. Under such conditions a back pressure turbine is capable of producing power (W) as long as the cogeneration potential is not exhausted. However, once this potential is exhausted, additional power demands can be satisfied using a condensing turbine (CT), as shown in Fig. 10.1D. On the other hand, the volume of steam produced by the boiler can be used as a basis for calculating fuel consumption, denoted by Qheat . It should be noted that the R-curve in its original form cannot be reliably used for evaluating more complex utility systems. However, in order to mitigate this

10.3 R-curve tool

issue, the notion of composite curves for the site can be used to account for steam heat demand, the potential for power generation, as well as the fuel consumption of a more complex industrial site. In order to produce complete site profiles, data on all hot and cold streams must be collected. In order to better illustrate this notion, it can be shown that in order to obtain the flow rate (m1 ) of the steam at the HP level for the heat demand of the HP steam, that is, QheatðHPÞ , the equation can be applied: m1 5

QheatðHPÞ qðHPÞ

(10.3)

In this equation, qðHPÞ signifies the specific heat content of the HP steam. In addition, m2 and m3 can be calculated using, QheatðMPÞ and QheatðLPÞ , and qðMPÞ and qðLPÞ , respectively. On the other hand, for the case where the flow rate of the steam is set for a CT, shown as m4 Fig. 10.1D, these steam flow rates, m1 ; m2 ; m3 ; and m4 , can be used for calculating the power generation of each of the expansion zones as well as the fuel consumption of the boiler. Afterward, the total cogeneration efficiency, denoted by ηcogen , associated with a specific Rratio can be computed. It can be deduced that by altering the value of m4 , it is possible to obtain different cogenerations for each of the expansion zones while estimating various total cogeneration efficiencies for different values of R. Therefore, in this way, the R-curve for a more complex utility system can be produced.

10.3 R-curve tool 10.3.1 Ideal R-curve or grassroots R-curve The grassroots R-curve can show the maximum cogeneration efficiency for a specific steam heat demand, regardless of the current configuration of the given utility system. The assumptions underlying these configurations are as follows: 1. Each of the expansion zones only includes a single steam turbine, which is responsible for the total power generation potential of that specific expansion zone. 2. Maximum full load is considered for all the steam turbines. These assumptions help achieve the maximum isentropic efficiency for the turbines, leading to the highest possible cogeneration efficiency for a given R-ratio. The grassroots R-curve is developed by considering a fixed heat demand and various changes in the power demand to calculate the R-ratio and the resultant maximum cogeneration efficiencies. In the following the computation procedure for the power and head of the steam as well as the gas turbines is discussed in detail [3].

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1. Only one steam turbine is located between two adjacent steam pressure levels. 2. All steam turbines operate at full loads. The ideal R-curve can be built for a fixed steam heat demand. The procedure for making grassroots R-curve is demonstrated in Fig. 10.2 [3].

10.3.2 Actual R-curve At the first step, all possible steam paths are determined. After considering a steam path, by varying the steam flow of the path the R-ratio is modified. The site heat demand is considered to be fixed that the R-curve is constructed. Due to increases of power demand the R-ratio site increases consequently. For all paths, this procedure should be repeated. However, it is necessary to indicate that path

FIGURE 10.2 Grassroots R-curve procedure [3].

10.3 R-curve tool

should be changed first and also what is the priority for modification. The passes with higher “path cogeneration efficiency” have higher rank priority for modifications. After ranking of steam paths the actual R-curve is built for an existing cogeneration site utility system as shown in Fig. 10.3 [6].

FIGURE 10.3 The procedure to construct the actual R-curve [6].

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10.4 Developing the extended R-curves 10.4.1 Cogeneration targeting In order to increase the accuracy of the calculations in R-curve, an accurate model [7] is applied for evaluating the cogeneration potential of site utility systems. This method uses the SUGCC (i.e., the site utility grand composite curve), which is another type of site composite curve. It is worth mentioning that the SUGCC is obtained based on the site composite curves that are plotted on the temperature
enthalpy axes for each of the steam mains with respect to their saturation temperature, steam generation capability, and usage loads from the source and sink profiles of the site composites. The VHP demand or the supply heat at each of the mains will be set by the difference between the steam generation and steam usage levels. This procedure utilizes the heat loads determined by SUGCC in order to compute the minimum required flow rate coming out of the steam generation unit as well as the levels of superheat in each of the steam mains. Moreover, the indices for the specified L steam mains are sorted from the highest pressure steam main in a descending order, and these indices are represented by i. In other words, for the VHP steam main, i 5 1, while for the high-pressure steam main, i 5 2, for the MP steam main, i 5 3, and for the low-pressure steam main, i 5 4. In addition, an expansion zone is considered between the two steam mains. The indices for the zones are determined from top to bottom as Z 5 1 for VHP
HP and so on. It should be noted that each zone only contains a single steam turbine. On the other hand, the isentropic efficiency can be considered as a function of the load, and by assuming turbines with fixed flow rates equal to full load, it is preferred to assume maximum efficiency. The isentropic efficiency was calculated by Varbanov et al. [8] using the thermodynamic model as follows: Wmax 5 

Wis;max 2 A B

 1 3:41443 3 106 A ηis 5 12 B Δhis M max

(10.4) (10.5)

where A and B signify constant values corresponding to the steam turbine as functions of the saturation temperature. These two constants can be computed using the following two equations: A 5 a0 1 a1 UΔTsat

(10.6)

B 5 a2 1 a3 UΔTsat

(10.7)

Table 10.1 presents the values for A and B. Steam tables can be used for calculating the enthalpy at the boiler outlet for specific pressures and temperatures. In general, the actual input enthalpy of a steam main can be calculated based on the value of the previous steam main. On

10.4 Developing the extended R-curves

Table 10.1 The regression coefficients used in the isentropic efficiency equation [8].

a0 (kW) a1 (kW/  C) a2 a3 ( C21)

Back pressure turbines

Condensing turbines

Wmax # 2000 kW

Wmax.2000 kW

Wmax # 2000 kW

Wmax.2000 kW

0 1.08

0 4.23

0 0.662

2463 3.53

1.097 0.00172

1.155 0.000538

1.191 0.000759

1.22 0.000148

the other hand, the input isentropic enthalpy of the steam main can be calculated in the superheated region. The efficiency can then be obtained. Afterward, the isentropic enthalpies and the efficiency can be substituted into the following equation to estimate the actual enthalpy, which can then be considered as the input enthalpy for the next zone: hi;actual 5 hi21;isentropic 2 η hi21;isentropic 2 hi;isentropic




(10.8)

This procedure utilizes an iterative procedure based on the desired value of the superheat in the low-pressure steam main in order to calculate the superheat temperature at each of the steam levels. It is required for this superheat to be set at the range of 10 C
20 C. However, if the value of the superheat in the lowpressure steam main is lower than the required value, the VHP operating conditions will be updated and iterated until the superheated conditions are satisfied for the low-pressure steam main. On the other hand, the mass balance for the ith turbine can be utilized in the following equation in order to estimate the mass flow rate of the steam going through the Zth turbine, which is denoted by mz : m_ z 5 m_ z21 1 m_ DEM 2 m_ GEN i i

(10.9)

m_ GEN i

where signifies the flow rate for the steam produced by the process, while m_ DEM denotes the flow rate of the steam demanded by the process. These two i flow rates can be calculated using the following two equations, respectively: m_ DEM 5 i

DEM Q_ i hactual 2 hf ;i i

(10.10)

5 m_ GEN i

GEN Q_ i hactual 2 hf ;i i

(10.11)

where hf ;i denotes the enthalpy of the saturated liquid enthalpy at the pressure present in the ith steam main.

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10.4.2 R-ratio against ED, CD, and BD It should be noted that the main objective of exergoeconomic evaluation and analysis is to compute the unit costs of products, including steam and electricity, while it also quantifies the monetary loss caused by irreversibility. This analysis can also be considered as a great tool for the optimal design and operation of highly complex thermal system. Currently, due to the importance of satisfactory estimations of the production cost for the profitable operation of plants, this type of analysis is in great demand. In these types of analysis, conservation laws of mass and energy are applied for individual components in a system. Afterward, the analysis carefully considers the quantitative balance of exergy and exergetic cost for individual components and the entire system as a whole. In order to visualize the process of cost formation as well as the productive interaction between the components, the exergoeconomic model was used to explore the productive structure of a given system. On the other hand, the exergoeconomic analysis is a potential tool for evaluating the location, magnitude, and source of the environmental effects of an energy conversion system. Therefore the exergoenvironmental analysis determines the environmental effects of individual exergy streams and the individual components in a specific plant. In this way the environmental effects of the exergy destruction of a specific component (B_D;k ) can be estimated. While keeping the steam heat demand constant and changing the level of power generation, we can obtain various R-ratios and their respective exergy destruction (ED) and exergy destruction cost (CD). Therefore the curves R-ratio against ED, R-ratio against CD, and R-ratio against BD are derived. These curves indicate the relationship between the power-to-heat ratios, that is, the R-ratio, and ED, CD, and BD.

10.4.3 Advanced representation of Exergy Destruction Level A number of variables, including the EDL (the exergy destruction level) and the ECDL (the exergy cost destruction level), account for the cost of exergy destruction and provide a deeper understanding of the overall performance of the plant. However, the current study proposes an additional variable of EEDL (the environmental effects of the exergy destruction level). Accordingly, graphical representations are derived for illustrating the performance of individual components. For each component the TV (target value) is considered as the exergetic criterion, showing the shaft work target or the exergy production.

10.4.4 The algorithm proposed for advanced analyses The extended grassroots R-curve based is presented in Fig. 10.4. The main steps of this algorithm are as follows:

10.4 Developing the extended R-curves

FIGURE 10.4 Extended R-curve construction [9].

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1. Collecting the data. 2. Plotting the SUGCC. 3. Increasing the mass flow to the full cogeneration point (i.e., the R-pinch) and computing the power based on the new targeting method, the R-ratio, the efficiency of cogeneration, the exergy destruction, the cost of exergy destruction, and the environmental effects. 4. Using the gas turbine or the condensate turbine to increase the power. 5. Once the power is increased, the advanced exergy destruction, the advanced cost of exergy destruction, and the advanced environmental effects can be calculated. 6. Selecting the best possible scenario. AV;EN 7. Finally, determining the avoidable and endogenous portions, that is, E_ D;k , AV;EN AV;EN C_ D;k , B_D;k , EDLAV;EN , ECDLAV;EN , EEDLAV;EN . k k k

10.5 Extended R-curve using in liquefied natural gas cogeneration In order to evaluate the performance of the extended R-curve for Total Site analysis, a case study was carried out on a liquefied natural gas (LNG) plant in Iran. This plant includes LNG trains in its initial phase. A number of dedicated facilities, producing raw gas from South Pars field in Iran, were responsible for supplying sour wet gas to this LNG plant. The plant uses the Siemens V.94.2 gas turbines manufactured by the MAPNA Group of Iran. However, the configuration of the LNG project required modifications and adjustments based on a new philosophy [7]. The main objective of the case study was to evaluate the optimal configuration and the process conditions for the combined cycle power station using the developed R-curve concept. The design of the plant was devised in order to fit the power demand of the worst possible conditions, that is, when the temperature was at the highest level. Therefore the gas turbines used in the plant had been designed to be able to operate at a temperature of 48 C, while the plant had been designed for normal operation at a temperature of 43 C, producing 703:6 MW. Nonetheless, the plant was capable of operating at 48 C by operating at a lower efficiency and a lower power generation capacity. Table 10.2 presents the maximum requirements for the steam and power levels in the LNG cogeneration plant. Table 10.3 determines capital cost estimation for each component. In addition, Table 10.4 indicates the estimation correlations for calculation of environmental impacts. In order to create the reduced superstructure, the following rules must be considered: 1. The new steam turbine must cover all the expansion zones, in which the current capacity is lower than the ideal capacity.

10.5 Extended R-curve using in liquefied natural gas cogeneration

Table 10.2 Power and steam specification for liquefied natural gas cogeneration [9]. Pressure (bar) Saturation temperature ( C) Mass flow requirements (t/h)

VHP

HP

101.9 312.38 60.3

10.84 183.42 400

HP, High pressure; VHP, very high pressure.

Table 10.3 Correlation for estimation of capital investment [9]. Component

Correlation for estimation of capital cost

GT package

2 CGT 5 0:0003 3 Pnet 1 0:1059 3 Pnet 1 6:2778 CGT : cost of gas turbine package ðMM$Þ Pnet : net power of gas turbine ðMWÞ

HRSG-2p

CHRSG-2p 5 5:805 2 0:1653 3 ΔTpinch 1 0:0153 3 mexhaust CHRSG-2p : cost of HRSG; two pressure levels ðMM$Þ ΔTpinch : HRSG pinch temperature ð CÞ
mexhaust : mass flow of exhausted gas kg=s CST 5 6:191 2 0:005573 3 ms 2 0:1156 3 Pin 2 3:743 3 1026 3 ms 2 1 0:0003415 3 ms 3 Pin 1 0:0005948 3 Pin 2 CST : cost of back pressure steam turbine ðMM$Þ
ms : mass flow of steam kg=s Pin : inlet pressure ðbarÞ CCT 5 3:165 1 0:1048 3 ms 1 0:01636 3 Pin CCT : cost of condensing steam
turbine ðMM$Þ ms : mass flow of steam kg=s in : inlet pressure ðbarÞ

ST

CT

CT, Condensing turbine; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

2. Each steam level must have an extraction, while the current extraction capacity is lower than the ideal capacity. The superstructure of cogeneration unit is demonstrated in Fig. 10.5A. According to these rules, the reduced superstructure can be designed for three different modes, shown in Fig. 10.5B. The relationship between the maximum efficiency of cogeneration, that is, ηcogen , and the power-to-heat ratio, that is, R, is depicted by the R-curve. Moreover, the R-curve shows the optimal configuration based on the required Rratio for a simple utility system (see Fig. 10.6 where the R-ratio is shown using horizontal arrows). According to Fig. 10.6, the R-curve allows the comparison of various configurations based on the efficiency of cogeneration. Moreover, this curve shows the

175

Table 10.4 Estimation of environmental impacts based on construction phase [9]. Component

Correlation for Yk (Pts of Eco-indicator 99)

GT 1 CC


Y_ AC 5 20:0076 3 Pnet 2 1 13:558 3 Pnet 2 18:583 3 0:0012 Y_ AC : environmental impact function of compressor ðPts=hÞ Pnet : net power of gas turbine ðMWÞ
Y_ GT1CC 5 20:0051 3 Pnet 2 1 3:582 3 Pnet 1 70:438 3 0:0066

HRSG-2p

Y_ GT1CC : environmental impact function of gas turbine ðincludes combustion chamberÞ ðPts=hÞ Pnet : net power of gas turbine ðMWÞ
Y_ HRSG-2p 5 715:5 2 27:88 3 ΔT pinch 1 2:143 3 mexhaust 3 9:7038e 2 04

AC

ST

CT

Y_ HRSG-2p : environmental impact function of HRSG; two pressure levels ðPts=hÞ ΔTpinch : HRSG pinch temperature ð CÞ
mexhaust :mass flow of exhuasted gas kg=s
Y_ ST 5 189:7 1 0:2677 3 ms 2 3:675 3 Pin 2 0:0002562 3 ms 2 1 0:008919 3 ms 3 Pin 1 0:022 3 Pin 2 3 0:0066 of back pressure steam turbine ðPts=hÞ Y_ ST : environmental impact function
ms : mass flow of steam kg=s Pin : inlet pressure ðbarÞ Y_ CT 5 ð78:57 1 4:486 3 ms 1 0:5066 3 Pin Þ 3 0:0066 of condensing steam turbine ðPts=hÞ Y_ CT : environmental impact function
ms : mass flow of steam kg=s Pin : inlet pressure ðbarÞ

AC, Air compressor; CC, combustion chamber; CT, condensing turbine; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

10.5 Extended R-curve using in liquefied natural gas cogeneration

FIGURE 10.5 Grassroot SUGCC design plan: (A) superstructure and (B) three selected modes [9]. SUGCC, Site utility grand composite curve.

superior efficiency of the GT 1 CT 1 BPT configuration over all the other configurations. Moreover, other analyses are performed for this configuration. Because of the nature of the notion of exergy, the efficiency of cogeneration exergy is lower than the efficiency of the cogeneration. In addition, the exergy destruction for individual configurations due to altering the R-ratio is shown by the R
ED curve. Based on Fig. 10.7, the advanced exergy destruction of the entire system and the individual components can be divided into avoidable/ unavoidable portions as well as endogenous/exogenous portions. While this figure is similar to the R-curve, it also accounts for the notion of exergy.

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FIGURE 10.6 Conventional R-curve representation [9].

The R-destruction cost curve is used for comparing various configurations based on the exergy destruction cost. According to Fig. 10.8, the advanced exergy destruction cost of the entire site and individual components can be divided into avoidable/unavoidable portions as well as endogenous/exogenous portions. While this figure is similar to the R-curve, it also accounts for the notion of exergoeconomics. The curve depicting R-ratio against the environmental effects is used for assessing individual configurations based on the environmental effects, which is shown in Fig. 10.9. This figure shows that the advanced environmental effects of the exergy destruction for the entire site and individual components can be divided into avoidable/unavoidable portions as well as endogenous/exogenous portions. It is worth mentioning that the best scenarios identified while searching for the optimal configuration all included a gas generation section, which included gas turbines equipped with an HRSG (heat recovery steam generator). Nonetheless, four gas turbines are not enough for providing the necessary power of 703.6 MW. Therefore, according to the n 1 1 philosophy, another gas turbine must be considered along with its additional costs. The best configuration of the LNG plant is depicted in Fig. 10.10. This extended R-curve model is capable of identifying the optimal values for the parameters of efficiency, exergoeconomic, environmental effects, and exergoenvironmental effects for the utility system of a Total Site (Table 10.5). Moreover, the R-ratio against exergoeconomic and exergoenvironmental parameters was plotted in order to determine the exergoenvironmental parameters of a utility system based on LCA (life cycle assessment) and exergoeconomic evaluation. Combining the concept of R-curves with representations of exergoeconomic, environmental effects, and exergoenvironmental parameters allows the improvement of the site utility based on the combination of operational, economic, environmental, exergy, and exergoeconomic perspectives.

10.5 Extended R-curve using in liquefied natural gas cogeneration

FIGURE 10.7 Advanced R-ratio versus exergy destruction: (A) total, (B) AC, (C) CC, (D) GT, (E) HRSG, (F) ST, and (G) CT [9]. AC, Air compressor; CC, combustion chamber; CT, condensing turbine; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

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FIGURE 10.7 Continued.

It is worth mentioning that the proposed method can be utilized for any thermal system in a systematic, graphical, and straightforward fashion in order to facilitate the synthesis of the site utility system. Based on the results of the current study, it can be concluded that while the conventional exergetic analysis may provide useful information, the advanced exergetic analysis provides more accurate and useful information. Moreover, the advanced exergetic analysis can provide additional information compared to the conventional exergetic analysis.

10.5 Extended R-curve using in liquefied natural gas cogeneration

FIGURE 10.8 Extended R-ratio versus exergy destruction cost: (A) total, (B) AC, (C) CC, (D) GT, (E) HRSG, (F) ST, (G) CT [9]. AC, Air compressor; CC, combustion chamber; CT, condensing turbine; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

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FIGURE 10.8 Continued.

10.6 Integrating the desalination systems with the help of R-curve 10.6.1 Reverse osmosis desalination In the cogeneration system the ratio of the power to heat rises by the power consumption of the integrated reverse osmosis (RO) desalination system. Considering the power-to-heat ratio before the integration, cogeneration efficiency is affected differently by integrating the desalination system to the off-grid cogeneration system with identical demand and generated powers, as shown in Fig. 10.11. A higher cogeneration efficiency is achieved when the system operates below Rpinch, as

10.6 Integrating the desalination systems with the help of R-curve

FIGURE 10.9 R-ratio versus total destruction environmental impact: (A) total, (B) AC, (C) CC, (D) GT, (E) HRSG, (F) ST, (G) CT [9]. AC, Air compressor; CC, combustion chamber; CT, condensing turbine; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

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FIGURE 10.9 Continued.

can be perceived from the associated R-curve. The operational point proceeds in the so-called below-the-pinch region of the curve and, consequently, results in a higher cogeneration efficiency. On the contrary, the cogeneration efficiency decreases for an initial R greater than Rpinch. Thus a lower cogeneration efficiency is achieved for the cogeneration system when its operating point goes forward.

10.6 Integrating the desalination systems with the help of R-curve

FIGURE 10.10 Optimum configuration of LNG cogeneration based on extended R-curve [9]. LNG, Liquefied natural gas.

10.6.2 Multieffect distillation desalination system The overall heat demand increases by integrating the multieffect distillation (MED) system as the heat user of the motive system. Fig. 10.12 shows how cogeneration efficiency is affected by increasing heat demand. For the fixed power demand the power-to-heat ratio declines when the heat demand increases. When the off-grid system is operating in the “below-the-pinch” region, the cogeneration efficiency drops by integrating the MED system into the process. Contrarily, a higher cogeneration efficiency is achieved for the “above-the-pinch” region. The impact of the integration is indicated in Fig. 10.12.

10.6.3 Integration effect on cogeneration efficiency factor As mentioned earlier, the energy consumption profile changes when the desalination system is integrated into the cogeneration system. Thus the fuel consumption increases or decreases when the desalination system is integrated into the cogeneration system, depending on its initial operational condition. The following metrics can be exploited for the evaluation of this effect: IECEF 5

Cc En

(10.12)

185

Table 10.5 Thermodynamic, exergoeconomic, and exergoenvironmental properties of each stream in optimum condition [9].

State

Material stream

Thermodynamic m (kg/ s) T (C)

P (bar)

EPH (MW)

ECH (MW)

1 2 3 4 5 6 7 8 9 37 38 39 40 41 42 43 44 45

Air Air FG FG FG NG Water Water Water Water Water Water Water Water Water Water Water Water

1455.2 1325.28 1351.6 1482 1268.8 26.488 326.7 272.4 54.2 16.7 111.1 57.2 57.2 199.1 199.1 199.1 8905.6 8905.6

1.013 13.68 13.13 1.04 1.013 25.18 1 101.9 10.84 101.9 10.84 101.9 10.84 101.9 0.4075 0.4075 1 1

0 571.6 1914.2 485.9 31 0 0.7 404.6 48.1 24.3 99.6 83.1 51.3 297.2 73 3.4 0 6.1

0 0 7.67 7.67 7.68 1748 0.82 0.69 0.13 0.04 0.27 0.14 0.14 0.51 0.51 0.51 22.24 22.2

25 396 1176.8 546 177.7 25 43 525 255 525 255 525 255 525 76.3 76.3 15 25

E (MW)

Exergoeconomic C c ($/s) ($/GJ)

Exergoenvironmental B (mPts/ b (Pts/ s) GJ)

0 571.6 1921.87 493.57 38.68 1748 1.52 405.29 48.23 24.34 99.87 83.24 51.44 297.71 73.51 3.91 22.24 28.3

0 42.2 50 12.8 0 7.9 0 11.6 1.4 0.7 2.9 2.4 1.5 8.5 2.1 0.1 0 0

0 47.3 56.5 14.5 0 9.3 0 13 1.5 0.8 3.2 2.7 1.7 9.6 2.4 0.1 0 0

0 73.8 26 26 0 4.5 28.6 28.6 28.6 28.6 28.6 28.6 28.6 28.6 28.6 28.6 0 0

0 82.7 29.4 29.4 0 5.3 32.1 32.1 32.1 32.1 32.1 32.1 32.1 32.1 32.1 32.1 0 0

10.6 Integrating the desalination systems with the help of R-curve

FIGURE 10.11 Integration of RO desalination and cogeneration system using ideal R-curve [10]. RO, Reverse osmosis.


where IECEF is the integration effect on cogeneration efficiency factor $=W ; En desalination system required energy ðWÞ: The performance indicator of total saving quantifies the impact of the adequate integration of the desalination and cooperation systems. The total saving is the summation of the difference of the average initial and final fuel consumption, and the difference of the average energy costs of the separate and integrated desalination units.

10.6.4 Case studies For each type of the desalination system, two distinguished scenarios were considered to analyze and exploit the studied concepts for various integration possibilities in different regions of the R-curve.

10.6.4.1 Specifications of desalination systems Input parameters for RO and MED systems are summarized in Tables 10.6 and 10.7.

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FIGURE 10.12 Integration of MED desalination and cogeneration system using ideal R-curve [10]. MED, Multieffect distillation.

10.6.4.2 First case study Willans line model is employed to specify the shaft power in part-load stream turbine, as follows: W 5 n 3 m 2 Wint

(10.13)

where n and Wint can be determined by referring to Ref. [11]. To model the boiler the following equation is used [12]:   Qfuel 5 Δhsteam aUmsteam 1 bUmsteam;max

(10.14)

where the performance coefficients are set to a 5 0.0126 and b 5 0.2156 [12]. A cogeneration system is illustrated in Fig. 10.13 along with its associated Rcurve and steam mains. Exporting electricity to the grid, importing electricity from the grid, and the fuel cost 0.85, 0.1, and 0.02 $/kWh, respectively. The cogeneration system operates 8000 hours a year. Results of a combination of RO and MED desalination systems with first case study are shown in Tables 10.8 and 10.9.

10.6 Integrating the desalination systems with the help of R-curve

Table 10.6 Specification of the reverse osmosis (RO) system [10]. SW30HR-380 RO membrane
type characteristics Active area Permeability of reference pure water Constant of salt permeability Max. operating pressure Max. feed flux Max. recovery rate Feed spacer thickness Typical stabilized salt rejection Specification of RO plant Total electric power Total production Feed flow

35 m2 2.7 3 1029 kg/m2 s pa 2.3 3 1025 kg/m2 s 82 bar 16.2 m3/h 30% 28 mil 99.7% 30 MW 219,554 m3/day 420,181 m3/day

Table 10.7 Specification of multieffect distillation system [10]. Average daily water production Last effect brine/steam temperature Number of effects GOR Seawater/product flow ratio Seawater flow Total heat to water plant

15,044 m3/day 300K 30 21.5 5.1 889 kg/s 17.8 MW(t)

GOR, Gain output ratio.

10.6.4.3 Second case study A cogeneration system, the major equipment of which includes boiler, gas turbine, back pressure steam turbine, and HRSG, is illustrated in Fig. 10.14 along with its associated R-curve and steam mains. Heat content (or equivalently, the steam generated in the HRGS), fuel consumption [3], and shaft power of the gas turbine [6] are, respectively, given in the following equations: Qfuel;GT 5 2:75 3 WGT 1 8:01

(10.15)

Qheat;HRSGðHPÞ 5 0:37 3 WGT 1 2:55

(10.16)

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WGT 5 1:2 3 mf 2 0:8

(10.17)

Results of a combination of RO and MED desalination systems with second case study are determined in Tables 10.10 and 10.11, respectively. The final and initial average values of R-ratio are obtained by considering the heat and power consumed in the desalination system as well as timetables of the demanded heat and power in the main plant. Two scenarios were developed to analyze the integration of the desalination system in various regions of the Rcurve.

Actual R-curve 0.75

Cogeneraon efficincy

190

0.7 0.65 0.6 0.55 0.5 0

0.1

0.2

0.3

0.4

0.5

R-rao FIGURE 10.13 Retrofit R-curve for the first case study [10.]

Table 10.8 Results of a combination of reverse osmosis (RO) desalination (first case) [10]. Average parameter Power requirement (MW) Desalination power demand (MW) Initial fuel demand (MW) Final fuel demand (MW) Fuel demand increase by integrated with RO (MW) Integrated RO fuel cost (MM$) IECEF (MM$/MW) Total saving (MM$/year) IECEF, Integration effect on cogeneration efficiency factor.

First scenario

Second scenario

36 30 381.031 411.042 42.135

72 30 492.459 564.037 48.077

4.802 20.065 14.396

11.453 0.125 1.095

10.6 Integrating the desalination systems with the help of R-curve

Table 10.9 Results of a combination of multieffect distillation (MED) desalination (first case) [10]. Average parameter Power requirement (MW) Heat demand desalination (MW) Initial fuel demand (MW) Final fuel demand (MW) Fuel demand increase by integrated with MED (MW) MED boiler fuel cost (MM$) Integrated MED fuel cost (MM$) IECEF (MM$/MW) Total saving (MM$/year)

First scenario

Second scenario

17 17.8 374.644 427.789 26.449

69.6 17.8 479.898 489.431 28.031

3.560 8.503 0.240 213.447

3.560 1.525 20.166 0.509

IECEF, Integration effect on cogeneration efficiency factor.

The integration of the RO in the “below-the-pinch” and decreasing regions of the R-curve is considered in the first and second scenarios, respectively. Two scenarios have distinct economic conditions with different signs for the corresponding integration effect on cogeneration efficiency factors (IECEFs). Results demonstrate that the two scenarios are significantly different even for the same RO desalination technology. Since the cogeneration efficiency drops, a considerable amount of energy wastes in the second scenario. The integration of the desalination and cogeneration systems is severely sensitive to its operational condition, as can be perceived by the considerably different values of the fuel saving in two scenarios. Finally, we analyze the integration of the MED system in the two considered case studies. The initial operation of the system operation is in the “below-thepinch” region for both case studies of the first scenario, unlike the second scenario, which is in “above-the-pinch” region. Both scenarios offer fuel saving. Furthermore, the consumed fuel differs considerably for the two considered case studies with MED integration. When the cogeneration system operates in the “below-the-pinch” region, the integration offers an IECEF with a negative sign for both case studies and, consequently, imposes a considerable financial load on the owner. Contrarily, a considerable saving is achieved if the desalination system is integrated into the cogeneration system with the “above-the-pinch” operational condition.

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R-curve 0.51

Cogeneraon efficiency

192

0.49 0.47 0.45 0.43 0.41 0.39 0.37 0.35 0

0.05

0.1

0.15

R-rao

FIGURE 10.14 Grassroots R-curve of the second case study [10].

0.2

0.25

0.3

References

Table 10.10 Results of a combination of reverse osmosis (RO) desalination (second case) [10]. Average parameter Average power demand (MW) Desalination power demand (MW) Initial fuel demand (MW) Final fuel demand (MW) Fuel demand increase by integrated with RO (MW) RO electricity cost from grid (MM$) Integrated RO fuel cost (MM$) IECEF (MM$/MW) Total saving (MM$/year)

First scenario

Second scenario

5 30 463.579 509.572 63.158

33 30 514.938 722.597 62.241

17.736 6.733 20.084 4.269

17.736 30.401 0.710 243.067

IECEF, Integration effect on cogeneration efficiency factor.

Table 10.11 Results of a combination of multieffect distillation (MED) desalination (second case) [10]. Average parameter Power requirement (MW) Heat demand desalination (MW) Initial fuel demand (MW) Final fuel demand (MW) Fuel demand increase by integrated with MED (MW) MED boiler fuel cost (MM$) Integrated MED fuel cost (MM$) IECEF (MM$/MW) Total saving (MM$/year)

First scenario

Second scenario

15 17.8 477.593 562.358 36.929

40 17.8 539.535 548.193 37.632

3.257 12.410 0.393 222.715

3.257 1.268 20.238 0.605

IECEF, Integration effect on cogeneration efficiency factor.

References [1] Dhole VR, Linnhoff B. Total site targets for fuel, co-generation, emissions, and cooling. Comput Chem Eng 1993;17:S101
9. [2] Raissi K. Total site integration. The University of Manchester; 1994. [3] Kimura H, Zhu XX. R-curve concept and its application for industrial energy management. Ind Eng Chem Res 2000;39(7):2315
35. [4] Kenney WF. Energy conservation in the process industries. Academic Press; 1984.

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[5] Sorin M, Hammache A. A new thermodynamic model for shaftwork targeting on total sites. Appl Therm Eng 2005;25(7):961
72. [6] Manninen J, Zhu XX. Optimal gas turbine integration to the process industries. Ind Eng Chem Res 1999;38(11):4317
29. [7] Khoshgoftar Manesh MH, et al. A new cogeneration targeting procedure for total site utility system. Appl Therm Eng 2013;54(1):272
80. [8] Varbanov PS, Doyle S, Smith R. Modelling and optimization of utility systems. Chem Eng Res Des 2004;82(5):561
78. [9] Khoshgoftar Manesh MH, et al. New procedure for optimal design and evaluation of cogeneration system based on advanced exergoeconomic and exergoenvironmental analyses. Energy 2013;59:314
33. [10] Salimi M, Amidpour M. Investigating the integration of desalination units into cogeneration systems utilizing R-curve tool. Desalination 2017;419:49
59. [11] Varbanov P, et al. Top-level analysis of site utility systems. Chem Eng Res Des 2004;82(6):784
95. [12] Smith R. Chemical process: design and integration. Wiley; 2005.

CHAPTER

Environmental impacts consideration

11

Chapter outline 11.1 Introduction ...............................................................................................195 11.2 Life cycle assessment .................................................................................196 11.2.1 Stages of life cycle assessment framework ................................197 11.2.2 Applications of life cycle assessment ........................................197 11.2.3 Benefits of life cycle assessment ..............................................198 11.2.4 Design a life cycle assessment project ......................................198 11.2.5 Real planning and process management ....................................198 11.2.6 How is life cycle assessment done? ...........................................199 11.3 Eco-indicator 99 ........................................................................................199 11.4 Exergoenvironmental analysis .....................................................................201 11.5 Estimation of greenhouse gas emissions ......................................................202 11.6 Footprint ....................................................................................................203 11.6.1 Carbon footprint ......................................................................203 11.6.2 Emission footprint ...................................................................203 11.6.3 Energy footprint ......................................................................203 11.6.4 Water footprint ........................................................................203 11.7 Environmental targeting ..............................................................................203 11.8 Case studies ..............................................................................................204 11.8.1 Case 1 ...................................................................................204 11.8.2 Case 2 ...................................................................................207 References ..........................................................................................................213

11.1 Introduction Due to the importance of environmental issues, the increasing production of pollutants and energy consumption, considering environmental issues in the design and sterilization of simultaneous production systems, this chapter examines environmental analysis. Given the importance of life cycle analysis and environmental impacts, these analyses are first addressed. In the following the method of calculating and estimating the production of pollutants is examined. It also looks at how carbon, pollutants, energy, and water are calculated. Accordingly, before implementing many development projects, the consequences and effects of such plans on the environment of a region are identified Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00011-2 © 2021 Elsevier Inc. All rights reserved.

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and predicted, and the necessary measures are taken to control and reduce them, and this is generally done through evaluation. The environmental impacts of development plans are based on the existing laws a country [1,2]. An activity is carried out to identify and predict the effects of a project on the environment. Environmental impact assessment is a systematic assessment of the effects of environmental indicators that can have an impact on the environment, including socioeconomic outcomes. The concept of the Environmental Impact Assessment Report is an official process that examines the likely outcomes and consequences of implementing a proposed project on the environment in order to predict adverse effects and understand how to prevent, reduce, or control those [1,2]. In the 21st century the debate on energy and environment has increased in many countries, including developing countries and developed countries. It is particularly worrying about climate change that has led to different policies. Energy policies can play an important role in counteracting climate change, because energy generation and consumption in transport, home, business, and industrial sectors account for a significant share of human in greenhouse gas (GHG) emissions. As a result, long-term strategies to achieve GHG emission reductions require changes in energy sources, infrastructure, and consumption [1,2]. Generating electricity using fossil fuels releases environmental pollutants, especially GHGs. Since the major share of power generation currently depends on fossil fuels, the potential for a significant reduction in GHG emissions through the development of other power generation technologies such as new energies and nuclear energy in the near future is a bit unexpected. But it is evident that in the long run, with the expansion of renewable energy technologies and nuclear power plants instead of thermal power plants, there is a tendency to see a decline in GHG emissions [1,2].

11.2 Life cycle assessment By increasing environmental awareness, industries have begun to evaluate the impact of their activities on the environment. The environmental performance of products and processes has become a key issue for industries to minimize the environmental impact of their activities. Many industries find it helpful to use nonpolluting strategies and environmental management systems to improve environmental performance. One of these environmental management tools is the life cycle assessment (LCA). LCA is a “cradle-to-grave” approach to assessing industrial systems. LCA provides an opportunity to estimate the cumulative environmental effects of all stages of the product’s life cycle. LCA is also a technique for assessing environmental aspects and potential impacts associated with a product, process, or service. In other words, it is a technique for evaluating all inputs and outputs (data and outputs), processes or services (inventory cycle inventory), waste assessment, effects on human health and ecological effects (evaluating the

11.2 Life cycle assessment

effect), and interpreting the evaluation results. Interpretation of the life cycle means that the entire life cycle of the product or process is investigated [1,2]. In ISO 14040 the following definition is provided for LCA [1,2]: A set of systematic methods for collecting and evaluating inputs and outputs and inputs and environmental effects associated with a product system throughout its life cycle.

Therefore LCA is a tool for analyzing the environmental impacts of products at all stages of their life cycle—from resource extraction to material production, production of parts and final product production, and product use until managed after disposal, including recycling, reuse, and disposal of final product—in other words, from cradle to grave. The entire system of unit processes involves the life cycle of the product system [1,2]. The LCA process can help decision-makers to select products or processes with the least environmental impact. LCA helps one to prevent the transmission of environmental problems from one stage to another [1,2].

11.2.1 Stages of life cycle assessment framework The LCA is a great and complex task with many variables in it. However, there is a general agreement on the formal structure of the LCA, which consists of the following three steps:

• defining purpose and scope • analyzing inventories • analyzing effect All phases end up interpreting the results (in some references, taking into account the stage of interpreting the results, four steps are considered) [1,2]. First, the goal and scope of the LCA are defined. Then both inventory and effect are analyzed. The interpretation of the results at each stage represents a potential analysis that may affect each step, so that the entire process will be flexible [1,2]. The LCA is a “cradle-to
grave” approach to assessing industrial systems. “Cradle to Gore” begins by collecting raw materials from the land for the production of the product and ends with the return of the product consumed to the ground. LCA provides an opportunity to estimate the cumulative environmental impacts generated by all stages of the product’s life cycle [1,2].

11.2.2 Applications of life cycle assessment The main applications of LCA include:

• • • •

analyzing the source of problems associated with a particular product; comparing the progress of the variables of a product; designing new products; and choosing between several comparable products.

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Of course, similar strategic-level applications can be identified in relation to government’s business and political strategies [1,2].

11.2.3 Benefits of life cycle assessment The LCA process can help decision-makers to select products or processes with the least environmental impact. This information can be used along with other factors such as cost and performance for product selection or process. LCA data identifies the transfer of environmental impacts from one environment to another and from one cycle to another. The LCA helps one to prevent the transfer of environmental problems from one stage to another. The ability to track environmental impacts can help decision-makers and managers understand the environmental characteristics of product or process choices. By doing LCA, analysts can [1,2]:

• systematically evaluate the environmental outcomes associated with the product;

• examine environmental impacts with one or more products or processes; • receive the acceptance of beneficiaries (community, etc.) for a planned program;

• spread air, water, and land at each stage of the life cycle; • understand the shift in environmental impacts in the life cycle and environment of the host;

• evaluate the human and ecological effects of substance use and releases into the environment in local, regional, and global communities;

• identify and compare the human and ecological effects between two or more products or processes; and

• estimate the effects of one or more affected environments [1,2].

11.2.4 Design a life cycle assessment project Implementing an LCA project is not just a study project. LCA results can be used in decision-making of industry, government, and NGOs. These results can determine their decisions based on capital, political issues, or strategic estimates. Therefore LCA project should be considered as an organizational process that can be carried out in a variety of ways [1,2].

11.2.5 Real planning and process management LCA implementation should be carefully aligned with the ultimate goal, which requires the LCA owner and other stakeholders to focus on the ultimate goal. Meanwhile, LCA researchers must also have a goal in mind, given how this will affect the project’s conditions. Also, the LCA owner should consider designing, organizing, and managing the process [1,2].

11.3 Eco-indicator 99

11.2.6 How is life cycle assessment done? LCA is a technique for assessing environmental aspects and potential impacts associated with a product, process, or service through [1,2]:

• collecting an inventory of energy and material inputs and releasing it to the environment;

• evaluating the potential environmental impacts associated with defined inputs and releases to the environment; and

• interpreting results to help make decisions. LCA is a technique for evaluating all product inputs and outputs (data and outputs), process or services (inventory cycle inventory), waste assessment, and effects on human health and ecological effects (impact assessment). The interpretation of the evaluation results (life cycle interpretation) means to take the entire life cycle of the product or process into consideration[1,2].

