Evidence map›Paper›PMID 38166879›Full record

ArticleBMC public health2024

A chemical accident cause text mining method based on improved accident triangle.

Zheng Li, Min Yao, Zhenmin Luo, Xinping Wang, Tongshuang Liu, Qianrui Huang, Chang Su

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Zheng LiCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China. zhengl0610@163.com.
Min YaoCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Zhenmin LuoCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Xinping WangCollege of Management, Xi'an University of Science and Technology, Xi'an, 710054, China.
Tongshuang LiuCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Qianrui HuangCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Chang SuCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.

Funding

Key R&D Project in Ningxia Hui Autonomous Region 2022BEE02001National Natural Science Foundation of China 51674193
6 · The paper itself

Abstract

backgroundWith the rapid development of China's chemical industry, although researchers have developed many methods in the field of chemical safety, the situation of chemical safety in China is still not optimistic. How to prevent accidents has always been the focus of scholars' attention.

methodsBased on the characteristics of chemical enterprises and the Heinrich accident triangle, this paper developed the organizational-level accident triangle, which divides accidents into group-level, unit-level, and workshop-level accidents. Based on 484 accident records of a large chemical enterprise in China, the Spearman correlation coefficient was used to analyze the rationality of accident classification and the occurrence rules of accidents at different levels. In addition, this paper used TF-IDF and K-means algorithms to extract keywords and perform text clustering analysis for accidents at different levels based on accident classification. The risk factors of each accident cluster were further analyzed, and improvement measures were proposed for the sample enterprises.

resultsThe results show that reducing unit-level accidents can prevent group-level accidents. The accidents of the sample enterprises are mainly personal injury accidents, production accidents, environmental pollution accidents, and quality accidents. The leading causes of personal injury accidents are employees' unsafe behaviors, such as poor safety awareness, non-standard operation, illegal operation, untimely communication, etc. The leading causes of production accidents, environmental pollution accidents, and quality accidents include the unsafe state of materials, such as equipment damage, pipeline leakage, short-circuiting, excessive fluctuation of process parameters, etc.

conclusionCompared with the traditional accident classification method, the accident triangle proposed in this paper based on the organizational level dramatically reduces the differences between accidents, helps enterprises quickly identify risk factors, and prevents accidents. This method can effectively prevent accidents and provide helpful guidance for the safety management of chemical enterprises.

Indexed as

AccidentsChemical Hazard ReleaseEnvironmental PollutionHumansRisk FactorsSafety ManagementAccident classificationAccident triangleK-means algorithmsRisk factorsText mining

Identifiers

PMID38166879
PMCPMC10762847

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.