Dynamic Risk Analysis Using Alarm Databases to Improve Process Safety and Product Quality: Part II – Bayesian Analysis

less than 1 minute read

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

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–Augé 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.

Keywords: dynamic risk analysis, alarm databases, chemical industry, Bayesian theory, fluid catalytic cracking unit