Recent studies have shown the importance of identifying near-misses to predict the probability of accidents (Meel and Seider, 2006) – and managing them to reduce the potential of accidents (Phimister et al., 2003; Cooke and Rohleder, 2006). In this paper, new methodologies involving near-miss utilization and management identify escalations in the probability of the occurrence of incidents [particularly shutdowns (trips)] – permitting operators to be alerted to incidents likely to occur in the near future. Also, they detect the onset and/or presence of inherent faults, or special-causes, likely to lead eventually to incidents. As an example, a typical chemical process undergoing normal operation with a few variables out of their normal operating ranges is considered. Shortly after a disturbance, many variables move out of their operating bounds, creating a flood of alarms. For such a dynamic process, using pattern recognition techniques, alerts to forewarn the operators of potential undesirable events before they occur are proposed. Thereafter, as the special-causes progress, with the potential for trips and accidents increasing, the frequency of alerts increases. The new techniques reduce the number of false-positives (alerts having lead-times greater than 24 or 48 hours) and false-negatives (undetected incidents) – providing more reliable and proactive safety and quality systems, leading to reduced risk levels. In addition, new indices to assess the safety and operational performances of industrial processes recorded in databases are proposed. These methods have been successfully applied to an industrial fluidized-catalytic-cracking-unit (FCCU), a large-scale unit at a refinery that processes 270,000 barrels of oil per day. Results and conclusions are presented.
1. Meel, A., and W. D. Seider, “Plant-specific dynamic failure assessment using Bayesian theory,” Chem. Eng. Sci., 61, 7036-7056 (2006).
2. Phimister J. R., U. Oktem, P. R. Kleindorfer, and H. Kunreuther, “Near-miss incident management in the chemical process industry,” Risk Analysis, 23, 445-459 (2003).
3. Cooke D.L., and T. R. Rohleder, “Learning from incidents: From normal accidents to high reliability.” System Dynamics Review, 22(3), 213-239 (2006).
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