Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control
- 1st Edition - February 4, 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
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.
- 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
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
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
- Edition: 1
- Published: February 4, 2022
- Language: English
CV
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, IndiaRK
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 DarussalamRead Optimal State Estimation for Process Monitoring, Fault Diagnosis and Control on ScienceDirect