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
1. 3.1 Introduction - p. 31 2. 3.2 Process representation for mechanistic model–based state estimation - p. 31 3. 3.3 Observability of nonlinear systems - p. 32 4. 3.4 Observers for state estimation in nonlinear systems - p. 36 5. 3.5 Stochastic filtering methods for state (parameter) estimation in nonlinear systems - p. 40 6. 3.5.1 Extended Kalman filter (EKF) - p. 40 7. 3.5.2 Steady-state extended Kalman filter - p. 48 8. 3.5.3 Two-level extended Kalman filter - p. 49 9. 3.5.4 Adaptive fading extended Kalman filter - p. 51 10. 3.5.5 Unscented Kalman filter (UKF) - p. 51 11. 3.5.6 Square root unscented Kalman filter (SRUKF) - p. 53 12. 3.5.7 Ensemble Kalman filter - p. 55 13. 3.5.8 Particle filter (PF) - p. 56 14. 3.6 Summary - p. 57 15. Chapter 4. Significance of stochastic and evolutionary optimization methods in advanced process control - p. 61 16. Chapter 5. Advanced process control algorithms - p. 75 17. 5.1 Introduction - p. 75 18. 5.2 Model predictive control - p. 75 19. 5.3 Nonlinear model-based control - p. 91 20. 5.4 Inferential control - p. 96 21. 5.5 Optimal and optimizing control - p. 97 22. 5.6 Intelligent control - p. 100 23. 5.7 Summary - p. 115 24. Chapter 6. Applications of generalized predictive control (GPC) and constrained generalized predictive control (CGPC) - p. 121 25. Chapter 7. Applications of model predictive control (MPC) to nonlinear processes - p. 129 26. 7.1 Introduction - p. 129 27. 7.2 Model predictive control of unstable processes - p. 129 28. 7.3 Model predictive control of ethyl acetate reactive distillation column - p. 132 29. 7.4 Self-adaptive model predictive cascade control of fluidized catalytic cracking unit - p. 136 30. 7.4.1 Brief description of FCCU - p. 136 31. 7.4.2 Mathematical model of FCCU - p. 138 32. 7.4.2.1 Feed and preheat systems - p. 138 33. 7.4.2.2 Reactor - p. 139 34. 7.4.2.3 Regenerator - p. 143 35. 7.4.2.4 Air blowers - p. 147 36. 7.4.2.5 Catalyst circulation - p. 150 37. 7.4.3 FCCU system variables - p. 151 38. 7.4.4 Controller structure - p. 151 39. 7.4.5 Predictive model, model identification, and controller formulation - p. 153 40. 7.4.6 Controller implementation and analysis of results - p. 153 41. 7.5 Summary - p. 157 42. Chapter 8. Applications of nonlinear model predictive control - p. 159 43. Chapter 9. Applications of generic model control - p. 177 44. Chapter 10. Applications of globally linearizing control - p. 191 45. Chapter 11. Applications of nonlinear internal model control - p. 203 46. Chapter 12. Applications of optimal control - p. 213 47. Chapter 13. Applications of optimizing control - p. 231 48. Chapter 14. Applications of inferential control - p. 241 49. Chapter 15. Applications of fuzzy logic control - p. 257 50. 15.1 Introduction - p. 257 51. 15.2 Fuzzy logic–based modeling and control of batch beer fermentation - p. 257 52. 15.3 Fuzzy model–based adaptive predictive control of a batch chemical reactor - p. 265 53. 15.4 Dynamic fuzzy learning control of pH process - p. 273 54. 15.5 Summary - p. 280 55. Chapter 16. Applications of neural network control - p. 283 56. Chapter 17. Applications of radial basis function network control - p. 293 57. Chapter 18. Applications of stochastic optimization-based control strategies - p. 305 58. Chapter 19. Future trends and challenges - p. 325 59. Index - p. 329
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
1. 3.1 Introduction - p. 31 2. 3.2 Process representation for mechanistic model–based state estimation - p. 31 3. 3.3 Observability of nonlinear systems - p. 32 4. 3.4 Observers for state estimation in nonlinear systems - p. 36 5. 3.5 Stochastic filtering methods for state (parameter) estimation in nonlinear systems - p. 40 6. 3.5.1 Extended Kalman filter (EKF) - p. 40 7. 3.5.2 Steady-state extended Kalman filter - p. 48 8. 3.5.3 Two-level extended Kalman filter - p. 49 9. 3.5.4 Adaptive fading extended Kalman filter - p. 51 10. 3.5.5 Unscented Kalman filter (UKF) - p. 51 11. 3.5.6 Square root unscented Kalman filter (SRUKF) - p. 53 12. 3.5.7 Ensemble Kalman filter - p. 55 13. 3.5.8 Particle filter (PF) - p. 56 14. 3.6 Summary - p. 57 15. Chapter 4. Significance of stochastic and evolutionary optimization methods in advanced process control - p. 61 16. Chapter 5. Advanced process control algorithms - p. 75 17. 5.1 Introduction - p. 75 18. 5.2 Model predictive control - p. 75 19. 5.3 Nonlinear model-based control - p. 91 20. 5.4 Inferential control - p. 96 21. 5.5 Optimal and optimizing control - p. 97 22. 5.6 Intelligent control - p. 100 23. 5.7 Summary - p. 115 24. Chapter 6. Applications of generalized predictive control (GPC) and constrained generalized predictive control (CGPC) - p. 121 25. Chapter 7. Applications of model predictive control (MPC) to nonlinear processes - p. 129 26. 7.1 Introduction - p. 129 27. 7.2 Model predictive control of unstable processes - p. 129 28. 7.3 Model predictive control of ethyl acetate reactive distillation column - p. 132 29. 7.4 Self-adaptive model predictive cascade control of fluidized catalytic cracking unit - p. 136 30. 7.4.1 Brief description of FCCU - p. 136 31. 7.4.2 Mathematical model of FCCU - p. 138 32. 7.4.2.1 Feed and preheat systems - p. 138 33. 7.4.2.2 Reactor - p. 139 34. 7.4.2.3 Regenerator - p. 143 35. 7.4.2.4 Air blowers - p. 147 36. 7.4.2.5 Catalyst circulation - p. 150 37. 7.4.3 FCCU system variables - p. 151 38. 7.4.4 Controller structure - p. 151 39. 7.4.5 Predictive model, model identification, and controller formulation - p. 153 40. 7.4.6 Controller implementation and analysis of results - p. 153 41. 7.5 Summary - p. 157 42. Chapter 8. Applications of nonlinear model predictive control - p. 159 43. Chapter 9. Applications of generic model control - p. 177 44. Chapter 10. Applications of globally linearizing control - p. 191 45. Chapter 11. Applications of nonlinear internal model control - p. 203 46. Chapter 12. Applications of optimal control - p. 213 47. Chapter 13. Applications of optimizing control - p. 231 48. Chapter 14. Applications of inferential control - p. 241 49. Chapter 15. Applications of fuzzy logic control - p. 257 50. 15.1 Introduction - p. 257 51. 15.2 Fuzzy logic–based modeling and control of batch beer fermentation - p. 257 52. 15.3 Fuzzy model–based adaptive predictive control of a batch chemical reactor - p. 265 53. 15.4 Dynamic fuzzy learning control of pH process - p. 273 54. 15.5 Summary - p. 280 55. Chapter 16. Applications of neural network control - p. 283 56. Chapter 17. Applications of radial basis function network control - p. 293 57. Chapter 18. Applications of stochastic optimization-based control strategies - p. 305 58. Chapter 19. Future trends and challenges - p. 325 59. Index - p. 329
