Advances in Process Control with Real Applications presents various advanced models for the control of nonlinear complex processes, including first principle, data driven and artificial intelligence type of models as well as inferential state estimation & stochastic and evolutionary optimization techniques. The book highlights the significance and importance of advanced controllers with several real applications concerning chemical and biochemical processes. It presents control approaches such as generalized predictive control (GPC) with and without constraints, linear & nonlinear model predictive control (MPC), dynamic matrix control (DMC), nonlinear control such as generic model control (GMC), globally linearizing control (GLC) and nonlinear internal model control (NIMC), optimal & optimizing control, inferential control, intelligent control based on fuzzy reasoning, neural network, machine learning and evolutionary computation.

• Describes a broad range of advanced control strategies with several real applications to various processes
• Highlights the formulation and design of different controllers are based on first principle, data driven and artificial intelligence type of models
• Incorporates inferential estimation and nature inspired optimization as an integral part of various model-based controllers