Advances in Process Control with Real Applications
- 1st Edition - July 9, 2025
- Latest edition
- Author: Ch. Venkateswarlu
- Language: English
Advances in Process Control with Real Applications presents various advanced controllers, including the formulation, design, and implementation of various advanced control st… Read more
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Description
Description
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.
Key features
Key features
- 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
Readership
Readership
Engineers, researchers, scientists and professionals in academic institutes, R&D establishments, and industries in chemical and bioengineering working on process operations and control and process systems; Master’s and PhD level students in chemical and bioengineering
Table of contents
Table of contents
1. Advanced process control & its significance
2. Types of models for advanced controllers
3. Role of state estimation in advanced process control
4. Significance of stochastic and evolutionary methods in advanced process control
5. Advanced process control algorithms
5.1 Model predictive control
5.1.1 Dynamic matrix control
5.1.2 Generalized predictive control
5.1.3 Constrained generalized predictive control
5.1.4 Linear model predictive control
5.1.5 Nonlinear model predictive control
5.2 Model based control
5.2.1 Generic model control
5.2.2 Globally linearizing control
5.2.3 Nonlinear internal model control5.3 Inferential control
5.4 Optimal and optimizing control
5.4.1 Optimal control
5.4.2 Optimizing control
5.5 Intelligent control
5.5.1 Fuzzy logic control
5.5.2 Neural network control
5.5.3 Radial basis function network control
6. Applications of generalized predictive control
6.1 Constrained generalized predictive control of unstable CSTR
6.2 Constrained generalized predictive control of unstable bioreactor
7. Applications of linear model predictive control to nonlinear systems
7.1 Model predictive control of unstable nonlinear systems
7.2 Multistep model predictive control of ethyl acetate reactive distillation column
7.3 Self adaptive model predictive control cascade control scheme for fluidized catalytic cracking unit (FCCU)
8. Applications of nonlinear model predictive control
8.1 Nonlinear model predictive control of reactive distillation Column
8.2 Nonlinear model predictive control of runaway batch chemical reactor
9. Applications of generic model control
9.1 Generic model control of distillation startup and operation
9.2 Generic model control of an exothermic batch chemical reactor - experimental aspects
10. Applications of globally linearizing control
10.1 Nonlinear geometric control of a chaotic reactor
10.2 Globally linearizing control of distillation startup and operation
10.1 Globally linearizing control of a chemical reactor using estimated heat release content
11. Applications of nonlinear internal model control
11.1 Nonlinear model-based control of complex dynamic chemical systems
11.2 Energy model based nonlinear internal model control of an exothermic batch chemical reactor
11.3 Nonlinear internal model control of distillation startup and operation
12. Applications of optimal control
12.1 An extended two step method for computing optimal control policy in a batch reactor
12.2 Optimal control of poly (b -hydroxybutyrate) fed-batch bioreactor by iterative dynamic programming
12.3 Advances in optimal control of polymerization reactors
13. Applications of optimizing control
13.1 Optimal state estimation and on-line optimization of a biochemical reactor
13.2 A two level EKF assisted on-line optimizing control of nonlinear processes
14. Applications of inferential control
14.1 Estimator based nonlinear control of a chemical reactor
14.2 Estimator based nonlinear control of a polymerization reactor
14.3 Soft sensor based nonlinear control of a chaotic reactor
14.4 Inferential control of metathesis reactive distillation column
15. Applications of fuzzy logic control
15.1 Temperature trajectory tracking of a chemical reactor using fuzzy linguistic controller
15.2 Fuzzy logic control of a runaway batch chemical reactor
15.3 Fuzzy modelling and control of batch beer fermentation
15.4 Dynamic fuzzy adaptive control of nonlinear unstable processes
15.5 Adaptive fuzzy model based predictive control of an exothermic batch chemical reactor
15.6 Fuzzy model based long range predictive control of continuous fermentor
15.7 Dynamic fuzzy adaptive controller for pH
16. Applications of neural network control
16.1 Neural network-based model predictive control of unstable biochemical reactor
16.2 Neural network-based model predictive control of unstable chemical reactor
17. Applications of radial basis function network control
17.1 Radial basis function network model predictive control of unstable bioreactor
17.2 Radial basis function network model predictive control of unstable chemical reactor
17.3 Radial basis function network model predictive control of polymerization reactor
18. Nonlinear process control based on evolutionary and stochastic optimizers
18.1 Simulated annealing based nonlinear model predictive control of runaway batch chemical reactor
18.2 Nonlinear model predictive control of reactive distillation based on stochastic optimization
18.3 Genetically tuned decentralized PI controllers for composition control of reactive distillation
19. Future trends and challenges
