Multi-Objective Optimization in Chemical Engineering : Developments and Applications.
Title:
Multi-Objective Optimization in Chemical Engineering : Developments and Applications.
Author:
Rangaiah, Gade Pandu.
ISBN:
9781118341698
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (530 pages)
Contents:
Multi-Objective Optimizationin Chemical Engineering -- Contents -- List of Contributors -- Preface -- Part I Overview -- 1 Introduction -- 1.1 Optimization and Chemical Engineering -- 1.2 Basic Definitions and Concepts of Multi-Objective Optimization -- 1.3 Multi-Objective Optimization in Chemical Engineering -- 1.4 Scope and Organization of the Book -- References -- 2 Optimization of Pooling Problems for Two Objectives Using the e-Constraint Method -- 2.1 Introduction -- 2.2 Pooling Problem Description and Formulations -- 2.2.1 p-Formulation -- 2.2.2 r-Formulation -- 2.3 e-Constraint Method and IDE Algorithm -- 2.4 Application to Pooling Problems -- 2.5 Results and Discussion -- 2.6 Conclusions -- Exercises -- References -- 3 Multi-Objective Optimization Applications in Chemical Engineering -- 3.1 Introduction -- 3.2 MOO Applications in Process Design and Operation -- 3.3 MOO Applications in Petroleum Refining, Petrochemicals and Polymerization -- 3.4 MOO Applications in the Food Industry, Biotechnology and Pharmaceuticals -- 3.5 MOO Applications in Power Generation and Carbon Dioxide Emissions -- 3.6 MOO Applications in Renewable Energy -- 3.7 MOO Applications in Hydrogen Production and Fuel Cells -- 3.8 Conclusions -- Acronyms -- References -- Part II Multi-Objective Optimization Developments -- 4 Performance Comparison of Jumping Gene Adaptations of the Elitist Non-dominated Sorting Genetic Algorithm -- 4.1 Introduction -- 4.2 Jumping Gene Adaptations -- 4.3 Termination Criterion -- 4.4 Constraint Handling and Implementation of Programs -- 4.5 Performance Comparison -- 4.5.1 Performance Comparison on Unconstrained Test Functions -- 4.5.2 Performance Comparison on Constrained Test Functions -- 4.6 Conclusions -- Exercises -- References.
5 Improved Constraint Handling Technique for Multi-Objective Optimization with Application to Two Fermentation Processes -- 5.1 Introduction -- 5.2 Constraint Handling Approaches in Chemical Engineering -- 5.3 Adaptive Constraint Relaxation and Feasibility Approach for SOO -- 5.4 Adaptive Relaxation of Constraints and Feasibility Approach for MOO -- 5.5 Testing of MODE-ACRFA -- 5.6 Multi-Objective Optimization of the Fermentation Process -- 5.6.1 Three-Stage Fermentation Process Integrated with Cell Recycling -- 5.6.2 Three-Stage Fermentation Process Integrated with Cell Recycling and Extraction -- 5.6.3 General Discussion -- 5.7 Conclusions -- Acronyms -- References -- 6 Robust Multi-Objective Genetic Algorithm (RMOGA) with Online Approximation