An Integrated Quantitative Bayesian Network in Risk Management for Complex Systems

doi:10.23940/ijpe.20.03.p4.354366

Abstract

Abstract: The development of complex systems such as industrial process plants is accompanied by a continuous improvement of industrial safety. This remains an important element such as production, in a world where accidents continue to cause a high number of fatalities and severe economic and material losses. In addition, these losses cannot avoid significant damages to the environment that have a negative effect on the present and future of society. A better way to deal with these complex systems is to use risk management, which is a necessary priority for our society and our companies today. It is essential to develop or integrate quantitative approaches in risk assessments to evaluate the safety of complex processes. The present work proposes a comprehensive risk assessment approach based on a bow tie diagram mapped to a Bayesian network, with the combination of a risk matrix. In this way, we firstly define the worst-case scenario by hazard analysis and then use a bow tie diagram to understand the flow of cause/effect relation between system components. This allows us to model the accidental scenario and then construct a Bayesian network. Secondly, a transformation operator is used to calculate the occurrence frequency of unwanted failures, which leads to the activation of various layers of protection within the system. Finally, a risk matrix is used to evaluate the residual risk with the help of a probability-severity ranking criterion. This proposed methodology has been applied to a gas treatment plant system based on risk management.

Key words: risk management, risk assessment, Bayesian network, complex systems, process safety

Mohammed Bougofa, Abderraouf Bouafia, and Ahmed Bellaouar. An Integrated Quantitative Bayesian Network in Risk Management for Complex Systems [J]. Int J Performability Eng, 2020, 16(3): 354-366.

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