(34d) Gas Pipeline System Integrity Management Software Platform Based on Dynamic Corrosion Bayesian Network Predictive Modeling | AIChE

(34d) Gas Pipeline System Integrity Management Software Platform Based on Dynamic Corrosion Bayesian Network Predictive Modeling

Pipeline failures are recurring over the last decades and are causing not only property damage but human injuries and lives as well. Improving the current pipeline integrity tools and techniques is becoming the main focus of operators around the world. Quantifying the health state of the pipeline system is the main challenge facing the operators due to the limited sensor monitoring points (limited budget) which can provide only partial health state data and non optimal maintenance schedules. As a result, the challenge is to find the optimal locations to place sensors to capture the health state of the pipeline system in the most accurate way possible and optimal inspection/maintenance schedules. This paper presents a software platform for gas pipeline system integrity management which includes novel sensor placement optimization framework that aims at providing the operators the optimal locations to place sensors at a minimal cost and the optimal mitigation actions and their timing. This framework maximizes the damage detection probability by the provided sensor network which captures as much as possible of the degrading parts of the pipeline. The proposed real-time optimization framework is based on dynamic corrosion Bayesian Network modeling where the changing operating parameters are updating the system simulation for optimal results. The real-time sensor placement optimization will provide the optimal sensor network layout and capture damages that could have been gone undetected and resulted in pipeline failures. In a few words, the proposed research work will help operators make risk-informed decisions to enhance pipeline system integrity management through optimal mitigation actions.