Experimental Study on the Escape Velocity of Miners during Mine Fire Periods

Zhang, Ruixin; Rongshan, Nie; Zhao, Hongze; Yanqiang, Fan

doi:10.1155/2018/9458785

Cited by 3 publications

(6 citation statements)

References 17 publications

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“…Ruixin et al (2018) performed an experiment to study escape velocity. It was carried out on the roadway of the Mu Chengjian Coal Mine.…”

Section: Methodsmentioning

confidence: 99%

“…In the experiment, the relationships between the miner's escape velocity and the inclination of the roadway, the area of the accessible section of the roadway, and the average wind velocity of the roadway were obtained through single-factor analysis and two multiple regression equations for the escape velocity were developed through multivariable linear regression analysis. From this experiment, a mathematical model was conducted to be used to predict the miner's escape velocity during mine fire periods under different roadway conditions (Equations (6) and (7)) (Ruixin et al 2018): …”

Section: Methodsmentioning

confidence: 99%

“…From this experiment, a mathematical model was conducted to be used to predict the miner's escape velocity during mine fire periods under different roadway conditions (Equations ( 6) and ( 7)) (Ruixin et al 2018):…”

Section: The Escape Velocity Of Miners During Mine Fire Periodsmentioning

confidence: 99%

“…We merged them all to create shapefiles and then converted all shapefiles to feature classes inside the geodatabase (Figure 4). The calculation of the escape velocity during the mine fire was conducted through Equations ( 6) and ( 7) (Ruixin et al 2018).…”

Section: Generation Of Gis Filesmentioning

confidence: 99%

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Optimization of escape routes during mine fire using GIS

2023

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One of the essential factors in underground mining operations is safety during potential mine accidents, like fires, for example. We used Geographical Information System (GIS) for network analysis to identify the best escape routes from different locations inside an underground mine during a fire to a safe place inside or outside the mine. We created an effective evacuation system with a more accurate 2D model based on fire dynamics, the actual dimensions of the mine, and its related elements. We applied our system to the Maghara coal mine to predict the possible fire outbreak scenarios. Our results could be used as a guide to evacuation and rescue plans during harmful events in underground mines, especially fires. Our system is an integrated system that analyses potential fire scenarios and elects the best evacuation plan using GIS technology.

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“…Ruixin et al (2018) performed an experiment to study escape velocity. It was carried out on the roadway of the Mu Chengjian Coal Mine.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: The Escape Velocity Of Miners During Mine Fire Periodsmentioning

confidence: 99%

Section: Generation Of Gis Filesmentioning

confidence: 99%

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Optimization of escape routes during mine fire using GIS

2023

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“…Based on the reviewed literature, two threshold values hold a central function during an evacuation in a smoke-filled environment [30,32]. The first threshold value is the visibility level at which evacuees in general can be expected to start reducing their evacuation speed.…”

Section: Model For the Conversion Of The Factors That Influence The Smentioning

confidence: 99%

Methodology for Optimal Fire Evacuations in Underground Mines Based on Simulated Scenarios

Adjiski¹,

Despodov²

2020

Fire Safety and Management Awareness

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The purpose of this chapter is to develop a methodology that will contribute in locating optimal evacuation routes in case of fire that are based on minimal carbon monoxide (CO) exposure during the evacuation procedures. The proposed methodology is tested using simulated fire scenarios from which CO concentration over time curve is extracted from all available evacuation routes and presented in a weighted form based on the accumulating effect of CO inhalation in the form of fractional effective dose (FED). The safety limits of the FED model on which the optimization process is based are determined using a model for the prediction of carboxyhemoglobin (COHb) levels in human blood. The COHb model is associated with predicted clinical symptoms that are the basis for determining the level of incapacitation at which the mineworkers are incapable of completing their evacuation. Also in the process of improving the fire risk analysis, the proposed methodology enables the development of evacuation plans that are based on the results of modeled fire scenarios combined together with the results of the anticipated hazards generated by CO inhalation. The results presented in this chapter indicate a more precise approach in the process of planning the evacuation system inside the underground mines.

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Research on the Prediction Method of Monthly Hidden Danger Quantity in Coal Mine Based on BP Neural Network Periodic Combination Model

Zhang

et al. 2022

Mathematical Problems in Engineering

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To better prevent the occurrence of hidden dangers of coal mine accidents and ensure the safety production of coal mine enterprises. This paper mines and analyses the pattern of historical monthly hidden danger quantity in the coal mine and constructs three models: the traditional backpropagation (BP) neural network model, the BP neural network based on the adaptive moment estimation optimization algorithm (Adam-BP) model, and the BP neural network prediction model with the introduction of monthly moderators (Month-Adam-BP). The experimental results show that the Adam-BP model can improve the prediction accuracy, in which the mean absolute percentage error (MAPE) improves by 8.93%, the root mean square error (RMSE) improves by 8.15%, the postdifference ratio C improves by 0.04, and the small error probability P improves by 0.12; the Month-Adam-BP model with the introduction of the monthly adjustment factor further improves the prediction accuracy, in which MAPE improves by 2.61%, RMSE improves by 5.41%, the postdifference ratio C improves by 0.06, and the small error probability P improves by 0.03. And the Month-Adam-BP model prediction accuracy reaches the level 2 standard with credible prediction effect; it can also be used to predict coal mines with periodic characteristics of hidden hazard data. Our prediction results show that the predicted number of hidden hazards in this coal mine for the next month is 29, which is an increase compared to the number of hidden dangers in the previous month. Thus, the coal mine safety managers need to strengthen the management of hidden hazards further to prevent accidents, which can better serve the standardization of coal mine safety production and ensure the smooth production of the coal mine.

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Experimental Study on the Escape Velocity of Miners during Mine Fire Periods

Cited by 3 publications

References 17 publications

Optimization of escape routes during mine fire using GIS

Optimization of escape routes during mine fire using GIS

Methodology for Optimal Fire Evacuations in Underground Mines Based on Simulated Scenarios

Research on the Prediction Method of Monthly Hidden Danger Quantity in Coal Mine Based on BP Neural Network Periodic Combination Model

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