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首页> 外文期刊>Transactions of The Institution of Chemical Engineers. Process Safety and Environmental Protection, Part B >Bayesian network-based risk analysis methodology: A case of atmospheric and vacuum distillation unit
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Bayesian network-based risk analysis methodology: A case of atmospheric and vacuum distillation unit

机译：基于贝叶斯网络的风险分析方法：大气和真空蒸馏装置的情况

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摘要

Chemical and petrochemical accidents, such as fires and explosions, do not happen frequently but have considerable consequences. These accidents compromise not only human safety but also cause significant economic losses and environmental contamination. The increasing complexity of chemical infrastructures increases the requirements of risk prevention. Thus, risk analysis for petrochemical systems is essential in helping analysts find the weakest process in the entire system and be used to strengthen the process and improve safety. Risk analysis has been previously studied; however, traditional methods have limitations. This study proposes a methodology that is based on Bayesian networks by giving a model for system risk analysis. The event is classified into three categories; cause, incident, and accident, according to criticality and thus, the model is analyzed as a three-layered structure. The application of the methodology is demonstrated by analyzing a vacuum distillation and an atmospheric unit. An exact reasoning method is used to infer the causality and probability within the events. After inferring the relationship between causes and accidents, mutual information and variance of beliefs are calculated to find the most sensitive event in an accident. Subsequently, means of strengthening operations to prevent accidents are suggested. This study may help companies decrease the cost of risk reduction.
机译：化学和石化事故，如火灾和爆炸，不会经常发生但具有相当大的后果。这些事故不仅妥协了人类安全，而且造成了显着的经济损失和环境污染。越来越复杂的化学基础设施的复杂性增加了风险预防的要求。因此，石化系统的风险分析对于帮助分析师找到整个系统中最弱的过程并用于加强过程并提高安全性。预先研究过风险分析;但是，传统方法有局限性。本研究提出了一种基于贝叶斯网络的方法，通过提供系统风险分析模型。该活动分为三类;原因，事件和事故，根据临界，因此分析模型作为三层结构。通过分析真空蒸馏和大气单元来证明方法的应用。确切的推理方法用于推断事件内的因果关系和概率。在推断出原因和事故之间的关系之后，计算信仰的相互信息和方差，以发现事故中最敏感的事件。随后，提出了加强行动来预防事故的方法。本研究可能有助于公司降低风险降低成本。

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