Methods of exploitation drainage, which is presently applied in polish hard coal mines in Upper Silesian Coal Basin(Poland), are not effective enough, high risk of methane hazard can be observed, and production capaci...Methods of exploitation drainage, which is presently applied in polish hard coal mines in Upper Silesian Coal Basin(Poland), are not effective enough, high risk of methane hazard can be observed, and production capacity of the mining plant is not fully used. Methane hazard, which may occur during planned coal exploitation, is presented in this paper. Following parameters are taken into consideration in the forecasts: coal extraction parameters, geological and mining conditions, deposit's methane saturation degree and impact of coal exploitation on the degasification coefficient of the seams, which are under the influence of relaxation zone. This paper presents the results of the analysis aiming to verify applicability of drainage ahead of mining of the coal seams by using surface directional wells. Based on the collected data(coal seams' structural maps, profiles of the exploratory wells, geological cross-sections), the lab tests of drilling cores and direct wells' tests, static model of the deposit was constructed and suitable grid of directional wells from the surface was designed. Comparison of forecasted methane emission volume between the two methods is investigated. The results indicated the necessity of performing appropriate deposit's stimulations in order to increase effectiveness of drainage ahead of mining.展开更多
In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio ...In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.展开更多
文摘Methods of exploitation drainage, which is presently applied in polish hard coal mines in Upper Silesian Coal Basin(Poland), are not effective enough, high risk of methane hazard can be observed, and production capacity of the mining plant is not fully used. Methane hazard, which may occur during planned coal exploitation, is presented in this paper. Following parameters are taken into consideration in the forecasts: coal extraction parameters, geological and mining conditions, deposit's methane saturation degree and impact of coal exploitation on the degasification coefficient of the seams, which are under the influence of relaxation zone. This paper presents the results of the analysis aiming to verify applicability of drainage ahead of mining of the coal seams by using surface directional wells. Based on the collected data(coal seams' structural maps, profiles of the exploratory wells, geological cross-sections), the lab tests of drilling cores and direct wells' tests, static model of the deposit was constructed and suitable grid of directional wells from the surface was designed. Comparison of forecasted methane emission volume between the two methods is investigated. The results indicated the necessity of performing appropriate deposit's stimulations in order to increase effectiveness of drainage ahead of mining.
基金supported by the Science and Research projects for Ph.D. candidates in the faculty of Xuzhou Normal University (No.08XLR12)Natural Science Foundation of Xuzhou Normal University (No.09XLA10)
文摘In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.
