| | Table of Contents | |
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| | Preface | IX |
| 1 | Introduction | 1 |
| 1.1 | Introductory Concepts of Process Control | 2 |
| 1.2 | Advanced Process Control Techniques | 5 |
| 1.2.1 | Key Problems in Advanced Control of Chemical Processes | 5 |
| 1.2.1.1 | Nonlinear Dynamic Behavior | 5 |
| 1.2.1.2 | Multivariable Interactions between Manipulated and Controlled Variables | 7 |
| 1.2.1.3 | Uncertain and Time-Varying Parameters | 7 |
| 1.2.1.4 | Deadtime on Inputs and Measurements | 8 |
| 1.2.1.5 | Constraints on Manipulated and State Variables | 9 |
| 1.2.1.6 | High-Order and Distributed Processes | 9 |
| 1.2.1.7 | Unmeasured State Variables and Unmeasured and Frequent Disturbances | 10 |
| 1.2.2 | Classification of the Advanced Process Control Techniques | 11 |
| 2 | Model Predictive Control | 15 |
| 2.1 | Internal Model Control | 15 |
| 2.2 | Linear Model Predictive Control | 17 |
| 2.3 | Nonlinear Model Predictive Control | 23 |
| 2.3.1 | Introduction | 23 |
| 2.3.2 | Industrial Model-Based Control: Current Status and Challenges | 26 |
| 2.3.2.1 | Challenges in Industrial NMPC | 30 |
| 2.3.3 | First Principle (Analytical) Model-Based NMPC | 32 |
| 2.3.4 | NMPC with Guaranteed Stability | 35 |
| 2.3.5 | Artificial Neural Network (ANN)-Based Nonlinear Model Predictive Control | 37 |
| 2.3.5.1 | Introduction | 37 |
| 2.3.5.2 | Basics of ANNs | 38 |
| 2.3.5.3 | Algorithms for ANN Training | 39 |
| 2.3.5.4 | Direct ANN Model-Based NMPC (DANMPC) | 43 |
| 2.3.5.5 | Stable DANMPC Control Law | 46 |
| 2.3.5.6 | Inverse ANN Model-Based NMPC | 47 |
| 2.3.5.7 | ANN Model-Based NMPC with Feedback Linearization | 49 |
| 2.3.5.8 | ANN Model-Based NMPC with On-Line Linearization | 51 |
| 2.3.6 | NMPC Software for Simulation and Practical Implementation | 52 |
| 2.3.6.1 | Computational Issues | 52 |
| 2.3.6.2 | NMPC Software for Simulation | 56 |
| 2.3.6.3 | NMPC Software for Practical Implementation | 58 |
| 2.4 | MPC General Tuning Guidelines | 59 |
| 2.4.1 | Model Horizon (n) | 61 |
| 2.4.2 | Prediction Horizon (p) | 61 |
| 2.4.3 | Control Horizon (m) | 62 |
| 2.4.4 | Sampling Time (T) | 62 |
| 2.4.5 | Weight Matrices ( l y and l u) | 62 |
| 2.4.6 | Feedback Filter | 63 |
| 2.4.7 | Dynamic Sensitivity Used for MPC Tuning | 63 |
| 3 | Case Studies | 73 |
| 3.1 | Productivity Optimization and Nonlinear Model Predictive Control (NMPC) of a PVC Batch Reactor | 73 |
| 3.1.1 | Introduction | 73 |
| 3.1.2 | Dynamic Model of the PVC Batch Reactor | 74 |
| 3.1.2.1 | The Complex Analytical Model of the PVC Reactor | 75 |
| 3.1.2.2 | Morphological Model | 86 |
| 3.1.2.3 | The Simplified Dynamic Analytical Model of the PVC Reactor | 91 |
| 3.1.3 | Productivity Optimization of the PVC Batch Reactor | 93 |
| 3.1.3.1 | The Basic Elements of GAs | 94 |
| 3.1.3.2 | Optimization of the PVC Reactor Productivity through the Initial Concentration of Initiators | 97 |
| 3.1.3.3 | Optimization of PVC Reactor Productivity by obtaining an Optimal Temperature Policy | 99 |
| 3.1.4 | NMPC of the PVC Batch Reactor | 101 |
| 3.1.4.1 | Multiple On-Line Linearization-Based NMPC of the PVC Batch Reactor | 104 |
| 3.1.4.2 | Sequential NMPC of the PVC Batch Reactor | 111 |
| 3.1.5 | Conclusions | 114 |
| 3.1.6 | Nomenclature | 116 |
| 3.2 | Modeling, Simulation, and Control of a Yeast Fermentation Bioreactor | 118 |
| 3.2.1 | First Principle Model of the Continuous Fermentation Bioreactor | 118 |
| 3.2.2 | Linear Model Identification and LMPC of the Bioreactor | 125 |
| 3.2.3 | Artificial Neural Network (ANN)-Based Dynamic Model and Control of the Bioreactor | 128 |
| 3.2.3.1 | Identification of the ANN Model of the Bioreactor | 128 |
| 3.2.3.2 | Using Optimal Brain Surgeon to Determine Optimal Topology of the ANN-Based Dynamic Model | 133 |
| 3.2.3.3 | ANN Model-Based Nonlinear Predictive Control (ANMPC) of the Bioreactor | 137 |
