Advances in Data-Driven Modeling, Fault Detection, and Fault Identification

Advances in Data-Driven Modeling, Fault Detection, and Fault IdentificationApplications to Chemical Processes\nAuthor(s): Mohamed N. Nounou, Hazem N. Nounou, Nour Basha, Byanne Malluhi\nFormat: Paperback\nPublisher: Elsevier - Health Sciences Division, United States\nImprint: Elsevier - Health Sciences Division\nISBN-13: 9780443334825, 978-0443334825\nSynopsis\nAdvances 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.\n\nThis 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 an.