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Multi-Objective Optimization in Chemical Engineering: Developments and Applications

ISBN: 978-1-118-34166-7
528 pages
May 2013
Multi-Objective Optimization in Chemical Engineering: Developments and Applications (111834166X) cover image

Description

For reasons both financial and environmental, there is a perpetual need to optimize the design and operating conditions of industrial process systems in order to improve their performance, energy efficiency, profitability, safety and reliability. However, with most chemical engineering application problems having many variables with complex inter-relationships, meeting these optimization objectives can be challenging. This is where Multi-Objective Optimization (MOO) is useful to find the optimal trade-offs among two or more conflicting objectives.

This book provides an overview of the recent developments and applications of MOO for modeling, design and operation of chemical, petrochemical, pharmaceutical, energy and related processes. It then covers important theoretical and computational developments as well as specific applications such as metabolic reaction networks, chromatographic systems, CO2 emissions targeting for petroleum refining units, ecodesign of chemical processes, ethanol purification and cumene process design.

Multi-Objective Optimization in Chemical Engineering: Developments and Applications is an invaluable resource for researchers and graduate students in chemical engineering as well as industrial practitioners and engineers involved in process design, modeling and optimization.

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Table of Contents

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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Related Websites / Extra

Adrían Bonilla-Petriciolet's Homepage Find out more about the author, Adrían Bonilla-Petriciolet, and his work on his website.
Wiley Book SupportPlease visit the book support website and enter the title, author or isbn to download excel files and corrected figures for chapter 17.
Gade Pandu Rangaiah's Homepage

 Find out more about the author, Gade Pandu Rangaiah, and his work on his website.

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