2. Types of models for advanced controllers
3. Role of state estimation in advanced process control
4. Significance of stochastic and evolutionary methods in advanced process control
5. Advanced process control algorithms
5.1 Model predictive control
5.1.1 Dynamic matrix control
5.1.2 Generalized predictive control
5.1.3 Constrained generalized predictive control
5.1.4 Linear model predictive control
5.1.5 Nonlinear model predictive control
5.2 Model based control
5.2.1 Generic model control
5.2.2 Globally linearizing control
5.2.3 Nonlinear internal model control5.3 Inferential control
5.4 Optimal and optimizing control
5.4.1 Optimal control
5.4.2 Optimizing control
5.5 Intelligent control
5.5.1 Fuzzy logic control
5.5.2 Neural network control
5.5.3 Radial basis function network control
6. Applications of generalized predictive control
6.1 Constrained generalized predictive control of unstable CSTR
6.2 Constrained generalized predictive control of unstable bioreactor
7. Applications of linear model predictive control to nonlinear systems
7.1 Model predictive control of unstable nonlinear systems
7.2 Multistep model predictive control of ethyl acetate reactive distillation column
7.3 Self adaptive model predictive control cascade control scheme for fluidized catalytic cracking unit (FCCU)
8. Applications of nonlinear model predictive control
8.1 Nonlinear model predictive control of reactive distillation Column
8.2 Nonlinear model predictive control of runaway batch chemical reactor
9. Applications of generic model control
9.1 Generic model control of distillation startup and operation
9.2 Generic model control of an exothermic batch chemical reactor - experimental aspects
10. Applications of globally linearizing control
10.1 Nonlinear geometric control of a chaotic reactor
10.2 Globally linearizing control of distillation startup and operation
10.1 Globally linearizing control of a chemical reactor using estimated heat release content
11. Applications of nonlinear internal model control
11.1 Nonlinear model-based control of complex dynamic chemical systems
11.2 Energy model based nonlinear internal model control of an exothermic batch chemical reactor
11.3 Nonlinear internal model control of distillation startup and operation
12. Applications of optimal control
12.1 An extended two step method for computing optimal control policy in a batch reactor
12.2 Optimal control of poly (b -hydroxybutyrate) fed-batch bioreactor by iterative dynamic programming
12.3 Advances in optimal control of polymerization reactors
13. Applications of optimizing control
13.1 Optimal state estimation and on-line optimization of a biochemical reactor
13.2 A two level EKF assisted on-line optimizing control of nonlinear processes
14. Applications of inferential control
14.1 Estimator based nonlinear control of a chemical reactor
14.2 Estimator based nonlinear control of a polymerization reactor
14.3 Soft sensor based nonlinear control of a chaotic reactor
14.4 Inferential control of metathesis reactive distillation column
15. Applications of fuzzy logic control
15.1 Temperature trajectory tracking of a chemical reactor using fuzzy linguistic controller
15.2 Fuzzy logic control of a runaway batch chemical reactor
15.3 Fuzzy modelling and control of batch beer fermentation
15.4 Dynamic fuzzy adaptive control of nonlinear unstable processes
15.5 Adaptive fuzzy model based predictive control of an exothermic batch chemical reactor
15.6 Fuzzy model based long range predictive control of continuous fermentor
15.7 Dynamic fuzzy adaptive controller for pH
16. Applications of neural network control
16.1 Neural network-based model predictive control of unstable biochemical reactor
16.2 Neural network-based model predictive control of unstable chemical reactor
17. Applications of radial basis function network control
17.1 Radial basis function network model predictive control of unstable bioreactor
17.2 Radial basis function network model predictive control of unstable chemical reactor
17.3 Radial basis function network model predictive control of polymerization reactor
18. Nonlinear process control based on evolutionary and stochastic optimizers
18.1 Simulated annealing based nonlinear model predictive control of runaway batch chemical reactor
18.2 Nonlinear model predictive control of reactive distillation based on stochastic optimization
18.3 Genetically tuned decentralized PI controllers for composition control of reactive distillation
19. Future trends and challenges
Review quotes
Review quotes
"This book presents 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 non- linear model predictive control; dynamic matrix control; nonlinear control and non-linear 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. It will be highly beneficial to students, researchers and industry professionals working in process design, process monitoring, process systems engineering, process operations and control etc." Review by Asian Dyer, August-September 2025
Product details
Product details
- Edition: 1
- Latest edition
- Published: July 10, 2025
- Language: English
About the author
About the author
CV
Ch. Venkateswarlu
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.
Affiliations and expertise
Chief Scientist (Retd.), Indian Institute of Chemical Technology, Tarnaka, Hyderabad, Telangana, IndiaView book on ScienceDirect
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