CHN Energy Shendong Coal Group Co. , Ltd.
School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing

研究煤巷掘进工作面瓦斯涌出量,对于煤巷掘进工作面瓦斯防治具有重要意义。利用深度学习理论与长短期记忆神经网络高效处理时间序列样本的特性,建立基于LSTM神经网络的煤巷掘进工作面瓦斯涌出量预测模型,依据训练过程中损失值的大小对模型超参数进行优化,选择并确定模型的最优超参数,借助煤巷掘进工作面瓦斯涌出量原始数据,验证模型的适用性和准确性,并根据预测结果分析工作面瓦斯涌出量在时间维度上的变化趋势。研究结果对预测煤巷掘进工作面瓦斯涌出变化趋势、判别工作面瓦斯异常涌出、提升掘进工作面瓦斯治理水平具有参考意义。

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.

国家自然科学基金项目(52130409,52121003)

李鹏,辛诗雨,闫凡壮,等.基于深度学习的煤巷掘进工作面瓦斯涌出量预测研究[J].煤炭工程,2024,56(12):115-124.