To curb the impacts of rising CO2 emissions, the Intergovernmental Panel on Climate Change report states that a net zero target needs to be achieved by the year 2055. Experts argue that this is a critical time to make important and accurate decisions. Thus, it is essential to have the right tools to efficiently plan and deploy future energy systems and supply chains. Mathematical models can provide decision-makers with the tools required to make well-informed decisions relating to development of energy systems and supply chains. This book provides an understanding of the various available energy systems, the basics behind mathematical models, the steps required to develop mathematical models, and examples/case studies where such models are applied. Divided into two parts, one covering basics for beginners and the other featuring contributed chapters offering illustrative examples, this book
Shows how mathematical models are applied to solve problems in energy systems and supply chains
Provides fundamentals of the working principles of various energy systems and their technologies
Offers basics of how to formulate and best practices for developing mathematical models, topics not covered in other titles
Features a wide range of case studies
Teaches readers to develop their own mathematical models to make decisions on energy systems
This book is aimed at chemical, process, mechanical, and energy engineers.
Author(s): Viknesh Andiappan, Denny K. S. Ng, Santanu Bandyopadhyay
Series: Green Chemistry and Chemical Engineering
Publisher: CRC Press
Year: 2022
Language: English
Pages: 240
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Series Preface
Preface
Foreword
Acknowledgments
Editors
Contributors
Part A Fundamentals
Chapter 1 Energy Systems and Supply Chains
1.1 Introduction
1.2 Energy Supply Chains
1.3 Energy Systems
1.3.1 Conventional Power Plants
1.3.2 Cogeneration Systems
1.3.3 Trigeneration Systems
1.3.4 Polygeneration Systems
1.3.5 Hybrid and Renewable Energy Systems
1.4 Process Systems Engineering
References
Chapter 2 Optimization of Energy Systems and Supply Chains
2.1 Introduction
2.2 Methodology of Mathematical Optimization
2.3 Illustrative Example
2.4 Conclusion
Chapter 3 Formulating Generalized Mathematical Models
3.1 Introduction
3.2 Recommended Practices
3.3 General Representations and Equations
3.3.1 Conversion
3.3.2 Branching Points
3.3.3 Summing Points
3.3.4 Other Aspects
3.4 Conclusion
Further Reading
References
Part B Applications
Chapter 4 Mixed-Integer Linear Programming Model for the Synthesis of Negative-Emission Biochar Systems
4.1 Introduction
4.2 Negative-Emission Biochar Systems
4.3 Methodology
4.4 Case Study 1: Synthesis of Bioenergy Plant with Biochar Production
4.5 Case Study 2: Synthesis of Bioenergy Plant Producing Multi-grade Biochars
4.6 Conclusion
4.7 Further Reading
4.8 Density and Calorific Value of Process Streams
References
Appendix
Chapter 5 A Comprehensive Guidance on Transitioning Toward Sustainable Hydrogen Network from Localized Renewable Energy System: Case Study of South Korea
5.1 Introduction
5.2 Problem Statement
5.3 Methodology
5.3.1 Mathematically Expressed Optimization Model
5.3.2 P-graph Optimization Model
5.3.3 Pareto Frontier and TOPSIS
5.4 Case Study Description
5.5 Results and Discussions
5.5.1 Increase of Hydrogen Demand
5.5.2 Overall Sustainable System Enhancement
5.6 Conclusion
Further Reading
Acknowledgments
References
Chapter 6 An Optimization Framework for Polygeneration System Driven by Glycerine Pitch and Diesel
6.1 Introduction
6.2 Methodology
6.2.1 Brayton Cycle
6.2.2 Steam Cycle
6.2.3 Waste Heat Recovery System
6.2.4 Utility Demand
6.2.5 Objective Function
6.3 Case Study
6.4 Results and Discussion
6.4.1 Scenario 1
6.4.2 Scenario 2
6.4.3 Cost Analysis
6.5 Conclusion
Nomenclature
Variable
Parameter
Superscript
Further Reading
References
Chapter 7 Multi-Objective Optimization of TEG Dehydration Process to Mitigate BTEX Emission under Feed Composition Uncertainty
7.1 Introduction
7.2 Methodology
7.2.1 Data Generation and Metamodel Development
7.2.2 Robust Multi-objective Optimization (RMOO)
7.3 Results and Discussions
7.3.1 Metamodel Generation
7.3.2 EACO-Based Robust Multi-objective Optimization
7.3.3 Pareto Front Generation
7.3.4 Value of Stochastic Solution (VSS)
7.4 Conclusions
Further Reading
Acknowledgments
References
Chapter 8 Constrained Production Planning with Parametric Uncertainties
8.1 Introduction
8.1.1 APP with Uncertainty: A Brief Overview
8.1.2 APP and Robust Optimization Methodologies
8.2 Methodology
8.2.1 Problem Statement
8.2.2 Deterministic Mathematical Formulation
8.2.3 Robust Mathematical Formulation Including Uncertainty
8.3 Case Study: Indian Steel Industry
8.4 Conclusion
Acknowledgments
Nomenclature
Sets
Parameters
Variables
References
Chapter 9 Linear Programming Models Based on the Input-Output Framework
9.1 Introduction
9.2 Methodology
9.2.1 Overview of the I-O Framework
9.2.2 LP Models Based on the I-O Framework
9.3 Case Study 1: Energy Allocation during a Shortage
9.4 Case Study 2: Decarbonization via Economic Restructuring
9.5 Conclusion
Further Reading
References
Appendix 9A: LINGO Code for Case Study 1
Appendix 9B: LINGO Code for Case Study 2
Chapter 10 Optimisation of Oil Palm-Based Biodiesel Supply Chain: Upstream Stages
10.1 Introduction
10.2 Methodology
10.2.1 Problem Statement
10.2.2 Material Flow
10.2.3 Plantation Expansion Cost
10.2.4 LUC Emission
10.3 Case Study Scenario
10.4 Results and Discussion
10.5 Conclusion
Future Reading
References
Appendix
Chapter 11 Optimal Design of Islanded Distributed Energy Systems Incorporating Renewable Energy for Rural Africa: A Namibian Case Study
11.1 Introduction
11.2 Problem Statement
11.3 Methodology
11.4 Case Study Scenario
11.4.1 Introduction
11.4.2 Findings of Study
11.4.2.1 Demand Profile
11.5 Conclusion
References
Appendix
Chapter 12 Multi-Objective Optimization for Energy Network Planning: Energy Storage and Distribution in Integrated Solar Powered Grids
12.1 Introduction
12.1.1 An Integrated Solar-Powered Electrical Distribution Grid
12.1.2 System Description
12.2 Methodology
12.2.1 Assumptions
12.2.2 Objective Function
12.2.3 Constraints
12.2.4 Solution Approach
12.3 Case Study
12.3.1 Base Run Results
12.3.2 Solar Production Site Breakdown
12.3.3 Battery Storage Breakdown
12.4 Conclusions and Recommendations
References
Appendix
Index
