基于深度学习的煤巷掘进工作面瓦斯涌出量预测研究
Prediction of gas emission from coal roadway heading face based on deep learning theory
李鹏 1辛诗雨 2闫凡壮 2周爱桃2
作者信息
- 1. 国能神东煤炭集团有限责任公司,陕西 榆林 719315
- 2. 中国矿业大学(北京) 应急管理与安全工程学院,北京 100083
- 折叠
摘要
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.
Abstract
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.
关键词
瓦斯涌出量/煤巷掘进工作面/深度学习/LSTM神经网络/预测模型Key words
gas emission/coal roadway heading face/deep learning/LSTM neural network/prediction model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据