Machine Learning in Chemical Safety and Health: Fundamentals with Applications - Hardcover

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Qingsheng Wang is Associate Professor of Chemical Engineering and George Armistead ‘23 Faculty Fellow at Texas A&M University. He has over 15 years of experience in the areas of process safety and fire protection. His experience is wide ranging, involving machine learning in chemical safety, flame retardant materials, fire and explosion dynamics, and composite manufacturing for safety and sustainability. He is a registered professional engineer (PE) and certified safety professional (CSP), and currently a principal member of the NFPA 18 and NFPA 30 committees. Professor Wang has established the Multiscale Process Safety Laboratory at Texas A&M and is currently leading the lab. He has published over 150 peer-reviewed journal publications and 6 book chapters. His work has been internationally recognized and heavily cited, and he is recognized as a world leader in the field of process safety.

Changjie Cai is Assistant Professor of Occupational and Environmental Health from Hudson College of Public Health at the University of Oklahoma Health Sciences Center. Dr Cai has formed an interdisciplinary research lab focusing on three major areas: (i) Developing portable and cost-effective devices to identify, assess and control the safety and health hazards; (ii) Integrating artificial intelligence techniques into safety and health fields; (iii) Modeling the hazard dispersion and their climate effects using chemical transport models.

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Machine Learning in Chemical Safety and Health

Introduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model Development

There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning implementation in chemical safety and health research and to provide guidance for implementing machine learning techniques and algorithms into chemical safety and health research.

Written by an international team of authors and edited by renowned experts in the areas of process safety and occupational and environmental health, sample topics covered within the work include:

• An introduction to the fundamentals of machine learning, including regression, classification and cross-validation, and an overview of software and tools
• Detailed reviews of various applications in the areas of chemical safety and health, including flammability prediction, consequence prediction, asset integrity management, predictive nanotoxicity and environmental exposure assessment, and more
• Perspective on the possible future development of this field

Machine Learning in Chemical Safety and Health serves as an essential guide on both the fundamentals and applications of machine learning for industry professionals and researchers in the fields of process safety, chemical safety, occupational and environmental health, and industrial hygiene.