11.3 Eco-indicator 99 An LCA is applied to calculate the environmental impact related to a product over its lifetime, [2] and it is performed based on the International Standards (ISO 14004) guidelines. The quantification of environmental impacts resulting from depletion and pollutions of a natural resource can be performed through different LCA procedures. The damage-oriented impact analysis approach is introduced the Eco-indicator 99. It defines based on three damage categories: (1) damage to human health, (2) damage to the ecosystem, and (3) resource depletion. After computing the environmental impacts in the different categories, the values are shown normalized, weighted arbitrary and the result is expressed and indicated in Eco-indicator points (Pts). In order to shift from the manufactured materials to the raw substances and emissions inventory, the software package SimaPro can be applied. Table 11.1 indicates some data collected and used for predicting the component-related environmental impact that happens during the construction phase. The unit of Eco-indicator’s values is based on dimensionless form and is defined the Eco-indicator point (Pt). In the Eco-indicator the unit milli-point (mPt) is usually applied, where 500 mPt 5 0.5 Pt. The scale is selected in such a way that the value of 1 Pt is a delegate for one thousand of the environmental loads of one average European resident yearly. where Y_ k is the environmental impacts that happen during the three phases of LCA: CO 1. construction phase: Y_ k OM 2. operation and maintenance phase: Y_ k DI 3. disposal phase: Y_ k

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Table 11.1 Eco-indicator for materials of some components in combined heat and power and combined cooling, heating, and power system. Components

Materials

Percentage of materials

Eco-indicator 99 (mPts=kg)

Points (mPts=kg)

Air compressor

Steel Steel low alloy Cast iron Steel Steel low alloy Cast iron Steel Steel low alloy Steel Steel high alloy Steel Steel high alloy Steel Steel

33 45 22 33 45 22 25 75 33 77 25 75 100 100

86 110 240 86 110 240 86 110 86 910 86 910 86 86


131

Fuel compressor Heat exchanger Afterburner Gas turbine Steam turbine Pump

131

104 729 704 86 86

Demange to resources

Inventory of all flows from and to all processes in the life cycle of a product

Resources Demange model for these flows

Demange to ecosystems

Weighting of these three demange categories

Indicator

Land use

Emissions

Damage to human health

FIGURE 11.1 General structure of LCA based on ECO99. LCA, Life cycle assessment.

The component-related environmental impact related to the kth equipment Y_ k : CO OM DI Y_ k 5 Y_ k 1 Y_ k 1 Y_ k

(11.1)

To simplify the discussion, in some cases the value of Y_ k is mainly associated CO with Y_ k . Fig. 11.1 indicates the LCA procedure based on Eco-indicator 99 to calculate environmental impacts.

11.4 Exergoenvironmental analysis

The environmental impact data for materials is used in system components in milli-point (mPt) of the Eco-indicator 99.

11.4 Exergoenvironmental analysis The exergoenvironmental analysis consists of three levels. First, an exergy analysis is calculated for each stream of the system, and in the second step the environmental impacts of each equipment on the manufacturing process are calculated, and finally in the third step the exergoenvironmental balance equations are formed to compute the environmental impact associated with each stream. The equation of exergoenvironmental balance related to each component is written as follows [3]. B_P;k 5 B_F;k 2 B_L;k 1 Y_ k X B: 1 B:w;k 5 B:q;k 2 B: 1 Y_ k e e;k i i;k

X

_ i B_i 5 bi UEx

(11.2) (11.3) (11.4)

The environmental impact rate related to exergy destruction of each component is found as: _ D;k B_ D;k 5 bF;k  Ex

(11.5)

The exergoenvironmental factor for each component is obtained as: fbk 5

Y_ k _ _ D;k Y k 1 bf ;k Ex

(11.6)

The relative environmental impact difference associated with each component is given as: rbk 5

bP;k 2 bF;k 1 2 εk Y_ k 5 1 _ P;k bF;k εk bf ;k Ex

(11.7)

In order to simplify the use of exergoenvironmental analysis in the optimal design and optimization of cogeneration and polygeneration systems, estimating the weight of equipment and components is a way forward. In this regard, some correlations for cogeneration system have been proposed by Morosuk et al. [4], Khoshgoftar Manesh et al. [5], and Cavalcanti et al. [3]. Environmental impact related to each component is estimated based on multiplying weight of each component and environmental impact per mass unit of it: yk 5 wk 3 bm;k

(11.8)

where yk is the environmental impact of the component in Pts, and wk is the weight of the component in tons.

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bm;k is environmental impact per mass unit of the component in Pts/t, which is a function of the component’s material, and it can be derived from Eco-indicator 99 knowing the material composition of each component.

11.5 Estimation of greenhouse gas emissions Carbone dioxide emissions as main GHG are estimated based on [CO2]Emiss (kg/s) and are associated with the value of fuel burnt, QFuel (kW), in a heating component as: 

½CO2 Emiss 5

QFuel NHV

  C% α 100

(11.9)

where α (53.67) is the ratio of CO2 molar masses and C, while NHV (kJ/kg) indicates the net heating value of a fuel with a carbon content of C (%). It determines that the type of fuel and the heating component result in the value of CO2 production. The heating component efficiency effects directly on the emission pollutions by the amount of fuel burnt. The effect of the fuel indicates by term of C%, NHV, and α. These effects are so-called fuel factor, FuelFact (kg/kJ), defined as: FuelFact 5

 α C% NHV 100

(11.10)

Gu¨lder introduced that the adiabatic flame temperature in the primary zone of the combustion chamber is expressed as [6]: Tpz 5 Aσα exp ðβ ðσ 1 λÞ2Þπxθyψz

(11.11)

where π is a dimensionless pressure p/pref. Also, p is associated with the combustion pressure p3 (and pref is 101.325 kPa. Furthermore, θ is a dimensionless temperature T/Tref and T is inlet temperature T3 and Tref is equal to 300K. In addition, ψ is the H/C atomic ratio ψ 5 4, the fuel is considered to be pure methane; Also, σ 5 ϕ for ϕ # 1 in which ϕ is related to the fuel-to-air equivalence ratio and σ 5 ϕ 2 0.7 for ϕ . 1 (it is assumed that ϕ 5 0.64); x, y, and z are quadratic functions of σ related to following correlations: 0:3 # ϕ # 1:0 and 0:92 # θ , 2:0

(11.12)

0:3 # ϕ # 1:0 and 2:0 # θ # 3:2

(11.13)

1:0 , ϕ # 1:6 and 0:92 # θ , 2:0

(11.14)

1:0 , ϕ # 1:6 and 2:0 # θ # 3:2

(11.15)

As Rizk and Mongia defined and proposed, the pollutant emissions in gram per kilogram of fuel were estimated as follows:   0:15E16τ 0:5 exp 271; 100=Tpz  0:5 P3 0:05 Δp3 =p3   0:179E9exp 7800=Tpz CO 5  0:5 p3 2 τ Δp3 =p3

NOx 5

(11.16)

(11.17)

11.7 Environmental targeting

11.6 Footprint 11.6.1 Carbon footprint The carbon footprint (CF) has become one of the most major environmental protection indicators recently [7]. CF indicates the quantities of GHGs. Usually. a period of 100 years is considered as a specific time horizon. The CF predicts that the land area needs to sequester atmospheric fossil CO2 emissions pollution through afforestation to target the maximum available area. This area is estimated as [8]: CF 5 MCO2 3

1 2 FCO2 3 EF SCO2

(11.18)

where CF is the indirect land occupation footprint by fossil fuel and cementrelated CO2 emissions (m2 year), MCO2 is the product-specific emission of CO2 (kg CO2), FCO2 is the fraction of CO2 absorbed by the oceans, SCO2 is the sequestration rate of CO2 by biomass (kg CO2/m2/year), and EF is the equivalence factor for forests. The unit of measuring footprint is expressed in m2.

11.6.2 Emission footprint The emission footprint (EMF) indicates the quantity of product or service-created emissions into the air, water, nitrogen and phosphorus, and soil. EMFs are calculated on a per-area basis [7].

11.6.3 Energy footprint The energy footprint considers different energy providers associated with different requirements [7]. The footprint is predicted by multiplying the last energy use of different energy transporters with their land required indices and adding these impacts to the footprint of the entire energy supply. This footprint is measured in m2. In the design phase, the maximum available area (m2) can be determined [7].

11.6.4 Water footprint The water footprint is based on the virtual water concept. Virtual water is defined as the water amount needs to produce a service or a product. Water footprint is indicated to summarize the portion of an activity or product to the environment decondensation. It is based on the utilization of the limited origin, water. Average water need for a specific class of service or product (m3) can be determined [7].

11.7 Environmental targeting The environmental targeting procedure has been proposed by Khoshgoftar Manesh et al. [9]. This procedure is associated with the prediction of GHG

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Calculate TAC with considering GHG taxes (lifetime) TAC–SUGCC with GHG taxes

Environmental strategy map

Estimation of footprints

Y-SUGCC new representation ESUGCC new representation

Calculate annualized operating cost with GHG taxes

Calculate annualized capital cost

Estimation of Eco-indicator 99

SUGCC

Estimation of GHG

Find main components parameters

Find operating parameters

WZ=m Z (hi – hi+1) Steam temperature and mass flows in boiler and each steam mains η, m iNET

Start Site utility data

Increase boiler exit temperature

Initial guess for boiler exit temperature

Yes No

For given steam levels Pi, Tsati

For given expansion zones assuming isentropic expansion (K=1) and find initial m iNET Find the total flow rates passing through each zone

No Yes

Correct the efficiency

For given steam levels correct hi and m

i

NET

FIGURE 11.2 Computation algorithm for environmental targeting [9].

emissions, carbon, water, energy, and EMFs. In addition, environmental impacts are considered based on the Eco-indicator 99. The computation procedure for environmental targeting is demonstrated in Fig. 11.2.

11.8 Case studies 11.8.1 Case 1 A simple cogeneration system is considered for exergoenvironmental modeling (Fig. 11.3). This system includes an air compressor, a combustion chamber, a

11.8 Case studies

3 2

Combustion chamber

1

4

Wnet Compressor

Gas turbine

6

5

HRSG 11 Pump

7

Wst

Steam turbine 10 Condenser 8

12

9

13 Process

FIGURE 11.3 A simple cogeneration system.

gas turbine, an heat recovery steam generator, a steam turbine, a condenser, a pump, and a process that needs steam. Exergoenvironmental balance equations for considered plant component are indicated in Table 11.2. The fuel and prod_ uct concept for exergoenvironmental analysis is determined in Table 11.3. BP _ and BF for each component are determined as shown in Table 11.3. For calculating Y_ k for each component, we use the Eco-indicator 99 method. By the steps of this method, we can obtain Y_ k in mPts/s [3]. Step 1: Identify the types and weight of materials used in the product (per unit) (Table 11.4). Step 2: Identify processes involved in manufacturing to process these materials (per unit) (Table 11.5).

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Table 11.2 Exergoenvironmental balance equation for case 1. Components

Auxiliary equations

Environmental impact balance equations _ AC 1 Y_ AC 5 b_ 2 3 E_ 2 b_ 1 3 E_ 1 1 b_ W;AC 3 W

Air compressor

b_ 1 5 0 b_ W;AC 5 b_ W;gt

Combustion chamber Gas turbine

b_ 3 5 fuel scores

b_ 2 3 E_ 2 1 b_ 3 3 E_ 3 1 Y_ CC 5 b_ 4 3 E_ 4 _ gt b_ 4 3 E_ 4 1 Y_ gt 5 b_ 5 3 E_ 5 1 b_ W;gt 3 W

HRSG

b_ 4 5 b_ 5 b_ 6 5 b_ 5

Steam turbine

b_ 7 5 b_ 12 b_ 9 5 b_ 8

_ st b_ 7 3 E_ 7 1 Y_ st 5 b_ 8 3 E_ 8 1 b_ 9 3 E_ 9 1 b_ W;st 3 W

Condenser

b_ 7 5 b_ 8 b_ 12 5 0

b_ 9 3 E_ 9 1 b_ 12 3 E_ 12 1 Y_ Condenser 5 b_ 10 3 E_ 10 1 b_ 13 3 E_ 13

Pump

b_ W;Pump 5 b_ W;st

_ Pump 1 Y_ Pump 5 b_ 10 3 E_ 10 b_ 9 3 E_ 9 1 b_ W;Pump 3 W

b_ 5 3 E_ 5 1 b_ 11 3 E_ 11 1 Y_ steam generator 5 b_ 6 3 E_ 6 1 b_ 7 3 E_ 7

HRSG, Heat recovery steam generator.

Table 11.3 Fuel and product environmental impacts of each component. Components Air compressor Combustion chamber Gas turbine HRSG Steam turbine Condenser Pump

Equations of product and fuel economic of each component B_ F ;Ac 5 B_ W B_ P;Ac 5 B_ 2 2 B_ 1 B_ F ;cc 5 B_ 2 1 B_ 3 B_ P;cc 5 B_ 4 B_ F ;GT 5 B_ 4 2 B_ 5 B_ P;GT 5 B_ W;GT _ 5 2 B_ 6 B_ F ;HRSG 5 B B_ P;HRSG 5 B_ 7 2 B_ 11 B_ F ;ST 5 B_ 7 2 B_ 8 2 B_ 9 B_ P;ST 5 B_ W;ST B_ F ;cond 5 B_ 12 2 B_ 13 B_ P;cond 5 B_ 10 2 B_ 9 B_ F ;pump 5 B_ W;pump B_ P;cond 5 B_ 11 2 B_ 10

HRSG, Heat recovery steam generator.

Another method for calculating the environmental impact related to each component is using for some estimation correlations. These correlations are presented in Table 11.6 based on Khoshgoftar Manesh et al. proposed for liquefied natural gas cogeneration plant [5].

11.8 Case studies

Table 11.4 The types and weight of materials for simple cogeneration system. No. AC

CC

GT

Materials FerrousMetals FerrousMetals FerrousMetals FerrousMetals FerrousMetals FerrousMetals FerrousMetals

HRSG

FerrousMetals FerrousMetals

ST

FerrousMetals FerrousMetals

Condenser Pump

FerrousMetals FerrousMetals FerrousMetals

Steel (80% primary, 20% scrap) Steel low alloy (93% Fe, 5% scrap, 1% alloy metals) Cast iron Steel high alloy (71% Fe,16% Cr, 13% Ni) Steel (80% primary, 20% scrap) Steel (80% primary, 20% scrap) Steel high alloy (71% Fe,16% Cr, 13% Ni) Steel (80% primary, 20% scrap) Steel high alloy (71% Fe,16% Cr, 13% Ni) Steel (80% primary, 20% scrap) Steel high alloy (71% Fe,16% Cr, 13% Ni) Steel (80% primary, 20% scrap) Steel (80% primary, 20% scrap) Cast iron

Indicator (mPts=kg) 86 110 240 910 86 86 910 86 910 86 910 86 86 240

AC, Air compressor; CC, combustion chamber; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

11.8.2 Case 2 In the second case study, we try to use environmental targeting for the steam and power production in a cogeneration site utility of process plant. It includes the four steam mains as very high pressure (VHP), high pressure (HP), medium pressure (MP), and low pressure (LP) at 120, 50, 14, and 3 bar, respectively. The steam requirements at HP, MP, and LP mains are 50, 40, and 85 MW, sequentially. The information about main parameters associated with steam mains are determined as indicated in Table 11.7. The required information for cogeneration targeting of case study 2 are demonstrated in Fig. 11.4. Environmental impact correlation for construction phase of case 2 is determined in Table 11.8. The feedwater for the boiler is at the temperature of 105 C and the degree of superheat for LP main is considered as 40 C. For foot print analysis the overall available area of the municipality is assumed to be 3.85E 1 11 m2 and total water resources of the municipality are 390,873.72 m3. The site utility grand composite curve (SUGCC) of considered problem is demonstrated in Fig. 11.5.

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Table 11.5 Identify manufacturing to process materials. No.

Processes

AC

PlasticsProcess MetalsProcess MetalsProcess PlasticsProcess MetalsProcess MetalsProcess MetalsProcess MetalsProcess PlasticsProcess MetalsProcess MetalsProcess PlasticsProcess MetalsProcess MetalsProcess MetalsProcess MetalsProcess MetalsProcess MetalsProcess MetalsProcess MetalsProcess MetalsProcess

CC Turbine

Heat exchanger Pump

Indicator (mPts/U) Forming Brazing Milling, turning, drilling Forming Milling, turning, drilling Shearing/stamping—steel Shearing/stamping—steel Brazing Forming Brazing Milling, turning, drilling forming Milling, turning, drilling Shearing/stamping—steel Milling, turning, drilling Brazing Shearing/stamping—steel Forming Extrusion Forming Brazing

5.3 4000 800 5.3 800 0.6e 2 4 0.6e 2 4 4000 5.3 4000 800 5.3 800 0.6e 2 4 800 4000 0.6e 2 4 5.3 72 5.3 5.3

AC, Air compressor; CC, combustion chamber.

Table 11.6 Correlations for the environmental impact estimation of each component in LNG cogeneration system. Component

Environmental impact correlation

Air compressor Combustion chamber 1 turbine

Pnet: net power of gas turbine Y_ 5 ð2 0:0051 3 Pnet 2 1 3:582 3 Pnet 2 70:438Þ 3 0:0066 Pnet: net power of gas turbine

Steam turbine

_ s 2 3:675 3 Pin 2 0:0002562 Y_ 5 ð189:7 1 0:2677 3 m

_ s 2 1 0:008919 3 m _ s 3 Pin 1 0:022 3 Pin 2 Þ 3 0:0066 3m ms: mass flow of steam Pin: inlet pressure Heat exchanger

Pump

_ exhaust Þ 3 9:7038e 2 04 Y_ 5 ð715:5 2 27:88 3 ΔTpinch 1 2:143 3 m

ΔTpinch: HRSG pinch temperature mexhaust: mass flow of exhausted gas _2 _ Y_ 5 0:0016 3 W pump 2 0:5559 3 W pump 1 12:66 _ W pump : net consumed power of pump

HRSG, Heat recovery steam generator.

11.8 Case studies

Table 11.7 Main parameters for case study 2 [9]. Parameters

VHP

Pressure (bar) SaturationTemperature ( C) Heat load (MW)

HP

120 324 0

MP

50 264 50

LP

14 195 40

3 133 85

HP, High pressure; LP, low pressure; MP, medium pressure; VHP, very high pressure.

VHP 120 bar Process steam generator HP 50 bar

Q ·net,i=Q·iDEM– Q·iGEN= 50 MW

Process steam generator MP 14 bar

Q ·net,i=Q·iDEM– Q·iGEN= 40 MW

Process steam demand

Q ·net,i=Q·iDEM– Q·iGEN= 85 MW

Process steam demand

Process steam generator LP 3 bar

Process steam demand

FIGURE 11.4 Total site utility system for case study 2 [9].

Table 11.8 Environmental impact correlation for construction phase of case study 2 [9]. Component Steam turbine

Environmental impact function, Yk (Pts of Eco-indicator 99)  Y_ ST 5 189:7 1 0:2677 3 ms 2 3:675 3 Pin 2 0:0002562 3 ms 2 1 0:008919 3 ms 3 Pin 1 0:022 3 Pin 2 Þ 3 0:0066 _ Y ST : Environmental impact of back pressure steam turbineðPts=hÞ  function  ms : mass flow of steam kg=s Pin : inlet pressure ðbarÞ

Boiler

Y_ Boiler 5 ð21:76 1 9:928 3 ms 2 87:14 3 mf Þ 3 9:7038E 2 04

Y_ Boiler : Environmental impact function of boilerðPts=hÞ   ms : mass flow of steam kg=s   mf : mass flow of fuel kg=s

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CHAPTER 11 Environmental impacts consideration

350

i=1(VHP, 120 bar) Saturation temperature (°C)

210

300

i=2(HP, 50 bar) 250

50 MW i=3(MP, 14 bar)

200

40 MW 150

i=4(LP, 3 bar)

85 MW 100

0

50

100

150

200

Heat load (MW)

FIGURE 11.5 The SUGCC of case study 2 [9]. SUGCC, Site utility grand composite curve.

The extended SUGCC based on combination with a T
S diagram as nominated (ESUGCC) for case study 2 is shown in Fig. 11.6. Table 11.9 indicates the result of shaft power targeting based on different cogeneration targeting approaches and simulation software. HP, High pressure; LP, low pressure; MP, medium pressure; VHP, very high pressure; IBTM, iterative bottom-to-top model; THM, turbine hardware model; SHM, Sorin and Hammache method. Furthermore, the share of Eco-indicator 99 (Y) for each equipment of case study 2 is indicated in Fig. 11.7. Y-SUGCC representation for case study 2 is shown in Fig. 11.8. The footprint values for case study 1 are indicated in Table 11.10. Moreover, the environmental strategy map based on foot print analysis for case study 2 is demonstrated in Fig. 11.9. As indicated, the CF is the lowest values and energy footprint is the highest values. The total area available of the municipality for case study 1 was 3.85E 1 11 m2 and total water resources of the municipality was 390,873.72 m3. As shown in the results of environmental targeting for case study 2, in addition to estimate the shaft work production, fuel consumption and steam used and production in Total Site of cogeneration plant with high accuracy, environmental impacts based on Eco-indicator 99 and foot print analysis are performed consequently. Furthermore, GHGs can be easily predicted by considering the fuel type, fuel consumption, and required constant as mentioned in Section 11.5.

11.8 Case studies

Temperature (°C) 600

1

550 500

2

450 400

Boiler 350

VHP_HP turbine

300

120 bar

250

50 bar

3

HP_MP turbine 200

14 bar MP_LP turbine

4

150

3 bar 100

– 18

–13

–8

–-3

2

Heat load (10MW)

7

Entropy (kJ/kgK)

FIGURE 11.6 ESUGCC of case study 2 [9]. ESUGCC, Extended site utility grand composite curve.

Table 11.9 Cogeneration targeting results for case study 2 [9]. Methodology IBTM THM-Model in STAR SHM Khoshgoftar Maensh et al. STAR simulation Thermoflex simulation

Error (%)

Total (MW)

“VHP
HP” (MW)

“HP
MP” (MW)

“MP
LP” (MW)

210.50 263.00

34.100 14.100

13.490 9.400

12.28 4.700

8.330 0

18.99 20.352

41.430 37.970

18.200 14.740

14.460 13.520

8.770 9.710

20.344


37.973 38.104

14.740 14.797

13.520 13.558

9.710 9.749

HP, High pressure; LP, low pressure; MP, medium pressure; VHP, very high pressure; IBTM, iterative bottom-to-top model; THM, turbine hardware model; SHM, Sorin and Hammache method.

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0.3545 1.088

Boiler 1.1968

YST (VHP–HP) YST (HP–MP) YST (MP–LP)

0.705

FIGURE 11.7 Share of Y(Pts/h) for each component (case study 2) [9].

FIGURE 11.8 Y-SUGCC for case study 2 [9]. SUGCC, Site utility grand composite curve.

Table 11.10 Footprint evaluation for case study 2 [9]. Parameters

Value

Carbon footprint (m2)

1,228,091

Emission footprint (m2) Energy footprint (m2)

26,337,527,415 166,206,621,545

Water footprint (m3)

97,718

References

Emission footprint 50% 40% 30% 20% 10%

Energy footprint

0%

Carbon footprint

Water footprint

FIGURE 11.9 Environmental strategy map for case study 2 [9].

References [1] Banerjee A, Tierney MJE. Comparison of five exergoenvironmental methods applied to candidate energy systems for rural villages in developing countries. Energy 2011;36 (5):2650
61. [2] Meyer L, et al. Exergoenvironmental analysis for evaluation of the environmental impact of energy conversion systems. Energy 2009;34(1):75
89. [3] Cavalcanti EJC. Exergoeconomic and exergoenvironmental analyses of an integrated solar combined cycle system. Renew Sustain Energy Rev 2017;67:507
19. [4] Morosuk T, et al. Advanced exergy-based analyses applied to a system including LNG regasification and electricity generation. Int J Energy Environ Eng 2012;3(1):1. [5] Khoshgoftar Manesh MH, et al. New procedure for optimal design and evaluation of cogeneration system based on advanced exergoeconomic and exergoenvironmental analyses. Energy 2013;59:314
33. [6] Rizk NK, Mongia HC. Semianalytical correlations for NOx, CO, and UHC emissions. J Eng Gas Turbines Power 1993;115(3):612
19. ˇ cek L, Klemeˇs JJ, Kravanja Z. A review of footprint analysis tools for monitoring [7] Cuˇ impacts on sustainability. J Clean Prod 2012;34:9
20. [8] De Benedetto L, Klemeˇs J. The environmental performance strategy map: an integrated LCA approach to support the strategic decision-making process. J Clean Prod 2009;17(10):900
6. [9] Khoshgoftar Manesh MH, et al. New emissions targeting strategy for site utility of process industries. Korean J Chem Eng 2013;30(4):796
812.

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Combined heating, cooling, hydrogen, and power production

12

Chapter outline 12.1 Introduction ...............................................................................................215 12.2 System description .....................................................................................219 12.3 Modeling and analysis ................................................................................222 12.3.1 Assumptions ...........................................................................222 12.3.2 Modeling and analysis .............................................................222 12.4 Validation of model ....................................................................................231 12.4.1 Performance evaluation ...........................................................232 References ..........................................................................................................234

12.1 Introduction The improvement of thermal systems’ efficiency has globally been regarded as great conflict since the problem with these systems is about the preparation and usage of energy sources which is becoming a worry around the world. It could be considered as a solution for improving efficiency of energy systems to merge them in the proper way for polygeneration applications throughout utilizing lowtemperature heating resources, including waste heat released from the industrial activities, geothermal heat, biomass, and solar ponds. The aforementioned multigenerational energy systems are capable of generating multiple products from one or many sorts of initial energy intake, which provide better utilization of first resources eventuating to diminishing loss of energy. Proper combining of basic power cycles, including Kalina cycle (KC), organic Rankine cycle (ORC), absorption power cycle with famous cycles such as vapor condensing refrigeration cycle, ejector refrigeration cycle (ERC), or absorption refrigeration cycle (ARC) for generating cooling heat pump cycles or domestic heater unit system to produce heat, proton-exchange membrane (PEM) electrolyzer for generating hydrogen or even applying various types of desalination technologies intended for producing fresh water seems to be promising and effective in creating a multigeneration energy system. Presenting and testing the new multigeneration system brings about enough effective methods by carrying out various types of first-form energy sources. In order to fulfill this objective, recently conducted studies investigated a varying range of heat sources, and inexhaustible heat resources have been proposed to be Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00012-4 © 2021 Elsevier Inc. All rights reserved.

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the principal and the base of the technologies. Solar energy as a suitable choice for multigeneration systems technology is constantly used for reproducible energy generation. With regard to evacuated solar collectors, a transient simulation of a new polygeneration system consisting of an ARC, a PEM electrolyzer, and Domestic Hot Water (DHW) production has been provided by Calise et al. [1], and the outcome showed that only in prominent public investment the reimbursement periods can be lucrative and cost-effective. A broad study regarding energy, exergy, and exergoeconomic on a new heliostat solar-driven polygeneration system was conducted by El-Emam and Dincer [2], and their outcome was a system with the ability to produce cooling output, heating output, fresh water, and hydrogen based on the 4 MW electricity demand that resulted in generating 1.25 kg/h hydrogen and 90 kg/s fresh water. A study with multiple aims of optimizing the efficiency of a biomass polygeneration system containing an ORC is conducted by Ahmadi et al. [3] to produce electricity, a double-effect ARC to generate cooling, a PEM electrolyzer to make hydrogen, a DHW to provide hot water, and a reverse osmosis (RO) desalination unit to supply fresh water and offered a closed-form equation of the relationship between exergy effectiveness and the total cost rate of the system. Geothermal as an energy form obtained from the heat with the earth could be a promising technology applied in multigeneration systems. More specifically, geothermal as the retained heat within the earth can be a promising technology for multigeneration systems. The geothermal power industry reached a capacity of about 3442 MW by the end of 2013 stated by Geothermal Energy Association (GEA). Moreover, GEA reported the growth of geothermal power industry to be at a constant rate of 4%
5% [4]. Certainly, the geothermal power could be implemented in polygeneration systems as geothermal own unique advantages comprising being friendly to the environment, its high potential capacity, great constancy, partial and low adverse effects, affordable, supply plentitude, etc. [4]. In this regard, Yilmaz et al. proposed a new biomass-assisted integrated plant for polygeneration. This plant was designed to generate useful commodities such as cooling, hydrogen, heating, drying, hot water, and power generations. This polygeneation system consists of the biomass gasifier unit, gas turbine cycle, Kalina cycle, RO unit, PEM electrolyzer, absorption cooling cycle, dryer, and heat pump. Because of these several positive features, many types of research have been conducted to further elaborate this energy form for polygeneration systems. For instance, a multigeneration system based on geothermal was offered by Akrami et al. [5] and consisted of an ORC, a DHW, an ARC, and a PEM electrolyzer to create electricity, heating, cooling, and hydrogen. In their study, they rendered energy, exergy, and exergoeconomic analyses and run an extended parametric study on the proposed system and also implied that the general term and exergy effectiveness could be attained by 34.98% and 49.17%, respectively. In addition, considering the geothermal temperature increase from 185 C to 215 C, the total cost of the highest and lowest was reported to be 23.18 $/GJ and 22.73$/GJ, respectively. A geothermal polygeneration system featuring an

12.1 Introduction

ORC, an AHT, a DHW, and a PEM electrolyzer, proposed by Parham and Assadi [6], was analyzed regarding the first thermodynamic law and performance parameters. They pointed out that enhancing the absorber temperatures led to a reduction of energy utilization factor. Because of the multiple advantages of the mixed implementation of several different sustainable energies, its synchronous application in the polygeneration system has been suggested in recent years. For instance, since solar energy use reaches the minimum level in some days or months in a year, the implementation of some other renewable sources as substitution might be a proper solution for these kinds of limitations. Taking this issue into account, Khalid et al. [7] applied a mix of solar
biomass heat source for a polygeneration system intended for a sustainable community. The suggested polygeneration system of the study was capable of generating power, space heating, cooling, and hot water with thermal and exergy effectiveness of 91% and 34.9%, respectively. The aforementioned study concluded that the suggested hybrid solar
biomass resource method was more efficient and affordable than the polygeneration system with solar or biomass sources alone. In another investigation conducted by Khalid et al. [8], the thermoeconomic analysis of a polygeneration system, including wind turbine, concentrated solar collector, ORC, and ground-source heat pump system for domestic usage, was performed. And their recommended polygeneration system was capable of generating electricity, hot water, heating, and cooling outputs at the same time. In addition, the planned polygeneration system was ameliorated and improved in terms of the total cost with 345,481$, electricity with 0.151$/kWh cost, and thermal and exergy efficacy of 46.1% and 7.3%, respectively. A polygeneration system, with solar
biomass as a hybrid heat resource, was offered by Sahoo et al. [9] and designed to generate commodities such as power, heating, cooling, and fresh water, was composed of an ARC, and ORC, a multieffect desalination system, and a heat exchanger (HE). In addition, a polygeneration system featured with a hybrid sustainable source was offered by Khan and Martin [10] for a rural area in Bangladesh. The system was provided by a PV array and animal and agricultural waste
fed digester while the cooling and lighting were supplied with excessive digester. The waste heat obtained from the process was applied to drive a membrane distillation unit for generating fresh water. They implied that the 0.4 m3 cooling fuel and 2
3 L pure drinking water are provided with a payback period of 3 and 4 years. A novel geothermal
solar polygeneration system intended for generating power, cooling, space heating, hot water, and heat for industrial usage was planned and presented by Al-Ali and Dincer [11]. The thermal efficacy of 16.4% and 78% and the exergy efficiency of 26.2% and 36.6% were recorded for the single GS and polygeneration system, respectively. In addition, it was concluded that 75% of the exergy destruction happens in solar collectors. Regarding thermodynamic- and thermoeconomic-based systems, a novel solar
geothermal polygeneration system producing electricity, heating, cooling, and fresh water for a small community within heating and cooling network of the district was executed

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by Calise et al. [12]. They indicated that the global exergy efficacy was variable from 40% to 50% in thermal recovery mode operation and 16% up to 20% in cooling mode operation. Moreover, the electricity, chilled water, cooling water, and distilled water exergoeconomic costs of the recommended system were varying at 0.1475
0.1722 h/kW h, 0.1863
0.1888 h/kW hex, 0.01612
0.01702 h/kW hex and 0.5695
0.6023 h/kW. A new polygeneration system adjusted for using three inexhaustible energies, including solar, geothermal, and ocean thermal, was developed by Azhar et al. [13] for multiple productions of heating, cooling, power, and fresh water. The recommended system consisted of an MSF desalination system for freshwater production and a direct steam generator was included in the system to generate electrical power. The energy and exergy efficacy of the system was reported to be 13.93% and 17.97%, respectively. Solar collector accounted for main exergy destruction followed by the desalination HE and turbine 2, in the order given. Natural gas liquefaction is applied for transferring natural gas to markets in which liquefied natural gas (LNG) is regasified to be suitable for being carried in the natural gas pipeline. Gas needs to be deposited in especial expensive tanks to be used in commercial activities. It must be stored in the special tanks that are highly expensive to be extensively utilized in commercial applications. Regasification, the process in which LNG is converted to its natural gas form at 2162 C and atmospheric temperature, is usually applied when the gas would be delivered to the consumers. During past decades, several researchers have pointed out the extraction of energy from thermal cycles via low-temperature heat resources such as LNG cold energy as a heat sink. For instance, Kanbur et al. [14] examined a little-sized cogeneration system by the use of cold energy obtained from LNG. Then the results have been made a comparison with the results of the ordinary micro-cogeneration system. The energy, exergy, and exergoecconomic were run in this comparison, and it was shown the product’s levelized and relative costs might be boosted up to 1.7 times and 75% for the cogeneration system in which LNG cold energy would apply, respectively, whereas the exergoeconomic factor showed a decline of up to 268%. A cascade Rankine cycle was implemented by Lee [15] to retrieve and restore the cold energy used in the liquefaction process of LNG that was affected by the HE in the process. In the study, two working fluids of ethane and propane were implemented in the cascade cycle. It was indicated that ethane
propane fluid application would result in higher power production. Concerning the above-discussed literature, there is an essential need to investigate geothermal energy for polygeneration systems to improve the efficacy of such systems. A study conducted by Zheng and Weng in 2009 [16], with the plan of mediating on the ordinary cooling and power cycles, indicated that the performance of the classic system could be optimized by extracting a direct heat form the system for heating applications and throughout installing a PEM electrolyzer and making use of the waste heat produced from the geothermal source for hydrogen energy

12.2 System description

demands. The extracting demands led the study to enlarge the mentioned conventional system in large-scale investigation in the most advanced monolithic power plans. First of all, a comprehensive thermodynamic and thermoeconomic appraisal of the developed system was exhibited, and the obtained results were further explained from the positive points. Moreover, the undiscovered system would be compared with some of the other similar systems in terms of efficiency and cost to shed light on the advantages and disadvantages of the proposed system for heat supplying and LNG as a heat sink. Taking the aforementioned studies into consideration and reviewing other similar works on this area, the system of Ebadollahi et al. [17] has more interesting due to consisting of an ORC, an ERC, and a PEM electrolyzer system and is driven by geothermal energy. Also, different analyses have been performed for the suggested system. In this chapter the suggested polygeneration system consists of an ORC, an ERC, and a PEM electrolyzer system and is driven by geothermal energy proposed by Ebadollahi et al. [17] are investigated.

12.2 System description As can be seen in Fig. 12.1, a schematic flow diagram of the recommended polygeneration system by Ebadollahi et al. [17] is demonstrated. The system runs via geothermal energy as a heat source and LNG acts as a heat sink, and it is composed of an ORC, an ERC, and a PEM electrolyzer system for producing multiple productions of cooling, heating, power, and hydrogen. As shown in Fig. 12.1, schematic diagram of the presented modern geothermal-based polygeneration system consuming LNG cold energy recovery is depicted. A combined cooling, heating, and power cycle based on the ORC and ERC is also demonstrated in Fig. 12.1. The investigated new system spends geothermal energy as a heat source and LNG cold energy as a heat reducer. By applying the geothermal energy, the saturated vapor leaves out the generator at state 2. The vapor at this stage enters turbine 1 to generate power within a certain amount of isentropic efficiency. Then, this expanded vapor (state 3) is separated into two streams before entering into the ejector. At stage 4, one of the streams entering the heater (state 4) produces a specific amount of heating capacity for heat consumers, and then coming to phase 5, it enters the regenerator (state 5). The other vapor stream coming into the ejector as the main flow (state 6) draws the lower pressure vapor from the evaporator (secondary fluid at state 17) into the ejector. It is essential to create a lower pressure in the nozzle at the entrance of the ejector in an isentropic process. Later, both primary and secondary fluids are combined inside the ejector (the ejector process is described in Section 12.3.2.1

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FIGURE 12.1 Schematic diagram of the geothermal-based polygeneration system using LNG cold energy recovery [17]. LNG, Liquefied natural gas.

in detail). For the next step the mixed flow gets in the condenser 1 (state 9). The condenser 1 start condensing the superheated vapor into a double-stream form, applying LNG, released regasified energy (state 10) is divided into two currents, the saturated vapor (state 11) and the saturated liquid stream (state 15). Theoretically, the input and output pressures of the stream divider are constant and the same. The drenched vapor is concentrated via the condenser 2 in the state 12. Then, using condenser 2, the generated heat is rejected for the regasification process of the LNG power generation subsystem. Producing more power in LNG turbine via cold energy generated during LNG regasification in condenser 2 will result in system performance improvement. Then the condensed liquid is pumped into the generator via pump 2 (state 13) and is combined with the output current

12.2 System description

of the heater. The blended flow (state 14) is then pumped back into the generator through pump 1 (state 1) to blend heating and power (CHP) subcycle operation. Meantime, the condensed liquid (state 15) and the saturated liquid (state 15) are throttled through the expansion valve to a lower pressure (state 16) and then get into the evaporator to generate cooling capacity through absorbing heat from its surrounding cold room. In the end the condensed vapor (state17) enters into the ejector as a subsidiary current to finish the refrigeration subcycle process. The potential heat of the condenser 2, which exists in the LNG power generation subcycle, is applied as a heat resource. The LNG is extracted from the LNG reservoirs at a very low temperature of 2161.7 C (state 18), and then it is pumped into the condenser 2 pressure via pump 3 (state 19). The heat of the condensed vapor is recovered by LNG in the process of condensation and then becomes a gasified as condensed natural gas (state 20). Later, the LNG turbine expands the natural gas to generate power output (state 21). Later, as a next step, the regasified LNG is heated in the condenser 1 and brought to the gas supplying system for city consumers (22). As demonstrated on the right side of Fig. 12.1, the PEM electrolyzer outline for H2 can be seen. PEM, as the electrolysis of water in a cell, is equipped with a solid polymer electrolyte responsible for protons conduction, gas product separation, and electrical insulation of the electrodes. The PEM electrolyzer is designed to solve the problem with the incomplete load, low flow, hydrogen density, and low-pressure operation that obstacle alkaline electrolyzers. PEM electrolysis is a crucial and helpful technology that generates hydrogen that is applied as an energy transmitter. Providing fast dynamic response times, large operational ranges, high efficiencies, and very high gas purities are some of the positive features of the PEM electrolyzer. As shown in Fig. 12.1, turbine 2 and geothermal waste heat from the generator both are responsible for requiring the needed electricity and heat for operating the electrochemical reactions of the PEM electrolyzer. The rest of geothermal energy, after being consumed by the generator, is also reused by a HE to increase water temperature before the PEM electrolyzer. The cooled stream (state 24) gets into the HE and then cooled down to state 28 by supplying excessive heat necessary for a PEM electrolyzer. Concurrently, the liquid water enters the HE (state 27) and delivers its temperature to that of the PEM electrolyzer and then enters the PEM electrolyzer (state 29). The hydrogen consuming a great amount of the power generated by the turbine 2 leaves the cathodes by creating dissipate heat to the environment and then cools down to the surrounding temperature (state 31). The produced oxygen gas at the anode on the other hand is extracted from the blend of water and oxygen. Next, it cools down to the reference environment temperature (state 30). Remained hot water is circulated toward the water supply current for the following hydrogen production cycle. The overall PEM electrolysis reaction is breaking down the water into its component hydrogen and oxygen via applying supplied

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electricity, heat, and water. As a result, the hydrogen can be deposited in the reservoir for the next applications.

12.3 Modeling and analysis In this section the recommended polygeneration system is simulated from thermodynamic and thermoeconomic point of view. First of all, thermodynamic assumptions considered in this investigation are presented, and afterward, the mathematical modeling of the ejector and PEM electrolyzer is thoroughly introduced and discussed. Then, the concepts of the thermodynamic and exert economic analysis are illustrated for the considered polygeneration system. The general performance criteria of the considered polygeneration system are formed according to the thermodynamic and exergoeconomic viewpoints.

12.3.1 Assumptions The following assumptions are used for simulation of the considered polygeneration system:

• All thermodynamic processes operate at constant status. • Heat carrier and pressure drops across all constituents are neglected. • The refrigerant leaving the condenser 2 (state 12), evaporator (state 17), • • •

generator (state 2), separator (states 11 and 15), and heater (state 5) is saturated, while a two-phase state for the condenser 1 output (state 10) is assumed. The flow across the expansion valve is assumed to be isenthalpic (h15 5 h16). Geothermal water enters at the temperature of 443K and a mass flow rate of 100 kg/s. Turbines and pumps running with specific values of isentropic efficiencies.

Instead of taking in to account the abovementioned assumptions, some other thermodynamic parameters need to be considered for mathematically evaluating the performance of the presented polygeneration system. These input parameters are listed in Table 12.1. Table 12.1 shows some required input parameters for simulating the case study. PEM, Proton-exchange membrane.

12.3.2 Modeling and analysis The thermodynamic analysis of the presented polygeneration system can be conducted based on the aforementioned presumptions. The subsection next is on the energy and exergy analysis of the considered system based on the thermodynamic performance. The mathematical modeling of the ejector and PEM electrolyzer is presented before the aforementioned analysis.

