综采面区段煤柱宽度预测GRNN模型构建与应用
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摘要
以近水平煤层综采工作面区段煤柱合理宽度预测为目标,分析了近水平综采工作面煤层内聚力、煤层厚度、弹性模量、内摩擦角、容重、泊松比、埋深、工作面长度、煤层倾角对区段煤柱留设的影响因素与关系。基于广义神经网络算法构建了近水平综采工作面区段煤柱留设宽度的神经网络预测模型。以某矿典型工作面为背景,运用所构建的广义回归神经网络进行预测并运用四折交叉验证算法对光滑因子进行优化,消除了模型构建参数选择的人为影响。预测结果表明与工作面实际区段煤柱仅有1%左右的误差。为验证广义回归神经网络的优越性,建立了普通BP神经网络模型进行对比,得出GRNN模型对于多种影响因素非线性耦合预测结果具有较好稳定性与精确性,为实现近水平综采工作面的精准开采提供了参考依据。
Based on the target of near horizontal coal seam section of fully mechanized working face reasonable coal pillar width prediction,the influence factors of the near horizontal coal seam of fully mechanized working face which include cohesion,coal seam thickness,elastic modulus,internal friction angle,unit weight,poisson's ratio,buried depth,length of working face,coal seam dip angle on section pillar and the relationship between them. Generalized regression neural network forecast model was established by generalized neural network algorithm to predict near horizontal section pillar width of fully mechanized working face. a typical mine working face was selected as the research background and spreads were optimized by four-fold cross validation algorithm,the artificial influence of model building parameter selection was eliminated. Forecast results show that there is only about 1% error width of section pillar. To verify the superiority of generalized regression neural network,an ordinary back propagation neural network model was set up,GRNN nonlinear coupling prediction results of various influencing factors have good stability and accuracy by contrast GRNN model and BPNN model,providing a reference for the precision mining of near horizontal fully mechanized working face.
Based on the target of near horizontal coal seam section of fully mechanized working face reasonable coal pillar width prediction,the influence factors of the near horizontal coal seam of fully mechanized working face which include cohesion,coal seam thickness,elastic modulus,internal friction angle,unit weight,poisson's ratio,buried depth,length of working face,coal seam dip angle on section pillar and the relationship between them. Generalized regression neural network forecast model was established by generalized neural network algorithm to predict near horizontal section pillar width of fully mechanized working face. a typical mine working face was selected as the research background and spreads were optimized by four-fold cross validation algorithm,the artificial influence of model building parameter selection was eliminated. Forecast results show that there is only about 1% error width of section pillar. To verify the superiority of generalized regression neural network,an ordinary back propagation neural network model was set up,GRNN nonlinear coupling prediction results of various influencing factors have good stability and accuracy by contrast GRNN model and BPNN model,providing a reference for the precision mining of near horizontal fully mechanized working face.
引文
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[19]阮永芬,叶燎原.用灰色系统理论与方法确定深基坑支护方案[J].岩石力学与工程学报,2003,22(7):1203-1206.RUAN Yong-fen,YE Liao-yuan. Determination of deep foundation pit supporting scheme by using the gray system theory and methord[J]. Chinese Journal of Rock Mechanics and Engineering,2003,22(7):1203-1206.
[20]贾义鹏,吕庆,尚岳全.基于粒子群算法和广义回归神经网络的岩爆预测[J].岩石力学与工程学报,2013,32(2):343-348.JIA Yi-peng,LV Qing,SHANG Yue-quan. Rockburst prediction using partice swarm optimization algorithm and grneral regression neural network[J]. Chinese Journal of Rock Mechanics and Engineering,2013,32(2):343-348.
[21]谭锋奇,李洪奇,孟照旭,等.数据挖掘方法在石油勘探开发中的应用研究[J].石油地球物理勘探,2010,45(1):85-91,164,172.TAN Feng-qi,LI Hong-qi,MENG Zhao-xu,et al. Studies on application of data mining method in oil exploration and development[J]. Oil Geophysical Prospecting,2010,45(1):85-91,164,172.
[2]袁亮.煤炭精准开采科学构想[J].煤炭学报,2017,42(1):1-7.YUAN Liang. Scientific conception of precision coal mining[J]. Journal of China Coal Society,2017,42(1):1-7.
[3]白矛,刘天泉.条带法开采中条带尺寸的研究[J].煤炭学报,1983,20(4):21-28.BAI Mao,LIU Tian-quan. Study on pillar size and mining width for partial mining[J]. Journal of China Coal Society,1983,20(4):21-28.
[4]张国华,张雪峰,蒲文龙,等.中厚煤层区段煤柱留设宽度理论确定[J].西安科技大学学报,2009,29(5):521-526.ZHANG Guo-hua,ZHANG Xue-feng,PU Wen-long,et al. Theoretical determination of the section coal pillar’s reserve width in medium thick coal seam[J]. Journal of Xi’an University of Science and Technology,2009,29(5):521-526.
[5] Guy Reed,Kent Mctyer,Russell Frith. An assessment of coal pillar system stability criteria based on a mechanistic evaluation of the interaction between coal pillars and the overburden[J]. International Journal of Mining Science and Technology,2017,27(1):9-15.
[6] ZHANG Jun-fei,JIANG Fu-xing,ZHU Si-tao,et al.Width design for gobs and isolated coal pillars based on overall burst-instability prevention in coal mines[J].Journal of Rock Mechanics and Geotechnical Engineering,2016,8(4):551-558.
