Research on Prediction and Prevention of Coal Spontaneous Combustion in Goaf Based on Optimized Random Forest

This paper takes the working face of Heiyanquan Mine as the research subject. Based on the mechanism of coal self-ignition and the gas generation pattern, combined with coal oxidation and temperature rise experiments and machine learning algorithms, a prediction and early warning model for spontaneous combustion risk in goaf areas is constructed. The results show that the double-layer random forest model optimized by grid search and cross-validation has a mean absolute error (MAE) of 6.47 and a determination coefficient (R2) of 0.9845, with a prediction relative error between 0 and 5%. When the working face was advancing slowly through a fault, the model projected that the peak temperature at a depth of 72 meters in the goaf area reached 49.2°C and surpassed 40°C for five consecutive instances, indicating a spontaneous combustion risk. After calculation and numerical simulation, a prevention and control plan was determined with a nitrogen injection flow rate of 9 m3/min and a nitrogen injection port 65 meters away from the working face. During the nitrogen injection process, the system continuously predicted and warned. When the measurement point No. 4 was located at a depth of 105 meters in the goaf area, it was determined that there was no risk of spontaneous combustion, and nitrogen injection was stopped. The findings offer a scientific, accurate, and efficient method for controlling spontaneous combustion in coal mine gob areas, which helps enhance the effectiveness of fire prevention and extinguishment.