Advances in Data-Driven Modeling, Fault Detection, and Fault Identification
Applications to Chemical Processes
- 1st Edition - November 1, 2025
- Authors: Mohamed N. Nounou, Hazem N. Nounou, Nour Basha, Byanne Malluhi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 4 8 2 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 4 8 3 - 2
Advances in Data-Driven Modeling, Fault Detection, and Fault Identification: Applications to Chemical Processes presents a comprehensive collection of research focused on data-d… Read more
Advances in Data-Driven Modeling, Fault Detection, and Fault Identification: Applications to Chemical Processes presents a comprehensive collection of research focused on data-driven modeling techniques for robust modeling, fault detection, and fault identification in chemical processes.
This accessible guide caters to both academic and industrial researchers seeking to enhance their work with data-driven methodologies. The book begins with an overview of key methods, emphasizing their significance in research and industry applications. Chapters delve into various chemical processes, such as the Tennessee Eastman Process and a Fischer-Tropsch bench scale setup, to validate and compare the discussed techniques. The content is organized into three main categories:
This accessible guide caters to both academic and industrial researchers seeking to enhance their work with data-driven methodologies. The book begins with an overview of key methods, emphasizing their significance in research and industry applications. Chapters delve into various chemical processes, such as the Tennessee Eastman Process and a Fischer-Tropsch bench scale setup, to validate and compare the discussed techniques. The content is organized into three main categories:
- Basic and advanced robust empirical techniques
- Prominent empirical statistical charts for detecting faults in multivariate systems
- Conventional and novel, multiclass classification, machine-learning techniques for accurately distinguishing between different fault types in batch or real-time scenarios
- Seamlessly bridges the gap between experts and beginners by offering in-depth mathematical formulations for advanced users and simplified explanations for newcomers, ensuring clarity and comprehension for all
- Offers step-by-step instructions for optimizing and tuning empirical methods toward specific goals, enabling users to replicate and validate results effectively
- Delivers targeted advice on the optimal use of each technique, empowering users to quickly harness the full potential of data-driven methods without the need for trial and error
Academic researchers, practicing (chemical) engineers, and senior/graduate students in systems engineering, control engineering, or process systems engineering
1. Introduction
2. Modeling
3. Fault detection
4. Fault identification
5. Appendix
2. Modeling
3. Fault detection
4. Fault identification
5. Appendix
- Edition: 1
- Published: November 1, 2025
- Language: English
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Mohamed N. Nounou
Dr. Mohamed Nounou is a Professor of Chemical Engineering in the College of Science and Engineering at Hamad Bin Khalifa University (HBKU), in Qatar. Prior to joining HBKU, he was with Texas A&M University at Qatar (TAMUQ) for 18 years. He received his B.S. degree (Magna Cum Laude) from Texas A&M University, College Station in 1995, and his M.S. and Ph.D. degrees from the Ohio State University in 1997 and 2000, respectively, all in chemical engineering. Then, he joined PDF Solutions, Inc. until 2002 when he joined the chemical and petroleum engineering department at the United Arab Emirates University (UAEU). Then, he joined TAMUQ in 2006, where he made significant contributions and received numerous awards at the college and university levels. In teaching, he received the AFS College-level and University-level Distinguished Achievement Teaching Awards in 2011 and 2012, respectively. He also received the “TAMUQ Faculty Excellence Award” in 2013. He later received the “Chemical Engineering Faculty of the Year Award” five times in 2013, 2014, 2016, 2017, 2018, and 2021, and the “Student Government Association CHEN Educator of the Year Award” in 2016. In service, Dr. Nounou was appointed a chair of the “Aggie Life” program, which is a university-wide initiative that aims at enhancing the college life experience of freshman students at TAMUQ. He has been chairing student advising since joining TAMUQ and also chaired the ABET Committee during the first accreditation cycle in 2008, where the Program received full accreditation, and he received a recognition award for this role. He also helped establish a chapter of AIChE at TAMUQ in 2007, and later established and advised the Gamma Delta chapter of the Omega Chi Epsilon honor society at TAMUQ, the first chapter outside the US. As a result, he received the “Student Organization Advisor of the Year Award” in 2014 and 2016. Dr. Nounou is also an accomplished researcher in the area process systems engineering. He received research funding over $5M and published a book and more than 200 journal and conference papers and book chapters. He served as an associated editor for several journals. He is an associate editor for the "Network Modeling Analysis in Health Informatics and Bioinformatics". He is a senior member of AIChE and IEEE.
Affiliations and expertise
College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarHN
Hazem N. Nounou
Dr. Hazem N. Nounou is a Professor and Program Chair of the Electrical Engineering Program in the College of Science and Engineering at Hamad Bin Khalifa University (HBKU), in Qatar. Prior to joining HBKU, he was the Senior Associate Dean for Undergraduate Studies and Student Success and Professor of Electrical and Computer Engineering at Texas A&M University at Qatar. He received the B.S. degree (Magna Cum Laude) from Texas A&M University, College Station, in 1995, and the M.S. and Ph.D. degrees from Ohio State University, Columbus, in 1997 and 2000, respectively, all in electrical engineering. In 2001, he was a Development Engineer for PDF Solutions, a consulting firm for the semiconductor industry, in San Jose, CA. Then, in 2001, he joined the Department of Electrical Engineering at King Fahd University of Petroleum and Minerals in Dhahran, Saudi Arabia, as an Assistant Professor. In 2002, he moved to the Department of Electrical Engineering, United Arab Emirates University, Al-Ain, UAE. In 2007, he joined the Electrical and Computer Engineering Program at Texas A&M University at Qatar, Doha, Qatar. He was the holder of Itochu Professorship from 2015-2017. He published more than 200 refereed journal and conference papers and book chapters. He served as an Associate Editor and in technical committees of several international journals and conferences. His research interests include data-based control, intelligent and adaptive control, control of time-delay systems, and system monitoring, identification and estimation. Dr. Nounou is a senior member of IEEE.
Affiliations and expertise
College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarNB
Nour Basha
Dr. Nour Basha is a currently a postdoctoral researcher in the college of science and engineering at Hamad Bin Khalifa University (HBKU). He completed his Ph.D. degree in chemical engineering from Texas A&M University at Qatar (TAMUQ) in 2024. Prior to his Ph.D., he competed his M.S. degree in chemical engineering and his B.S. degree in electrical engineering all from TAMUQ. His research interests include process modeling and monitoring, fault detection, fault classification, and fuzzy control.
Affiliations and expertise
College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarBM
Byanne Malluhi
Byanne Malluhi is a Ph.D. student in chemical engineering at Texas A&M University (TAMU). She holds both a B.S. and an M.S. in chemical engineering from Texas A&M University at Qatar (TAMUQ). She briefly worked as an associate process engineer in McDermott from Oct 2019 to May 2020. Her research interests in the process system engineering field include process modeling, monitoring, control, and data analytics.
Affiliations and expertise
Texas A&M University, Qatar