Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series

Tianjun Zhang,Shaobo Pan,Liyun Han,Shuang Song,Shugang Li,Li Ma

doi:10.3390/en12010161

Tianjun Zhang, Shaobo Pan + Show 4 more

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https://doi.org/10.3390/en12010161

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Abstract

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.

Highlights

China is a large coal consuming and producing country
Combined with the characteristics of gas concentration data, this paper proposes a gas concentration prediction model based on Long short-term memory (LSTM) that can effectively predict the gas concentration in the time period and provide a strong basis for formulating a reasonable gas drainage plan, improving coal mine safety management
Data was divided into a test set, a training set, a backtracking set, and a verification set, with the training set used for model training, the test set used to test the model learning effect, and the backtrack set used as an input variable to predict the time period

Summary

Introduction

China is a large coal consuming and producing country. Gas emissions from coal seams increase sharply during the coal mining process; gas overruns and coal and gas outbursts are frequent; and it has become a challenging topic for safe coal mine production. Energies 2019, 12, 161 has done a lot of such research, and proposed the gas concentration of hybrid kernel least squares support vector machine based on phase space reconstruction theory and adaptive chaos particle swarm optimization theory [1,2,3,4]. Scholars such as Wu used support vector machine (SVM) and the differential evolution (DE) algorithm to establish a prediction model, and based on the Markov chain, the residual correction predicted the gas concentration change trend, compared with the direct SVM prediction of the granular data [5]. Zhang proposed a dynamic nerve network gas concentration real-time prediction model for improved prediction accuracy of gas concentration and less running time [8]

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