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.
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Table of Contents
1. Introduction2. Thermal modeling and analysis3. Advanced Thermal Models 4. Combined thermal, economic, and environmental models5. Soft computing and statistical tools for developing analytical models6. Optimization basics7. Decision-making in optimization and assessment of energy systems8. Real-time optimization of energy systems using the soft-computing approaches 9. Conclusion
Authors
Hoseyn Sayyaadi Professor of Mechanical Engineering, Faculty of Mechanical Engineering-Energy Division, K.N. Toosi University of Technology, Tehran, Iran. Dr Sayyaadi is Professor of Mechanical Engineering at K.N. Toosi University of Technology, focusing on design, modelling, and optimization of energy systems.He has a number of publications in this field with 79 journal articles and 98 conference papers up to Aug. 2020.
His research interests are exergy and exergoeconomic analyses, modelling and optimization of energy systems, multi-objective optimization and decision making, machine learning tools for modelling and optimization of energy systems including soft-computing and statistical tools (SCST), fuzzy inference system (FIS), and artificial neuro-fuzzy inference system (ANFIS), hydrogen production, heat exchangers, Stirling engines, power generation systems, and HVAC and refrigeration systems.
