PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 19, 2024International Journal of Occupational Safety and Ergonomics5 citations

24Model-based comparative analysis of two catastrophic hazardous chemical pipeline accidents

View Full Paper
JWJianhao WangMZMengmeng ZhangHXHuacai Xian

Key Points

Key points are not available for this paper at this time.

Abstract

Objectives. This study conducted a comparative analysis of two catastrophic pipeline accidents in China in order to identify some common mistakes and lessons learned to prevent similar accidents. Methods. The 24Model was used in this study, which provides a universal pathway for accident analysis from the individual level to the organizational level. Results. There were similarities between the two cases in the aspects of the occurrence, development, emergency and causation at different levels: both were caused by leaks of pipelines and evolved into multiple explosions during emergency response; both leaks were caused by the corrosion of pipelines in the confined space of a damp or salt-spray environment; both were classified as 'responsibility accidents', and unsafe acts, such as the failure to identify hidden hazards of pipelines that were the direct cause of accidents, reflected the shortcomings of individual safety habitual behaviour in terms of knowledge, awareness, habits and psychology; weaknesses in the organizational management mainly concerned hazard identification, pipeline maintenance, emergency disposal, etc.; and there is not a good safety climate within the organization. Conclusions. Organizations should develop a closed-loop management system and strengthen the construction of safety culture, and the government should supervise the implementation of procedures.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Wang et al. (2024) studied this question.

synapsesocial.com/papers/68e73613b6db6435876af18ahttps://doi.org/10.1080/10803548.2024.2325258
Ask AI
Helpful
Bookmark
Share
View Full Paper