Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks

Yuan Zhi · Nima Khakzad · Faisal Irshad Khan · Paul R Amyotte

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Risk and Safety Analysis Industrial and Mining Safety Combustion and Detonation Processes

Abstract

In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow-tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures.

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References

2001Improving the analysis of dependable systems by mapping fault trees into Bayesian networks 2011Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches 2012Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network 2013Quantitative risk analysis of offshore drilling operations: A Bayesian approach 2012Dynamic risk analysis using bow-tie approach 2012Domino Effect Analysis Using Bayesian Networks 2005ARAMIS project: A more explicit demonstration of risk control through the use of bow–tie diagrams and the evaluation of safety barrier performance 2005Lees' Loss Prevention in the Process Industries

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2018Fuzzy evidence theory and Bayesian networks for process systems risk analysis 2016Domino effect analysis of dust explosions using Bayesian networks 2023Exploring hazardous chemical explosion accidents with association rules and Bayesian networks 2017Clarifying the concept of operational risk assessment in the oil and gas industry 2018A quantitative risk analysis model considering uncertain information

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