화학공학소재연구정보센터
Process Safety and Environmental Protection, Vol.129, 8-16, 2019
Prediction of gas and coal spontaneous combustion coexisting disaster through the chaotic characteristic analysis of gas indexes in goaf gas extraction
The gas and coal spontaneous combustion coexisting disasters have become a common mode of major and extraordinarily serious accident of coal mine, so it has important significance to predict the coexistence disaster for prevention and treatment. This study took the gas samples from gas drainage pipeline in the goaf of 1203 working face of Hongyang No. 2 Coal Mine, and then analyzed the chaotic characteristics of coal gas indexes from gas drainage pipeline by using the R/S analysis method. The results show that the Hurst index of time series of gas indicators related to coal spontaneous combustion shows stable statistical characteristics when the oxygen concentration fluctuates smoothly. However, when the oxidation of coal in goaf accelerates, the Hurst index will be smaller than 0.77025. The Hurst index of time series of gas indicators related to gas in the goaf of 1203 working face is greater than 0.5, which indicates that gas has persistent correlation. The Hurst index can quantitatively reflect the inherent tendency characteristics and persistent intensity of the gas concentration variation process. The Hurst index of time series of gas indicators related to coal spontaneous combustion at the monitoring point can be used to judge the time tendency of coal spontaneous combustion in goaf and further judge the spontaneous combustion state of coal. Therefore, methods for judging whether the coal in goaf is in the dangerous stage of spontaneous combustion and for determining the change trend of gas concentration in the goaf were proposed. On this basis, the coexisting disaster were forecasted, moreover, this kind of prediction method can reduce the misinformation of the coexisting disaster. The study provides a meaningful new idea and method for improving the theory of coexisting disaster prediction. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.