基于分形—混沌理论的煤与瓦斯突出预测研究
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摘要
煤与瓦斯突出是含瓦斯煤岩体发生的极其复杂的动力现象,属于复杂非线性动力系统在时空演化过程中的灾变行为,是影响煤矿安全生产的主要灾害之一。探索煤与瓦斯突出现象背后的规律性,对预防突出具有非常重要的意义。为此,本文应用混沌和分形的相关理论从非线性上对动态瓦斯涌出数据进行研究,探索突出预测的新方法。论文首先对进行过预处理的时间序列进行相空间重构,分析了时间序列的混沌特征量,证明了其就是混沌时间序列,并根据时间序列的混沌特征量提出了此时间序列的最大可预测时间。在此基础上,用分形理论对涌出时间序列进行研究,其结果表明,时间序列表面上杂乱无章,但可以用分维数来定量描述其无标度区的标度对称性,并且指出了有无突出危险性情况下分形指标的不同。最后,建立了基于Lyapunov指数的时间序列预测模式和基于混沌和分形特征的神经网络预测模型。
Coal and gas outburst is a very complicated dynamic phenomenon of coal rock which contains gas, it can be regarded as a catastrophe behavior of a complicated non-linear dynamic system during its evolving process in space and time, and also be one of the major disasters influencing safe production of coal mine. It has very theoretical significance and engineering applying value for effective and forecasting on coal seam fire to explore the intrinsic rule in the coal and gas outburst. Therefore, in this paper, from the view of non-linear science, some researches on dynamic gas emission has been done on chaos and fractal theory, and a new approach is proposed to predict coal and gas outburst. By reconstructing phase spaces to the representative time-series date of dynamic gas emission which through data pretreatment, and discussed its chaos parameters, consequently it is proved that time-series possess chaos and fractal characteristics, and the maximal forecasting time can be got by the method based on the maximal Lyapunov exponent. Researching on the time series of coal autoxidation indicates that time series can be qualitication described with fractal dimension under no-scaled zone. And it obeys the fractal distributions, although it is disorderly and unsystematic on the surface. Finally, the time series have been forecasted by the method based on the maximal Lyapunov exponent and an Artificial Neural Network model is set up for outburst prediction of coal seam tunneling face based on the chaos and fractal characteristics of time-series.
Coal and gas outburst is a very complicated dynamic phenomenon of coal rock which contains gas, it can be regarded as a catastrophe behavior of a complicated non-linear dynamic system during its evolving process in space and time, and also be one of the major disasters influencing safe production of coal mine. It has very theoretical significance and engineering applying value for effective and forecasting on coal seam fire to explore the intrinsic rule in the coal and gas outburst. Therefore, in this paper, from the view of non-linear science, some researches on dynamic gas emission has been done on chaos and fractal theory, and a new approach is proposed to predict coal and gas outburst. By reconstructing phase spaces to the representative time-series date of dynamic gas emission which through data pretreatment, and discussed its chaos parameters, consequently it is proved that time-series possess chaos and fractal characteristics, and the maximal forecasting time can be got by the method based on the maximal Lyapunov exponent. Researching on the time series of coal autoxidation indicates that time series can be qualitication described with fractal dimension under no-scaled zone. And it obeys the fractal distributions, although it is disorderly and unsystematic on the surface. Finally, the time series have been forecasted by the method based on the maximal Lyapunov exponent and an Artificial Neural Network model is set up for outburst prediction of coal seam tunneling face based on the chaos and fractal characteristics of time-series.
