Advances in Process Control with Real Applications

Häftad, Engelska, 2025

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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

Produktinformation

Utgivningsdatum2025-07-10
Mått216 x 276 x undefined mm
Vikt980 g
FormatHäftad
SpråkEngelska
Antal sidor348
FörlagElsevier Science
ISBN9780443238574

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Dr. Ch. Venkateswarlu M.Tech., Ph. D, has formerly worked as Scientist, Senior Principal Scientist and Chief Scientist at Indian Institute of Chemical Technology (IICT), Hyderabad, a premier research and development (R&D) institute of Council of Scientific and Industrial Research (CSIR), India. Later, he worked as Director R&D at BV Raju Institute of Technology (BVRIT), Narsapur, Greater Hyderabad. Prior to Director R&D at BVRIT, he worked as Professor, Principal and Head of Chemical Engineering Department of the same institute. He did his graduation from Andhra University as well as from Indian Institute of Chemical Engineers, and post-graduation and Ph. D in Chemical Engineering from Osmania University, Hyderabad, India. He holds 35 years R&D and industry experience along with 20 years teaching experience. His research interests lie in the areas of conventional process control & advanced process control, dynamic process modelling & simulation, process identification & dynamic optimization, process monitoring & fault diagnosis, state estimation & soft sensing, applied engineering mathematics & evolutionary computing, artificial intelligence & expert systems, and bioprocess engineering & bio-informatics. He published more than 120 research papers in peer journals of repute along with few international and national proceeding publications. He is also credited with 150 technical paper presentations and invited lectures. He authored two books published by Elsevier along with few book chapters. He is also in editorial boards of few international journals. He has executed several R&D projects sponsored by DST and Industry. He is a reviewer of several international research journals and many national and international research project proposals. He has guided several postgraduate and Ph. D students. He served as a long-term guest faculty for premier institutes like Bhaba Atomic Research Centre Scientific Officers Training, BITS Pilani MS (off-campus) and IICT-CDAC Bioinformatics Programs. He is a Fellow of Andhra Pradesh Akademi of Sciences and Telangana State Academy of Sciences.

1. Advanced process control & its significance2. Types of models for advanced controllers3. Role of state estimation in advanced process control4. Significance of stochastic and evolutionary methods in advanced process control5. Advanced process control algorithms6. Applications of generalized predictive control7. Applications of linear model predictive control to nonlinear systems8. Applications of nonlinear model predictive control9. Applications of generic model control10. Applications of globally linearizing control11. Applications of nonlinear internal model control12. Applications of optimal control13. Applications of optimizing control14. Applications of inferential control15. Applications of fuzzy logic control16. Applications of neural network control17. Applications of radial basis function network control18. Nonlinear process control based on evolutionary and stochastic optimizers19. Future trends and challenges