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

Zheng Li1, Min Yao2,3, Zhenmin Luo2

  • 1College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China. zhengl0610@163.com.

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|January 3, 2024
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Summary
This summary is machine-generated.

Developing an organizational-level accident triangle for China

Keywords:
Accident classificationAccident triangleK-means algorithmsRisk factorsText mining

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Area of Science:

  • Chemical Engineering
  • Industrial Safety
  • Risk Management

Background:

  • China's chemical industry growth presents ongoing safety challenges.
  • Despite research, chemical accident prevention remains a critical focus.
  • Optimizing safety management is vital for sustainable industrial development.

Purpose of the Study:

  • To develop an organizational-level accident triangle model.
  • To analyze accident patterns and risk factors in Chinese chemical enterprises.
  • To propose targeted accident prevention strategies.

Main Methods:

  • Developed an organizational-level accident triangle (group, unit, workshop levels).
  • Analyzed 484 accident records using Spearman correlation.
  • Applied TF-IDF and K-means for keyword extraction and text clustering.
  • Identified risk factors and proposed improvement measures.

Main Results:

  • Reducing unit-level accidents effectively prevents group-level incidents.
  • Accidents categorized into personal injury, production, environmental, and quality types.
  • Unsafe employee behaviors drive personal injury accidents.
  • Unsafe material conditions (e.g., equipment failure) cause other accident types.

Conclusions:

  • The organizational-level accident triangle simplifies accident analysis.
  • It aids rapid identification of risk factors for effective prevention.
  • The model offers practical guidance for chemical enterprise safety management.