Study on the temperature prediction model of residual coal in goaf based on ACO-KELM

Existing studies on the temperature prediction of residual coal in goafs have mainly focused on the relationship between temperature and gas concentration, with limited attention given to the complex nonlinear relationships between the residual coal temperature in the goaf, the distance from the working face, and the air leakage velocity. To address this gap, a prediction model based on ant colony optimization (ACO) and kernel extreme learning machine (KELM) (ACO-KELM) was proposed. In the 21404 working face goaf of Hulususu Coal Mine, beam tubes and distributed fiber optics were arranged to collect data on O2 concentration, CO concentration, CO2 concentration, and temperature within the goaf. Simultaneously, the air leakage intensity and horizontal distances from the working face were incorporated to construct the KELM model. ACO was employed to optimize the regularization coefficients and kernel parameters in the KELM model, thereby obtaining the best-performing hyperparameter combination and generating the optimal KELM model. Compared to the prediction models based on extreme learning machine (ELM) and random forest (RF) algorithms, the ACO-KELM model achieved an average absolute error of 0.0701 ℃ and a root mean square error (RMSE) of 0.0748 ℃ on the test set, reducing these errors by 65% and 195%, respectively, compared to the ELM-based model, and by 53% and 156%, respectively, compared to the RF-based model. The coefficient of determination (R2) for the ACO-KELM model on the test set was 0.9635, which was only 0.01 lower than that of the training set, indicating that the model was not overfitted and demonstrated a high degree of accuracy.