Journal of Loss Prevention in the Process Industries ( IF 3.5 ) Pub Date : 2020-03-26 , DOI: 10.1016/j.jlp.2020.104108 Pranav Kannan , Susmitha Purnima Kotu , Hans Pasman , Sreeram Vaddiraju , Arul Jayaraman , M. Sam Mannan
Microbiologically influenced corrosion (MIC) is a microbial community assisted degradation of materials affecting chemical processing and oil and gas industries. MIC has been implicated in incidents involving loss of containment of hazardous hydrocarbons which have led to fires and explosions, economic and environmental impact. The interplay between abiotic environmental factors and dynamic biotic factors in MIC are poorly understood. There is a lack of mechanistic understanding of MIC and very few models are available to predict or assess MIC threat. Here we report on the development of a model to assess the susceptibility to MIC. The high-resolution model utilizes 60 independent nodes, including operational and historical failure analysis data, and is built by combining empirical relationships between the abiotic and biotic variables impacting MIC. Both static and dynamic Bayesian-network (BN) approaches were used to combine heuristic and quantitative states of variables to ultimately yield a susceptibility measure for MIC. A confidence-in-information metric was generated to reflect the amount of data used in the estimation. A susceptibility to MIC of 45%–60% was estimated by the model for ten different scenarios simulated using case-studies from literature. The susceptibility to MIC estimated by these scenarios was further interpreted in the context of these cases. This systems-based MIC model can be utilized as an independent estimator of susceptibility or can be incorporated as a sub-model within comprehensive safety threat assessment models currently utilized in industry.
中文翻译:
使用静态和动态贝叶斯网络实现基于系统的微生物影响腐蚀建模方法
微生物影响的腐蚀(MIC)是一种微生物群落,其材料的降解会影响化学加工以及石油和天然气工业。MIC已牵涉到涉及危险碳氢化合物失去包容性的事件,这些事件导致火灾和爆炸,经济和环境影响。MIC中非生物环境因素和动态生物因素之间的相互作用了解甚少。缺乏对MIC的机械理解,很少有模型可用于预测或评估MIC威胁。在这里,我们报告了一种评估MIC敏感性的模型的开发。高分辨率模型利用60个独立的节点,包括运行和历史故障分析数据,通过结合影响MIC的非生物和生物变量之间的经验关系来构建。静态和动态贝叶斯网络(BN)方法都用于结合变量的启发式和定量状态,以最终产生MIC的敏感性度量。生成了信息可信度度量,以反映估计中使用的数据量。通过使用文献中的案例研究模拟的十种不同情景,该模型对MIC的敏感性估计为45%–60%。在这些情况下,进一步解释了通过这些场景估计的MIC易感性。这种基于系统的MIC模型可以用作磁化率的独立估计器,也可以作为子模型合并到当前行业中使用的综合安全威胁评估模型中。静态和动态贝叶斯网络(BN)方法都用于结合变量的启发式和定量状态,以最终产生MIC的敏感性度量。生成了信息可信度度量,以反映估计中使用的数据量。通过使用文献中的案例研究模拟的十种不同情景,该模型对MIC的敏感性估计为45%–60%。在这些情况下,进一步解释了通过这些场景估计的MIC易感性。这种基于系统的MIC模型可以用作磁化率的独立估计器,也可以作为子模型合并到当前行业中使用的综合安全威胁评估模型中。静态和动态贝叶斯网络(BN)方法都用于结合变量的启发式和定量状态,以最终产生MIC的敏感性度量。生成了信息可信度度量,以反映估计中使用的数据量。通过使用文献中的案例研究模拟的十种不同情景,该模型对MIC的敏感性估计为45%–60%。在这些情况下,进一步解释了通过这些场景估计的MIC易感性。这种基于系统的MIC模型可以用作磁化率的独立估计器,也可以作为子模型合并到当前行业中使用的综合安全威胁评估模型中。