Prediction of gas concentration in the upper corner of mining working face based on the FEDformer-LGBM-AT architecture

LIANG Yunpei; LI Shang; LI Quangui; GUO Yabo; SUN Wanjie; ZHENG Menghao; WANG Chengcheng

doi:10.13225/j.cnki.jccs.YG24.1512

Abstract：In the context of the intelligent upgrading of coal mines,mining high-quality information from massive monit-oring data of working faces to construct scientific models that enhance prediction duration and accuracy is crucial for pre-venting excessive gas concentration in the upper corner.However,there are many factors that affect the gas concentration in the upper corner,and there is a lack of utilization of massive data.The prediction accuracy of gas concentration is high but the duration is short,only 0～30 minutes,while the prediction accuracy and generalization ability are poor for medi-um to long duration 30～60 minutes.In order to solve this problem,this article takes a coal mining face in Shanxi Province as the research object.Firstly,the coal seam gas content of the face is dynamically extracted,and a feature set of coal seam gas content,gas concentration,coal mining machine,and wind speed is constructed.Then,the feature set is pre-processed,and different features are screened based on correlation analysis.Further construct short-term trends,stable trends,periodic trends,and concatenated features of relevant features.Firstly,a gas concentration prediction layer based on frequency enhanced decomposition transformer(FEDformer)is constructed,and a residual correction layer based on lightweight gradient boosting machine(LGBM)is constructed.Then,adaptive thresholding(AT)technology is introduced to construct a threshold perception layer.Finally,a three-layer gas concentration prediction model architecture is formed to predict the gas concentration in the upper comer within the next 60 minutes,and the prediction performance was investig-ated by recalling rate,false positive rate,MAE and MAPE.The research results indicate that the short-term recall rate of the upper corner gas concentration prediction model based on the FEDformer-LGBM-AT architecture is 0.956,the false alarm rate is 0.035,the MAE is 0.033,and the MAPE is 0.183;The recall rate of long-term prediction is 0.940,the false positive rate is 0.035,the MAE is 0.047,and the MAPE is 0.262;Compared with traditional models such as Grey Model(GM),Support Vector Machine(SVM),Backpropagation(BP),Gated Recurrent Unit(GRU),Particle Swarm Optimized Long Short Term Memory(PSO-LSTM),Transformer,etc.,the FEDformer-LGBM-AT architecture model has better long-term prediction accuracy and generalization ability.The adaptive threshold perception makes the model sensitive to high-value gas concentrations.This architecture model compensates for the limitations and generalization of short-term predic-tion,supports on-site gas exceedance prevention measures,and can provide certain reference and guidance for intelligent prediction of gas concentration in mining face.