Optimal State Estimation for Process Monitoring Fault Diagnosis and Control 1st Edition by Venkateswarlu, Rama Rao Karri ISBN 0323900682 9780323900683

Part I: Basic details and state estimation algorithms

Chapter 1. Optimal state estimation and its importance in process systems engineering

Abstract

1.1 Introduction

1.2 Significance of state estimation

1.3 Role of state estimation in process systems engineering

1.4 Outline of this book

1.5 Summary

Chapter 2. Introduction to stochastic processes and state estimation filtering

Abstract

2.1 Introduction

2.2 Probability and stochastic variables

2.3 Probability distributions and distribution functions

2.4 White Gaussian noise and colored noise

2.5 Stochastic/random processes

2.6 Filtering, estimation, and prediction problem

2.7 Summary

References

Chapter 3. Linear filtering and observation techniques

Abstract

3.1 Introduction

3.2 Representation of a system and associated variables

3.3 Concepts of observability and controllability

3.4 Recursive weighted least squares estimator

3.5 Luenberger observer for state estimation

3.6 Reduced order Luenberger observer for state estimation

3.7 Kalman filter for state estimation

3.8 State estimation applications of linear filtering and observation techniques

3.9 Summary

References

Chapter 4. Mechanistic model-based nonlinear filtering and observation techniques for optimal state/parameter estimation

Abstract

4.1 Introduction

4.2 General nonlinear system and system models

4.3 Observability of nonlinear systems

4.4 Extended Kalman filter

4.5 Steady state extended Kalman filter

4.6 Two-level extended Kalman filter

4.7 Adaptive fading extended Kalman filter

4.8 Unscented Kalman filter

4.9 Square root unscented Kalman filter

4.10 Ensemble Kalman filter

4.11 Particle filter

4.12 Reduced order Luenberger observer

4.13 Reduced order extended Luenberger observer

4.14 Nonlinear observer

4.15 State estimation applications of nonlinear filtering and observation techniques

4.16 Summary

References

Chapter 5. Data-driven modeling techniques for state estimation

Abstract

5.1 Introduction

5.2 Principal component analysis

5.3 Projection to latent structures

5.4 Artificial neural networks

5.5 Radial basis function networks

5.6 Nonlinear iterative partial least squares

5.7 State estimation applications of data-driven modeling techniques

5.8 Summary

References

Chapter 6. Optimal sensor configuration methods for state estimation

Abstract

6.1 Introduction

6.2 Brief review on sensor configuration methods

6.3 Optimal sensor configuration: classical methods

6.4 Optimal sensor configuration: gramian-based methods for linear systems

6.5 Optimal sensor configuration for nonlinear systems

6.6 Summary

References

Part II: Optimal state estimation for process monitoring

Chapter 7. Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in multicomponent batch distillation

Abstract

7.1 Introduction

7.2 Batch distillation process and its dynamic model

7.3 Simplified dynamic model of batch distillation

7.4 The application system

7.5 Measurements configuration for state estimation

7.6 Performance criteria

7.7 Extended Kalman filter for compositions estimation

7.8 Steady state Kalman filter for compositions estimation

7.9 Adaptive fading extended Kalman filter for compositions estimation

7.10 Comparative performance of composition estimators

7.11 Summary

References

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

Abstract

8.1 Introduction

8.2 Reactive batch distillation process and its dynamic model

8.3 Simplified dynamic model of reactive batch distillation

8.4 The application system

8.5 Sensor configuration for state estimation

8.6 Performance criteria

8.7 Extended Kalman filter for compositions estimation

8.8 Summary

References

Chapter 9. Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation in complex nonlinear dynamical systems

Abstract

9.1 Introduction

9.2 Nonlinear dynamical CSTR

9.3 Optimal state estimation in nonlinear dynamical CSTR

9.4 Nonlinear dynamical homopolymerization reactor

9.5 Optimal state estimation in nonlinear dynamical homopolymerization reactor

9.6 Summary

References

Chapter 10. Application of mechanistic model-based nonlinear filtering and observation techniques for optimal state estimation of a kraft pulping digester

Abstract

10.1 Introduction

10.2 Experimental system and dynamic modeling

10.3 Optimal state estimation of kraft pulping digester

10.4 State estimation results

10.5 Summary

References

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

Abstract

11.1 Introduction

11.2 The process and its mathematical model

11.3 Optimal sensor configuration using empirical observability grammians

11.4 State estimator design

11.5 Estimator performance measure for optimality of sensor configuration

11.6 Analysis of results

11.7 Summary

References

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

Abstract

12.1 Introduction

12.2 The process and its mathematical model

12.3 Method of solution

12.4 Results of numerical solution

12.5 Optimal sensor configuration in a catalytic tubular reactor

12.6 Optimal state estimation using unscented Kalman filter

12.7 Summary

References

Chapter 13. Applications of data-driven model-based methods for process state estimation

