Risk analysis of urban low-pressure natural gas networks based on hybrid dynamic Bayesian networks

Abstract

With the accelerating pace of urbanization, natural gas has become an increasingly vital component of urban energy supply due to its status as a clean energy source. As the "last mile" of gas transportation, the reliability of low-pressure natural gas pipeline networks directly affects both the quality of life for urban residents and public safety. However, these networks face numerous uncertainties, such as equipment aging, environmental changes, and operational errors, all of which pose significant safety risks. To effectively assess the risk level of urban natural gas networks and enhance their reliability, this paper proposes a risk analysis approach based on a hybrid Dynamic Bayesian Network for urban low-pressure natural gas pipeline systems. First, failure data from an urban low-pressure network is statistically analyzed to identify operational characteristics and potential failure factors, leading to the establishment of a comprehensive risk analysis index system. Then, combining the dynamic Bayesian network model with an improved failure probability model with historical accident data, real-time monitoring data, and expert experience, the proposed approach dynamically updates the network's safety status, predicting the failure probability trend over future time periods and tracing critical weak points in the network. Furthermore, through an integrated reliability evaluation, targeted optimization strategies and improvement measures are proposed to ensure the long-term safe and stable operation of the low-pressure natural gas network. This study provides theoretical support and technical solutions for enhancing the safety and reliability of urban natural gas pipeline systems.