Plantwide Control

Prof. Gade Pandu Rangaiah is currently Professor and Deputy Head in the Department of Chemical & Biomolecular Engineering at the National University of Singapore. His research interests are in control, modeling and optimization of chemical, petrochemical and related processes. Prof. Rangaiah published nearly 120 papers in international journals and presented around 90 papers in conferences. He received several awards for his teaching including Annual Teaching Excellence Awards from the National University of Singapore for four consecutive years. Prof. Rangaiah edited two books (one on multi-objective optimization and another on global optimization) published by World Scientific. Dr Vinay Kariwala is an Assistant Professor in the School of Chemical and Biomedical Engineering at the Nanyang Technological University, Singapore. He got his Ph.D. degree in Chemical Engineering (Computer Process Control) from the University of Alberta, Canada, in 2004. During 2004-2005, he worked as a postdoctoral fellow at the Norwegian University of Science and Technology, Trondheim, Norway. He has published more than 25 papers in international journals and refereed conference proceedings in the broad areas of plant-wide control and control structure design. Recently, his contributions were recognized with the best reviewer award by Journal of Process Control for the year 2009.

• Preface Section I: Overview and Perspective1 Introduction1.1 Background 11.2 Plant-Wide Control 21.3 Scope and Organization of the Book 4References 102 Industrial Perspective on Plant-Wide Control2.1 Introduction 12.2 Design Environment 32.3 Disturbances and Measurement System Design 62.4 Academic Contributions 82.5 Conclusions 11References 12Section II: Tools and Heuristics3 Control Degrees of Freedom Analysis for Plant-Wide Control of Industrial Processes3.1 Introduction 23.2 Control Degrees of Freedom (CDOF) 43.3 Computation Methods for Control Degrees of Freedom (CDOF): A Review 73.4 Computation of CDOF Using Flowsheet-Oriented Method 143.4.1 Computation of Restraining Number for Unit Operations 163.5 Application of Flowsheet-Oriented Method to Distillation Columns and the Concept of Redundant Process Variables 193.6 Application of Flowsheet-Oriented Method to Compute CDOF to Complex Integrated Processes 223.7 Conclusions 23References 244 Selection of Controlled Variables Using Self-Optimizing Control Method4.1 Introduction 24.2 General Principle 44.3 Brute-Force Optimization Approach for CV Selection 84.4 Local Methods 114.4.1 Minimum Singular Value (MSV) Rule 124.4.2 Exact Local Method 144.4.3 Optimal Measurement Combination 164.4.3.1 Null Space Method 164.4.3.2 Explicit Solution 174.4.3.3 Toy Example 194.5 Branch and Bound Methods 214.6 Constraint Handling 234.7 Case Study: Forced Circulation Evaporator 264.8 Conclusions and Discussion 324.9 Acknowledgements 34References 345 Input-Output Pairing Selection for Design of Decentralized Controller5.1 Introduction 25.1.1 State of the Art 35.2 Relative Gain Array and Variants 5Steady-State RGA 65.2.2 Niederlinski Index 85.2.3 The Dynamic Relative Gain Array 95.2.4 The Effective Relative Gain Array 115.2.5 The Block Relative Gain 125.2.6 Relative Disturbance Gain Array 145.3 µ-Interaction Measure 155.4 Pairing Analysis Based on the Controllability and Observability 175.4.1 The Participation Matrix 175.4.2 The Hankel Interaction Index