Analysis on causes of chemical industry accident from 2015 to 2020 in Chinese mainland: A complex network theory approach |
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| Institution: | 1. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, 100029, China;2. Safety Integrated and Coordinated Division of the Department of Emergency Management, Beijing, 100012, China;3. Changzhou Fire and Rescue Detachment, Changzhou, 213000, Jiangsu, China;4. Shanghai Zhonghe Insurance Brokerage Co., Ltd., Shanghai, 210019, China;5. Process Safety and Disaster Prevention Laboratory, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan;1. School of Chemical Engineering, Anhui University of Science and Technology (AUST), 168, Taifeng St., Anhui, 232001, China;2. Department of Chemical and Materials Engineering, National Yunlin University Science and Technology (YunTech), 123, University Rd. Sec. 3, Douliou, 64002, Yunlin, Taiwan;3. School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, China;1. College of Chemistry and Chemical Engineering, China University of Petroleum (East China), Qingdao, 266580, China;2. College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, 266580, China;1. Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology (YunTech), 123 University Rd., Sec. 3, Douliou, Yunlin, 64002, Taiwan, ROC;2. Department of Safety, Health, and Environmental Engineering, YunTech, 123 University Rd., Sec. 3, Douliou, Yunlin, 64002, Taiwan, ROC;1. Department of Materials, Textiles and Chemical Engineering, Ghent University Technologiepark 46, B9052, Ghent, Belgium;2. Umicore R&D, B2250, Olen, Belgium;1. Department of Safety, Health and Environmental Engineering, National Kaohsiung University of Science and Technology, 1 University Road, Yanchao District, Kaohsiung, Taiwan, ROC;2. Occupational Safety and Health Administration, Ministry of Labor, 439 Zhongping Road, Xinzhuang District, New Taipei, Taiwan, ROC |
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| Abstract: | Chemical manufacturing is a long-process industry, where an end product may pass through numerous dangerous and complex steps. In such long chains of coordinated activity, accidents remain common. This study made loss-prevention recommendations for the chemical industry after conducting a review of accident reports and creating a complex network model. A human factor analysis and classification system (HFACS) was used to classify data from 109 investigation reports from the Chinese mainland (2015–2020). Levels Ⅱ and Ⅲ of the HFACS output were fed into a complex network model to generate a map of causes and chains of risk. It was shown that most accidents were directly or indirectly caused by human action, and human factors played a decisive role in occurrence, evolution, and resolution. The model used was visualized in Gephi, and the key cause nodes were identified by their topological characteristics. A modularity algorithm was used to derive the community structures and segment the network map. Crucial nodes in each community were compared with factors for each class in the HFACS model. It was also found that there was a biasing factor in the causal processes of explosive accidents and poisoning and asphyxiation accidents according to the associations classified by modularity. Risk abatement strategies were proposed for the crucial factors. |
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| Keywords: | Accident reports Complex network model HFACS Crucial nodes Risk abatement strategies |
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