Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis

Methane is one of the most dangerous gases encountered in the mining industry. During mining operations, methane can be broadly classified into three states: mining excavation, stoppage safety check, and abnormal methane concentration, which is usually a precursor to a gas accident, such as a coal a...

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Bibliographic Details
Published in:Energies (Basel) Vol. 15; no. 6; p. 2262
Main Authors: Meng, Xiangrui, Chang, Haoqian, Wang, Xiangqian
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2022
Subjects:
Decomposition
deep learning
methane concentration prediction
mining safety
Neural networks
recurrent neural network
Sensors
Time series
time series analysis
Trends
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:Methane is one of the most dangerous gases encountered in the mining industry. During mining operations, methane can be broadly classified into three states: mining excavation, stoppage safety check, and abnormal methane concentration, which is usually a precursor to a gas accident, such as a coal and gas outburst. Consequently, it is vital to accurately predict methane concentrations. Herein, we apply three deep learning methods—a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU)—to the problem of methane concentration prediction and evaluate their efficacy. In addition, we propose a novel prediction method that combines classical time series analysis with these deep learning models. The results revealed that GRU has the least root mean square error (RMSE) loss of the three models. The RMSE loss can be further reduced by approximately 35% by using the proposed combined approach, and the models are also less likely to result in overfitting. Therefore, combining deep learning methods with classical time series analysis can provide accurate methane concentration prediction and improve mining safety.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en15062262