12.3 Modeling and analysis

Table 12.1 Input data for thermodynamic simulation of the considered polygeneration system [17]. Parameter

Value

Ambient temperature, T0 (K) Ambient pressure, P0 (bar) The inlet temperature of geothermal, TGth (K) Generator terminal temperature difference, TTDg (K) Condensers terminal temperature difference, TTDc (K) Heater terminal temperature difference, TTDh (K) Evaporator temperature, Te (K) Geothermal mass flow rate mGth (kg/s) Cold room temperature, TCR (K) Isentropic efficiency of turbine, ηis, (%) Turbine 1 expansion ratio, TER1 Turbine 2 expansion ratio, TER2 Pumps isentropic efficiency, ηis (%) Ejector’s nozzle efficiency (%) Ejector’s mixer efficiency (%) Ejector’s diffuser efficiency (%) Inlet water temperature, Tw, (K) Oxygen pressure, PO2 (bar) Hydrogen pressure, PH2 (bar) Electrolyzer temperature, TElec (K) Anode activation energy, Eact,a (kJ/mol) Cathode activation energy, Eact,c (kJ/mol) Membrane surface water of anode, λa (Ω21) Membrane surface water of cathode, λc (Ω21) Membrane thickness, D (μm) 2 Anode preexponential factor, Jref a ðA=m Þ ref Cathode preexponential factor, Jc ðA=m2 Þ Faraday constant, F (C/mol) PEM efficiency, ηPEM (%)

293 1.01 443 8 3 3 278 100 Te 1 8 90 6 9 95 85 90 85 293 1.01 1.01 340 76 18 14 10 100 1.7 3 105 4.6 3 103 96,486 85

PEM, Proton-exchange membrane.

12.3.2.1 Ejector modeling The ejector mathematical modeling being applied in thermodynamic analysis of the new proposed polygeneration system is presented in detail. To make a simple analysis of the ejector, some of the considered assumptions are as follows:

• Flow inside the ejector is one-dimensional. • The internal chamber of the ejector is adiabatic.

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• The motive and suction streams reach the same pressure at the entrance of the • •

constant area mixing section of the ejector, and no mixing takes place before the mixing section. The kinetic energy of primary and secondary streams at the input and output of the ejector is overlooked. Effects of viscosity and mixing losses are taken into account as the nozzle, mixer, and diffuser isentropic efficiencies of 90%, 85%, and 85%, respectively.

The early current velocity in the nozzle (upf ;n ) can be neglected. In this case the speed of the output flow of the nozzle can be demonstrated as follows: upf ;n 5

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2ηn ðhpf ;n 2 hpf ;n;s Þ

(12.1)

where hpf ;n;s is the primary flow ideal enthalpy under isentropic expansion condition. The ejector mass entrainment ratio (μ) is expressed as the mass flow rate of the secondary flow (m_ sf ) to the primary flow (m_ pf ): μ5

m_ sf m_ pf

(12.2)

Momentum conservative equation applied in the mixer may be expressed as the following equation: m_ pf upf ;n 1 m_ sf usf ;n 5 ðm_ pf 1 m_ sf Þumf ;m;s

(12.3)

The secondary flow velocity (usf ;n ) can be overlooked I compared to the main flow velocity (upf ;n ) at the nozzle. Therefore the speed of the ideal mixed stream (umf ;m;s ) can be determined from the following relation: umf ;m;s 5

upf ;n 11μ

(12.4)

Meantime, the mixer isentropic efficiency can be expressed as follows: ηm 5

u2 mf ;m u2 mf ;m;s

(12.5)

Thus the real fleetness of the mixed flow can be mentioned as follows: umf ;m 5

pffiffiffiffiffiffi upf ;n ηm 11μ

(12.6)

Energy balance equation for the mixer is indicated as follows:         u2 pf ;n u2 sf ;n u2 mf ;m m_ pf hpf ;n 1 1 m_ sf hsf ;n 1 5 m_ pf 1 m_ sf hmf ;m 1 2 2 2

(12.7)

The enthalpy of mixed flow can be computed from the following equation: hmf ;m 5

hpf ;n 1 μhsf u2 mf ;m 2 11μ 2

(12.8)

12.3 Modeling and analysis

The kinetic energy of the mixed stream in the mixing chamber is modified into pressure energy. Overleaping the speed of the combined flow at the output of the diffuser and applying diffuser-isentropic efficacy, the real enthalpy of the mixed flow (stream) can be obtained in term of the following equation: hmf ;d 5 hmf ;m 1

hmf ;d;s 2 hmf ;m ηd

(12.9)

where hmf ;d;s is the optimal enthalpy of the combined stream in the isentropic expansion condition, and ηd is the isentropic efficiency of the nozzle. According to the aforementioned relations, the ratio of the ejector mass entrainment can be finally declared as follows: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi ηn ηm ηd hpf ;n 2 hpf ;n;s 21 μ5 hmf ;d;s 2 hmf ;m

(12.10)

As can be seen, the schematic of ejector and pressure losses along with the velocity profile throughout the ejector is demonstrated in Fig. 12.2.

12.3.2.2 Proton-exchange membrane electrolyzer The thermodynamic analysis of the PEM electrolyzer system can be carried out by electrochemical modeling as follows: ΔH 5 ΔG 1 TΔS

(12.11)

where ΔG and TΔS are the Gibbs free energy and required thermal energy, respectively. The hydrogen molar rate can be expressed as the following equation: NH2 ;out 5

J 5 NH2 O;out 2F

(12.12)

where J is the current density in A/m2, F stands for the Faraday constant in C/mol, NH2 O;out is the rate of the molar mass stream for the water entering the PEM electrolyzer in mol/s, and NH2 ;out is calculated to equal to the molar mass flow rate of hydrogen in mol/s. The energy delivered to the PEM electrolyzer can be defined as follows: E_ electric 5 JV

(12.13)

where EElec is the delivered energy to the PEM electrolyzer system, and V is the necessary electric potential that is attained according to the following equation: V 5 V0 1 Vact0 a 1 Vact0 c 1 Vohm

(12.14)

Using the Nernst equation, V0 (the reversible potential) can be indicated as follows: V0 5 1:229 2 8:5 3 1024 ðTPEM 2 298Þ

(12.15)

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FIGURE 12.2 Schematic diagram of the ejector.

where Vact0 c is the cathode activation overpotential, Vact;a is the anode activation over potential, and Vohm is ohmic overpotential. The local ionic conductivity of the PEM electrolyzer (x) is attained from the following relation [18]:    1 1 σ½λðxÞ 5 ½0:5139λðxÞ 2 0:326exp 1268 2 303 T

(12.16)

In Eq. (12.16), x indicates the calculated depth of the membrane from the surface of the cathode membrane. λ(x) is the membrane surface water and can be implied in terms of the membrane layer width (D), anode membrane surface water (λa), and cathode membrane surface water (λc) as follows: λðxÞ 5

λa 2 λc x 1 λc L

(12.17)

12.3 Modeling and analysis

Moreover, the overall resistance of the system (RPEM) is computed via the following equation [18]: RPEM 5

ðL 0

dx σ½λðxÞ

(12.18)

The ohmic overpotential can be identified from the ohm law as follows [36]: Vohm 5 JRPEM

(12.19)

In addition, the activation of the overpotential can be numerated from following elation [18]:   RT J 21 sinh Vact;i 5 F 2J0;i

i 5 a; c

(12.20)

where J0,i, is the exchange current denseness that is defined as follows [18]:   Eact;i i 5 a; c J0;i 5 Jiref exp 2 RT

(12.21)

where Jiref is the preexponential element, and Eact;i is the activation energy. The amount of the heat delivered to the water in the HE of the PEM electrolyzer (HE) is formulated as follows:   Q_ H2 O 5 m_ H2 O hout;HE 2 hin;HE 5 m_ H2 O q_Q;PEM

(12.22)

where m_ H2 O is the mass stream rate of water, and q_Q;PEM is the certain heat delivered to the heater. In the same way the PEM electrolyzer thermal efficacy is calculated by the following equation: ηth;PEM 5

LHVH2 3 N_ H2 ;out E_ electric 1 Q_ Q;PEM

(12.23)

12.3.2.3 Energy and exergy analysis For the other components the energy balance equations are indicated in Table 12.2. In addition, exergy balance equation is determined in Table 12.2. HE, Heat exchanger; PEM, proton-exchange membrane.

12.3.2.4 Exergoeconomic modeling Table 12.3 shows the cost balance equation, auxiliary equations, and component cost equation for each component. HE, Heat exchanger; PEM, proton-exchange membrane. For HEs the overall heat transfer coefficients are indicated in Table 12.4. The overall heat transfer coefficient (Uk) is calculated based on the following equation: Uk 5

Q_ k Ak ΔTLMTD;k

(12.24)

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Table 12.2 Energy and exergy balance equations. Component Generator Condenser 1 Condenser 2 Evaporator Heater HE PEM electrolyzer Pump 1 Pump 2 Pump 3 Turbine 1 Turbine 2 Expansion valve Regenerator Separator Ejector

Energy balance equation

_g 5m _ 1 ðh2 2 h1 Þ Q _ _ 1 ðh23 2 h24 Þ Qg 5 m _ c1 5 m _ 9 ðh9 2 h10 Þ Q _ c1 5 m _ 21 ðh22 2 h21 Þ Q _ c2 5 m _ 11 ðh11 2 h12 Þ Q _ c2 5 m _ 19 ðh20 2 h19 Þ Q _e 5m _ 17 ðh17 2 h16 Þ Q

_h 5m _ 4 ðh4 2 h5 Þ Q _h 5m _ 26 ðh26 2 h25 Þ Q _ HE 5 m _ 24 ðh24 2 h28 Þ Q _ HE 5 m _ 29 ðh29 2 h27 Þ Q See Section 12.3.2.2

: _ _ P1 ¼ m1 ðh1 2 h14 Þ W ηis;P1 5 hh1s1 22hh1414 : _ _ P1 ¼ m12 ðh13 2 h12 Þ W 2 h12 ηis;P2 5 hh13s 2h : _ 13 12 _ W P3 ¼ m18 ðh19 2 h18 Þ 2 h18 ηis;P3 5 hh19s 2h : _ 19 18 _ W T1 ¼ m2 ðh2 2 h3 Þ ηis;T1 5 hh22 22hh3s3 : _ _ T2 ¼ m20 ðh20 2 h21 Þ W 21 ηis;T2 5 hh2020 22hh21s h16 5 h15 _ 14 h14 5 m _ 5 h5 1 m _ 13 h13 m _ 14 5 m _ 5 1m _ 13 m _ 10 h10 5 m _ 11 h11 1 m _ 15 h15 m _ 10 5 m _ 11 1 m _ 15 m See ejector modeling subsection

Exergy balance equation     _ 2 2 Ex _ 23 2 Ex _ 24 2 Ex _ 1 _ g 5 Ex Ex D     _ 9 2 Ex _ 21 2 Ex _ 22 2 Ex _ 10 _ c1 5 Ex Ex D     _ 11 2 Ex _ 19 2 Ex _ 20 2 Ex _ 12 _ c2 5 Ex Ex D     _ e T0 2 1 _ 17 2 Ex _ 16 2 Q _ e 5 Ex Ex D TCR     _ 26 2 Ex _ 4 2 Ex _ 5 2 Ex _ 25 _ h 5 Ex Ex D     _ 29 2 Ex _ 24 2 Ex _ 28 2 Ex _ 27 _ HE 5 Ex Ex D   _ PEM2Elec 2 Ex _ 30 1 Ex _ 31 2 Ex _ 29 _ PEM2Elec 5 W Ex D   _ P1 2 Ex _ 1 2 Ex _ 14 _ P1 5 W Ex D   _ P2 5 W _ P2 2 Ex _ 13 2 Ex _ 12 Ex D   _ P3 2 Ex _ 19 2 Ex _ 18 _ P3 5 W Ex D   _ T1 _ 2 2 Ex _ 3 2W _ T1 5 Ex Ex D   _ T2 _ 20 2 Ex _ 21 2 W _ T2 5 Ex Ex D _ EV 5 Ex _ 15 2 Ex _ 16 Ex D   _ 14 2 Ex _ 5 1 Ex _ 13 _ reg 5 Ex Ex D   _ 10 2 Ex _ 11 1 Ex _ 15 _ sep 5 Ex Ex D   _ 9 _ 17 1 Ex _ 6 2 Ex _ ej 5 Ex Ex D

HE, Heat exchanger; PEM, proton-exchange membrane.

12.3.2.5 Overall performance evaluation To evaluate an overall performance analysis, the criteria for performance evaluation are developed based on the first and second laws of thermodynamic and economic viewpoints as shown in Table 12.5. SUCP, Sum unit cost of the product.

Table 12.3 Exergoeconomic balance and component cost equations. Auxiliary equations

Component

Cost balance equations

Generator

_2 5C _ 23 1 C _ 1 1 Z_ g _ 24 1 C C

Condenser 1

_ 10 5 C _ 21 1 C _ 9 1 Z_ c1 _ 22 1 C C

c23 5 c24 c23 5 15:24$=GJ c9 5 c10

Condenser 2

_ 20 5 C _ 19 1 C _ 11 1 Z_ c2 _ 12 1 C C

c11 5 c12

Evaporator

_ q:e 5 C _ 16 1 Z_ e _ 17 1 C C

c16 5 c17

Heater

_5 5C _ 25 1 C _ 4 1 Z_ h _ 26 1 C C

HE

_ 29 5 C _ 27 1 C _ 24 1 Z_ HE _ 28 1 C C

PEM electrolyzer Pump 1

_ 31 5 C _ 29 1 C _ PEM2Elec 1 Z_ PEM2Elec _ 30 1 C C

c4 5 c5 c25 5 0 c24 5 c28 c27 5 0 cPEM2Elec 5 cw:T2

_ 14 1 C _ w;p1 1 Z_ p1 _1 5C C

cw;p1 5 cw;T1

Pump 2

_ 12 1 C _ w;p2 1 Z_ p2 _ 13 5 C C

cw;p2 5 cw;T1

Pump 3

_ 18 1 C _ w;p2 1 Z_ p2 _ 19 5 C C

cw;p3 5 cw;T2 c18 5 6:98$=GJ

Turbine 1

_ w;T1 5 C _ 2 1 Z_ T1 _3 1C C

c2 5 c3

Turbine 2

_ w;T2 5 C _ 20 1 Z_ T2 _ 21 1 C C

c21 5 c20

Component cost equations

0:78 Ag Zg 5 130 3 0:093  Ac1 0:78 0:093  Ac2 0:78 Zc2 5 130 3 0:093  Ae 0:78 Ze 5 130 3 0:093  A 0:78 h Zh 5 130 3 0:093 Zc1 5 130 3



ZHE 5 130 3

A

HE

0:78

0:093

_ PEM2Elec ZPEM2Elec 5 1000 3 W  0:26

0:5 _ 12ηis:P1 ZP1 5 2100 3 W10P1 η is:P1



_ ZP2 5 2100 3 W10P2

0:26

12ηis:P2 ηis:P2

0:5

 0:26

0:5 _ 12ηis:P3 ZP3 5 2100 3 W10P3 η is:P3

_ 0:7 1 1 ZT1 5 3880 3 W T1 _ ZT2 5 3880 3 W T2 1 1 0:7

 

0:05 12ηis:T1 0:05 12ηis:T2

3 ! 3 !

 

1 1 5 3 eðT2 2866Þ



1 1 5 3 eðT20 2866Þ



(Continued)

Table 12.3 Exergoeconomic balance and component cost equations. Continued Component

Cost balance equations

Auxiliary equations

Component cost equations

Expansion valve Regenerator

_ 15 1 Z_ EV _ 16 5 C C




_ 15 ZEV 5 114:5m




_ 14 0:67 Zreg 5 280:3m

Separator

_5 1C _ 13 1 Z_ reg _ 14 5 C C _ 15 5 C _ 10 1 Z_ sep _ 11 1 C C




_ 10 0:67 Zreg 5 114:5m

Ejector

_6 1C _ 17 1 Z_ ej _9 5C C




Division point

_4 1C _6 _3 5C C

_6 _ C 5 C4_ Ex_6 Ex 4

Zej 5 1:5 3 0:45 3 20 3


0:05 T6 P6

3 ðP9 Þ20:75

12.4 Validation of model

Table 12.4 The overall heat transfer coefficients for heat exchangers. Component

UðkW m2 KÞ

Generator Evaporator Condenser Recovery heat exchanger

1.6 0.9 1.1 1

Table 12.5 The overall heat transfer coefficients for heat exchangers. Parameter Overall energy efficiency Overall input heat Overall net output power Overall exergy efficiency Cost of cooling Cost heating Cost of power Cost of hydrogen SUCP

Equation _e 1Q _h _ H ;out 1 W _ net 1 Q LHVH2 3 N 2 _ Qin _g 1Q _ HE _ in 5 Q Q   _ T1 1 1 2 η _ _ _ _ net 5 W _ W th;PEM W T2 2 W P1 2 W P2 2 W P3   _ T =T 2 1 1 Ex _ 25 1 Ex _ net 1 Q _ _ 31 2 Ex W  e ðð 0 CR Þ Þ  26  ηex 5 _ 28 1 Ex _ 22 _ 23 2 Ex _ 18 2 Ex Ex ηth 5

_ q;e C ccooling 5 cQe 5 _ Qe ððT0 =TCR Þ 2 1Þ _ cHeating 5 c26 5 _C26 Ex26 _ C cW;net 5 _W;net W net _ chydrogen 5 c31 5 _C31 Ex31 _ W;net 1 C _ Q;e 1 C _ 26 1 C _ 31 C SUCPsys 5 _ _ e ððT0 =TCR Þ 2 1Þ 1 Ex _ 31 _ 26 1 Ex W net 1 Q

12.4 Validation of model To confirm the outcomes acquired from the thermodynamic simulation for the considered polygeneration system, two main parts as ERC and PEM electrolyzer systems in which the veracity of each validation is demonstrated in the discussion next. The results of the ERC simulation of the first study are presented in Table 12.6 and are compared with those of experiments. For this simulation, motive vapor and suction pressures are set at 0.77 and 0.2 MPa, in the order stated. Also, the saturation temperatures of motive vapor and the evaporator are fixed at 55 C and 7 C, in the order given. In addition, the motive vapor and suction flows are highly heated by 8 C and 6.5 C, respectively. Based on these input parameters, the result of the considered mode on ERC [17] seems to be congruent ´ with those of Smierciew et al.

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Table 12.6 Ejector refrigeration cycle model validation between the model ´ discussed here [17] and Smierciew et al. [19]. Parameter

Considered model [17]

Ref. [19]

Relative error (%)

Generator duty, Qg (kW) Cooling output, Qe (kW) Condenser duty, Qc (kW) Pump power, Wp (kW) Mass entrainment ratio, μ Coefficient of performance, COP

8.713 1.706 9.826 0.01701 0.2422 0.1954

9 1.75 11.28 0.02 0.24 0.19

3.18 2.51 12.89 17.64 0.91 2.84

Table 12.7 Proton-exchange membrane model validation between the model discussed here [17] and Ahmadi et al. [20]. Parameter

Considered model [17]

Ref. [20]

Relative error (%)

Electrolyzer temperature (K) Water primary temperature (K) Water pressure (bar) Net output power (MW) First-law efficiency (%) Exergy efficiency (%) Electrolyzer exergy efficiency (%) Hydrogen production rate (kg/h)

353 298 1.01 0.101 3.75 23.1 57.15 1.197

353 298 1.01 0.10196 3.6 22.7 56.34 1.2

0 0 0 0.5 4 1.7 1.4 0.2

The second part of the confirmatory model is conducted on a PEM electrolyzer. In the mathematical part the working temperature of the electrolyzer is set to be at 80 C, and oxygen and hydrogen pressures are fixed at 1 atm. As Table 12.7 shows the computed errors for different parameters fall in an acceptable range from 0.2% to 4% showing that the result of the current study is appropriately in concordance with those of Ahmadi et al. [20].

12.4.1 Performance evaluation To appraise the advantages and disadvantages of the offered integrated system, a comprehensive comparative study between the result of the present and those of Ahmadi et al. [20], Akrami et al. [21], and Rostamzadeh et al. [22] was conducted. In the following the results of this comparison are pinpointed in terms of energy, exergy, and economics. In the first scenario a novel polygeneration system run by industrial waste heat that was proposed by Ahmadi et al. [20] is considered, and its results were compared with those of the present study (Table 12.8). In this regard, six of the most important parameters, including net output power, heating capacity, cooling capacity,

12.4 Validation of model

Table 12.8 An evaluation between the results of the considered study and Ahmadi et al. [20]. Parameters

Unit

Consider study [17]

Ahmadi et al. [20]

Net power output Cooling demand Heating demand Mass flow rate of Hydrogen Energy efficiency Exergy efficiency

Wnet (kW) Qcooling (kW) Qheating (kW) mhydrogen (kg/h) ηth (%) ηex (%)

180.5 133.9 186.23 1.51 37.38 40.86

116.5 20.9 811.1 0.61 24.1 55.1

Table 12.9 An evaluation between the results obtained by considered study and Akrami et al. [21]. Parameters

Unit

Considered study [17]

Akrami et al. [21]

Hot water mass flow rate Net power output Cooling capacity Hot water mass flow rate

mhydrogen (kg/h) Wnet (kW) Qcooling (kW)   mHw kg=s

2.078 923.6 1276 3.14

0.18 816.7 1896 7.06

Energy efficiency Exergy efficiency

ηth (%) ηex (%)

40.77 35.78

33.92 43.59

hydrogen mass flow rate, thermal efficiency, and exergy efficiency, were reported for both cases. Under the same input parameter, it was determined that the presented system in the present study is capable of producing more power, cooling capacity, and hydrogen than that of Ahmadi et al. It has also produced lower heating capacity since Ahmadi et al. have applied domestically water heater. From a thermodynamic point of view, the currently considered system has higher thermal efficiency (around 55%), compared to the one presented by Ahmadi et al. with lower exergy efficiency of around 25%. Thus the current polygeneration system outperforms that of Ahmadi et al. from the first law of thermodynamic vantage point. The low amount of exergy efficiency can be explained by the fact that in contrast to the study of Ahmadi et al., the present polygeneration system that is proposed in this chapter applies cold energy recovery of the regasification process, where a high value of exergy destruction occurs in the first HE recovery due to the heat transfer relations. In the second scenario, another novel polygeneration system driven by geothermal heat proposed in a study conducted by Akrami et al. [21] is considered, and its results are compared with those of the present study (Table 12.9). Six important parameters, including net output power, hot water mass flow rate, cooling capacity, hydrogen mass flow rate, thermal efficiency, and exergy efficiency, are reported for both cases.

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Table 12.10 An evaluation between the results obtained by considered study and Rostamzadeh et al. [22]. Parameters

Unit

Considered study [17]

Rostamzadeh et al. [22]

Net power output Cooling capacity Thermal efficiency Exergy efficiency SUCP of the system

Wnet (kW) Qcooling (kW) ηth (%) ηex (%) ($/GJ)

68.65 11.91 40.33 37.76 397.3

53.44 17.85 34.05 33.59 362.5

The presented system can produce more power and hydrogen than that of Akrami et al. by producing a lower mass flow rate of hot water and cooling capacity. From a thermodynamic point of view the currently proposed system has higher thermal efficiency than that of presented in the study of Akrami et al. with thermal efficacy of around 20%, and has lower exergy efficiency (around 18%). Thus regarding the first law of thermodynamic vantage point, the current polygeneration system outperforms that of Akrami et al. The low amount of exergy efficiency can be explained by the fact that in contrast to the study of Akrami et al., the present polygeneration system proposed in this chapter uses cold energy recovery of regasification process, where a high value of exergy destruction occurs in the first HE recovery based on heat transfer relations. In the third scenario, another novel integrated system driven by a low-temperature heat, which was proposed by Rostamzadeh et al. [22], is considered and its results are compared with those of the present study (Table 12.10). Five key parameters, including net output power, cooling capacity, thermal efficiency, exergy efficiency, and SUCP of the system, are reported for both cases. Under the same input parameter, it can be said that the current presented system can produce more power than that of Rostamzadeh et al, although the presented system in the current study has produced lower cooling capacity. From thermodynamic point of view, the currently proposed system has higher thermal and exergy efficiencies than those of the system presented by Rostamzadeh et al. by 18.44% and12.41%, respectively. Thus the current polygeneration system outperforms that of Rostamzadeh et al. from the first and the second laws of thermodynamic vantage point. From an economic standpoint the results indicated that the present polygeneration system has excelled the system presented by Rostamzadeh et al. since two surplus commodities of heating and hydrogen are installed in the present system and make the system much more efficient. SUCP, Sum unit cost of the product.

References [1] Calise F, Ferruzzi G, Vanoli L. Transient simulation of polygeneration systems based on PEM fuel cells and solar heating and cooling technologies. Energy 2012;41(1):18
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66. Available from: https://doi.org/10.1016/j.energy.2013.04.024. [4] Yilmaz F, Ozturk M, Selbas R. Development and techno-economic assessment of a new biomass-assisted integrated plant for multigeneration. Energy Convers Manage 2019;202:112154. [5] Akrami E, Chitsaz A, Nami H, Mahmoudi SMS. Energetic and exergoeconomic assessment of a multi-generation energy system based on indirect use of geothermal energy. Energy 2017;124:625
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82. [7] Khalid F, Dincer I, Rosen MA. Thermoeconomic analysis of a solar-biomass integrated multigeneration system for a community. Appl. Therm. Eng. 2017;120:645
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geothermal system. Appl Therm Eng 2014;71(1):16
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geothermal polygeneration system producing energy and water. Energy Convers Manage 2016;115:200
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´ [19] Smierciew K, Gagan J, Butrymowicz D, Karwacki J. Experimental investigations of solar driven ejector air-conditioning system. Energy Build 2014;80:260
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CHAPTER

Modern polygeneration systems

13

Chapter Outline 13.1 Introduction ...............................................................................................237 13.1.1 Fuel cell .................................................................................238 13.1.2 Solar energy ...........................................................................238 13.2 Fuell cell integration ..................................................................................239 13.2.1 Fuel cell 1 thermoelectric generator ..........................................239 13.2.2 Fuel cell 1 heat pump/refrigeration ...........................................245 13.3 Fuel cell 1 absorption chillers .....................................................................246 13.3.1 Fuel cell—desalination systems ................................................247 13.3.2 Microbial cell integration .........................................................248 13.4 Solar energy ..............................................................................................250 13.4.1 General overview .....................................................................250 13.4.2 Polygeneration with solar energy ...............................................254 13.4.3 Photovoltaic/thermal/CPVT collector driven systems ..................264 13.5 Hybrid solar polygeneration systems ............................................................267 13.5.1 Integrated solar biomass-driven devices ...................................267 13.5.2 Hybrid solar geothermal .........................................................271 13.5.3 Hybrid photovoltaic/thermal ocean ..........................................277 13.5.4 Hybrid solar power tower wind turbines ....................................277 13.5.5 Hybrid solar wind/ocean .........................................................277 13.5.6 Other hybrid models ................................................................282 References ..........................................................................................................282

13.1 Introduction Various products can be manipulated by systems having multiple generations based on their functions and requirements. The modern polygeneration systems are classified based on using fuel cells (FCs) and solar energy with a combination of other components to generate multiple products.

Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00013-6 © 2021 Elsevier Inc. All rights reserved.

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13.1.1 Fuel cell FC technologies are a promising method in point of view of high performance and environment-friendly. FCs operate with the electrochemical reaction associated with hydrogen and oxygen to generate water and electricity. The various types of FCs consist of alkaline FCs, proton exchange membrane (PEM) FCs (PEMFCs), solid oxide FCs (SOFC), molten carbonate FCs (MCFCs), etc. The operating temperature range and material composition are the key parameters for the type of FCs. Due to chemical reactions in the FCs, there are fewer moving parts so are noiseless. As FCs do not release pollutant emission, it is a major advantage of using FCs. Accordingly, FCs are significantly developing and being used by various applications.

13.1.2 Solar energy We can consume solar power as the primary source of energy or as a hybrid with other exhaustible or sustainable sources of energy to operate these systems. Systems with multiple generations operating on solar energy are interesting because of solar power and associated technological items being broadly available. This chapter aims to thoroughly analyze, classify, and research various outlines, advantages, capabilities, hardships, the direction of future research, and market perspectives for systems having multiple generations operating on solar energy. In addition, the current study analyzes the way systems of solar energy can be utilized to make hybrid systems to have multiple generations. Despite utilizing backup sources of fossil fuels, such systems are considered (1) operating solely with solar energy and (2) operating with solar energy in a hybrid, where solar energy is consumed with other inexhaustible sources. According to the literature review, various methods were used for creating systems with multiple generations operating on solar energy by putting various cycles and devices together. These systems include many advantages, for example, increasing the effectiveness and decreasing the total and operational expenses, and the release of carbon dioxide. Pairing solar energy with other inexhaustible power resources allows for operating constantly and managing dispatchable potentials. It is indicated in this review that to outline the choice and modeling, we should consider various thermodynamics and aspects related to the economy and environment. There is a need for additional studies among multiple disciplines from varying standpoints of these systems to improve the development of systems with multiple generations operating on solar energy. Researches should concentrate, especially, on creating several sample models and carrying out trial evaluations. Moreover, new reasons and deals for buying the products continuously must be determined. We can utilize solar energy as the most important source of power or in a hybrid with other sources of energy that are either exhaustible or sustainable, such as fuels coming from fossils, to operate a system of multiple generations. Systems

13.2 Fuell cell integration

with multiple generations operating on solar energy are the cases of interest because of solar energy being broadly available in many places on the planet and various technological items are suitable for the application present. Systems with multiple generations operating on solar energy have several advantages, for example, creating several practical inexhaustible and environment-friendly products, which enhances the total effectiveness of the thermodynamics; thus decreasing the total amount of prices dedicated to investment and expenses related to products, through distributing the parts and reducing the release of greenhouse gases. We use the collectors of solar energy to employ the existing solar energy, dividing these collectors into two main groups: Photovoltaic (PV) collectors and several solar thermal collectors: comprising water heater collectors, air heater collectors, and concentrating solar collectors. Regarding the previous review and reasoning, employing the systems with multiple generations operating on solar energy is a case of interest and includes various advantages for both areas of residence and industrial activity. Regardless of the fossil fuels utilized as sources are backed up or not, the systems with multiple generations are mainly categorized into two groups: (1) systems with multiple generations operating solely on solar energy where the only source of energy that is inexhaustible is the Sun, and (2) systems with multiple generations operating on solar energy as a hybrid, where solar energy is consumed combined with other inexhaustible sources of energy, for instance, biomass, geothermal, wind, and ocean.

13.2 Fuell cell integration 13.2.1 Fuel cell 1 thermoelectric generator The thermoelectric generator (TEG) is a form of electrical producer that works to generate power after a heat source is imposed to one end of the module underneath the Seebeck effect principle (refer to Fig. 13.1 for some standard examples [1]). When a tiny fraction of such heat is already transformed into energy, the remaining heat is discharged into a low-grade heat sink like the ambient environment. In general, a higher temperature gradient between the hot and cold ends of the TEG might well lead to greater electrical efficiency that is significantly related to the Carnot theory’s connections. TEGs have often been included in the sector of FC studies to harness electrochemical waste heat from the FC to generate extra power. Relative to the gas turbine (GT), consequently, the TEG can boost the total electric productivity. The TEG, in contrast, seems to have a much simpler structure than the GT, and it also does not contain moving parts that would potentially receive regular maintenance. However, the conversion efficiency of the TEG, because of the same temperature gradient, seems to be generally lower than the GT. In addition, a single TEG module could only embrace a restricted quantity of heat (e.g., the commercial

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FIGURE 13.1 Conventional approaches to the combination of TEG to FCs (A) directly, (B) indirectly by the FC exhaust pipe, and (C) indirectly by a liquid cooling circuit [1]. FC, Fuel cell; TEG, thermoelectric generator.

TEC1-12730 module only can tolerate 300 W at most), so scaling the TEG structure to a large application ( . 30 kW) would mean massive dimensions. Consequently, for assimilation with high-temperature FCs of massive scale, the TEG is often less preferred than the GT. From another point of view, through small- or medium-scale medium-temperature FCs, including high-temperature PEMFC (the average operating temperature of 120 C 180 C), the TEG does seem more promising. The methods widely considered for physically incorporating the TEG into FCs as shown schematically in Fig. 13.1. While this approach seems to be ideal in thermal context, it is sadly not practical due to so many primary causes. First, the FC stack has geometrical restrictions as the reactants and products require port connectivity, which in turn restricts the positions where the TEG could be properly positioned. Furthermore, while evaluating an air-cooled FC stack, the oxygen provided is mostly used to directly cool the FC, implying that a substantial percentage of the heat can escape the stack through the product emissions. Likewise, when analyzing a liquid-cooled FC stack, the cooling liquid is indeed reducing the large proportion of the heat. Thereby, for both situations,

13.2 Fuell cell integration

when it is attached directly to the stack structure of the FC, there would be a very constrained quantity of heat accessible for the TEG, leading in TEG output powers that are so tiny to be applied in practice. So instead of connecting directly like Fig. 13.1A, a more realistic model would be Fig. 13.1B C. Above all, Fig. 13.1B comprises the installation of the TEG downstream of the exhaust pipe of the FC stack [1]. This approach is most suitable for an air-cooled FC stack because the reactant side air generates major cooling and therefore provides the whole (or most) waste heat into the exhaust. Chen et al. [2] pioneered the concept of assimilation, which could be the first 0.35%. In addition, this framework may be recycled in scenario the TEG is applied as a Peltier heater for heating; a plugin that is efficient in preventing the startup time for the FC stack. This combined concept of TEG and Peltier heater was successfully established by Kwan et al. [3] that developing an electronic device that could deliberately decide whether the thermoelectric module should act as a TEG or a Peltier heater/cooler. The Peltier heater/cooler is also recognized, in particular, as the thermoelectric cooler (TEC), which is a heat pump. Lately, the hybrid device of FC TEG has been introduced which contains a regenerator shown in Fig. 13.2. Many styles of FCs have been intensively researched, including those mentioned by Zhang et al. [4]. The regenerator is classified as a heat exchanger unit that reuses the waste heat in the reactants. Such a notion hypothetically enhances the thermal energy efficiency of the system by optimizing the loss of heat via the exhaust. Consequently, resolving the principal decay of implementation in Fig. 13.1A is a simple alternative, although no experimental research is currently underway to validate all these. Nondimensional designs for the FC, the regenerator, and the TEG modules are devised in the main recent publication of hybrid FC TED structure. These submodels are then assimilated to obtain a coherent thermodynamic steady-state template for assessing the overall system performance. The TEG initially appeared to play a key role in elevated temperature of FCs, consistent with previously recorded productivity improvements. For example, the phosphoric acid FC, the operating temperature of which ranges commonly

FIGURE 13.2 FC TEG integrated system with a regenerator element [1]. FC, Fuel cell; TEG, thermoelectric generator.

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FIGURE 13.3 TEG integrated system with both the regenerator and thermoelectric [1]. TEG, Thermoelectric generator.

around 150 C and 200 C, demonstrates efficiency improvements of only 1.7% 2.8%, whereas improvements of up to 50% are confirmed for a high-temperature FC, including the direct carbon FC. In Fig. 13.3, “space cooling” refers to the principle from the literature that the TEG power output is attached directly to a separate TEC that supplies cooling in a separate space.

13.2.1.1 Fuel cell—gas turbine Generally, emissions of high-temperature FCs, including SOFCs or MCFCs, have high temperatures (over 500 C) and produce high-grade heat that is recyclable for power generation. At the same time, the GT is indeed a mechanical rotational technology that generates a high-temperature gas passes through and pushes the GT’s rotor with the assistance of an electric engine. Consequently, placing the hightemperature FC to supply its high-temperature emissions to the GT (so that creating the FC GT or FC 1 GT hybrid model) seems to be a very popular notion for substantially improving the electrical output of the single FC over. Furthermore, since the FC stack often does not consume the fuel for SOFCs or MCFCs and leaks into the product emission, it is indeed a standard procedure to similarly combust the product exhaust in an afterburner further to improve the exhaust temperature as well as consequently further enhance the capacity of the GTs. The emissions product can be used to preheat the air and/or fuel supply to the FC stack. It lessens the cooling effect generated by feeding the low-temperature reactants directly to the stack in the FC. Noticeably, hydrogen is not a naturally available resource because of its unstable chemical nature; so, it is a usual policy to attach an in situ unit that extracts hydrogen from a more standard fuel, such as natural gas or biofuel. Perna et al. [5] published research in this regard using an in situ biomass gasifier to generate hydrogen for the SOFC. This research demonstrated that when the steam-tocarbon ratio of the reaction at the SOFC inlet is 0, the efficiency is only 24%. By comparison, including the GT, the electrical efficiency was increased to 35%, which strongly suggests the improvements supported by the GT integration. As illustrated in Fig. 13.4, the basic framework of a SOFC GT system comprises a

13.2 Fuell cell integration

FIGURE 13.4 The basic common configuration of FC 1 GT combination system implementations [1]. FC, Fuel cell; GT, gas turbine.

compressor, an air/fuel preheater, a reformer, a SOFC stack, an afterburner, and a GT. In addition, to the basic configuration, the FC and GT elements can be randomly reconfigured to form unique operating systems that can boost the functionality between the individual subsystems or simply increase the hybrid system’s overall performance. The top and bottom cycles are two generally recognized configurations that are under rigorous study. Illustration of Fig. 13.5A demonstrates a topping process that is often alluded to as a direct cycle since this GT is motivated in the afterburner by the combusted products. In this scenario, since the FC stack remains downstream of the compressor, it slightly serves as the heat source of a Brayton process. Therefore a highpressure (HP) situation is required, which could influence the FC’s lifetime. For studies considering the top process, some examples have been included [5 7]. In a loop to the bottom seen in Fig. 13.5B, the air flows into the compressor and then exchanges heat from the afterburner of high-temperature gas. After the heat is absorbed the air may push the GT to generate significant power before reaching the main reactant, the FC stack. In this scenario, since the FC is situated downstream of the GT, this could lead to a lower pressure, thereby enhancing its lifetime and safety. However, because the combustion heat implicitly controls the GT, this configuration’s electrical output becomes typically inferior over the topping cycle. Cuneo et al. [8] studied an example considering the bottom cycle. In the meanwhile, Pirkandi et al. [9] and Yi et al. [10] simultaneously conducted studies comparing the efficiency of the top and bottom cycles and reported the findings that the top cycle provides higher electrical efficiency. Microturbine services generally operate at 30 kW 30 MW, while an industrial-scale turbine is required to function at 100 1000 MW [11]. Besides, GTs are seldom available on the market at lower power (,30 kW), being too costly. Consequently, based on the literature by Refs. [11,12] or as a power plant for large buildings, cities, or towns [13], the FC 1 GT model is commonly used in larger scale applications, such as in aircraft. The FC 1 GT hybrid model is capable of achieving overall electrical efficiency

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FIGURE 13.5 Conventional combination of the FC 1 GT system (A) top cycle and (B) bottom cycle [1]. FC, Fuel cell; GT, gas turbine.

improvements up to 70%, which is certainly higher than when the FC or GT is employed as the stand-alone unit. Similarly, the processing of fuel often plays a key role in the hybrid FC 1 GT process, which is a critical aspect of further improving performance. Numerous researches devoted to the analysis of the FC 1 GT hybrid service’s stable-state features, such as energy or exergy efficiency. In certain situations, such as Camblong et al. [14] the study of energy or exergy performance was also combined with techno-economic research to assess the economic viability of the FC 1 GT framework. So many research developments have also been conducted concentrating on the control strategy, experimental investigation, and off-design efficiency analysis. Regrettably, as discussed earlier [15,16], the capital expense of the high-temperature FCs (such as the SOFC) seems to be the key barrier preventing the hybrid device from achieving widespread marketing. One way to reduce the cost of capital would be to develop methods of generating reusable and carbon-neutral sources of fuel, like biogas. In the other research, Ghorbani and Khoshgoftar Manesh [17] proposed an integration of an internal

13.2 Fuell cell integration

FIGURE 13.6 Combination of IRSOFC GT ORC system [17]. GT, Gas turbine; IRSOFC, internal reforming solid oxide fuel cell; ORC, organic Rankine cycle.

reforming SOFC (IRSOFC) GT organic Rankine cycle (ORC) system (Fig. 13.6). Conventional and advanced exergetic and exergoeconomic analyses have been performed. Results demonstrate that overall cycle efficiency and the net power associated with the new integrated system are increased by 7.7% and 1.1 MW, respectively, rather base SOFC GT configuration.

13.2.2 Fuel cell 1 heat pump/refrigeration The heat pump, the refrigerator, is a term that recognizes any machine that, with the aid of an engineering program, compels the heat transfer from a lowtemperature heat storage tank (i.e., a cold environment) to a higher temperature

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FIGURE 13.7 Configuration of (A) stand-alone AC and (B) integrated FC AC system [1]. AC, Absorption chiller; FC, fuel cell.

storage tank (i.e., hot environment). The core idea of a heat pump can be seen in Fig. 13.7A where electricity or mechanical energy is produced. On the one side of the table, heat pumps can be applied for cooling applications; for instance the vapor compression cycle is quite popularly employed in air conditioners [18,19] to supply the residents with personal comforts. Another example of this is the TEC [20], which is more ideal for smaller implementations, including CPU cooling due to its structural simplicity. Heat pumps could also be employed as productive heaters that are more effective than electric heating because the heating effort slightly requires energy from the low-temperature storage tank. All in all, the latest researches [1] have already shown that the FC HP hybrid network is extremely effective in reducing the home’s energy demand when the FC is used as a micro Combined Heat and Power (µCHP) supply.

