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

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.

SUBMITTER: Wu Y

PROVIDER: S-EPMC8328327 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPostoperative nausea and vomiting (PONV) is a common complication after surgery. However, drugs cannot prevent it completely, and acupuncture therapy shows the potential in preventing PONV, yet the best choice hasn't been demonstrated.ObjectiveThis network meta analysis aimed to evaluate the effectiveness of different acupuncture therapies used for preventing PONV in abdominal operation.MethodsAuthors searched articles from PubMed/Medline, Cochrane library, Web of Science, Ebsco and Ovid/Embase, and established database from setup time to June 2019. Quality evaluation of included studies was performed with Cochrane risk-of-bias tool (ROB 2.0). Pairwise and network meta analysis were conducted by RevMan and Addis respectively.ResultsTwenty studies with 2862 patients were included in this research. Pairwise meta analysis shows that compared with placebo, transcutaneous electric nerve stimulation had lower risk of postoperative nausea (PON) (odds ratio (OR) = 0.42, 95%confidence interval (CI): 0.30-0.60), postoperative vomiting (POV) (OR = 0.53, 95%CI: 0.36-0.78), PONVs (OR = 0.46, 95%CI: 0.31-0.68), and postoperative rescue (POR) (OR = 0.61, 95%CI: 0.41-0.90), Capsicum had lower risk of PON (OR = 0.16, 95%CI: 0.09-0.28), PONVs (OR = 0.23, 95%CI: 0.12-0.45), Acupressure had lower risk of POV (OR = 0.42, 95%CI: 0.25-0.70), POR (OR = 0.42, 95%CI: 0.27-0.64). In network meta analysis, compared with usual care, the probability rank suggested that Acupoint Injection showed lowest risk of PON (OR = 0.02, 95%CI: 0.00-0.11), POV (OR = 0.06, 95%CI: 0.01-0.49), Usual care for PONVs (OR = 0.31, 95%CI: 0.13-0.75), and Capsicum for POR (OR = 0.39, 95%CI: 0.07-2.33). Further study should be carried out to verify this result.ConclusionBoth pairwise and network meta analysis showed acupuncture therapy was superior to placebo and usual care. Different acupuncture therapy regimens may have advantages in different aspects. And compared with POV, PON seems easier to control. Research results may provide guidance for the prevention of PONV.Systematic review registration: PROSPERO CRD42019147556.

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

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