Study on Hadoop platform based method for gas outburst prediction and early warning

doi:10.16265/j.cnki.issn1003-3033.2017.11.011

Abstract

Abstract: In order to prevent coal and gas (methane) outburst accidents, this paper was aimed at working out a method for prediction and early warning of coal and gas outburst based on Hadoop platform. Firstly, the Holt exponential smoothing method was used for preprocessing the gas concentration monitoring data in real time, so as to improve the accuracy and integrity of the monitoring data. Secondly, characteristic parameters of the gas concentration were extracted based on the BP neural network model. A gas outburst prediction and early warning model was built on the basis of both characteristic parameters extracted and the outburst prevention detection data. Finally, field application of the method was made at the Wu9-10-21050 working face of Pingdingshan No.8 mine. The results show that the method can be used for dealing with and analyzing large quantities of gas concentration data, improving the reliability of both the prediction and the early warning.

Key words: Hadoop platform, monitoring data, coal mine gas, early warning, coal and gas outburst

CLC Number:

X936

HAO Tianxuan, ZHANG Chunlin. Study on Hadoop platform based method for gas outburst prediction and early warning[J]. China Safety Science Journal, 2017, 27(11): 61-66.

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