Multi-Objective Optimization in Chemical Engineering: Developments and Applications

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.

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