Analysis of Geological Factors Affecting Coal Seam Gas Content and Prediction

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Abstract:

In order to determine coal seam gas content accurately, the single factor method was adopted to study geological factors that affect coal seam gas content of a coal mine. Bedrock depth, 50m influence coefficient and coal thickness are identified as the main controlling factors of gas content. And the corresponding correlation coefficient are: 0.6011, 0.3899, 0.2527. Factors such as effective trapping thickness, moisture content and ash content are regarded as secondary factors. Then the SVM theory was used to set up a nonlinear prediction model between coal seam gas content and main controlling factors, and the gas content was predicted. Results show that single factor method can be used to determine main controlling factors of coal seam gas and the gas content SVM model can predict gas content accurately. Besides, the research findings can provide technical basis for gas drainage and gas outburst prevention.

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Periodical:

Advanced Materials Research (Volumes 634-638)

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3645-3649

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January 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] B.F. Yu: Prevention of Coal-mine Gas Hazards and Utilization Manual (Coal Industry Press,China 2005).

Google Scholar

[2] W.Y. Cheng, K.J. Wang: Mining Safety and Environmental Protection, Vol.27 (2000) No.5, pp.20-21. (In Chinese)

Google Scholar

[3] Ayers Jr., W.B. and Ambrose: Chicago Gas Research Institute Topical ReportGRI-91/0072, (1991), pp.9-46.

Google Scholar

[4] Kaiser, W.R. and Hamilton: Journal of the Geological Society of London, Vol.151 (1994), p.417–420.

Google Scholar

[5] L.W. Zhong, Y.Z Zheng and Z.R. Yun: Journal of China Coal Society, Vol.27 (2002) No.6, pp.581-585. (In Chinese)

Google Scholar

[6] G. Cui: Mining Safety, Vol.2 (1992), pp.22-24. (In Chinese)

Google Scholar

[7] Y.J. Tian: Support Vector Regression and its Application (Ph.D., China Agricultural University, China 2005).

Google Scholar