This book presents the concepts and algorithms of advanced industrial process control and on-line optimization within the framework of a multilayer structure. It describes the interaction of three separate layers of process control: direct control, set-point control, and economic optimization. The book features illustrations of the methodologies and algorithms by worked examples and by results of simulations based on industrial process models.

Risk Analysis and Control for Industrial Processes - Gas, Oil and Chemicals provides an analysis of current approaches for preventing disasters, and gives readers an overview on which methods to adopt. The book covers safety regulations, history and trends, industrial disasters, safety problems, safety tools, and capital and operational costs versus the benefits of safety, all supporting project decision processes. Tools covered include present day array of risk assessment, tools including HAZOP, LOPA and ORA, but also new approaches such as System-Theoretic Process Analysis (STPA), Blended HAZID, applications of Bayesian data analytics, Bayesian networks, and others. The text is supported by valuable examples to help the reader achieve a greater understanding on how to perform safety analysis, identify potential issues, and predict the likelihood they may appear.

Industrial Process Identification and Control Design is devoted to advanced identification and control methods for the operation of continuous-time processes both with and without time delay, in industrial and chemical engineering practice. The simple and practical step- or relay-feedback test is employed when applying the proposed identification techniques, which are classified in terms of common industrial process type: open-loop stable; integrating; and unstable, respectively. Correspondingly, control system design and tuning models that follow are presented for single-input-single-output processes. Furthermore, new two-degree-of-freedom control strategies and cascade control system design methods are explored with reference to independently-improving, set-point tracking and load disturbance rejection. Decoupling, multi-loop, and decentralized control techniques for the operation of multiple-input-multiple-output processes are also detailed. Perfect tracking of a desire output trajectory is realized using iterative learning control in uncertain industrial batch processes. All the proposed methods are presented in an easy-to-follow style, illustrated by examples and practical applications. This book will be valuable for researchers in system identification and control theory, and will also be of interest to graduate control students from process, chemical, and electrical engineering backgrounds and to practising control engineers in the process industry.

Risk Analysis and Control for Industrial Processes - Gas, Oil and Chemicals provides an analysis of current approaches for preventing disasters, and gives readers an overview on which methods to adopt. The book covers safety regulations, history and trends, industrial disasters, safety problems, safety tools, and capital and operational costs versus the benefits of safety, all supporting project decision processes. Tools covered include present day array of risk assessment, tools including HAZOP, LOPA and ORA, but also new approaches such as System-Theoretic Process Analysis (STPA), Blended HAZID, applications of Bayesian data analytics, Bayesian networks, and others. The text is supported by valuable examples to help the reader achieve a greater understanding on how to perform safety analysis, identify potential issues, and predict the likelihood they may appear. - Presents new methods on how to identify hazards of low probability/high consequence events - Contains information on how to develop and install safeguards against such events, with guidance on how to quantify risk and its uncertainty, and how to make economic and societal decisions about risk - Demonstrates key concepts through the use of examples and relevant case studies

Overview of Industrial Process Automation, Second Edition, introduces the basics of philosophy, technology, terminology, and practices of modern automation systems through the presentation of updated examples, illustrations, case studies, and images. This updated edition adds new developments in the automation domain, and its reorganization of chapters and appendixes provides better continuity and seamless knowledge transfer. Manufacturing and chemical engineers involved in factory and process automation, and students studying industrial automation will find this book to be a great, comprehensive resource for further explanation and study. - Presents a ready made reference that introduces all aspects of automation technology in a single place with day-to-day examples - Provides a basic platform for the understanding of industry literature on automation products, systems, and solutions - Contains a guided tour of the subject without the requirement of any previous knowledge on automation - Includes new topics, such as factory and process automation, IT/OT Integration, ISA 95, Industry 4.0, IoT, etc., along with safety systems in process plants and machines

This book reviews current design paths for soft sensors, and guides readers in evaluating different choices. The book presents case studies resulting from collaborations between the authors and industrial partners. The solutions presented, some of which are implemented on-line in industrial plants, are designed to cope with a wide range of applications from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation.

Detailed coverage of the practical aspects of multivariate statistical process control (MVSPC) based on the application of Hotelling's T2 statistic. MVSPC is the application of multivariate statistical techniques to improve the quality and productivity of an industrial process. Provides valuable insight into the T2 statistic.

This brief reviews concepts of inter-relationship in modern industrial processes, biological and social systems. Specifically ideas of connectivity and causality within and between elements of a complex system are treated; these ideas are of great importance in analysing and influencing mechanisms, structural properties and their dynamic behaviour, especially for fault diagnosis and hazard analysis. Fault detection and isolation for industrial processes being concerned with root causes and fault propagation, the brief shows that, process connectivity and causality information can be captured in two ways: · from process knowledge: structural modeling based on first-principles structural models can be merged with adjacency/reachability matrices or topology models obtained from process flow-sheets described in standard formats; and · from process data: cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian networks can be used to identify pair-wise relationships and network topology. These methods rely on the notion of information fusion whereby process operating data is combined with qualitative process knowledge, to give a holistic picture of the system.

Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.

Includes: Digital signals and systems. Digital controllers for process control applications. Design of digital controllers. Control of time delay systems. State-space concepts. System identification. Introduction to discrete optimal control. Multivariable control. Adaptive control. Computer aided design for industrial control systems. Reliability and redundancy in microprocessor controllers. Software and hardware aspects of industrial controller implementations. Application of distributed digital control algorithms to power stations. An expert system for process control.