## 5 Conclusions and Future Work  
In this study, an improved integrated framework is proposed, which maps Fault Tree Analysis (FTA), the Fuzzy Set Theory (FST), and expert knowledge into a Bayesian Network (BN) to achieve risk assessment of fuzzy data in complex systems. The weakest \(t\)-norm algorithm processes the intersection operations between fuzzy sets by taking the minimum value, which can conservatively estimate the intersection results to the greatest extent, avoiding the overestimation of risks. The Similarity Aggregation Method (SAM) is used to aggregate different experts' views on basic events. Expert interviews and the fuzzy set theory can effectively handle the issue of insufficient leakage data. The proposed integrated approach can effectively overcome the challenges and uncertainties of risk events in the process of probabilistic risk assessment of complex systems where data are unavailable. Through fuzzy operations, the qualitative data are translated into quantitative form. Compared with some other safety risk assessment methods, this approach offers certain improvements: (i) It takes into account the divergence and ambiguity of expert opinions, using the Similarity Aggregation Method (SAM) to aggregate the similarities between experts. (ii) It combines FTA, fuzzy sets, and BNs for probabilistic reasoning, which can yield relatively reliable risk indicators and capture the dependencies between events. In the case study, the leakage risk of an ethane storage tank was assessed, and the causes of this accident were analyzed with a fault tree being established accordingly. Due to the incompleteness of basic event data, expert interviews were combined to obtain the bottom events of the system fault tree. Finally, the proposed method was used to evaluate the occurrence probabilities of the top and bottom events of the system. The results indicate that high cyclical load fluctuations, corrosive media, thinning of internal coatings, failure of cathodic protection, and failure of corrosion insulation are key risk factors for ethanestorage tank leakage. In summary, professionals in oil and gas storage and transportation, as well as safety research professionals, can leverage the findings of this article to minimize the probability of ethane storage tank leakage incidents. The results of this study also make practical contributions to risk assessment in ethane storage and transportation by identifying critical bottom events. The findings offer a novel framework and methodology for better comprehending the progression of ethane tank leakage incidents, enabling proactive hazard prevention and control measures. However, the method proposed in this paper focuses more on static analysis and does not fully consider the dynamic characteristics of the system over time, such as equipment aging, changes in operating conditions, and environmental impacts, where risk levels may evolve over time. In future work, this paper intends to combine FTA with a Dynamic Fuzzy Bayesian Network (DFBN) to overcome the limitations of current static models in capturing time-dependent and system dynamic changes. The dynamic Bayesian network, which is capable of updating the risk scenarios in real time and reflecting the complexity of ethane storage tank leakage risk as a function of the environment and operating conditions, can be used to dynamically investigate the accident development process by inputting new data at different times, thus enhancing the sensitivity and responsiveness to potential risks. Conceptualization, M.P. ; methodology, M.P. and Z.Z. ([PERSON]); software, Z.Z. ([PERSON]); writing--original draft, Z.Z. ([PERSON]); writing--review and editing, Z.Z. ([PERSON]); supervision, Q.L. All authors have read and agreed to the published version of the manuscript. This study was supported by the following grants: Major Program of the National Social Science Foundation of China: Research on the Path of High-Quality Development of Natural Gas Industry Driven by Energy Revolution. Project number: 22&ZD1050. Not applicable.