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 processesHighlights the formulation and design of different controllers are based on first principle, data driven and artificial intelligence type of modelsIncorporates inferential estimation and nature inspired optimization as an integral part of various model-based controllers INDICE: 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 6. Applications of generalized predictive control 7. Applications of linear model predictive control to nonlinear systems 8. Applications of nonlinear model predictive control 9. Applications of generic model control 10. Applications of globally linearizing control 11. Applications of nonlinear internal model control 12. Applications of optimal control 13. Applications of optimizing control 14. Applications of inferential control 15. Applications of fuzzy logic control 16. Applications of neural network control 17. Applications of radial basis function network control 18. Nonlinear process control based on evolutionary and stochastic optimizers 19. Future trends and challenges
- ISBN: 978-0-443-23857-4
- Editorial: Elsevier
- Encuadernacion: Rústica
- Páginas: 350
- Fecha Publicación: 01/01/2025
- Nº Volúmenes: 1
- Idioma: Inglés