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Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

Author

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  • Yaju Wu
  • Kaili Xu
  • Ruojun Wang
  • Xiaohu Xu

Abstract

Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.

Suggested Citation

  • Yaju Wu & Kaili Xu & Ruojun Wang & Xiaohu Xu, 2021. "Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-16, August.
    • Handle: RePEc:plo:pone00:0254861
    DOI: 10.1371/journal.pone.0254861
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    Cited by:

    1. Fakhradin Ghasemi & Mohammad Babamiri & Zahra Pashootan, 2022. "A comprehensive method for the quantification of medication error probability based on fuzzy SLIM," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-17, February.

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