under Interval Uncertainty -- 6.1 Introduction -- 6.2 Background and Definition -- 6.2.1 Multi-Objective Genetic Algorithm (MOGA) -- 6.2.2 Multi-Objective Robustness with Interval Uncertainty: Basic Idea -- 6.3 Robust Multi-Objective Genetic Algorithm (RMOGA) -- 6.3.1 Nested RMOGA -- 6.3.2 Sequential RMOGA -- 6.3.3 Comparison between Nested and Sequential RMOGA -- 6.4 Online Approximation-Assisted RMOGA -- 6.4.1 Steps in Approximation-Assisted RMOGA -- 6.4.2 Sampling -- 6.4.3 Metamodeling and Verification -- 6.4.4 Sample Selection and Filtering -- 6.5 Case Studies -- 6.5.1 Numerical Example -- 6.5.2 Oil Refinery Case Study -- 6.6 Conclusions -- References -- 7 Chance Constrained Programming to Handle Uncertainty in Nonlinear Process Models -- 7.1 Introduction -- 7.2 Uncertainty Handling Techniques -- 7.3 Chance-Constrained Programming: Fundamentals -- 7.3.1 Calculation of P (hk (x, ξ) ≥ 0) ≥ p (k = 1, . . . , u) -- 7.3.2 Calculation of max f
7.5 Conclusions -- Nomenclature -- Appendices -- A.1 CCP for Normally Distributed Uncertain Parameters -- A.2 Calculation of Mean and Variance for General Function -- References -- 8 Fuzzy Multi-Objective Optimization for Metabolic Reaction Networks by Mixed-Integer Hybrid Differential Evolution -- 8.1 Introduction -- 8.2 Problem Formulation -- 8.2.1 Primal Multi-Objective Optimization Problem -- 8.2.2 Resilience Problem -- 8.3 Optimality -- 8.4 Mixed-Integer Hybrid Differential Evolution -- 8.4.1 Algorithm -- 8.4.2 Constraint Handling -- 8.5 Examples -- 8.6 Conclusions -- Exercises -- References -- Part III Chemical Engineering Applications -- 9 Parameter Estimation in Phase Equilibria Calculations Using Multi-Objective Evolutionary Algorithms -- 9.1 Introduction -- 9.2 Particle Swarm Optimization (PSO) -- 9.2.1 Multi-Objective Particle Swarm Optimization (MO-PSO) -- 9.3 Parameter Estimation in Phase Equilibria Calculations -- 9.4 Model Description -- 9.4.1 Vapor Liquid Equilibrium -- 9.4.2 Heat of Mixing -- 9.5 Multi-Objective Optimization Results and Discussion -- 9.6 Conclusions -- Nomenclature -- Exercises -- References -- 10 Phase Equilibrium Data Reconciliation Using Multi-Objective Differential Evolution with Tabu List -- 10.1 Introduction -- 10.2 Formulation of the Data Reconciliation Problem for Phase Equilibrium Modeling -- 10.2.1 Data Reconciliation Problem -- 10.2.2 Data Reconciliation for Phase Equilibrium Modeling -- 10.3 Multi-Objective Optimization using Differential Evolution with Tabu List -- 10.4 Data Reconciliation of Vapor-Liquid Equilibrium by MOO -- 10.4.1 Description of the Case Study -- 10.4.2 Data Reconciliation Results -- 10.5 Conclusions -- Exercises -- References -- 11 CO2 Emissions Targeting for Petroleum Refinery Optimization -- 11.1 Introduction -- 11.1.1 Overview of the CDU -- 11.1.2 Overview of the FCC.