Array 195.4.3 The Dynamic Input-Output Pairing Matrix 19Input-Output Pairing for Uncertain Multivariable Plants 21RGA in the Presence of Statistical Uncertainty 22RGA in the Presence of Norm-Bounded Uncertainties 23DIOPM and the Effect of Uncertainty 26Input-Output Pairing for Nonlinear Multivariable Plants 285.6.1 Relative Order Matrix 295.6.2 The Nonlinear RGA 305.7 Conclusions and Discussion 31References 336 Heuristics for Plantwide Control6.1 Introduction 26.2 Basics of Heuristic Plantwide Control 46.2.1 Plumbing 56.2.2 Recycle 66.2.2.1 Effect of Recycle on Time Constants 66.2.2.2 Snowball Effects in Liquid Recycle Systems 76.2.2.3 Gas Recycle Systems 86.2.3 Fresh Feed Introduction 86.2.3.1 Ternary Example 96.2.3.2 Control Structures 116.2.3.3 Ternary Process with Altered Volatilities 126.2.4 Energy Management and Integration 126.2.5 Controller Tuning 136.2.5.1 Flow and Pressure Control 136.2.5.2 Level Control 146.2.5.3 Composition and Temperature Control 166.2.5.4 Interacting Control Loops 176.2.6 Throughput Handle 186.3 Application to HDA Process 186.3.1 Process Description 196.3.2 Application of Plantwide Control Heuristics 206.3.2.1 Throughput Handle 206.3.2.2 Maximum Gas Recycle 206.3.2.3 Component Balances (Downs Drill) 206.3.2.4 Flow Control in Liquid Recycle Loop 216.3.2.5 Product Quality and Constraint Loops 216.4 Conclusion 217 Throughput Manipulator Location Selection for Economic Plantwide Control7.1 Introduction 27.2 Throughput Manipulation, Inventory Regulation and Plantwide Variability Propagation 37.3 Quantitative Case Studies 67.3.1 Case Study I: Recycle Process 77.3.1.1 Alternative Control Structures 77.3.1.2 Quantitative Back-Off Results 87.3.1.3 Salient Observations 107.3.2 Case Study II: Recycle Process with Side Reaction 117.3.2.1 Economically Optimal Process Operation 117.3.2.2 Self Optimizing Variables for Unconstrained Degrees of Freedom 147.3.2.3 Plantwide Control System Design 157.3.2.4 Dynamic Simulation Results 187.4 Discussion 197.5 Conclusions 237.6 Acknowledgments 237.7 Supplementary Information 23References 248 Influence of Process Variability Propagation in Plant-Wide Control8.1 Introduction 28.2 Theoretical Background 58.3 Local Unit Operation Control 128.3.1 Heat Exchanger 128.3.2 Extraction Process 138.4 Inventory Control 158.4.1 Pressure Control in Gas Headers 158.4.2 Parallel Unit Operations 178.4.3 Liquid Inventory Control 18Plant-Wide Control Examples 218.5.1 Distillation Column Control 218.5.2 Esterification Process 228.6 Conclusion 25References 27Section III: Methodologies9 A Review of Plant-Wide Control Methodologies and Applications9.1 Introduction 19.2 Review and Approach-Based Classification of PWC Methodologies 39.2.1 Heuristics-Based PWC Methods 49.2.2 Mathematical-Based PWC Methods 69.2.3 Optimization-Based PWC Methods 89.2.4 Mixed PWC Methods 99.3 Structure-Based Classification of PWC Methodologies 129.4 Processes Studied in PWC Applications 149.5 Comparative Studies on Different Methodologies 169.6 Concluding Remarks 18References 2010 Integrated Framework of Simulation and Heuristics for Plant-Wide Control System Design10.1 Introduction 110.2 HDA Process: Overview and Simulation 210.2.1 Process Description 210.2.2 Steady-State and Dynamic Simulation 410.3 Integrated Framework Procedure and Application to HDA Plant 510.4 Evaluation of the Control System 1710.5 Conclusions 18References 2011 Economic