Research on Predictive Model of Gas Concentration for Driving Ventilation in Coal Mine Based on RBF and BP

Abstract:

In order to predict accurately gas concentration and design ventilation scheme in driving ventilation process under different gas emission in coal mine, based on the analysis of various ventilation factors, the prediction model structure of gas concentration for driving ventilation was designed based on RBF and BP neural network in this paper. Then MATLAB software and the observation data obtained from the coal mine sites were used to compare and analyze the prediction errors of two models, and a RBF neural network model with higher prediction precision was obtained. After that, the prediction model was used for practical application research on the gas concentration of the heading face in concrete coal mines. The research shows that the settled prediction model can not only predict the gas concentration precisely of driving ventilation, but also provide a certain theory basis for different driving ventilation equipment layout and parameters configuration in the driving ventilation process of coal mines.

• Periodical: Advanced Materials Research (Volumes 706-708)
• Pages: 1805-1809
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.706-708.1805
• Online since: June 2013
• Authors: Xiao Yan Gong, Jun Guo, He Xue, Dong Hui Yan, Zhe Wu
• Keywords: BP Neural Network (BPNN), Driving Ventilation, Gas Concentration, Prediction Model, RBF Neural Network

References

[1] XY Gong, YG Dang. Analysis of vibration modals of blade of FBD series local fan used in mine[J]. Mining & Processing Equipment, 2010, 12(38): 54-57.

[2] JJ Cheng, YG Jia, SR Cheng. Problems and solutions in head ventilation[J]. Safety & Environmental Protection, 2005, 32(1): 72-75.

[3] HJ Chan, GS Wei. Talk about local ventilation in heading face[J]. Heilongjiang Science and Technology Information, 2010, 96(10): 1.

[4] WG Hou. Analysis and application of gas concentration law from heading face in Liyazhuang Mine[J]. Shaanxi coal, 2010, 20(2): 52-55.

[5] ZX Hu, BS Wang. Research on a point gas concentration in heading face[J]. Coal Science and Technology in shandong, 2005, 25(5): 67-69.

[6] XY Gong. The local ventilation system reliability assessment and enhancement measures in mine[D]. Journal, Xi'an Mining Institute, 1993.

[7] XY Gong. Fault diagnosis approach of local ventilation system in coal mines based on multidisciplinary technology. Chinese Journal of Mining Sciences & Technology (English Edition), 2006, 9, 16(3): 317-320.

[8] XR Meng, YY Xu, J Wang. Neural network identification model for technology selection of fully-mechanized top-coal caving mining[J]. Chinese Journal of Mining Sciences & Technology, 2001, 11(2):199-203.