Machine Learning-Based Full-Cycle Evaluation Method for Gas Extraction Performance in Coal Mines

With the continuous increase in coal mining depth, gas-related disasters have become a critical constraint for the safe and efficient extraction of coal resources. To address prominent issues such as the lack of scientific evaluation in extraction design, insufficient dynamic monitoring during the process, and delayed effectiveness assessment, this study establishes a full-cycle evaluation framework for gas extraction, encompassing the entire process from design and construction to implementation. A Bayesian optimization-based Random Forest Regression model (BO-RFR) is proposed for the preliminary evaluation of extraction schemes, while a reduced-order prediction model integrating deep neural networks and convolutional autoencoders (DNN-CAE) is developed to enable dynamic prediction and reconstruction of residual gas pressure fields. Field tests demonstrated that the BO-RFR model achieved a residual gas pressure prediction error of less than 0.02 MPa, while the DNN-CAE dynamic evaluation model yielded an average mean squared error (MSE) of 2.73 × 10^-5 and mean absolute error (MAE) of 0.00493, confirming the high accuracy and reliability of the proposed method in engineering applications. The results of this study can provide scientific decision-making support for coal mine gas control, enabling precise control and intelligent management of the extraction process. This holds significant practical value for enhancing the safety of coal mine production.