This paper presents numerical studies on the effects of atmospheric pressure fluctuations on hill-side coal fires and their surface anomalies. Based on the single-particle reaction–diffusion model, a formula to estim...This paper presents numerical studies on the effects of atmospheric pressure fluctuations on hill-side coal fires and their surface anomalies. Based on the single-particle reaction–diffusion model, a formula to estimate oxygen consumption rate at high temperature controlled by oxygen transport is proposed.Daily fluctuant atmospheric pressure was imposed on boundaries, including the abandoned gallery and cracks. Simulated results show that the effects of atmospheric pressure fluctuations on coal fires and surface anomalies depend on two factors: the fluctuant amplitude and the pressure difference between inlet(s) and outlet(s) of the air ventilation system. If the pressure difference is close to the fluctuant amplitude, atmospheric pressure fluctuations greatly enhance gas flow motion and temperatures of the combustion zone and outtake(s). If the pressure difference is much larger than the fluctuant amplitude, atmospheric pressure fluctuations exert no impact on underground coal fires and surface anomalies.展开更多
Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics o...Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.展开更多
文摘This paper presents numerical studies on the effects of atmospheric pressure fluctuations on hill-side coal fires and their surface anomalies. Based on the single-particle reaction–diffusion model, a formula to estimate oxygen consumption rate at high temperature controlled by oxygen transport is proposed.Daily fluctuant atmospheric pressure was imposed on boundaries, including the abandoned gallery and cracks. Simulated results show that the effects of atmospheric pressure fluctuations on coal fires and surface anomalies depend on two factors: the fluctuant amplitude and the pressure difference between inlet(s) and outlet(s) of the air ventilation system. If the pressure difference is close to the fluctuant amplitude, atmospheric pressure fluctuations greatly enhance gas flow motion and temperatures of the combustion zone and outtake(s). If the pressure difference is much larger than the fluctuant amplitude, atmospheric pressure fluctuations exert no impact on underground coal fires and surface anomalies.
基金Supported by the National Natural Science Foundation of China （40971275, 50811120111）
文摘Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.
