Based on Fuzzy Bayesian Network of Oil Wharf Handling Risk Assessment

Abstract EN

In order to make the risk assessment method of oil wharf handling more reasonable, basic data calibration method more accurate, and assessment findings more objective, the fuzzy weights of the relative probability of basic events are calibrated by ANP decision-making (Analytic Network Process).

ANP decision-making is appropriate for reflecting the dependence between the basic events and the feedback relationship.

The calibration value is used as the probability value of each basic event.

Based on the fault tree model, the relationship between the accidents caused by the Bayesian network is constructed, and the important degree of the basic events is quantitatively evaluated.

The case focuses on wharf handling gasoline fire and explosions, using ANP method to calibrate probability, and analyzing and sorting the structural importance, the probability importance, and critical degree of each basic event through forward and backward reasoning.

The results showed that the evaluation model can better characterize the effect of the basic events on the top events, which can be targeted to identify security weaknesses in oil wharf handling process.

It has some practical significance for finding security risks and improving working conditions and the overall system safety level.