Advances in Data-Driven Modeling, Fault Detection, and Fault Identification: Applications to Chemical Processes

by Mohamed N. Nounou, Hazem N. Nounou, Nour Basha MS, Byanne Malluhi MS

Overview

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:

- 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 scenariosWhether a researcher or practitioner, this book is an essential resource for leveraging data-driven approaches in chemical engineering fields.

- 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

Product Details

ISBN-13:	9780443334832
Publisher:	Elsevier Science
Publication date:	11/01/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	500
File size:	23 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. Mohamed Nounou is a professor of Chemical Engineering in the College of Science and Engineering at Hamad Bin Khalifa University (HBKU). 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.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). 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.

Table of Contents

1. Introduction2. Modeling3. Fault detection4. Fault identification5. Appendix

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From the Publisher

A comprehensive and easy-to-use guide to essential data-driven modeling techniques in statistical data analysis, featuring methods like principal component analysis, Gaussian processes, and neural networks