基于非线性理论的煤与瓦斯突出预测技术研究
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摘要
我国是一个资源大国，但也是地质灾害最严重的国家，煤与瓦斯突出就是其中的一种较普遍的矿井生产自然灾害。我国煤与瓦斯突出矿井数量多，分布范围广且突出事故频繁。尤其是随着矿井开采深度的增大，开采地质条件日益复杂，瓦斯压力不断增大，大大提高了突出的危险性。因此，致力于煤与瓦斯突出预测预报和预处理的方法研究对提高矿井的安全生产有着非常重大的现实意义。
本文采用非线性理论对煤与瓦斯突出机理及危险性预测方法进行了较深入研究，通过分析煤与瓦斯突出非线性机理，以表征地应力大小的声发射指标和表征瓦斯状况的瓦斯浓度指标为影响瓦斯突出的敏感指标。并以此分别对声发射和瓦斯单项指标进行了研究，编制相应的BP神经网络程序，建立了ANNS声发射预测模型和ANNC瓦斯浓度指标预测模型。
在总结和分析煤与瓦斯突出机理及影响因素的数字表征基础上，运用滚动预测法，确定了1小时声发射频数值和10分钟瓦斯浓度平均值为样本的输入参数，并探讨了网络模型结构的设计，包括输入输出层单元指标的选取、隐层单元、训练样本的确定以及输入输出数据的处理。在此基础上运用层次分析法得出了声发射1小时和8小时6个指标的权重，从而计算出声发射的综合指标值，最后结合瓦斯浓度指标得出了预测煤与瓦斯突出的综合指标CI，并给出了危险临界值。
通过对现场数据进行预测分析，对比常规指标，验证了综合指标在预测工作面前方后续时间段内突出危险状况是准确且可行的。
China is a country with extensive resources, but also is one of the most affected countriesof geological disasters, coal and gas outburst is one of the common natural disasters in mineproduction. Large numbers, wide distribution and the frequent accidents are the characteristicsof coal and gas outburst mine in our country, especially with the increase of mining depth, thegrowing complex geological conditions and the gas pressure increasing constantly, magnitudeof coal and gas outburst is greatly enhanced. Therefore, the research of prediction andpretreatment method of coal and gas outburst contributed to improve the safety of mine.
In this paper, based on the nonlinear theory mechanism and prediction method of coaland gas outburst were further studied. By analyzing the theory, the acoustic emission indexwas used to display the ground stress value and the gas concentration index was used tocharacterize the status of gas, both of the two indices were made as the sensitive indicatorsaffecting the coal and gas outburst. And on this basis, acoustic emission and gas index werestudied, the corresponding BP neural network was programmed, ANNS AE model and ANNCprediction model of gas concentration were established.
Based on the summary and analysis of gas outburst mechanism and the number land ofinfluencing factors, using rolling forecast method, determined the sample input parameterswith 1 hour acoustic emission frequency value and 10 minutes gas concentration average, anddiscussed the design of network model structure, including the selection of input or outputlayer unit index, the determination of hidden layer unit and training sample, and the process ofinput or output data. Based on these above, the weighting of six indexes including 1 hour and8 hours and so on, were obtained by the analytical hierarchy process (AHP) as well as theacoustic emission comprehensive index. Finally, comprehensive index CI ,used to predict coaland gas outburst, and the critical value of risk were got by synthetically analyzing both the acoustic emission comprehensive index and gas density index.
Through analysis of field data to predict the contrast of conventional indicators, we havebeen validated for the front side indicators in forecasting the follow-up time period outburstsituation is accurate and feasible.
本文采用非线性理论对煤与瓦斯突出机理及危险性预测方法进行了较深入研究，通过分析煤与瓦斯突出非线性机理，以表征地应力大小的声发射指标和表征瓦斯状况的瓦斯浓度指标为影响瓦斯突出的敏感指标。并以此分别对声发射和瓦斯单项指标进行了研究，编制相应的BP神经网络程序，建立了ANNS声发射预测模型和ANNC瓦斯浓度指标预测模型。
在总结和分析煤与瓦斯突出机理及影响因素的数字表征基础上，运用滚动预测法，确定了1小时声发射频数值和10分钟瓦斯浓度平均值为样本的输入参数，并探讨了网络模型结构的设计，包括输入输出层单元指标的选取、隐层单元、训练样本的确定以及输入输出数据的处理。在此基础上运用层次分析法得出了声发射1小时和8小时6个指标的权重，从而计算出声发射的综合指标值，最后结合瓦斯浓度指标得出了预测煤与瓦斯突出的综合指标CI，并给出了危险临界值。
通过对现场数据进行预测分析，对比常规指标，验证了综合指标在预测工作面前方后续时间段内突出危险状况是准确且可行的。
China is a country with extensive resources, but also is one of the most affected countriesof geological disasters, coal and gas outburst is one of the common natural disasters in mineproduction. Large numbers, wide distribution and the frequent accidents are the characteristicsof coal and gas outburst mine in our country, especially with the increase of mining depth, thegrowing complex geological conditions and the gas pressure increasing constantly, magnitudeof coal and gas outburst is greatly enhanced. Therefore, the research of prediction andpretreatment method of coal and gas outburst contributed to improve the safety of mine.
