Method and Validation of Coal Mine Gas Concentration Prediction by Integrating PSO Algorithm and LSTM Network

• Published in: Processes Vol. 12; no. 5; p. 898
• Main Authors: Yang, Guangyu, Zhu, Quanjie, Wang, Dacang, Feng, Yu, Chen, Xuexi, Li, Qingsong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Algorithms; Artificial intelligence; Coal industry; Coal mines; Coal mining; Comparative analysis; Forecasts and trends; Long short-term memory; Methods; Neural networks; Particle swarm optimization; Performance prediction; Prediction models; Rankings; Support vector machines; China; Guizhou China
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr12050898

Gas concentration monitoring is an effective method for predicting gas disasters in mines. In response to the shortcomings of low efficiency and accuracy in conventional gas concentration prediction, a new method for gas concentration prediction based on Particle Swarm Optimization and Long Short-Term Memory Network (PSO-LSTM) is proposed. First, the principle of the PSO-LSTM fusion model is analyzed, and the PSO-LSTM gas concentration analysis and prediction model is constructed. Second, the gas concentration data are normalized and preprocessed. The PSO algorithm is utilized to optimize the training set of the LSTM model, facilitating the selection of the training data set for the LSTM model. Finally, the MAE, RMSE, and coefficient of determination R2 evaluation indicators are proposed to verify and analyze the prediction results. Gas concentration prediction comparison and verification research was conducted using gas concentration data measured in a mine as the sample data. The experimental results show that: (1) The maximum RMSE predicted using the PSO-LSTM model is 0.0029, and the minimum RMSE is 0.0010 when the sample size changes. This verifies the reliability of the prediction effect of the PSO-LSTM model. (2) The predictive performance of all models ranks as follows: PSO-LSTM > SVR-LSTM > LSTM > PSO-GRU. Comparative analysis with the LSTM model demonstrates that the PSO-LSTM model is more effective in predicting gas concentration, further confirming the superiority of this model in gas concentration prediction.