Elsevier Artificial Intelligence in Safety Science and Engineering

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