Abstract To prevent coal spontaneous combustion, it is critical to accurately simulate and predict the low-temperature oxidation process of coal. In this study, an experiment system was constructed to investigate the temperature and gas concentration of two typical coals during low temperature oxidation. The back propagation neural network (BPNN) was proposed to simulate this process under six factors, including activation energy, void fraction, moisture content, air flow rate, stacking time and location of measuring point. The average relative error of oxygen concentration, temperature and CO concentration are 1.34 %, 1.09 % and 1.15 %, respectively. Besides, the statistical performance indicators are precise. Most importantly, the predicted temperature and gas concentration fields can be utilized to analyze coal spontaneous combustion process which allows us to track down the crucial factors on spontaneous combustion. By applying our BPNN modeling method, the odds of coal spontaneous combustion can be lowered.

中文翻译：

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基于BPNN的低温煤氧化温度场和CO浓度场预测

摘要 为防止煤炭自燃，准确模拟和预测煤炭的低温氧化过程至关重要。在这项研究中，建立了一个实验系统来研究低温氧化过程中两种典型煤的温度和气体浓度。提出了反向传播神经网络（BPNN）在六个因素下模拟该过程，包括活化能、空隙率、水分含量、空气流速、堆叠时间和测量点位置。氧浓度、温度和CO浓度的平均相对误差分别为1.34%、1.09%和1.15%。此外，统计性能指标准确。最重要的是，预测的温度场和瓦斯浓度场可用于分析煤自燃过程，使我们能够追踪自燃的关键因素。通过应用我们的 BPNN 建模方法，可以降低煤自燃的几率。