In the study of the application effectiveness of deep-hole controlled pre-splittingblasting technology,it was found through laboratory micro test and field study on a mine insouth China that under the technology,coal ...In the study of the application effectiveness of deep-hole controlled pre-splittingblasting technology,it was found through laboratory micro test and field study on a mine insouth China that under the technology,coal masses produce many irreversible cracks.Afterblasting,the nearer the distance from blasting hole,the larger the BET surface areaand volume ratio of the infiltration pore are;they increased by 11.47%and 5.73%,respectively.The coefficient of air permeability is increased 4 times.After 3 months,the gasdrainage rate was increased by 66%.In the first 15 days,the cumulative pumped gas was1.93 times of blasting before.The average absolute gas emission decreased by 63.46%.Experimental results show that deep-hole controlled pre-splitting blasting not only preventscoal and gas outburst,but also gives good economic results.展开更多
In order to solve the problems of top-coal inadequate destruction and large amounts of gas emission in mining extra thick and hard coal seam,this study investigated the pre-splitting for deep borehole blasting and gas...In order to solve the problems of top-coal inadequate destruction and large amounts of gas emission in mining extra thick and hard coal seam,this study investigated the pre-splitting for deep borehole blasting and gas pre-draining technologies on top coal.The mechanism of the technologies was systematically expounded based on hard top-coal cracks development obtained by numerical simulation and theoretical analysis.The results show that explosive blasting in the hard rock results in a large number of cracks and large displacement in the rock mass due to the effect of explosion stress.Meanwhile,the thick top-coal caves,and desorbing gas flows along the cracks improve gas extraction.Finally,the pre-splitting for deep borehole blasting and gas pre-draining technologies was applied in No.3802 working face of Shui Liandong Coal Mine,which increases monthly output in the face to 67.34 kt and the drained gas concentration to 86.2%.The drained gas average concentration from each borehole reaches 40%,and the effect is remarkable.展开更多
Blasting technology is widely used to prevent coal bursts by presplitting the overburden in underground coal mines.The control of blasting intensity is important in achieving the optimal pre-split effectiveness and re...Blasting technology is widely used to prevent coal bursts by presplitting the overburden in underground coal mines.The control of blasting intensity is important in achieving the optimal pre-split effectiveness and reducing the damage to roadway structures that are subjected to blasting vibrations.As a critical parameter to measure the blasting intensity,the peak particle velocity(PPV)of vibration induced by blasting,should be accurately predicted,and can provide a useful guideline for the design of blasting parameters and the evaluation of the damage.In this paper,various factors that influence PPV,induced by roof pre-split blasting,were analyzed using engineering blasting experiments and numerical simulations.The results showed that PPV was affected by many factors,including charge distribution design(total charge and maximum charge per hole),spacing of explosive centers,as well as propagation distance and path.Two parameters,average charge coefficient and spatial discretization coefficient were used to quantitatively characterize the influences of charge distribution and spacing of explosive centers on the PPV induced by roof pre-split blasting.Then,a model consisting of the combination of artificial neural network(ANN)and genetic algorithm(GA)was adopted to predict the PPV that was induced by roof presplit blasting.A total of 24 rounds of roof pre-split blasting experiments were carried out in a coal mine,and vibration signals were collected using a microseismic(MS)monitoring system to construct the neural network datasets.To verify the efficiency of the proposed GA-ANN model,empirical correlations were applied to predict PPV for the same datasets.The results showed that the GA-ANN model had superiority in predicting PPV compared to empirical correlations.Finally,sensitivity analysis was performed to evaluate the impacts of input parameters on PPV.The research results are of great significance to improve the prediction accuracy of PPV induced by roof pre-splitting blasting.展开更多
基金Supported by Project from National Natural Science Foundation of China(50674111)the National key Technology R&D Program in 10th Five Years Plan of China
文摘In the study of the application effectiveness of deep-hole controlled pre-splittingblasting technology,it was found through laboratory micro test and field study on a mine insouth China that under the technology,coal masses produce many irreversible cracks.Afterblasting,the nearer the distance from blasting hole,the larger the BET surface areaand volume ratio of the infiltration pore are;they increased by 11.47%and 5.73%,respectively.The coefficient of air permeability is increased 4 times.After 3 months,the gasdrainage rate was increased by 66%.In the first 15 days,the cumulative pumped gas was1.93 times of blasting before.The average absolute gas emission decreased by 63.46%.Experimental results show that deep-hole controlled pre-splitting blasting not only preventscoal and gas outburst,but also gives good economic results.
基金financially supported by the National Natural Science Fund of China(Nos.51004003 and 51474009)Anhui Province Education Department Natural Science Fund Key Project of China(No.KJ2010A091)
文摘In order to solve the problems of top-coal inadequate destruction and large amounts of gas emission in mining extra thick and hard coal seam,this study investigated the pre-splitting for deep borehole blasting and gas pre-draining technologies on top coal.The mechanism of the technologies was systematically expounded based on hard top-coal cracks development obtained by numerical simulation and theoretical analysis.The results show that explosive blasting in the hard rock results in a large number of cracks and large displacement in the rock mass due to the effect of explosion stress.Meanwhile,the thick top-coal caves,and desorbing gas flows along the cracks improve gas extraction.Finally,the pre-splitting for deep borehole blasting and gas pre-draining technologies was applied in No.3802 working face of Shui Liandong Coal Mine,which increases monthly output in the face to 67.34 kt and the drained gas concentration to 86.2%.The drained gas average concentration from each borehole reaches 40%,and the effect is remarkable.
基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX21_2378)National Natural Science Foundation of China(Grant Nos.51874292 and 51804303).
文摘Blasting technology is widely used to prevent coal bursts by presplitting the overburden in underground coal mines.The control of blasting intensity is important in achieving the optimal pre-split effectiveness and reducing the damage to roadway structures that are subjected to blasting vibrations.As a critical parameter to measure the blasting intensity,the peak particle velocity(PPV)of vibration induced by blasting,should be accurately predicted,and can provide a useful guideline for the design of blasting parameters and the evaluation of the damage.In this paper,various factors that influence PPV,induced by roof pre-split blasting,were analyzed using engineering blasting experiments and numerical simulations.The results showed that PPV was affected by many factors,including charge distribution design(total charge and maximum charge per hole),spacing of explosive centers,as well as propagation distance and path.Two parameters,average charge coefficient and spatial discretization coefficient were used to quantitatively characterize the influences of charge distribution and spacing of explosive centers on the PPV induced by roof pre-split blasting.Then,a model consisting of the combination of artificial neural network(ANN)and genetic algorithm(GA)was adopted to predict the PPV that was induced by roof presplit blasting.A total of 24 rounds of roof pre-split blasting experiments were carried out in a coal mine,and vibration signals were collected using a microseismic(MS)monitoring system to construct the neural network datasets.To verify the efficiency of the proposed GA-ANN model,empirical correlations were applied to predict PPV for the same datasets.The results showed that the GA-ANN model had superiority in predicting PPV compared to empirical correlations.Finally,sensitivity analysis was performed to evaluate the impacts of input parameters on PPV.The research results are of great significance to improve the prediction accuracy of PPV induced by roof pre-splitting blasting.
