Operational risk assessment of chemical industries by exploiting accident databases

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dc.contributor.authorMeel, A.
dc.contributor.authorO'Neill, L. M
dc.contributor.authorLevin, J. H
dc.contributor.authorSeider, Warren D.
dc.contributor.authorOktem, U.
dc.contributor.authorKaren, N.
dc.date2023-05-17T01:09:19.000
dc.date.accessioned2023-05-22T12:41:09Z
dc.date.available2023-05-22T12:41:09Z
dc.date.issued2007-03-01
dc.date.submitted2007-08-01T09:16:55-07:00
dc.description.abstractAccident 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, the 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.
dc.description.commentsPostprint version. Published in Journal of Loss Prevention in the Process Industries, Volume 20, Issue 2, March 2007, pages 113-127. Publisher URL: http://dx.doi.org/10.1016/j.jlp.2006.10.003
dc.identifier.urihttps://repository.upenn.edu/handle/20.500.14332/5909
dc.legacy.articleid1098
dc.legacy.fulltexturlhttps://repository.upenn.edu/cgi/viewcontent.cgi?article=1098&context=cbe_papers&unstamped=1
dc.source.issue90
dc.source.journalDepartmental Papers (CBE)
dc.source.peerreviewedtrue
dc.source.statuspublished
dc.subject.otherrisk
dc.subject.otherfrequency modeling
dc.subject.otherconsequence modeling
dc.subject.otherabnormal events
dc.subject.otherchemical plants
dc.titleOperational risk assessment of chemical industries by exploiting accident databases
dc.typeArticle
digcom.contributor.authorMeel, A.
digcom.contributor.authorO'Neill, L. M
digcom.contributor.authorLevin, J. H
digcom.contributor.authorisAuthorOfPublication|email:seider@seas.upenn.edu|institution:University of Pennsylvania|Seider, Warren D.
digcom.contributor.authorOktem, U.
digcom.contributor.authorKaren, N.
digcom.identifiercbe_papers/90
digcom.identifier.contextkey331382
digcom.identifier.submissionpathcbe_papers/90
digcom.typearticle
dspace.entity.typePublication
relation.isAuthorOfPublication54d076cd-7f9e-46eb-8e03-524d2d1e543e
relation.isAuthorOfPublication54d076cd-7f9e-46eb-8e03-524d2d1e543e
relation.isAuthorOfPublication.latestForDiscovery54d076cd-7f9e-46eb-8e03-524d2d1e543e
upenn.schoolDepartmentCenterDepartmental Papers (CBE)
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