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PDF(3639 KB)
PDF(3639 KB)
高压储氢系统的多米诺事故Bayes网络分析
Bayesian network analysis of domino accidents in high-pressure hydrogen storage systems
由于氢气易泄漏燃爆的物化特性, 氢气集中存储设施如大规模电解水制储氢罐区和分布式加氢站等面临较高的多米诺事故风险。针对高压储氢罐区和加氢站多米诺事故传播模式, 该文提出一种Bayes网络(BN)分析方法。首先建立面向涉氢装置的泄漏燃爆场景事件树, 然后基于氢气专用事故后果评估模型, 枚举区域内的所有潜在多米诺事故场景。随后, 对每一潜在初始事故设备自动构建描述多米诺事故传播的BN模型, 模拟获得整个储氢系统内的综合风险分布, 并给定多样证据, 开展因果推理和诊断推理。模型分析显示:储氢罐区存储压强在2~15 MPa时, 多米诺风险占罐区总风险的25%以上, 爆炸是导致多米诺事故的主要事故类型; 加氢站综合事故风险主要包括压缩机自失效风险和储氢瓶多米诺事故风险, 后者主要由喷射火导致。研究结果为建立储氢系统先进定量风险评估方法提供了重要参考。
Objective: High-pressure gaseous hydrogen storage systems, such as large-scale hydrogen tank farms and distributed hydrogen refueling stations, are prone to hydrogen leakage, fire, and explosion because of the unique physicochemical properties of hydrogen. These events could set off a series of more serious accidents that would cause domino accidents. This study proposes a Bayesian network (BN)-based analysis method for the assessment of internal domino risk distribution within such systems. Methods: First, event tree models were established for various leakage scenarios in hydrogen-related facilities. Thereafter, all potential domino accident scenarios within the area were enumerated in calculation using accident consequence assessment models for hydrogen facility leaks. Next, BN models were automatically constructed to describe the propagation of domino accidents for each potential initial accident device. Finally, using BN models to analyze the magnitude and sources of overall risk for these systems, as well as the patterns of accident propagation and leakage scenarios. Results: The overall risk in hydrogen refueling stations mainly originates from the self-failure risk of compressors and the domino risk of hydrogen storage cylinders; jet fire (JF) and vapor cloud explosion (VCE) contribute 76% and 23.4% to the domino risk of all hydrogen cylinders, respectively. When the storage pressure in hydrogen tank farms is between 2 and 15 MPa, the domino risk comprises >25% of the overall risk, with explosions serving as the predominant accident type resulting in domino accidents. Causal reasoning indicates that a JF from a medium hole is the most probable domino accident scenario for both the hydrogen storage cylinders in the hydrogen refueling stations affected by the JF and the spherical tanks in the hydrogen tank farms affected by the explosion. Diagnostic reasoning for initial accident scenarios indicates that rupture and large-hole leakage of hydrogen spherical tanks and cylinders, respectively, are the most probable cause, provided that a multistage domino accident has occurred. Conclusions: Regarding the common 2-MPa hydrogen spherical tank employed in Chinese green hydrogen projects, the cumulative self-failure risk and domino risk of all tanks in the tank farms is 3.5×10-5 and 1.88×10-5 a-1, respectively, with the latter accounting for ~35%. In the future, decreasing the storage pressure to 1-1.7 MPa or increasing it to 10-15 MPa might lower the contribution of domino risk to < 30% and maintain cumulative self-failure risk at a level of 10-5 a-1. At 70-MPa hydrogen refueling stations, the domino risk to hydrogen cylinders from the compressors and pipeline is ~2.9×10-4 and ~4.4×10-5 a-1, respectively. In the abovementioned hydrogen storage systems, explosions are a notable accident type that can trigger domino accidents. Therefore, the implementation of explosion-suppression measures to decrease the probability of ignition is a key focus for mitigating the overall risk of hydrogen storage systems. Our findings indicate that future quantitative risk assessments for high-pressure hydrogen storage systems should consider the possibility of domino accidents. We believe these results serve as notable references for the establishment of advanced quantitative risk assessment methods customized to high-pressure hydrogen storage systems.
储氢系统 / 多米诺事故 / 定量风险评估 / 事件树 / Bayes网络
high-pressure hydrogen storage systems / domino accidents / quantitative risk assessment / event trees / Bayesian network
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