Citation

Lu J, Jia X, Guo X. China Saf. Sci. J. 2022; 32(11): 74-81.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.11.0326

PMID

unavailable

Abstract

In order to clarify the relationship between the sensitive indexes of coal and gas outburst, firstly, the grey relational model was adopted, combined with the initial velocity method of drilling gas and the drilling cutting index method, the initial gas emission velocity qm of drilling gas was selected as the reference sequence, and the analysis index of drilling cuttings gas Δh2, K1 and the amount of drilling cuttings S were selected as the comparison sequences to carry out the correlation analysis of the sensitive indexes of coal and gas outburst and determine the key factors affecting the coal and gas outburst accidents. Then the classical Gray Prediction model (GM (1,1)) was improved by introducing buffer weakening operators and automatic optimization and weighting method. The model was built, and the correlations among the qm, Δh2, K1 and S were quantitatively analyzed. Finally, each index parameter was calculated using the measured gas data from a coal mining face in Shanxi, China.

RESULTS show that in the mentioned coalmine, the order of influence of coal and gas outburst sensitive indexes on outburst risk is followed as Δh2> S > K1. And there is a crossing relation between Δh2 K1, S and qm. The small error probability value of the improved grey prediction model increased from 0. 69 to 0. 87, the ratio of the posterior error decreased from 0. 500 0 to 0. 431 7, and the prediction grade was improved from pass to good. © 2022 China Safety Science Journal. All rights reserved.

Language: zh