A human, organizational and technical factors (HOTF) -based gas transmission station functional division risk analysis method using EW-TOPSIS and Adversarial ISM

Abstract

Gas transmission station is the hub for long-distance natural gas transportation. Once an accident occurs at the station, the stability of gas supply will be affected. It is necessary to conduct a comprehensive risk analysis for the gas transmission station. Gas transmission station is seen as a complex system involving human, organizational and technical factors (HOTF). Most studies in the field of gas transmission station risk analysis have only focused on evaluating risk of technical factors. The present study constructs the safety index evaluation system from the aspects of human, technical and organizational risk factors. By incorporating Entropy Weight improved Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS) and Adversarial Interpretive Structure Modeling (AISM), this paper proposes a new gas transmission station risk analysis method. Taking eleven functional divisions of gas transmission stations as the research object, the weight of safety index is determined using EW and the weighted distance from each index to positive ideal solutions and negative ones is calculated using TOPSIS method, based on which the risk levels of different functional divisions are determined. Further considering the interaction of risk associations between different functional divisions, AISM is adopted to classify the risk association levels of functional division. The high-risk divisions of gas transmission station are identified based on both safety level sorting and risk correlation hierarchy division. The findings can contribute to a better understanding of risk distribution in gas transmission stations, which helps the rational allocation of safety management resources.