Research on prediction model of coal spontaneous combustion temperature based on SSA-CNN

Abstract

To predict the coal spontaneous combustion temperature accurately and efficiently, this study proposes a model based on the Sparrow Search Algorithm (SSA) and Convolutional Neural Network (CNN). Firstly, the study analyzes the main gas reactions during the coal oxidation to pyrolysis process. Six gas indicators, namely O2, CO, C2H4, CO/ΔO2, C2H4/C2H6, and C2H6, are closely related to coal temperature. Subsequently, a prediction indicator system is established. Then, the excellent data mining capabilities of CNN are leveraged through deep learning, along with their unique advantages in local perception and weight sharing, and a CNN prediction model framework is constructed. Moreover, the comparison between the algorithm performances is executed and SSA is selected for optimization. Utilizing its exceptional global search capability and adaptability, SSA optimizes the seven hyper-parameters of the model, significantly enhancing prediction accuracy. In the final step, SSA-CNN is compared with five reference models on test samples. The SSA-CNN model showcases a maximum relative error of 0.155, outperforming other models. Moreover, the RMSE of this model yields 8.4500, which is also lower than other models. The results suggest that the combination of the selected gas indicators with the SSA-CNN model can accurately predict the spontaneous combustion temperature of coal.