Title: Prediction model for coal-gas outburst using the genetic projection pursuit method

Authors: Yueqiang Liang; Deyong Guo; Zhanfeng Huang; Xihui Jiang

Addresses: School of Resources and Safety Engineering, China University of Mining and Technology, Beijing, Beijing, 100083, China ' School of Resources and Safety Engineering, China University of Mining and Technology, Beijing, Beijing, 100083, China ' School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, Henan Province, 467036, China; School of Resources and Safety Engineering, China University of Mining and Technology, Beijing, Beijing, 100083, China ' School of Resources and Safety Engineering, China University of Mining and Technology, Beijing, Beijing, 100083, China

Abstract: This paper proposes to solve the coupling result problem of individual indexes and to improve the prediction accuracy of coal-gas outburst. According to gas geology research, the prediction index system is established from the context of an outburst-prone tectonophysical environment. A new model for coal-gas outburst prediction is developed through genetic algorithm and projection pursuit method. This model indicates the dangerous degree of occurrence of potential coal-gas outburst by calculating a one-dimensional projection eigenvalue which reflects the dangerous degree of coal-gas outburst. The prediction model is applied in the tunnelling working face 31041 of the Chaohua coal mine, and the dangerous degree of prediction is primarily identical with the in situ records. The case study demonstrates the reliability of the genetic projection pursuit method in predicting coal-gas outburst. [Received: October 16, 2015; Accepted: October 6, 2016]

Keywords: coal-gas outburst; prediction model; genetic algorithm; projection pursuit; gas geology; dangerous degree; prediction accuracy.

DOI: 10.1504/IJOGCT.2017.087042

International Journal of Oil, Gas and Coal Technology, 2017 Vol.16 No.3, pp.271 - 282

Received: 11 Dec 2015
Accepted: 06 Oct 2016
Published online: 05 Oct 2017 *

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