Probability Analysis of Hazardous Chemicals Storage Tank Leakage Accident Based on Neural Network and Fuzzy Dynamic Fault Tree
Source abstract
Abstract
Aiming at the problems of complex calculation processes, insufficient risk data, and reliance on experts’ subjective judgments that exist in traditional probability analysis methods, this paper proposes a probability analysis method for hazardous chemical storage tank leakage accidents based on neural networks and fuzzy dynamic fault trees (Fuzzy DFT). This method combines fuzzy set theory (FST) and Bootstrap technology to accurately quantify the failure probabilities of basic events (BEs) and reduce the dependence on experts’ subjective judgments. Furthermore, an artificial neural network (ANN) model for tank failures is constructed. This model can accurately calculate the probability of tank leakage accidents by taking into account the dependency relationships among basic events. Finally, a long short-term memory (LSTM) network is utilized to analyze the dynamic evolution trend of the probability of storage tank accidents over time. In this paper, this method is applied to the case of the “11.28” Shenghua vinyl chloride leakage accident. The results show that the calculation results of this method are highly consistent with the actual situation of the accident, indicating that it is a scientific and effective method for analyzing the probability of hazardous chemical storage tank leakage accidents.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Risk and Safety Analysis · Fault Detection and Control Systems · Safety and Risk Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China
Jiangsu Key Laboratory of Oil-Gas Storage and Transportation Technology, Changzhou 213164, China
Funding
National Key R&D Plan “Internet of Things and Smart City Key Technologies and Demonstration” Key Special Project · National Key R&D Program of China · Natural Science Research Project of Higher Education Institutions of Jiangsu Province · Open Project of Jiangsu Key Laboratory of Oil and Gas Storage and Transportation Technology · Jiangsu Graduate Research and Practice Innovation Project
References (46)
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