Abstract

13.1 Introduction

13.2 Projection to latent structures model-based compositions estimator for multicomponent batch distillation

13.3 Artificial neural network model-based compositions estimator for multicomponent batch distillation

13.4 Radial basis function network model-based compositions estimator for multicomponent batch distillation

13.5 NIPALS–RBFN model-based compositions estimator for multicomponent batch distillation

13.6 Summary

References

Part III: Optimal state estimation for process fault diagnosis

Chapter 14. Optimal state and parameter estimation for fault detection and diagnosis in continuous stirred tank reactor

Abstract

14.1 Introduction

14.2 General structure of model-based fault detection and diagnosis

14.3 General process description for fault detection and diagnosis

14.4 Nonlinear CSTR, its mathematical model and fault cases considered

14.5 Method of extended Kalman filter

14.6 Method of reduced order extended Luenberger observer and extended Kalman filter

14.7 Method of two-level extended Kalman filter

14.8 Method of a discrete version of extended Kalman filter and sequential least squares

14.9 Method of discrete version of extended Kalman filter and simultaneous least squares

14.10 Summary

References

Chapter 15. Optimal state and parameter estimation for fault detection and diagnosis of a nonlinear batch beer fermentation process

Abstract

15.1 Introduction

15.2 General structure and general process description for model-based fault detection and diagnosis

15.3 Batch beer fermentation process, its mathematical model and fault cases

15.4 Method of extended Kalman filter

15.5 Method of reduced-order extended Luenberger observer and extended Kalman filter

15.6 Method of two-level extended Kalman filter

15.7 Method of discrete version of extended Kalman filter and sequential least squares

15.8 Method of discrete version of extended Kalman filter and simultaneous least squares

15.9 Summary

References

Chapter 16. Optimal state and parameter estimation for fault detection and diagnosis of a high-dimensional fluid catalytic cracking unit

Abstract

16.1 Introduction

16.2 Process representation

16.3 Fluid catalytic cracking unit

16.4 Mathematical model of fluid catalytic cracking unit

16.5 Fluid catalytic cracking unit system variables

16.6 Fault cases considered in fluid catalytic cracking unit

16.7 Design of discrete version of extended Kalman filter

16.8 Design of unscented Kalman filter

16.9 Analysis of results

16.10 Summary

References

Part IV: Optimal state estimation for process control

Chapter 17. Optimal state estimator-based inferential control of continuous reactive distillation column

Abstract

17.1 Introduction

17.2 Process and the dynamic model

17.3 The process characteristics

17.4 Classical proportional-integral/proportional-integral-derivative controllers for distillation column

17.5 Brief description of genetic algorithms

17.6 Design of genetically tuned proportional-integral controllers

17.7 Design of composition estimator

17.8 Analysis of results

17.9 Summary

References

Chapter 18. Optimal state estimation for nonlinear control of complex dynamic systems

Abstract

18.1 Introduction

18.2 Optimal state estimation and estimator-based control of chaotic chemical reactor

18.3 Optimal state estimation and estimator-based control of homopolymerization reactor

18.4 Summary

References

Chapter 19. Optimal state estimator based control of an exothermic batch chemical reactor

Abstract

19.1 Introduction

19.2 Experimental system and its mathematical model

19.3 State/parameter estimation

19.4 Control algorithms

19.5 Design of estimator based controllers for the esterification reactor

19.6 Analysis of results

19.7 Summary

References

Part V: Optimal state estimation for online optimization

Chapter 20. Optimal state and parameter estimation for online optimization of an uncertain biochemical reactor

Abstract

20.1 Introduction

20.2 The process and its mathematical model

20.3 State and parameter estimation using extended Kalman filter

20.4 State and parameter estimation using two-level extended Kalman filter

20.5 Online optimization problem

20.6 Functional conjugate gradient method

20.7 Extended Kalman filter-assisted online optimizing control of the biochemical reactor

20.8 Two-level extended Kalman filter-assisted online optimizing control strategy

20.9 Summary

References

Chapter 21. Overview, opportunities, challenges, and future directions of state estimation

Abstract

21.1 Overview

21.2 Opportunities

21.3 Challenges

21.4 Future directions

21.5 Summary

Index