Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control

Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control

1st Edition - January 31, 2022

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  • Authors: Ch. Venkateswarlu, Rama Rao Karri
  • eBook ISBN: 9780323900683
  • Paperback ISBN: 9780323858786

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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

  • 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

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

  • 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: Application of data driven model-based methods for process state estimation
  • 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

Product details

  • No. of pages: 366
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: January 31, 2022
  • Imprint: Elsevier
  • eBook ISBN: 9780323900683
  • Paperback ISBN: 9780323858786

About the Authors

Ch. Venkateswarlu

Dr. Ch. Venkateswarlu, 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. Recently, he retired as the 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 dynamic process modelling & simulation, process identification & dynamic optimization, process monitoring & fault diagnosis, state estimation & soft sensing, statistical process control & advanced process control, 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 a book 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. He received various awards in recognition to his R&D and academic contributions.

Affiliations and Expertise

Chief Scientist (Retd.), Indian Institute of Chemical Technology, (CSIR-IICT), Hyderabad, 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

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