Dynamic risk analysis using alarm databases to improve process safety and product quality: Part IIuBayesian analysis

作者：Pariyani Ankur; Seider Warren D^*; Oktem Ulku G; Soroush Masoud

来源：AICHE Journal, 2012, 58(3): 826-841.

DOI：10.1002/aic.12642

• abnormal events
• near-misses
• copulas
• unsafe incidents
• process safety
• product quality
• Bayesian theory
• Monte-Carlo simulation
• probabilistic risk analysis
• petroleum refinery

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 databasesto 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 CuadrasAuge copula, are used. To perform Bayesian simulation, the random-walk, multiple-block, MetropolisHastings 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.

• 出版日期2012-3