Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry

Ching Wu Cheng, Hong Qing Yao, Tsung-Chih Wu

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Accidents that occur in the petrochemical industry frequently result in serious social issues. Behind every occupational accident, there are safety management problems requiring investigation. This study collected 349 cases of major occupational accidents in the petrochemical industry between 2000 and 2010 in Taiwan for analysis. Using descriptive statistics, we elucidated the factor distribution of these major occupational accidents. The data mining classification and regression tree (CART) was used to examine the distribution and rules governing the factors of the disasters. This study found that for equipment such as pipelines and control valves, devising high-quality safety and protective devices/ maintenance/renewal plans and pipeline setups/design plans can effectively prevent accidents such as fires, explosions, and poisoning caused by material leakage, as well as employees being caught in/rolled up in machinery. Furthermore, implementing safety management measures, such as worker safety educational training, and enforcing standards for inspections, operations, and risk assessments personnel, has become an important factor in accident prevention. This study suggests the use of the following measures: for abnormal conditions such as pipeline cracking/damage or rusting, hightemperatures caused by material leakage into the inner protective layer of pipelines should be prevented. Considering overlapping pipelines, rusting issues caused by pipelines touching each other should be avoided, and maintenance and repair should be performed to ensure the safety of work environments. These measures can eliminate the risk of work injuries and resulting social issues.

Original languageEnglish
Pages (from-to)1269-1278
Number of pages10
JournalJournal of Loss Prevention in the Process Industries
Volume26
Issue number6
DOIs
Publication statusPublished - 2013 Jan 1

Fingerprint

occupational accidents
Occupational Accidents
petrochemicals
Data Mining
Petrochemicals
Protective Devices
Data mining
Safety Management
Industry
Accidents
Pipelines
industry
Maintenance
Accident Prevention
Safety
Explosions
Disasters
accidents
Taiwan
Poisoning

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Food Science
  • Chemical Engineering(all)
  • Safety, Risk, Reliability and Quality
  • Energy Engineering and Power Technology
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

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Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry. / Cheng, Ching Wu; Yao, Hong Qing; Wu, Tsung-Chih.

In: Journal of Loss Prevention in the Process Industries, Vol. 26, No. 6, 01.01.2013, p. 1269-1278.

Research output: Contribution to journalArticle

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