Prediction method and application of gas emission from mining workface based on STL-EEMD-GA-SVR

Authors

LIN Haifei, College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; Coal Industry Western Mine Gas Intelligent Drainage Engineering Research Center, Xi’an University of Science and Technology, Xi’an 710054, ChinaFollow
LIU Shihao, College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
ZHOU Jie, College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
XU Peiyun, College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
SHUANG Haiqing, College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China; Coal Industry Western Mine Gas Intelligent Drainage Engineering Research Center, Xi’an University of Science and Technology, Xi’an 710054, China

Abstract

Accurate prediction of gas emission can provide important basis for mine ventilation and the prevention and measures of gas disasters. In order to improve the prediction accuracy of gas emission in the mining workface, the monitoring data of gas emission were decomposed into the trend term, periodic term and irregular fluctuation term by the Seasonal-Trend decomposition procedure based on Loess (STL) based on the monitoring data of gas emission from the mining workface of Huangling Mine in Shaanxi. Besides, the irregular fluctuation term was further broken down into the Intrinsic Mode Functions (IMFs) components with different characteristics and the residual margins by the Ensemble Empirical Mode Decomposition (EEMD). Then, each decomposed data was predicted by the Support Vector Regression (SVR) through parameter optimization by Genetic Algorithms (GA). Moreover, the prediction result of each component model was superposed to obtain the final prediction result of gas emission. In addition, the evaluation indicators for precision of STL-EEMD-GA-SVR model (hereinafter referred to as SEGS), EEMD-GA-SVR model, GA-SVR model and Gaussian Process Regression (GPR) model were analyzed comparatively in the 3 scenarios with 247, 147 and 70 groups of prediction set. According to the results, SEGS model is the best, of which the fitting degree R² was 0.81, 0.92 and 0.99 respectively, and the average relative error at the peak point was 3.15%, 2.33% and 1.04% respectively. In general, the constructed SEGS model could accurately predict the gas emission of mining workface.

Keywords

gas emission, machine learning, seasonal-trend decomposition, ensemble empirical mode decomposition, time series prediction

DOI

10.12363/issn.1001-1986.22.04.0218

Recommended Citation

LIN Haifei, LIU Shihao, ZHOU Jie, et al. (2022) "Prediction method and application of gas emission from mining workface based on STL-EEMD-GA-SVR," Coal Geology & Exploration: Vol. 50: Iss. 12, Article 14.
DOI: 10.12363/issn.1001-1986.22.04.0218
Available at: https://cge.researchcommons.org/journal/vol50/iss12/14

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