Title:
Model based control : case studies in process engineering
Publication Information:
Weinheim : Wiley-VCH, 2006
ISBN:
9783527315451
Added Author:
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010132544 | TP155.75 M624 2006 | Open Access Book | Book | Searching... |
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Summary
Summary
Filling a gap in the literature for a practical approach to the topic, this book is unique in including a whole section of case studies presenting a wide range of applications from polymerization reactors and bioreactors, to distillation column and complex fluid catalytic cracking units. A section of general tuning guidelines of MPC is also present.These thus aid readers in facilitating the implementation of MPC in process engineering and automation. At the same time many theoretical, computational and implementation aspects of model-based control are explained, with a look at both linear and nonlinear model predictive control. Each chapter presents details related to the modeling of the process as well as the implementation of different model-based control approaches, and there is also a discussion of both the dynamic behaviour and the economics of industrial processes and plants. The book is unique in the broad coverage of different model based control strategies and in the variety of applications presented. A special merit of the book is in the included library of dynamic models of several industrially relevant processes, which can be used by both the industrial and academic community to study and implement advanced control strategies.
Author Notes
Professor Paul Serban Agachi professor of process control at the Department of Chemical Engineering of "Babes-Bolyai" University, Cluj-Napoca
Zoltan Kalman Nagy a faculty member at Loughborough University. UK
Mircea Vasile Cristea present Associate Professor at "Babes-Bolyai" University
Arpad Imre-Lucaci worked in the Chemical Engineering Department of "Babes-Bolyai" University Cluj-Napoca, Romania
Table of Contents
| Preface | p. IX |
| 1 Introduction | p. 1 |
| 1.1 Introductory Concepts of Process Control | p. 2 |
| 1.2 Advanced Process Control Techniques | p. 5 |
| 1.2.1 Key Problems in Advanced Control of Chemical Processes | p. 5 |
| 1.2.1.1 Nonlinear Dynamic Behavior | p. 5 |
| 1.2.1.2 Multivariable Interactions between Manipulated and Controlled Variables | p. 7 |
| 1.2.1.3 Uncertain and Time-Varying Parameters | p. 7 |
| 1.2.1.4 Deadtime on Inputs and Measurements | p. 8 |
| 1.2.1.5 Constraints on Manipulated and State Variables | p. 9 |
| 1.2.1.6 High-Order and Distributed Processes | p. 9 |
| 1.2.1.7 Unmeasured State Variables and Unmeasured and Frequent Disturbances | p. 10 |
| 1.2.2 Classification of the Advanced Process Control Techniques | p. 11 |
| 2 Model Predictive Control | p. 15 |
| 2.1 Internal Model Control | p. 15 |
| 2.2 Linear Model Predictive Control | p. 17 |
| 2.3 Nonlinear Model Predictive Control | p. 23 |
| 2.3.1 Introduction | p. 23 |
| 2.3.2 Industrial Model-Based Control: Current Status and Challenges | p. 26 |
| 2.3.2.1 Challenges in Industrial NMPC | p. 30 |
| 2.3.3 First Principle (Analytical) Model-Based NMPC | p. 32 |
| 2.3.4 NMPC with Guaranteed Stability | p. 35 |
