基于HFACS模型的化工事故致因分析

doi:10.16265/j.cnki.issn1003-3033.2018.09.014

摘要/Abstract

摘要： 为探究我国化工企业安全管理应重点防范的问题,首先,基于化工事故特点,识别人因分析和分类系统(HFACS)中各致因类别在化工领域的具体表现形式;然后,完善HFACS模型,并以某化工企业2012—2016年的102起事故为样本,应用该模型分析事故致因,针对具体致因提出相应的控制与改善措施。结果表明:员工的不安全行为主要包括习惯性违规和技能型失误,其主要形式为未对设备/工艺进行风险评估;不安全行为的前提条件主要源于操作者注意力不集中、警惕性不强以及作业时未穿戴个人保护设备(PPE);不安全监管的主要形式为未进行监督和风险评估;组织影响主要形式为安全氛围未形成。

关键词: 化工事故, 人因分析和分类系统(HFACS), 事故致因, 个人保护设备(PPE), 不安全行为

Abstract: In order to reveal the vulnerability of safety management systems in Chinese chemical industry, firstly,based on the characteristics of chemical accidents,the specific manifestations of each cause category in HFACS were identified. Then ,HFACS model was supplemented,and an analysis of causes of accidents was made using the supplemented HFACS model on the basis of data on a certain chemical enterprise's 102 accidents occurred in 2012-2016. Finally, corresponding improvement and control measures were proposed to prevent chemical accidents in the future.The results show that the unsafe behavior of employees is mainly manifested in the routine violations and skill-based errors, and its main cause is lack of risk assessment for equipment or process, that the root causes of the operator's unsafe behavior are the poor concentration and vigilance of operator in adverse mental state factors, and without wearing PPE, that main forms of unsafe supervision are lack of providing both safety supervision and risk assessment,and that organizational influence factors are mainly manifested in lack of a safety climate.

Key words: chemical accidents, human factors analysis and classification system (HFACS), accident-cause, personal protective equipment(PPE), unsafe acts

中图分类号:

X928.5

王晶, 樊运晓, 高远. 基于HFACS模型的化工事故致因分析[J]. 中国安全科学学报, 2018, 28(9): 81-86.

WANG Jing, FAN Yunxiao, GAO Yuan. Analysis of causes of accidents in chemical industry based on HFACS model[J]. China Safety Science Journal, 2018, 28(9): 81-86.

参考文献

[1] 蒋涛,王延平,边敏. 国内外石油化工行业事故预警预防管理对比研究[J]. 安全、健康和环境, 2014,14(3): 1-4.
JIANG Tao, WANG Yanping, BIAN Min. Comparative study on early-warning and prevention management of accidents in petrochemical industry at home and abroad [J]. Safety Health and Environment, 2014,14(3): 1-4.
[2] 刘蓉,王保民. 2014年我国化工生产事故统计分析[J]. 山西化工,2015,35(2): 70-72.
LIU Rong, WANG Baomin. Statistics on Chinese chemical production accidents in 2014 [J].Shanxi Chemical Industry, 2015,35(2): 70-72.
[3] 杜红岩,王延平,卢均臣. 2012年国内外石油化工行业事故统计分析[J]. 中国安全生产科学技术, 2013,9(6): 184-188.
DU Hongyan, WANG Yanping, LU Junchen. Statistical analysis on petroleum and chemical accidents at home and abroad during the period of 2012 [J]. Journal of Safety Science and Technology, 2013,9(6): 184-188.
[4] 张羽,贺娟茹. 高危系统社会监管双链机制设计:以天津港爆炸事故为例[J]. 工业安全与环保,2017,43(3): 1-4.
ZHANG Yu, HE Juanru. Design of the double:chain mechanism for social supervision in the high risk system: the explosion in Tianjin port as an example[J]. Industrial Safety and Environmental Protection, 2017,43(3): 1-4.
[5] 王起全. 输油管道泄漏火灾爆炸事故演化及应急疏散分析[J]. 中国安全科学学报, 2016,26(5): 24-29.
WANG Qiquan. Analysis of oil pipeline leak fire and explosion accident evolution and emergency evacuation [J].China Safety Science Journal,2016,26(5): 24-29.
[6] THEOPHILUS S C, ESENOWO V N, AREWA A O, et al. Human factors analysis and classification system for the oil and gas industry (HFACS-OGI)[J]. Reliability Engineering and System Safety, 2017,167: 168-176.
[7] 卢均臣,王延平,袁纪武,等. 2012年全球炼油与化工事故统计分析[J]. 安全、健康和环境,2014,14(2):5-7,11.
LU Junchen, WANG Yanping, YUAN Jiwu, et al. Statistical analysis of global refining and chemical accidents in 2012 [J].Safety Health and Environment, 2014,14(2):5-7, 11.
[8] 叶永峰,夏昕,李竹霞. 化工行业典型安全事故统计分析[J]. 工业安全与环保, 2012,38(9):49-51, 55.
YE Yongfeng, XIA Xin, LI Zhuxia. Statistical analysis of typical chemical industry accidents [J]. Industrial Safety and Environmental Protection, 2012, 38(9):49-51, 55.
[9] 刘蓉. 化工生产企业事故分析与预测研究[D].太原:中北大学, 2015.
LIU Rong. Research of accident analysis and prediction of chemical manufacturers [D].Taiyuan: North University of China, 2015.
[10] 刘瑾,孙宾宾. 化工安全事故的发生与预防措施[J]. 广州化工,2016,44(18): 251-253.
LIU Jin, SUN Binbin. Discussion on chemical safety accidents and preventive measures [J]. Guangzhou Chemical Industry, 2016, 44(18): 251-253.
[11] WIEGMANN D A, SHAPPELL S A. A human error analysis of commercial aviation accidents using the human factors analysis and classification system[R]. Office of Aviation Medicine Federal Aviation Administration, 2001.
[12] 傅贵,索晓,孙世梅. HFACS的细节层级元素在2-4Model中的对应研究[J].中国安全科学学报, 2016,26(10): 1-6.
FU Gui, SUO Xiao, SUN Shimei. A comparative research on classification of granularity level elements in HFACS into 24Model [J].China Safety Science Journal, 2016,26(10): 1-6.
[13] 赵娜,李亨英,曾建潮. 基于HFACS的煤矿瓦斯事故人因分析[J].煤矿安全, 2014,45(9): 172-174.
ZHAO Na, LI Hengying, ZENG Jianchao. Human factors analysis of coal mine gas accidents based on HFACS [J]. Safety in Coal Mines, 2014, 45(9): 172-174.

