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Multi-objective optimization : techniques and applications in chemical engineering /
Optimization has been playing a key role in the design, planning and operation of chemical and related processes for nearly half a century. Although process optimization for multiple objectives was studied by several researchers back in the 1970s and 1980s, it has attracted active research in the la...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hackensack, N.J. :
World Scientific,
©2009.
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| Colección: | Advances in process systems engineering ;
v. 1. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; Contents; Chapter 1 Introduction Gade Pandu Rangaiah; 1.1 Process Optimization; 1.2 Multi-Objective Optimization: Basics; 1.3 Multi-Objective Optimization: Methods; 1.4 Alkylation Process Optimization for Two Objectives; 1.4.1 Alkylation Process and its Model; 1.4.2 Multi-Objective Optimization Results and Discussion; 1.5 Scope and Organization of the Book; References; Exercises; Chapter 2 Multi-Objective Optimization Applications in Chemical Engineering Masuduzzaman and Gade Pandu Rangaiah; Abstract; 2.1 Introduction; 2.2 Process Design and Operation.
- 2.3 Biotechnology and Food Industry2.4 Petroleum Refining and Petrochemicals; 2.5 Pharmaceuticals and Other Products/Processes; 2.6 Polymerization; 2.7 Conclusions; References; Chapter 3 Multi-Objective Evolutionary Algorithms: A Review of the State-of-the-Art and some of their Applications in Chemical Engineering Antonio López Jaimes and Carlos A. Coello Coello; Abstract; 3.1 Introduction; 3.2 Basic Concepts; 3.2.1 Pareto Optimality; 3.3 The Early Days; 3.4 Modern MOEAs; 3.5 MOEAs in Chemical Engineering; 3.6 MOEAs Originated in Chemical Engineering.
- 3.6.1 Neighborhood and Archived Genetic Algorithm3.6.2 Criterion Selection MOEAs; 3.6.3 The Jumping Gene Operator; 3.6.4 Multi-Objective Differential Evolution; 3.7 Some Applications Using Well-Known MOEAs; 3.7.1 TYPE I: Optimization of an Industrial Nylon 6 Semi-Batch Reactor; 3.7.2 TYPE I: Optimization of an Industrial Ethylene Reactor; 3.7.3 TYPE II: Optimization of an Industrial Styrene Reactor; 3.7.4 TYPE II: Optimization of an Industrial Hydrocracking Unit; 3.7.5 TYPE III: Optimization of Semi-Batch Reactive Crystallization Process.
- 3.7.6 TYPE III: Optimization of Simulated Moving Bed Process3.7.7 TYPE IV: Biological and Bioinformatics Problems; 3.7.8 TYPE V: Optimization of a Waste Incineration Plant; 3.7.9 TYPE V: Chemical Process Systems Modelling; 3.8 Critical Remarks; 3.9 Additional Resources; 3.10 Future Research; 3.11 Conclusions; Acknowledgements; References; Chapter 4 Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations Manojkumar Ramteke and Santosh K. Gupta; Abstract; 4.1 Introduction; 4.2 Genetic Algorithm (GA); 4.2.1 Simple GA (SGA) for Single-Objective Problems.
- 4.2.2 Multi-Objective Elitist Non-Dominated Sorting GA (NSGA-II) and its JG Adaptations4.2.2.1 Jumping Genes/Transposons (Stryer, 2000); 4.2.2.2 (Variable-Length) Binary-Coded NSGA-II-JG (Kasat and Gupta, 2003); 4.2.2.3 (Fixed-Length) NSGA-II-aJG; 4.2.2.4 NSGA-II-mJG ('modified' JG); 4.2.2.5 NSGA-II-saJG ('specific adapted' JG); 4.2.2.6 NSGA-II-sJG ('specific' JG); 4.3 Simulated Annealing (SA); 4.3.1 Simple Simulated Annealing (SSA) for Single-Objective Problems; 4.3.2 Multi-Objective Simulated Annealing (MOSA).