Improving Process Safety and Product Quality using Large Databases

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Ankur Pariyani, Warren D. Seider, Ulku Oktem, and Masoud Soroush

This paper introduces a novel modeling and statistical framework (based on Bayesian theory) that utilizes extensive distributed control system and emergency shutdown databases, to perform thorough risk and vulnerability assessment of chemical/petrochemical plants. Quality variables are utilized, in addition to safety (or process) variables, to enhance both process safety and product quality. To effectively achieve these objectives, new concepts of abnormal events and upset states are defined, which permit the identification of near-miss events from the databases. The databases for a fluid catalytic cracking unit at a major petroleum refinery are used to demonstrate the application and performance of the techniques introduced herein. The results show that with the novel utilization of near-miss data, one can perform robust risk calculations using both product-quality and safety data.

Keywords: safety improvement, risk analysis, alarm databases, Bayesian theory