11.1.3 Pinch Analysis -- 11.1.4 Multi-Objective Optimization (MOO) -- 11.2 MOO-Pinch Analysis Framework to Target CO2 Emissions -- 11.3 Case Studies -- 11.3.1 Case Study 1: Direct Heat Integration -- 11.3.2 Case Study 2: Total Site Heat Integration -- 11.4 Conclusions -- Nomenclature -- Exercises -- Appendices -- A.1 Modeling of CDU and FCC -- A.2 Preliminary Results with Different Values for NSGA-II Parameters -- A.3 Pinch Analysis Techniques -- A.3.1 Composite Curves (CC) -- A.3.2 Grand Composite Curve (GCC) -- A.3.3 Total Site Profiles -- References -- 12 Ecodesign of Chemical Processes with Multi-Objective Genetic Algorithms -- 12.1 Introduction -- 12.2 Numerical Tools -- 12.2.1 Evolutionary Approach: Multi-Objective Genetic Algorithms -- 12.2.2 Choice of the Best Solutions -- 12.3 Williams-Otto Process (WOP) Optimization for Multiple Economic and Environmental Objectives -- 12.3.1 Process Modelling -- 12.3.2 Optimization Variables -- 12.3.3 Objectives for Optimization -- 12.3.4 Problem Constraints -- 12.3.5 Implementation -- 12.3.6 Procedure Validation -- 12.3.7 Tri-Objective Optimization -- 12.3.8 Discussion -- 12.4 Revisiting the HDA Process -- 12.4.1 HDA Process Description and Modelling Principles -- 12.4.2 Optimization Variables -- 12.4.3 Objective Functions -- 12.4.4 Multi-Objective Optimization -- 12.5 Conclusions -- Acronyms -- References -- 13 Modeling and Multi-Objective Optimization of a Chromatographic System -- 13.1 Introduction -- 13.2 Chromatography-Some Facts -- 13.3 Modeling Chromatographic Systems -- 13.4 Solving the Model Equations -- 13.5 Steps for Model Characterization -- 13.5.1 Isotherms and the Parameters -- 13.5.2 Selection of Isotherms -- 13.5.3 Experimental Steps to Generate First Approximation -- 13.6 Description of the Optimization Routine-NSGA-II -- 13.7 Optimization of a Binary Separation in Chromatography.
13.7.1 Selection of the Objective Functions -- 13.7.2 Selection of the Decision Variables -- 13.7.3 Selection of the Constraints -- 13.8 An Example Study -- 13.8.1 Schemes of the Optimization Studies -- 13.8.2 Results and Discussion -- 13.9 Conclusions -- References -- 14 Estimation of Crystal Size Distribution: Image Thresholding Based on Multi-Objective Optimization -- 14.1 Introduction -- 14.2 Methodology -- 14.3 Image Simulation -- 14.3.1 Camera Model -- 14.3.2 Process Model -- 14.3.3 Assumptions -- 14.4 Image Preprocessing -- 14.5 Image Segmentation -- 14.5.1 Image Thresholding Based on Single Objective Optimization -- 14.5.2 Multi-Objective Optimization -- 14.5.3 Problem Formulation -- 14.5.4 Results and Discussion -- 14.6 Feature Extraction -- 14.6.1 Results and Discussion -- 14.7 Future Work -- 14.8 Conclusions -- Nomenclature -- References -- 15 Multi-Objective Optimization of a Hybrid Steam Stripper-Membrane Process for Continuous Bioethanol Purification -- 15.1 Introduction -- 15.2 Description and Design of a Hybrid Stripper-Membrane System -- 15.2.1 Hybrid Stripper-Membrane System of Huang et al. -- 15.2.2 Modified Design of the Hybrid Stripper-Membrane System -- 15.3 Mathematical Formulation and Optimization -- 15.3.1 Problem Formulation -- 15.3.2 Optimization Methodology for MOO Problems in Cases A and B -- 15.4 Results and Discussion -- 15.4.1 Maximize Ethanol Purity (fpurity) and Minimize Operating Cost/kg of Bioethanol (fcost) -- 15.4.2 Minimize Ethanol Loss (floss) and also Operating Cost/kg of Bioethanol (fcost) -- 15.4.3 Detailed Analysis of a Selected Optimal Solution -- 15.5 Conclusions -- Exercises -- References -- 16 Process Design for Economic, Environmental and Safety Objectives with an Application to the Cumene Process -- 16.1 Introduction -- 16.2 Review and Calculation of Safety Indices.