In this paper, based on the nonlinear theory mechanism and prediction method of coaland gas outburst were further studied. By analyzing the theory, the acoustic emission indexwas used to display the ground stress value and the gas concentration index was used tocharacterize the status of gas, both of the two indices were made as the sensitive indicatorsaffecting the coal and gas outburst. And on this basis, acoustic emission and gas index werestudied, the corresponding BP neural network was programmed, ANNS AE model and ANNCprediction model of gas concentration were established.
Based on the summary and analysis of gas outburst mechanism and the number land ofinfluencing factors, using rolling forecast method, determined the sample input parameterswith 1 hour acoustic emission frequency value and 10 minutes gas concentration average, anddiscussed the design of network model structure, including the selection of input or outputlayer unit index, the determination of hidden layer unit and training sample, and the process ofinput or output data. Based on these above, the weighting of six indexes including 1 hour and8 hours and so on, were obtained by the analytical hierarchy process (AHP) as well as theacoustic emission comprehensive index. Finally, comprehensive index CI ,used to predict coaland gas outburst, and the critical value of risk were got by synthetically analyzing both the acoustic emission comprehensive index and gas density index.
Through analysis of field data to predict the contrast of conventional indicators, we havebeen validated for the front side indicators in forecasting the follow-up time period outburstsituation is accurate and feasible.
引文
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[30]纪洪广等.无损伤监测中常用声发射参数分析与评价[J].无损检测,2001,23(7):289～292.
[31]西安科技大学西安西科测控设备有限责任公司. GDD-1矿用矿压动态传感器(矿用本质安全型)使用说明书.2004.4.
[32]西安科技大学西安西科测控设备有限责任公司. KJ110N型矿井安全综合监控系统软件使用说明书.2004.8.
[33]苏燹等.工作面突出危险性实时跟踪预测技术及装备的研究科学技术报告[R].煤炭科学研究总院重庆分院.2000.2.
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[35]李萍丰.浅谈煤与瓦斯突出机理的假说—二相流体假说[J].煤矿安全,1989(11):29～35.
[36]梁冰，章梦涛等.煤和瓦斯突出的固流耦合失稳理论[J].煤炭学报, 1995,20 (5):492～496.
[37]蒋承林，俞启香.煤与瓦斯突出机理的球壳失稳假说[J].煤矿安全, 1995,16 (2):17～25.
[38]卢连宁，张迎新等.煤与瓦斯突出机理的球壳失稳假说[J].黑龙江科技学院学报,1995,17(1):17~25.
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[2]柴兆喜.各国煤和瓦斯突出概况[M].世界煤炭技术,1984,4,25～26.
[3]王凯，俞启香.煤与瓦斯突出的非线性特征及预测模型[M].徐州:中国矿业大学出版社,2005,3.
[4]焦作矿业学院瓦斯地质研究室.瓦斯地质概论[M].北京:煤炭工业出版社,1990,52～56.
[5]张玉功，王魁军，范启炜.北票矿区煤与瓦斯突出预测预报的研究与应用[J].煤矿安全,1991(1),17~22.
[6] ITAKURA K，NAKAJIMA I,WATANABE Y. AE activity in cross-measure derivatesagainst outburst-prone coal seams—study on AE activity prior to gas outburst (1streport). Journal of the Mining and Metallurgical Institute of Japan，1988，104(1206)：495~503.
[7]石显鑫，蔡栓荣，冯宏等.利用声发射技术预测预报煤与瓦斯突出[J].煤田地质与勘探,1998,26,60～65.
[8]秦汝祥.煤与瓦斯突出预报研究现状综述[J].能源技术与管理,2005,1,7～8.
