Machine Learning in Chemical Safety and Health: Fundamentals with Applications

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.