16.2.1 Integrated Inherent Safety Index (I2SI).
Abstract:
Preface xv Part I Overview 1 Introduction 3 Adrian Bonilla-Petriciolet and Gade Pandu Rangaiah 1.1 Optimization and Chemical Engineering 3 1.2 Basic Definitions and Concepts of Multi-Objective Optimization 5 1.3 Multi-Objective Optimization in Chemical Engineering 8 1.4 Scope and Organization of the Book 9 2 Optimization of Pooling Problems for Two Objectives Using the ε-Constraint Method 17 Haibo Zhang and Gade Pandu Rangaiah 2.1 Introduction 17 2.2 Pooling Problem Description and Formulations 19 2.3 ε-Constraint Method and IDE Algorithm 25 2.4 Application to Pooling Problems 27 2.5 Results and Discussion 28 2.6 Conclusions 32 3 Multi-objective Optimization Applications in Chemical Engineering 35 Shivom Sharma and Gade Pandu Rangaiah 3.1 Introduction 35 3.2 Multi-Objective Optimization Applications in Process Design and Operation 37 3.3 Multi-Objective Optimization Applications in Petroleum Refining, Petrochemicals, and Polymerization 57 3.4 Multi-Objective Optimization Applications in the Food Industry, Biotechnology, and Pharmaceuticals 57 3.5 Multi-Objective Optimization Applications in Power Generation and Carbon Dioxide Emissions 66 3.6 Multi-Objective Optimization Applications in Renewable Energy 66 3.7 MOO Applications in Hydrogen Production and Fuel Cells 82 3.8 Conclusions 82 Part II Multi-Objective Optimization Developments 4 Performance Comparison of Jumping-Gene Adaptations of the Elitist Nondominated Sorting Genetic Algorithm 105 Shivom Sharma, Seyed Reza Nabavi and Gade Pandu Rangaiah 4.1 Introduction 105 4.2 Jumping-Gene Adaptations 107 4.3 Termination Criterion 110 4.4 Constraints Handling and Implementation of Programs 112 4.5 Performance Comparison 114 4.6 Conclusions 124 5 Improved Constraint Handling Technique for Multi-objective Optimization with Application to
Two Fermentation Processes 129 Shivom Sharma and Gade Pandu Rangaiah 5.1 Introduction 129 5.2 Constraint Handling Approaches in Chemical Engineering 131 5.3 Adaptive Constraint Relaxation and Feasibility Approach for SOO 132 5.4 Adaptive Relaxation of Constraints and Feasibility Approach for MOO 133 5.5 Testing of MODE-ACRFA 136 5.6 Multi-Objective Optimization of the Fermentation Process 139 5.7 Conclusions 153 6 Robust Multi-Objective Genetic Algorithm (RMOGA) with Online Approximation under Interval Uncertainty 157 Weiwei Hu, Adeel Butt, Ali Almansoori, Shapour Azarm and Ali Elkamel 6.1 Introduction 157 6.2 Background and Definition 159 6.3 Robust Multi-Objective Genetic Algorithm (RMOGA) 163 6.4 Online Approximation-Assisted RMOGA 168 6.5 Case Studies 172 6.6 Conclusion 178 7 Chance Constrained Programming to Handle Uncertainty in Nonlinear Process Models 183 Kishalay Mitra 7.1 Introduction 183 7.2 Uncertainty Handling Techniques 184 7.3 Chance-Constrained Programming: Fundamentals 186 7.4 Industrial Case Study: Grinding 193 7.5 Conclusion 206 8 Fuzzy Multi-objective Optimization for Metabolic Reaction Networks by Mixed-Integer Hybrid Differential Evolution 217 Feng-Sheng Wang and Wu-Hsiung Wu 8.1 Introduction 217 8.2 Problem Formulation 219 8.3 Optimality 223 8.4 Mixed-Integer Hybrid Differential Evolution 228 8.5 Examples 233 8.6 Summary 240 Part III Chemical Engineering Applications 9 Parameter Estimation in Phase Equilibria Calculations using Multi-Objective Evolutionary Algorithms 249 Sameer Punnapala, Francisco M. Vargas and Ali Elkamel 9.1 Introduction 249 9.2 Particle Swarm Optimization (PSO) 250 9.3 Parameter Estimation in Phase Equilibria Calculations 253 9.4 Model Description 253 9.5 Multi-Objective Optimization Results and Discussions 256 9.6 Conclusions 260 10