13.3 Fuel cell 1 absorption chillers Absorption chillers (ACs) seem to be technologies that supply cooling utilizing high-temperature heat as the primary source of energy (as opposed to electricity). The AC employs a fluid system composed of two loops to obtain the cooling purpose (as can be seen in Fig. 13.7A), where one cycle encompasses a salt solution

13.3 Fuel cell 1 absorption chillers

and the other entails only the solvent of that solution. Water as the solvent and lithium bromide (LiBr) as the solute would be a widely accepted salt solution applied in ACs. The absorber and generator are placed at the adjacent points of different loops; at the generator, high-temperature heat (often over 100 C) is utilized in the salt solution to vaporize the solvent and the vaporized solvent transports along the left cycle. The solvent generates heat at the condenser throughout this cycle and moves across a throttle valve to lessen the pressure. By integrating the two processes the temperature of the solvent declines underneath ambient temperature; consequently, cooling energy at the evaporators is supplied and extracted. Ultimately, the solvent at the absorber reintegrates with the salt solution. Besides that, the solute’s absolute flow rate stays unchanged across the entire right cycle; so, a strong solution happens with the throttle valve in this side path as a result of a particular quantity of the solvent that has been vaporized and expelled at the generator. Since the performance of the AC is significantly greater when higher grade heat is provided as the source of power, ACs are more widely seen in higher temperature FCs, including SOFCs [1,21], where efficiency is improved by 40% 50%.

13.3.1 Fuel cell—desalination systems Desalination is described as the procedure of purification of raw water from a particular source (such as the ocean) into fresh water, which is appropriate for daily use. In areas lacking freshwater resources, this system has profound importance. Desalination can broadly be classified into two classes based on thermal or membrane filtration. Multistage flash (MSF) and multieffect distillation (MED) are generally recognized as thermal-based techniques that refer to a process of evaporating water from an impure blend and compressing the subsequent water vapor in some kind of a clean reservoir. Admittedly, such a process requires an active source of heat to compel water to evaporate. Likewise, desalination could also be obtained through membrane filtering techniques, where reverse osmosis (RO) becomes a famous example, encompassing a semipermeable membrane to segregate the water from its impurities. Unlike thermal-oriented approaches, RO requires applying pressure to resolve the water’s osmotic resistance; so, enabling water to move across the membrane while retaining the solutes on the pressurized side. Typically, the source of energy for the pressurized procedure is supplemented by a compressor or piston, which needs electricity. However, a source of heat in RO is not mandatory, attaching one could then enhance the rate of water production since, according to Al-Mutaz and Ghunaimi [22] higher temperature of the membrane would also significantly boost its permeability or salt passage by around 3%. In another phrase, electricity is the major source of energy for membrane filtration techniques, but alternatively, thermal energy could be utilized to maximize the efficiency of water production. Admittedly, the FC can endorse the heat prerequisite of thermal desalination processes or the power consumption of RO. Doing so, an FC desalination hybrid model could be developed to harvest both electricity and fresh water (and possibly heat to some extent) through suitably sizing the FC and desalination plants.

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FIGURE 13.8 A common configuration of the integrated FC and desalination system: (A) thermal-based desalination system such as MSF or MED (B) RO desalination [1]. FC, Fuel cell; MED, multieffect distillation; MSF, multistage flash; RO, reverse osmosis.

The configuration of the application depends on the characteristics of the desalination plant; Fig. 13.8 shows typical examples of thermal plants (A) and RO (B). Clearly, in Fig. 13.8A the heat supply for the thermal desalination plant, generally added by a boiler, should be altered by the FC stack electrochemical waste heat. The electric output power of the FC stack is sometimes employed to endorse the thermal plant’s auxiliary units like the seawater pump. Until now, numerous researches only evaluate the steady-state attributes of this system (energy and exergy efficiency, costs, etc.), but few studies have focused on the system’s dynamic perspective, which is significant in a practical scenario involving time-varying environmental conditions as well as energy and water requirements. In addition, there is reportedly no observational research enforcing this concept, which is meaningful for proving the validity in real life.

13.3.2 Microbial cell integration Microbial FCs (MFCs) were launched in the last two decades as a potential treatment technology with bioelectricity generation accompanied by simultaneous carbon and nutrient removal. Fig. 13.9 represents the most advantages of MFC. The microbial desalination cell (MDC) is a particular class of FC that incorporates both the MFC and the wastewater and/or seawater treatment electrodialysis

13.3 Fuel cell 1 absorption chillers

FIGURE 13.9 Advantages and applications of MFC [23]. MFC, Microbial fuel cell.

FIGURE 13.10 Schematic flow diagram of a common MDC unit [25]. MDC, Microbial desalination cell.

(ED) unit. The MFC produces energy in MDCs by oxidizing organic material stored in wastewater, while the ED device segregates salt ions from the seawater. Multiple kinds of MDC plants exist, such as air cathode MDC, biocathode MDC, and desalination and chemical processing cell (MEDC) and microbial electrolysis [24]. Fig. 13.10 depicts a schematic MEDC system that involves anode, cathode, and a central desalination compartment. Every compartment needs to be separated

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by a proper membrane. Saline water resources approach the central desalination compartment where cations and anions (signed as Naþ and Cl in Fig. 13.10, respectively) flow to the cathode and anode. Wastewater containing rich organic contents reaches the anode compartment as the main energy source, and the trapped organic matter is oxidized to produce CO2, H1 ions, and electrons. Electrons flow toward the cathode through an external circuit to minimize waterair mixture into OH-ions. This control scheme obtained trigeneration since wastewater treatment, desalination of seawater, and generation of electricity are performed. The abovementioned process is indeed so superior compared with the popular forms of MSF/MED or reversing osmosis.

13.4 Solar energy 13.4.1 General overview Industrial procedures can effectively use energy if they combine various kinds of resources and/or recover the energies that are considered as waste. Systems using several resources were conceptualized and developed as a result, which can produce several kinds of helpful resources. A system using both solar and wind energies can be mentioned as a common instance of procedures using several resources. Procedures using several resources or the act of putting them together as a hybrid are possible solutions to the issues related to the irregularity that cause delays in utilizing solar energy on a grand scale, even if temporary. Fig. 13.11 exhibits various systems using solar energy incorporated along with resources besides the main ones that are gathered with common types of products.

Power Cooling Heating Desalinated water Hydrogen Others

Polygeneration system

Other fuel resources

FIGURE 13.11 A simple schematic of a solar-hybrid polygeneration system.

13.4 Solar energy

Solar-driven polygeneration system

Products

Auxiliary resources

Backup fossil fuel

Biomass

Geothermal

Wind

Power

Ocean

Heating

Cooling

Fresh water

Hydrogen

Chemical products

Liquid and gaseous fuel

Solar energy system

CPS

PT

SPT

PV/PVT

LF

CPV/CPVT

Solar collectores

PD

FIGURE 13.12 Solar-driven polygeneration system [23].

Solar collector

Nonconcentrating collectors

Evacutaed tube collectors (ETC)

Direct flow ETC

Heat pipe ETC

Flat plate collectors

Concentrating collectors

Compound parabolic concentrator

Parabolic through

Parabolic dish

Synthetic aroumatic fluid

Molten salt

Direct system generation

Solar tower

FIGURE 13.13 Solar thermal collectors.

Fig. 13.12 demonstrates a solar-driven polygeneration system based on auxiliary resources, solar energy systems, and different products. Many suggested that systems with multiple generations operating on solar energy use this energy as thermal energy that the thermal collectors give them, although PVs or technologies of different types possibly affect a number of these kinds of systems. Systems with multiple generations operating on solar energy use collectors of solar thermal energy. We can categorize this type of energy into two types of technologies: concentrating and nonconcentrating (Fig. 13.13). The collectors of the nonconcentrating type can only be used in procedures that need low-grade thermal energy. Flat-plate collectors are considered as one of the most common kinds of nonconcentrating collectors. They are mostly utilized to

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heat water and air for heating the space or drying. Hybrid PV/thermal (PVT) collectors comprising PV/water heaters and PV/air heaters can be mentioned as another kind of solar collector of the nonconcentrating type. PVT has a PV module and uses a mechanism to remove heat from this module and decrease its temperature so that the effectiveness gets enhanced. The heat that was removed from the PV can be employed to produce hot water or hot air in an outline with multiple generations. The concentered systems of PV (CPV) also uses this concept, which employs mirrors that are curvy so they can cause the solar rays to get concentrated into small areas or cells [23]. The technologies using concentrated solar power (CSP), having concentration values of high amounts, offer thermal energy of a high grade that can be employed to make various products in a system with multiple generations. Different CSP technologies that can be mentioned are parabolic trough (PT), solar power tower (SPT), linear Fresnel, and parabolic dish (PD). Many papers analyze various aspects of CSP technology. A literature review exhibited collectors of the PT type and the heliostat field type as two technologies using CSP being mainly utilized in systems with multiple generations operating on solar energy. The technology related to the collectors of the PT type uses some mirrors of the parabolic type to redirect the solar rays toward the receiver that is placed in line with the focal point of the collector [23]. A type of fluid that conducts heat, some instances of which are synthetic oil or molten salt, is used to transfer the existing energy of the solar thermal kind to the specified target place. There are some references such as Fuqiang et al. [26], and Ferna´ndez-Garc´ıa et al. [27] that discuss the collectors of the parabolic type more thoroughly. The collectors of the heliostat field type have a set of mirrors and a receiver that is placed over a tower [28]. These mirrors are used to reflect the solar rays on the exterior area of the receiver, to reach a higher ratio of concentration and temperature during operation. We advise enthusiastic readers to refer to Pfahl et al. [29], Gadalla and Saghafifar [30], and Ho [31], where they can find it thoroughly discussed collectors of the heliostat field type. Parabolic dish shaped mirrors are used in the collectors of the PD type so that they can reflect the solar radiation on the receiver placed in line with their focal point. Collectors of the heliostat field type are more developed and less expensive than this technology. They reviewed thoroughly the collectors of the PD type. Systems with multiple generations operating on solar energy might utilize fuels from the fossils as their use of fossil fuel as an alternative source. Solar energy partly provides these systems with thermal energy that collectors of the solar thermal type absorb, while fossil fuels supply the rest of it. Other sources and systems using inexhaustible energy can be incorporated with collectors of the solar thermal type in systems having multiple generations. Many studies in the literature have researched systems using CSP/PV as a hybrid. A more efficient production of electricity with power quality, in comparison with a PV alone, and less expensive production of electricity, in comparison with power plants of CSP alone, are mainly the beneficial aspects that these systems have [32]. The review by Ju et al. on such systems is another possible case we can mention incorporating CSP with the energy coming from the wind.

13.4 Solar energy

An issue that such systems have is how they schedule, and this is because such inexhaustible sources have an irregular essence. We can also utilize geothermal energy with CSP as thermal energy sources. The geothermal source type and temperature can determine how it is incorporated with CSP. We can also consider the energy from biomass as another source of inexhaustible energy to incorporate it with CSP. In addition, the energy of biomass can partially solve the issue of CSP irregularity. Besides, systems having integrated biomass or CSP can enter the market better than systems using biomass or CSP alone [33]. Pramanik and Ravikrishna offered an overview of these power plants using CSP as a hybrid. Desalination procedures and systems operating on thermal energy can be utilized in systems with multiple generations operating on solar energy to produce cooling or refrigeration and clean water using existing waste or low-grade heat. Systems operating on thermal energy with cooling and refrigerating abilities mostly have various types of absorption refrigeration systems using water lithium bromide (H2O LiBr) combination chillers and ammonia water (NH3 H2O). Shirazi et al. suggested a comprehensive and critical review of ACs operating on solar energy [34]. Technologies using thermal desalination operate depending on procedures of phase change, such as water evaporating and condensing [35]. Examples of technologies using thermal water desalination are MED, MSF distillation, thermal vapor compression, and humidification dehumidification. We can also utilize water desalination systems based on the membrane where a part of the power produced is consumed for producing water. Three basic technologies of membrane-based desalination that can be mentioned are RO, ED, and membrane distillation, which is a procedure operating on thermal energy and studied desalination systems. Mohammadi et al. [36] have thoroughly reviewed the desalination systems operating on CSP. We can make use of systems with multiple generations operating on solar energy to produce fuels in the liquid and gas phases, instances of which are hydrogen and methanol or chemical and industrial products. Acar and Dincer [37] have suggested a thorough list of techniques to use for generating hydrogen. These procedures have been classified according to the primary energy source into different groups of electrical, thermal, photonic, and hybrid methods. A system with multiple generations operating on solar energy can give us the energy coming from electricity and/or thermal energy that such methods need to generate hydrogen. The literature has suggested and assessed many systems with multiple generations operating on solar energy. The suggested systems were categorized into two basic groups: (1) systems with multiple generations operating solely on solar energy and (2) systems with multiple generations operating on solar energy as a hybrid. Notice that both of them utilize alternative systems using fossil fuels to make sure that the helpful products are operated and produced when there is low or zero solar radiation available. Systems with multiple generations operating solely on solar energy indicate the type of systems that use solar energy as the only source of inexhaustible energy. Systems with multiple generations are operating on solar energy as a hybrid system of solar energy with other sources of inexhaustible energy, the instances of which are the energy of biomass,

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geothermal energy, and the energy coming from the wind and the ocean, to produce several helpful products.

13.4.2 Polygeneration with solar energy The review of the conducted studies on systems with multiple generations operating on solar energy is included in this part. As mentioned, while some systems may be prepared with an alternative source using fuels coming from fossils, they are classified as systems operating mainly on solar energy. So far, various technologies using solar energy have been utilized to make systems with multiple generations operating on solar energy, such as collector of the PT type using solar energy, power tower systems operating on solar energy, PVT and Concentrated photovoltaic thermal (CPVT), collectors of the PD type using solar energy, and other collectors, including collectors of flat-plate and evacuated tube type using solar energy. In the upcoming sections the systems with multiple generations operating on solar energy put forward in the literature are categorized more into several groups, based on the technologies using solar energy. The studies are shown and analyzed in sequential order for each group. In this chapter the group of the systems using collectors of flat-plate and evacuated tube type using solar energy is classified as systems operating on collectors of the solar thermal type.

13.4.2.1 The parabolic trough type Collectors of the PT type using solar energy, systems of the SPT, and collectors of the PD type using solar energy can provide thermal energy with a high temperature. We can utilize them to produce electricity through a power cycle, or we can directly provide other systems with them so that they can produce various products. Common heat-conductive fluids that PT solar fields utilize are thermal oil, water or steam, and molten salts. The highest amount of thermal energy temperature can vary based on the fluid type that the fields offer. The highest amount of temperature is typically less than 400 C for fluids based on thermal oil, whereas for the water or steam, which uses the concept of producing direct steam, the highest amount of temperature can be the same as the temperature of steam in power plants operating on coal and natural gas. The highest amount of temperature for molten salts can go up to 600 C 650 C [38]. Molten salts, and water or steam, are also utilized on a large scale, as heat conductor fluids for usage in power tower plants using solar energy. Almahdi et al. [39] developed a polygeneration solar power system (Fig. 13.14), integrated with a hot and cold thermal backup device. The module consists of PT collectors, three ORCs, a heat pump R-123, a chiller H2O LiBr, a chiller NH3 H2O, and an electrolyzer. A dryer attached to the hot/cold thermal storage device supplied cooling through driving the ORC at night. The device generated electricity, heating, cooling, hydrogen, and dry sawdust biomass at multiple outputs. The system’s overall energy and exergy efficiency improved up to 20.7% and 13.7%, respectively. It was reported that the energy and exergy efficiency of hybrid

13.4 Solar energy

FIGURE 13.14 Solar PTC driven polygeneration system proposed by Almahdi et al. [39]. PTC, parabolic trough collector.

systems were 11.9% and 0.4% higher than the single generation system. The peak energy loss for the sawdust belt dryer was 64%. A polygeneration PT-based device (Fig. 13.15) for the production of electricity, heat, fresh water, and hydrogen under residential applications (38 flats) was presented by Ozlu and Dincer [40]. When solar energy was not available or had a significant fluctuation, hydrogen was produced for energy storage to counterbalance the gap between demand and supply. The findings revealed that the device had 36% and 44% of optimum energy and exergy efficiencies, respectively. For a solar collector area of 24 m2 the network will produce 0.04 kg/s of fresh water and a maximum turbine power of 116 kW. The installation of the device has been environmentally beneficial leading to a decline in carbon dioxide emissions of 476 t/year. Yuksel et al. [41] developed a solar-based polygeneration model based on a parabolic solar field, a double-stage ORC, an electrolyzer for the PEM, a PEM fuel cycle, and a quadruple absorption cooling system as demonstrated in Fig. 13.16. The polygeneration pattern provides electricity, hydrogen, hot water, heating, and

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FIGURE 13.15 Solar PTC driven polygeneration system proposed by Ozlu and Dincer [40].

FIGURE 13.16 Solar PTC driven polygeneration system proposed by Yuksel et al. [41].

13.4 Solar energy

FIGURE 13.17 Solar PTC driven polygeneration system proposed by Sharifishourabi and Arab Chadegani [42].

cooling. They implemented a parametric analysis and demonstrated that the maximum exergy destruction pertained to PT collectors, PEMFCs, and turbines, respectively, admitting that design improvements of these components lead to higher polygeneration system performance. Sharifishourabi and Arab Chadegani [42] developed a dynamic polygeneration hybrid solar system (Fig. 13.17) consisting of a solar parabolic field that used molten salt, an ORC, a triple-effect AC H2O LiBr, and an electrolyzer. Electric power, hot water, heating, cooling, conditioned air, and hydrogen were generated by this device. The system’s net power generation capacity was 421 kW with an exergetic utilizing factor of 0.39. ORC’s energy and exergy efficiencies were both 14.4% and 26%, respectively. The system had a cooling and heating capacity of 2114 and 2380 kW, with hydrogen production of 0.001664 kg/s, respectively. From an environmental perspective, the implementation of the device in low ambient temperatures was discovered more advantageous. Islam et al. [43] suggested different arrangements (Figs. 13.18 and 13.19) for the power generation, cooling, domestic hot water, space heating, and hydrogen through a solar-driven parabolic polygeneration system. The system comprised two ORCs, an AC H2O LiBr, a TEG, and an electrolyzer. Layouts differed concerning the TEG implementation. In both configurations the power produced by TEG led an electrolyzer to make hydrogen. The application of TEG in Configuration 2 resulted in a higher overall performance of work output and efficiencies in energy and exergy. The total energy and exergy efficiencies for Configuration 1 were 57.3% and 49.7% and 59.6% and 51.7% for Configuration 2, respectively.

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FIGURE 13.18 Solar PTC driven polygeneration system with a thermoelectric generator driven by Therminol VP-1 proposed by Islam et al. [43].

FIGURE 13.19 Solar PTC driven polygeneration system with a thermoelectric generator driven by with thermoelectric generator driven by isobutane proposed by Islam et al. [43].

13.4 Solar energy

FIGURE 13.20 Solar PTC driven polygeneration system proposed by El-Emam and Dincer [44].

El-Emam and Dincer [44] introduced a solar polygeneration system consisting of solar parabolic collectors, a thermal energy storage system, an ORC, a single H2O LiBr chiller, an RO unit mounted with an energy recovery mechanism, and an electrolyzer. The device illustrated in Fig. 13.20 could produce hot water, electricity, cooling, fresh water, hydrogen, and urban hot water. The model was installed to produce 200 500 kW of electricity, 40 kg/s of fresh water, 50 C domestic hot water, and 500 800 kW air-conditioning cooling effect. The outcomes of thermoeconomic analysis and multiobjective optimization techniques identified a set of optimum points and the highest and lowest efficiencies of exergy. Tukenmez et al. [45] analyzed an innovative polygeneration system driven by PT collectors, demonstrated graphically in Fig. 13.21. The device could generate electricity, hot and fresh water, compressed hydrogen, cooling, heating, and drying. The device was made of a thermal energy storage machine, an MED unit, a hydrogen production system, a Kalina cycle, an ORC, an ejector cooling system, and a drying unit. The scheme could yield 3545 kW of power, 1284 kW of cooling, 808 kW of heating, 1136 kW of drying, 00043 kg/s of hydrogen, and 8.3 kg/s of fresh water based on the research process. The system’s total energy and exergy efficiencies were 59.34% and 56.21%, respectively.

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FIGURE 13.21 Solar PTC driven polygeneration system proposed by Tukenmez et al. [45].

Bamisile et al. [46] introduced a new polygeneration model (Fig. 13.22), relying on PT collectors, single- and double-effect H2O LiBr chillers, two steam Rankine cycles, and a PEM electrolyzer. The presented device could produce 1027 kW of energy, 188.5 kW of hot water, 11.23 kg/s of cooling, and 0.9785 kg/ h of hydrogen. The polygeneration system’s overall energy and exergy efficiencies were extracted as 71.6% and 24.5%, respectively.

13.4 Solar energy

FIGURE 13.22 Solar PTC driven polygeneration system proposed by Bamisile et al. [46].

13.4.2.2 Solar power tower driven systems SPT technology had already received increasing importance among all CSP systems in that it gives a higher concentration rate and greater temperature thermal energy compared to PT technology and also uses thermal storage quicker than PD technology. This is a motivation to recruit SPT technologies to accumulate thermal energy at high temperatures for polygeneration reasons. The power tower devices with an HP receiver have been used in the literary works for such purposes, in addition to molten salt-based SPTs. The high-temperature solar energy (up to 1000 C) can be embedded directly into a GT cycle employing an HP receiver, reducing the gas and CO2 emissions [47]. Siddiqui et al. [48] developed a polygeneration supporting the notion by the solar tower to generate energy, hydrogen, cooling, and hot water. The designed methodology (Fig. 13.23) included an integrated solar tower platform with molten salt storage, a hybrid cycle SOFC GT, a molten alkaline electrolyte ammonia FC, a PEM electrolyzer, and an NH3 H2O chiller. A portion of the energy produced during the first Rankine cycle was used through the electrolyzer to extract hydrogen. The polygeneration device’s energy and exergy efficiencies were 39.1% and 38.7%, respectively; 19.3% and 17.8% greater compared to the single generation systems. The integrated cycle of SOFC GT energy and exergy efficiencies was 68.5% and 55.9%, respectively.

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FIGURE 13.23 Solar power tower driven polygeneration system proposed by Siddiqui et al. [48].

El-Emam and Dincer [49] have developed an SPT polygeneration model to produce power, hydrogen, fresh water, and hot water at home. The module consists of a Rankine cycle of reheat steam embedded with a solar tower device, an H2O LiBr chiller, an electrolyzer, and an RO unit of marine water with an energy recovery machine. The steam remaining from the HP turbine diverged into two sources. First, one component was used to produce hot water and then transferred to the chiller. The second flux was employed to supply the electrolyzer’s thermal energy. The technique produced 4 MW of power, 1.25 kg/h of hydrogen, and 90 kg/s of fresh water (Fig. 13.24). Yilmaz [50] developed a hybrid cycle-based polygeneration solar energy tower-GT (Fig. 13.25) unit for electricity generation, heating, cooling, hydrogen, and freshwater production. The comprehensive thermodynamic evaluation was carried out to assess the efficiency of the model that consisted of an SPT-driven GT with an absorption cooling and heating system, a flash desalination unit, a steam Rankine cycle as a bottom cycle, an ORC, and a PEM electrolyzer. The energy, hydrogen, and clean water were extracted in this system as 18.992 kW, 0.04663 kg/s, and 0.8862 kg/s, respectively. Efficiencies in energy and exergy have expanded nearly to 78.93% and 47.56%, respectively.

13.4 Solar energy

FIGURE 13.24 Schematic of polygeneration system proposed by El-Emam and Dincer [49] based on solar heliostat steam turbine.

FIGURE 13.25 Solar power tower driven polygeneration system proposed by Yilmaz [50].

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13.4.2.3 Parabolic dish driven systems An innovative polygeneration solar energy device (Fig. 13.26) was examined by Yilmaz et al. [51] for power generation, heating, cooling, drying, hydrogen, and liquefaction based on PD solar system. The other significant requirements, recommended in this study, included a critical CO2 power cycle, a high-temperature steam electrolyzer, a dryer unit, a heat pump, and a single-effect AC. The system’s energetic and exergetic performance exceeded 60.14% and 58.37%, respectively. The solar tower also demonstrated the maximum irreversibility of 18,775 kW among all of its elements. Besides, the polygeneration service’s sustainability index was measured between 2.2 and 3.05.

13.4.3 Photovoltaic/thermal/CPVT collector driven systems Though CPVT collectors are far less advanced and therefore costlier than PVTs, they were used for polygeneration objectives as appealing alternative energy. The probability of obtaining a substantially higher performance and supplying superior thermal power appears to make CPVTs more desirable compared to PVTs. Contrary to PVTs, however, CPVT devices are geographically restricted to regions that experience the huge potential for Direct Normal Irradiance (DNI). Calise et al. [52] implemented a polygeneration system that also included a CPVT domain, an alkaline electrolyzer, a PEM fuel storage, and a single-effect

FIGURE 13.26 Parabolic dish driven polygeneration system proposed by Yilmaz et al. [51].

13.4 Solar energy

FIGURE 13.27 Solar CPVT driven polygeneration system proposed by Calise et al. [52].

H2O LiBr AC. The machine is designed to generate electricity, cooling, heating, and hot water as illustrated in Fig. 13.27. The CPVT sector supplied the energy needed for the electrolyzer to produce hydrogen and oxygen. The hydrogen was accumulated in a tank and then delivered for power generation to the FC, and selling the oxygen. The hydrogen collected by the electrolyzer could produce only 4.3% of the demand for fuel storage. The FC’s electrical and thermal performances were, respectively, 35.0% and 43.4%. The device’s energetic productivity was indeed a function of the size of the solar field and the nominal power of the FC. The system demonstrated potential energy savings and productivity from both an energetic and economic perspective if an opportunity is given.

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FIGURE 13.28 Solar CPVT driven polygeneration system proposed by Khan et al. [53].

A CPVT collector based polygeneration device (Fig. 13.28) was recommended by Khan et al. [53] to effectively apply and maintain the electricity and thermal energy produced by the CPVTs in Doha, Qatar. The author employed an innovative CPVT thermal energy storage system called the Boiling Heat Transfer Nucleate Pool. The package involved a hydrogen storage system, an H2O LiBr chiller, a PEMFC, and an electrolyzer, a space cooling, dehumidification and ventilation device, and a space heating and hot water system that could produce energy, hot water, space cooling, and drinkable water. A part of CPVT’s electricity was used to operate electrolyzers to produce hydrogen and oxygen, which were then reused by PEMFCs to generate extra power mostly overnight or when additional electricity was needed. The CPV’s energetic and exergetic efficiency improvements have been 40.8% and 49.7%,

13.5 Hybrid solar polygeneration systems

respectively, while they reached up to 67.52% and 34.89%, respectively, for the polygeneration model.

13.5 Hybrid solar polygeneration systems This section provides a literature review conducted for the development of polygeneration hybrid solar power platforms. In such technologies, other renewable energy sources, including biomass, geothermal, wind, ocean, or a mixture of some of these resources, are employed, in addition to solar power, to assist subsystems to reach multiple products. These devices are primarily categorized into four types according to the renewable energy sources employed in conjunction with solar power: (1) hybrid solar biomass platforms, (2) combined solar geothermal systems, (3) integrated solar wind/ocean models, and (4) other hybrid systems that combine more than one renewable energy with solar power. Just like with the solar-only-driven models discussed in Section 13.3, hybrid solar systems in the categories of (1) and (3) are also listed based on the solar energy equipment utilized. The research works and systems for each group have been evaluated in chronologically.

13.5.1 Integrated solar biomass-driven devices In the literature the hybrid solar biomass devices used PT solar collectors, solar energy towers, CPVT, and solar collectors of evacuated tubes.

13.5.1.1 Hybrid parabolic trough collectors biomass Karellas and Braimakis [54] investigated the efficiency of an innovative co/trigeneration microscale with economic and thermodynamic perspectives. The machine (Fig. 13.29) involved PT collectors, a boiler for biomass, an ORC, and a compression chiller for vapor. The ORC’s needed heat was provided employing the biomass boiler and PT collectors. In the situation of no overheating and 90 C evaporation temperature, 50 kWth heat input, and 5 kWth cooling capacity, total efficiency, energy production, heating capacity, and exergy performance were approximately 2.38%, 1.42 kWe, 53.5 kWth, and 7%, respectively. The ORC’s highest efficiency was 5.5% when considering R245fa as a fluid. The economic studies revealed that the decline in fuel oil and electricity demand was a payback period of 7 years and an internal return rate of 12%. Ghasemi et al. [55] performed thermodynamic and thermo-economic analysis techniques and optimization algorithms for a modern polygeneration hybrid solar biomass device (Fig. 13.30), producing electricity, cooling, heating, liquefied natural gas (LNG), and fresh water. The presented polygeneration scheme consisted of a PT solar field, a steam Rankine cycle, a dual-effect AC, a heater, a liquefaction framework for natural gas, an MED structure, and a biomass combustion

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FIGURE 13.29 Hybrid Solar PTC biomass polygeneration system proposed by Karellas and Braimakis [54].

burner. The machine supplied electricity of 16.11 kW, 28.94 kW heating, 23.41 kW cooling, fresh water of 8.8 kg/h, and LNG of 0.02 m3/h. The energy and exergy efficiencies reached, respectively, 46.8% and 11.2%, and the cost rate was $15.16/h. Enforcing a multiobjective optimization resulted in a rate of exergetic performance and cost of production up to 9.9% and $13.2/h, respectively.

13.5.1.2 Hybrid solar power tower biomass Khalid et al. [56] developed a polygeneration integrated solar biomass structure for generating power, cooling, heating, hot water, and heated air. The idea demonstrated in Fig. 13.31 involved a solar tower, an open GT cycle, a confined GT cycle, two steam Rankine cycles, a single-effect H2O LiBr chiller, and a heat exchanger designed to generate hot air. The device could achieve more than 47.5 MW of energy through all four power cycles. It revealed that the total platform’s energy and exergy efficiencies were 66.5% and 39.7%, respectively. In comparison, when only the biomass network operated these corresponding values became 64.5% and 37.6%, respectively; while when just the solar system regulated, they become 27.3% and 44.3%, respectively.

13.5.1.3 Hybrid CPVT collectors biomass Calise et al. [57] suggested a new polygeneration model based on solar and biomass energies to achieve complex priorities. The configuration of the developed framework for generating electricity, fresh water, cooling, and heating is

FIGURE 13.30 Hybrid solar PTC biomass-driven polygeneration system proposed by Ghasemi et al. [55].

FIGURE 13.31 Hybrid solar tower biomass-driven polygeneration system proposed by Khalid et al. [56].

13.5 Hybrid solar polygeneration systems

FIGURE 13.32 Hybrid solar CPVT-biomass-driven polygeneration system proposed by Calise et al. [57].

illustrated in Fig. 13.32. The core elements were CPVT collectors that could function up to 100 C, a supplementary heater for biomass, an AC, an MED, and three mechanisms for thermal energy storage [57]. For four various scenarios that significantly affected the unit’s cost, the prices of each product generated through exergoeconomic results were evaluated. The power, heating, cooling, and fresh water supplied could be available to customers at h0.1748/kWh, h0.044/kWh, h0.056/kWh, and h2.748/m3, respectively.

13.5.2 Hybrid solar geothermal For integrated solar geothermal models the solar energy devices were employed using PT solar collectors, SPT, and PVT/CPVT collectors. The conceptual systems have been described and evaluated in the following pages, focusing on these models of solar power systems.

13.5.2.1 Hybrid parabolic trough collectors geothermal A hybrid solar geothermal polygeneration scheme (Fig. 13.33) introduced by AlAli and Dincer [58] could provide electricity, cooling, heating, hot water, and industrial heat. The designed methodology composed of a geothermal solar power cycle that included a PT solar field, two ORCs, and an AC H2O LiBr. The solar field was applied to elevate water temperature to heat the ORC2 evaporator. The single generation (just electricity) and polygeneration systems’ energy performances were 16.4% and 78%, respectively, while, their exergy efficiencies

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FIGURE 13.33 Hybrid solar PTC geothermal-driven polygeneration system proposed by Al-Ali and Dincer [58].

were 26.2% and 36.6%, respectively. In addition, the solar power system possessed around 75% of the exergy destruction. Islam and Dincer [59] introduced a method for setting up of polygeneration system, as can be seen in Fig. 13.34, including sources of solar and geothermal energy. The device consists of a geothermal well, PT collectors, an ORC with two turbines, two thermal energy storages, a chiller H2O LiBr, a heat pump R140, and a drying system. Before reaching the second turbine, the solar field was used to raise the heat of the organic fluid, boosting its electricity generation. Thermal energy storage was employed even at dark to reserve heat exiting of the first and second turbines to supply industrial heat and urban hot water. The polygeneration platform’s energy and exergy efficiencies were 51% and 62%, respectively; and the corresponding values became 22% and 54%, respectively, for the single generation. The maximum exergy destruction of 64.5% and 24.8% were pertained, respectively, to the solar field and geothermal structure. Karapekmez and Dincer [60] developed a hybrid solar geothermal polygeneration model (Fig. 13.35) for the production of electricity, cooling, heating, hydrogen, drying air, and hot water. The integrated model involved a PT solar field configured with a power cycle supported by thermal energy storage, the ORC, a single NH3 H2O AC, a SOFC, and an electrolyzer. They employed a piece of equipment to create hydrogen from the hydrogen sulfide (H2S) harvested from the geothermal power plants. To

13.5 Hybrid solar polygeneration systems

FIGURE 13.34 Hybrid solar PTC geothermal-driven polygeneration system proposed Islam and Dincer [59].

produce energy the hydrogen was fed to the FC. The steam from the geothermal well was applied at 218 C to produce the cooling capacity and then transmitted to the ORC boiler. Polygeneration’s maximum energy and exergy improvements were 78.37% and 58.40%, respectively.

13.5.2.2 Hybrid solar power tower geothermal Panchal et al. [61] designed a tower geothermal polygeneration integrated solar energy device. The model generated energy, cooling, hot water, heating, and drying as shown in Fig. 13.36. The designed methodology consisted of a solar

273

FIGURE 13.35 Hybrid solar PTC geothermal-driven polygeneration system proposed by Karapekmez and Dincer [60].

FIGURE 13.36 Hybrid solar tower geothermal-driven polygeneration system proposed by Panchal et al. [61].

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energy tower, a Rankin cycle of reheat steam, a cycle of geothermal water, an AC of H2O LiBr, and a drying device. The findings demonstrated that the energetic productivity for the single generation was approximately 7% compared to 37% for polygenerations. The AC’s energetic and exergetic coefficient of performance (COP) were 0.7 and 0.11, respectively. The AC might also supply 1895 kW of cooling. Among all the elements of the model, the maximum exergy destruction appeared in the boiler and condenser of the Rankine cycle.

13.5.2.3 Hybrid photovoltaic/thermal/CPVT collectors geothermal For a tiny Mediterranean island, Calise et al. [62] presented a hybrid CPVT collector geothermal polygeneration framework (Fig. 13.37) to produce electric power, cooling and heating, fresh water, and hot water. The module consists of 400 CPVT collectors, 85 C geothermal wells, an MED unit, a single-effect H2O LiBr chiller, and tanks for the saving thermal power. The CPVT developed electricity and thermal power up to 100 C. A portion of the solar collectors’ thermal energy was utilized through the chiller for heating and cooling. The MED module supplied thermal energy by both solar collectors and geothermal power. In addition, hot domestic water was supplied using the geothermal wells’ thermal energy. The device provided outstanding power and economic efficiency with a straightforward 3.45-year payback period and could prevent 4870 t of carbon dioxide emissions each year and massively reduce diesel fuel usage.

FIGURE 13.37 Hybrid PVT/CPVT geothermal-driven polygeneration system proposed by Calise et al. [62]. PVT, Photovoltaic/thermal.

13.5 Hybrid solar polygeneration systems

13.5.3 Hybrid photovoltaic/thermal ocean Ahmadi et al. [63] developed a hybrid solar ocean polygeneration device for power production, cooling, freshwater, and hydrogen production. The developed scheme (Fig. 13.38) composed of a PVT unit, flat-plate solar collectors, an Ocean Thermal Energy Conversion (OTEC) device, an H2O LiBr chiller, a PEM electrolyzer, and an RO with an energy recovery instrument. Hot seawater was employed to warm the working fluid of the ORC for the generation of energy to operate the electrolyzer. The thermal energy from PVT was applied to generate cooling by driving the AC. Further, for the freshwater generation, the power produced by PVT was supplied to the RO system. The exergy efficiency and overall cost rate for the optimized plan were reported to be 60% and $154/h, respectively.

13.5.4 Hybrid solar power tower wind turbines Ishaq et al. [64] introduced a trigeneration device with an SPT and wind turbines and analyzed the efficiency from energy and exergy perspectives. Fig. 13.39 illustrates the schematic structure of the suggested model to produce electricity, heating, and hydrogen. The scheme consists of an SPT, an integrated Cu Cl cycle with a hydrogen compression device, and a wind turbine. At 750 C the solar tower unit conveys heat to the molten salt. A portion of the electricity produced by the wind turbine was provided to the hydrogen compression unit’s electrolyzer and compressors. The heat used in the compressors’ intercooling process was applied to boil the water. The generated power by the wind turbines was 11.98 MW, with the hydrogen production rate of 455.1 kg/h. The platform’s energetic and exergetic efficiencies were, respectively, 49% and 48.2%.

13.5.5 Hybrid solar wind/ocean So far, PT solar collectors, solar energy tower plants, and PVTs have been employed for the production of hybrid solar wind/ocean models. Luqman et al. [65] tested an innovative polygeneration method for solar wind hybrids, Fig. 13.40, combined with an H2 O2 combustion unit to generate a variety of valuable products such as electric power, cooling, hydrogen, and oxygen, fresh water, drying food, and hot water. The unit consists of a PT solar field, wind turbine, Rankine cycle, MSF desalination scheme, electrolyzer, R-134 vapor compression refrigeration, food-drying module, oxyhydrogen combustor, water heater, and hydrogen and oxygen stores. To ensure the system’s continuous operation a thermal energy storage unit was employed. In the H2 O2 combustor, hydrogen was treated as a fuel when solar energy was inaccessible. The findings revealed that the plant developed for polygeneration could achieve 11.4 MW of electricity, 828 m3/day of pure water, 36 kg/s of drying hot air, 31 kg/s of heat water, 920 kg/ day of hydrogen, and 2.26 MW of cooling. Multi- and single-generation energetic

277

FIGURE 13.38 Hybrid solar PVT ocean-driven polygeneration system proposed by Ahmadi et al. [63]. PVT, Photovoltaic/thermal.

13.5 Hybrid solar polygeneration systems

FIGURE 13.39 Hybrid solar tower wind turbine-driven polygeneration system proposed by Ishaq et al. [64].

FIGURE 13.40 Hybrid solar-wind turbine ocean-driven polygeneration system proposed by Luqman et al. [65].

279

FIGURE 13.41 Hybrid solar PTC geothermal ocean-driven polygeneration system proposed by Azhar et al. [66].

FIGURE 13.42 Hybrid heliostat field/CPVT, wind turbines, and a biogas-driven polygeneration system proposed by Bamisile et al. [67].

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efficiencies were 50% and 15%, respectively; while their exergetic improvements were 34% and 16%, respectively.

13.5.6 Other hybrid models The developed methods that used solar energy in conjunction with two other renewable energies have been classified as other hybrid models. Azhar et al. [66] presented a hybrid system (Fig. 13.41) to produce electricity, fresh water, industrial heating, and cooling powered by solar, geothermal, and ocean sources of energy. The method consists of a PT solar field, a geothermal double flash power system, an MSF distillation, a single-effect AC, and an OCTE scheme. To decrease the pumping power the seawater supplied to the OTEC was employed for the MSF plant. The overall power generation of the unit was about 55 MW, the production of fresh water was 18.54 kg/s, and the production of industrial heat was 39.48 MW. The OTEC also supplied 30.49 kW of electricity with 0.73% efficiency. The platform’s energy and exergy efficiencies were 13.93% and 17.97%, respectively. Bamisile et al. [67] developed a polygeneration framework relying on heliostat field/CPVT, wind turbines, and a biogas module. The suggested scheme (Fig. 13.42) could generate energy, hot air, hydrogen, fresh water, hot water, and cooling. Our scenarios were considered, including CPVT/wind, wind/biogas, CPVT/biogas, and only biogas. The field of heliostat focused the solar radiation on the CPVT machine, which released both electricity and thermal energy. The device could yield 3.4 MW of power, 12.41 L/s of hydrogen, 279.4 kW of cooling capacity, 17.546 kg/s of hot air, 144.18 L/min of hot water, and 10.31 L/min of fresh water. The energetic and exergetic efficiencies were around 64.91% 71.06% and 31.80% 53.81%, respectively.

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CHAPTER

Optimization of cogeneration and polygeneration systems

14

Chapter outline 14.1 Introduction ...............................................................................................287 14.2 Optimization problem ..................................................................................288 14.2.1 System boundaries ..................................................................288 14.2.2 Objective functions and system criteria .....................................289 14.2.3 Decision variables ...................................................................289 14.2.4 Constraints .............................................................................289 14.3 Optimization techniques ..............................................................................290 14.3.1 Classical optimization ..............................................................290 14.3.2 Numerical optimization techniques ...........................................291 14.3.3 Metaheuristic optimization techniques ......................................291 14.4 Multiobjective optimization .........................................................................291 14.5 Case studies ..............................................................................................295 14.5.1 Case 1: Solar hybrid cogeneration plant ....................................295 14.5.2 Case 2: Optimal design of utility systems using targeting strategy 304 14.5.3 Grassroots case study ..............................................................306 14.5.4 Optimization results ................................................................306 14.5.5 Case 3: Optimal design of thermoelectric generator-parabolic trough collector-driven polygeneration system ......................................310 14.5.6 Case 4: Biomass
solar-driven polygeneration system .................316 References ..........................................................................................................322

14.1 Introduction Mathematically, optimization is a process during which a function subject to given constraints is either maximized or minimized for several variables, each within a specific range. Optimization entails searching for the ideal configuration for a certain problem subject to practical constraints. Polygeneration system is a multiinput and multioutput system that generates different products simultaneously. The optimal design of this system is done with one or multiresources to obtain different objective functions (OFs). In this regard, a single- and multi-OFs are introduced [1].

Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00014-8 © 2021 Elsevier Inc. All rights reserved.

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To optimal design of the polygeneration systems, the numerical optimization approaches are applied mainly to the sizing of the equipment properly, setting resources to achieve maximum economic and environmental benefits. Singleobjective optimization (SOO) has been widely used over several decades. SOO applies to the process in which the optimum solution is being searched where the optimization problem includes a single-OF only. Therefore it assumes the solution to the problem regarding a single metric. Multiobjective optimization (MOO) emerged in response to the need to assume multiple OFs and its significance. These optimization problems are typically referred to as multicriteria decision-making in management fields. Intrinsically, the majority of real-life optimization problems possess more than one OF. Optimization cannot reasonably fulfill its principles and purposes with a single OF only where other objectives are critical as well. The subsequent four sections define and explain several significant optimization terms and concepts.

14.2 Optimization problem Optimization is a branch of mathematics that tries to find the best solution based on related OFs. Optimization is a key role in finding and solving engineering and real-life problems in the wide range area from mathematical programming, economics, management science, business, medicine, life sciences, artificial intelligence, etc. Mathematically, an optimization problem can be expressed as [2]: Find X 5 ðx1 ; x2 ; . . . ; xn Þ minimizes fi ðX Þi 5 1; 2; . . . ; n0 subject to gi ðX Þ # 0; j 5 1; 2; . . . ; ng hk ðX Þ 5 0; k 5 1; 2; . . . ; ne xlm # xm # xum ; m 5 1; 2; . . . ; ns

where X is related to n design variables, fi ðX Þ is an OF, gi ðX Þ is inequality, and hk ðX Þ is equality constraints. xlm and xum are the lower and upper limits for the xm design variable. The n0 , ng , ne , and ns are the number of OFs, number of inequality, equality, and constraints, respectively.

14.2.1 System boundaries Setting the system boundaries is the initial phase of any optimization problem. All subsystems affecting the performance of the system need to be involved. The system should be decomposed into smaller subsystems where it is extremely complex. In this context, optimization should be performed on each subsystem separately, meaning that subsystems need to be suboptimized.

14.2 Optimization problem

14.2.2 Objective functions and system criteria The second phase is to specify system criteria, occasionally referred to as OFs. An OF is either minimized or maximized at decision-maker’s will. Optimization criteria may differ significantly. For example, they may depend on economic goals (e.g., environmental impact cost, exergy destruction costs, total annual levelized costs, and total capital investment), efficiency goals (e.g., energy and exergy), other technological goals (e.g., production rate, production time, and total weight), and environmental impact objectives (e.g., pollutant emission reduction, environmental impacts related to exergy destruction, carbon footprint, water footprint, and energy footprint) [1]. Notably, multiple OFs can be assumed when trying to find the best possible solution to an optimization problem through MOO. The OF components may be driven by the economy, environment, and energy efficiency/conservation. The economic function is estimated by net cost, profit, or total cost (TC). Net cost equals TC subtracted by system earnings. Environmental objectives are involved in life cycle analysis, principally alluding to the environmental impact of human-mediated carbon dioxide (CO2) emissions. They can be calculated by fossil fuel combustion
related emissions during energy production processes, device/equipment manufacturing
related impacts, or global environmental impacts (Eco-indicator 99 or EI-99). EI-99 is employed to evaluate the environmental impacts regarding human well-being, natural resources, and the quality of the ecosystem for a long time horizon. Impacts of sulfur dioxide (SO2) and fine particulate matter (PM2.5) emissions are assumed as well. Energy conservation objective can be estimated by savings from polygeneration energy systems (PESs) and polygeneration system emissions relative to the independent supply of various energy products. The advantages of polygeneration systems can be underscored by utilizing the standard criteria already mentioned.

14.2.3 Decision variables The next key step in optimization problem formulation is choosing independent decision variables that describe feasible design options sufficiently. Three factors should be taken into account in selecting decision variables: (1) all essential variables potentially affecting the system’s efficiency and performance must be included, (2) less critical variables must be excluded, and (3) independent variables with open-to-change values must be distinguished. The only amenable-tochange variables in optimization problems are decision variables only. Dependent variables are those with their values measured concerning independent variables by utilizing mathematical models.

14.2.4 Constraints Design problems would face constraints owing to fundamental principles of conservation requiring to be fulfilled, limited physical variable ranges, as well as other

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restrictions. Variables may be restricted, possibly because of limited equipment, materials, and space used. In other words, the flow rate of material, generated force, low/high temperatures attainable by components, maximum allowable operating pressure, system physical dimensions, etc. may be restricted. Furthermore, minimum temperature values can be determined for plastic thermoforming and ignition to take place in an engine. Thus constraints may include both maximum and minimum values of design variables. Thermal systems confront numerous constraints due to specifically energy-, mass-, and momentum-associated conservation laws. For example, under steadystate conditions, total mass entering the system must be equal to total mass leaving. Thus an equation arises requiring to be satisfied by pertinent design variables, thereby limiting values, probably used in finding an optimum. In a similar vein, considerations for the energy balance are crucial in thermal systems as they probably lead to limited heat fluxes, dimensions, temperature ranges, etc., which may be employed. During simulation and modeling a number of these constraints are typically fulfilled since governing equations are based on the principles of conservation. Hence, such constraints are considered in the already optimized OFs. In these instances, only further restrictions defining design domain boundaries are subject to further consideration.

14.3 Optimization techniques 14.3.1 Classical optimization Classical optimization techniques can be applied to search for an optimal solution or unconstrained minimum or maximum of differentiable and continuous functions. Accordingly, the following characteristics can be enumerated for classical optimization:

• Some analytical techniques utilize differential calculus in to locate the optimal solution.

• Classical techniques cannot be widely used due to having discontinuous or differentiable OFs.

• These techniques consider the function to be twice differentiable regarding • • • •

design variables, with continuous derivatives. Classical optimization techniques can address three major problem types: single-variable functions, unconstrained multivariable functions, and multivariable functions with both inequality and equality constraints.

The method of Lagrange multipliers can be employed for equality-constrained problems. The Karush
Kuhn
Tucker conditions must be satisfied to find the optimal solution for inequality-constrained problems.

14.4 Multiobjective optimization

14.3.2 Numerical optimization techniques There are several categories of this optimization technique [3]:

• Linear programming (LP): It is used when an OF f is linear, and Set A, in • • • • • •

which A is the design variable space, is determined by employing linear inequalities and equalities only. Integer programming: It is used for linear programs where some/all variables are constrained to take integer values. Quadratic programming: It enables an OF to have quadratic (squared) terms, whereas Set A needs to be determined with linear inequalities and equalities. Nonlinear programming: It is generally used when an OF or constraints or both contain nonlinear components. Stochastic programming: It is used when certain constraints are dependent upon random variables. Dynamic programming: It is used when the optimization strategy is founded on breaking down the problem into smaller subproblems. Combinatorial optimization: It is associated with problems in which the feasible solution set is discrete or can be reduced to a discrete set of feasible solutions.

14.3.3 Metaheuristic optimization techniques In the past three decades, metaheuristic approaches have been rapidly developed to solve optimization problems. These approaches are principally and mainly do not have a theoretical base and intuitive and do not have theoretical support. Some well-known metaheuristic methods are Genetic Algorithm (GA) [4], Simulating Annealing (SA) [5], Tabu Search [6], Ant Colony [7], Particle Swarm Optimization [8], and Cuckoo Search (CS) [9], Firefly Algorithm [10], Bat Algorithm [11], and Kirill Herd [12]. Fig. 14.1 represents the classification of numerical optimization approaches and a brief history of the optimization methods is depicted in Fig. 14.2. Table 14.1 also indicates a classification of optimization problems.

14.4 Multiobjective optimization In general, optimal conditions heavily depend upon the selected OF. However, some performance elements usually need to be taken into account for that to be practically implemented. In the design of energy and thermal systems, specific typical quantities need to be maximized, such as efficiency (exergy and/or energy), rates of production, output, quality, and rate of thermal conduction. Meanwhile, other quantities need to be minimized, including costs, environmental impact, input, and pressure. Either one of the above can be selected as the problem OF, though considering multiple OFs is commonly more helpful.

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FIGURE 14.1 Classification of numerical optimization approaches [2].

Those who adopt a simple optimization approach would be capable of determining the maximum and minimum of a single-variable function. They can take advantage of first- and second-derivative methods to find the maximum value of a particular function. At an advanced level, optimization users can find the maximum value of multivariable functions. They can also solve constrained multivariable optimization problems. Constrained (or constraint) optimization is practically a crucial issue as the majority of real-life problems include constraints. MOO has been recently widely utilized and comprehensively reviewed. It is included in a vast array of algorithms, applications, and case studies. Multiple OFs can often be dealt with by being integrated into a single-OF, which is to be subsequently maximized or minimized. For instance, the thermal conduction rate should be maximized in designing cooling/heating systems for electronic devices. Nevertheless, this is usually achieved to the detriment of the raised rate of fluid flow and associated frictional pressure drop. A MOO problem has either maximized or minimized OFs. Similar to SOO, MOO contains some constraints that must be satisfied in any feasible solution, including the optimal solution. X is one of the solutions to this problem, a vector with n design parameters and decision variables. Here, the final constraint set is referred to as variable bounds, restricting the searching bound. Every solution value for decision variables needs to fall within a lower bound (x(L) i) and an upper bound (x(U) i). To demonstrate, two OFs will be considered: OF1 and OF2. It is assumed that they should be minimized; however, they can also be maximized in a similar vein as it resembles minimizing its negative.

14.4 Multiobjective optimization

FIGURE 14.2 A brief history of optimization methods [2].

The values of both OFs at five design points are demonstrated in Fig. 14.3A. According to this figure, the second design is preferred over the fourth design as values for both OFs are lower for the former. Likewise, the third design is preferred over the fifth design. Moreover, the first, second, and third designs are not dominated by any other designs.

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Table 14.1 Classification of optimization problems [2]. Basic of classification

Group

Specifications

Number of design variables

Single variable

The vector of design variables includes only one variable. The vector of design variables includes more than one variable. There is one criterion expressed as an objective function. Many criteria are considered together to determine an optimum solution. A minimum or maximum of objective function without any limitation is attempted. Some constraints define the set of feasible solutions. Objective functions and constraints are linear.

Multivariable Number of objective functions

Single objective Multiobjective

Presence of constraints

Unconstrained Constrained

Features of constraints and objective functions

Nature of design variables

Linear programming Nonlinear programming

Static Dynamic

Type of design variables

Discrete Continuous mixed

Nature of design variables and design input data

Deterministic

Probabilistic

Nature of objective functions and design constraints

Crisp

Fuzzy

Some of the objective functions and constraints can be nonlinear. Quadratic programming and geometric programming problems are two specific kinds of nonlinear optimization problems. Design variables are independent. They are not functions of other parameters. Design variables are functions of other parameters, for example, time. Design variables can only take integer or discrete values. Design variables take any real value. Some design variables take integer values and others take real values. All design variables or preassigned parameters such as loads acting on a structure are assumed to be deterministic. All or some of the design variables or preassigned parameters are described by random or probabilistic variables within a given range. The constraints and objective functions are expressed by nonfuzzy and unambiguous expressions or responses. Some of the constraints and objective functions are described by fuzzy expressions or responses.

The set of all nondominated designs constituted the Pareto front, which portrays the optimum set of design points, as depicted in Fig. 14.3B. Notably, any point on the Pareto front may be assumed as an optimum design condition. Decision-makers (usually designers or engineers) are responsible for choosing a particular design from a population of points that form the Pareto front.

14.5 Case studies

FIGURE 14.3 Two-objective optimization with two objective functions OF1 and OF2: (A) dominant designs and (B) the Pareto frontier [3].

14.5 Case studies 14.5.1 Case 1: Solar hybrid cogeneration plant 14.5.1.1 General overview This section applies the GA for the optimal design of a solar hybrid cogeneration cycle as proposed by Soltani et al. [13], and using CS algorithm by Khoshgoftar Manesh and Ameryan to examine the optimum solution for hybrid solar CGAM (C. Frangopoulos, G. Tsatsaronis, A. Valero, M. Spakovsky) [14]. As a newly established population-based algorithm, CS is inspired by the behavior of certain cuckoo species along with the Le´vy flight behavior of individual fruit flies and birds. Moreover, solar power tower (aka “central tower” power plants or “heliostat” power plants) technology can be used in traditional fossil fuel power cycles, partially because it can reach temperatures up to 1000 C. A solar hybrid cogeneration cycle has undergone an exergoeconomic optimization here. The popular prescribed simple cogeneration (CGAM) problem is modified via hybridization by the proper design of the heliostat field layout around the heliostat power plant to satisfy its annual demand. The schematic of the hybrid solar cogeneration cycle is shown in Fig. 14.4A and the flow diagram of the well-known CGAM cogeneration plant is indicated in Fig. 14.4B. Following are the equations of the physical model governing a solar receiver: :

:

:

ma h3 1 Qs;absorbed 5 ma h30 :

Qs;absorbed 5 DNI 3 Amirror 3 ηfield 3 ηreceiver ηfield 5 ηatmospheric 3 ηreflection 3 ηCos 3 ηshading 3 ηblocking

(14.1) (14.2) (14.3)

3 ηAttenuation 3 ηIntercept p30 5 p3 ð1 2 Δpreceiver Þ

(14.4)

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FIGURE 14.4 (A) Hybrid cogeneration cycle. (B) Flow diagram for the CGAM cogeneration plant [13]. CGAM, C. Frangopoulos, G. Tsatsaronis, A. Valero, M. Spakovsky.

14.5 Case studies

The implemented technique has been presented in Section 14.5.1.2 to extract a general equation for solar energy collected and additional design details.

14.5.1.2 Solar field design Some factors influence solar field design and general performance, such as attenuation, cosine effects, intercept, reflectivity, and atmospheric conditions, as well as shading and blocking, typically evaluated regarding efficiency. The present study presumes reduced land costs since the system in question is supposed to be situated in the Mojave Desert. Thus mirrors are installed to stay properly distant to avert blocking and shading effects, thereby overlooking them. According to heliostat manufacturing information, the reflection coefficient is supposed to be constant, thus overlooking the degradation and the soiling of the mirrors. Notwithstanding, the behavior of cosine and atmospheric efficiencies is a function of time, which requires to be specified by transient functions to reach specific outcomes. Accordingly, the transient value of the existing solar power contiguous to the receiver cluster is now measured by formulating the respective MATLAB codes with proper time steps. To speed up the optimization process the general conclusion of the corresponding solar field is converted into principal optimization codes by a unitderived equation. A certain number of mirrors are required only to measure solar power on the brink of the receiver. A huge dataset could be generated by executing solar codes at proper intervals of the number of mirrors (i.e., 6000
9000). The solar power verging on the receiver was measured for 6000
9000 heliostats and was curve-fit as shown in the following equation: Qs;field 5 6:499 3 n mirror 1 3823

(14.5)

Table 14.2 describes the solar field design in detail. Fig. 14.5 depicts the layout of the solar field designed. Besides, mirrors are southward-directed, depending on the location of the field.

14.5.1.3 Optimization This study formulates and implements a MOO scheme for the two plants to discover solutions satisfying exergoeconomic objectives. The optimization process is implemented using specific types of search algorithms (e.g., multiobjective CS and GA algorithms) for evaluation. The total operating cost rate (should be minimized) and the exergy efficiency (aka second-law or rational efficiency) of the cogeneration plant (should be maximized) are two OFs for MOO problem assumed here. The two objectives, exergetic and economic, can be mathematically formulated as follows: Exergetic

:

:

ξ 5 W net 1 msteam ðe9 2 e8 Þ : mfuel 3 efuel

(14.6)

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Table 14.2 Some information about the solar field design [13]. Location of field

Heliostat area Receiver cluster area Total field area Number of mirrors Tower height ηCosine ηReflectivity ηAtmospheric

Daggett, Mojave, California, United States

16 m2 with 4 m 3 4 m dimensions 120 m2 1700 m 3 800 m 8800 (obtained through optimization) 150 m 0.82 (calculated annual average dynamic value) 0.90

ηBlocking

0.59 (calculated annual average dynamic value) 1

ηShading

1

ηIntercept

0.85

ηAttenuation ηReceiver

0.92 0.56 [15]

FIGURE 14.5 Solar field layout [13].

Long.

116.88

Lat.

34.86

Alt.

700 m

14.5 Case studies

:

Economic

:

:

Ctotal 5 Csteam 1 Celectricity

(14.7)

Decision variables may be modified during optimization processes. There are five-degree-of-freedom (5-DOF) economic and thermodynamic models of the CGAM problem. For optimization process acceleration and search space definition for the optimization process, a specific variation range is determined for each of the decision variables as follows: Instead of T3, which has been used in the original CGAM problem, the variable εap is employed to mitigate the number of infeasible solutions occurring during optimization. Following are two decision variables assumed as well for a hybrid cycle:

• Nmirror (i.e., no. of heliostat mirrors): 7500 # Nmirror # 10,000 • T30 (i.e., solar thermal temperature): 1200 # T30 # 1300 The present study utilizes the CS algorithm [16] to optimize the CGAM problem.

14.5.1.4 Physical constraints Heat transfer between cold and hot streams in an air preheater (APH) and a heat recovery steam generator (HRSG) must satisfy the following feasibility constraints:

• APH: T5 . T3; T6 . T2 • HRSG: ΔTP 5 T7P 2 T9 . 0; T6 $ T9 1 ΔTP; T7 $ T8 1 ΔTP; ηhrsg # 1 Another constraint (relative to the original CGAM problem) is enforced on the exhaust gas temperature, not allowed to be lower than 380 K. The net target power is kept constant at 30 MW for the regular power cycle. However, in the hybrid mode, the net power is supposed to have been deviated from an annual base design by 400 kW to achieve a higher number of feasible design points due to fluctuations in solar radiation. Furthermore, in earlier phases in the design of the plant, a group of sufficient design points was not derived for a constant 30 MW power in the hybrid mode, thus paving the way for taking additional steps for optimization. Following are the two newly formulated constraints: 29; 600 , Wnet , 30; 400

(14.8)

_ fuelhybrid , m _ fuelconventional m

(14.9)

14.5.1.5 Optimization runs 14.5.1.5.1 Conventional case First, the base case study is determined by optimizing the normal CGAM cycle to be compared to hybridization cases by utilizing CS and GA algorithms. Fig. 14.6

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30,000

Total cost rate of product ($/h)

300

25,000 20,000 EP

15,000 10,000 5000 51.4

51.6

51.8

52

52.2 52.4 52.6 52.8 Total exergetic efficiency (%)

53

53.2

53.4

FIGURE 14.6 Pareto front optimal solution using GA for base case [13]. GA, Genetic Algorithm.

demonstrates Pareto optimal solutions for the CGAM cycle for the OFs suggested by employing the GA algorithm. As seen, the plant’s overall second-law efficiency is increased by 52.4%, though the TC product rate rises marginally. A 52.4%
52.7% increase in the overall exergy efficiency leads to a slight rise in the TC product rate. A 152.7% increase in the overall exergy efficiency leads to a sharp rise in the TC rate. MOO entails a decision-making process to select the final optimal solution from existing solutions. The decision-making process is typically conducted assisted by a theoretical point [i.e., equilibrium point (EP)] illustrated in Fig. 14.6. For this point the two objectives have their optimal values apart from the other objective. Having the two objectives at their optimum point concurrently is impracticable clearly. The EP is not a Pareto optimal solution, as depicted in Fig. 14.6. On the Pareto front the nearest point to the EP may be assumed as a desired final solution. In selecting the final optimum point, a better value should be attained for each of the objectives compared to their initial value taken from the base case problem. Besides, where one objective is variable, the stability of the point selected is of paramount importance. Thus a section of the Pareto front is selected for decision-making, as shown in Fig. 14.7, overlapping the area between the vertical and horizontal lines. A final optimum solution with a TC product rate of 10,851.47 $/h and a rational efficiency of 52.73% is selected, as shown in Fig. 14.7. Notably, an optimal solution can be selected based on each decision-maker criteria and preferences. Accordingly, each of them might select a distinct point as an optimal solution to better fit his or her needs. Table 14.3 lists the values associated with each decision variable and both OFs (overall exergy efficiency and TC product rate) using MOO by CS and GA.

14.5 Case studies

Total cost rate of product ($/h)

19,000 17,000 15,000 13,000 11,000 9000 52.6

52.73981866, 10851.47376 52.65

52.7

52.75 52.8 52.85 Total exergetic efficiency (%)

52.9

52.95

53

FIGURE 14.7 The optimum point details [14].

Table 14.3 introduces and compares the two plants in terms of cycle performances [13] following associated optimal points by utilizing GA and CS algorithms. The optimal decision variables obtained are assumed as input parameters to find out the behavior of the cycle. The total net electrical energy produced in the hybrid cycle deviates 60 kW (an acceptable value) from the fixed power generated for the CGAM problem.

14.5.1.5.2 Solar hybrid case Based on Toffolo and Lazzaretto’s MOO study in Ref. [17], the final point in the cycle, as an available section of HRSG, is evidently of greater importance in the optimization process. Hence, they split up their study into two separate parts: constrained and free exhaust temperatures, as done here. Nonetheless, in conventional case optimization, no substantial change has been examined by removing exhaust temperature as a constraint. The temperature of gases exiting the HRSG significantly contributes to the verification of the optimal workable design point for the hybrid cycle. Although it is the final stage of monitoring the performance of the cycle as its final point, it fails to affect each state thermodynamically directly. No possible design point is identified in the first run of the hybrid cycle by employing decision-variable intervals presented. This is an unavoidable consequence since gas exergy (entering HRSG) shows a steep decline (about 1/2) due to a substantial reduction in fuel
air (F/A) ratio in the hybrid cycle (the final design target). This leads to low exergy to generate the needed saturated steam (i.e., 14 kg/s). However, a coherent set of design points would be generated following optimization, provided that while the exhaust temperature constraint is neglected, as shown in Fig. 14.8. Thus the excellent overall performance of the plant is manifest.

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Table 14.3 Evaluation of optimum values in conventional and solar hybrid cases [14]. Conventional (GA)

Solar hybrid (GA)

Conventional (CS)

Solar hybrid (CS)


0.521

1590 0.8602 0.9087 10.6184

1207.09 0.889 8800 9.69 1582.08 0.8632 0.9193 9.55


0.53

1591.2 0.8612 0.9091 10.6214

1208.25 0.89 8800 9.69 1583.11 0.8644 0.9196 9.56

818.53 611.01 74.50 1.5663 30,000 436.5533

962.67 594.90 80.25 0.82 30,060 385.13

819.24 611.24 74.52 1.5663 30,000 436.5821

962.94 595.12 80.29 0.82 30,060 385.79

2.4827 0.0184
23.92 1.52 9.94

1.79 1.71 34.78 11.64 1.44 2.6

2.4827 0.0184
23.92 1.52 9.94

1.79 1.71 34.78 11.64 1.44 2.6


90.06 23.13 0.4247

59.8 91.1 12.47 1.03


90.06 23.13 0.4247

59.8 91.1 12.47 1.03

10,851.47 52.74

20,296.98 41.6

10,852.81 52.77

20,299.01 41.83

81.08 37.89


94 53.98 35

81.08 37.89


94 53.98 35

Decision variables T30 (K) εap Nmirror (kg/s) T4 (K) ηsc (%) ηst (%) r_c Dependent variables T3 (K) T2 (K) (kg/s) (kg/s) (kW) T7 (K) Exergy destruction rate AC (MW) APH (MW) Solar (MW) CC (MW) GT (MW) HRSG (MW) Exergy rate for significant streams 30 (MW) 4 (MW) (MW) 7 (loss) (MW) Objectives Total cost rate of product ($/h) Overall exergy efficiency (%) Other results Total input exergy rate (MW) Total exergy destruction rate (MW)

(Continued)

14.5 Case studies

303

Table 14.3 Evaluation of optimum values in conventional and solar hybrid cases [14]. Continued

Average solar share in hybrid mode (%) Annual average solar share (%) (considering night hours) Specific cost of elec. ($/kWh) Specific investment for CO2 reduction ($/kgCO2) CO2 emission rate (t/year)

Conventional (GA)

Solar hybrid (GA)

Conventional (CS)

Solar hybrid (CS)




14




14

0.06526


0.08533 2.13

0.06528


0.08536 2.13

114,375

86,115

114,375

86,115

AC, Air compressor; APH, air preheater; CC, combustion chamber; GT, gas turbine; HRSG, heat recovery steam generator.

Total cost rate of product ($/h)

32,000 30,000 28,000 26,000 24,000 22,000 20,000

45

45.3

45.6

45.9 46.2 46.5 46.8 Total exergetic efficiency (%)

47.1

47.4

47.7

FIGURE 14.8 The Pareto front optimal solution for solar CGAM [13]. CGAM, C. Frangopoulos, G. Tsatsaronis, A. Valero, M. Spakovsky.

Consequently, two modifications are performed: (1) the interval of “the effectiveness of the APH (εap)” is decreased to 0.30 and (2) the combustion temperature (T4) is increased. It should be pointed out that the permissible combustion temperature regarding technological limitations for the first-stage gas turbine blade is 1650K. Notwithstanding, in such a case, T4 is allowed to rise to 1750K to find a feasible design availability. Finally, an estimated illogical cost is followed as the optimal point, which is approximately sixfold its free temperature, leading to the hybrid mode.

CHAPTER 14 Optimization of cogeneration and polygeneration systems

20,800

Total cost rate of product ($/h)

304

20,700 20,600 20,500 20,400 20,300 20,200 20,100 40.9

41

41.1

41.2

41.3

41.4

41.5

41.6

41.7

41.8

41.9

Total exergy efficiency (%)

FIGURE 14.9 The Pareto front optimal solution for solar CGAM [13]. CGAM, C. Frangopoulos, G. Tsatsaronis, A. Valero, M. Spakovsky.

The allowable range is as follows:

• m8 (injected water in HRSG): 5 # m8 # 15 The Pareto optimal solutions for the hybrid system are shown in Fig. 14.9. The plant’s overall exergy efficiency rises to 41.3%, while the TC product rate increases marginally. A 41.3%
41.6% increase in the overall exergy efficiency leads to a slight rise in the TC product rate. A 141.6% increase in the overall exergy efficiency leads to a surge in the TC product rate. The same process used for the conventional cycle applies to select the final optimal solution out of existing solutions. Details of the optimum point using GA in solar hybrid CGAM is shown in Fig. 14.10.

14.5.2 Case 2: Optimal design of utility systems using targeting strategy Central utility systems are a major part of most processing industries that produce simultaneous steam at different levels and shaft power. The “grassroots problem” refers to some instances in which a brand-new utility system, along with its operating conditions, must be developed for multiple scenarios that maximize or minimize the task objective and, at the same time, satisfy the associated constraints. Regarding information required, grassroots cases do not entail specifying any available tools. Nevertheless, similar to prior problem types, the same operational

14.5 Case studies

20,500

Total cost rate of product ($/h)

20,450 20,400 20,350 20,300 41.60225421, 20296.98924

20,250 20,200 20,150 20,100 41.5

41.55

41.6 Total exergy efficiency (%)

41.65

41.7

FIGURE 14.10 Details of the optimum point. Using GA in solar hybrid CGAM [13]. CGAM, C. Frangopoulos, G. Tsatsaronis, A. Valero, M. Spakovsky; GA, Genetic Algorithm.

data still needs to be provided. Moreover, compared to retrofitting work, new tools should be conveniently substituted by supplying additional options. There exist increasing alternatives for the newly added component in the grassroots design. Accordingly, scant information is required for these types of problems, and some more variables and constraints can be applied compared to retrofitting cases. In addition, the variables that maximize or minimize the task objective can be achieved from the solution space. This is a far greater space because each unit in the configuration superstructure can be of various dimensions (a continuous range of sizes) within practical limits. Therefore genuine retrofit and operational problems are regarded as particular instances of a more general formulation of grassroots design according to which all particular tasks are definable. Rad et al. [18] proposed a systematic procedure for the grassroots design of the central utility system of process industries based on targeting strategy. Fig. 14.11 illustrates the algorithm of the steps taken for the grassroots design suggested by Rad et al. [18]. A comparison was made between the outcome of the presented algorithm and STAR’s. The latter takes advantage of a mathematical programming approach to finding optimal solutions. Another comparison was made between the turbine hardware model )THM( model [18] and bottom-up/top-down combined models [19] and the proposed targeting model to assess whether or not the latter can be applied for optimum design. To this end, three procedures were established and assessed following three cogeneration targeting models. The first procedure is founded on the targeting model developed by Rad et al. [18]. The second procedure takes advantage of the THM. Finally, the third procedure is a combination of top-down and bottom-up procedures.

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FIGURE 14.11 Algorithm of Pooyan Rad for the optimal design of a utility system (grassroots design) [18].

Table 14.4 Site conditions for case study [18]. Parameter

Unit

Value

Total working hours LHV: fuel oil 2 LHV: natural gas Ambient temperature Relative humidity Steam (hot utility) price Electric price: peak Electric price: off-peak Peak hours per day Price: fuel oil 2 Price: natural gas Price: raw water

h/year kJ/kg kJ/kg  C % $/kWh $/kWh $/kWh h $/kg $/kg $/t

8600 45,000 50,244 10 60 0.16 0.07 0.05 7 0.19 0.22 0.05

14.5.3 Grassroots case study The optimization model proposed here is applied to a site utility of a new petrochemical complex, outlined by Kapil et al. [20]. It is assumed that both condensate return and boiler feedwater have the same temperature, that is, 105 C. Tables 14.4 and 14.5 list site and steam conditions for our case study, respectively.

14.5.4 Optimization results As shown in Fig. 14.12, the optimal solution is achieved, taking into account that the superstructure is subject to the results of targeting and exergoeconomic optimization by employing GA. The optimal trade-offs between CP and CD rates,

14.5 Case studies

Table 14.5 Steam header conditions for case study [18]. Parameter

Unit

Value

VHP pressure HP pressure MP pressure LP pressure Deaerator pressure Condenser pressure VHP steam demand VHP steam generation HP steam demand HP steam generation MP steam demand MP steam generation LP steam demand LP steam generation

bara bara bara bara bara bara MW MW MW MW MW MW MW MW

101 20.6 4.1 2.7 1.1 0.98 110.8 0.0 141.4 120 57 47.7 89 15.4

HP, High pressure; LP, low pressure; MP, medium pressure; VHP, very high-pressure.

FIGURE 14.12 Superstructure associated with targeting strategy [18].

CD rate and exergy efficiency, and CP rate and exergy efficiency (for OFs) are demonstrated in Figs. 14.13
14.16 (for variable interest rates), and Fig. 14.15 (for fuel prices), respectively.

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FIGURE 14.13 Pareto front for optimal solution (Cp fuel 5 0.004 $/MJ, i 5 5%) [18].

FIGURE 14.14 Pareto front for optimal solution (Cp fuel 5 0.004 $/MJ, i 5 5%) [18].

14.5 Case studies

FIGURE 14.15 Pareto front for optimal solution (Cp fuel 5 0.004) [18].

FIGURE 14.16 Pareto front for optimal solution [18].

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Flows: kg/s Boiler Fuel: 2.7

Fuel: 2.7

Boiler

40.0

101 bar 530°C

40.0

0.0 MW

110.8 MW LD1

~

T1

0.1

~

T2 1.57 MW

4.3

4.8

LD2 1.2

~

T3

20.6 bar 344°C

120.0 MW

141.4 MW

~

T4 3.46 MW

2.05 MW

3.5

5.9

LD3

1.41 MW

4.1 bar 202°C

47.7 MW

57.0 MW 6.7

T5

~

9.35 MW

14.5

T6

~

9.61 MW

14.9

2.7 bar 170°C

15.4 MW

5 89.0 MW

Deaerator

FIGURE 14.17 Optimum arrangement for case study [18].

Fig. 14.17 shows the optimal operational configuration of the steam network obtained by the novel procedure, the targets for shaft work, fuel and steam flow rates, and heat loads in a steam boiler.

14.5.5 Case 3: Optimal design of thermoelectric generator-parabolic trough collector-driven polygeneration system 14.5.5.1 General overview This section introduces a polygeneration system containing a parabolic trough solar collector (PTSC), a thermoelectric generator (TEG), a Rankine cycle (RC), and a proton-exchange membrane (PEM) proposed by Habibollahzade et al. [21]. The system mentioned previously is integrated by setting up a TEG in place of a condenser as the cooling and power generating unit following the transfer of the excess power generated by the TEG to the polymer electrolyte membrane electrolyzer for producing hydrogen. Thorough modeling is conducted on the integrated renewable energy system. Moreover, to gain a better insight into the performance of the system, the impact of the influential parameters on exergoeconomic indicators was examined by performing a parametric study. The system is demonstrated in Fig. 14.18. The input values of the mentioned system are determined in Table 14.6. Table 14.7 shows the cost equations of considered equipment and Table 14.8 indicates thermophysical and exergoeconomic properties of each state point.

14.5 Case studies

FIGURE 14.18 Schematic diagram of polygeneration system proposed by Habibollahzade et al. [21].

Table 14.6 Values related to input parameters for the considered system [21]. Parameter

Value

Parameter

Value

Parameter

Value

Steam Rankine cycle

PTSC (for single collector)

PEM electrolyzer

ηt ηp

0.85 0.95

WðmÞ LðmÞ

5.76 12.27

PO2 ðkPaÞ PH2 ðkPaÞ

101.3 101.3

P5 ðkPaÞ P6 ðkPaÞ ΔTp;p ΔTsup TEG unit ZTM T7
T8 ( C) T8 ( C) T9 ( C)

1100 80 10 10

Dri ðmÞ Dro ðmÞ Dci ðmÞ Dco ðmÞ εC ðmÞ; β εr K γ ξ; α

0.066 0.07 0.115 0.121 0.94 0.15 1 0.93 0.96

TPEM ( C) Eact;a ðkJ=molÞ Eact;c ðkJ=molÞ λa λc
2 Jref a A=m
2 Jref c A=m
F C=mol T 0 ðK Þ

80 76 18 14 10 1.7 3 105 4.6 3 103 96,486 298.15

0.8 5 25 35



Aap;tot m2 5 14; 000 G kW=m2 5 0:9. PTSC, parabolic trough solar collector; PEM, proton-exchange membrane; TEG, Thermoelectric generator.

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CHAPTER 14 Optimization of cogeneration and polygeneration systems

Table 14.7 Cost equations of considered equipment [21]. Zk ð$Þ


_ p 0:71 Zp 5 a1 W a1 5 3540 $=kW0:71
_ t 0:7 Zt 5 a2 W a2 5 6000 $=kW0:7 _ 4 1 a5 ðm _ 2 Þ1:2 ZSG 5 a3 ½ðQec =ΔTlm;ec Þ0:8 1 ðQev =ΔTlm;ev Þ0:8  1 a4 m 0:8 a3 5 6570 $=ðkWKÞ a4 5 21; 276$=ðkgsÞ a5 5 1184:4$=ðkgsÞ1:2 _ TEG ZTEG 5 a6 W a6 5 1500 $=kW ZPTSC 5 a7 Aap;tot a7 5 240 $=m2 _ PEM ZPEM 5 a8 W a8 5 1000 $=kW _6 Zcond 5 a9 m a9 5 1773$=ðkgsÞ

Component Pump Steam turbine Steam generator

TEG unit PTSC field and solar fluid PEM electrolyzer Condenser (for comparison)

PTSC, parabolic trough solar collector; PEM, proton-exchange membrane; TEG, Thermoelectric generator.

Table 14.8 Thermophysical and exergoeconomic properties of each state point [21]. Stream

T ( C)

P ðkPaÞ

_ ðkg=sÞ m

E_ ðkWÞ

c ð$=GJÞ

_ ð$=hÞ C

1 2 3 4 5 6 7 8 9

172.3 290 172.3 30 191.8 93.55 30 25 35

250 237.5 225.6 1100 1045 80 76 101.3 101.3

26.03 26.03 26.03 2.496 2.496 2.496 2.496 120.5 120.5

1266 3712 1265 83.93 2094 1037 81.3 601.9 684.5

24.87 24.87 24.87 39.79 37.6 37.6 37.6 0 49.31

332.4 332.4 113.2 12.03 283.4 140.3 11 0 121.5

_ 12 Þ 5 1:181 kg=h. Hydrogen production rate ðm

14.5.5.2 Multiobjective optimization method In thermal design systems, multiple contradictory objectives typically exist, requiring to be disentangled at the same time. Unlike SOO, MOO allows for obtaining a set of optimum solution points, namely, Pareto front. The latter mainly aims to find out an optimal solution point, unfluctuating between exergy

14.5 Case studies

Table 14.9 Chosen parameters and their ranges as the input of the multiobjective optimization [21]. Aap m2




5000
2000

ΔT p;p

P6 ðkPaÞ

2
20

80
150

T2 ( C) 270
390

P5 ðkPaÞ 700
1500

ZTM 0.2
1.6

FIGURE 14.19 Procedure for modeling and optimization of the polygeneration system [21].

efficiency and product cost. It may be somewhat challenging to find these points due to numerous influential design parameters. Meanwhile, ascertaining them can be cost-effective concerning long-lasting system operation while maintaining proper performance. In these cases a GAbased MOO approach can help ascertain the optimum design point and specify the most desired values for plant design parameters. Thus this study applies an EA-based MOO by employing a MATLAB code to find out the appropriate design point. For a MOO-mediated system optimization, six of the most critical influential parameters are selected and demonstrated in Table 14.9. A GA-based MOO technique was applied regarding exergy efficiency and corresponding overall costs as the two OFs of the integrated system, and a convergent solution was reached after 256 iterations. Fig. 14.19 demonstrates the procedure for modeling and optimization of the polygeneration system [21]. Fig. 14.20 illustrates optimum solution points as a Pareto front. As shown, TC increases with an increase in the exergy efficiency. From Point C to Point A, there is a 10.01%
13.29% and 60.21
66.96 $/GJ in total exergy efficiency and TC, respectively. Likewise, Points A and C are optimal points for exergy efficiency and economic aspect, respectively. The ideal point indicating the

313

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CHAPTER 14 Optimization of cogeneration and polygeneration systems

FIGURE 14.20 The Pareto frontier of the optimal solution points [21].

maximum exergy efficiency and the minimum TC can hardly be achieved. A comparison between the ideal point above and the Pareto front suggests that the two OFs obviously cannot obtain their optimum values at the same time. Point B (the nearest one to the ideal point on the Pareto front) is regarded as the best solution point where the system maintains its stability. To gain a better insight into the impact of the said parameters on system performance, their scatter plot is depicted in Fig. 14.21. The aperture area distribution is displayed in Fig. 14.21A. An aperture area of 14,000
20,000 m2 encompasses all the optimal solution point. Near-19,400 m2 values are seen as better design points as the majority of optimal points are near this value. All optimal solution points are above Aap 5 14,000 m2; thus the design points fall within the range 14,000
19,400 m2. The pinch-point temperature difference distribution is depicted in Fig. 14.21B. According to this figure, optimal solution points fell within the range 10 C , ΔTp,p , 20 C. Values near 10 C are favored since the majority of optimal points are near this value. Any value less than 10 C is not considered as an appropriate decision value because no optimum point falls within this range. Fig. 14.21C portrays the impact of inlet TEG pressure and distribution of this value. It is better to maintain the pressure within the range of 140
150 kPa and close to 100 kPa. Fig. 14.21D illustrates a scatter plot of solar outlet temperature. As shown, the range 290 C
345 C encompasses all optimal solution points, most of which near 290 C. Therefore better operating performance can be guaranteed for the system if the parameter above is kept near

14.5 Case studies

FIGURE 14.21 Scatter distribution of the effective parameters (A) the distribution of the aperture area; (B) the distribution of the pinch pointtemperature difference; (C) Effect of TEG inlet pressure and distribution of this value; (D) Scatter distribution of the solar outlet temperature; (E) Scatter distribution of the turbine inlet pressure; (F) Scatter distribution of the figure of merit [21].

290 C. Fig. 14.21E and F demonstrated a scatter plot of the two parameters already mentioned, that is, the inlet pressure of the turbine and figure of merit, respectively. It may be inferred that setting the inlet pressure of the turbine at its maximum is desirable where all optimal solution points are situated. Being

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CHAPTER 14 Optimization of cogeneration and polygeneration systems

FIGURE 14.22 Schematic of solar
biomass-based polygeneration system proposed by Ghasemi et al. [22].

sensitive, modifying this parameter would lead the system to lose its optimal performance. The “figure of merit” distribution shows that optimal solution points fall within the range of 0.2
1.5. Accordingly, as optimal solution points have a wide-range dispersion, “figure of merit” is not sensitive. For system optimization a MOGA-based MATLAB code already developed is implemented to characterize system-optimal operating conditions. According to the results, the optimal working conditions are measured to be 61.69 $/GJ and 12.76% for TC and exergy efficiency, respectively. The results of MOO also indicate that the last optimum point (consistent between TC and exergy efficiency) has a TC of 63.96 $/GJ and maximum exergy efficiency of 13.29%, respectively. These are 60.21 $/GJ and 10.01% for optimal working conditions in economic terms. Likewise, the rate of hydrogen production would be 2.28 kg/h under stable operating conditions. Finally, it was concluded that the establishment of a TEG unit in place of a condenser would guarantee optimized system performance and declined TC.

