中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (11): 59-66.doi: 10.16265/j.cnki.issn1003-3033.2023.11.1087

• 安全社会科学与安全管理 • 上一篇    下一篇

利益相关者视角下化工安全关键致因网络分析

刘丹1,2(), 朱卫昌1,2, 李墨潇1,2, 金青松3, SAIDAH Saad3   

  1. 1 武汉理工大学 中国应急管理研究中心,湖北 武汉 430070
    2 武汉理工大学 安全科学与应急管理学院,湖北 武汉 430070
    3 马来西亚国立大学 信息科学与技术学院,马来西亚雪兰莪州 万宜新镇 43600
  • 收稿日期:2023-06-20 修回日期:2023-09-13 出版日期:2023-11-28
  • 作者简介:

    刘丹 (1985—),男,湖南邵阳人,副教授,博士生导师,主要从事公共安全与应急管理、机器学习与智能计算、风险评估与应急决策等方面的研究。E-mail:

    李墨潇 副教授

    金青松 高级工程师

    SAIDAH Saad 教授

  • 基金资助:
    国家社会科学基金资助(23BGL280); 国家自然科学基金资助(52209146)

Network analysis of chemical safety critical causation from perspective of stakeholders

LIU Dan1,2(), ZHU Weichang1,2, LI Moxiao1,2, JIN Qingsong3, SAIDAH Saad3   

  1. 1 China Research Center for Emergency Management, Wuhan University of Technology
    2 School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan Hubei 430070, China
    3 Faculty of Information Science and Technology, National University of Malaysia, UKM Bangi Selangor 43600, Malaysia
  • Received:2023-06-20 Revised:2023-09-13 Published:2023-11-28

摘要:

针对化工事故致因分析较少考虑利益相关者间复杂耦合关系的问题,首先,以2016—2021年间80个典型的化工事故为案例,提取出6个利益相关者和26个相关的风险因素,构建风险矩阵,利用Netdraw软件可视化矩阵,得到化工安全风险网络图;然后,整体层面上,运用社会网络分析(SNA)中的块模型密度矩阵和像矩阵得到核心块中的7个风险因素;个体层面上,运用节点度值和中心度确定9个风险因素;将整体和个体层面筛选出的风险因素取交集,确定网络中的4个核心风险因素;最后,从整体网络密度和聚类系数2方面检测管控后的风险网络效果。研究结果表明:核心风险因素为违章作业,化工企业不具备安全生产条件,安全评价机构预评价、现状验收评价不到位,材料设备性能、质量不足。管控核心风险因素后,新风险网络的密度降低35%,聚类系数降低21%,有效降低了因某一关键风险的引发而导致化工事故的可能性。

关键词: 利益相关者, 化工安全, 关键致因因素, 社会网络分析(SNA), 块模型

Abstract:

In order to address the issue of the lack of complex coupling relationships among stakeholders in the causes analysis of chemical accidents, firstly, taking 80 typical chemical accidents from 2016 to 2021 as cases, 6 stakeholders and 26 related risk factors were extracted, and the risk matrix was constructed. Then, Netdraw was used to visualize the matrix, and obtain the chemical safety risk network diagram. Secondly, on an overall level, the density matrix and image matrix of the block model in SNA were used to obtain 7 risk factors in the core block. On the individual level, node degree values and centrality were used to determine 9 risk factors. Then, the risk factors selected on the overall and individual levels were intersected to determine the four core risk factors in the network. Finally, the effect of the controlled risk network was tested from two aspects of overall network density and clustering coefficient. The results show that the core risk factors are four, namely illegal operations, chemical enterprises don't have the conditions for safe production, the pre-evaluation of safety evaluation institutions and the acceptance evaluation of the status quo are not in place, and the performance and quality of materials and equipment are insufficient. After controlling the core risk factors, the density of the new risk network is reduced by 35%, and the clustering coefficient is reduced by 21%, which effectively reduces the possibility of chemical accidents caused by a key risk.

Key words: stakeholders, chemical safety, key causative factors, social network analysis (SNA), block model