[7]冯夏庭,杨成祥.智能岩石力学———参数与模型的智能辨识[J].岩石力学与工程学报,1999,18(3):350-353.FENG Xia-ting,YANG Cheng-xiang. Intelligent rock mechanics and its contents[J]. Chinese Journal of Rock Mechanics and Engineering,1999,18(3):350-353.
[8]蔡美峰,王双红.地应力状态与围岩性质的关系研究[J].中国矿业,1997(6):38-42.CAI Mei-feng,WANG Shuang-hong. Relation between ground stress behaviour and properties of surrounding rock[J]. China Mining Magazine,1997(6):38-42.
[9]刘思峰.灰色系统理论的产生与发展[J].南京航空航天大学学报,2004,36(2):267-272LIU Si-feng. Emergence and development of grey system theory and its forward trends[J]. Journal of Nanjing University of Aeronautics&Astronautics,2004,36(2):267-272.
[10]魏峰远,陈俊杰,邹友峰.影响保护煤柱尺寸留设的因素及其变化规律[J].煤炭科学技术,2006,34(10):85-87.WEI Feng-yuan,CHEN Jun-jie,ZOU You-feng. Influence factors and change law of protection coal pillars left[J]. Coal Science and Technology,2006,34(10):85-87.
[11]赵雁海,宋选民,刘宁波.浅埋煤层群中煤柱稳定性及巷道布置优化研究[J].煤炭科学技术,2015,43(12):12-17.ZHAO Yan-hai,SONG Xuan-min,LIU Ning-bo. Research on stability of coal pillar and roadway layout optimization in shallow multi-seams[J]. Coal Science and Technology,2015,43(12):12-17.
[12]王永革,肖辉赞.大采高工作面区段煤柱合理尺寸的数值模拟[J].辽宁工程技术大学学报,2011,30(5):645-648.WANG Yong-ge,XIAO Hui-zan. Numerical simulation on reasonable size of section coal pillar in the face of big mining height[J]. Journal of Liaoning Technical University,2011,30(5):645-648.
[13]郑仰发,鞠文君,康红普,等.基于三维应变动态监测的大采高综采面区段煤柱留设综合试验研究[J].采矿与安全工程学报,2014,31(3):359-365.ZHENG Yang-fa,JU Wen-jun,KANG Hong-pu,et al.Combined test research on coal pillar width setting of district sublevel for fully-mechanized face with large mining height based on 3D dynamic strain monitoring[J]. Journal of Mining and Safety Engineering,2014,31(3):359-365.
[14]耿耀强,陈通,黄克军,等.庙哈孤矿区浅埋煤层护巷煤柱合理宽度研究[J].中国矿业,2016,25(1):97-100.GENG Yao-qiang,CHEN Tong,HUANG Ke-jun,et al.Research into rational width of chain coal-pillar of shallow coal seam in Miaohagu mining area[J]. China Mining Magazine,2016,25(1):97-100.
[15]宋选民,窦江海.浅埋煤层回采巷道合理煤柱宽度的实测研究[J].采矿与安全工程学报,2003,34(3):31-33.SONG Xuan-min,DOU Jiang-hai. Study on the technology of reinforcing roadways under complex conditions[J]. Journal of Mining and Safety Engineering,2003,34(3):31-33.
[16]黄庆享,杜君武,刘寅超,等.浅埋煤层群工作面合理区段煤柱留设研究[J].西安科技大学学报,2016,36(1):19-23.HUANG Qing-xiang,DU Jun-wu,LIU Yin-chao,et al.Study on section coal pillar of group mining in shallow buried coal seams[J]. Journal of Xi’an University of Science and Technology,2016,36(1):19-23.
[17]张新荣.浅埋深综采工作面区段煤柱宽度优化研究[J].煤炭科学技术,2012,40(5):14-16.ZHANG Xin-rong. Study on width optimization of sectional coal pillar for fully mechanized coal mining face in shallow depth mine[J]. Coal Science and Technology,2012,40(5):14-16.
[18]蒋力帅,刘洪涛,连小勇,等.浅埋中厚煤层护巷煤柱合理宽度研究[J].煤矿开采,2012,17(4):105-108.JIANG Li-shuai,LIU Hong-tao,LIAN Xiao-yong,et al.Research on rational width of coal-pillar in shallow-buried medium-thick coal seam[J]. Coal Mining Technology,2012,17(4):105-108.
[19]阮永芬,叶燎原.用灰色系统理论与方法确定深基坑支护方案[J].岩石力学与工程学报,2003,22(7):1203-1206.RUAN Yong-fen,YE Liao-yuan. Determination of deep foundation pit supporting scheme by using the gray system theory and methord[J]. Chinese Journal of Rock Mechanics and Engineering,2003,22(7):1203-1206.
[20]贾义鹏,吕庆,尚岳全.基于粒子群算法和广义回归神经网络的岩爆预测[J].岩石力学与工程学报,2013,32(2):343-348.JIA Yi-peng,LV Qing,SHANG Yue-quan. Rockburst prediction using partice swarm optimization algorithm and grneral regression neural network[J]. Chinese Journal of Rock Mechanics and Engineering,2013,32(2):343-348.
[21]谭锋奇,李洪奇,孟照旭,等.数据挖掘方法在石油勘探开发中的应用研究[J].石油地球物理勘探,2010,45(1):85-91,164,172.TAN Feng-qi,LI Hong-qi,MENG Zhao-xu,et al. Studies on application of data mining method in oil exploration and development[J]. Oil Geophysical Prospecting,2010,45(1):85-91,164,172.
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