Bøger af Ch. Venkateswarlu

Advances in Process Control with Real Applications presents advanced models for the control of nonlinear complex processes, including first principle, data driven, and artificial intelligence models, along with 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 covers 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), and more. Additional sections shine a light on 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.

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimators with a special emphasis on their development and applications to process monitoring, fault diagnosis and control. The design and analysis of different state estimators are highlighted with a number of applications and case studies concerning to various real chemical and biochemical processes. The book starts with the introduction of basic concepts, extending to classical methods and successively leading to advances in this field. Design and implementation of various classical and advanced state estimation methods to solve a wide variety of problems makes this book immensely useful for the audience working in different disciplines in academics, research and industry in areas concerning to process monitoring, fault diagnosis, control and related disciplines.