Association analysis of accident factors in petrochemical storage tank farms |
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| Institution: | 1. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China;2. Faculty of Technology, Policy and Management, Safety and Security Science Group (S3G), TU Delft, 2628 BX, Delft, the Netherlands;3. Faculty of Applied Economics, Antwerp Research Group on Safety and Security (ARGoSS), Universiteit Antwerpen, 2000, Antwerp, Belgium;4. CEDON, KULeuven, 1000, Brussels, Belgium;1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, China;2. Beijing Key Laboratory of Comprehensive Emergency Response Science, China;1. College of Urban Construction and Safety Engineering, Shanghai Institute of Technology, Shanghai, 201418, PR China;2. College of Safety Engineering, China University of Labor Relations, Beijing, 100048, PR China;3. Process Safety and Disaster Prevention Laboratory, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan;1. China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing, 210096, China;2. School of Safety and Ocean Engineering, China University of Petroleum, Beijing, 102249, China;3. Trenchless Technology Center, Louisiana Tech University, LA, 71270, United States;1. Department of Safety Engineering, College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, 100029, China;2. University Gustave Eiffel, UPEC, CNRS, Laboratory Multi Scale Modeling and Simulation, (MSME/UMR 8208), 5 bd Descartes, 77454, Marne-la-Vallee, France;3. Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 211816, Jiangsu, China;4. Process Safety and Disaster Prevention Laboratory, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan, ROC |
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| Abstract: | In order to identify and clarify the association between the factors leading to accidents in a petrochemical tank area, this study analyzes investigation reports of 212 petrochemical tank farm accidents and combines this with the “association rule” mining and science related to complex networks. The main risk factors are determined and a risk factor data set is constructed; 75 association rules are extracted from the factor data set based on the Apriori algorithm. Then the obtained association rules are used to construct an accident factors network of the petrochemical storage tank area, and the topology characteristics of the network are further analyzed to reveal the importance of factors. Factors with large node degree, betweenness, and clustering coefficients are obtained, such as “violation of operating regulations”, “high concentration of flammable gas in the air”, “lack of experience and professional skills”, etc. These factors play an important role in the formation and development of accidents. The results also show that the accident cause network of the petrochemical storage tank area has a small average shortest path length and a large cluster coefficient, indicating a relatively close connection between the accident factors. The contributions of this study is not only extracting the hidden relationships among contributory factors to tank farm accidents using association analysis, but also revealing which factors are more important for the tank farm safety through the complex network. |
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| Keywords: | Tank farm accidents Association rule mining Complex networks Important accident factors |
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