引文
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[2] 于不凡.国内外煤和瓦斯突出日常预测研究综述.煤与瓦斯突出预测资料汇编.煤炭科学研究院重庆研究所编,1987
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[13] 张宏伟.活动断裂研究与矿井动力现象预测.煤炭学报.1998,(2)
[14] 刘明举.计算机模式识别技术在煤与瓦斯突出预测预报中的应用.瓦斯地质.1989
[15] 张铁岗.矿井瓦斯综合治理技术.北京:煤炭工业出版社,2001,3
[16] 聂百胜,等.煤与瓦斯突出预测技术研究现状及发展趋势.中国安全学报,2003(6),40～43
[17] 吴祥兴,陈忠.混沌学导论,上海:上海科学技术文献出版社,1996
[18] 吕金虎,陆君安等.混沌时间序列分析及其应用,武汉:武汉大学出版社,2002
[19] 仪垂祥.非线性科学及其在地质学忠的应用.北京:气象出版社,1995.60～95
[20] 安鸿志,陈敏.非线性时间序列分析.上海:上海科学技术出版社,1998,254～71
[21] Takens F. Detecting strange in turbulence. In: Dynamical systems and turbulence, Lecture Notes in Mathematics, 1981, 898
[22] Packard N H, Grutchfield J P. Farmer J D, et al. Geometry from a time series. Phys Rev Lett, 1980, 45
[23] Kugiumtzis D. State space reconstrucrtion parameters in the analysis of chaotic time series-the role of the time window length. Physica D, 1996, 95
[24] 辛厚文.分形理论及其应用.合肥:中国科学技术大学出版社,1993
[25] 朱令人等.地震分形.北京:地震出版社,2000
[26] 曾文曲,王向阳等.分形理论与分形的计算机模拟.沈阳:东北大学出版社,2000
[27] 廖明,张文明,方湄等.时间序列分形特征的判别.北京科技大学学报。1998
[28] 曹星平,易东云等.基于神经网络的时间序列预测方法进展,电脑与信息技术,1996,6
[29] 张立明.人工神经网络的模型及其应用.上海.复旦大学出版社,1993
[30] 李孝安,张晓溃.神经网络与神经计算导论.西安:西北工业大学出版社,1994
[31] 胡守仁等.神经网络导论.长沙:国防科技大学出版社,1993,113
[31] 戴群.改进型前向神经网络的时间序列预测及其性能比较.南京航空航天大学,2003,1
[32] 赵庆闲.基于分形与混沌的煤自燃参数及状态预报研究.西安科技大学.2003,5
[33] 王凯,俞启香等.煤与瓦斯突出的非线性特征及预测模型.徐州:中国矿业大学出版社.2005,3
[34] 秦汝祥.基于监测系统的瓦斯与煤突出实时预报研究.安徽理工大学.2003,6
[2] 于不凡.国内外煤和瓦斯突出日常预测研究综述.煤与瓦斯突出预测资料汇编.煤炭科学研究院重庆研究所编,1987
[3] 柴兆喜.各国煤和瓦斯突出概况.世界煤炭技术,1984(4)
[4] 姚宝魁、孙广忠、尹代勋.煤与瓦斯突出的区域性预测.北京:中国科学出版社,1993
[5] 焦作矿业学院瓦斯地质研究室.瓦斯地质概论.北京:煤炭工业出版社,1990
[6] 于不凡.煤和瓦斯突出的预测.世界煤炭技术,1985(2)
[7] 唐修义,等译.煤矿瓦斯动力现象的预测和预防.北京:煤炭工业出版社,1992
[8] 张玉功.北票矿区煤与瓦斯突出预测预报的研究与应用.煤矿安全1997(1)
[9] 彭立世.瓦斯地质研究现状及前景展望.焦作矿业学院学报,1995,14
[10] 何学秋,刘明举,含瓦斯煤岩破坏电磁动力学.徐州:中国矿业大学出版社,1995
[11] 煤炭工业部煤炭科学研究院重庆研究所.坑道无线电透视在我国煤矿地质中的应用.重庆:1984.4
[12] 段克信.北票矿区地质动力区划.煤炭学报.1995,20
[13] 张宏伟.活动断裂研究与矿井动力现象预测.煤炭学报.1998,(2)
[14] 刘明举.计算机模式识别技术在煤与瓦斯突出预测预报中的应用.瓦斯地质.1989
[15] 张铁岗.矿井瓦斯综合治理技术.北京:煤炭工业出版社,2001,3
[16] 聂百胜,等.煤与瓦斯突出预测技术研究现状及发展趋势.中国安全学报,2003(6),40～43
[17] 吴祥兴,陈忠.混沌学导论,上海:上海科学技术文献出版社,1996
[18] 吕金虎,陆君安等.混沌时间序列分析及其应用,武汉:武汉大学出版社,2002
[19] 仪垂祥.非线性科学及其在地质学忠的应用.北京:气象出版社,1995.60～95
[20] 安鸿志,陈敏.非线性时间序列分析.上海:上海科学技术出版社,1998,254～71
[21] Takens F. Detecting strange in turbulence. In: Dynamical systems and turbulence, Lecture Notes in Mathematics, 1981, 898
[22] Packard N H, Grutchfield J P. Farmer J D, et al. Geometry from a time series. Phys Rev Lett, 1980, 45
[23] Kugiumtzis D. State space reconstrucrtion parameters in the analysis of chaotic time series-the role of the time window length. Physica D, 1996, 95
[24] 辛厚文.分形理论及其应用.合肥:中国科学技术大学出版社,1993
[25] 朱令人等.地震分形.北京:地震出版社,2000
[26] 曾文曲,王向阳等.分形理论与分形的计算机模拟.沈阳:东北大学出版社,2000
[27] 廖明,张文明,方湄等.时间序列分形特征的判别.北京科技大学学报。1998
[28] 曹星平,易东云等.基于神经网络的时间序列预测方法进展,电脑与信息技术,1996,6
[29] 张立明.人工神经网络的模型及其应用.上海.复旦大学出版社,1993
[30] 李孝安,张晓溃.神经网络与神经计算导论.西安:西北工业大学出版社,1994
[31] 胡守仁等.神经网络导论.长沙:国防科技大学出版社,1993,113
[31] 戴群.改进型前向神经网络的时间序列预测及其性能比较.南京航空航天大学,2003,1
[32] 赵庆闲.基于分形与混沌的煤自燃参数及状态预报研究.西安科技大学.2003,5
[33] 王凯,俞启香等.煤与瓦斯突出的非线性特征及预测模型.徐州:中国矿业大学出版社.2005,3
[34] 秦汝祥.基于监测系统的瓦斯与煤突出实时预报研究.安徽理工大学.2003,6