Plantwide ControlIntroduction 1Control Layers and Time Scale Separation 3Plantwide Control Procedure 7Degrees of Freedom for Operation 911.5 Skogestad’s Plantwide Control Procedure 12Top-Down Part 12Discussion 29Conclusion 30REFERENCES 3012 Performance Assessment of Plant-Wide Control Systems12.1 Introduction 212.2 Desirable Qualities of a Good Performance Measure 412.3 Performance Measure Based on Steady State: Steady-State Operating Cost/Profit 512.4 Performance Measures Based on Dynamics 612.4.1 Process Settling Time Based on Overall Absolute Component Accumulation 612.4.2 Process Settling Time Based on Plant Production 712.4.3 Dynamic Disturbance Sensitivity (DDS) 812.4.4 Deviation from the Production Target (DPT) 812.4.5 Total Variation (TV) in Manipulated Variables 1012.5 Application of the Performance Measures to the HDA Plant Control Structure 1112.5.1 Steady-State Operating Cost 1212.5.2 Process Settling Time Based on Overall Absolute Component Accumulation 1212.5.3 Process Settling Time Based on Plant Production 1312.5.4 Dynamic Disturbance Sensitivity (DDS) 1412.5.5 Deviation from the Production Target (DPT) 1512.5.6 Total Variation (TV) in Manipulated Variables 1512.6 Application of the Performance Measures for Comparing PWC Systems 1512.7 Discussion and Recommendations 1712.7.1 Disturbances and Set-Point Changes 1712.7.2 Performance Measures 1912.8 Concluding Remarks 21References 21Section IV: Applications Studies13 Design and Control of a Cooled Ammonia Reactor13.1 Introduction 213.2 Cold-Shot Process 413.2.1 Process Flowsheet 413.2.2 Equipment Sizes, Capital and Energy Costs 613.3 Cooled-Reactor Process 713.3.1 Process Flowsheet 713.3.2 Reaction Kinetics 913.3.3 Optimum Economic Design of the Cooled-Reactor Process 1013.3.3.1 Effect of Pressure 1013.3.3.2 Effect of Reactor Size 1213.3.4 Comparison of Cold-Shot and Cooled-Reactor Processes 1213.4 Control 1313.5 Conclusion 1613.6 Acknowledgement 16References 1614 Design and Plant-Wide Control of a Biodiesel Plant14.1 Introduction 114.2 Steady-State Plant Design and Simulation 414.2.1 Process Design 414.2.1.1 Feed and Product Specifications 414.2.1.2 Reaction Section 514.2.1.3 Separation Section 614.2.2 Process Flowsheet and HYSYS Simulation 814.3 Optimization of Plant Operation 1014.4 Application of IFSH to Biodiesel Plant 1214.5 Validation of the Plant-Wide Control Structure 1814.6 Conclusions 20References 2015 Plant-Wide Control of a Reactive Distillation Process15.1 Introduction 215.2 Design of Ethyl Acetate Reactive-Distillation Process 315.2.1 Kinetic and Thermodynamic Models 315.2.2 The Process Flowsheet 415.2.3 Comparison of the Process Using Either Homogeneous or Heterogeneous Catalyst 615.3 Control Structure Development of the Two Catalyst Systems 815.3.1 Inventory Control Loops 815.3.2 Product Quality Control Loops 1015.3.3 Tuning of the Two Temperature Control Loops 12Closed-Loop Simulation Results 1315.3.5 Summary of PWC Aspects 1515.4 Conclusions 17References 1716 Control System Design of a Crystallizer Train for Para-Xylene Recovery16.1 Introduction 316.1 Process 516.2 Description 516.2.1 Para-Xylene Production Process 516.2.2 Para-Xylene Recovery Based on Crystallization Technology 616.3 Process Model 816.3.1 Crystallizer (Units 1–5) 816.3.2 Cyclone Separator (Units 9, 11) 1016.3.3 Centrifugal Separator (Units 8, 10) 1116.3.4 Overall Process Model 1216.4 Control System Design 1416.4.1 