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, the Journal of Hazardous Material, 130,[33][34][35][36][37][38][39][40][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 plantand 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. 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 wit...
Basing on the extensive analysis of both native and foreign scientific publications, the authors have concluded that the problem of classifying risks and factors of their occurrence, and the risk assessment, as well, is well-publicized. In some works, strategies are proposed to mitigate consequences of the onset of risks by creating reserves. However, all of them are mechanisms of the management response during implementation of an investment and construction project, i.e., the reactive position. The article proposes to move from a reactive to a proactive position, which essence is to implement the goal set by the investor, regardless of the conditions, circumstances, and the likelihood of the manifestation of internal or external negative impacts. This problem is solved by the Business Impact Analysis (BIA) method, which logic is only in assessing the fact of breaching contracts by the subjects, but not the frequency of occurrence of events causing risks. The method used does not consider or observe the content of various events, their cause-and-effect relationships, but the only fact of nonfulfillment of the contract terms in relation to duration or estimated cost by a business entity implementing investment and construction projects.
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