Abstract
To improve the level of safety in coal mine production, it is important to enhance the accuracy of coal mine gas concentration prediction. In the context of deep learning, we proposed a mine gas concentration prediction model based on gated recurrent units (GRUs). The GRU model is not only simple in structure but also offers high prediction accuracy, and it can make full use of the time-series characteristic of mine gas concentration data. First, we apply the Pauta criterion and Lagrange interpolation to preprocess mine gas concentration monitoring data. Then, a spatial reconstruction method is used to construct the training set for the prediction model. Finally, the mean square error (MSE) is used as the loss function and adaptive moment estimation (Adam) is used as the optimization algorithm to determine the learning parameters of the GRU model for predicting gas concentration values. Experimental results show that compared with models based on support vector regression (SVR), a backpropagation neural network (BPNN), a recurrent neural network (RNN) and a long short-term memory (LSTM) network, the proposed GRU-based model for gas concentration prediction achieves reduced error on the test set, and moreover, the GRU model is more efficient than the LSTM model in terms of run time. Thus, the accuracy and efficiency of gas concentration prediction are both improved, showing that the proposed model is of high practical value.
Highlights
Coal is an important pillar of China’s primary energy consumption, and it is related to the economic and energy security of China
An experimental evaluation shows that the error of the proposed gated recurrent units (GRUs)-based gas concentration prediction model is reduced by 7.9% compared with that of a model based on a long short-term memory (LSTM) network and that its run-time efficiency is simultaneously improved by 13.05%, endowing it with better practical value for application
The input layer is responsible for preprocessing the original time series of gas concentration data to satisfy the requirements for the input to the GRU model; in the hidden layer, GRU neurons are used to construct a 1-layer loop neural network; and the output layer is mapped to one-dimensional sequence of data through a fully connected layer to realize gas concentration prediction
Summary
Coal is an important pillar of China’s primary energy consumption, and it is related to the economic and energy security of China. Coal enterprises have installed safety monitoring systems, but the main functions provided by these systems are the short-term identification of and response to disasters. These monitoring systems fail to fully exploit the value of the available gas data, resulting in insufficient forecasting ability for mine gas disasters. An experimental evaluation shows that the error of the proposed GRU-based gas concentration prediction model is reduced by 7.9% compared with that of a model based on a long short-term memory (LSTM) network and that its run-time efficiency is simultaneously improved by 13.05%, endowing it with better practical value for application.
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