Yuan Zhi, Khakzad Nima, Khan Faisal, Amyotte Paul
Safety and Risk Engineering Group (SREG), Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St. John's, NL, Canada, A1B 3×5.
Risk Anal. 2015 Feb;35(2):278-91. doi: 10.1111/risa.12283. Epub 2014 Sep 26.
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.
在本研究中，提出了一种基于贝叶斯网络的粉尘爆炸场景风险分析方法。我们的方法还借助蝴蝶结图来更好地表示粉尘爆炸的促成因素与后果之间存在的逻辑关系。在本研究中，评估了粉尘爆炸场景的风险，同时考虑了共同原因失效以及根事件与可能后果之间的依赖性。通过诊断分析，确定粉尘颗粒特性、氧气浓度和员工安全培训是导致粉尘爆炸的最关键根事件。概率适配概念也用于顺序更新，从而从过去的粉尘爆炸事故中学习，这在动态风险评估和管理中非常重要。我们还将所提出的方法应用于一个案例研究，以模拟粉尘爆炸场景、估计设想的风险，并识别需要额外安全措施的系统脆弱部分。
