Machine Learning in Chemical Safety and Health - Fundamentals With Applications

Informationen zum Autor 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. Klappentext Introduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model DevelopmentThere 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 fieldMachine 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. Zusammenfassung Introduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model DevelopmentThere 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, sam...

List of contents

List of Contributors xiii
 
Preface xvii
 
1 Introduction 1
 
Pingfan Hu and Qingsheng Wang
 
1.1 Background 2
 
1.2 Current State 5
 
1.2.1 Flammability Characteristics Prediction Using Quantitative Structure-Property
 
Relationship 5
 
1.2.2 Consequence Prediction Using Quantitative Property-Consequence
 
Relationship 6
 
1.2.3 Machine Learning in Process Safety and Asset Integrity Management 6
 
1.2.4 Machine Learning for Process Fault Detection and Diagnosis 7
 
1.2.5 Intelligent Method for Chemical Emission Source Identification 7
 
1.2.6 Machine Learning and Deep Learning Applications in Medical Image Analysis 7
 
1.2.7 Predictive Nanotoxicology: Nanoinformatics Approach to Toxicity Analysis of
 
Nanomaterials 8
 
1.2.8 Machine Learning in Environmental Exposure Assessment 8
 
1.2.9 Air Quality Prediction Using Machine Learning 8
 
1.3 Software and Tools 9
 
1.3.1 R 9
 
1.3.2 Python 12
 
References 13
 
2 Machine Learning Fundamentals 19
 
Yan Yan
 
2.1 What Is Learning? 19
 
2.1.1 Machine Learning Applications and Examples 20
 
2.1.2 Machine Learning Tasks 21
 
2.2 Concepts of Machine Learning 22
 
2.3 Machine Learning Paradigms 24
 
2.4 Probably Approximately Correct Learning 25
 
2.4.1 Deterministic Setting 26
 
2.4.2 Stochastic Setting 29
 
v
 
0005453285.3D 5 30/8/2022 8:51:33 PM
 
2.5 Estimation and Approximation 31
 
2.6 Empirical Risk Minimization 32
 
2.6.1 Empirical Risk Minimizer 32
 
2.6.2 VC-dimension Generalization Bound 33
 
2.6.3 General Loss Functions 34
 
2.7 Regularization 35
 
2.7.1 Regularized Loss Minimization 35
 
2.7.2 Constrained and Regularized Problem 36
 
2.7.3 Trade-off Between Estimation and Approximation Error 37
 
2.8 Maximum Likelihood Principle 38
 
2.8.1 Maximum Likelihood Estimation 39
 
2.8.2 Cross Entropy Minimization 40
 
2.9 Optimization 41
 
2.9.1 Linear Regression: An Example 42
 
2.9.2 Closed-form Solution 42
 
2.9.3 Gradient Descent 43
 
2.9.4 Stochastic Gradient Descent 45
 
References 46
 
3 Flammability Characteristics Prediction Using QSPR Modeling 47
 
Yong Pan and Juncheng Jiang
 
3.1 Introduction 47
 
3.1.1 Flammability Characteristics 47
 
3.1.2 QSPR Application 48
 
3.1.2.1 Concept of QSPR 48
 
3.1.2.2 Trends and Characteristics of QSPR 48
 
3.2 Flowchart for Flammability Characteristics Prediction 49
 
3.2.1 Dataset Preparation 51
 
3.2.2 Structure Input and Molecular Simulation 52
 
3.2.3 Calculation of Molecular Descriptors 53
 
3.2.4 Preliminary Screening of Molecular Descriptors 54
 
3.2.5 Descriptor Selection and Modeling 55
 
3.2.6 Model Validation 57
 
3.2.6.1 Model Fitting Ability Evaluation 57
 
3.2.6.2 Model Stability Analysis 59
 
3.2.6.3 Model Predictivity Evaluation 60
 
3.2.7 Model Mechanism Explanation 61
 
3.2.8 Summary of QSPR Process 61
 
3.3 QSPR Review for Flammability Characteristics 62
 
3.3.1 Flammability Limits 62
 
3.3.1.1 LFLT and LFL 62
 
3.3.1.2 UFLT and UFL 64
 
3.3.2 Flash Point 65
 
3.3.3 Auto-ignition Temperature 68
 
3.3.4 Heat of Combustion 69
 
vi Contents
 
0005453285.3D 6 30/8/2022 8:51:33 PM
 
3.3.5 Minimum Ignition Energy 70
 
3.3.6 Gas-liquid Critical Tem

Product details

• Authors: Qingsheng Cai Wang
• Assisted by: Cai (Editor), Changjie Cai (Editor), Qingsheng Wang (Editor)
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 01.12.2022
• EAN: 9781119817482
• ISBN: 978-1-119-81748-2
• No. of pages: 320
• Subjects: Natural sciences, medicine, IT, technology > Chemistry > Miscellaneous; Chemie, Arbeitssicherheit, Informatik, Künstliche Intelligenz, Maschinelles Lernen, Artificial Intelligence, computer science, chemistry, Chemical Engineering, chemische Verfahrenstechnik, Chemical and Environmental Health and Safety, Arbeitssicherheit u. Umweltschutz i. d. Chemie, Prozesssicherheit, Process Safety