Int. J. of Oil, Gas and Coal Technology   »   2016 Vol.13, No.2

 

 

Title: Bayesian network-based model for assessing the intensity of outbursts of coal and gas

 

Authors: Wen Nie; Hongwei Yang; Hailong Zhang; Jiabo Geng; Xin Wu

 

Addresses:
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
Institute of Technology, Sichuan Normal University, Chengdu, Sichuan 610101, China

 

Abstract: Fully understanding the major parameters contributing to an outburst is significant in the evaluation of coal and gas outbursts. However, these parameters can also affect each other which significantly increase the difficulty of evaluation. Considering related uncertainties, a Bayesian network (BN) which reflects major variable parameters affects the object without knowing the clear relations between the parameters. In this study, a conceptual BN model is developed to estimate the probability of outburst intensities in the laboratory. A total of 2,000 groups of random samples based on physical experiments are produced by trace linear or 3D interpolation for these factors and outbursts, respectively. A total of 1,800 groups of random samples are used to construct the BNs while the other 200 random samples are used for validation purposes. The proposed BN model estimates the intensity of the outburst with a root-mean-square deviation (RMSD) (3.8) and a linear correlation (0.94) compared with validation values of 200 random samples. [Received: December 2, 2014; Accepted: May 2, 2015]

 

Keywords: Bayesian networks; gas and coal outbursts; probability; interpolation; mini-outbursts; outburst intensity; coal mining.

 

DOI: 10.1504/IJOGCT.2016.10000264

 

Int. J. of Oil, Gas and Coal Technology, 2016 Vol.13, No.2, pp.200 - 213

 

Available online: 20 Aug 2016

 

 

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