| 2.3.5 Artificial Neural Network (ANN)-Based Nonlinear Model Predictive Control | p. 37 |
| 2.3.5.1 Introduction | p. 37 |
| 2.3.5.2 Basics of ANNs | p. 38 |
| 2.3.5.3 Algorithms for ANN Training | p. 39 |
| 2.3.5.4 Direct ANN Model-Based NMPC (DANMPC) | p. 43 |
| 2.3.5.5 Stable DANMPC Control Law | p. 46 |
| 2.3.5.6 Inverse ANN Model-Based NMPC | p. 47 |
| 2.3.5.7 ANN Model-Based NMPC with Feedback Linearization | p. 49 |
| 2.3.5.8 ANN Model-Based NMPC with On-Line Linearization | p. 51 |
| 2.3.6 NMPC Software for Simulation and Practical Implementation | p. 52 |
| 2.3.6.1 Computational Issues | p. 52 |
| 2.3.6.2 NMPC Software for Simulation | p. 56 |
| 2.3.6.3 NMPC Software for Practical Implementation | p. 58 |
| 2.4 MPC General Tuning Guidelines | p. 59 |
| 2.4.1 Model Horizon (n) | p. 61 |
| 2.4.2 Prediction Horizon (p) | p. 61 |
| 2.4.3 Control Horizon (m) | p. 62 |
| 2.4.4 Sampling Time (T) | p. 62 |
| 2.4.5 Weight Matrices ([Gamma subscript l superscript y] and [Gamma subscript l superscript u]) | p. 62 |
| 2.4.6 Feedback Filter | p. 63 |
| 2.4.7 Dynamic Sensitivity Used for MPC Tuning | p. 63 |
| 3 Case Studies | p. 73 |
| 3.1 Productivity Optimization and Nonlinear Model Predictive Control (NMPC) of a PVC Batch Reactor | p. 73 |
| 3.1.1 Introduction | p. 73 |
| 3.1.2 Dynamic Model of the PVC Batch Reactor | p. 74 |
| 3.1.2.1 The Complex Analytical Model of the PVC Reactor | p. 75 |
| 3.1.2.2 Morphological Model | p. 86 |
| 3.1.2.3 The Simplified Dynamic Analytical Model of the PVC Reactor | p. 91 |
| 3.1.3 Productivity Optimization of the PVC Batch Reactor | p. 93 |
| 3.1.3.1 The Basic Elements of GAs | p. 94 |
| 3.1.3.2 Optimization of the PVC Reactor Productivity through the Initial Concentration of Initiators | p. 97 |
| 3.1.3.3 Optimization of PVC Reactor Productivity by obtaining an Optimal Temperature Policy | p. 99 |
| 3.1.4 NMPC of the PVC Batch Reactor | p. 101 |
| 3.1.4.1 Multiple On-Line Linearization-Based NMPC of the PVC Batch Reactor | p. 104 |
| 3.1.4.2 Sequential NMPC of the PVC Batch Reactor | p. 111 |
| 3.1.5 Conclusions | p. 114 |
| 3.1.6 Nomenclature | p. 116 |
| 3.2 Modeling, Simulation, and Control of a Yeast Fermentation Bioreactor | p. 118 |
| 3.2.1 First Principle Model of the Continuous Fermentation Bioreactor | p. 118 |
| 3.2.2 Linear Model Identification and LMPC of the Bioreactor | p. 125 |
| 3.2.3 Artificial Neural Network (ANN)-Based Dynamic Model and Control of the Bioreactor | p. 128 |
| 3.2.3.1 Identification of the ANN Model of the Bioreactor | p. 128 |
| 3.2.3.2 Using Optimal Brain Surgeon to Determine Optimal Topology of the ANN-Based Dynamic Model | p. 133 |
| 3.2.3.3 ANN Model-Based Nonlinear Predictive Control (ANMPC) of the Bioreactor | p. 137 |
| 3.2.4 Conclusions | p. 141 |
| 3.2.5 Nomenclature | p. 143 |
| 3.3 Dynamic Modeling and Control of a High-Purity Distillation Column | p. 145 |
| 3.3.1 Introduction | p. 145 |
| 3.3.2 Dynamic Modeling of the Binary Distillation Column | p. 146 |
| 3.3.2.1 Model A: 164th Order DAE Model | p. 148 |
| 3.3.2.2 Model B: 84th Order DAE Model | p. 150 |
| 3.3.2.3 Model C: 42nd Order ODE Model | p. 150 |
| 3.3.2.4 Model D: 5th Order ODE Model | p. 151 |
| 3.3.2.5 Model E: 5th Order DAE Model | p. 152 |
| 3.3.2.6 Comparison of the Models | p. 153 |
