Development of machine learning based prediction models for hazardous properties of chemical mixtures |
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| Institution: | 1. School of Chemistry and Materials Engineering, Changshu Institute of Technology, Changshu, Jiangsu, 215500, China;2. Center for Corporate Sustainability (CEDON), KU Leuven, Brussels, 1000, Belgium;3. School of Water Conservancy & Environment Engineering, Changchun Institute of Technology, Changchun, Jilin, 130000, China;4. College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, 210009, China;5. Faculty of Applied Economics, Antwerp Research Group on Safety and Security (ARGoSS), University of Antwerp, Antwerp, 2000, Belgium;6. Faculty of Technology, Policy and Management, Safety and Security Science Group (S3G), TU Delft, Delft, 2628, the Netherlands;1. Deanship of Graduate Studies, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;2. Physics Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia;3. Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, 342111, Ondo State, Nigeria;4. Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;5. Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria;6. Computer Information System Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31433, Saudi Arabia;1. Mary Kay O’Connor Process Safety Center, Texas A&M University, College Station, TX 77843, United States;2. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United States |
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| Abstract: | Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis. |
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| Keywords: | Machine learning Flammability limit Auto-ignition temperature Flash point Quantitative structure-property relationship |
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