Abstract
Accurate predictions of the coal temperature in coal spontaneous combustion (CSC) are important for ensuring coal mine safety. Gas coal (the Zhaolou coal mine in Shandong Province, China) was used in this paper. A large CSC experimental device was adopted to obtain its characteristic temperatures from the macroscopic characteristics of gas production. A simulated annealing-support vector machine (SA-SVM) prediction model was proposed to reflect the complex nonlinear mapping between characteristic gases and the coal temperature. The risk degree of CSC was estimated in the time domain, and the model was verified by using in situ data from an actual working face. Furthermore, back-propagation neural network (BPNN) and single SVM methods were adopted for comparison. The results showed that the BPNN could not adapt to the small-sample problem due to overfitting and the output of a single SVM was unstable due to its strong dependence on the setting of hyperparameters. Through the SA global optimization process, the optimal combination of hyperparameters was obtained. Therefore, SA-SVM had higher prediction accuracy, robustness, and error tolerance rate and better environmental adaptability. These findings have certain practical significances for eliminating the hidden danger of CSC in the gob and providing timely warnings about potential danger.