Abstract:The corresponding engineering effects are inevitable and occurred when the coal mines working face mining. The main manifestations are distribution of mining stress and fracture of overlying rock seams. The mining effects play a controlling role in the occurrence of dynamic disasters such as disastrous coal mine earthquakes and rock bursts. In view of this, taking No. 1305 working face mining of Dongtan coal mine for background, the distribution and transfer characteristics of mining stress in working face and… Show more
“…If the ventilation in the mining area is not good enough, the continuous explosion caused by combustion of coal and gas can worsen the disaster and make the situation more complicated [10][11][12][13][14][15][16]. Moreover, the propagation of shock waves in complex wind networks can damage the ventilation system, once the decision-making misplays, it might expand and deepen the scope and degree of personnel damage [17][18][19][20][21][22][23][24]. Therefore, researching the identification and prediction methods of thermodynamic disasters during deep coal mining is a very important work for the design of mine emergency systems and the decision-making of mine rescue and personnel evacuation.…”
Researching the methods to identify and predict thermodynamic disasters during deep coal mining is a very important work for the design of mine emergency systems and the decision-making of mine rescue and personnel evacuation, however, existing studies only built static models without evaluating or predicting the development trend of thermodynamic disasters, the research on dynamic modeling methods and rescue decision-making is insufficient, and they generally ignored the mechanism of mutual conversion between fire and gas explosion in deep coal mines. Thus, this paper aims to study the identification and prediction of thermodynamic disasters during deep coal mining. At first, the method for analyzing the thermal field in deep coal mining areas is introduced in detail, and the finite element thermal analysis method is adopted to study the thermodynamic disasters during deep coal mining; then, this paper establishes a thermodynamic disaster prediction model based on the improved Kernel-based Extreme Learning Machine (KELM), and introduces the improved Crow Search Algorithm (CSA) to solve the instability of prediction results caused by artificial selection of model parameters. At last, this paper uses experimental results to verify the validity of the proposed model.
“…If the ventilation in the mining area is not good enough, the continuous explosion caused by combustion of coal and gas can worsen the disaster and make the situation more complicated [10][11][12][13][14][15][16]. Moreover, the propagation of shock waves in complex wind networks can damage the ventilation system, once the decision-making misplays, it might expand and deepen the scope and degree of personnel damage [17][18][19][20][21][22][23][24]. Therefore, researching the identification and prediction methods of thermodynamic disasters during deep coal mining is a very important work for the design of mine emergency systems and the decision-making of mine rescue and personnel evacuation.…”
Researching the methods to identify and predict thermodynamic disasters during deep coal mining is a very important work for the design of mine emergency systems and the decision-making of mine rescue and personnel evacuation, however, existing studies only built static models without evaluating or predicting the development trend of thermodynamic disasters, the research on dynamic modeling methods and rescue decision-making is insufficient, and they generally ignored the mechanism of mutual conversion between fire and gas explosion in deep coal mines. Thus, this paper aims to study the identification and prediction of thermodynamic disasters during deep coal mining. At first, the method for analyzing the thermal field in deep coal mining areas is introduced in detail, and the finite element thermal analysis method is adopted to study the thermodynamic disasters during deep coal mining; then, this paper establishes a thermodynamic disaster prediction model based on the improved Kernel-based Extreme Learning Machine (KELM), and introduces the improved Crow Search Algorithm (CSA) to solve the instability of prediction results caused by artificial selection of model parameters. At last, this paper uses experimental results to verify the validity of the proposed model.
“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”
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