| 3.2.4 | Conclusions | 141 |
| 3.2.5 | Nomenclature | 143 |
| 3.3 | Dynamic Modeling and Control of a High-Purity Distillation Column | 145 |
| 3.3.1 | Introduction | 145 |
| 3.3.2 | Dynamic Modeling of the Binary Distillation Column | 146 |
| 3.3.2.1 | Model A: 164th Order DAE Model | 148 |
| 3.3.2.2 | Model B: 84th Order DAE Model | 150 |
| 3.3.2.3 | Model C: 42nd Order ODE Model | 150 |
| 3.3.2.4 | Model D: 5th Order ODE Model | 151 |
| 3.3.2.5 | Model E: 5th Order DAE Model | 152 |
| 3.3.2.6 | Comparison of the Models | 153 |
| 3.3.3 | A Computational Efficient NMPC Approach for Real-Time Control of the Distillation Column | 158 |
| 3.3.3.1 | NMPC with Guaranteed Stability of the Distillation Column | 158 |
| 3.3.3.2 | Direct Multiple Shooting Approach for Efficient Optimization in Real-Time NMPC | 160 |
| 3.3.3.3 | Computational Complexity and Controller Performance | 164 |
| 3.3.4 | Using Genetic Algorithm in Robust Optimization for NMPC of the Distillation Column | 180 |
| 3.3.4.1 | Motivation | 180 |
| 3.3.4.2 | GA-Based Robust Optimization for NMPC Schemes | 180 |
| 3.3.5 | LMPC of the High-Purity Distillation Column | 184 |
| 3.3.6 | A Comparison Between First Principles and Neural Network Model-Based NMPC of the Distillation Column | 184 |
| 3.3.7 | Conclusions | 189 |
| 3.3.8 | Nomenclature | 190 |
| 3.4 | Practical Implementation of NMPC for a Laboratory Azeotropic Distillation Column | 192 |
| 3.4.1 | Experimental Equipment | 192 |
| 3.4.2 | Description of the Developed Software Interface | 193 |
| 3.4.3 | First Principles Model-Based Control of the Azeotropic Distillation Column | 200 |
| 3.4.3.1 | Experimental Validation of the First Principles Model | 200 |
| 3.4.3.2 | First Principle Model-Based NMPC of the System | 206 |
| 3.4.4 | ANN Model-Based Control of the Azeotropic Distillation Column | 208 |
| 3.4.5 | Conclusions | 211 |
| 3.5 | Model Predictive Control of the Fluid Catalytic Cracking Unit | 213 |
| 3.5.1 | Introduction | 213 |
| 3.5.2 | Dynamic Model of the UOP FCCU | 214 |
| 3.5.2.1 | Reactor Model | 215 |
| 3.5.2.2 | Regenerator Model | 218 |
| 3.5.2.3 | Model of the Catalyst Circulation Lines | 219 |
| 3.5.3 | Model Predictive Control Results | 221 |
| 3.5.3.1 | Control Scheme Selection | 221 |
| 3.5.3.2 | Different MPC Control Schemes Results | 223 |
| 3.5.3.3 | MPC Using a Model Scheduling Approach | 227 |
| 3.5.3.4 | Constrained MPC | 227 |
| 3.5.4 | Conclusions | 229 |
| 3.5.5 | Nomenclature | 230 |
| 3.6 | Model Predictive Control of the Drying Process of Electric Insulators | 233 |
| 3.6.1 | Introduction | 233 |
| 3.6.2 | Model Description | 233 |
| 3.6.3 | Model Predictive Control Results | 236 |
| 3.6.4 | Neural Networks-Based MPC | 238 |
| 3.6.4.1 | Neural Networks Design and Training | 238 |
| 3.6.4.2 | ANN-Based MPC Results | 239 |
| 3.6.5 | Conclusions | 241 |
| 3.6.6 | Nomenclature | 243 |
| 3.7 | The MPC of Brine Electrolysis Processes | 244 |
| 3.7.1 | The Importance of Chlorine and Caustic Soda | 244 |
| 3.7.2 | Industrially Applied Methods for Brine Electrolysis | 244 |
| 3.7.3 | Mathematical Model of the Mercury Cell | 245 |
| 3.7.3.1 | Model Structure | 247 |
| 3.7.3.2 | The Main Equations of the Mathematical Model | 247 |
| 3.7.4 | Mathematical Model of Ion-Exchange Membrane Cell | 250 |
| 3.7.4.1 | Model Structure | 251 |
| 3.7.4.2 | The Main Equations of the Mathematical Model | 252 |
| 3.7.5 | Simulation of Brine Electrolysis | 255 |
| 3.7.5.1 | Simulation of the Mercury Cell Process | 255 |
| 3.7.5.2 | Simulation of the Ion-Exchange Membrane Cell Process | 255 |
| 3.7.6 | Model Predictive Control of Brine Electrolysis | 255 |
| 3.7.6.1 | MPC of Mercury Cell | 259 |
| 3.7.6.2 | MPC of IEM Cell | 261 |
| 3.7.7 | Conclusions | 264 |
| 3.7.8 | Nomenclature | 264 |
| | Index | 275 |
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