Optimum Machine Learning on Gas Extraction and Production for Adaptive Negative Control

To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume, which adversely impacts gas utilization efficiency in mines, a gas extraction pure volume prediction model was developed using Support Vector Regression (SVR) and Random Forest (RF), with hyperparameters fine-tuned via the Genetic Algorithm (GA). Building upon this, an adaptive control model for gas extraction negative pressure was formulated to maximize the extracted gas volume within the pipeline network, followed by field validation experiments. Experimental results indicate that the GA-SVR model surpasses comparable models in terms of mean absolute error, root mean square error, and mean absolute percentage error. In the extraction process of bedding boreholes, the influence of negative pressure on gas extraction concentration diminishes over time, yet it remains a critical factor in determining the extracted pure volume. In contrast, throughout the entire extraction period of cross-layer boreholes, both extracted pure volume and concentration exhibit pronounced sensitivity to fluctuations in extraction negative pressure. Field experiments demonstrated that the adaptive control model enhanced the average extracted gas volume by 5.08% in the experimental borehole group compared to the control group during the later extraction stage, with a more pronounced increase of 7.15% in the first 15 days. The research findings offer essential technical support for the efficient utilization and long-term sustainable development of mine gas resources. The research findings offer essential technical support for gas disaster mitigation and the sustained, efficient utilization of mine gas.