A spatio-temporal temperature prediction model for coal spontaneous combustion based on back propagation neural network

Coal spontaneous combustion (CSC) occurs with the change of temperature and gas concentration which is useful for early warning. To explore the intrinsic correlation between temperature evolution and gas distribution, a Back Propagation Neural Network (BPNN) model of coal temperature - gas composition - spatial distance was constructed, which is based on the gas characteristics and spatial distribution of coal temperature in large-scale coal spontaneous combustion experiments. The results showed that the O2 concentration reached the lowest value (8.058%) at 45 cm on Day 52 with the time. CO and CO2 also formed concentration peaks at 45 cm, and the gas distribution continuously moved towards the inlet position. The hydrocarbon gas components showed periodic increases, with values below 100 ppm. When the supply air time increased, the temperature rise rate in the initial stage of oxidation was slow but relatively uniform. Affected by ventilation conditions and coal density, two high-temperature zones were observed at 45 cm and 85 cm in the experimental furnace. With the inclusion of spatial distance parameters in coal temperature prediction, the BPNN model exhibits accurate predictive capability, with a total error of 0.94997. This can provide a method for predicting temperature changes in coal mine.