A Method for Predicting Coal Temperature Using CO with GA-SVR Model for Early Warning of the Spontaneous Combustion of Coal
Guo Qing, Wanxing Ren, Wei Lu
Abstract
Temperature is the key factor influencing the spontaneous combustion of coal, but it is difficult to obtain accurate temperature data because of the complex physical environment of the mining area. A mathematical model relating coal temperature to CO concentration was derived from data collected from a low-temperature oxidation experiment. Subsequently, a model is established that uses a genetic algorithm to select and optimize penalty factor C and kernel function parameter g of a support-vector regression model (GA-SVR). Taking O2, CO2 and C2H6 as independent variables, the GA-SVR model is then employed to calculate CO concentration. This predicted CO concentration is then used to calculate coal temperatures and assess the risk of spontaneous combustion. The performance of the GA-SVR model is compared with standard SVR, random forest and back propagation neural network models. The results demonstrate that the GA-SVR model has superior accuracy and generalization capabilities. This model can be used to predict coal temperatures within mines and provide an early warning for spontaneous combustion.