基于动态贝叶斯网络气化炉供料系统风险分析

针对当前大型气化装置在动态风险分析方面的不足，提出一种基于动态贝叶斯网络的气化炉供料系统风险分析方法。利用气化炉供料系统各单元失效形式的相关资料，建立故障树模型，并将故障树模型转化为贝叶斯网络模型，利用K?2算法优化贝叶斯网络模型，结合β因子法处理共因失效对系统的影响。考虑维修因素对系统各时刻失效率的影响，同时采用GeNIe软件和Monte Carlo方法进行计算，验证所提方法的准确性和可行性。结果表明，动态贝叶斯网络在气化炉供料系统风险分析中，具有动态风险分析准确和能够识别系统薄弱环节的优点，并且依照分析结果可以得到合理的可靠性分配策略。

Risk Analysis of Feeding System of Gasifier Based on Dynamic Bayesian Network

Aiming at the shortcomings of dynamic risk analysis in large gasification units, a method of dynamic Bayesian network based risk analysis of gasifier feed system was proposed. The fault tree model was established by using the relevant data of failure forms of each unit of gasifier feeding system, and the fault tree model was transformed into a Bayesian network model. The bayesian network model was optimized by K?2 algorithm, and the influence of common cause failure on the system was processed by β factor method. Considering the influence of maintenance factors on the failure rate of the system at each moment, the accuracy and feasibility of the proposed method were verified by using the Bayesian network analysis software GeNIe and Monte Carlo. The results show that the dynamic Bayesian network has the advantages of accurate dynamic risk analysis and the ability to identify the weak links in the gasifier feed system risk analysis. According to the analysis results, a reasonable reliability allocation strategy can be obtained.