[1]	于溪芮, 何旭, 孔得朋. 基于Apriori算法的氢安全事故统计分析[J]. 中国安全科学学报, 2024, 34(4): 128-134.
[2]	郑霞忠, 刘奕成, 邵波, 王硕, 柯善钢. 基于文本挖掘的水电工程施工物体打击事故致因分析[J]. 中国安全科学学报, 2024, 34(4): 50-57.
[3]	王军武, 何娟娟, 宋盈辉, 刘一鹏, 陈兆, 郭婧怡. 基于RF-SFLA-SVM的装配式建筑高空作业工人不安全行为预警[J]. 中国安全科学学报, 2024, 34(3): 1-8.
[4]	王亮, 赖佳燕, 张自欣, 王应明. 基于文本挖掘和改进DEMATEL法的化工事故关键因素识别[J]. 中国安全科学学报, 2024, 34(3): 20-28.
[5]	刘丹, 罗颜声, 李诗轩, 许钢焱, 李墨潇. 面向飞行全过程的民航事故致因关联网络分析[J]. 中国安全科学学报, 2024, 34(3): 84-92.
[6]	袁晨辉, 傅贵, 吴治蓉, 赵金坤. 24Model与LCM原因因素定义对比研究[J]. 中国安全科学学报, 2024, 34(1): 27-34.
[7]	倪国栋, 杨栎, 安瑶瑶, 郑雨茁. 不安全行为信息在建筑工人群体中的传染机制[J]. 中国安全科学学报, 2024, 34(1): 43-52.
[8]	莫俊文, 王锐锐, 滕仓国. 高海拔铁路工程施工人员不安全行为评估[J]. 中国安全科学学报, 2023, 33(8): 30-38.
[9]	郑彬彬, 冯婷婷, 王佳贺, 肖远, 孙文浩. 基于文本挖掘的城镇燃气事故致因及关联分析[J]. 中国安全科学学报, 2023, 33(7): 190-195.
[10]	李乃文, 房小凯, 牛莉霞. 非正式群体凝聚力对矿工不安全行为的影响研究[J]. 中国安全科学学报, 2023, 33(7): 9-15.
[11]	宋炜, 傅贵. STPA与24Model的对比分析[J]. 中国安全科学学报, 2023, 33(6): 1-10.
[12]	刘全龙, 张晓霖, 张悦倩, 邱尊相. 基于事故致因的标准化煤矿建筑规范库构建[J]. 中国安全科学学报, 2023, 33(5): 128-133.
[13]	李磊, 李江娥, 任俞霏, 唐小小. 基于HFACS模型的城轨列车延误风险致因研究[J]. 中国安全科学学报, 2023, 33(5): 152-157.
[14]	王超, 徐楚昕, 董杰, 王志锋. 基于ST-GCN的空中交通管制员不安全行为识别[J]. 中国安全科学学报, 2023, 33(5): 42-48.
[15]	胡飞翔, 周建亮, 林欣燕, 张家琪, 戴可欣. 情绪对建筑工人不安全行为影响的认知试验[J]. 中国安全科学学报, 2023, 33(4): 75-83.