4.3 Article

Line prediction technology for forecasting coal and gas outbursts during coal roadway tunneling

Journal

JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING
Volume 34, Issue -, Pages 412-418

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jngse.2016.07.010

Keywords

Coal and gas outbursts; Line prediction; Initial gas emissions; Gas emission per meter of borehole

Funding

  1. Natural Science Foundation of Jiangsu Province [BK20150180, BK20150181, BK20150195]
  2. Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
  3. Graduate Research and Innovation Projects of Jiangsu Province [KYLX15_1431]

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The advantages and disadvantages of existing methods for predicting coal and gas outbursts in coal roadways were analyzed, and the primary factors influencing coal and gas outbursts, including the expansion energy of the initially released gases, were studied. To this end, a theoretical formula for calculating the amount of gas released from boreholes during drilling was deduced. In addition, it was determined that the gas emission per unit borehole length was related to the ground stress, gas pressure, and coal strength around the borehole during drilling. By collecting these continuously released gases from boreholes during drilling, that is, by adopting line prediction technology, the influence of those parameters (ground stress, gas pressure, coal strength) on the outburst process can be determined, and the outburst hazard can be predicted. To test this approach, a specially developed line prediction device was applied to the No. 3 coal seam of the Xinyuan coal mine, Shanxi Province, China. The test results demonstrate that the gas emissions per meter of borehole, Q(Lm), measured by the line prediction technique differed between outburst and non-outburst samples. Additionally, compared with existing indices used for predicting coal and gas outbursts in coal roadways, including K-1 and S-max, the line prediction technique was more sensitive and could reflect the outburst hazard in front of working faces more accurately. (C) 2016 Elsevier B.V. All rights reserved.

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