IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v238y2024i6p1209-1223.html
   My bibliography  Save this article

Analysis of accident risk factors in chemical industry based on ISM-BN

Author

Listed:
  • Yiming Ma
  • Mingguang Zhang
  • Mingliang Wang

Abstract

The chemical industry involves the production, storage, and use of many flammable, explosive, toxic, and other hazardous chemicals. Once an accident occurs, it will cause serious harm to human and economic activities. In order to prevent chemical accidents, this paper combines Interpretive Structural Modeling (ISM) and Bayesian network (BN) to quantitatively study the relationship and interaction strength among accident risk factors in chemical industry. Through the analysis of accident cases and questionnaire survey, 21 accident risk factors in chemical industry are selected. According to the decision of experts, the influence relationship between risk factors is determined, and a multi-level directed graph of ISM is obtained. And the ISM model is transformed into a quantitative BN model. The BN model is applied to forward reasoning, sensitivity analysis, and reverse reasoning. The results indicate that there is a positive correlation between various risk factors and chemical accidents, and the supervision mechanism has the highest probability of occurrence in production activities. Illegal operation has the highest sensitivity and the greatest impact on chemical accidents. Inherent hazards of materials and products is the most likely cause of accidents. Based on the research results, feasible measures have been proposed to improve safety management in the chemical industry.

Suggested Citation

  • Yiming Ma & Mingguang Zhang & Mingliang Wang, 2024. "Analysis of accident risk factors in chemical industry based on ISM-BN," Journal of Risk and Reliability, , vol. 238(6), pages 1209-1223, December.
  • Handle: RePEc:sae:risrel:v:238:y:2024:i:6:p:1209-1223
    DOI: 10.1177/1748006X231205382
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X231205382
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1748006X231205382?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:238:y:2024:i:6:p:1209-1223. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.