我国智能绿色矿业发展战略研究

为了对瓦斯超限和煤与瓦斯突出事故进行提前预警，建立了数据预处理、瓦斯浓度预测模型，研发了具有三级判识标准和流程的预警系统。通过基于LSTM算法的瓦斯浓度数据训练解决深度学习中的梯度消失与梯度爆炸，通过瓦斯特征分析反映瓦斯浓度时空相关性，通过研究样本时长、预测时长与预测精度之间的关系得出最优预测方法。结果表明：当子样本时间长度为1.0 h时，5 min的超前预测精度最高，符合1%的评判标准；当子样本的时间长度为1.5 h时，超前预测10 min和15 min的精度最高且符合评判标准；当子样本时间长度为2.0 h时，需要超前预测20 min才符合标准。瓦斯浓度预测分析与防突预警系统能够反映煤巷掘进过程中瓦斯浓度的变化规律并进行瓦斯浓度预测和实时防突预警，为安全高效的瓦斯防治工作提供技术支撑。

Design of gas concentration prediction and early warning system for outburst accident based on LSTM

In order to make early warning of gas overlimit and coal and gas outburst accidents, the model of data preprocessing and gas concentration prediction were established, and an early warning system with three-level of identification criteria and procedures was developed. Through the training of gas concentration data based on LSTM algorithm, the gradient disappearance and gradient explosion in deep learning were solved. The temporal and spatial correlation of gas concentration was reflected through the analysis of gas characteristics, and the optimal forecasting method was obtained by studying the relationship among sample duration, forecasting duration and forecasting accuracy. The results show that when the time of sub-sample is 1.0 h, the prediction accuracy of 5 min in advance is the highest, which meets the evaluation standard of 1%. When the time of sub-sample is 1.5 h, the prediction accuracy of 10 min and 15 min in advance is the highest, which meets the evaluation criteria. When the time of sub-sample is 2.0 h, it takes 20 min in advance to meet the standard. The prediction and analysis of gas concentration and the prewarning system can reflect the variation law of gas concentration in the process of coal drift driving, and carry out gas concentration prediction and real-time anti-outburst prewarning, providing technical support for gas prevention and control safely and efficiently.