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Geomechanics and Engineering
  Volume 13, Number 5, November 2017 , pages 861-879
DOI: https://doi.org/10.12989/gae.2017.13.5.861
 


Hazard prediction of coal and gas outburst based on fisher discriminant analysis
Liang Chen, Enyuan Wang, Junjun Feng, Xiaoran Wang and Xuelong Li

 
Abstract
    Coal and gas outburst is a serious dynamic disaster that occurs during coal mining and threatens the lives of coal miners. Currently, coal and gas outburst is commonly predicted using single indicator and its critical value. However, single indicator is unable to fully reflect all of the factors impacting outburst risk and has poor prediction accuracy. Therefore, a more accurate prediction method is necessary. In this work, we first analyzed on-site impacting factors and precursors of coal and gas outburst; then, we constructed a Fisher discriminant analysis (FDA) index system using the gas adsorption index of drilling cutting h2, the drilling cutting weight S, the initial velocity of gas emission from borehole q, the thickness of soft coal h, and the maximum ratio of post-blasting gas emission peak to pre-blasting gas emission Bmax; finally, we studied an FDA-based multiple indicators discriminant model of coal and gas outburst, and applied the discriminant model to predict coal and gas outburst. The results showed that the discriminant model has 100% prediction accuracy, even when some conventional indexes are lower than the warning criteria. The FDA method has a broad application prospects in coal and gas outburst prediction.
 
Key Words
    coal and gas outburst; prediction; fisher discriminant analysis; indicator
 
Address
Liang Chen: 1) School of Energy & Environment Engineering, Zhongyuan University of Technology, 450007 Zhengzhou,
Henan, China
2) Key Laboratory of Safety and High-efficiency Coal Mining, Ministry of Education
Anhui University of Science and Technology, 232001 Huainan, Anhui, China

Enyuan Wang, Junjun Feng, Xiaoran Wang and Xuelong Li:Key Laboratory of Coal Methane and Fire Control, Ministry of Education,
China University of Mining and Technology, 221116 Xuzhou, Jiangsu, China
 

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