Phase Equilibrium Data Reconciliation using Multi-Objective Differential Evolution with Tabu List 267 A. Bonilla-Petriciolet, Shivom Sharma and Gade Pandu Rangaiah 10.1 Introduction. 267 10.2 Formulation of the Data-Reconciliation Problem for Phase Equilibrium Modeling 270 10.3 Multi-Objective Optimization using Differential Evolution with Tabu List 274 10.4 Data Reconciliation of Vapor-Liquid Equilibrium by MOO 277 10.5 Conclusions 287 11 CO2 Emissions Targeting for Petroleum Refinery Optimization 293 Mohmmad A. Al-Mayyahi, Andrew F.A. Hoadley and Gade Pandu Rangaiah 11.1 Introduction 293 11.2 MOO-Pinch Analysis Framework to Target CO2 Emissions 303 11.3 Case Studies 304 11.4 Case Studies 305 11.5 Conclusions 315 12 Ecodesign of Chemical Processes with Multi-Objective Genetic Algorithms 335 Catherine Azzaro-Pantel and Luc Pibouleau 12.1 Introduction 335 12.2 Numerical Tools 337 12.3 Williams-Otto Process (WOP) Optimization for Multiple Economic and Environmental Objectives 338 12.4 Revisiting the HDA Process 346 12.5 Conclusions and Perspectives 361 13 Modeling and Multi-objective Optimization of a Chromatographic System 369 Abhijit Tarafder 13.1 Introduction 369 13.2 Chromatography-Some Facts 371 13.3 Modeling Chromatographic Systems 373 13.4 Solving the Model Equations 376 13.5 Steps for Model Characterization 377 13.6 Description of the Optimization Routine-NSGA-II 387 13.7 Optimization of a Binary Separation in Chromatography 387 13.8 An Example Study 390 13.9 Conclusion 396 14 Estimation of Crystal Size Distribution: Image Thresholding based on Multi-Objective Optimization 399 Karthik Raja Periasamy and S. Lakshminarayanan 14.1 Introduction 399 14.2 Methodology 401 14.3 Image Simulation 402 14.4 Image Preprocessing 404 14.5 Image Segmentation 404 14.6 Feature Extraction 413
14.7 Future Work 417 14.8 Conclusions 418 15 Multi-Objective Optimization of a Hybrid Steam Stripper-Membrane Process for Continuous Bioethanol Purification 423 Krishna Gudena, Gade Pandu Rangaiah and S Lakshminarayanan 15.1 Introduction 423 15.2 Description and Design of a Hybrid Stripper-Membrane System 426 15.3 Mathematical Formulation and Optimization 431 15.4 Results and Discussion 435 15.5 Conclusions 445 15.5 Exercises 445 16 Process Design for Economic, Environmental and Safety Objectives with an Application to the Cumene Process 449 Shivom Sharma, Zi Chao Lim and Gade Pandu Rangaiah 16.1 Introduction 449 16.2 Review and Calculation of Safety Indices 451 16.3 Cumene Process, its Simulation and Costing 455 16.4 I2SI Calculation for Cumene Process 459 16.5 Optimization using EMOO Program 462 16.6 Optimization for Two Objectives 464 16.7 Optimization for EES Objectives 469 16.8 Conclusions 471 17 New PI Controller Tuning Methods Using Multi-Objective Optimization 479 Allan Vandervoort, Jules Thibault and Yash Gupta 17.1 Introduction 479 17.2 PI Controller Model 480 17.3 Optimization Problem 481 17.4 Pareto Domain 481 17.5 Optimization Results 488 17.6 Controller Tuning 490 17.7 Application of the Tuning Methods 491 17.8 Conclusions 498 Index.
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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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