Basic Regulatory Control 1416.4.2 Steady State Optimal Operation Policy 1516.4.2.1 Maximization of Para-Xylene Recovery 1516.4.2.2 Load Distribution 1716.4.3 Design of Optimizing Controllers 1916.4.3.1 Multiloop Controller 2016.4.3.2 Multivariable Controller 2016.4.3.3 Simulation 2116.4.4 Incorporation of Steady State Optimizer 2216.4.4.1 LP Based Steady State Optimizer 2216.4.4.2 Simulation 2416.4.5 Justification of MPC Application 2516.5 Conclusions 2616.6 5.A Linear Steady State Model and Constraints 27References 2917 Modeling and Control of Industrial Off-Gas Systems17.1 Introduction 317.2 Process Description 5Off-Gas System Model Development 717.3.1 Roaster off-Gas Train 817.3.2 Furnace Off-Gas Train 1217.4 Control of Smelter Off-Gas Systems 1417.4.1 Roaster Off-Gas System 1517.4.1.1 Degree of Freedom Analysis 1517.4.1.2 Definition of Optimal Operation 1617.4.1.3 Optimization 1717.4.1.4 Production Rate 1917.4.1.5 Structure of the Regulatory and Supervisory Control 2117.4.1.6 Validation of the Proposed Control Structure 2217.4.2 Furnace Off-Gas System 2217.4.2.1 Manipulated Variables and Degree of Freedom Analysis 2217.4.2.2 Definition of Optimal Operation 2317.4.2.3 Optimization 2417.4.2.4 Production Rate 2617.4.2.5 Structure of the Regulatory and Supervisory Control Layer 2717.4.2.6 Validation of the Proposed Control Structures 2817.5 Conclusion 28Notation 29Subscripts 32References 33Section V: Emerging Topics18 Plant-Wide Control via a Network of Autonomous Controllers18.1 Introduction 218.2 Process and Controller Networks 718.2.1 Representation of Process Network 718.2.2 Representation of Control Network 10Plant-Wide Stability Analysis Based on Dissipativity 1318.4 Controller Network Design 1818.4.1 Transformation of the Network Topology 18Plant-Wide Connective Stability 2518.4.3 Performance Design 2718.5 Case Study 3118.5.1 Process Model 3218.5.2 Distributed Control System Design 3418.6 Discussions and Conclusion 35References 4019 Co-Ordinated, Distributed Plant-Wide Control19.1 Introduction 2Co-Ordination Based Plant-Wide Control 819.2.1 Price-Driven Co-Ordination 1119.2.1.1 The Price Decomposition Principle 1119.2.1.2 Algorithm 12Price-Driven Co-Ordination Procedure: 1419.2.1.4 Summary 1519.2.2 Augmented Price-Driven Method 1519.2.2.1 The Newton Based Price Update Method as a Negotiation Principle 1719.2.3 Resource Allocation Co-Ordination 1819.2.3.1 Resource Allocation Principle 1819.2.3.2 Algorithm and Interpretation 1819.2.4 Prediction-Driven Co-Ordination 2119.2.4.1 Prediction-Driven Principle 2119.2.4.2 Algorithm and Interpretation 2319.2.4.3 Prediction Driven Co-Ordination Procedure 2319.2.5 Economic Interpretation 2419.3 Case Studies 2519.3.1 A Pulp Mill Process 2519.3.1.1 Problem Formulation 25Plant-Wide Coordination and Performance Comparison 2719.3.2 A Forced-Circulation Evaporator System 2919.3.2.1 Problem Formulation 30Plant-Wide Co-Ordination and Performance 3219.4 The Future 34References 3820 Determination of Plant-Wide Control Loop Configuration and Eco-Efficiency20.1 Introduction 120.2 Relative Gain Array (RGA) and Relative Exergy Gain Array (REA) 420.2.1 Relative Gain Array (RGA) 420.2.2 Relative Exergy Array (REA) 620.2.2.1 Exergy 620.2.2.2 Relative Exergy Array 820.3 Exergy Calculation Procedure 1020.4 Case Study 1320.4.1 Distillation Column 1320.4.2 Case Study 2 1520.5 Summary 19References

Review copy sent 25/04/12: Book News