A Novel Underlying Algorithm for Reducing Uncertainty in Process Industry Risk Assessment

Abstract

Normal fuzzy fault tree is a classic model in the field of process industry risk assessment, and it can provide reliable prior knowledge for machine learning. However, it is difficult to adapt the traditional approximate calculation method to highly nonlinear problems, and this may introduce model uncertainty. To solve this problem, this study proposes an accurate calculation algorithm. In the proposed algorithm, first, an exact α-cut set of normal fuzzy fault tree is derived according to the exact calculation formula of normal fuzzy numbers and in combination with the cut-set theorem. Subsequently, the relationship between the membership function and the exact cut set is derived based on the representation theorem. Finally, according to the previous derivation, the coordinates of the point on the exact membership curve are found within the range of x from 0 to 1. Based on this, an accurate membership graph is drawn, the membership curve is evenly divided with the area enclosed by the x-axis, and the fuzzy median is obtained. The results of the two chemical accident cases demonstrate that the proposed algorithm has a strong ability to handle uncertainty and can significantly reduce the uncertainty of the process industry risk assessment results. The results also reveal that the superiority of the accurate calculation algorithms becomes more obvious when the mean failure probability of basic events is larger or the accident tree is more complex. This study provides a high-accuracy underlying algorithm for process industry risk assessment, and it is of great value for improving system security.