Acoustic emission monitoring technology for coal and gas outburst
Технология мониторинга акустической эмиссии для выбросов угля и газа
2019-02-07
SCID: 54.1/3nkpbq8x
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AE continuous monitoringAE evolutionary characteristicsacoustic emissioncoal and gas outburstgas concentration variation
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Abstract (AI)
Abstract In recent years, with the increase in mining depth and strength, coal‐rock gas dynamic disasters, such as coal and gas outburst, have shown an increasing trend. Acoustic emission (AE) technology has been viewed as a promising method that can effectively forecast coal and gas dynamic disasters. This paper first tests the AE characteristics of coal and rock samples during loading. Then, self‐developed AE continuous monitoring and early warning equipment is used to monitor and predict the coal and gas outburst dynamic disasters on the working face. And it is found that the coal samples primarily show ductile failure, and the AE exhibits the evolutionary characteristics of “rise‐peak‐fall”. The rock samples primarily exhibit brittle failure, and the AE evolution mode is almost no falling stage. Coal and gas outbursts occur after the stress peak. Before coal and gas outbursts occur, there is a clear increasing trend in the AE ahead of the gas concentration variation. When the gas‐bearing coal is damaged by the load, the coal body first breaks due to the stress, and the AE value increases. Then, due to the fracture of the coal body, the crack penetrates, gas rushes out, and the gas concentration increases. The research results can provide an advanced technical method for the monitoring and early warning of coal–rock gas dynamic disasters, and improve the prediction accuracy for dynamic disasters.
Key Findings
1
AE increases when gas-bearing coal is damaged by load; coal fracture and crack penetration lead to subsequent rapid gas release and gas concentration rise.
2
Coal and gas outbursts occur after the stress peak, with a clear AE increase preceding gas concentration changes.
3
Laboratory loading tests show coal samples primarily undergo ductile failure with AE signals following a rise–peak–fall evolutionary pattern.
4
Rock samples primarily undergo brittle failure with AE evolution lacking a distinct falling stage.
5
Self-developed continuous AE monitoring and early-warning equipment can monitor and predict coal–gas outburst dynamic disasters, improving prediction accuracy.
Research Object
Acoustic emission monitoring system applied to coal and rock samples and to working-face coal seams for coal and gas outburst detection
Research Subject
AE signal characteristics and their evolution (rise–peak–fall, absence of fall), relationship between AE trends and gas concentration changes, and the use of continuous AE monitoring for early warning/prediction of coal and gas outbursts
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2019-02-07
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