A temperature prediction model for coal spontaneous combustion based on PSO-SRU deep artificial neural networks - JIA Pengtao; LIN Kaiyi; GUO Fengjing (Gong-kuang zidonghua, 2022)
Traditional temperature prediction models for coal spontaneous combustion typically have low generality and robustness. This paper improves them by proposing a coal spontaneous combustion temperature prediction model based on particle swarm optimization and simple recurrent unit(PSO-SRU). It firstly pre-processes the gas concentration data collected from temperature programmed oxidation tests, selects the concentration data of O2, CO, CO2, CH4, C2H4 that highly relate to the coal temperature as the prediction indicators, and further separates the indicators into training and testing data sets. Then, a SRU based prediction model over the training data set is trained to learn the nonlinear relationship between the coal spontaneous combustion temperature and the indicators. Mean absolute error(MAE) forms the fitness function and PSO algorithms are involved to optimize the SRU prediction model's parameters. Finally, the PSO-SRU model with optimized parameters are applied over the testing data set to predict the coal spontaneous combustion temperature. Experiments show the PSO-SRU model can improve the prediction accuracy, as the model's MAE and root mean square error(RMSE), comparing with those generated by support vector regression(SVR), random forest(RF), and back propagation(BP), decreases by 12.58, 7.65, 5.91 ℃, and 22.65, 17.45, 8.94 ℃ respectively. The PSO-SRU model also demonstrates a good generality and robustness, as the difference of determination coefficient (R2) of the model over the training and testing data sets is only 0.03.