14.5.6 Case 4: Biomass
solar-driven polygeneration system 14.5.6.1 General overview This section is based on a biomass
solar-based PES proposed by Ghasemi et al. [22] (Fig. 14.22). It then analyzes the system mentioned previously

14.5 Case studies

FIGURE 14.23 Representation of a simple Linde
Hampson liquefaction cycle using by Ghasmei et al. [22].

thermoeconomically and thermodynamically based on MOO. For electricity supply, cooling/heating power and an RC with a double-effect absorption chiller, a heater, and a turbine are used. On the other hand, a Hampson
Linde cycle (Fig. 14.23), a multiple-effect desalination plant, and biomass combustion, a burner, and a PTSC are utilized for natural gas liquefaction, seawater desalination, and exploitation of solar energy. Input data for modeling and simulation of the considered polygeneration system are determined in Table 14.10 and the results of the thermodynamic simulation of this system are shown in Table 14.11.

14.5.6.2 Optimization In this study, decision variables and their range of variation were selected following sensitivity analysis (SA) results where the impact of these variables on OFs has been examined. Table 14.12 lists decision variables and their possible ranges owing to technical constraints.

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Table 14.10 Input data for modeling of the considered polygeneration system [22]. Parameters

Value 

Collector outlet temperature, T19 ( C) Turbine inlet temperature, T21 ( C) Turbine inlet pressure, P21 ðkPaÞ Temperature difference of evaporator 1, ΔTeva1 ( C) Temperature difference of heater, ΔTH ( C) Temperature of the stack gasses of bagasse burner, T17 ( C) Evaporator 2 temperature, ΔTeva2 ( C)
Salinity of water, SW g=L
LHV of bagasse, LHVbio MJ=kg
Compression ratio of compressor P34 =P36 ð 2 Þ LNG temperature, T39 ( C)

352.2 500 15,000 10 20 500 10 40 17.7 200 2161.5

LHV, Lower heating value; LNG, Liquefied natural gas.

The present study involves two contradictory OFs, exergy efficiency and product cost rate, to be maximized and minimized, respectively. The implementation of GA in MATLAB GA toolbox resulted in Pareto-optimal sets. Every point on the Pareto front reflects system-optimal performance concerning minimizing product cost rate and maximizing exergy efficiency. Qualified optimal points on the Pareto front can be selected by a suitable decision-making technique. In this problem, various selection techniques depend on the ideal point at which product cost rate is minimized, and exergy efficiency is maximized. Since the ideal point is not on the Pareto front, it is impractical. Notwithstanding, each point on the Pareto front closer to the ideal point can be seen as a desirable optimal solution. When the distance to the ideal point is being measured, product cost rate and exergy efficiency are not about the same size. Thus the LP technique for multidimensional analysis of preferences (LINMAP) method is employed for objective nondimensionalization as in the following. This section introduces and discusses the results of implementing GA for MOO of a PES. Pareto fronts determined for the system mentioned previously are shown in Fig. 14.24. Pareto optimality indicates a contradictory relationship between two predetermined objectives. As illustrated, it may be concluded that the maximum product cost rate and exergy efficiency is located at design point op3 concurrently. Likewise, minimum product cost rate and exergy efficiency occur at design point op1. The two said design points (i.e., op1 and op3) could be optimum conditions at which “product cost rate” and “exergy efficiency” can serve as a system’s singleOFs. According to LINMAP results, op2 is the closest point on the Pareto front (in Fig. 14.24) to the ideal point, potentially regarded as a final optimal solution. The optimum values for single and two objective functions optimization have been indicated in Table 14.13.

14.5 Case studies

319

Table 14.11 Results of thermodynamic simulation of polygeneration system (Case 4) [22].

State no.

Temperature ( C)

PressureðkPaÞ

Mass flow rate
kg=s

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

35.03 65 95.63 99.43 65.36 65.36 97.63 33.09 10 10 15 30 15 30 20 15 350 89 352.2 347.2 500 99.63 99.53 99.63 102.3 69.63 89.63

342.2 342.2

15 2161.5 15 258.23 2161.5 2161.5 15

22.97 22.97 22.97 22.97 22.97 5.058 22.97 5.058 1.23 1.23 300 300 300 300 300 300 101.3 101.3 17,000 17,000 15,000 100 100 100 15,000 150 150

15,000 15,000

20,000 101.2 101.3 20,000 101.2 101.2 101.2

0.11 0.11 0.11 0.1 0.1 0.1 0.005 0.01 0.01 0.01 0.37 0.37 0.22 0.22 1.11 1.11 0.003 0.003 1.72 1.72 0.021 0.021 0.021 0.021 0.021 0.36 0.36

0.022 0.022

0.01 0.01 0.01 0.01 0.01 0.003 0.001

Specific enthalpy
kJ=kg

Specific entropy   kJ=ðkg KÞ

Specific exergy
kJ=kg

75.13 139.5 206.3 217.4 145.8 145.8 2681 138.6 138.6 2519 63.1 125.9 63.2 125.9 84.12 63.2 631.7 363 701.5 689.1 3309 2449 1772 417.5 439.7 291.6 375.5

1610 2610

2218.2 2400.1 223.2 2512.3 2512.3 2911 59.45

0.22 0.31 0.4 0.5 0.4 0.4 8.8 0.4 0.5 8.8 0.2 0.5 0.2 0.4 0.3 0.2 6.2 5.8 1.5 1.5 6.3 6.7 4.9 1.3 1.3 0.9 1.2

3.7 5.3

23.3 22.1 20.01 24.5 23.1 26.6 0.2

475.2 482.6 495.1 563.1 549.6 549.6 363.5 2.184 21.622 243.65 0.2 1.77 0.2 1.77 0.37 0.2 116.5 8.2 263.7 256.9 1483 506 352 44 61 19.4 34.8 17.9 16.2 550.5 1083

52,134 51,599 51,393 52,179 51,776 52,406 0 (Continued)

320

CHAPTER 14 Optimization of cogeneration and polygeneration systems

Table 14.11 Results of thermodynamic simulation of polygeneration system (Case 4) [22]. Continued State no.

Temperature ( C)

PressureðkPaÞ

Mass flow rate
kg=s

41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

341.3 15 84 100 99.92 99.11 98.75 99.83 88.44 89.83 89 100 99.92 99.83 89.83 99.92 99.83 63.1 33.09 59.9 70.24 43.8 34.93 87.63

17,000 101.3 101.3 101.3 101 101.3 101.3 100.7 101.3 101 101 101.3 101 100.7 101 101 101.3 22.97 5.058 5.058 5.058 1.228 1.228 5.058

1 1.72 0.002 0.002 0.0001 0.008 0.007 0.002 0.001 0.002 0.002 0.001 0.0001 0.0001 0.002 0.0002 0.002 0.005 0.005 0.11 0.11 0.11 0.11 0.004

Specific enthalpy
kJ=kg

Specific entropy   kJ=ðkg KÞ

Specific exergy
kJ=kg

674.6 63.01 351.8 949.6 2676 389.1 380.1 2675 331.5 2676 2224 419.1 418.7 418.4 418.7 418.7 418.9 264.1 264.1 142.6 71.98 71.98 75.11 3124

1.4 0.2 1.1 2.7 7.3 1.2 1.1 7.3 1.2 7.3 6.1 1.3 1.3 1.3 1.3 1.3 1.3 0.89 0.89 0.35 0.41 0.25 0.23 8.7

249 0 30.1 165.3 558.8 40.6 40.1 558.4 31.8 558.8 456 44.2 44.1 44 44.1 44.1 44.2 15.05 9.6 551 462.8 508.8 475.4 618.8

Based on the results, the system mentioned previously can produce 16.11 kW of electricity, 23.41 kW of cooling power, 28.94 kW of heating power, 0.02 m3/h of liquefied natural gas, and 8.8 kg/h of freshwater. The energy efficiency, exergy efficiency, and product cost rate of this system are 46.8%, 11.2%, and 15.16 $/h, respectively. Thorough modeling is performed by applying energy, exergy, material, and thermoeconomic balances to all parts of the PES. We can delve further into the topic by an SA to inspect whether thermoeconomic and thermodynamic performance depends on decision variables like temperature of stack gasses, or a temperature difference between evaporator 1, evaporator 2, and turbine. Ultimately, the optimized system performance is established by GA and derivation of Pareto front by taking into account product cost rate and exergy efficiency as OFs. The optimized PES could give a product cost rate and exergy efficiency of 13.32 $/h and 9.9%, respectively.

14.5 Case studies

Table 14.12 Decision variables for polygeneration system (Case 4) [22]. Variable Temperature of the stack gasses of bagasse burner, T17 ( C) Temperature difference of evaporator 1, ΔTeva1 ( C) Evaporator 2 temperature, Teva2 ( C) Turbine inlet temperature, T21 ( C)

Range 350
600 5
20 5
20 400
800

FIGURE 14.24 Pareto frontier of the multigeneration energy system [22].

Table 14.13 The optimum values for single and two objective functions optimization [22].

Parameters Exergy efficiency (%) Product cost rate ($/h) Temperature of the stack gasses of bagasse burner ( C) Temperature difference of evaporator 1 ( C) Evaporator 2 temperature ( C) Turbine inlet temperature ( C)

Base case

Exergetic optimization (op1)

Economic optimization (op2)

Exergetic and economic optimization (op3)

Ideal case

11.17

16.18

9.87

13.89

16.18

15.16

19.40

13.32

17.14

13.32

350

557.2

543.5

560.5




10

6.7

13.8

7.6




10

19.6

16.6

19.9




500

798.2

400.8

634.1




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CHAPTER 14 Optimization of cogeneration and polygeneration systems

References [1] Rong A, Su Y. Polygeneration systems in buildings: a survey on optimization approaches. Energy Build 2017;151:439
54. [2] Sahab MG, Toropov VV, Gandomi AH. 2
A review on traditional and modern structural optimization: problems and techniques. In: Gandomi AH, et al., editors. Metaheuristic applications in structures and infrastructures. Oxford: Elsevier; 2013. p. 25
47. [3] Dincer I, Rosen MA, Ahmadi P. Optimization of energy systems. Wiley; 2017. [4] McCall J. Genetic algorithms for modelling and optimisation. J Comput Appl Math 2005;184(1):205
22. [5] Xiao Y, Konak A. A simulating annealing algorithm to solve the green vehicle routing & scheduling problem with hierarchical objectives and weighted tardiness. Appl Soft Comput 2015;34:372
88. [6] Gendreau M. An introduction to tabu search. In: Glover F, Kochenberger GA, editors. Handbook of metaheuristics. Boston, MA: Springer US; 2003. p. 37
54. [7] Dorigo M, Stu¨tzle T. Ant colony optimization: overview and recent advances. In: Gendreau M, Potvin J-Y, editors. Handbook of metaheuristics. Boston, MA: Springer US; 2010. p. 227
63. [8] Piotrowski AP, Napiorkowski JJ, Piotrowska AE. Population size in particle swarm optimization. Swarm Evol Comput 2020;58:100718. [9] Joshi AS, et al. Cuckoo search optimization
a review. Mater Today Proc 2017;4 (8):7262
9. [10] Johari N, et al. Firefly algorithm for optimization problem. Appl Mech Mater 2013;421. [11] Yang X-S. Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 2013;5. [12] Bolaji ALA, et al. A comprehensive review: Krill Herd algorithm (KH) and its applications. Appl Soft Comput 2016;49:437
46. [13] Soltani R, et al. Multi-objective optimization of a solar-hybrid cogeneration cycle: application to CGAM problem. Energy Convers Manage 2014;81:60
71. [14] Khoshgoftar Manesh MH, Ameryan M. Optimal design of a solar-hybrid cogeneration cycle using Cuckoo Search algorithm. Appl Therm Eng 2016;102:1300
13. [15] Schwarzbo¨zl P, et al. Solar gas turbine systems: design, cost and perspectives. Sol Energy 2006;80(10):1231
40. [16] Yang X-S, Deb S. Multiobjective cuckoo search for design optimization. Comput Oper Res 2013;40(6):1616
24. [17] Toffolo A, Lazzaretto A. Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design. Energy 2002;27(6):549
67. [18] Rad MP, et al. New procedure for design and exergoeconomic optimization of site utility system considering reliability. Appl Therm Eng 2016;94:478
90. [19] Ghannadzadeh A, Perry S, Smith R. Cogeneration targeting for site utility systems. Appl Therm Eng 2012;43:60
6. [20] Kapil A, et al. Site-wide low-grade heat recovery with a new cogeneration targeting method. Chem Eng Res Des 2012;90(5):677
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References

[21] Habibollahzade A, et al. Multi-criteria optimization of an integrated energy system with thermoelectric generator, parabolic trough solar collector and electrolysis for hydrogen production. Int J Hydrogen Energy 2018;43(31):14140
57. [22] Ghasemi A, Heidarnejad P, Noorpoor A. A novel solar-biomass based multigeneration energy system including water desalination and liquefaction of natural gas system: thermodynamic and thermoeconomic optimization. J Cleaner Prod 2018;196:424
37.

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15

Reliability and availability of cogeneration and polygeneration systems Chapter Outline 15.1 15.2 15.3 15.4

Introduction ...............................................................................................325 Definitions .................................................................................................327 Reliability modeling of utility system ...........................................................327 Case studies ..............................................................................................330 15.4.1 Case 1 ...................................................................................330 15.4.2 Case 2 ...................................................................................336 References ..........................................................................................................341

15.1 Introduction The process industries face cost challenges, environmental concerns, and efficiency improvement issues. In-between, the cost has almost always got the most attention for all kinds of systems, as well as for utility systems. One approach to this problem is system optimization. Utility systems have several elements, operating under various environmental conditions; all of these elements must be working for the system to function properly. These systems provide electrical power, fuel, heating, and cooling water and steam for consumer systems (e.g., refinery industries). The utility systems should be reasonably reliable; on the other hand, the design and operation of a utility plant should recognize that the equipment is not fully reliable to avoid large economic penalties [1]. Through consideration of reliability, it is possible to improve utility systems design and operation to cope with uncertainties in operation and unexpected circumstances such as demand variations and fluctuations, and equipment failures. Therefore studying system reliability captures a better perception of the system and helps to make better decisions. There are two approaches addressing reliability: (1) system components reliability enhancement and (2) adding redundant components to the desired subsystems [2]. The probability of failure is required to make the system more reliable. There are several methods for calculating such probabilities. They include expert analysis, failure mode and effects analysis, parts count analysis, reliability block diagram (RBD), hybrid techniques [combinations of RBDs and Markov or fault tree analysis (FTA)], FTA, Markov analysis, and enhanced Markov analysis. Among them the Markov method considers the most aspects of system Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00015-X © 2021 Elsevier Inc. All rights reserved.

325

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CHAPTER 15 Reliability and availability of cogeneration

functioning, including safety ranking/comparison, probability of dangerous failure, availability prediction, effects of redundancy, common cause failure, systematic failure, the effect of diagnostics, the effect of on-line test and repair, effect of off-line test and repair, and time/sequence dependences. But Markov does not perform sensitivity and uncertainty analyses [3]. While it has wide applications, the Markov practicality is limited due to computer storage requirements and the rounding errors occurred while solving it for large systems [4]. System reliability measurement via Markov method is an intricate and timeconsuming task for large-scale systems. There are some researches on this matter. Knegtering and Brombacher [5] rearranged the block diagram to reduce the number of states of a system in a Markov model and introduced a new procedure ˇ called “micro Markov model.” Snipas et al. [6] applied the stochastic automata networks for modeling the power systems reliability. They acknowledged the state-space explosion problem in large-scale systems. Their power system casestudy had 17 components, which resulted in 217 different states. They assumed that the disconnected item cannot be faulted, so they reduced the size of the state space and the number of interactions. Puerto-Santana et al. [7] introduced asymmetric hidden Markov models (HMM-As) for problems with multivariate time. Li et al. [8] employed the aggregation method for decreasing the dimension of the state space and calculating several reliability indexes. Sun and Liu [9] designed reliable and flexible steam and power systems with fail, hot and cold standby, and start-up operation modes of the components. Rouvroye and Brombacher [10] worked on redundant reliability problems with multiple choices of the components. Miryousefi Aval et al. [11] have assessed the reliability of combined cooling, heating, and power systems. Their equation calculates the probability of the state for large systems and is an appropriate method for series or parallel systems. Adefarati and Bansal [12] proposed a reliability assessment for a distribution system with a wind turbine generator, an electric storage system, and photovoltaic collectors. They determined the functioning of major components as global states, including several series substates; so, the state space of the whole system is divided into small state spaces, which simplifies the probability calculation. Shu and Zhao [13] presented a modified Markov method that avoids huge Markov models; using Markov analysis on all of the channels of a proposed safety instrumented system they proposed a simplified method. Hou et al. [14] decreased the state-space size and calculated the probability in three steps: integrating high order states, merging normal states into one state, and a time discretization technique before computer analysis. Son et al. [15] decomposed the system into independent subsystems with an optional number of subsystems. They constructed a Markov model at the subsystems level and the system level, using failure rates or unavailability rates. The binary-valued variables, introduced by Smith et al. [16], reduces the computational storage requirements and uses different algorithms for the system characteristics prediction. In this chapter attempted to reduce large state spaces, such as works aimed to avoid the exploded Markov model or to decrease the computation time in multipart systems. Cogeneration systems have several repairable and/or similar

15.3 Reliability modeling of utility system

components with the same reliability characteristics. Here, a developed Markov procedure to reduce the state-space dimension, without the elimination of the affective states is considered.

15.2 Definitions Reliability means the probability of a device or system to perform a specified function at a given time under specific conditions. Reliability refers to either the system itself or the system components. Systems usually categorized as repairable and nonrepairable. Repairable types are devices that may fail to perform at least one of its required functions multiple times during its lifetime [17]. Instead of replacement, the system is restored by repairing or by substituting the failed part [18]. The repair time is not negligible in repairable systems in comparison to the operation time, while in a nonrepairable system it is assumed to happen in a short time. Note that reliability is a concept usually used for nonrepairable systems. The concept of availability is usually considered for repairable systems. Availability is the probability that a device performs its required function during a certain interval of time (including maintenance periods), operated under specific conditions. Availability measures are concerned with the fraction of time a unit provides services.

15.3 Reliability modeling of utility system In repairable systems as utility systems, Markov modeling is an appropriate method to estimate the system reliability. They describe a system concerning a set of states and transitions. The Markov states are all possible conditions of a system. The state space simulates the state transition process between up and down states [19]. The system can only be in one state at a time. The transition between states happens with a certain frequency or rate, whereas the transition rate from the up-state to the down-state is usually defined as the failure rate and the transition rate from the failed state back to the operating state is defined as the repair rate. A Markov model does not remember the transitions, and further transitions to the other states depend only on the current state of the system. The failure rate (λ) and repair rate (μ) are the main considered indices in the concept of reliability. The failure rate is the frequency at which an engineered system or component fails, and the repair rate is the frequency of the repair [20]. The failure rate typically follows a “bathtub” curve as illustrated in Fig. 15.1. In Markov models, it is assumed that the failure and repair rate of a component is constant. These constant values are the averages of the component failure rates and repair rates. Each component in a utility system is either in functioning or failure mode and only one component fails at a time. The probabilities of these occurrences are scarce

327

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CHAPTER 15 Reliability and availability of cogeneration

FIGURE 15.1 Failure rate with time (Bathtub curve) [21].

System construction

Define operating condition

Remove unimportant states with three failed elements and more

Define system states

Constitute transition matrix

Solve equations

Use 1 state for same states with two failed elements

Constitute transition matrix and solve equations

Minus states probabilities with two failed elements

Present states probabilities

Remove states with two failed elements and more

Constitute transition matrix for states less than two failed elements and solve equations

FIGURE 15.2 Comparison of analytical and presented procedures algorithms [23].

and could be neglected. Therefore a system with n components has 2n states and accordingly has 2n equations to be solved to capture the system probability states [22]. The Markov method comprises three main steps (blue path in Fig. 15.2): 1. capturing the possible states of the system, 2. capturing the transition processes, and 3. determining the probabilities of each state.

15.3 Reliability modeling of utility system

The probability of state I, Pi ðtÞ, is captured using the following equation [24]: dPi ðtÞ 5 MPi ðtÞ dt

(15.1)

MPi 5 0

(15.2)

where M is the transitions matrix. For steady-state conditions, Eq. (15.1) becomes Eq. (15.2) has 2n subequations and, instead of one of them, it may use the following equation: n X

Pi 5 1

(15.3)

i51

The important states should be identified at first to drop some states and decrease the number of states. Therefore the states with maximum inlet transition rate and minimum outlet transition rate are selected and the probability of the other states are set to zero, which has a probability of less than 0.0001%. The λ=μ index evaluates the effect of the components on the system availability; a larger value of the index λ=μ suggests it is prudent to reduce the failure rate or increase the repair rate so that the system availability can be improved the most effectively [25]. The following steps illustrate the modified method to decrease the state-space size: Step 1. Capture the modes of systems such as functioning, failing, standby, and half load. Step 2. Eliminate states with four or more failed elements, which have a low probability and can be simply ignored in complex utility systems. Step 3. Eliminate states with three failed elements, which have the minimum inlet transition rate and maximum outlet transition rate. The minimum inlet and maximum outlet imply that the probability of the related state reduces to zero and accordingly may be neglected. Again, these states have probabilities of less than 0.0001%. Step 4. Capture the representative state for the rest of the states with two or more failed elements, which are the same, based on their constant probabilities. This is done because the probability of these states has no reasonable variation, so they all may be treated as with two and three failed elements or as one representative state. Step 5. Determine M for the reduced state space and solve Eq. (15.2). This step calculates the probability of all states. Step 6. Calculate Eq. (15.4). Although we capture one representative state for states with two or three failed elements, it is important to note that perhaps the number of these states was large. So, Eq. (15.4) is used to revise the probability of the state with one failed component: z X

Pi 5 1 2

X

probability of states with two and three failed elements

(15.4)

i51

where z is the number of states with less than two failed elements. Step 7. Determine M and solve Eq. (15.2) for states with less than two failed elements using Eq. (15.4) instead of Eq. (15.3). Step 8. Determine the probability of all states. Step 9. Calculate the availability.

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CHAPTER 15 Reliability and availability of cogeneration

This procedure results in the probability of each state and the system availability can be determined. Fig. 15.2 shows the algorithm and compares the conventional solution and the modified counterpart.

15.4 Case studies 15.4.1 Case 1 A simple utility system example is selected and the proposed procedure is applied to it. Fig. 15.3 illustrates the considered system and Table 15.1 represents the header conditions. The utility system has eight important elements comprising two boilers and six turbines (the letdown and the deaerator have been ignored). So, if the operation modes take-on two modes of functioning and failing, the system has 28 5 256 states. According to Table 15.2, the failure rate of the boiler is greater than that of the turbine and its repair rate is less than that of the turbine. So, the

Fuel: 2.7

Fuel: 2.7

40 kg/s

40 kg/s

0.1

1.57 MW

~ 4.8

1.2

~

3.46 MW

5.9

6.7

9.35 MW

~

9.61 MW

14.5

De-Aerator

FIGURE 15.3 Utility system used as case study 1 [23].

~

14.9

2.05 MW

~

3.5

1.41 MW

~ 4.3

15.4 Case studies

Table 15.1 Specification of steam main for case study 1 [23]. Parameter

Condition

VHP pressure HP pressure MP pressure LP pressure Deaerator pressure Condenser pressure VHP steam demands VHP steam generation HP steam demands HP steam generation MP steam demands MP steam generation LP steam demands LP steam generation

bara bara bara bara bara bara MW MW MW MW MW MW MW MW

101 20.6 4.1 2.7 1.1 0.98 110.8 0.0 141.4 120 57 47.7 89 15.4

HP, High pressure; LP, low pressure; MP, medium pressure; VHP, very high pressure.

Table 15.2 Failure and repair rates for devices [23]. Site component

Failure rate (h21)

Repair rate (h21)

Source reference

Boiler Steam turbine HRSG Gas turbine

115.58E 2 6 48.42E 2 6 83.33E 2 6 1136.63E 2 6

7017.54E 2 6 10,384.2E 2 6 7916.66E 2 6 42,168.97E 2 6

[18] [18] [20] [26]

effect of the states with a failed boiler on the system is greater than that of states with a failed turbine. The method includes the following steps: Step 1. Determine the modes of each functioning and failing components. Step 2. Drop the states with four or more failed elements, for example, one boiler and three turbines failed or four turbines failed. The reason is that the probability of simultaneous failure of four or more elements in the cogeneration system converges to zero. Step 3. Drop the states with three failed elements, which have the minimum inlet transition rate and the maximum outlet transition rate. All states with three turbines are removed. HRSG, Heat recovery steam generator. Due to the probability of three failed elements which have the minimum inlet transition rate and the maximum outlet transition rate simultaneously in the cogeneration system are near zero, the states more than four equipment. Turbines

331

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CHAPTER 15 Reliability and availability of cogeneration

All elements operate

T1

`

T3

T2

T1, T2

Low input rate

Boiler high output rate

T4

B1,T1, T3

T1, T2, T3, B1

B2

B1

T6

B1, B2

T1, B1

T1, T3

T1,T2 , T3

T5

B1, B2, T1

B1, B2, T1, T3

Dash line related to removed states

FIGURE 15.4 State space for case study 1 [23].

have the minimum inlet and the maximum outlet transition rate. Consequently, all states related to failures of three turbines have been neglected. Step 4. Capture one representative state for the rest of the states with two or more failed elements, based on their constant probability assumption. That means the representatives are two turbines two boilers, one turbine and one boiler, two turbines and one boiler, two boilers and one turbine failed. One state associated with failure states of two boilers is specified. For failure states of one boiler and steam turbine, 12 states are considered. Also, 15 failures states are considered related to two steam turbines’ failure states simultaneously. The detailed state-space system for case study 1 is demonstrated in Fig. 15.4. Furthermore, the failure states regarding to case study 1 are determined in Fig. 15.5. The states for which have four or more components failed are eliminated based on step 2 procedure. In accordance with steps 24 the failure states are reduced. In this regard, all states associated with the failure states of one component are considered. By considering the failure of two components simultaneously as mention in the step 4, three candidates are selected. The states with three failed components which have the maximum outlet transition rate and the minimum inlet transition rate are removed as indicated in step 3. Therefore all states with three turbines are eliminated, and only two candidates are selected.

15.4 Case studies

FIGURE 15.5 Failure states of system regarding case study 1 [23].

The six failure states are selected for one steam turbine and two boilers. In addition, 30 failure states are accepted in the case of one boiler and two turbines, respectively. The 120 failure states are considered for cases of three turbines. However, because of near zero failure rates associated with steam turbines, all these states are ignored. The Table 15.3 indicates the failure states for case study 1. Also, one state is associated with operating all elements. Therefore there are 9 states (i.e., 8 states regarding to fB1 ; B2 ; T1 ; T2 ; T3 ; T4 ; T5 ; T6 g 1 1 state 5 9 states). For the two components failure and for one representative state for the rest of the states with two or more failed components, same as B2T1T2, B1T1T2, and T1T2T3, failure states 973 based on steps 3 and 4 are extracted and selected as represented in Figs. 15.4 and 15.5. Following step 3 and based on the failure of B1 and B2, one state is selected, for the failure of the boiler and the turbine, 12 failure states are accepted, and for the failure of turbine and turbine, 15 failure states are considered. Therefore the number of failure states considered is 28 (12 1 15 1 1) based on step 3 consequently. Based on step 4, as indicated for two boilers and one steam turbine failures, 6 failure states are candidate. Also, for the (BTT) simultaneous failure, 30 failure

333

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CHAPTER 15 Reliability and availability of cogeneration

Table 15.3 Failure states for case study 1. No

Failure state

Notation

1 2 3 4 5 6 7 8

Boiler 1 Boiler 2 Steam turbine Steam turbine Steam turbine Steam turbine Steam turbine Steam turbine

B1 B2 T1 T2 T3 T4 T5 T6

1 2 3 4 5 6

states are selected. In addition, for the (TTT) simultaneous failure, 120 states happen. Although, all related states for (TTT) have been neglected because of the low failure rates of turbines. There are 36 states associated with step 4, 28 states are regarding to step 3, and 8 states are based on step 2 accordingly. Moreover, 1 state is associated with operating all components. Lastly, 73 failure states are selected and used for case 1. Step 5. Determine M for the reduced state space and solve Eq. (15.2). Table 15.4 shows the selected states and Table 15.5 indicates the calculated probability of all states. Step 6. Calculate Eq. (15.4). The right-hand side of Eq. (15.4) shows the multiplied number of states with two turbines, one turbine and one boiler, two boilers, two turbines and one boiler, two boilers and one turbine failed, respectively. That is, z X i51

     X  6  6 2 2 Pi 5 1 2 3 0:00002 1 3 0:000072 1 3 0:000256 2 1 1 2       6 2 6 2 1 301 3 0 5 1 2 0:00142 5 0:99858 (15.5) 2 1 1 2

The value of 0.00142 indicates all of the states. Step 7. Determine M and solve Eq. (15.2) for states with less than two failed elements using Eq. (15.4) instead of Eq. (15.3). Table 15.6 shows the resulting accurate values of probabilities. Step 8. Determine the probability of all states. Table 15.7 shows the resulting probabilities of all states. Step 9. Calculate the availability. It is assumed that the utility system continues to function if all of its components are operating. So, the system availability is as follows: Availabilitysystem 5 P1 5 94:1242

(15.6)

Table 15.4 Selected states for case 1 [23]. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

All: O

T1: F

T2: F

T3: F

T4: F

T5: F

T6: F

B1: F

B2: F

T1,T2: F

T1,B1: F

B1,B2: F

T1,T2,B1: F

T1,B1, B2: F

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CHAPTER 15 Reliability and availability of cogeneration

Table 15.5 Initial probability (case study 1). Probability (%)

Probability (%)

State

Condition

Initial presented method

Markov

1 2 3 4 5 6 7 8 9 10 11 12 13 14

All components operate Turbine 1 failed Turbine 2 failed Turbine 3 failed Turbine 4 failed Turbine 5 failed Turbine 6 failed Boiler 1 failed Boiler 2 failed Two turbines failed One turbine and one boiler failed Two boilers failed Two turbines and one boiler failed One turbine and two boilers failed

94.2251 0.4394 0.4394 0.4394 0.4394 0.4394 0.4394 1.5519 1.5519 0.0020 0.0072 0.0256 0.0000 0.0000

94.2251 0.4394 0.4394 0.4394 0.4394 0.4394 0.4394 1.5519 1.5519 0.0020 0.0072 0.0256 0.0000 0.0000

If each turbine in each zone compensates for the failure of one of the neighbors inside that zone, the system availability increases to: Availabilitysystem 5 P1 1 P2 1 P3 1 P4 1 P5 1 P6 1 P7 1 6 3 P10 5 96:7696

(15.7)

To determine other removed states the minimum states that make up the desired states must be taken into account.

15.4.2 Case 2 Fig. 15.6 shows a system constructor comprising a gas turbine, a heat recovery steam generator, and a steam turbine. If the letdown stations are ignored, the number of remaining states equals 212 5 4096. Table 15.8 indicates specification of case study 2. Table 15.9 shows 27 considered important states. The probabilities are calculated as follows: Step 1. Determine the modes of each functioning and failing components. Step 2. Drop the states with four or more failed elements, for example, one gas turbine and three turbines failed. Step 3. Drop the states with three failed elements, which have the minimum inlet transition rate and the maximum outlet transition rate. All states with three turbines are removed.

15.4 Case studies

Table 15.6 New probabilities regarding states that remain in step 7 (case study 1) [23]. Probability (%)

Probability (%)

State

Condition

Initial presented method

Markov

1 2 3 4 5 6 7 8 9 10 11 12 13 14

All components operate Turbine 1 failed Turbine 2 failed Turbine 3 failed Turbine 4 failed Turbine 5 failed Turbine 6 failed Boiler 1 failed Boiler 2 failed Two turbines failed One turbine and one boiler failed Two boilers failed Two turbines and one boiler failed One turbine and two boilers failed

94.2251 0.4394 0.4394 0.4394 0.4394 0.4394 0.4394 1.5519 1.5519 0.0020 0.0072 0.0256 0.0000 0.0000

94.2251 0.4394 0.4394 0.4394 0.4394 0.4394 0.4394 1.5519 1.5519 0.0020 0.0072 0.0256 0.0000 0.0000

Table 15.7 Final probabilities regarding states of system (case study 1) [23]. Probability (%)

Probability (%)

State

Condition

Presented method

Markov

1 2 3 4 5 6 7 8 9

All components operate Turbine 1 failed Turbine 2 failed Turbine 3 failed Turbine 4 failed Turbine 5 failed Turbine 6 failed Boiler 1 failed Boiler 2 failed

94.1242 0.4389 0.4389 0.4389 0.4389 0.4389 0.4389 1.5502 1.5502

94.2251 0.4394 0.4394 0.4394 0.4394 0.4394 0.4394 1.5519 1.5519

337

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CHAPTER 15 Reliability and availability of cogeneration

189.3 MW

189.3 MW

~ 171.3

31.4 MW

~ 171.3

~ 14.7 T(C) = 540

0

91.1

34.1

12.8

125.3

96.6 0 T1 19.3 MW

T(C) = 370 37.3

0

106.2 T4 12.8 MW

T2 60 MW

T3 8.1 MW

T(C) = 235 0.9 312.9 T6

60 MW

T5

21.9 MW T(C) = 160

Flow rate: kg/s

107.6

FIGURE 15.6 Flow sheet of case study 2 [27].

Table 15.8 Specification of steam main (case study 2) [27]. Parameter

Condition

VHP pressure HP pressure MP pressure LP pressure Deaerator pressure Condenser pressure VHP steam demands VHP steam generation HP steam demands HP steam generation MP steam demands MP steam generation LP steam demands LP steam generation

bara bara bara bara bara bara kg kg kg kg kg kg kg kg

104.4 42.4 11.4 4.5 1.1 0.98 0 18.5 108.2 35.6 213.9 11 107.60 0

HP, High pressure; LP, low pressure; MP, medium pressure; VHP, very high pressure.

15.4 Case studies

Table 15.9 Initial probability (case study 2) [23].

State

Condition

1 27 810 1113 14 15 16 1718 19 20

All components operate One ST failed One GT failed One HRSG failed Two STs failed One ST and one GT failed One ST and one HRSG failed Two GTs failed One GT and one HRSG (same) failed One GT and one HRSG (not same) failed Two HRSGs failed One ST and one GT and one HRSG failed One ST and two GTs failed three GTs failed Two GTs and one HRSG failed One GT and two HRSGs failed

2122 23 24 25 26 27

Probability (%)

Probability (%)

Initial presented method

Markov

87.1611 0.4064 2.3528 0.9174 0.0019 0.0110 0.0042 0.0634 0.0204 0.0248

87.1611 0.4064 2.3528 0.9174 0.0019 0.0110 0.0042 0.0634 0.0204 0.0248

0.0094 0.0001

0.0094 0.0001

0.0003 0.0017 0.0006 0.0002

0.0003 0.0017 0.0006 0.0002

GT, Gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

Step 4. Capture one representative state for the rest of the states with two or more failed elements. These are the same, based on their constant probability assumption. Table 15.10 shows 27 considered important states. Step 5. Determine M for the reduced state space and solve Eq. (15.2). Table 15.11 shows the probabilities of all selected states. Step 6. Calculate Eq. (15.4). The right-hand side of Eq. (15.4) shows the products of the number of states their probabilities, for states 1427. That is,         6 6 3 6 3 Pi 5 1 2 3 0:000019 1 3 0:000110 1 3 0:000042 2 1 1 1     1    3 3 6 3 1 3 0:000634 1 3 3 0:000204 1 6 3 0:000248 1 3 0:000094 1 2 2 1         1 6 3 3 3 3 3 6 3 0:000001 1 3 0:000003 1 3 0:000017 1 3 0:000006 2 2 1 1 2  3 3 1 3 0:000002 5 1 2 0:007904 5 0:992096 (15.8) 1 2 P

The value of 0.007904 indicates all states.

339

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CHAPTER 15 Reliability and availability of cogeneration

Table 15.10 New probabilities regarding states that remain in step 7 (case study 2) [23]. Probability (%)

Probability (%)

State

Condition

Present method

Markov

1 2 3 4 5 6 7 8 9 10 11 12 13

All components operate Steam turbine 1 failed Steam turbine 2 failed Steam turbine 3 failed Steam turbine 4 failed Steam turbine 5 failed Steam turbine 6 failed GT 1 failed GT 2 failed GT 3 failed HRSG 1 failed HRSG 2 failed HRSG 3 failed

86.6404 0.4040 0.4040 0.4040 0.4040 0.4040 0.4040 2.3351 2.3351 2.3351 0.9120 0.9120 0.9120

87.1611 0.4064 0.4064 0.4064 0.4064 0.4064 0.4040 2.3528 2.3528 2.3528 0.9174 0.9174 0.9174

GT, Gas turbine; HRSG, heat recovery steam generator.

Step 7. Determine M and solve Eq. (15.2) for states with less than two failed elements using Eq. (15.4) instead of Eq. (15.3). Table 15.10 determines the resulting accurate values of probabilities. Step 8. Determine the probabilities of all states. Table 15.11 shows the resulting probabilities of all states. Step 9. Calculate the availability. It is assumed that the utility system continues to function if all of its components are operating. So, Availabilitysystem 5 P1 5 86:6406 These probabilities represent the future function of the system. The Markov method deals with a large number of equations; so, most researchers of the repairable systems drop many important states to simplify the calculations. Therefore probabilities calculated for the remaining states have insufficient accuracy and have errors. The new procedure avoids these errors, so the probability of each state can be determined with reasonable accuracy. It is considered a representative for removed states with reasonable probability and assumed that the probabilities of eliminated states are equal to the same representative state. So, the probabilities of all removed states are calculated. Because of the impact of removed states on states with one failure states, the probability of states with one failure was separately recalculated, by assuming that the sum of them is less than one. The obtained probabilities could be applied for calculation of reliability, availability, maintenance program, system safety, and quality. The reduced method applied for a complex cogeneration system and polygeneration which represents an intricate

References

Table 15.11 Final probabilities regarding system states (case study 2) [23].

State

Condition

1 27 810 1113 1428 2946 4764 6567 6870 7176

All components operate One ST failed One GT failed One HRSG failed Two STs failed One ST and one GT failed One ST and one HRSG failed Two GTs failed One GT and one HRSG (same) failed One GT and one HRSG (not same) failed Two HRSGs failed One ST and one GT and one HRSG failed One ST and two GTs failed Two GTs failed Two GTs and one HRSG failed One GT and two HRSGs failed

7779 80115 116133 134136 137139 140142

Probability (%)

Probability (%)

Presented method

Markov

86.6406 0.4040 2.3351 0.9112 0.0019 0.0110 0.0042 0.0634 0.0204 0.0248

87.1611 0.4064 2.3528 0.9174 0.0019 0.0113 0.0044 0.0636 0.0206 0.0249

0.0094 0.0001

0.0096 0.0003

0.0003 0.0017 0.0006 0.0002

0.0004 0.0018 0.0008 0.0003

GT, Gas turbine; HRSG, heat recovery steam generator; ST, steam turbine.

system. Accordingly, it applies to the other similar cases. An important remark for this procedure was the reduction of the computational time.

References [1] Aguilar O, et al. Design and optimization of flexible utility systems subject to variable conditions: Part 1: modelling framework. Chem Eng Res Des 2007;85(8):113648. [2] Coelho LdS. Reliabilityredundancy optimization by means of a chaotic differential evolution approach. Chaos Solitons Fractals 2009;41(2):594602. [3] Chen T-C, You P-S. Immune algorithms-based approach for redundant reliability problems with multiple component choices. Comput Ind 2005;56(2):195205. [4] Zhu D. Power system reliability analysis with distributed generators. Electrical engineering. Blacksburg, VA: Virginia Polytechnic Institute and State University; 2003. [5] Knegtering B, Brombacher AC. A method to prevent excessive numbers of Markov states in Markov models for quantitative safety and reliability assessment. ISA Trans 2000;39(3):3639.

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ˇ [6] Snipas M, Radziukynas V, Valakeviˇcius E. Modeling reliability of power systems substations by using stochastic automata networks. Reliab Eng Syst Saf 2017;157:1322. [7] Puerto-Santana C, Bielza C, Larran˜aga P. Asymmetric hidden Markov models with continuous variables. Advances in artificial intelligence. Cham: Springer International Publishing; 2018. [8] Li Y, Cui L, Lin C. Modeling and analysis for multi-state systems with discrete-time Markov regime-switching. Reliab Eng Syst Saf 2017;166:419. [9] Sun L, Liu C. Reliable and flexible steam and power system design. Appl Therm Eng 2015;79:18491. [10] Rouvroye JL, Brombacher AC. New quantitative safety standards: different techniques, different results? Reliab Eng Syst Saf 1999;66(2):1215. [11] Miryousefi Aval SM, Ahadi A, Hayati H. Adequacy assessment of power systems incorporating building cooling, heating and power plants. Energy Build 2015;105:23646. [12] Adefarati T, Bansal RC. Reliability assessment of distribution system with the integration of renewable distributed generation. Appl Energy 2017;185:15871. [13] Shu Y, Zhao J. A simplified Markov-based approach for safety integrity level verification. J Loss Prev Process Ind 2014;29:2626. [14] Hou K, et al. A continuous time Markov chain based sequential analytical approach for composite power system reliability assessment. IEEE Trans Power Syst 2016;31 (1):73848. [15] Son KS, et al. Study on the systematic approach of Markov modeling for dependability analysis of complex fault-tolerant features with voting logics. Reliab Eng Syst Saf 2016;150:4457. [16] Smith, KN, et al. Automated Markov-chain based analysis for large state spaces. In: Annual IEEE international systems conference (SysCon). 2017. [17] Billinton R, Allan RN. Reliability evaluation of engineering systems: concepts and techniques. Springer; 1992. [18] Stapelberg RF. Handbook of reliability, availability, maintainability and safety in engineering design. London: Springer; 2009. [19] Shan X, Wang P, Lu W. The reliability and availability evaluation of repairable district heating networks under changeable external conditions. Appl Energy 2017;203:68695. [20] Hosseini SR, Amidpour M, Behbahaninia A. Thermoeconomic analysis with reliability consideration of a combined power and multi stage flash desalination plant. Desalination 2011;278(1):42433. [21] Smith DJ. 2 - Understanding terms and jargon. In: Smith DJ, editor. Reliability, maintainability and risk. 7th ed. Oxford: Butterworth-Heinemann; 2005. p. 1123. [22] Lewis EE. Introduction to reliability engineering. Wiley; 1987. [23] Khoshgoftar Manesh MH, Pouyan Rad M, Rosen MA. New procedure for determination of availability and reliability of complex cogeneration systems by improving the approximated Markov method. Appl Therm Eng 2018;138:6271. [24] Rausand M, Høyland A. System reliability theory: models, statistical methods, and applications. Wiley; 2003.

