Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

The effective prediction of gas concentration and the reasonable formulation of corresponding safety measures have important significance for improving the level of coal mine safety. To improve the accuracy of gas concentration prediction and enhance the applicability of the models, this paper starts with actual coal mine production monitoring data, improves the accuracy of gas concentration prediction through multi-parameter fusion prediction, and constructs a recurrent neural network (RNN)-based multi-parameter fusion prediction of coal face gas concentration. We determined the performance evaluation index of the model’s prediction method; used the grid search method to optimize the hyperparameters of the batch size; and used the number of neurons, the learning rate, the discard ratio, the network depth, and the early stopping method to prevent overfitting. The gas concentration prediction models—based on RNN and PSO-SVR and PSO-Adam-BP neural networks—were compared and analyzed experimentally with the mean absolute percentage error (MAPE) as the performance evaluation index. The result show that using the grid search method to adjust the batch size, the number of neurons, the learning rate, the discard ratio, and the network depth can effectively find the optimal hyperparameter combination. The training error can be reduced to 0.0195. Therefore, Adam’s optimized RNN gas concentration prediction model had higher accuracy and stability than the BP neural network and SVR. During training, the mean absolute error (MAE) could be reduced to 0.0573, and the root mean squared error (RMSE) could be reduced to 0.0167; however, the MAPE could be reduced to 0.3384% during prediction. The RNN gas concentration prediction model and parameter optimization method based on Adam optimization can effectively predict gas concentration. This method shows high accuracy in the prediction of gas concentration time series and can be used as a reference model for predicting mine gas concentration.

Tectonic coal is a kind of soft coal that is generated during tectonic movement. Gas outbursts usually occur in seams containing both virgin coal and tectonic coal. To reveal the adsorption characteristics of this type of coal seam (containing both virgin coal and tectonic coal), both tectonic coal and virgin coal were collected from the same longwall face and a series of laboratory tests were conducted, including coal sorption tests and pore specific surface measurements. Both the tectonic coal and virgin coal were crushed into coal powder (0.18–0.25 mm) for the coal sorption tests. In these laboratory tests, different mass ratios between tectonic coal and virgin coal were tested. We found that with the increase of the percentage of tectonic coal, the adsorption volume showed a rising trend, reached its maximum value, and then decreased. The specific surface areas of the mixed coal samples had the same evolution trends as those of the adsorption volume. From the laboratory tests, we found that when the mass ratio of virgin coal to tectonic coal was 1:1, both the adsorption volume and the specific surface areas reached their maximum values. Due to the percentage variation of the tectonic coal in the panel with the advancement of the longwall face, when the tectonic coal accounted for 50% of the total coal, the gas content would rise. Thus, proper measures should be adopted for outburst hazards control. The mathematical model between the change of specific surface area and the stress and strain of pore expansion before and after gas adsorption was established, and the relationship between the change of pore structure and gas emission before and after gas adsorption was obtained. It provides a theoretical basis for further research on coal and gas outburst mechanisms.

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.