| 3.3.3 A Computational Efficient NMPC Approach for Real-Time Control of the Distillation Column | p. 158 |
| 3.3.3.1 NMPC with Guaranteed Stability of the Distillation Column | p. 158 |
| 3.3.3.2 Direct Multiple Shooting Approach for Efficient Optimization in Real-Time NMPC | p. 160 |
| 3.3.3.3 Computational Complexity and Controller Performance | p. 164 |
| 3.3.4 Using Genetic Algorithm in Robust Optimization for NMPC of the Distillation Column | p. 180 |
| 3.3.4.1 Motivation | p. 180 |
| 3.3.4.2 GA-Based Robust Optimization for NMPC Schemes | p. 180 |
| 3.3.5 LMPC of the High-Purity Distillation Column | p. 184 |
| 3.3.6 A Comparison Between First Principles and Neural Network Model-Based NMPC of the Distillation Column | p. 184 |
| 3.3.7 Conclusions | p. 189 |
| 3.3.8 Nomenclature | p. 190 |
| 3.4 Practical Implementation of NMPC for a Laboratory Azeotropic Distillation Column | p. 192 |
| 3.4.1 Experimental Equipment | p. 192 |
| 3.4.2 Description of the Developed Software Interface | p. 191 |
| 3.4.3 First Principles Model-Based Control of the Azeotropic Distillation Column | p. 200 |
| 3.4.3.1 Experimental Validation of the First Principles Model | p. 200 |
| 3.4.3.2 First Principle Model-Based NMPC of the System | p. 206 |
| 3.4.4 ANN Model-Based Control of the Azeotropic Distillation Column | p. 208 |
| 3.4.5 Conclusions | p. 211 |
| 3.5 Model Predictive Control of the Fluid Catalytic Cracking Unit | p. 213 |
| 3.5.1 Introduction | p. 213 |
| 3.5.2 Dynamic Model of the UOP FCCU | p. 214 |
| 3.5.2.1 Reactor Model | p. 215 |
| 3.5.2.2 Regenerator Model | p. 218 |
| 3.5.2.3 Model of the Catalyst Circulation Lines | p. 219 |
| 3.5.3 Model Predictive Control Results | p. 221 |
| 3.5.3.1 Control Scheme Selection | p. 221 |
| 3.5.3.2 Different MPC Control Schemes Results | p. 223 |
| 3.5.3.3 MPC Using a Model Scheduling Approach | p. 227 |
| 3.5.3.4 Constrained MPC | p. 227 |
| 3.5.4 Conclusions | p. 229 |
| 3.5.5 Nomenclature | p. 230 |
| 3.6 Model Predictive Control of the Drying Process of Electric Insulators | p. 233 |
| 3.6.1 Introduction | p. 233 |
| 3.6.2 Model Description | p. 233 |
| 3.6.3 Model Predictive Control Results | p. 236 |
| 3.6.4 Neural Networks-Based MPC | p. 238 |
| 3.6.4.1 Neural Networks Design and Training | p. 238 |
| 3.6.4.2 ANN-Based MPC Results | p. 239 |
| 3.6.5 Conclusions | p. 241 |
| 3.6.6 Nomenclature | p. 243 |
| 3.7 The MPC of Brine Electrolysis Processes | p. 244 |
| 3.7.1 The Importance of Chlorine and Caustic Soda | p. 244 |
| 3.7.2 Industrially Applied Methods for Brine Electrolysis | p. 244 |
| 3.7.3 Mathematical Model of the Mercury Cell | p. 245 |
| 3.7.3.1 Model Structure | p. 247 |
| 3.7.3.2 The Main Equations of the Mathematical Model | p. 247 |
| 3.7.4 Mathematical Model of Ion-Exchange Membrane Cell | p. 250 |
| 3.7.4.1 Model Structure | p. 251 |
| 3.7.4.2 The Main Equations of the Mathematical Model | p. 252 |
| 3.7.5 Simulation of Brine Electrolysis | p. 255 |
| 3.7.5.1 Simulation of the Mercury Cell Process | p. 255 |
| 3.7.5.2 Simulation of the Ion-Exchange Membrane Cell Process | p. 255 |
| 3.7.6 Model Predictive Control of Brine Electrolysis | p. 255 |
| 3.7.6.1 MPC of Mercury Cell | p. 259 |
| 3.7.6.2 MPC of IEM Cell | p. 261 |
| 3.7.7 Conclusions | p. 264 |
| 3.7.8 Nomenclature | p. 264 |
| Index | p. 275 |
