Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control

1st Edition - January 31, 2022
Authors: Ch. Venkateswarlu, Rama Rao Karri
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 8 7 8 - 6
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 0 6 8 - 3

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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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.

Cover image
Title page
Table of Contents
Copyright
About the authors
Preface
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

Ch. Venkateswarlu

Dr. Ch. Venkateswarlu M.Tech., Ph. D, has formerly worked as Scientist, Senior Principal Scientist and Chief Scientist at Indian Institute of Chemical Technology (IICT), Hyderabad, a premier research and development (R&D) institute of Council of Scientific and Industrial Research (CSIR), India. Later, he worked as Director R&D at BV Raju Institute of Technology (BVRIT), Narsapur, Greater Hyderabad. Prior to Director R&D at BVRIT, he worked as Professor, Principal and Head of Chemical Engineering Department of the same institute. He did his graduation from Andhra University as well as from Indian Institute of Chemical Engineers, and post-graduation and Ph. D in Chemical Engineering from Osmania University, Hyderabad, India. He holds 35 years R&D and industry experience along with 20 years teaching experience. His research interests lie in the areas of conventional process control & advanced process control, dynamic process modelling & simulation, process identification & dynamic optimization, process monitoring & fault diagnosis, state estimation & soft sensing, applied engineering mathematics & evolutionary computing, artificial intelligence & expert systems, and bioprocess engineering & bio-informatics. He published more than 120 research papers in peer journals of repute along with few international and national proceeding publications. He is also credited with 150 technical paper presentations and invited lectures. He authored two books published by Elsevier along with few book chapters. He is also in editorial boards of few international journals. He has executed several R&D projects sponsored by DST and Industry. He is a reviewer of several international research journals and many national and international research project proposals. He has guided several postgraduate and Ph. D students. He served as a long-term guest faculty for premier institutes like Bhaba Atomic Research Centre Scientific Officers Training, BITS Pilani MS (off-campus) and IICT-CDAC Bioinformatics Programs. He is a Fellow of Andhra Pradesh Akademi of Sciences and Telangana State Academy of Sciences.

Affiliations and expertise

Chief Scientist (Retd.), Indian Institute of Chemical Technology, Tarnaka, Hyderabad, Telangana, India

Rama Rao Karri

Dr. Rama Rao Karri is a Professor (Sr. Asst) in the Faculty of Engineering, Universiti Teknologi Brunei, Brunei Darussalam. He has a Ph.D. from the Indian Institute of Technology (IIT) Delhi, Master’s from IIT Kanpur in Chemical Engineering. He has worked as a Post-Doctoral research fellow at NUS, Singapore for about six years and has over 18 years of working experience in Academics, Industry, and Research. He has experience of working in multidisciplinary fields and has expertise in various evolutionary optimization techniques and process modelling. He has published 150+ research articles in reputed journals, book chapters, and conference proceedings with a combined Impact factor of 611.43 and has an h-index of 28 (Scopus - citations: 2600+) and 27 (Google Scholar -citations: 3000+). He is an editorial board member in 10 renowned journals and a peer-review member for more than 93 reputed journals and has peer-reviewed more than 410 articles. Also, he handled 112 articles as an editor. He also has the distinction of being listed in the top 2% of the world’s most influential scientists in the area of environmental sciences and chemicals for the Years 2021 & 2022. The List of the Top 2% of Scientists in the World compiled and published by Stanford University is based on their international scientific publications, the number of scientific citations for research, and participation in the review and editing of scientific research. He held a position as Editor-in-Chief (2019-2021) in the International Journal of Chemoinformatics and Chemical Engineering, IGI Global, USA. He is also an Associate editor in Scientific Reports, Springer Nature & International Journal of Energy and Water Resources (IJEWR), Springer Inc. He is also a Managing Guest editor for Spl. Issues: 1) “Magnetic nanocomposites and emerging applications", in Journal of Environmental Chemical Engineering (IF: 5.909), 2) “Novel CoronaVirus (COVID-19) in Environmental Engineering Perspective", in Journal of Environmental Science and Pollution Research (IF: 4.223), Springer. 3) “Nanocomposites for the Sustainable Environment”, in Applied Sciences Journal (IF: 2.679), MDPI. He along with his mentor, Prof. Venkateswarlu is authoring an Elsevier book, “Optimal state estimation for process monitoring, diagnosis, and control”. He is also co-editor and managing editor for 8 Elsevier, 1 Springer and 1 CRC edited books. Elsevier: 1) Sustainable Nanotechnology for Environmental Remediation, 2) Soft computing techniques in solid waste and wastewater management, 3) Green technologies for the defluoridation of water, 4) Environmental and health management of novel coronavirus disease (COVID-19), 5) Pesticides remediation technologies from water and wastewater: Health effects and environmental remediation, 6) Hybrid Nanomaterials for Sustainable Applications, 7) Sustainable materials for sensing and remediation of noxious pollutants. Springer: 1) Industrial wastewater treatment using emerging technologies for sustainability. CRC: 1) Recent Trends in Advanced Oxidation Processes (AOPs) for micro-pollutant removal.

Affiliations and expertise

Senior Assistant Professor, Universiti Teknologi Brunei, Brunei Darussalam

Life sciences

Physical sciences & Engineering

Social sciences & Humanities

Health