Modeling, Assessment, and Optimization of Energy Systems

Description

Modelling, Assessment, and Optimization of Energy Systems provides comprehensive methodologies for the thermal modelling of energy systems based on thermodynamic, exergoeconomic and exergoenviromental approaches. It provides advanced analytical approaches, assessment criteria and the methodologies to obtain analytical expressions from the experimental data. The concept of single-objective and multi-objective optimization with application to energy systems is provided, along with decision-making tools for multi-objective problems, multi-criteria problems, for simplifying the optimization of large energy systems, and for exergoeconomic improvement integrated with a simulator EIS method. This book provides a comprehensive methodology for modeling, assessment, improvement of any energy system with guidance, and practical examples that provide detailed insights for energy engineering, mechanical engineering, chemical engineering and researchers in the field of analysis and optimization of energy systems. Front Cover Modeling, Assessment, and Optimization of Energy Systems Copyright Dedication Contents Preface Acknowledgment Chapter 1: Introduction 1.1. Preface 1.2. Outline 1.3. Classification of models for energy systems 1.4. Problem formulation 1.4.1. The mathematical form of the problem 1.4.2. Classification of the problem: 1.4.3. Degree of freedom in the optimization problem 1.4.4. Simplification of the model 1.4.5. Examples of problem formulation 1.5. Required steps for correct modeling and optimization 1.6. Summary 1.7. Exercise References Chapter 2: Thermal modeling and analysis 2.1. Introduction 2.2. Chapter's outline 2.3. Review of thermodynamic principles 2.3.1. The first law of thermodynamics 2.3.1.1. First-law thermodynamic analysis of reactive systems and definition of formation enthalpy 2.3.2. The second law of thermodynamics 2.3.2.1. The second law of thermodynamics for closed thermodynamic systems 2.3.2.2. The second law of thermodynamics for open thermodynamic systems 2.3.3. Gibbs functions and chemical potential 2.4. Fundamental of exergetic analysis 2.4.1. Definitions 2.4.1.1. Quality of the energy and definition of the ordered energy and disordered energy 2.4.1.2. Definition of the exergy 2.4.1.3. Environment and different types of equilibrium with the environment 2.4.2. Different types of exergy for analysis of the open systems (control volumes) 2.4.2.1. Exergy transfer due to the work transfer 2.4.2.2. Exergy transfer due to the heat transfer 2.4.2.3. Flow exergy 2.4.2.4. Balance of the exergy in a control volume 2.4.3. Nonflow exergy for analysis of closed systems (control masses) 2.5. Thermal assessment of energy system based on the exergy concepts 2.5.1. Exergy destruction vs. exergy loss 2.5.2. Exergetic efficiency 2.5.2.1. Overall exergetic efficiency of sample energy systems 2.5.2.2. Examples of the exergetic efficiency at the component level 2.5.2.3. Exergetic efficiency for assessment and optimization of energy systems 2.5.2.4. Exergetic balance equation based on the definition of fuel and product's exergies 2.5.2.5. Exergetic efficiency for assessment and optimization of energy systems 2.5.3. Efficiency defect and relative irreversibility 2.5.3.1. Efficiency defect 2.5.3.2. Relative irreversibility 2.5.4. Suggested approaches to enhance the thermal performance of energy systems 2.5.5. Graphical presentation of the exergetic analysis 2.6. Precise exergetic evaluation 2.6.1. Separation of exergy destruction into avoidable and unavoidable terms 2.6.2. Separation of exergy destruction into endogenous and exogenous terms 2.6.2.1. First method 2.6.2.2. Second method 2.6.3. Remarks on the concepts of the precise exergetic analysis 2.7. Case study 2.8. Summary 2.9. Exercises References Chapter 3: Advanced thermal models 3.1. Introduction 3.2. Chapter's outline 3.3. Finite-time thermodynamics 3.3.1. Limitation on the thermal efficiency of heat engines driven by the heat transfer 3.3.2. Limitation on the thermal efficiency of heat engines due to the