scholarly journals t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines

2021 ◽  
Author(s):  
Prasanjit Dey ◽  
K. Saurabh ◽  
C. Kumar ◽  
D. Pandit ◽  
S. K. Chaulya ◽  
...  
Keyword(s):  
Neural Network ◽  
Coal Mines ◽  
Gas Concentration ◽  
Underground Coal Mines

Research on Predictive Model of Gas Concentration for Driving Ventilation in Coal Mine Based on RBF and BP

2013 ◽  
Vol 706-708 ◽  
pp. 1805-1809
Author(s):  
Xiao Yan Gong ◽  
Jun Guo ◽  
He Xue ◽  
Dong Hui Yan ◽  
Zhe Wu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Rbf Neural Network ◽  
Coal Mines ◽  
Observation Data ◽  
Prediction Errors ◽  
Application Research ◽  
Gas Concentration ◽  
Mine Sites

In order to predict accurately gas concentration and design ventilation scheme in driving ventilation process under different gas emission in coal mine, based on the analysis of various ventilation factors, the prediction model structure of gas concentration for driving ventilation was designed based on RBF and BP neural network in this paper. Then MATLAB software and the observation data obtained from the coal mine sites were used to compare and analyze the prediction errors of two models, and a RBF neural network model with higher prediction precision was obtained. After that, the prediction model was used for practical application research on the gas concentration of the heading face in concrete coal mines. The research shows that the settled prediction model can not only predict the gas concentration precisely of driving ventilation, but also provide a certain theory basis for different driving ventilation equipment layout and parameters configuration in the driving ventilation process of coal mines.

A new simple methodology for evaluation of explosion risk in underground coal mines

2013 ◽  
Vol 26 (6) ◽  
pp. 1524-1529 ◽  
Author(s):  
Ljiljana Medic Pejic ◽  
Javier García Torrent ◽  
Enrique Querol ◽  
Kazimierz Lebecki
Keyword(s):  
Coal Mines ◽  
Underground Coal Mines ◽  
Explosion Risk
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