Accident databases (NRC, ATSDR’s, RMP, and others) contain records of incidents (e.g., releases and spills) that have occurred in United States chemical plants during recent years. For various chemical industries, Kleindorfer and coworkers summarize the accident frequencies and severities in the RMP*Info database. Also, Anand and coworkers 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. Probability density distributions are formulated for their causes (e.g., equipment failures, operator errors, etc.), and equipment items are 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, the NRC database is exploited to model the rate of occurrence of incidents in various chemical and petrochemical companies using Bayesian theory. 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 risk within an industry utilizing the frequency and severity distributions.
Keywords: RMP database, accident prevention, Bayesian theory, operational risk assessment