References

[25] Karami-Horestani A, Hamedani Golshan ME, Hajian-Hoseinabadi H. Reliability modeling of TCRFC type SVC using Markov process. Int J Electr Power Energy Syst 2014;55:30511. [26] Autor O, Autor SIM. OREDA: offshore reliability data handbook. OREDA; 2002. [27] Aguilar O, et al. Availability and reliability considerations in the design and optimisation of flexible utility systems. Chem Eng Sci 2008;63(14):356984.

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CHAPTER

Software tools

16

Chapter outline 16.1 Introduction ...............................................................................................345 16.1.1 Power plants ...........................................................................346 16.1.2 Process industries ...................................................................352 16.1.3 Renewable energy ...................................................................359 16.1.4 Computer code .......................................................................361 References ..........................................................................................................362

16.1 Introduction Simulation tools are very important and vital for the analysis and evaluation of existing plants and new plants being designed. This software helps the user to have a correct and deep understanding of the process as well as to analyze the appropriate sensitivity on the decision variables. It is important to evaluate existing systems to optimize energy consumption, pollutant emissions, total annualized, and production costs. Investigating the performance of equipment in different situations is another feature of this software. Due to the wide range of software available for the design and simulation of energy systems, to classify more accurately, the classification of this software is based on the main application of these tools. For this purpose, simulation software and design of cogeneration and polygeneration systems are divided into four special categories. The first category of these software tools is dedicated to special applications in the field of power plants industry. These software tools have a special feature for designing, simulating, and analyzing thermal power plants and combined cycle, as well as cogeneration plants. In this set of software, more attention is paid to the precise modeling of conventional power plant equipment such as gas power plants, combined cycle, and steam thermal power plants. Dynamic simulation capability can also be done for the main components of power plants. Gas turbines, steam turbines, heat recovery boilers, boilers, and feedwater heaters can be accurately simulation and the detailed mechanical design is performed. Some of these software have economic and financial analysis. The second category is dedicated to professional software in the field of simulation of oil, gas, petrochemical processes, and processing industries in general. Cogeneration and Polygeneration Systems. DOI: https://doi.org/10.1016/B978-0-12-817249-0.00016-1 © 2021 Elsevier Inc. All rights reserved.

345

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CHAPTER 16 Software tools

Cogeneration and polygeneration software

Power plants GT Pro. GT Master STEAM PRO STEAM Master THERMOFLEX GateCycle Ebsilon Cycle-Tempo

Process industries Aspen Plus Aspen Hysys Petro-SIM UniSIM ProMax AVEVA PRO II i_STEAM STAR

Renewbale energy TRNSYS HOMER PRO RET Screen SAM Advisor

Computer codes EES Thermolib Matlab

FIGURE 16.1 Classification of cogeneration and polygeneration software tools.

Due to the development of polygeneration systems in the production of valuable chemical products such as hydrogen, ethanol, methanol, carbon dioxide, and desalinated water in power and cogeneration plants, the use of these simulators to accurate simulation, analyze, and design of considered chemical systems is inevitable. These software tools usually have dynamic simulation capability along with steady-state stimulation. Other features of this software include economic analysis and process optimization. One of the key features and major advantages of process simulation software is a precise thermodynamic and thermophysical data library for a wide range of materials. The third category is software that specializes in designing, simulating, and analyzing renewable energy systems. This software can easily receive incoming data according to the situation and needs of limitable energy systems, which are sometimes subject to environmental conditions. Also, this software works dynamically and can perform analytics on energy systems temporarily. The fourth category is dedicated to coding software, which is developed by professional users and is usually used academically. Fig. 16.1 shows the classification of cogeneration and polygeneration software tools.

16.1.1 Power plants 16.1.1.1 GT PRO GT PRO is professional software that is used for the design of a combined cycle or gas turbine cogeneration plant design. It is a user-friendly software that helps the user to easily and rapidly achieve an optimal configuration and technical specifications. The user can enter the design criteria, start with the big picture, and progresses in a logical

16.1 Introduction

FIGURE 16.2 A typical configuration of a subcritical coal power plant in GT PRO environment [2].

sequence in more detail. The software designs the new plant, computes its performance, its detailed mass and heat balances, and builds its major component details of the design. It can be integrated with the optional PEACE (Plant Engineering and Cost Estimation) module. As a result, it typically needs only a few minutes to build a new power plant and cogeneration design. This authorizes the user to build and evaluate many plant options, and variations within each, to find the most appropriate design, techno-economically [1]. Without a doubt, GT PRO has very accurate models for simulating gas turbines as well as thermal recovery boilers. Any GT PRO model can be exported into GT MASTER for detailed off-design analysis or annual operating economics. It can also be integrated with THERMOFLEX for modeling unique details design. GT PRO models can be operated from MS Excel with intermediated input and output through Excel-E-link. It also has different main types of gasifiers that have been used for integrated gasification combined cycle (IGCC); CO2 capture processes, both postcombustion and precombustion. GT Pro has also the different types of desalination systems, including multistage flash (MSF), multiple effect distillation (MED), and reverse osmosis (RO). Fig. 16.2 shows the typical configuration of a subcritical coal power plant in the GT PRO environment.

16.1.1.2 GT MASTER GT MASTER simulates the performance of a gas turbine combined cycle and cogeneration plant at different operating conditions, such as different loads and control set points at different and ambient conditions. It is defined by its mechanical specification, by integrated with a GT PRO model that is defined by the assumptions used to create the hardware. More than 4000 inputs associated with plant hardware specification and control set points are all relied on importing GT PRO design [3]. The detailed economic analysis based on the annual operating cycle, consisting of transient conditions, is performed. A transient model is done using GT-TRAN module. Any GT MASTER file can be exported into THERMOFLEX for 24-hour transient modeling and application, such as integration with concentrated solar thermal receivers. Plant Design Expert )PDE( is an excellent platform that applies the GT PRO/ GT MASTER design and simulation engines.

347

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16.1.1.3 STEAM PRO STEAM PRO applies for the process design of conventional fossil fuel and nuclear steam power plants, helping the user to achieve an optimal arrangement and its specification easily and rapidly. Without a doubt, this software very accurately simulates thermal steam boilers, steam turbines, and feedwater heaters rather than other existing simulators. It also provides excellent information on detailed design and economic analysis of equipment. The software designs the new plant, computes its performance, its detailed mass and heat balances, and builds its major component details of the design. It can be integrated with the optional PEACE module. As a result, it typically needs only a few minutes to build a new power plant and cogeneration design. This authorizes the user to build and evaluate many plant options, and variations within each, to find the most appropriate design, techno economically [4]. This software very accurately simulates thermal boilers and feed preheaters. It also provides excellent information on detailed design and economic analysis of equipment. STEAM PRO can be simulating accurately the back-pressure units for cogeneration, small/medium condensing turbines, extraction turbines, medium/ large plants with single reheat cycles, and supercritical double-reheat plants. It can be simulated up to 12 feedwater heaters of different types, and a broad configuration of boiler feedwater pump types and drive configuration. A broad range of cooling systems consisting of the various types of condenser and cooling tower are included within the program. It will build preliminary designs for all typical boiler types, including pulverized coal, circulating and bubbling fluidized beds, and different grate-fired arrangements. It can consider the various types of fuel, including solid, liquid, and gases and its characteristics; STEAM PRO has a broad range of biomass, coal types, or municipal solid waste, gaseous or liquid fuels of several grades. STEAM PRO also equipped with CO2 capture and the main desalination systems consisting of MSF, multiple effect distillation (MED), and RO.

16.1.1.4 STEAM MASTER STEAM MASTER applies for performance analysis and evaluation of a given plant at different operating conditions, such as feedwater heater bypass and part loads. It is an off-design simulator of a plant investigated by its hardware specification, and this hardware is all starting from the STEAM PRO design but keeps available for setting, tuning, and adjustment by the user. A STEAM MASTER file can be exported into THERMOFLEX for modeling special details design. Its models can be run from MS Excel via E-LINK add-in [5].

16.1.1.5 THERMOFLEX THERMOFLEX is the software from THERMOFLOW that applies for thermodynamic simulation and mass and energy balances. It is a graphic user interface with a fully flexible software within which the user builds a configuration of the cycle. The program can be used for both design and off-design simulation and models all types of combined cycles, conventional steam cycles, repowering,

16.1 Introduction

FIGURE 16.3 Schematic diagram of the conventional gas turbine CCHP as simulated in Thermoflex [7]. CCHP, Combined cooling, heating, and power.

renewable energy systems, and refrigeration cycles, and various thermodynamic cycles and energy systems [6]. Models built by GT PRO, GT MASTER, PDE, and STEAM PRO can be imported into THERMOFLEX/PEACE for further flexible design, application, and analysis. Fig. 16.3 demonstrates a process diagram of the conventional gas turbine combined cooling, heating, and power (CCHP) as simulated in Thermoflex [7]. Also, Fig. 16.4 shows a schematic diagram of the integrated linear fresnel reflector (LFR) gas turbine trigeneration plant as simulated in Thermoflex [7].

16.1.1.6 GateCycle GE’s GateCycle software is fully flexible energy and mass balance program. It is used to model for the steady-state design and off-design performance of cogeneration and thermal power plants [8]. It supplies a palette of conventional power plant component icons that can be used to create detailed models of fossil, combined cycle, simple cycle, and nuclear power plants. GateCycle also can model desalination plants as well. GateCycle models may be applied to performance evaluation and monitoring, detailed engineering design, retrofit and repowering, and acceptance test calculations. GateCycle also has an Excel user interface CycleLink for optimization.

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FIGURE 16.4 Schematic diagram of the integrated LFR gas turbine tri-generation plant as simulated in Thermoflex [7].

16.1.1.7 EBSILON EBSILON software is planning and simulation software, which can be used to simulate any thermodynamic cycle process. It is capable of modeling arbitrary entire plant, conceptual design, and detailed design, and also optimizing the performance of the selected operation. It computes the performance of a plant under the possible range of operating conditions. EBSILON works with conventional power plants, desalination plants, fuel cell applications, nuclear power plants, gasification processes, reforming processes, solar power plants, and user-defined processes [9]. The ability of this software is a simulation of some main chemical reactions such as gasification, reforming, and electrochemical reactions in fuel cells. A sample cogeneration system that simulates in EBSILON is shown in Fig. 16.5.

16.1.1.8 Cycle-Tempo Cycle-Tempo is used for design, analyses, optimization, and monitoring of the energy systems. It has a wide range of model library that includes a conventional component of energy systems, innovative components such as IGCC, refrigeration absorption systems, organic Rankine cycle (ORC) turbogenerators, fuel cells, and CO2 capture plants. Also, exergy analysis for the evaluation and optimization of the design and operation of the selected system is employed. For performance evaluation, real-time combination with existing plant-wide data monitoring

16.1 Introduction

FIGURE 16.5 EBSILON Professional model of biomass-driven cogeneration plant with ORC unit [10]. ORC, Organic Rankine cycle.

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FIGURE 16.6 A biomass-driven CHP configurations:Boiler 1 ST 1 ORC [12]. CHP, Combined heat and power; ORC, organic Rankine cycle.

systems and troubleshooting is established [11]. Fig. 16.6 demonstrates the CycleTempo environment for biomass-CHP (combined heat and power) configuration simulation proposed by Pantaleo et al. [12].

16.1.2 Process industries 16.1.2.1 Aspen Plus Aspen Plus is the leading and most well-known in chemical process simulator in the market and also be used for simulation of the thermal power plant, desalination processes, site utility, cogeneration, and polygeneration systems. It uses mathematical models to estimate the performance of the process. One of the major advantages is that Aspen Plus has already an existing database of species for pure/binary parameters. It has a database of very powerful thermodynamic properties for a wide range of materials and applications. Aspen Plus can simulate very complex processes such as chemical reactors, common and advanced separation processes, and electrolyte solutions. Aspen Plus is very fine for modeling and simulation of polymers and electrolytes. Aspen Plus can be used for steady-state and transient simulation, and also plant economic calculation and evaluation [13]. Fig. 16.7 shows the Aspen Plus model for the steam and double-stage ORC for the polygeneration system proposed by Ishaq et al. [14]. Aspen Plus model for Cu Cl cycle integrated with the hydrogen compression system proposed by Ishaq et al. is shown in Fig. 16.8.

16.1.2.2 Aspen HYSYS Aspen HYSYS is a process simulator applied to mathematically model chemical processes for process and power plant industries. Aspen HYSYS is used extensively in

16.1 Introduction

FIGURE 16.7 Aspen Plus model for the steam and double-stage organic Rankine cycle for the polygeneration system proposed by Ishaq et al. [14].

the industry due to its steady-state, dynamic simulation, optimization process design, and performance evaluation [15]. Aspen HYSYS especially uses for simulation of petrochemical, petroleum refining, and oil assays and relevant processes. When you want to model petroleum assays, Aspen HYSYS is preferred. Fig. 16.9 demonstrates the process flow diagram of the polygeneration system proposed by Hosseini et al. [16] simulated with Aspen HYSYS. Also, an Aspen HYSYS model for solar geothermal power plants proposed by Keshvarparast et al. [17] and simulated in Aspen HYSYS is demonstrated in Fig. 16.10.

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FIGURE 16.8 Aspen Plus model for Cu Cl cycle integrated with the hydrogen compression system proposed by Ishaq et al. [14].

16.1.2.3 Petro-SIM Petro-SIM is KBC’s leading process simulation software for steady-state, dynamic simulation of process industries such as petrochemical, oil, and gas process and also capable of optimization of processes. It has a user-friendly environment and can be used for simulation of process industries, cogeneration, and polygeneration systems. Petro-SIM is capable of simulation for all process plants, including downstream, midstream, and upstream [18].

16.1 Introduction

FIGURE 16.9 The detailed process flow diagram of the hybrid system [16].

16.1.2.4 UniSim Honeywell’s UniSim software performs online and off-design simulation and optimization of unit operations in process industries and also in power plant sectors, site utility, cogeneration, and polygeneration systems. It employs to determine mass and energy balances, rating, and the sizing of components. Users can work with UniSim easily to improve the performance of the unit operation and equipment, and also maximize the profitability of the overall plant [19].

16.1.2.5 ProMAX ProMax is a chemical process software for simulation, design, troubleshooting developed by Bryan Research and Engineering, Inc. This software is widely used for simulation

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FIGURE 16.10 Aspen HYSYS mode of an integrated solar-geothermal power plant equipped with air-cooled condenser [17].

and sensitivity analysis of gas and oil refineries and related processes [20]. Fig. 16.11 represents the ProMax model for the acid gas cleaning process [21].

16.1.2.6 AVEVA PRO/II AVEVA PRO/II software uses for simulation, applied optimization of process plants, and also rating and sizing of each component. Designed to compute heat and material balance computations for a wide range of chemical processes, AVEVA PRO/II has a wide range of thermodynamic models to uses in every industry [22]. Fig. 16.12 demonstrates a schematic of the IGCC coal power plant proposed and simulated by Lee et al. in PRO/II [23]. Also, Fig. 16.13 represents the modeling of a gasifier in PRO/II.

16.1.2.7 i-Steam i-Steam is a software employed for modeling, analysis, simulation, sensitivity analysis, and applied optimization of site utilities, power, and steam systems. It uses nonlinear modeling and optimization approaches to improve the performance of existing utility and cogeneration systems and also to decrease energy consumptions and operating costs. It can be used for complex revamp projects with different scenarios. One of the main advantages of i-Steam is to create a hydraulic model for the steam pipeline network realistically and also optimization of integration of CHPs with process units and optimal driver selection configurations quickly and accurately [24].

16.1 Introduction

FIGURE 16.11 ProMax model for the acid gas cleaning process [21].

FIGURE 16.12 Schematic of the IGCC plant proposed and simulated by Lee et al. in PRO/II [23].

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FIGURE 16.13 Modeling of a gasifier in PRO/II [23].

FIGURE 16.14. STAR model for simulation of a steam network of a process industry [26].

16.1.2.8 STAR STAR is a software for the simulation, design, and optimization of cogeneration, and site utility systems in process industries. The interactions between the central utility systems and each process, the steam network, steam boilers, gas turbines, steam turbines, fired heaters, heat exchangers, expansion valves, and all cooling systems are all simulated and evaluated using STAR. It can be applied for reducing operating costs and technoeconomic evaluation of diffrent scenarios [25]. The most profitable strategy can be found. STAR can also be employed to promote the reduction of emission pollutions to meet better performance of environmental regulations in point of view. Fig. 16.14 shows a STAR model for the simulation of a steam network of a process industry [26].

16.1 Introduction

16.1.3 Renewable energy 16.1.3.1 TRNSYS TRNSYS is a transient system that uses for dynamic simulation with a modular configuration. The TRNSYS library consists of different components of energy systems. The main capability of TRNSYS is to handle time-dependent forcing functions, weather data, and time-dependent simulation. TRNSYS is well-known for the analysis and evaluation of any time-dependent system. One of the most important applications of TRNSYS simulation is related to a solar system consisting of solar thermal and solar photovoltaic systems, renewable energy, fuel cell, heating, ventilation, and air conditioning system (HVAC), and cogeneration and trigeneration systems [27]. Fig. 16.15 demonstrates TRNSYS model of renewable CCHP system proposed by Chua [28].

16.1.3.2 HOMER Pro The HOMER Pro is developed for simulating and optimizing microgrid design in all locations and situations, from village and island to grid-connected campuses

FIGURE 16.15 TRNSYS model of renewable CCHP system proposed by Chua [28]. CCHP, Combined cooling, heating, and power.

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and military bases. This software is promoted and distributed by HOMER Energy. HOMER is a Hybrid Optimization Model for Multiple Energy Resources software that has three powerful tools in one software package that performs the techno-economic analysis. HOMER evaluates all possible integration of system types and different scenarios in a single run and then prepares the results based on the selection of optimization variables [29]. Fig. 16.16 shows the configuration analysis for a campus microgrid in HOMER.

FIGURE 16.16 Configuration analysis for a campus microgrid in HOMER [30].

16.1 Introduction

16.1.3.3 RETScreen RETScreen is a software for energy efficiency, simulation, performance analysis, and feasibility evaluation of renewable energies and cogeneration plants. This program as rapidly professional and powerful decision-makers helps users to find optimum operating parameters from technical and economical point of views. It can use different types of energy-efficient technologies and renewable energies. RETScreen is also optimized integrated energy-efficient design in different sectors. RETScreen has some valuable databases, consisting of global climate data from NASA satellites, for energy projects. RETScreen tries to select us easier and faster by promoting cleaner energy decisions [31].

16.1.3.4 System Advisor Model The System Advisor Model (SAM) is free software that employs for technoeconomic decision-making for researchers, engineers, policy analysts, technology developers, and managers in the renewable energy industry [32]. SAM can model many and different types of renewable energy systems as PV systems, from small residential application to large utility-scale systems, various types of battery applications, different concentrating solar power consisting of parabolic trough, linear Fresnel, and power tower for power generation, process heating from linear Fresnel and parabolic trough and systems, wind power in wide ranges of applications, wave and tidal systems as marine energy, solar water heating, geothermal power generation, biomass combustion for power generation, and high concentration photovoltaic systems [32].

16.1.4 Computer code 16.1.4.1 EES EES is an equation solver program that can solve thousands of nonlinear algebraic, and integral equations and differential equations numerically around 24,000 simultaneous equations in a 64-bit professional version. It can also be used for uncertainty analyses and optimization problems. A significant advantage of EES is the high-accuracy transport property and thermodynamic database (100 fluids) that is supplied for hundreds of substances that promote it to be used with the equation solving capability in modeling, simulation, and applied optimization of power cycles, cogeneration, and energy systems. It also has a heat transfer library database and functions for radiation, conduction, and convection coefficient. It can be linked to C/C11, Excel, and MATLAB, Fortran, and Python [33].

16.1.4.2 Thermolib Thermolib is a toolbox for thermodynamic computations for energy systems that is used in MATLAB and Simulink simultaneously. Thermolib used to thermodynamic modeling and simulation of energy systems in various industries. This

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program provides a Simulink block set for simulations and a set of MATLAB command-line functions for thermodynamic computations. Fundamental thermodynamic creating blocks provide to design of the user-defined elements. The thermodynamic and thermophysical database supplies from the JANAF tables and can be used and developed by the user easily and rapidly. Thermolib can be used for calculation of real gas behavior associated with the Peng-Robinson EOS and uses IAPWS-IF97 formulation for water and steam [34].

16.1.4.3 MATLAB MATLAB is one of the most comprehensive programming languages that can be used for simulation, optimization, sensitivity analysis, risk analysis, and reliability for thermodynamic cycles, power plants, cogeneration, and polygeneration systems. The main advantages of MATLAB are very high capabilities and fully flexible of this program in solving equations as well as various optimization algorithms and also the possibility of direct or indirect linking with other simulation software. In the case of thermodynamic and thermophysical databases concerns, existing MATLAB codes for this purposes such as X-Steam [35] (for water/steam thermophysical properties) and REFPROP [36] (for a wide range of substances) can be used, or by using artificial intelligence techniques and machine learning methods using existing thermophysical tables, suitable models can be created to predict thermodynamic properties.

References [1] GT PRO: gas turbine combined cycle & cogeneration plant creation. 2018. Available from: http://www.thermoflow.com/products_gasturbine.html. [2] Gonzalez-Salazar MA, Kirsten T, Prchlik L. Review of the operational flexibility and emissions of gas- and coal-fired power plants in a future with growing renewables. Renew Sustain Energy Rev 2018;82:1497 513. [3] GT MASTER: gas turbine combined cycle & cogeneration plant simulation. 2018. Available from: www.thermoflow.com/products_gasturbine.html. [4] STEAM PRO: conventional steam power plant creation. 2018. Available from: https:// www.thermoflow.com/products_conventionalsteam.html. [5] STEAM MASTER: conventional steam power plant simulation. 2018. Available from: https://www.thermoflow.com/products_conventionalsteam.html. [6] General Purpose Program for design & simulation of gas turbine combined cycles, cogeneration systems, conventional steam plants, renewable energy and other thermal systems. 2018. Available from: https://www.thermoflow.com/products_generalpurpose.html. [7] Dabwan YN, Pei G, Gao G, Li J, Feng J. Performance analysis of integrated linear Fresnel reflector with a conventional cooling, heat, and power tri-generation plant. Renew Energy 2019;138:639 50.

References

[8] GE Power Systems selects GateCycle. 2001. Available from: https://www.poweronline.com/doc/ge-power-systems-selects-gatecycle-0001. [9] Design and engineer your plants with EBSILON®Professional STEAG’s innovative, world-leading planning tool. 2020. Available from: https://www.ebsilon.com/en/. ´ [10] Kalina J, Swierzewski M, Szega M. Simulation based performance evaluation of biomass fired cogeneration plant with ORC. Energy Procedia 2017;129:660 7. [11] Cycle-Tempo. 2020. Available from: http://www.asimptote.nl/software/cycle-tempo/. [12] Pantaleo AM, Ciliberti P, Camporeale S, Shah N. Thermo-economic assessment of small scale biomass CHP: steam turbines vs ORC in different energy demand segments. Energy Procedia 2015;75:1609 17. [13] ASPEN PLUS. 2020. Available from: https://www.aspentech.com/en/products/engineering/aspen-plus. [14] Ishaq H, Dincer I, Naterer GF. Multigeneration system exergy analysis and thermal management of an industrial glassmaking process linked with a Cu Cl cycle for hydrogen production. Int J Hydrogen Energy 2019;44(20):9791 801. [15] Aspen HYSYS. Available from: https://www.aspentech.com/en/products/engineering/ aspen-hysys. [16] Hosseini SS, Mehrpooya M, Alsagri AS, Alrobaian AA. Introducing, evaluation and exergetic performance assessment of a novel hybrid system composed of MCFC, methanol synthesis process, and a combined power cycle. Energy Convers Manage 2019;197:111878. [17] Keshvarparast A, Ajarostaghi SSM, Delavar MA. Thermodynamic analysis the performance of hybrid solar-geothermal power plant equipped with air-cooled condenser. Appl Therm Eng 2020;172:115160. [18] Petro-SIM. 2020. Available from: https://www.kbc.global/software/process-simulation-software/. [19] UNISIM. 2020. Available from: https://www.honeywellprocess.com/en-US/ explore/products/advanced-applications/unisim/unisim-competency-suite/Pages/ default.aspx. [20] PROMAX. 2020. Available from: https://www.bre.com/ProMax-Main.aspx. [21] Younas O, Banat F. Parametric sensitivity analysis on a GASCO’s acid gas removal plant using ProMax simulator. J Nat Gas Sci Eng 2014;18:247 53. [22] AVEVA PRO/II. 2020. Available from: https://sw.aveva.com/engineer-procure-construct/process-engineering-and-simulation/pro-ii-process-engineering. [23] Lee JC, Lee HH, Joo YJ, Lee CH. Process simulation and thermodynamic analysis of an IGCC (integrated gasification combined cycle) plant with an entrained coal gasifier. Energy 2014;64:58 68. [24] i-Steam. 2020. Available from: https://www.processint.com/software/i-steam/. [25] STAR software. 2020. Available from: https://www.ceas.manchester.ac.uk/cpi/ research/resources/software/. [26] Jafari Nasr MR. An optimization approach to refinery steam management with consideration of CO2 emission. J Pet Sci Technol 2014;4(1):73 84. [27] TRNSYS. 2020. Available from: https://sel.me.wisc.edu/trnsys/features/features.html. [28] Chua KJ, Yang WM, Er SS, Ho CA. Sustainable energy systems for a remote island community. Appl Energy 2014;113:1752 63. [29] HOMER PRO. 2020. Available from: https://www.homerenergy.com/products/pro/ index.html.

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[30] Iqbal F, Siddiqui AS. Optimal configuration analysis for a campus microgrid—a case study. Prot Control Mod Power Syst 2017;2(1):23. [31] RETScreen. 2020. Available from: https://www.nrcan.gc.ca/maps-tools-publications/ tools/data-analysis-software-modelling/retscreen/7465. [32] SAM. 2020. Available from: https://sam.nrel.gov/. [33] EES. 2020. Available from: http://fchartsoftware.com/ees/. [34] Thermolib. 2020. Available from: https://www.mathworks.com/products/connections/ product_detail/thermolib.html. [35] X-Steam. 2020. Available from: https://www.mathworks.com/matlabcentral/fileexchange/9817-x-steam-thermodynamic-properties-of-water-and-steam. [36] REFPROP. 2020. Available from: https://www.nist.gov/srd/refprop.

APPENDIX

A A Calculation of thermodynamic properties for several substances 1. At T ref 5 298:15K ð25
CÞ; pref 5 1 bar (Table A.1) Table A.l Variation of specific heat, enthalpy, absolute entropy, and Gibbs function with the temperature at 1 bar for various substances in units of kJ=kmol or kJ=kmol K [1].


Substance

Formula

cp


h

Carbon (graphite) Sulfur (rhombic) Nitrogen Oxygen Hydrogen Carbon monoxide Carbon dioxide Water Water Methane Sulfur dioxide Hydrogen sulfide Ammonia

C(s) S(s) N2 ðgÞ O2 ðgÞ H2 ðgÞ COðgÞ CO2 ðgÞ H2 OðgÞ H2 OðlÞ CH4 ðgÞ SO2 ðgÞ H2 SðgÞ NH3 ðgÞ

8.53 22.77 28.49 28.92 29.13 28.54 35.91 31.96 75.79 35.05 39.59 33.06 35.59

0 0 0 0 0 2110,528 2393,521 2241,856 2285,829 274,872 2296,833 220,501 246,111

s


g


5.740 32.058 191.610 205.146 130.679 197.648 213.794 188.824 69.948 186.251 284.094 205.757 192.451

21711 29558 257,128 261,164 238,961 2169,457 2457,264 2298,153 2306,685 2130,403 2370,803 281,847 2103,491

2. For 298:15 , T # T max ; pref 5 1 bar; with y 5 1023 T (Table A.2) Table A.2 Correlation for calculation of specific heat, enthalpy, absolute entropy, and Gibbs function. Property

Equation


No. 22

Specific heat

c p 5 a 1 by 1 cy

Enthalpy

b d h
5 103 3 H1 1 ay 1 y 2 2 cy21 1 y 3 2 3




1 dy

2



(A1) (A2) (Continued)

365

366

Appendix A

c d Absolute entropys
5 S1 1 a 3 lnðT Þ 1 by 2 y22 1 y2 (A3)Gibbs functiong
5 h
2 Ts
2 2 0 (A4) The parameters H ; S1 ; a; b; c; and d needed for calculation of thermody-

namic properties are obtained for selected substances in Table A.3. The maximum temperature, Tmax , is 368K for SðsÞ, 1100K for CðsÞ, 500K for H2 OðlÞ, and SO2 ðgÞ; 2000K for CH4 ðgÞ and H2 SðgÞ, 1500K for NH3 ðgÞ, and 3000K for all remaining substances. To evaluate the absolute entropy at states where the pressure p differs from pref 5 1 bar, correlations (A.5) and (A.6) (Table A.4) should be used [1]. 0

Table A.3 Constant values for H ; S 1 ; a; b; c; and d for selected substances [1]. 0

Substance

Formula

H

S1

c

d

Carbon (graphite)

C(s)

22.101

26.540

0.109

38.940

20.146

217.385

Sulfur (rhombic)

S(s)

25.242

259.014

14.795

24.075

0.071

0

Nitrogen

N2 ðgÞ

29.982

16.203

30.418

2.544

20.238

0

Oxygen

O2 ðgÞ

29.589

36.116

29.154

6.477

20.184

21.017

a

b

Hydrogen

H2 ðgÞ

27.823

222.966

26.882

3.586

0.105

0

Carbon monoxide

COðgÞ

2120.809

18.937

30.962

2.439

20.280

0

Carbon dioxide

CO2 ðgÞ

2413.886

287.078

51.128

4.368

21.469

0

20.423

0

Water

H2 OðgÞ

2253.871

211.750

34.376

7.841

Water

H2 OðlÞ

2289.932

267.147

20.355

109.198

2.033

0

Methane

CH4 ðgÞ

281.242

96.731

11.933

77.647

0.142

218.414

Sulfur dioxide

SO2 ðgÞ

2315.422

243.725

49.936

4.766

2 1.046

0

Hydrogen sulfide

H2 SðgÞ

232.887

1.142

34.911

10.686

20.448

0

Ammonia

NH3 ðgÞ

260.244

229.402

37.321

18.661

20.649

0

Table A.4 Absolute entropy for pure substance and mixture. Property

Equation

Absolute entropy (ideal gas)

sðT; pÞ 5 s % ðT Þ 2 Rln

Absolute entropy (ideal gas mixture)





P Pref  
xk P s k ðT; pÞ 5 s k ðT Þ 2 Rln Pref o

No. (A5) (A6)

Appendix A

B Seawater properties correlations B.1 Specific volume and density of seawater ν sw 5

1 ρsw

(B.1)

  ρsw 5 ρw 1 ws a1 1 a2 T 1 a3 T 2 1 a4 T 3 1 a5 ws T 2 ρw 5 9:999 3 102 1 2:034 3 1022 T 2 6:162 3 1023 T 2 1 2:261 3 1025 T 3 2 4:657 3 1028 T 4

(B.2) (B.3)

where ν sw is the specific volume of seawater in m =kg; ρsw and ρw are the density of seawater and pure water, respectively, in m3 =kg; T is the temperature in
C, ws is the salt concentration in kgs =kgsw [2]. 3

a1 5 8:020 3 102 ;

a2 5 22:001;

a4 5 23:060 3 1025 ;

a3 5 1:677 3 1022

a5 5 21:613 3 1025

(B.4)

B.2 Specific enthalpy of seawater and pure water   hsw 5 hw 2 ws b1 1 b2 ws 1 b3 ws 2 1 b4 ws 3 1 b5 T 1b6 T 2 1 b7 T 3 1 b8 ws T 1 b9 w2s T 1 b10 ws T 2 (B.5)

hw 5 141:355 1 4202:070 3 T 2 0:535 3 T 2 1 0:004 3 T 3

(B.6)

where hsw and hw are the specific enthalpy of seawater and pure water respectively in J=kg, 10 # T # 120
C; 0 # ws # 0:12 kg=kg, and b1 5 22:348 3 104 ; b2 5 3:152 3 105 ; b3 5 2:803 3 106 ; b4 5 21:446 3 107 ; b5 5 7:826 3 103 ; b6 5 24:417 3 101 ; b7 5 2:139 3 1021 ; b8 5 21:991 3 104 ;

(B.7)

b9 5 2:778 3 104 ; b10 5 9:728 3 101

B.3 Specific entropy of seawater and pure water   ssw 5 sw 2 ws c1 1 c2 ws 1 c3 ws 2 1 c4 ws 3 1 c5 T 1c6 T 2 1 c7 T 3 1 c8 ws T 1 c9 w2s T 1 c10 ws T 2 (B.8) sw 5 0:1543 1 15:383 3 T 2 2:996 3 1022 3 T 2 1 8:193 3 1025 3 T 3 2 1:370 3 1027 3 T 4

(B.9)

where ssw and sw are the specific entropy of seawater and pure water respectively in J=kgK, 10 # T # 120
C; 0 # ws # 0:12kg=kg, and c1 5 2 4:231 3 102 ; c2 5 1:463 3 104 ; c3 5 2 9:880 3 104

367

368

Appendix A

c4 5 3:095 3 105 ; c5 5 2:562 3 101 ; c6 5 2 1:443 3 1021 c7 5 5:879 3 1024 ; c8 5 2 6:111 3 101 ; c9 5 8:041 3 101 c10 5 3:035 3 1021

(B.10)

C Cost functions Table C.1 Table C.1 Purchased equipment cost (PEC) functions. Component

Cost function ($) _a m

Air compressor

71.1 3 0:92 2 η

Combustion chamber

46:08 3

Gas turbine Steam turbine Air cooled condenser Low pressure economizer Pump

AC

Reference

  3 rp 3 ln rp

[3]

_a m 0:995 2 ðP4 =P2 Þ _ fg m

479:34 3

0:93 2 ηGT

3 ð1 1 exp ð0:018 3 T4 2 26:4ÞÞ  3 ln PP45 3 ð1 1 exp ð0:036 3 T4 2 54.4))

_ ST1 2210 3 W _ CASC 1773 3 m  0:8 _ _ fg 1:2 1 1184:4 3 m 6570 3 QΔTLPECO LPECO 0:7

 0:26  0:5 _ P1 3 12η 2100 3 W10P1 η

[3] [3] [3] [3] [3] [3]

P1

"

_ ECO Q

0:8

    # _ SUP 0:8 _ EVA 0:8 _ fg 1:2 _ w 1 1184:4 3 m 1 Q 1 Q 1 21; 276 3 m ΔTEVA ΔTSUP

Heat recovery steam generator

6570 3

ORC turbine

479:34 3

ORC reheater

_ 235 3 Q

ORC condenser

_ CAORC 1773 3 m  0:26  0:5 _ ORCP 2100 3 WORCP 3 12η ; [24] 10 η

[5]

Solar collector

PECsf 5 355 $=Area

[6]

Heat recovery vapor generator

_ Z 5 235 3 Q

[5]

ORC pump

ΔTECO

_ ORC m 0:92 2 ηORCT

3 ln



P31 P32



3 ð1 1 exp ð0:036 3 T31 2 54.4))

0:75

[3] [4] [5]

[5]

ORCP

Feedwater heaters

Deaerator

SOFC

0:1 1 TTTD 1a3 a3 5 3-ðLPFWHÞ a3 5 6-ðHPFWHÞ _ DEA Þa5 a 4 3 ðm a4 5 143; 315 ½$=ðkg=sÞ a5 5 0:7 ZSOFC 5 ASOFC ð2:96TSOFC 2 1907Þ  0:7 Zinv 5 105 Wcell =500 Zaux 5 0:1 3 ZSOFC _ 66 3 Q



0:75

[6]

[6]

[7]

(Continued)

Appendix A

Table C.1 Purchased equipment cost (PEC) functions. Continued Component

Cost function ($)

Solar heliostat

    2 ð8:37E 1 7Þ 1 8:2E 2 3Qrec 2 2 85:75 ΔT 2

Reference Present study

21 201:67 3 Q 3 ΔTlm 3 dp20:15 3 dp20:15 t s  0:05 Ti _ 3 2 3 8:07 3 0:989 3 m 3 Pe 20:75 Pi

[8]

TVC RO

PECmembrane 1 PECpretreat 1 PECRO2pump 1 PECRO2valve

[9,10]

MED

[9]

PECmembrane 5 NOmembranes 3 PECone2membrane PECone2membrane 5 7846  0:8 _ 3 24 3 3600 PECpretreat 5 996 3 ξ 1 m RO2feed ρ ξ 1 5 1:399: inflation factor PECRO2pump 5 393; 000ξ 1 1 701:19 3 14:5 3 PRO2feed  0:05 T _ 3 i PECRO2valve 5 8:07 3 0:989 3 m 3 Pe 20:75 Pi
 0:8  0:8  ec ev _ 4 1 a5 ðm _ 2 Þ1:2 1 a4 m a3 ΔTQlm;ec 1 ΔTQlm;ev

Steam generator

[11]

a3 5 6570 ½$=ðkW=KÞ0:8  a4 5 21; 276 ½$=ðkg=sÞ a5 5 1184:4 ½$=ðkg=sÞ1:2  _ TEG a6 W

TEG

[11]

a6 5 1500 ð$=kWÞ

_ PEM W

PEM electrolyzer

[11]

a8 5 1000 ð$=kWÞ Condenser (for comparison)

_6 a9 m

PTSC field and solar fluid

a7 Aap;tot

[11]

a9 5 1773 ½$=ðkg=sÞ [11]

a7 5 240 ð$=m2 Þ

MED, Multieffect distillation; ORC, organic Rankine cycle; PEM, proton exchange membrane; RO, reverse osmosis; SOFC, solid oxide fuel cell; TEG, thermoelectric generator; TVC, thermal vapor compression.

D Weight function Table D.1 Table D.1 The weight functions of some component in tons. Component

Relation

Units

Reference

Air compressor

100Pout  D FS ; 2σ

P in kPa; D in m _ net in kW W

[3]

Combustion chamber

FS 5 2; v 5 15 m=s; σ 5 15:8   120:48 _ net 1 2 0:0090 3 W _ 1:223 3 W net 2 1484:59 100Pout  D FS ; 2σ

FS 5 1:6; v 5 6:2 m=s; σ 5 45:6  2:37 _ net 1 2 0:000106 1 0:0057 3 W

_ net 10;482:37 2 9:65 3 W



P in kPa; D in m _ net in kW W

Present study [3] Present study

(Continued)

369

370

Appendix A

Table D.1 The weight functions of some component in tons. Continued Component

Relation

Gas turbine

100Pout  D FS ; 2σ

Heat exchanger

Units

Reference

P in kPa; D in m

[3]

0:06 3 Wnet 1 1:13 3 1028 3 Wnet 3 2 17:48 2 4:54 3 1025 3 Wnet 2

Wnet in kW

Present study

2:989Q0:97 P , 25bar

Q in kW

[3]

Q in kW

Present study

FS 5 2; v 5 13 m=s; σ 5 5:9

1:15

2:340Q

P . 25bar

2:14 3 Q0:7 HX Turbine

_ 4:90W

_ in kW W

[3]

Cooler

_ 0:99 0:073Q

_ in kW Q

[3]

Pump

_ 0:95 0:0061  W

_ in kW W

[3]

_ fg in kg/s m

Present study

0:73



CO2 capture

_ fg 1 0:13 3 m _ 2fg 10 3 37:27 3 m

Superheater

2 2 80:67 3 ΔTpinch 3511:49 1 0:87 3 ΔTpinch

ΔTpinch in K

Present study

Evaporator

6948:11 2 1109:97 3 logΔTpinch

ΔTpinch in K

Present study

Solar field

_ desired 148:437 1 5550:51 3 m

Present study

ORC pump

_ ORC;P 31:22 3 W

_ desired in m kg/s _ in kW W

Condenser

_ 0:099 0:073 3 Q cond

_ in kW Q

Present study

ORC turbine

_ ORC;T 14 3 W

W in kW

Present study

Present study

ORC, Organic Rankine cycle.

