A systems-based approach for modeling of microbiologically influenced corrosion implemented using static and dynamic Bayesian networks

• Published in: Journal of loss prevention in the process industries 2020-05, Vol.65, p.104108, Article 104108
• Main Authors: Kannan, Pranav, Kotu, Susmitha Purnima, Pasman, Hans, Vaddiraju, Sreeram, Jayaraman, Arul, Mannan, M. Sam
• Format: Article
• Language: English
• Subjects: Bayesian network; Dynamic Bayesian network; Microbiologically influenced corrosion; Process safety; Systems based model
• ISSN: 0950-4230
• DOI: 10.1016/j.jlp.2020.104108

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. •A systems-based model was developed to estimate the susceptibility of MIC.•This model integrates various types of information starting with material properties.•It further encompasses operational data, and maintenance history with failure analysis history.•This model was used to determine susceptibility of MIC in twelve different scenarios.•This model was also implemented to estimate the susceptibility of MIC using a dynamic Bayesian network.