Dynamic safety risk modeling of process systems using bayesian network

Abstract

Process complex systems in particular oil and gas plants due to dealing with hazardous materials at severe process conditions are much prone to catastrophic accidents. In this context, safety risk analysis is a crucial tool to develop effective strategies to prevent accident and provide mitigative measures. Dynamic risk analysis (DRA) is one of the most practical approaches for risk analysis that helps provide safer operations of complex process systems. The present work is aimed at demonstrating the application of an integrated DRA approach to comprehensive quantitative modeling and analysis of the both aspects of risk, that is, probability and consequence assessments. In this approach, first, the worst case scenario is identified and then a robust tool is developed for dynamic accident scenario modeling and risk assessment by means of Bayesian Network. This approach is applied to risk analysis of a flammable liquid storage system at a gas refinery. The work provides valuable information on the identification and comprehensive analysis of worst case accident scenarios, their main consequences, critical basic events, and minimal cut sets which lead to accident scenarios and also for dynamic updating of probabilities and risk. The obtained results are more appropriate and rigorous to developing preventive and mitigative strategies for potential accident scenarios and thus increase the safety level in the complex process systems. © 2017 American Institute of Chemical Engineers Process Saf Prog 36: 399–407, 2017