Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control
- 1st Edition - January 31, 2022
- Latest edition
- Authors: Ch. Venkateswarlu, Rama Rao Karri
- Language: English
Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control presents various mechanistic model based state estimators and data-driven model based state estimator… Read more
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Description
Description
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.
Key features
Key features
- Describes various classical and advanced versions of mechanistic model based state estimation algorithms
- Describes various data-driven model based state estimation techniques
- Highlights a number of real applications of mechanistic model based and data-driven model based state estimators/soft sensors
- Beneficial to those associated with process monitoring, fault diagnosis, online optimization, control and related areas
Readership
Readership
Engineers, researchers and scientists working in academic institutes, R&D establishments and industries. Engineers, researchers and scientists working with process systems engineering field-oriented areas such as process monitoring, diagnosis, control and online optimization of chemical and biochemical processes
Table of contents
Table of contents
Part I - BASIC DETAILS AND STATE ESTIMATION ALGORITHMS
1. Optimal state estimation and its importance in process systems engineering
2. Stochastic process and filtering theory
3. Linear filtering and observation techniques with examples
3.1. Introduction
3.2. Kalman filter
3.3. Luenberger observer
3.4. Examples
3.5. Summary
4. Mechanistic model-based nonlinear filtering and observation techniques for state estimation
4.1. Introduction
4.2. Extended Kalman filter.
4.3. Steady-state Kalman filter.
4.4. Adaptive fading Kalman filter.
4.5. Unscented Kalman filter
4.6. Square root unscented Kalman filter
4.7. Nonlinear observer
4.8. Summary
5. Data-driven modelling techniques for state estimation
5.1. Introduction
5.2. Principal component analysis (PCA)
5.3. Projection to latent structures (PLS)
5.4. Nonlinear iterative partial least squares (NIPALS)
5.5. Artificial neural networks (ANN)
5.6. Radial basis function networks (RBFN)
5.7. Summary
6. Optimal sensor configuration methods for state estimation
6.1. Introduction
6.2. Sensitivity matrix
6.3. Singular value decomposition
6.4. Principal component analysis
6.5. Observability grammians
6.6. Summary
Part II - APPLICATION OF MECHANISTIC MODEL-BASED NONLINEAR FILTERING AND OBSERVATION TECHNIQUES FOR STATE ESTIMATION IN CHEMICAL PROCESSES
7. Optimal state estimation in multicomponent batch distillation
7.1. Introduction
7.2. Extended Kalman filter for state estimation in multicomponent batch distillation
7.3. Steady-state Kalman filter for state estimation in multicomponent batch distillation
7.4. Adaptive fading Kalman filter for state estimation in multicomponent batch distillation
7.5. Summary
8. Optimal state estimation in multicomponent reactive batch distillation with optimal sensor configuration
8.1. Introduction
8.2. Optimal sensor configuration in a reactive distillation column
8.3. The method of extended Kalman filter for compositions estimation
8.4. Summary
9. Optimal state estimation in complex nonlinear dynamical systems
9.1. Introduction
9.2. State estimation of a nonlinear dynamical polymerization CSTR using extended Kalman filter.
9.3. State estimation of a nonlinear dynamical polymerization reactor using an extended Kalman filter.
9.4. Summary
10. Optimal state estimation of a kraft pulping digester
10.1. Introduction
10.2. Optimal state estimation of a kraft pulping digester using extended Kalman filter.
10.3. Optimal state estimation of a kraft pulping digester using a nonlinear observer.
10.4. Summary
11. Optimal State Estimation of a High Dimensional Fluid Catalytic Cracking Unit
11.1. Introduction
11.2. Optimal State Estimation using extended Kalman filter
11.3. Optimal State Estimation using unscented Kalman filter
11.4. Optimal State Estimation using square root unscented Kalman filter
11.5. Summary
12. Optimal state estimation of continuous distillation column with optimal sensor configuration
12.1. Introduction
12.2. Optimal sensor configuration based on observability grammians
12.3. The method of extended Kalman filter for compositions estimation
12.4. Summary
13. Optimal state and parameter estimation in nonlinear CSTR
13.1. Introduction
13.2. State and parameter estimation using the method of extended Kalman filter
13.3. State and parameter estimation using the method of reduced order extended Luenberger observer and extended Kalman filter
13.4. State and parameter estimation using the method of extended Kalman filter and simultaneous least squares
13.5. State and parameter estimation using the method of extended Kalman filter and sequential least squares.
13.6. State and parameter estimation using the method of two-level extended Kalman filter
13.7. Summary
Part III - APPLICATION OF QUANTITATIVE MODEL-BASED NONLINEAR FILTERING AND OBSERVATION TECHNIQUES FOR STATE ESTIMATION IN BIOCHEMICAL PROCESSES
14. Optimal state and parameter estimation in the nonlinear batch beer fermentation process
14.1. Introduction
14.2. State and parameter estimation of nonlinear using the method of extended Kalman filter
14.3. State and parameter estimation using the method of extended Kalman filter and simultaneous least squares
14.4. State and parameter estimation using the method of extended Kalman filter and sequential least squares.
14.5. State and parameter estimation using the method of two-level extended Kalman filter
14.6. Summary
15. Optimal state and parameter estimation for online optimization of an uncertain biochemical reactor
15.1. Introduction
15.2. Optimal state and parameter estimation using extended Kalman filter
15.3. Optimal state and parameter estimation using two-level extended Kalman filter
15.4. Summary
Part IV - APPLICATION OF DATA-DRIVEN MODEL-BASED TECHNIQUES FOR STATE ESTIMATION IN CHEMICAL PROCESSES
16. Data-driven methods for state estimation in multi-component batch distillation
16.1. Introduction
16.2. Partial least squares model-based estimator for estimation of compositions
16.3. ANN model-based estimator for estimation of compositions
16.4. RBFN model-based estimator for estimation of compositions
16.5. NIPALS-RBFN model-based estimator for estimation of compositions
16.6. Summary
17. Hybrid schemes for state estimation
18. Future development, prospective and challenges in the application of soft sensors in industrial applications
Product details
Product details
- Edition: 1
- Latest edition
- Published: January 31, 2022
- Language: English
About the authors
About the authors
CV
Ch. Venkateswarlu
RK