Abstract
Fuzzy Bayesian network (FBN) has been widely used for risk assessment of accidents in process industries to deal with complex causality and uncertainty arising from complex interdependence among risk factors, insufficient data and complex environments. The similarity aggregation method (SAM) is a method of aggregating fuzzy opinions considering consensus degree. However, SAM does not take into account the impact of individual differences on consistency, which will bring a certain degree of uncertainty. Therefore, this work proposes an improved SAM based FBN model to better deal with various types of uncertainty. This methodology makes the prediction results of the storage tank accident more accurate and reliable. The result analysis indicates that the improved SAM is of significance to improve the reliability of the input data of FBN. Then, the critical analysis of the root node shows the effectiveness and reliability of FBN in identifying the critical events of the storage tank accident. The proposed method can predict the probability of storage tank accidents, determine the proportion of main contributing factors and identify the critical causes of storage tank accidents more reliably and accurately. It can provide important supporting information for decision-makers to optimize risk management strategies.
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