Prediction of gas emission from coal roadway heading face based on deep learning theory
The study of gas outflow from coal roadway development face is of great significance for the prevention and control of gas in coal roadway face.Using the characteristics of deep learning theory and long and short-term memory neural network to process the time series samples efficiently,a prediction model of gas emission prediction model based on LSTM neural network is established.The hyperparameters of the model are optimized according to the size of the loss value in the training process,and the optimal hyperparameters are selected and determined.With the help of the original data of gas emission from the coal roadway heading face,the applicability and accuracy of the model are verified,and the variation trend of gas emission in time dimension is analyzed according to the predicted results.The results of the study are of reference significance for predicting the trend of gas outflow in coal roadway heading face,identifying abnormal gas emission in the face,and improving the level of gas control.
gas emissioncoal roadway heading facedeep learningLSTM neural networkprediction model
NETL
NSTL
万方数据
