Due to the complexity of the process operation and the requirements for high quality, low cost, safety and the protection of the environment, an increasing number of pulp and paper companies are in need of an advanced control technology to improve their process operation. This publication presents, for the first time, the theory of such an advanced control technology as well as various industrial applications associated especially with Paper Making. The reader will gain a better understanding of the most popular and advanced process control techniques and applications of these techniques in an important real-time process industry. The contents are based on the authors' own research on modeling and advanced control in this field.
Master process control hands on, through practical examples and MATLAB(R) simulations This is the first complete introduction to process control that fully integrates software tools--enabling professionals and students to master critical techniques hands on, through computer simulations based on the popular MATLAB environment. Process Control: Modeling, Design, and Simulation teaches the field's most important techniques, behaviors, and control problems through practical examples, supplemented by extensive exercises--with detailed derivations, relevant software files, and additional techniques available on a companion Web site. Coverage includes: Fundamentals of process control and instrumentation, including objectives, variables, and block diagrams Methodologies for developing dynamic models of chemical processes Dynamic behavior of linear systems: state space models, transfer function-based models, and more Feedback control; proportional, integral, and derivative (PID) controllers; and closed-loop stability analysis Frequency response analysis techniques for evaluating the robustness of control systems Improving control loop performance: internal model control (IMC), automatic tuning, gain scheduling, and enhancements to improve disturbance rejection Split-range, selective, and override strategies for switching among inputs or outputs Control loop interactions and multivariable controllers An introduction to model predictive control (MPC) Bequette walks step by step through the development of control instrumentation diagrams for an entire chemical process, reviewing common control strategies for individual unit operations, then discussing strategies for integrated systems. The book also includes 16 learning modules demonstrating how to use MATLAB and SIMULINK to solve several key control problems, ranging from robustness analyses to biochemical reactors, biomedical problems to multivariable control.
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.
This compact and original reference and textbook presents the most important classical and modern essentials of control engineering in a single volume. It constitutes a harmonic mixture of control theory and applications, which makes the book especially useful for students, practicing engineers and researchers interested in modeling and control of processes. Well written and easily understandable, it includes a range of methods for the analysis and design of control systems.
Offering a different approach to other textbooks in the area, this book is a comprehensive introduction to the subject divided in three broad parts. The first part deals with building physical models, the second part with developing empirical models and the final part discusses developing process control solutions. Theory is discussed where needed to ensure students have a full understanding of key techniques that are used to solve a modeling problem. Hallmark Features: Includes worked out examples of processes where the theory learned early on in the text can be applied. Uses MATLAB simulation examples of all processes and modeling techniques- further information on MATLAB can be obtained from www.mathworks.com Includes supplementary website to include further references, worked examples and figures from the book This book is structured and aimed at upper level undergraduate students within chemical engineering and other engineering disciplines looking for a comprehensive introduction to the subject. It is also of use to practitioners of process control where the integrated approach of physical and empirical modeling is particularly valuable.
This book fills the gap between basic control configurations (Practical Process Control) and model predictive control (MPC). For those loops whose performance has a direct impact on plant economics or product quality, going beyond simple feedback or cascade can improve control performance, or specifically, reduce the variance about the target. However, the effort required to implement such control technology must be offset by increased economic returns from production operations. The economic aspects of the application of the various advanced control technologies are stressed throughout the book.
This text offers a modern view of process control in the context of today's technology. It provides the standard material in a coherent presentation and uses a notation that is more consistent with the research literature in process control. Topics that are unique include a unified approach to model representations, process model formation and process identification, multivariable control, statistical quality control, and model-based control. This book is designed to be used as an introductory text for undergraduate courses in process dynamics and control. In addition to chemical engineering courses, the text would also be suitable for such courses taught in mechanical, nuclear, industrial, and metallurgical engineering departments. The material is organized so that modern concepts are presented to the student but details of the most advanced material are left to later chapters. The text material has been developed, refined, and classroom tested over the last 10-15 years at the University of Wisconsin and more recently at the University of Delaware. As part of the course at Wisconsin, a laboratory has been developed to allow the students hands-on experience with measurement instruments, real time computers, and experimental process dynamics and control problems.
Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing Detailed resource on the “Why,” “What,” and “How” of integrated process modeling, advanced control and data analytics explained via hands-on examples and workshops for optimizing polyolefin manufacturing. Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing discusses, as well as demonstrates, the optimization of polyolefin production by covering topics from polymer process modeling and advanced process control to data analytics and machine learning, and sustainable design and industrial practice. The text also covers practical problems, handling of real data streams, developing the right level of detail, and tuning models to the available data, among other topics, to allow for easy translation of concepts into practice. Written by two highly qualified authors, Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing includes information on: Segment-based modeling of polymer processes; selection of thermodynamic methods; estimation of physical properties for polymer process modeling Reactor modeling, convergence tips and data-fit tool; free radical polymerization (LDPE, EVA and PS), Ziegler-Natta polymerization (HDPE, PP, LLPDE, and EPDM) and ionic polymerization (SBS rubber) Improved polymer process operability and control through steady-state and dynamic simulation models Model-predictive control of polyolefin processes and applications of multivariate statistics and machine learning to optimizing polyolefin manufacturing Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing enables readers to make full use of advanced computer models and latest data analytics and machine learning tools for optimizing polyolefin manufacturing, making it an essential resource for undergraduate and graduate students, researchers, and new and experienced engineers involved in the polyolefin industry.
This book offers a modern view of process control in the context of today's technology. It provides innovative chapters on the growth of educational, scientific, and industrial research among chemical engineers. It presents experimental data on thermodynamics and provides a broad understanding of the main computational techniques used for chemical
An application-oriented approach to process control. The reference text systematically explains process identification, control and optimization, the three key steps needed to solve a multivariable control problem. Theory is discussed as far as it is needed to understand and solve the defined problem, while numerous examples written in MATLAB illustrate the problem-solving approach.