[9] T. Xua, C.A. Tanga, T.H. Yangc, W.C. Zhu, J. Liu. Numerical investigation of coal andgas outbursts in underground collieries International Journal of Rock Mechanics &Mining Sciences 43 (2006) ,905～919.
[10]何学秋，刘明举.含瓦斯煤岩破坏电磁动力学[M].中国矿业大学出版社,1995,52～54.
[11]王恩元，何学秋，聂百胜等.电磁辐射法预测煤与瓦斯突出原理[J].中国矿业大学学报,2000,2,225～229.
[12]聂百胜，何学秋，王恩元等.用电磁辐射法非接触预测煤与瓦斯突出[J].煤矿安全，2000,2,41～43.
[13] Xiao-Zhao Li, An-Zeng Hua. Prediction and prevention of sandstone-gas outbursts incoal mines. International Journal of Rock Mechanics & Mining Sciences 43 (2006), 2～18.
[14] Yaqin Wu, Kai Wang, Maoguang Wang.Study on compound genetic and back propag-ation algorithm for prediction of coal and gas outburst risk. Intelligent InformationProcessing III (2006),233～241.
[15]蔡成功，侯锦秀.层次分析、定性数据定量化在煤与瓦斯突出预测中的应用[J].数学的实践与认识,2005,35(10):59~64.
[16]南存全，冯夏庭.基于SVM的煤与瓦斯突出区域预测研究[J].岩石力学与工程学报,2005,24(2):263~267.
[17]赵耀江，王冶.基于神经网络建立煤与瓦斯突出预测模型[J].中国安全科学报,1997.7,23～25.
[18]于洪仕，付兴武.小波包变换在煤与瓦斯突出预测中的应用[J].黑龙江科技学院学报,2006,16(5):279~281.
[19]高雷阜.煤与瓦斯突出的混沌动力系统演化规律研究[D].辽宁:辽宁工程技术大学,2006.
[20]范金志，郭德勇，张建国.层次分析法确定煤与瓦斯突出影响因素的权重[J].矿业安全与环保.2001.31(3):4～7.
[21]吕绍林，何继善.瓦斯突出煤体的粒度分形研究[J].煤炭科学技术,1999,27(2):46~49.
[22] M.B. Wold, L.D. Connell, S.K. Choi. The role of spatial variability in coal seamparameters on gas outburst behavior during coal mining. InternationalJournal of CoalGeology (2008), doi: 10.1016/j.coal.2008.01.
[23]凌复华.突变理论[M].上海出版社,1989,13~58.
[24]于不凡，王佑安.煤矿瓦斯灾害防治及利用技术手册[M].北京:煤炭工业出版,2006.
[25]李忠峰.煤与瓦斯突出机理及其条件评述[J].煤炭科学技术,1997(11).
[26]唐春安，王淑红，傅宇方著.岩石破裂过程数值试验[M].北京:科学出版社,2003.5.
[27]于不凡.煤和瓦斯突出机理[M].北京:煤炭工业出版社,1985.
[28]霍多特B B.煤与瓦斯突出[M].宋士钊，王佑安译.北京:中国工业出版社,1966.
[29]胡千庭.煤与瓦斯突出的力学作用机理及应用研究[D].北京:中国矿业大学(北京),2007.
[30]纪洪广等.无损伤监测中常用声发射参数分析与评价[J].无损检测,2001,23(7):289～292.
[31]西安科技大学西安西科测控设备有限责任公司. GDD-1矿用矿压动态传感器(矿用本质安全型)使用说明书.2004.4.
[32]西安科技大学西安西科测控设备有限责任公司. KJ110N型矿井安全综合监控系统软件使用说明书.2004.8.
[33]苏燹等.工作面突出危险性实时跟踪预测技术及装备的研究科学技术报告[R].煤炭科学研究总院重庆分院.2000.2.
[34]周世宁，何学秋.煤和瓦斯突出机理的流变假说[J].中国矿业大学学报,1990(2):1～9.
[35]李萍丰.浅谈煤与瓦斯突出机理的假说—二相流体假说[J].煤矿安全,1989(11):29～35.
[36]梁冰，章梦涛等.煤和瓦斯突出的固流耦合失稳理论[J].煤炭学报, 1995,20 (5):492～496.
[37]蒋承林，俞启香.煤与瓦斯突出机理的球壳失稳假说[J].煤矿安全, 1995,16 (2):17～25.
[38]卢连宁，张迎新等.煤与瓦斯突出机理的球壳失稳假说[J].黑龙江科技学院学报,1995,17(1):17~25.
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