氢安全风险评价技术发展现状与展望

doi:10.3981/j.issn.2097-0781.2024.04.008

前瞻科技 ›› 2024, Vol. 3 ›› Issue (4): 91-104.DOI: 10.3981/j.issn.2097-0781.2024.04.008

氢安全风险评价技术发展现状与展望

张嘉欣¹(), 姜雅宁¹, 孔祥领², 姚晨奕¹, 巴清心¹^,³, 李雪芳¹^,³^,^†()

1.山东大学热科学与工程研究中心（高等技术研究院），济南 250061
2.中国石油技术开发有限公司，北京 100028
3.山东大学高效储能及氢能利用山东省工程研究中心，济南 250061

收稿日期:2024-09-30 修回日期:2024-10-17 出版日期:2024-12-20 发布日期:2024-12-24
通讯作者: †
作者简介:张嘉欣，博士研究生。主要从事氢安全风险评价、低温氢射流理论模型研究。电子信箱：202134242@mail.sdu.edu.cn。
李雪芳，副教授，博士研究生导师。中国动力工程学会青年工作委员会委员，中国汽车工程学会汽车火灾安全技术分会委员，国际氢能协会（IAHE）会员，中国可再生能源学会会员（氢能专业委员会），中国消防协会会员。主要从事氢能与氢安全研究。电子信箱：lixf@email.sdu.edu.cn。
基金资助:
国家重点研发计划(2023YFB4004501)

Current Status and Prospects of Hydrogen Risk Assessment Technologies

ZHANG Jiaxin¹(), JIANG Yaning¹, KONG Xiangling², YAO Chenyi¹, BA Qingxin¹^,³, LI Xuefang¹^,³^,^†()

1. Institute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, China
2. China Petroleum Technology and Development Corporation, Beijing 100028, China
3. Shandong Engineering Research Center for High-efficiency Energy Storage and Hydrogen Energy Utilization, Shandong University, Jinan 250061, China

Received:2024-09-30 Revised:2024-10-17 Online:2024-12-20 Published:2024-12-24
Contact: †

摘要/Abstract

摘要：

面向氢能系统和设施的风险评价对预防氢事故、确保氢能安全应用至关重要。随着氢能商业化应用和推广，氢能应用场景的多样化对风险评价技术提出了更高的要求。文章总结了定性及定量风险评价的基础理论及方法，综述了氢能行业现有风险评价技术的发展现状和主要应用场景，并介绍了动态贝叶斯网络和人工神经网络等新兴的风险评价技术。在此基础上，指出目前在氢安全数据、动态化定量风险评价、风险评价流程和可接受标准、仿真与评价工具等方面所面临的挑战，并提出了未来氢安全风险评价技术发展的4点建议。

关键词: 氢能, 氢安全, 风险评价

Abstract:

Conducting risk assessments for hydrogen energy systems and facilities is essential for preventing hydrogen accidents and ensuring the safe use of hydrogen energy. With the advancement of commercial applications of hydrogen energy, diversified hydrogen energy application scenarios have put higher demands on risk assessment technologies. This paper summarized the fundamental theories and methods of both qualitative and quantitative risk assessments and reviewed the current development status and main application scenarios of existing risk assessment technologies in the hydrogen energy industry. Emerging risk assessment technologies, such as dynamic Bayesian networks and artificial neural networks, were introduced as well. On this basis, this paper highlighted current challenges in hydrogen safety data, dynamic quantitative risk assessment, risk assessment procedures and acceptable standards, and simulation and assessment tools. Finally, four recommendations were proposed for the future development of hydrogen risk assessment technologies.

Key words: hydrogen energy, hydrogen safety, risk assessment

张嘉欣, 姜雅宁, 孔祥领, 姚晨奕, 巴清心, 李雪芳. 氢安全风险评价技术发展现状与展望[J]. 前瞻科技, 2024, 3(4): 91-104.

ZHANG Jiaxin, JIANG Yaning, KONG Xiangling, YAO Chenyi, BA Qingxin, LI Xuefang. Current Status and Prospects of Hydrogen Risk Assessment Technologies[J]. Science and Technology Foresight, 2024, 3(4): 91-104.

图/表 9

图1 典型氢安全事故

Fig. 1 Typical hydrogen safety accidents

图2 HAZOP与FMEA结合使用示例

Fig. 2 Joint application of HAZOP and FMEA

图3 氢安全事故领结图

Fig. 3 Bow-tie diagram of hydrogen safety accidents

图4 定量风险评价的基本步骤

Fig. 4 Procedures of quantitative risk assessment

表1 氢事故数据库

Table 1 Hydrogen accident database

数据库名称	国家/地区	记录的事故数量	数据管理方
高压气体事故数据库^[32]	日本	未专门统计氢能事故	高压气体安全研究所（日本）
H₂ Tools^[33]	美国	221	太平洋西北国家实验室（美国）
氢事件和事故数据库（HIAD）^[34]	全球范围	755	欧盟联合研究中心（荷兰）

图5 HIAD统计的氢气事故分类

Fig. 5 Statistics of hydrogen accidents by HIAD

图6 贝叶斯网络示意图

Fig. 6 Bayesian network

图7 不同国家的社会风险可接受标准上边界线为不可容忍线，下边界线为可忽略线。

Fig. 7 Social risk acceptance criteria in various countries

图8 神经网络和机器学习在安全风险评价体系中的应用

Fig. 8 Application of neural network and machine learning in risk assessment system

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Current Status and Prospects of Hydrogen Risk Assessment Technologies

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