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.

介紹機器學習技術和工具，並提供如何將機器學習應用於化學安全和健康相關模型開發的指導。

在化學安全和健康相關模型開發中，應用機器學習算法的興趣日益增長，應用範圍包括性質和毒性預測、後果預測和故障檢測等領域。本書是首部回顧機器學習在化學安全和健康研究中的實施現狀並提供指導的著作。

本書由國際作者團隊撰寫，並由工藝安全、職業與環境健康領域的知名專家編輯，其中涵蓋的主題包括：

- 機器學習基礎知識介紹，包括回歸、分類和交叉驗證，以及軟體和工具概述
- 在化學安全和健康領域的各種應用詳細評論，包括易燃性預測、後果預測、資產完整性管理、預測納米毒性和環境暴露評估等
- 對該領域未來發展的展望

《化學安全和健康中的機器學習》是工業專業人士和工藝安全、化學安全、職業與環境健康以及工業衛生研究人員的重要指南。

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 published 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, the Occupational and Environmental Health & Artificial Intelligence (OEH-AI) Lab. Research in the lab focuses on three major areas: (1) identifying and assessing exposure of safety and health hazards; (2) integrating AI techniques into occupational and environmental health fields; (3) studying the environmental hazards and their climate effects using regional chemical transport models.

Qingsheng Wang是德克薩斯農工大學的化學工程副教授和George Armistead '23教職員。他在過程安全和防火保護領域擁有超過15年的經驗。他的經驗涵蓋範圍廣泛，包括化學安全中的機器學習、阻燃材料、火災和爆炸動力學，以及安全和可持續性的複合材料製造。他是一名註冊專業工程師（PE）和認證安全專業人員（CSP），目前是NFPA 18和NFPA 30委員會的主要成員。王教授在德克薩斯農工大學建立了多尺度過程安全實驗室，並目前領導該實驗室。他發表了150篇同行評審的期刊論文和6章書籍。他的工作在國際上得到了認可並且被廣泛引用，被譽為過程安全領域的世界領導者。

Changjie Cai是俄克拉荷馬大學健康科學中心Hudson公共衛生學院的職業與環境健康助理教授。蔡博士成立了一個跨學科研究實驗室，名為職業與環境健康與人工智能（OEH-AI）實驗室。該實驗室的研究重點包括三個主要領域：（1）識別和評估安全和健康危害的暴露；（2）將人工智能技術應用於職業和環境健康領域；（3）使用區域化學傳輸模型研究環境危害及其氣候效應。