Operational risk assessment of chemical industries by exploiting accident databases |
| |
| Institution: | 1. Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104-6393, USA;2. Risk Management and Decision Processes Center, Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6340, USA;3. Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011-3130, USA;1. Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea;2. Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency (KOSHA), 400 Jongga-ro Jung-gu, Ulsan 681-230, Republic of Korea;3. Department of Energy and Chemical Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 406-772, Republic of Korea;1. Department of Safety Science and Engineering, School of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China;2. Safety and Security Science, Delft University of Technology, Delft, The Netherlands;3. Antwerp Research Group on Safety and Security (ARGoSS), Faculty of Applied Economics, Universiteit Antwerpen, Antwerp, Belgium;4. LISES – Dipartimento di Ingegneria Civile, Chimica, Ambientale e dei Materiali, Alma Mater Studiorum – Università di Bologna, Bologna, Italy;5. Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, Canada;6. Research group CEDON, Faculty of Economics and Management, Brussels campus, KULeuven, Belgium;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, KU Leuven, 1000 Brussels, Belgium |
| |
| Abstract: | Accident databases (NRC, RMP, and others) contain records of incidents (e.g., releases and spills) that have occurred in the USA chemical plants during recent years. For various chemical industries, Kleindorfer, P. R., Belke, J. C., Elliott, M. R., Lee, K., Lowe, R. A., & Feldman, H. I. (2003). Accident epidemiology and the US chemical industry: Accident history and worst-case data from RMP*Info. Risk Analysis, 23(5), 865–881.] summarize the accident frequencies and severities in the RMP*Info database. Also, Anand, S., Keren, N., Tretter, M. J., Wang, Y., O’Connor, T. M., & Mannan, M. S. (2006). Harnessing data mining to explore incident databases. Journal of Hazardous Material, 130, 33–41.] use data mining to analyze the NRC database for Harris County, Texas.Classical statistical approaches are ineffective for low frequency, high consequence events because of their rarity. Given this information limitation, this paper uses Bayesian theory to forecast incident frequencies, their relevant causes, equipment involved, and their consequences, in specific chemical plants. Systematic analyses of the databases also help to avoid future accidents, thereby reducing the risk.More specifically, this paper presents dynamic analyses of incidents in the NRC database. The NRC database is exploited to model the rate of occurrence of incidents in various chemical and petrochemical companies using Bayesian theory. Probability density distributions are formulated for their causes (e.g., equipment failures, operator errors, etc.), and associated equipment items utilized within a particular industry. Bayesian techniques provide posterior estimates of the cause and equipment-failure probabilities. Cross-validation techniques are used for checking the modeling, validation, and prediction accuracies. Differences in the plant- and chemical-specific predictions with the overall predictions are demonstrated. Furthermore, extreme value theory is used for consequence modeling of rare events by formulating distributions for events over a threshold value. Finally, the fast-Fourier transform is used to estimate the capital at risk within an industry utilizing the frequency and loss-severity distributions. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
