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
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
A critical challenge of any blast simulation facility is in producing the widest possible pressure-impulse range for matching against equivalent high-explosive events.Shock tubes and blast simulators are often constra...A critical challenge of any blast simulation facility is in producing the widest possible pressure-impulse range for matching against equivalent high-explosive events.Shock tubes and blast simulators are often constrained with the lack of effective ways to control blast wave profiles and as a result have a limited performance range.Some wave shaping techniques employed in some facilities are reviewed but often necessitate extensive geometric modifications,inadvertently cause flow anomalies,and/or are only applicable under very specific configurations.This paper investigates controlled venting as an expedient way for waveforms to be tuned without requiring extensive modifications to the driver or existing geometry and could be widely applied by existing and future blast simulation and shock tube facilities.The use of controlled venting is demonstrated experimentally using the Advanced Blast Simulator(shock tube)at the Australian National Facility of Physical Blast Simulation and via numerical flow simulations with Computational Fluid Dynamics.Controlled venting is determined as an effective method for mitigating the impact of re-reflected waves within the blast simulator.This control method also allows for the adjustment of parameters such as tuning the peak overpressure,the positive phase duration,and modifying the magnitude of the negative phase and the secondary shock of the blast waves.This paper is concluded with an illustration of the potential expanded performance range of the Australian blast simulation facility when controlled venting for blast waveform tailoring as presented in this paper is applied.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
Blast furnace(BF)operation state was difficult to characterize,measure,and predict.To solve this problem,an intelligent evaluation and advanced prediction method of BF operation state based on industry big data and ma...Blast furnace(BF)operation state was difficult to characterize,measure,and predict.To solve this problem,an intelligent evaluation and advanced prediction method of BF operation state based on industry big data and machine learning was proposed.Based on the criteria of high productivity,low consumption,high quality,smooth running and long life,five BF parameters were extracted according to production experience and metallurgy process.Using the unsupervised learning,a 4-grade evaluation rule was established to realize the intelligent rating of BF operation state.Based on Kendall and maximal information coefficient,70 BF parameters with the most characteristic power of BF operation state were determined.The weights of BF parameters were calculated by applying the criteria importance through intercriteria correlation and the grey correlation degree.The weights of raw material,fuel,gas distribution,cooling stave,BF hearth,and iron and slag were 0.241,0.213,0.140,0.098,0.117 and 0.191,respectively.The weight of data interval was calculated by using the grading algorithm and the monotonicity,and then,the intelligent scoring mechanism based on the multiple weights was formed.It was beneficial to qualitatively and quantitatively characterizing the“black box”BF operation state.Furthermore,combining the algorithm and the evaluation mechanism,a graded prediction model of BF operation state was developed and proposed.It was shown that,compared with the conventional prediction model,the mean absolute error and mean square error of the graded prediction model were reduced by 0.35 and 1.29,respectively,while the explained variation was increased by 14.56%,the hit rate was increased by 5.1%within the error of 3%,and the average hit rate was more than 90.6%.It could be applied to reliably predict the score of BF operation state in the next hour and accurately provide the support for the practical controlling of the running BF.展开更多
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funded partially by the Australian Government through the Australian Research Council’s Linkage Infrastructure,Equipment and Facilities (LIEF)funding scheme (LE130100133)。
文摘A critical challenge of any blast simulation facility is in producing the widest possible pressure-impulse range for matching against equivalent high-explosive events.Shock tubes and blast simulators are often constrained with the lack of effective ways to control blast wave profiles and as a result have a limited performance range.Some wave shaping techniques employed in some facilities are reviewed but often necessitate extensive geometric modifications,inadvertently cause flow anomalies,and/or are only applicable under very specific configurations.This paper investigates controlled venting as an expedient way for waveforms to be tuned without requiring extensive modifications to the driver or existing geometry and could be widely applied by existing and future blast simulation and shock tube facilities.The use of controlled venting is demonstrated experimentally using the Advanced Blast Simulator(shock tube)at the Australian National Facility of Physical Blast Simulation and via numerical flow simulations with Computational Fluid Dynamics.Controlled venting is determined as an effective method for mitigating the impact of re-reflected waves within the blast simulator.This control method also allows for the adjustment of parameters such as tuning the peak overpressure,the positive phase duration,and modifying the magnitude of the negative phase and the secondary shock of the blast waves.This paper is concluded with an illustration of the potential expanded performance range of the Australian blast simulation facility when controlled venting for blast waveform tailoring as presented in this paper is applied.
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.
基金supported by the National Natural Science Foundation of China(Nos.52404343,52274326,and 52404341)the Fundamental Research Funds for the Central Universities(N2425031,N25BJD007)+1 种基金the China Postdoctoral Science Foundation(2024M760370)the Liaoning Province Science and Technology Plan Joint Program(Key Research and Development Program Project)(2023JH2/101800058).
文摘Blast furnace(BF)operation state was difficult to characterize,measure,and predict.To solve this problem,an intelligent evaluation and advanced prediction method of BF operation state based on industry big data and machine learning was proposed.Based on the criteria of high productivity,low consumption,high quality,smooth running and long life,five BF parameters were extracted according to production experience and metallurgy process.Using the unsupervised learning,a 4-grade evaluation rule was established to realize the intelligent rating of BF operation state.Based on Kendall and maximal information coefficient,70 BF parameters with the most characteristic power of BF operation state were determined.The weights of BF parameters were calculated by applying the criteria importance through intercriteria correlation and the grey correlation degree.The weights of raw material,fuel,gas distribution,cooling stave,BF hearth,and iron and slag were 0.241,0.213,0.140,0.098,0.117 and 0.191,respectively.The weight of data interval was calculated by using the grading algorithm and the monotonicity,and then,the intelligent scoring mechanism based on the multiple weights was formed.It was beneficial to qualitatively and quantitatively characterizing the“black box”BF operation state.Furthermore,combining the algorithm and the evaluation mechanism,a graded prediction model of BF operation state was developed and proposed.It was shown that,compared with the conventional prediction model,the mean absolute error and mean square error of the graded prediction model were reduced by 0.35 and 1.29,respectively,while the explained variation was increased by 14.56%,the hit rate was increased by 5.1%within the error of 3%,and the average hit rate was more than 90.6%.It could be applied to reliably predict the score of BF operation state in the next hour and accurately provide the support for the practical controlling of the running BF.
