Scenarios simulation of high-temperature molten metal tipping accidents with RealFlow software

Zhao, Yiqing; Jiang, Zhengsong; Jin, Aibing; Jiang, Linjing; Mtoni, Mtaita C.; Tianjian, Sun

doi:10.1016/j.ssci.2020.104934

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…In order to achieve safe production in different process industries, many scholars have conducted various studies for the determination of accident causes, simulation of accident consequences, and prevention of accident occurrence. For instance, Zhao et al 4 identified the root causes that triggered ladle tipping accidents in steel industry in China between 2006 and 2017, imported 3D models into Real Flow for simulation of accidents, and obtained the maximum‐risk‐radius regression lines under different ladle height tipping. Based on over 100 interviews with 20 chemical/pharmaceutical companies, Phimister et al 5 developed a seven‐stage management framework capable of analyzing the near‐miss incidents in companies: identification of attempted incidents, reporting, prioritization, cause analysis, development of solutions to prevent recurrence, dissemination of contingency measures, and, finally, problem resolution.…”

Section: Introductionmentioning

confidence: 99%

Significance ranking and correlation identification of accident causes in process industry based on system thinking and statistical analysis

Zhang

Zhong

Shi

et al. 2023

Process Safety Progress

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Accidents in process industry occur frequently with serious casualties and property losses. This paper builds an accident causation model of process industry based on system thinking by dividing the accident causation system into 4 subsystems and 22 factors. A combination of grey relational analysis and correspondence analysis is conducted to carry out a structured analysis of the collected data. The research contains three main parts: (1) Grey relational analysis is used to obtain the significance ranking of 22 cause factors in process industry, and three critical cause factors are identified as “Security inspection,” “Risk identification,” and “Security awareness.” (2) Through correspondence analysis, the correlations between three sets of variables are analyzed and the cause factors requiring focused attention are identified as “Electric spark,” “Temperature,” “Raw material control,” “Punching phenomenon,” “Equipment clogging,” and “Combustible gases.” (3) An intelligent monitoring scheme is developed for the critical factors of each subsystem, which aims to achieve real‐time monitoring and early warning by means of video surveillance and sensor placement for the human, equipment, and environment subsystems. The conclusions obtained from this study can be used to enhance the efficiency of safety management and reduce the probability of accident occurrence in the process industry.

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Section: Introductionmentioning

confidence: 99%

Significance ranking and correlation identification of accident causes in process industry based on system thinking and statistical analysis

Zhang

Zhong

Shi

et al. 2023

Process Safety Progress

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“…It is also pointed out that the high-temperature molten metal is still the focus of monitoring in the process of metal smelting and production, and it is a major risk factor which is prone to mass fatalities and mass injuries. Statistics show that from 2010 to 2013, 31% of accidents in metallurgical enterprises are related to high-temperature molten metal [ 2 ]. In 2015, the most common cause of casualties in iron and steel enterprises is still violation of operating procedures or labor discipline [ 3 ], causing 23 deaths, 7 serious injuries and 7 minor injuries, respectively accounting for 60.52% of the total number of deaths, 58.33% of the total number of serious injuries and 62.71% of the total number of casualties.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2021

PLoS ONE

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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.

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Occupational hazards and safety in secondary steelmaking industry: a review

Goyal,

Singh,

Bhunia

2024

Saf. Extreme Environ.

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Scenarios simulation of high-temperature molten metal tipping accidents with RealFlow software

Cited by 3 publications

References 9 publications

Significance ranking and correlation identification of accident causes in process industry based on system thinking and statistical analysis

Significance ranking and correlation identification of accident causes in process industry based on system thinking and statistical analysis

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

Occupational hazards and safety in secondary steelmaking industry: a review

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