Advances in Process Control with Real Applications presents various advanced controllers, including the formulation, design, and implementation of various advanced control strategies for a wide variety of processes. These strategies include generalized predictive control with and without constraints; linear and nonlinear model predictive control; dynamic matrix control; nonlinear control, such as generic model control, globally linearizing control, and nonlinear internal model control; optimal and optimizing control; inferential control; intelligent control based on fuzzy reasoning and neural networks; and controllers based on stochastic and evolutionary optimization.
This book will be highly beneficial to students, researchers, and industry professionals working in process design, process monitoring, process systems engineering, process operations and control, and related areas.

Describes various advanced controllers for the control of complex nonlinear processes
Provides the fundamentals, algorithms, approaches, control strategies, and implementation procedures systematically
Highlights the significance and importance of advanced process control with many real applications

Includes bibliographical references and index.

"The new edition blends conventional topics with a modern perspective of integrated process operation, control, and information systems. Updated throughout, it addresses smart manufacturing, new data preprocessing techniques, and machine learning and artificial intelligence concepts. It guides the reader to resources needed to solve modeling, classification, and monitoring problems. It introduces the link between process optimization and process control and links discussion of modern architectures of industrial computer control systems with real case studies and applications to pilot-scale operations. It features exercises throughout and downloadable MATLAB toolboxes to reinforce learning"-- Provided by publisher.