Includes bibliographical references and index.

Preface. 1 Introduction. 1.1 Introductory Concepts of Process Control. 1.2 Advanced Process Control Techniques. 1.2.1 Key Problems in Advanced Control of Chemical Processes. 1.2.1.1 Nonlinear Dynamic Behavior. 1.2.1.2 Multivariable Interactions between Manipulated and Controlled Variables. 1.2.1.3 Uncertain and Time-Varying Parameters. 1.2.1.4 Deadtime on Inputs and Measurements. 1.2.1.5 Constraints on Manipulated and State Variables. 1.2.1.6 High-Order and Distributed Processes. 1.2.1.7 Unmeasured State Variables and Unmeasured and Frequent Disturbances. 1.2.2 Classification of the Advanced Process Control Techniques. 2 Model Predictive Control. 2.1 Internal Model Control. 2.2 Linear Model Predictive Control. 2.3 Nonlinear Model Predictive Control. 2.3.1 Introduction. 2.3.2 Industrial Model-Based Control: Current Status and Challenges. 2.3.2.1 Challenges in Industrial NMPC. 2.3.3 First Principle (Analytical) Model-Based NMPC. 2.3.4 NMPC with Guaranteed Stability. 2.3.5 Artificial Neural Network (ANN)-Based Nonlinear Model Predictive Control. 2.3.5.1 Introduction. 2.3.5.2 Basics of ANNs. 2.3.5.3 Algorithms for ANN Training. 2.3.5.4 Direct ANN Model-Based NMPC (DANMPC). 2.3.5.5 Stable DANMPC Control Law. 2.3.5.6 Inverse ANN Model-Based NMPC. 2.3.5.7 ANN Model-Based NMPC with Feedback Linearization. 2.3.5.8 ANN Model-Based NMPC with On-Line Linearization. 2.3.6 NMPC Software for Simulation and Practical Implementation. 2.3.6.1 Computational Issues. 2.3.6.2 NMPC Software for Simulation. 2.3.6.3 NMPC Software for Practical Implementation. 2.4 MPC General Tuning Guidelines. 2.4.1 Model Horizon (n). 2.4.2 Prediction Horizon (p). 2.4.3 Control Horizon (m). 2.4.4 Sampling Time (T). 2.4.5 Weight Matrices ( l y and l u). 2.4.6 Feedback Filter. 2.4.7 Dynamic Sensitivity Used for MPC Tuning. 3 Case Studies. 3.1 Productivity Optimization and Nonlin

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.