The OED–BO–RFR model for predicting CBM pressure during the pre-drainage process

Abstract

In actual coal mining operations, hazards like coal and gas outbursts pose significant threats to safety. Extracting coal seam gas through boreholes is the main method of coal mine gas disaster management. Currently, coal seam gas pressure often obtained through direct measurement in underground boreholes requires substantial human and material resources. Therefore, by integrating orthogonal experimental design (OED), random forest regression (RFR) algorithms and Bayesian optimization (BO), a model named OED–BO–RFR was proposed for predicting coal seam gas pressure during pre-drainage processes. The OED–BO–RFR model was tested against RFR, deep neural network (DNN) and support vector regression (SVR) models using a test dataset, revealing its substantial superiority in forecasting coal seam gas pressure during pre-drainage. Furthermore, analyzing the importance of various factors in the OED–BO–RFR model’s construction confirmed its scientific robustness and effectiveness. Subsequently, the OED–BO–RFR, RFR, DNN and SVR models were validated in situ for coal seam gas pre-drainage engineering. The OED–BO–RFR exhibited the best performance among the four models. These results demonstrate that the OED–BO–RFR model can swiftly and accurately predict gas pressure in borehole pre-drained coal seams, providing valuable insights and references for gas disaster management in coal mining enterprises.

Keywords

• Gas disaster control
• gas pre-drainage
• random forest regression
• coalbed gas pressure prediction
• Bayesian optimization