| 基于灰色关联度BP神经网络预测煤层瓦斯含量 |
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| 引用本文： | 高锡擎,王来斌,沈金山,郑飞.基于灰色关联度BP神经网络预测煤层瓦斯含量[J].煤炭技术,2011,30(8). |
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| 作者姓名： | 高锡擎 王来斌 沈金山 郑飞 |
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| 作者单位： | 安徽理工大学地球与环境学院,安徽淮南,232001 |
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| 基金项目： | 安徽省教育厅自然基金(2006kj002B) |
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| 摘 要： | 以淮南矿区潘三矿13-1煤层为例,在分析潘三矿瓦斯地质资料的基础上,结合灰色关联度分析,确定煤层埋深、地质构造、煤层倾角、煤层厚度以及顶板岩性为影响煤层瓦斯含量的主要因素,建立瓦斯含量预测BP神经网络模型。对已建立的模型进行训练和检验,并预测煤层未开采区域瓦斯含量。结果表明:建立的预测模型能满足煤矿实际安全生产的要求,为矿井瓦斯灾害防治提供一定的参考依据。
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| 关 键 词： | 瓦斯含量预测 灰色关联度 BP神经网络 定量评价 |
Prediction of Seam Gas Content Based on Grey Correlation Degree and BP Neural Network |
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| GAO Xi-qing,WANG Lai-bin,SHEN Jin-shan,ZHENG Fei.Prediction of Seam Gas Content Based on Grey Correlation Degree and BP Neural Network[J].Coal Technology,2011,30(8). |
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| Authors: | GAO Xi-qing WANG Lai-bin SHEN Jin-shan ZHENG Fei |
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| Affiliation: | GAO Xi-qing,WANG Lai-bin,SHEN Jin-shan,ZHENG Fei(School of Earth and Environment,Anhui University of Science and Technology,Huainan 232001,China) |
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| Abstract: | The paper presented a BP neural network model to predict content of coalbed gas based on analyzing grey correlation degree and gas-geological characteristic of NO.13-1 coal bed of Pansan Mine in Huainan coal mining area.It's determined by the coal buried depth,thickness,lithologic properties of roof,seam angle and coal rank which are the important factors of controlling gas content of coal.The model has been established for training and testing,and forecasting the gas content of coal area without mining.The... |
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| Keywords: | gas content predication grey correlation degree BP neural network quantitative assessment |
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