Stochastic Risk Assessment with Bayesian Networks in Esfahan Refinery

Abstract

Refineries are among the industrial centers that supply the energy and raw materials to downstream industries. To achieve sustainable development goals, creating appropriate balance between economic and environmental goals has always been the focus of managers and policy makers in the societies. Bayesian Network model has become a robust tool in the field of risk assessment and uncertainty management in refineries. The focus of this research is to prioritizing different units from the point of view of social and ecological aspects for facilitating the decision-making process in the context of waste material treatment in Esfahan refinery in line with the sustainable development goals. The methodology of this research is based on risk assessment with the aid of Bayesian Networks. To this end, first material flow analysis of the processes procured risk identification, subsequently influence diagram and Bayesian Network structure were designed. After completing conditional probability tables, risk factors were prioritized. According to the risk assessment results, Fuel unit was classified as the most significant risk factor, whereas Pipelines and Plant air & instrument air system were identified as the most environmentally friendly units.