mechanical friction 3.3.3. Limitation on the thermal efficiency of heat engines due to the mechanical friction and heat transfer resistance 3.3.4. Limitation on the thermal efficiency of heat engine driven by chemical reactions 3.3.5. Finite-time exergetic analysis of heat engines 3.3.5.1. Thermal exergy associated with the heat transfer based on the FTT 3.3.5.2. Nonflow exergy prediction based on the FTT 3.3.5.3. Exergetic efficiency of endoreversible heat engines based on the FTT 3.3.6. Other aspects of the finite-time thermodynamics 3.3.6.1. The optimal time and optimal path of the thermal process 3.3.7. Case studies of heat engines analyzed by the FTT model 3.3.7.1. Otto cycle (i) Otto cycle in the classical thermodynamics: (ii) Otto cycle in the finite-time thermodynamics: 3.3.7.2. Stirling cycle (i) Stirling cycle in the classical thermodynamics: (ii) Stirling cycle in the finite-time thermodynamics 3.4. Finite-speed thermodynamics 3.4.1. Case studies in the FST 3.5. Combined finite-time/finite-speed models 3.5.1. Combined finite-time/finite-speed model for the Otto cycle 3.5.2. Evaluation of thermal energy of the exhaust gases from Otto engines 3.5.3. Case study 3.6. Quasi-steady models (case study: Stirling engines) 3.6.1. Schmidt model 3.6.2. Adiabatic model 3.6.3. Simple model 3.6.3.1. Nonideal heat transfer 3.6.3.2. Pumping loss effects 3.7. Comprehensive combined thermal models (case study: Stirling engines) 3.7.1. CAFS thermal model 3.7.2. Simple-II thermal model 3.7.3. Polytropic thermal model 3.7.3.1. PSVL model 3.7.3.2. Modified PSVL and CPMS models (i) Temperature distribution in the Stirling engine's heat exchangers (ii) The real value of polytropic indexes in Stirling engines (iii) Solution procedure of the modified PSVL and CPMS models 3.7.4. Rotational speed's effect 3.7.4.1. Inertial effect 3.7.4.2. Effect of engine's speed on the gas temperature in heat exchangers 3.7.4.3. Sophisticated mechanical friction model 3.7.4.4. Solution method and results of the modified thermal model 3.7.5. Comparison of all thermal models of Stirling engines 3.7.6. Generalizing of models 3.8. Summary 3.9. Exercises References Chapter 4: Combined thermal, economic, and environmental models 4.1. Introduction 4.2. Chapter's outline 4.3. Exergoeconomic modeling 4.3.1. Economic analysis 4.3.1.1. Basic economic principle 4.3.1.2. Time value of money 4.3.1.3. Compounding frequency 4.3.1.4. Annuities and capital recovery factor 4.3.1.5. Inflation, escalation, and levelization 4.3.1.6. Economic models used in exergoeconomic analysis 4.3.1.7. Simple economic model 4.3.1.8. Total revenue requirement, TRR, model 4.3.1.9. TRR model for usage in exergoeconomics 4.3.1.10. Remarks regarding the selection of different economic model 4.3.2. Exergoeconomic analysis 4.3.3. Exergoeconomic assessment 4.4. Exergoenvironmental modeling 4.4.1. Life cycle analysis, LCA 4.4.1.1. Inventory process 4.4.1.2. Damage modeling 4.4.1.3. Weighting process 4.4.1.4. Uncertainties 4.4.1.5. LCA software 4.4.2. Exergoenvironmental analysis 4.4.3. Exergoenvironmental assessment 4.5. Exergoenvironomic modeling 4.5.1. Exergoenvironmental analysis 4.5.2. Exergoenvironmental assessment 4.6. Case studies 4.6.1. Case study (I): A gas turbine-based cogeneration plant 4.6.1.1. Exergoeconomic model of the case study (I) 4.6.1.2. Exergoenvironmental model of the case study (I) 4.6.2. Case study (II): A nuclear power plant with pressurized water reactor, PWR 4.6.2.1. Exergoeconomic model of the proposed PWR nuclear power plant 4.6.3. Remark on case studies 4.7. Summary 4.8. Exercises References Chapter 5: Soft computing and statistical tools for developing analytical models 5.1. Preface 5.2. Outline 5.3. Artificial neural network (ANN) 5.4. Group method of data handling (GMDH) type neural network 5.5. Genetic programming (GP) 5.6. Stepwise regression method (SRM) 5.7. Multiple linear regression (MLR) 5.8. Using computer codes and toolboxes to develop statistical models 5.8.1. Neural fitting toolbox (nftool) 5.8.2. Jacobsons's toolbox for GMDH 5.8.3. GPLAB 5.8.4. The ``LinearModel.Stepwise´´ and ``LinearModel.Fit´´ commands 5.9. Case studies 5.9.1. Case study (I): Cellulose direct evaporative cooler (DEC) 5.9.2. Case study (II): Dew-point (M-cycle) indirect evaporative cooler 5.9.2.1. Modeling a cross-flow dew-point cooler by GMDH 5.9.2.2. Modeling counter and perforated counter dew-point coolers 5.9.2.3. Modeling a counter-flow dew-point evaporative cooler by SRM 5.9.3. Case study (III): Desiccant-enhanced indirect evaporative (DEVap) cooler 5.9.4. Case study (IV): A heat pump 5.9.5. Case study (V): A polymer electrolyte membrane fuel cell (PEMFC) 5.9.6. Case study (VI): A Stirling engine 5.10. Summary 5.11. Exercises Acknowledgment References Chapter 6: Optimization basics 6.1. Preface 6.2. Outline 6.3. General definition 6.3.1. Unimodality and multimodality 6.3.2. Local and global optimums 6.3.3. Theory of convexity (and concavity) 6.4. Theory of optimization 6.4.1. Theory of unconstraint optimization 6.4.2. Theory of constraint optimization 6.5. Mathematical optimization 6.5.1. Unconstraint optimization 6.5.1.1. Direct optimization methods 6.5.1.2. Indirect optimization methods 6.5.1.3. Remarks on direct and indirect optimization methods 6.5.2. Constraint optimization 6.5.2.1. Linear programming (optimization) 6.5.2.2. Nonlinear programming (optimization) 6.5.2.3. IP and MINLP problems 6.6. Metaheuristic optimization approaches 6.6.1. Genetic algorithm 6.6.1.1. Tuning parameters 6.6.1.2. Encoding data to chromosome forms 6.6.1.3. Generating new population via genetic operators 6.6.1.4. Decoding the final population to reach the value of the optimal solution 6.6.1.5. General remarks regarding GA 6.6.1.6. The implication of GA for energy systems 6.6.2. Other metaheuristic optimization methods 6.6.2.1. Particle swarm optimization, PSO 6.6.2.2. Simulated annealing, SA 6.7. Hybrid optimization approaches 6.8. Multiobjective optimization 6.8.1. Mathematical multiobjective optimization 6.8.1.1. Weighted sum method 6.8.1.2. Weighted metric method 6.8.1.3. -Constraint method 6.8.2. Metaheuristic multiobjective optimization 6.9. Optimization toolbox of the MATLAB software 6.10. Dynamic optimization of energy systems 6.11. Optimization of large energy systems 6.12. Case studies 6.12.1. Case study (I): A gas turbine-based cogeneration plant 6.12.2. Case study (II): A nuclear power plant with pressurized water reactor, PWR 6.12.3. Case study (III): GPU-3 Stirling engine 6.13. Results 6.14. Summary 6.15. Exercises References Chapter 7: Decision-making in optimization and assessment of energy systems 7.1. Preface 7.2. Outline 7.3. LINMAP method 7.4. TOPSIS method 7.5. Fuzzy Bellman-Zadeh method 7.6. AHP and fuzzy-AHP methods 7.6.1. Conventional AHP method 7.6.2. Fuzzy-AHP method 7.7. Decision-making software 7.8. Case studies 7.8.1. Case study (I): Recuperative gas cycle 7.8.2. Case study (II): The best air conditioning system at different weathers 7.9. Summary 7.10. Exercises References Chapter 8: Real-time optimization of energy systems using the soft-computing approaches 8.1. Introduction 8.2. Outline of this chapter 8.3. Iterative exergoeconomic optimization 8.4. Fuzzy inference system, FIS, for real-time optimization 8.4.1. The concept of the fuzzy inference system, FIS 8.4.2. The FIS method for real-time optimization of energy systems 8.5. Case studies for real-time optimization using the FIS 8.5.1. Case study (I)-The CGAM problem 8.5.2. Case study (II)-A steam power plant 8.6. Assessment of the FIS for real-time optimization of energy systems 8.7. Adaptive neuro-fuzzy inference system, ANFIS, for real-time optimization 8.7.1. The concept of the adaptive neuro-fuzzy inference system, ANFIS 8.7.2. The ANFIS method for real-time optimization of energy systems 8.8. Case studies for real-time optimization using the ANFIS 8.8.1. Case study (I)-The CGAM problem 8.8.2. Case study (II)-A steam power plant 8.9. Assessment of the ANFIS for real-time optimization of energy systems 8.10. Comparing FIS, ANFIS, and conventional optimization methods 8.11. Summary 8.12. Exercise References Chapter 9: Conclusion Appendix Index Back Cover