Bayesian modeling for natural gas pipeline corrosion management

Pipeline corrosion analysis is considered a challenging topic due to the complexity and uncertainty of the factors involved. The uncontrolled consequences of corrosion impact the ''natural ecosystem, society, and economy.'' Investigating corrosion plays a crucial role in managing incidents. This paper aims to provide an effective management tool for predicting corrosion using Bayesian modeling. This study illustrates how to integrate Bayesian modeling with ''incomplete data, scientific information in various formats, and expert knowledge,'' utilizing it effectively. Employing Netica software, a fault tree representing important elements affecting the natural gas transmission lines corrosion is converted into a cause-and-effect diagram of the Bayesian belief network. Natural gas transmission lines corrosion the model's output response is then analyzed after defining the relationships between these corrosion-affecting variables. The correlation between failures and corrosion is considered through a bivariate normal distribution as the likelihood function in the Bayesian update, and the model was validated using OpenBUGS software. In the next step, sensitivity analysis and scenario analysis were conducted in two industrial zones located in the central regions of the country. The final findings showed that the suggested model can produce accurate results for Corrosion monitoring systems in the Natural Gas Industry, providing an efficient approach to assess safety, quantitative risk analysis, and forming the basis for decisions aimed at averting pipeline episodes.