Author(s)

Wenke Luo^1,2, Shiliang Shi^1,2, Yi Lu^1,2*, Shenghua Zou^1,2, Zaian Chen³, Liliang Chen⁴

¹Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines, Xiangtan, China.
²School of Energy and Safety Engineering, Hunan University of Science and Technology, Xiangtan, China.
³Jinzhushan Mining Company of Hunan Coal Group, Lenshuijiang, China.
⁴Baishanping Mining of Hunan Black Gold Era Co., LTD, Leiyang, China.

Affected by many involved factors, different dimensions, data with large difference, incomplete information and so on, the most optimal selection of regional outburst prevention measures for outburst mine has become a complicated system project. The traditional way of outburst prevention measure selection belongs to qualitative method, which may cause high-cost of gas control, huge quantities of drilling work, long construction time and even secondary disaster. To solve the above-mentioned problems, in light of occurrence status of coal seam gas in No. 21 mining area of Jinzhushan Tuzhu Mine, through grey fixed weight clustering theory and a combination method of qualitative and quantitative analysis, the judging model with multi-objective classification for optimization of outburst prevention measures was established. The three weight coefficients of outburst prevention technology scheme are sorted, in order to determine the advantages and disadvantages of each outburst prevention technology scheme under the comprehensive evaluation of multi-target. Finally, the problem of quantitative selection for regional outburst prevention technology scheme is solved under the situation of multi-factor mode and incomplete information, which provides reasonable and effective technical measures for prevention of coal and gas outburst disaster.

Coal-Gas Outburst, Grey Theory, Fixed Weight Clustering Analysis, Regional Outburst Prevention Measures

Luo, W. , Shi, S. , Lu, Y. , Zou, S. , Chen, Z. and Chen, L. (2016) Optimization Study of Outburst Prevention Measures for Tuzhu Coal Mine Based on Fixed Weight Clustering Analysis. Journal of Geoscience and Environment Protection, 4, 153-161. doi: 10.4236/gep.2016.41016.