Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (3): 354-366.

### An Integrated Quantitative Bayesian Network in Risk Management for Complex Systems

Mohammed Bougofaa,*, Abderraouf Bouafiab, and Ahmed Bellaouara

1. aTransport Engineering and Environment Laboratory, Frères Mentouri Constantine 1 University, Constantine, 25000, Algeria;
bChemical Engineering and Environment Laboratory of Skikda, University of 20 Août 1955 de Skikda, Skikda, 21000, Algeria
• Submitted on  ;  Revised on  ; Accepted on
• Contact: Mohammed Bougofa E-mail:mohamed.bogoffa@umc.edu.dz
• About author:Mohammed Bougofa received his M.S degree in health and industrial safety from the Institute of Hygiene and Industrial Safety at the University of Batna in 2015. He is a Ph.D. student at Frères Mentouri Constantine University. His research interests include complex system safety, reliability, and availability.
Abderraouf Bouafia received his M.S degree in health and industrial safety from the Institute of Hygiene and Industrial Safety at the University of Batna in 2015. He is a Ph.D. student at the University of Août. His research interests include quantitative risk assessment and the domino effect of complex processes.
Ahmed Bellaouar is a professor in the Transportation-Engineering Department at the University of Frères Mentouri Constantine. His research interests include mechanics, maintenance, transportation, logistics, and safety.

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.