E Eco-indicator for some components Table E.1 Table E.1 The weight functions of some component in tons. Component

Materials composition

Eco’99 (mPts/kg)

Air compressor

Steel 33.33% Steel low allow 44.5% Cast iron 22.22% Steel 33.34% Steel high alloy 66.66% Steel 25% Steel high alloy 75% Steel 33.33% Steel low allow 44.5% Cast iron 22.22%

86 110 240 86 910 86 910 86 110 240

Combustion chamber Gas turbine Fuel compressor

Total (mPts/kg) 71.7

585.5 645.5 71.7

(Continued)

Appendix A

Table E.1 The weight functions of some component in tons. Continued Component

Materials composition

Eco’99 (mPts/kg)

Heat exchanger

Steel 75% Steel low allow 25% Steel 33.34% Steel high alloy 66.66% Steel 100% Steel 100% Steel 100% Steel 100% Steel 100% Steel 100% Steel 20% Cast iron 60% Copper 15% Aluminum—primary 5%

86 110 86 910 86 86 86 86 86 86 86 240 1400 780

After burner HRVG ORC turbine ORC pump ORC condenser HRSG TVC Motor/generator

Total (mPts/kg) 46.1 585.5 28 28 28 28 28 28 410

HRSG, Heat recovery steam generator; ORC, organic Rankine cycle; TVC, thermal vapor compression.

References [1] Bejan A, Tsatsaronis G, Moran MJ. Thermal design and optimization. Wiley; 1995. [2] Lienhard JH, et al. Chapter 4—Thermodynamics, exergy, and energy efficiency in desalination systems. In: Arafat HA, editor. Desalination sustainability. Elsevier; 2017. p. 127206. [3] Cavalcanti EJC. Exergoeconomic and exergoenvironmental analyses of an integrated solar combined cycle system. Renew Sustain Energy Rev 2017;67 50719. [4] Nami H, Mahmoudi SMS, Nemati A. Exergy, economic and environmental impact assessment and optimization of a novel cogeneration system including a gas turbine, a supercritical CO2 and an organic Rankine cycle (GT-HRSG/SCO2). Appl Therm Eng 2017;110:131530. [5] Bonyadi N, Johnson E, Baker D. Technoeconomic and exergy analysis of a solar geothermal hybrid electric power plant using a novel combined cycle. Energy Convers Manage 2018;156:54254. [6] Dincer I, Rosen MA, Ahmadi P. Optimization of energy systems. Wiley Online Library; 2017. [7] Shirazi A, et al. Thermaleconomicenvironmental analysis and multi-objective optimization of an internal-reforming solid oxide fuel cellgas turbine hybrid system. Int J Hydrogen Energy 2012;37(24):1911124.

371

372

Appendix A

[8] Mabrouk AA, Nafey AS, Fath HES. Thermoeconomic analysis of some existing desalination processes. Desalination 2007;205(1):35473. [9] El-Sayed YM. The thermoeconomics of energy conversions. Elsevier Science; 2013. [10] Park C, et al. Stochastic cost estimation approach for full-scale reverse osmosis desalination plants. J Membr Sci 2010;364(1):5264. [11] Habibollahzade A, et al. Multi-criteria optimization of an integrated energy system with thermoelectric generator, parabolic trough solar collector and electrolysis for hydrogen production. Int J Hydrogen Energy 2018;43(31):1414057.

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A “Above-the-pinch” region, 191 Absolute entropy correlation for calculation, 365t for pure substance and mixture, 366t variation, 365t Absorption chillers (ACs), 246 247 fuel cell 1 absorption chillers, 246 250 fuel cell, 247 248 microbial cell integration, 248 250 Absorption power cycle, 215 Absorption refrigeration cycle (ARC), 215, 217, 219 Absorption refrigeration systems, 253 AC. See Air compressor (AC) ACs. See Absorption chillers (ACs) Actual enthalpy, 170 171 Actual R-curve, 168 169 procedure to construct, 169f Actual steam level temperature model, 150 151 Adiabatic flame temperature, 202 Advanced exergetic evaluation of cogeneration advanced exergy destruction level representation, 83 84 advanced exergy-based variables, 76 77 application of advanced exergy-based analysis, 84 93 methodology for splitting variables, 77 82 endogenous and exogenous parts, 79 82 unavoidable and avoidable parts, 78 79 Advanced exergy destruction, 91 92 Advanced exergy-based analysis, 77f, 84 application CGAM problem, 84 88 LNG cogeneration, 88 93 Advanced exergy-based variables, 76 77 avoidable/unavoidable, 76 77 endogenous/exogenous, 76 Air compressor (AC), 16 Air preheater (APH), 299 Alkaline, 26 Annualized cost method, 61 APH. See Air preheater (APH) ARC. See Absorption refrigeration cycle (ARC) Aspen HYSYS, 352 353 Aspen Plus software, 151, 352 Asymmetric hidden Markov models (HMM-As), 326

Austria, cogeneration and polygeneration in, 35 Automated targeting method (ATM), 151 Auxiliary equations, 227, 228t Availability, concept of, 327 AVEVA PRO/II software, 356 Avoidable/unavoidable exergy destruction, 76 77, 78t

B Back-pressure steam turbine plant, 14, 15f Bandyopadhyay model, 147 148 Bathtub curve, 327 Below-the-pinch region of curve, 182 184, 191 Biomass solar-driven polygeneration system, 316 321, 316f decision variables, 321t input data, 318t optimization, 317 321 optimum values, 321t Pareto frontier of multigeneration energy system, 321f thermodynamic simulation, 319t Boiling Heat Transfer Nucleate Pool, 266 267 Brine heater, 117

C C. Frangopoulos, G. Tsatsaronis, A. Valero, M. Spakovsky problem (CGAM problem), 84 88. See also CGAM cogeneration plant advanced exergoeconomic analysis, 87t combination splitting of cost of exergy destruction, 87t of exergy destruction in components, 86t of investment costs, 87t conventional exergoeconomic analysis, 87t endogenous curve equations, 85t splitting of cost of exergy destruction, 86t of exergy destruction in components, 86t of investment costs of selected components, 87t “Calculator” function of Aspen Plus, 152 153 Capital investment cost, 91 92 Carbon dioxide emissions (CO2 emissions), 289 Carbon footprint (CF), 203 Carbone dioxide emissions, 202 Carnot factor, 140 142, 145

373

374

Index

Cascade Rankine cycle, 218 CC. See Combustion chamber (CC) CCHP system. See Combined cooling, heating, and power system (CCHP system) CCs. See Composite curves (CCs) Central utility systems, 304 305 CF. See Carbon footprint (CF)Conversion factor (CF) CGAM cogeneration plant. See also C. Frangopoulos, G. Tsatsaronis, A. Valero, M. Spakovsky problem (CGAM problem) exergy and exergoeconomic modeling of, 65 70 exergetic cost rate of fuels and product, 69t exergetic product, fuel, and efficiency, 67t exergy calculation of each stream, 66t fixed variables for economic model, 69t purchased equipment cost, 68t modeling, 40 41, 41f relations, inputs, and outputs, 42t specification of each stream, 43t Chemical exergy, 59 60 China, cogeneration and polygeneration in, 36 CHP system. See Combined heat and power system (CHP system) Classical optimization, 290 Close-cycle system, 4 Closed system. See Control mass system CM control mass, 39 Coefficient of performance (COP), 273 276 Cogeneration, 1, 345 application, 30 32, 32t commercial, 32 industrial, 31 institutional, 32 case study, 155 158, 157f CCHP and stand-alone system comparison, 7 8 China, 36 CHP system analysis, 3 4 comparison of different methods, 155, 156t components, 13 combined cycle-based cogeneration plants, 19 21 fuel cell, 26 gas turbine, 15 19 internal combustion engine, 21 24 steam turbines, 14 15 Stirling engines, 24 26 deployment, 9 efficiency, 165 166, 182 184 fundamental, 2 history, 9 in integrated system, 108 110 issues, 139 140

Japan, 35 36 law, 33 models, 140 155 plants, 115 and polygeneration system, 96 potential, 95 by IBTM, 157f by Kapil method, 158f by Khoshgoftar Manesh method, 158f prospects in Europe, 32 35 Austria, 35 Fiona Riddoch, COGEN Europe, Belgium, 33 Germany, 33 34 Spain, 34 35 software, 155 steam mains parameters, 156t system, 3f, 189 192, 326 327 targeting, 170 171 regression coefficients in isentropic efficiency equation, 171t United States, 36 Combinatorial optimization, 291 Combined cooling, heating, and power system (CCHP system), 3, 5 8, 13, 30 31, 43f, 348 349 comparison with stand-alone system, 7 8 exergy and exergoeconomic modeling of, 71 plant, 8f principle, 6f relations, inputs, and outputs, 44t thermodynamic modeling, 41 43 Combined cycle (CC) CC-based cogeneration plants, 19 21 with steam user unit, 21f power plant, 19 20, 124 systems, 19 20 open cycle of cogeneration based on, 20f Combined heat and power system (CHP system), 2, 13, 15, 22, 29, 31 analysis, 3 4 principle, 6f in Spain, 34 35 Combined heating, cooling, hydrogen, and power production modeling and analysis, 222 230 system description, 219 222 validation of model, 231 234 Combined pinch and exergy representation (CPER), 154 Combined solar geothermal systems, 267 Combustion chamber (CC), 17, 21 22, 91 Combustion engines, 13 Commercial applications for cogeneration and polygeneration, 32

Index

Component cost equation, 227, 228t Composite curves (CCs), 96 Computer code, 361 362 EES equation solver program, 361 MATLAB, 362 Thermolib, 361 362 Concentered systems of PV (CPV), 251 252 Concentrated heating, 14 Concentrated photovoltaic thermal (CPVT), 254 collectors, 264 polygeneration device, 266 Concentrated solar power (CSP), 252 253 Condensation extraction steam turbine turbines, 14 Condensing turbine (CT), 166 Constrained optimization, 292 Constraints, 289 290 Control mass system, 39 Conventional exergoeconomic analysis, 84, 85t Conventional exergy-based analysis, 75 Conventional R-curve representation, 178f Conventional TSA, 102 105 steam levels and cooling mains data for Total Site, 102t steam profiles with steam boilers providing VHP steam, 102f, 104f steam used and steam generated across Total Site, 103t SUGCC with maximum heat recovery, 105f Total Site profiles with potential steam heat recovery, 103f Conversion factor (CF), 142 Cooling heat pump cycles, 215 COP. See Coefficient of performance (COP) Cost balance equation, 227, 228t Cost functions, 368 Cost reductions, 29 CPER. See Combined pinch and exergy representation (CPER) CPV. See Concentered systems of PV (CPV) CPVT. See Concentrated photovoltaic thermal (CPVT) CS. See Cuckoo search (CS) CSP. See Concentrated solar power (CSP) CT. See Condensing turbine (CT) Cuckoo search (CS), 291 Cycle-Tempo, 350 352

Decision-makers, 294 Decision-making process, 299 300 Degree of superheat (DSH), 142, 148, 155 DES. See District energy systems (DES) Desalinated water production desalination technologies, 116 124, 116t integration with of gas turbines, 127 129 integration with site utility industrial plants, 129 134 integration with thermal power plants, 124 127 Desalination, 115, 247 248, 253 FC, 247 248 plants, 115 technologies, 116t, 215 MED desalination, 119 122 MSF distillation desalination, 116 119 RO desalination, 122 124 Desalination systems, 247 248 integration with help of R-curve advanced R-ratio vs. exergy destruction, 180f cogeneration system, 189 192 IECEF, 185 187 MED desalination system, 185 RO desalination, 182 184 specifications, 187 Willans line model, 188 DG. See Distributed generation (DG) DHW. See Domestic Hot Water (DHW) Diesel cycle, 23 24 Diesel engine, 21 22 power-generation cycle, 24, 24f, 25f Direct Normal Irradiance (DNI), 264 Directive 2004/8/EC, 33 Distributed generation (DG), 8 District energy systems (DES), 10 District heating, 13 DNI. See Direct Normal Irradiance (DNI) 5-DOF. See Five-degree-of-freedom (5-DOF) Domestic heater unit system, 215 Domestic Hot Water (DHW), 215 216 DPEV. See Deaerator pressure evaporator (DPEV) DSH. See Degree of superheat (DSH) Dynamic polygeneration hybrid solar system, 257 Dynamic programming, 291

D

EBSILON software, 350 ECDL. See Exergy cost destruction level (ECDL) Eco-indicator for components, 370 for materials, 200t point, 199 Eco-indicator 99 (EI-99), 199 201, 203 204, 289

Damage-oriented impact analysis, 199 Dead state, 56 limited, 56 Deaerator pressure evaporator (DPEV), 46 Decision variables, 289

E

375

376

Index

Economizers, 51 53 ED. See Electrodialysis (ED) EDL. See Exergy destruction level (EDL) EEDL. See Environmental effects of exergy destruction level (EEDL) EES (equation solver program), 361 EI-99. See Eco-indicator 99 (EI-99) Ejector modeling, 223 225 Ejector refrigeration cycle (ERC), 215, 232t Electrical efficiency, 4 Electrodialysis (ED), 248 249 EMF. See Emission footprint (EMF) Emission footprint (EMF), 203 Endogenous and exogenous parts, 79 82 engineering approach, 81 82 simple approach, 80 81 step-by-step connection of elements, 80f thermodynamic approach, 81 Endogenous exergy destruction, 76 Endogenous part, 82 Endogenous/exogenous exergy destruction, 76, 78t Energy, 163 165. See also Solar energy analysis, 227 balance equation, 224 consumption, 195 conversion efficiency, 4 systems, 75 footprint, 203 of fuel, 165 166 geothermal, 216, 219 losses, 30 policies, 196 resources, 29 system, 39 Energy Tax and Electricity Tax Acts, 34 Engineering approach, 81 82, 83f Entropy, 40 Enthalpy correlation for calculation, 365t variation, 365t Environmental effects of exergy destruction level (EEDL), 83, 172 advanced representation of, 172 Environmental Impact Assessment Report, 196 Environmental impacts, 195 assessment, 196 case studies case 1, 204 206 case 2, 207 212 Eco-indicator 99, 199 201 environmental targeting, 203 204 exergoenvironmental analysis, 201 202 footprint, 203

greenhouse gas emissions estimation, 202 LCA, 196 199 Environmental strategy map, 210, 213f Environmental targeting, 203 204 EP. See Equilibrium point (EP) Equilibrium point (EP), 299 300 ERC. See Ejector refrigeration cycle (ERC) ESUGCC. See Extended SUGCC (ESUGCC) EU. See European Union (EU) European Directive (1996), 33 European Union (EU), 32 Evaporators, 51 53 Exergetic cost rates, 71 for fuel and product, 73t of fuels and product for CGAM problem, 69t Exergetic efficiency, 142 Exergetic model, 140 142 Exergoeconomic analysis, 88 balance, 61 factor, 62 Exergoeconomic modeling, 61 63, 227 of CCHP, 71 exergetic product, fuel, and efficiency for each component, 69t fuel and product exergy streams of equipment, 71t purchase equipment cost for CCHP case, 70t purchase equipment cost of the equipment, 72t of CGAM cogeneration plant, 65 70 exergoeconomic balance and component cost equations, 229t of polygeneration system, 71 72 exergetic cost rates for fuel and product, 73t Exergoenvironmental analysis, 88, 201 202 balance equations, 204 205, 206t Exergy, 55 57 analysis, 55 56, 58, 227 environment from perspective of, 57 of CCHP, 71 exergetic product, fuel, and efficiency for each component, 69t fuel and product exergy streams of equipment, 71t purchase equipment cost for CCHP case, 70t purchase equipment cost of the equipment, 72t of CGAM cogeneration plant, 65 70 exergetic cost rate of fuels and product, 69t exergetic product, fuel, and efficiency, 67t exergy calculation of each stream, 66t fixed variables for economic model, 69t

Index

purchased equipment cost, 68t dead state, 56 limited, 56 destruction, 60 61, 75, 81 and exergoeconomic modeling of polygeneration system, 71 72 exergetic cost rates for fuel and product, 73t losses, 56 thermoeconomic evaluation, 57 58 and thermoeconomic modeling algorithm for evaluation of thermal plants, 65f chemical exergy, 59 60 ECDL, 63 64, 64f EDL, 63 64, 64f exergoeconomic modeling, 61 63 physical exergy, 58 59 standard chemical values, 60t Exergy cost destruction level (ECDL), 63 64, 64f, 83, 91f, 92f, 172 advanced representation of, 172 Exergy destruction level (EDL), 63 64, 64f, 83, 91f, 92f, 172 advanced representation of, 172 Exhaust gases, 127 128 Extended R-curves advanced representation of EDL, ECDL, and EEDL, 172 algorithm proposed for advanced analyses, 172 174 cogeneration targeting, 170 171 regression coefficients in isentropic efficiency equation, 171t construction, 173f exergy destruction cost, vs., 182f using in LNG cogeneration, 174 181 R-ratio against ED, CD, and BD, 172 R-ratio vs. total destruction environmental impact, 184f Extended SUGCC (ESUGCC), 210 Extractable energy, 144 145 Extractable power concept, 154 Extraction steam turbine flow diagram, 14, 16f

F Failure rate, 327, 328f Fault tree analysis (FTA), 325 326 FCs. See Fuel cells (FCs) Feedwater preheater (FWPH), 46 Fiona Riddoch, COGEN Europe, Belgium, 33 First law of thermodynamics, 2, 40, 140 First thermodynamic law, 216 217 Five-degree-of-freedom (5-DOF), 299

Flash chamber mass balances, 118 Flat-plate collectors, 251 252 Footprint carbon, 203 emission, 203 energy, 203 evaluation for case study 2, 212t water, 203 Fossil fuels, 137 FTA. See Fault tree analysis (FTA) Fuel cell 1 heat pump/refrigeration, 245 246 Fuel cell 1 thermoelectric generator, 239 245 Fuel cells (FCs), 26, 237 238 desalination systems, 247 248 fuel cell 1 ACs, 246 250 gas turbine, 242 245 integration, 239 246 fuel cell 1 heat pump/refrigeration, 245 246 fuel cell 1 TEG, 239 245, 240f, 241f Fuel consumption, 98 Fuel factor, 202 FWPH. See Feedwater preheater (FWPH)

G GA. See Genetic algorithm (GA) Gain output ratio (GOR), 132 Gas engine, 9 10 power-generation cycle based on, 24, 24f, 25f Gas turbines (GTs), 13, 15 19, 91, 127 129, 239 241 fuel cell—gas turbine, 242 245 integration of gas turbine turbo compressor with HRSG, 128f Nadoshan cogeneration plant, 128t water cost, net power, and payback period, 129t Gas diesel combustion engines, 4 GateCycle, 349 GCCs. See Grand composite curves (GCCs) GEA. See Geothermal Energy Association (GEA) Generating electricity, 196 Genetic algorithm (GA), 291 Geothermal energy, 216, 219 Geothermal Energy Association (GEA), 216 Geothermal polygeneration system, 216 217 Geothermal solar polygeneration system, 217 218 Germany, cogeneration and polygeneration in, 33 34 GHG emissions. See Greenhouse gas emissions (GHG emissions) Gibbs function correlation for calculation, 365t

377

378

Index

Gibbs function (Continued) variation, 365t Global energy consumption, 137 GOR. See Gain output ratio (GOR) Grand composite curves (GCCs), 96, 138 139, 154 Grassroots case study, 306 Grassroots problem, 304 305 Grassroots R-curve, 165, 167 168, 168f grassroot SUGCC design plan, 177f Green approach, 35 Greenhouse gas emissions (GHG emissions), 196 estimation, 202 GT MASTER software, 347 GT PRO software, 346 347, 347f GTs. See Gas turbines (GTs)

H Hampson Linde cycle, 316 317, 317f Harell method, 144 145 Heat exchanger (HEX), 48, 217 Heat generation, 14 from cogeneration, 13 Heat production, 14 Heat recovery and power targeting method, 154 Heat recovery boilers (HRBs), 19 21 Heat recovery steam generator (HRSG), 15 16, 40 43, 83 84, 127 128, 128f, 178, 299, 331 gas turbine based cogeneration plant with, 18f gas turbine based cogeneration plant with supplementary boiler, 18f with MED-TVC and RO desalination, 46 Heat sink profiles, 96 98 Heating, ventilation, and air conditioning system (HVAC system), 359 Heliostat field/CPVT, 282 Heliostat solar-driven polygeneration system, 215 216 HEX. See Heat exchanger (HEX) High-pressure (HP), 151 153, 207, 243 steam levels, 102 High-pressure economizers (HPECO2 and HPECO1), 46 High-pressure evaporator (HPEV), 46 High-pressure superheater (HPSH), 46 Higher cogeneration efficiency, 182 184 HMM-As. See Asymmetric hidden Markov models (HMM-As) HOMER. See Hybrid Optimization Model for Multiple Energy Resources (HOMER) HP. See High-pressure (HP) HPEV. See High-pressure evaporator (HPEV)

HPSH. See High-pressure superheater (HPSH) HRBs. See Heat recovery boilers (HRBs) HRSG. See Heat recovery steam generator (HRSG) HVAC system. See Heating, ventilation, and air conditioning system (HVAC system) Hybrid CPVT collectors biomass, 268 271 Hybrid cycle-based polygeneration solar energy tower-GT, 262 Hybrid desalination systems, 115 Hybrid Optimization Model for Multiple Energy Resources (HOMER), 359 360 HOMER Pro, 359 360 Hybrid parabolic trough collectors biomass, 267 268, 268f Hybrid parabolic trough collectors geothermal model, 271 273, 272f, 273f Hybrid photovoltaic/thermal collectors (Hybrid PVT collectors), 251 252 Hybrid photovoltaic/thermal/CPVT collectors geothermal model, 276, 276f Hybrid photovoltaic/thermal ocean model, 277 Hybrid PVT collectors. See Hybrid photovoltaic/ thermal collectors (Hybrid PVT collectors) Hybrid solar polygeneration systems, 267 282 hybrid photovoltaic/thermal ocean model, 277 hybrid solar power tower wind turbines, 277 hybrid solar geothermal model, 271 276 hybrid solar wind/ocean model, 277 282 integrated solar biomass-driven devices, 267 271 other hybrid models, 282 Hybrid solar power tower biomass, 268 tower geothermal model, 273 276, 275f tower wind turbines, 277, 279f Hybrid solar biomass platforms, 267 Hybrid solar geothermal cogeneration plant, 50f thermodynamic modeling of, 48 50 Hybrid solar geothermal model, 271 276 hybrid photovoltaic/thermal/CPVT collectors geothermal model, 276 hybrid PTC geothermal model, 271 273, 272f, 273f hybrid solar power tower geothermal model, 273 276 Hybrid solar wind/ocean model, 277 282, 278f, 279f, 280f, 281f

I i-Steam software, 356 357 IBTM. See Iterative bottom-to-top model (IBTM) Ideal R-curve, 167 168

Index

IECEF. See Integration effect on cogeneration efficiency factor (IECEF) IGCC. See Integrated gasification combined cycle (IGCC) Industrial applications for cogeneration and polygeneration, 31 Injector pump, 22 Innovative polygeneration solar energy device, 264 system, 259 Institutional applications for cogeneration and polygeneration, 32 Integer programming, 291 Integrated gasification combined cycle (IGCC), 346 347 Integrated process site utility system, 108 110 Integrated solar biomass-driven devices, 267 271 hybrid CPVT collectors biomass, 268 271, 271f hybrid parabolic trough collectors biomass, 267 268, 268f, 269f hybrid solar power tower biomass, 268, 270f Integrated solar wind/ocean models, 267 Integration effect on cogeneration efficiency factor (IECEF), 185 187 Internal combustion engine, 21 24 Internal reforming SOFC (IRSOFC), 243 245 Iran LNG plant, 88 IRSOFC GT ORC system, 243 245 Isentropic efficiency, 146, 149 ISO 14004, 199 Iterative bottom-to-top model (IBTM), 148 149 cogeneration potential by, 157f

J Japan, cogeneration and polygeneration in, 35 36

K Kalina cycle (KC), 215 Kapil model, 149 150 cogeneration potential obtained by, 158f Karush Kuhn Tucker conditions, 290 Khoshgoftar Manesh method, 63, 83 cogeneration potential by, 157f, 158f TSA, 95 96 Kinetic energy, 39 Kinetic exergy, 59

L LCA. See Life cycle assessment (LCA) Levelized cost method. See Annualized cost method LFR. See Linear Fresnel reflector (LFR) Life cycle analysis. See Life cycle assessment (LCA)

Life cycle assessment (LCA), 195 199 applications, 197 198 benefits, 198 Eco-indicator 99, 199 201 project design, 198 real planning and process management, 198 stages, 197 Linear Fresnel reflector (LFR), 252 253, 348 349 Linear programming (LP), 291 LINMAP method. See LP technique for multidimensional analysis of preferences method (LINMAP method) Liquefied natural gas (LNG), 88, 218 cogeneration, 88 93, 174 conventional exergetic, exergoeconomic, and exergoenvironmental analysis, 89t data parameters for LNG case, 89t extended R-curves using in, 174 181 optimum configuration, 185f optimum configuration of Iran LNG’s steam, 88f power and steam specification, 175t splitting capital investment cost within kth component, 90t splitting exergy destruction within kth component, 89t thermodynamic, exergoeconomic, and exergoenvironmental properties, 186t power generation subcycle, 221 Lithium bromide (LiBr), 246 247 Live steam, 14 LNG. See Liquefied natural gas (LNG) Low pressure (LP), 117, 151 153, 207 steam generation, 104 105 LP. See Linear programming (LP)Low pressure (LP) LP technique for multidimensional analysis of preferences method (LINMAP method), 318

M Macroscopic potential, 39 Markov model, 326 329, 340 341 MATLAB, 362 MCFCs. See Molten carbonate FCs (MCFCs) MDC. See Microbial desalination cell (MDC) Mean effective pressure (MEP), 24 MED. See Multiple effect distillation (MED) MED-TVC. See Multieffect distillation system equipped with thermal vapor compression (MED-TVC) Medina-Flores and Pico´n-Nu´n˜ez model, 146 147 Medium pressure (MP), 104 105, 129, 149, 151 153, 207 MEP. See Mean effective pressure (MEP)

379

380

Index

Metaheuristic optimization techniques, 291 MFCs. See Microbial FCs (MFCs) Micro Combined Heat and Power (µCHP), 245 246 Micro Markov model, 326 Microbial cell integration, 248 250 Microbial desalination cell (MDC), 248 249 Microbial FCs (MFCs), 248, 249f Microcogeneration units, 35 Microturbine services, 243 245 Modern polygeneration systems, 237 fuel cell 1 absorption chillers, 246 250 fuel cells, 238 integration, 239 246 hybrid solar polygeneration systems, 267 282 solar energy, 238 239, 250 267 Mollier graph, 144, 144f Mollier representation, 144 Molten carbonate FCs (MCFCs), 26, 238, 242 243 Momentum conservative equation, 224 MOO. See Multiobjective optimization (MOO) MP. See Medium pressure (MP) MSF. See Multistage flash (MSF) MSF RO desalination systems. See Multistage flash reverse osmosis desalination systems (MSF RO desalination systems) Multieffect distillation system equipped with thermal vapor compression (MED-TVC), 46, 49t, 120, 120f, 123t Multigeneration system, 215 216 Multigenerational energy systems, 215 Multiobjective optimization (MOO), 288, 291 294 Multiple effect distillation (MED), 115, 247 248, 346 348 desalination, 119 122, 185 integration, 188f specification, 189t Multistage flash (MSF), 115, 119t, 247 248, 346 347 desalination system, 218 distillation, 116 desalination, 116 119, 117f Multistage flash reverse osmosis desalination systems (MSF RO desalination systems), 124

N Nadoshan pipeline gas station in Iran, 128 Natural gas liquefaction, 218 Nernst equation, 225 226 Nonlinear programming, 291

Nonrepairabl system, 327 Numerical optimization techniques, 287 288, 291

O Objective functions (OFs), 287, 289 Ocean Thermal Energy Conversion (OTEC), 277, 282 OFs. See Objective functions (OFs) Open system. See Volume control systems Open-cycle systems, 4 Optimization, 287 case studies, 295 321 grassroots case study, 306 optimal design of TEG-PTC-driven polygeneration system, 310 316 optimization results, 306 310 solar hybrid cogeneration plant, 295 304 utility system optimal design using targeting strategy, 304 305 MOO, 291 294 problem, 288 290, 294t constraints, 289 290 decision variables, 289 objective functions and system criteria, 289 system boundaries, 288 techniques, 290 291, 293f Organic Rankine cycle (ORC), 48 50, 215 217, 219, 243 245, 350 352 Original R-curve, 165 Osmotic pressure difference, 123 124 OTEC. See Ocean Thermal Energy Conversion (OTEC) Otto cycle, 23 24 ideal thermal efficiency, 23

P Parabolic dish (PD), 252 253 PD driven systems, 264 Parabolic trough (PT), 252 253 solar field, 282 Parabolic trough collectors (PTC), 254 260 hybrid PTC biomass, 267 268, 268f, 269f hybrid PTC geothermal model, 271 273, 272f, 274f Parabolic trough solar collector (PTSC), 310 Pareto front, 294 Particulate matter emissions (PM2.5 emissions), 289 Path cogeneration efficiency, 168 169 PD. See Parabolic dish (PD) PDE. See Plant Design Expert (PDE) PEACE module. See Plant Engineering and Cost Estimation module (PEACE module)

Index

PEM. See Proton-exchange membrane (PEM) PEMFC. See Proton exchange membrane FC (PEMFC) Performance evaluation, 232 234 PESs. See Polygeneration energy systems (PESs) Petro-SIM software, 354 Petrochemical processes, 95 Phosphoric acid, 26 Photovoltaic collectors (PV collectors), 239 Photovoltaic/thermal/CPVT collector driven systems, 264 267 parabolic dish driven polygeneration system, 264f solar CPVT driven polygeneration system, 265f, 266f Physical exergy, 58 59 Pinch analysis, 140 Plant Design Expert (PDE), 347 Plant Engineering and Cost Estimation module (PEACE module), 346 347 Pollutant emissions, 202 Pollutants, 195 Polygeneration, 2, 10, 345 application, 30 32, 32t commercial, 32 industrial, 31 institutional, 32 China, 36 components of, 13 combined cycle-based cogeneration plants, 19 21 fuel cell, 26 gas turbine, 15 19 internal combustion engine, 21 24 steam turbines, 14 15 Stirling engines, 24 26 hybrid solar power platforms, 267 integrated solar biomass, 268 Japan, 35 36 modeling and analysis, 222 230 assumptions, 222 ejector modeling, 223 225 energy and exergy analysis, 227 exergoeconomic modeling, 227 overall performance evaluation, 228 230 proton-exchange membrane electrolyzer, 225 227 PT-based device, 255 with solar energy, 254 264 parabolic dish driven systems, 264 parabolic trough type, 254 260 solar power tower driven systems, 261 263 solar PTC driven polygeneration system, 256f, 257f, 258f, 259f, 260f

system, 46f, 132, 216, 218 219, 287 exergetic cost rates for fuel and product, 73t exergy and exergoeconomic modeling, 71 72 geothermal-based polygeneration system, 220f input data for thermodynamic simulation of, 223t performance evaluation, 232 234 relations, inputs, and outputs, 47t, 49t system description, 219 222 thermodynamic modeling of, 46 48 validation of model, 231 234 United States, 36 Polygeneration energy systems (PESs), 289 Power cycles, 215 Power generation technologies, 196 Power plants, 346 352 Cycle-Tempo, 350 352 EBSILON software, 350 GateCycle, 349 GT MASTER software, 347 GT PRO software, 346 347, 347f STEAM MASTER software, 348 STEAM PRO software, 348 THERMOFLEX software, 348 349 Power-generation cycle based on gas or diesel engine, 24, 24f, 25f Power-to-heat ratios, 172, 175, 182 185 Probability of state, 329 Process industries, 100, 352 358 Aspen HYSYS, 352 353 Aspen Plus, 352 AVEVA PRO/II software, 356 i-Steam software, 356 357 Petro-SIM software, 354 ProMAX software, 355 356 STAR software, 358 UniSim software, 355 ProMAX software, 355 356 Proton exchange membrane FC (PEMFC), 26, 238 Proton-exchange membrane (PEM), 222, 310 electrolyzer, 215, 218 219, 221 222, 225 227, 232t “Pseudo-terminal” translates, 17 19 PT. See Parabolic trough (PT) PTC. See Parabolic trough collectors (PTC) PTSC. See Parabolic trough solar collector (PTSC) Purchased equipment cost functions (PEC functions), 368t Pure water specific enthalpy, 367 specific entropy, 367 368 PV collectors. See Photovoltaic collectors (PV collectors)

381

382

Index

Q Qom CC power plant, 124, 125f Quadratic programming, 291

parameters of RO system, 125t Reversible process, 17 19 RO. See Reverse osmosis (RO)

R

S

R-curve, 165 desalination system integration with help of Rcurve, 182 192 multieffect distillation system, 189t notation, 165 167 tool actual R-curve, 168 169 grassroots R-curve, 167 168, 168f ideal R-curve, 167 168 R-destruction cost curve, 178 R-pinch, 174 R-ratios, 166, 168 169 Rankine cycle (RC), 51 53, 262, 310 RBD. See Reliability block diagram (RBD) RC. See Rankine cycle (RC) Refining processes, 95 REFPROP, 362 Refrigerator, 245 246 Reliability, 325 327 case studies, 330 341 failure and repair rates for devices, 331t failure states of system, 333f, 334t final probabilities regarding states of system, 337t, 341t flow sheet, 338f initial probability, 336t, 339t new probabilities, 337t, 340t specification of steam main, 331t, 338t state space, 332f utility systems, 330f modeling of utility system, 327 330 Reliability assessment for distribution system, 326 Reliability block diagram (RBD), 325 326 Ren et al. model, 151 154, 153f Renewable energy, 359 361 HOMER Pro, 359 360 RETScreen, 361 SAM, 361 sources, 267 TRNSYS transient system, 359 Repair rate, 327 Repairable system, 327 Retrofit R-curve, 165 RETScreen, 361 Reverse osmosis (RO), 115, 247 248, 346 347 desalination, 46, 122 124, 124f, 182 184 integration, 187f specification, 189t

SA. See Sensitivity analysis (SA)Simulating annealing (SA) Saline water resources, 115 Salisbury approximation, 142, 147 SAM. See System Advisor Model (SAM) SCA. See Steam cascade analysis (SCA) Seawater specific enthalpy, 367 specific entropy, 367 368 specific volume and density, 367 Second law of thermodynamics, 40 Sensitivity analysis (SA), 317 Shaft power production, 144 145 Shaft work targeting, 139 142 result, 159f SimaPro package, 199 Simple cogeneration system, 204 205, 205f types and weight of materials for, 207t Simulating annealing (SA), 291 Simulation tools, 345 Single-OF, 292 Singleobjective optimization (SOO), 287 288 Site profiles, 98 of integrated site utility, 111f Site sink profile, 96 98 Site source and sink profiles (SSSPs), 140 Site utility grand composite curve (SUGCC), 98, 104, 129 131, 139 140, 142, 155, 170, 207 of base process site utility, 109f construction, 149f of integrated system, 111f with maximum heat recovery, 105f modified SUGCC of steam network, 112f utility system layout, 139f Site utility industrial plants, 129 134 base site utility case, 130f freshwater production price and simple payback, 135t parameters assumptions of MED TVC and MSF, 134t RO desalination system, 134t site profile associated with integration of base case, 133f site specification per period, 131t SUGCC-base case site utility, 132f techno-economic evaluation of base case, 134t total site profiles of base case, 131f total site utility demands, 130t

Index

Site utility integration, 106 112, 108f modified SUGCC of steam network, 112f process flow diagram of integrated steam power plant, 110f properties of steam requirements of base process site utility, 109t site profiles of integrated site utility, 111f site utility process plant, 112f steam generation and use, 109t and use integrated system, 110t SUGCC of base process site utility, 109f of integrated system, 111f Small-scale cogeneration units, 35 SOFC. See Solid oxide FCs (SOFC) Software tools, 345 cogeneration and polygeneration, 346f computer code, 361 362 power plants, 346 352 process industries, 352 358 renewable energy, 359 361 Solar energy, 215 217, 237 239, 250 254. See also Energy photovoltaic/thermal/CPVT collector driven systems, 264 267 polygeneration with, 254 264 solar thermal collectors, 251f solar-driven polygeneration system, 251f solar-hybrid polygeneration system, 250f Solar hybrid cogeneration plant, 295 304 optimization, 297 304 conventional case, 299 301, 302t solar hybrid case, 301 304, 302t physical constraints, 299 solar field design, 297, 298f, 298t Solar polygeneration system, 259 Solar power tower (SPT), 252 253 polygeneration system, 263f SPT driven systems, 261 263 Solar thermal collectors, 239 Solar-based polygeneration model, 255 257 Solar-steam Rankine cycle, 48 Solid oxide FCs (SOFC), 26, 238, 242 243 SOO. See Singleobjective optimization (SOO) Sorin and Hammache method, 145 146 Space cooling, 241 242 Spain, cogeneration and polygeneration in, 34 35 Specific density of seawater, 367 Specific enthalpy of seawater and pure water, 367 Specific entropy of seawater and pure water, 367 368 Specific heat correlation for calculation, 365t variation, 365t

Specific volume of seawater, 367 SPT. See Solar power tower (SPT) SSSPs. See Site source and sink profiles (SSSPs) Stand-alone system, 7 8 Standard chemical values, 60t STAR software, 155, 358 State space, 326 327 Steam generation and use integrated system, 110t heat recovery, 103 mains parameters, 156t Steam cascade analysis (SCA), 151 algebraic targeting technique, 152f STEAM MASTER software, 348 STEAM PRO software, 348 Steam turbines, 13 15 cogeneration plant with, 17f Stirling engines, 24 26 Stochastic programming, 291 SUCP. See Sum unit cost of product (SUCP) SUGCC. See Site utility grand composite curve (SUGCC) Sulfur dioxide (SO2), 289 Sum unit cost of product (SUCP), 228, 234 Sun, 239 Superheaters, 51 53 “Sys” substrate, 58 System Advisor Model (SAM), 361 System boundaries, 288 System environment, 39 System optimization, 325

T Target value (TV), 63, 83 TC. See Total cost (TC) TEC. See Thermoelectric cooler (TEC) TEG. See Thermoelectric generator (TEG) Temperature enthalpy diagram, 163 165 T H model, 142 143 Thermal power plant, 106 112 Thermal power plants, integration with, 124 127 integration of CC with MSF RO, 126f net power, desalinated water production, 127t power cost and desalinated water price, 127t specification of steam in Qom CC plant, 126t Thermal systems, 290 efficiency, 215 Thermal vapor compression (TVC), 120 121, 120f Thermodynamic analysis, 222, 267 268 of PEM electrolyzer, 225 Thermodynamic approach, 81, 82t Thermodynamic modeling, 39, 170 of CCHP, 41 43

383

384

Index

Thermodynamic modeling (Continued) CGAM cogeneration plant modeling, 40 41, 41f first law of thermodynamics, 40 of hybrid solar geothermal cogeneration plant, 48 50, 50f of polygeneration system, 46 48 second law of thermodynamics, 40 Thermodynamic properties calculation, 365 366 Thermoeconomic analysis, 267 268 of polygeneration system, 217 Thermoeconomic evaluation, 57 58 Thermoeconomic modeling, 58 64 Thermoeconomic variables, 58 Thermoelectric cooler (TEC), 239 241 Thermoelectric generator (TEG), 239, 310 fuel cell 1 TEG, 239 245, 240f, 241f FC, 242 245 optimal design TEG-PTC-driven polygeneration system, 310 316 cost equations of considered equipment, 312t multiobjective optimization method, 312 316 polygeneration system, 311f thermophysical and exergoeconomic properties, 312t values related to input parameters, 311t THERMOFLEX software, 347 349 Thermolib, 361 362 THM. See Turbine hardware model (THM) Total cost (TC), 289 Total Site, 95, 97f, 138 140, 138f conditions, 165 integration, 95 96, 97f methodology, 95 procedure, 98 101, 101f target construction for exergetic method, 141f utility cogeneration potential targeting, 151 Total Site analysis (TSA), 95 96 conventional TSA, 102 105 integration of site utility and thermal power plant, 106 112 Total Site profiles (TSPs), 96 98, 108f combining hot and cold streams in, 100f generation with GCC, 99f and potential of cogeneration, 101f steam generation and used in, 100f utilities demand and grand composite curves, 98f Transshipment model, 95 Trigeneration, 5 7

plant energy balance schematics, 5f TRNSYS transient system, 359 T s diagram for steam turbine expansion process, 145, 145f TSA. See Total Site analysis (TSA) TSPs. See Total Site profiles (TSPs) Turbine hardware model (THM), 143, 154, 305 TV. See Target value (TV) TVC. See Thermal vapor compression (TVC)

U Unavoidable and avoidable parts, 78 79, 78t investment cost and exergy destruction for kth component, 80f Unavoidable exergy destruction, 76 per product exergy, 84, 85t UniSim software, 355 United States, cogeneration and polygeneration in, 36 Utility systems, 325, 330 331, 330f layout, 139 140 on SUGCC, 139f optimal design using targeting strategy, 304 305

V Vacuum back vacuum, 14 Vapor condensing refrigeration cycle, 215 Vapor pressure reduction stations, 14 Variable bounds, 292 Very high-pressure (VHP), 129, 140, 151, 170, 207 steam generation, 104 Virtual water, 203 Volume control systems, 39

W Waste heat, 5 recovery, 22 23 Water, 246 247 footprint, 203 Weight function, 369, 370t Willans line model, 143, 154, 188 results of combination MED desalination, 190t results of combination RO desalination, 190t retrofit R-curve, 190f

X X-Steam, 362