Operational risk assessment of chemical industries by exploiting accident databases

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, 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...

Knowledge Management and Risk Management

“…We can find numerous work devoted to managing risks in energy [5,6,7,8], in chemistry and pharmaceutical industry [9,10,11,12] aeronautic and space [13,14], automotive [15] and recently those related to the use of information technology [16,17]. Other fields are also concerned, such as finance and banking [18], insurance and health [19].…”

Risk Assessment System for Verifying the Safeguards Based on the HAZOP Analysis

“…Therefore, various methods are proposed to solve this problem. For example, the system used in the risk assessment to create a statistical model based on the accident database [3]. In chemical plants, the safeguards are installed to prevent the accidents and the damage from spreading.…”

“…The PFD used in this system reference to "Guidelines for Process Equipment Reliability Data with Data Tables" [8]. The Probability of consequence events caused by equipment failure can be calculated by equation (3).…”

Improving Process Safety and Product Quality using Large Databases

“…In our previous work, Bayesian analysis was used for dynamic risk analysis [2,14,15]. Assumed prior distributions were updated dynamically with data (incident consequences), to yield mean failure and incident probabilities.…”