This article provides a systematic framework for the analysis and improvement of near-miss programs in the chemical process industries. Near-miss programs improve corporate environmental, health, and safety (EHS) performance through the identification and management of near misses. Based on more than 100 interviews at 20 chemical and pharmaceutical facilities, a seven-stage framework has been developed and is presented herein. The framework enables sites to analyze their own near-miss programs, identify weak management links, and implement systemwide improvements.

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...

A novel framework to model the chronology of incidents is presentedsdepicting the relationship of initiating events with the various regulating and protection systems of the processseventually leading to consequences, varying from zero to high severities. The key premise is that the departures and subsequent returns of process and product quality variables, from and to their normal operating ranges, are recognized as near-misses, which could have propagated to incidents. This leads to the availability of vast near-miss information recorded in distributed control and emergency shutdown systems databases that monitor the dynamics of the process. New performance indices, which utilize this abundant information, are introduced to conduct quantitative and qualitative (absolute and relative) assessment of the real-time safety and operability performances of an industrial fluidized-catalytic-cracking unit (FCCU) at a petroleum refinery. Also, new techniques for abnormal event tracking and recovery-time analysis are presented, which help to identify the variables that experience operational difficulties. It is shown how this information can be used to suggest improvements in the alarmsystem structures for the FCCU.

Part II presents step (iii) of the dynamic risk analysis methodology; that is, a novel Bayesian analysis method that utilizes near-misses from distributed control system (DCS) and emergency shutdown (ESD) system databases-to calculate the failure probabilities of safety, quality, and operability systems (SQOSs) and probabilities of occurrence of incidents. It accounts for the interdependences among the SQOSs using copulas, which occur because of the nonlinear relationships between the variables and behavior-based factors involving human operators. Two types of copula functions, multivariate normal and Cuadras-Auge´copula, are used. To perform Bayesian simulation, the random-walk, multiple-block, Metropolis-Hastings algorithm is used. The benefits of copulas in sharing information when data are limited, especially in the cases of rare events such as failures of override controllers, and automatic and manual ESD systems, are presented. In addition, product-quality data complement safety data to enrich near-miss information and to yield more reliable results.Step (iii) is applied to a fluidized-catalytic-cracking unit (FCCU) to show its performance.

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