Outlines the fundamentals of machine/deep learning methods applicable to process safetyDetails advanced machine and deep learning methodologies for risk assessment and management in chemical process engineeringOffer theoretical and.

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Bibliographic Details

Title: Smart Technologies and Artificial Intelligence in Safety Engineering
Publisher: Elsevier Science
Publication Date: 2026
Language: English
ISBN 10: 0443363420
ISBN 13: 9780443363429
Binding: Soft cover
Condition: New

About this title

Synopsis

Artificial Intelligence in Safety Science and Engineering serves as a comprehensive guide to the latest Artificial Intelligence (AI) research and the ways in which it can be applied to multiple crucial topics in process engineering. This book couples a strong theoretical overview and real-world case studies and methodology details, to help with experimental design and troubleshooting when implementing smart technologies and AI. The book covers the latest applications of smart technologies and AI as they can be applied to many prominent topics in the fields of process safety, such as the fundamental of AI technologies, the advanced methods of machine learning, the application to process accidental modeling and analysis, the application to process risk assessment, the application to process fault diagnosis and prognosis, the application to predictive maintenance, the application to process emergency response optimization, the application to simulation and training and the application to process incident prediction and prevention.

Artificial Intelligence in Safety Science and Engineering will be a valuable resource for academics, students and researchers working in the process safety domain, particularly those working with chemical processes at the industrial level. This book will also be a useful guide for government policy makers and decision makers in industry focusing on process safety regulation, standards, legislation and risk management.

Outlines the fundamentals of machine/deep learning methods applicable to process safety
Details advanced machine and deep learning methodologies for risk assessment and management in chemical process engineering
Offer theoretical and practical advice for using smart technology for process fault diagnosis and prognosis
Discusses recent advances in smart AI techniques to process safety, as applied to industrial chemistry and chemical processes
Covers applications of smart technology and AI in human behavior analysis for risk analysis

About the Authors

Xinyan Huang is an Associate Professor of Dept of Building Environment and Energy Engineering at The Hong Kong Polytechnic University. He received his PhD from Imperial College London, MSc from UC San Diego, and BEng from Southeast University, China. Before joining PolyU, he was a Postdoc and Lecturer at the UC Berkeley, where he explored the Microgravity Fire Safety for the International Space Station with NASA. Dr Huang is a Combustion Scientist and a Fire Safety Engineer who has co-authored 180+ journal papers, edited one book, and supervised over 10 Postdoc and 20 PhD students. He is a board member of Int. Association for Fire Safety Science (IAFSS) and Int. Association of Wildland Fire (IAWF), an Associate Editor of Fire Technology and International Journal of Wildland Fire, an editorial member of J. Building Engineering, Fire Safety J. and Fire and Materials, a Chartered Building Services and Fire Engineer, a committee member for HK Fire Safety Code, and High Court Fire Expert. He is a winner of the NSFC Excellent Young Scientists Fund, Bernard Lewis Fellowship and Best Paper Award from Combustion Institute, IAFSS Early Career Award, Ricardo Award from Institute of Physics, HKIE Fire Engineering Grand Award, “5 under 35� and Bono Award from Society of Fire Protection Engineers (SFPE).

Jihao Shi is a Research Assistant Professor of Dept of Building Environment and Energy Engineering at The Hong Kong Polytechnic University. received his BEng (First Class) degree and PhD degree from China University of Petroleum in 2013, and 2018, respectively. Dr Shi was also a China Scholarship Council (CSC) visiting PhD student of Curtin University in Australia from 2016 to 2018. Before joining PolyU, he worked as an Associate Professor and Postdoctoral Researcher at China University of Petroleum. Dr Shi’s research interests focus on physics-based AI for process safety management. Based on these research topics, Dr Shi has published 50+ SCI journal papers including 31 papers as the first/corresponding author. His works were funded by National Key R&D Program of China, NSFC and various industrial partners etc. Dr Shi is an associate editor of Petroleum science and served as the chief Guest Editors of Reliability Engineering & System Safety, Process Safety and Environmental Protection. He is also a winner of the Best PhD Thesis of Safety Science and Engineering in China.

Ming Yang is an Assistant Professor in the section of Safety and Security Science at Delft University of Technology. His scientific work evolves around reliability, risk, and resilience modelling to support safety and security-related decision-making problems in the process industries and offshore operations. He holds a B.Eng in Chemical and Material Engineering and minor in Management Science, a MSc. in Environmental System Engineering, and a PhD in Oil and Gas Engineering. He is Associate Editor of ACS Journal of Chemical Health and Safety, Subject Editor of Process Safety and Environmental Protection, Safety in Extreme Environments, Editorial Board Member of Journal of Loss Prevention in the Process Industries, Engineering Applications of Artificial Intelligence, and Hygiene and Environmental Health Advances.

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