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.
1. Chapter 2 : Introduction to stochastic processes and state estimation filtering - p. Page 4 2. Chapter 3 : Linear filtering and observation techniques - p. Page 20 3. Chapter 4 : Mechanistic model-based nonlinear filtering and observation techniques for optimal state/parameter estimation - p. Page 56 4. Chapter 5 : Data-driven modeling techniques for state estimation - p. Page 88 5. Chapter 6 : Optimal sensor configuration methods for state estimation - p. Page 110 6. Chapter 7 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in multicomponent batch distillation - p. Page 121 7. Chapter 8 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in multicomponent reactive batch distillation with optimal sensor configuration - p. Page 140 8. Chapter 9 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in complex nonlinear dynamical systems - p. Page 161 9. Chapter 10 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation of a kraft pulping digester - p. Page 183 10. Chapter 11 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation of a continuous reactive distillation column with optimal sensor configuration - p. Page 198 11. Chapter 12 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation of a catalytic tubular reactor with optimal sensor configuration - p. Page 215 12. Chapter 13 : Applications of data-driven model-based methods for process state estimation - p. Page 229 13. Chapter 14 : Optimal state and parameter estimation for fault detection and diagnosis in continuous stirred tank reactor - p. Page 246 14. Chapter 15 : Optimal state and parameter estimation for fault detection and diagnosis of a nonlinear batch beer fermentation process - p. Page 270 15. Chapter 16 : Optimal state and parameter estimation for fault detection and diagnosis of a high-dimensional fluid catalytic cracking unit - p. Page 287 16. Chapter 17 : Optimal state estimator-based inferential control of continuous reactive distillation column - p. Page 311 17. Chapter 18 : Optimal state estimation for nonlinear control of complex dynamic systems - p. Page 330 18. Chapter 19 : Optimal state estimator based control of an exothermic batch chemical reactor - p. Page 342 19. Chapter 20 : Optimal state and parameter estimation for online optimization of an uncertain biochemical reactor - p. Page 357 20. Chapter 21 : Overview, opportunities, challenges, and future directions of state estimation - p. Page 371 21. Index - p. Page 375
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.
1. Chapter 2 : Introduction to stochastic processes and state estimation filtering - p. Page 4 2. Chapter 3 : Linear filtering and observation techniques - p. Page 20 3. Chapter 4 : Mechanistic model-based nonlinear filtering and observation techniques for optimal state/parameter estimation - p. Page 56 4. Chapter 5 : Data-driven modeling techniques for state estimation - p. Page 88 5. Chapter 6 : Optimal sensor configuration methods for state estimation - p. Page 110 6. Chapter 7 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in multicomponent batch distillation - p. Page 121 7. Chapter 8 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in multicomponent reactive batch distillation with optimal sensor configuration - p. Page 140 8. Chapter 9 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in complex nonlinear dynamical systems - p. Page 161 9. Chapter 10 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation of a kraft pulping digester - p. Page 183 10. Chapter 11 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation of a continuous reactive distillation column with optimal sensor configuration - p. Page 198 11. Chapter 12 : Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation of a catalytic tubular reactor with optimal sensor configuration - p. Page 215 12. Chapter 13 : Applications of data-driven model-based methods for process state estimation - p. Page 229 13. Chapter 14 : Optimal state and parameter estimation for fault detection and diagnosis in continuous stirred tank reactor - p. Page 246 14. Chapter 15 : Optimal state and parameter estimation for fault detection and diagnosis of a nonlinear batch beer fermentation process - p. Page 270 15. Chapter 16 : Optimal state and parameter estimation for fault detection and diagnosis of a high-dimensional fluid catalytic cracking unit - p. Page 287 16. Chapter 17 : Optimal state estimator-based inferential control of continuous reactive distillation column - p. Page 311 17. Chapter 18 : Optimal state estimation for nonlinear control of complex dynamic systems - p. Page 330 18. Chapter 19 : Optimal state estimator based control of an exothermic batch chemical reactor - p. Page 342 19. Chapter 20 : Optimal state and parameter estimation for online optimization of an uncertain biochemical reactor - p. Page 357 20. Chapter 21 : Overview, opportunities, challenges, and future directions of state estimation - p. Page 371 21. Index - p. Page 375
