Quantitative risk estimation of CNG station by using fuzzy bayesian networks and consequence modeling

Abstract

As one of the potential explosions and inflammation, compressed natural gas (CNG) stations in urban areas cause irreparable losses and casualties. Estimating risk assessment in gas stress based on coherent uses can reduce accidents in urban areas. The purpose of the present study was to estimate a small risk estimation at one of the CNG multipurpose stations, LPG, using combined models of the Fuzzy Bayesian Network, Bow-tie Diagram, and consequence modeling. This study was conducted based on the basic and 25 intermediate events. This study formed a seven-person safety team to identify the primary events and build the Bow-tie diagram. Then, because of the lack of a proper database, fuzzy theory was used to determine the probability of significant events. Bayesian networks were drawn based on the Bow-tie model using GeNLe software. Finally, the main events of the two Bow-tie, Bayesian network modeling, and risk estimation were performed with the help of PHAST/SAFETI (V8.22). The geographical information system software was used to zone the explosion effects. The Risk assessment result showed that the social risks and the Bayesian network model are more than Bow-tie, and the Bow-tie diagram is unacceptable. Therefore, using incompatible land uses in the vicinity of the CNG stations gives rise to the effects of accident scenarios in particular residential and administrative land uses, which decision-makers and city managers should consider. Based on the findings of this study, the obtained results can be utilized to implement effective control measures. These measures encompass devising a response plan tailored to address specific emergency conditions and conducting comprehensive training programs for the individuals and residents residing within the study area.