| 王福生,张志明,董宪伟.基于BP神经网络的煤自燃倾向性预测——以唐山矿及荆各庄矿为例[J].唐山学院学报,2020,33(3):16-20 |
| 基于BP神经网络的煤自燃倾向性预测——以唐山矿及荆各庄矿为例 |
| Forecast of Coal Spontaneous Combustion Tendency Based on BP Neural Network: With Tangshan Mine and Jinggezhuang Mine as an Example |
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| DOI：10.16160/j.cnki.tsxyxb.2020.03.004 |
| 中文关键词: 煤自燃倾向性 BP神经网络 预测模型 唐山矿 荆各庄矿 |
| 英文关键词: spontaneous combustion tendency of coal BP neural network forecast model Tangshan Mine Jinggezhuang Mine |
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| 中文摘要: |
| 为准确预测煤的自燃倾向性，在总结和分析煤自燃倾向性研究现状的基础上，选取煤的组成与结构方面的碳含量、镜质组含量、固定碳含量、比表面积、微孔占比与羟基含量6项主要影响因素建立了基于BP神经网络的煤自燃倾向性预测模型，确立了建模所需的样本，并运用Matlab软件进行网络训练并完成模型检验。将该模型应用于唐山矿9煤层、11煤层及荆各庄矿的煤自燃倾向性预测，结果显示误差均小于5%，证明基于BP神经网络的煤自燃倾向性预测模型的准确度较高，可用于工程实际。 |
| 英文摘要: |
| In order to accurately forecast the spontaneous combustion tendency of coal, a forecast model based on BP neural network is established, where six main factors influencing the composition and texture of coal are selected, including the carbon content, vitrinite content, fixed carbon content, specific surface area, micropore ratio and hydroxy content, with the research status about this topic being summarized and analyzed . After the samples needed for modeling are established and the Matlab software is used for net training, the model test is completed. This model has been applied to forecast the coal spontaneous combustion tendency for coal seams No.9 and No. 11 in Tangshan Mine and the coal in Jinggezhuang Mine. The results show that the errors are less than 5%, which proves that this forecast model can achieve high accuracy and be used in the practice. |
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