In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hy...In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.展开更多
The size distribution of a muck pile depends not on only the blasting standard but also on the mechanical properties,joint system,and crack density of the rock mass. As,the cracks in the rock masses are especially hea...The size distribution of a muck pile depends not on only the blasting standard but also on the mechanical properties,joint system,and crack density of the rock mass. As,the cracks in the rock masses are especially heavily developed at the limestone quar- ries in Japan,they,along with the joints,have a large impact on the effects of blasting, such as the size of the muck pile.Therefore,if the joint system and/or crack density in a rock mass can be determined and quantitatively evaluated,the blasting operation can be conducted more effectively,efficiently and safely.However,guidelines for designing ap- propriate blasting standards based on the rock mass conditions have not yet been scien- tifically developed.Therefore,blasting tests were conducted on different mines and faces, under different geological conditions and blasting standards,in order to determine the im- pacts of each factor on the effects of blasting.Summarized the results of a series of blast- ing tests and described the impacts of geological conditions on the size of the muck pile produced by blast.展开更多
Studied forecasting and controlling the blasting fragmentation by using artifi- cial neural network for multi-ingredients. At the same time, according to the characteris- tic of multi-parameters input to network model...Studied forecasting and controlling the blasting fragmentation by using artifi- cial neural network for multi-ingredients. At the same time, according to the characteris- tic of multi-parameters input to network model, the gray correlation theory was employed to find out key factors, which can not only save time of computation and parameters in- put, but improve the stability of the model.展开更多
The effect of solid inertants like rock dust on explosion suppression was experimentally tested.By adding solid inertants with different concentrations into three kinds of coal dust,the maximum explosion pressure P ma...The effect of solid inertants like rock dust on explosion suppression was experimentally tested.By adding solid inertants with different concentrations into three kinds of coal dust,the maximum explosion pressure P max and the rate of explosion pressure rise(d p/d t)max were acquired.Based on this,the suppression effect of rock dust on coal dust explosion was analyzed.The experimental and analytical results show that there are two major factors that play an important role in explosion suppression:composition of solid inertant and particle size of solid inertant.The higher the concentration of solid inertant and the smaller the particle size of solid inertant,the better the suppression effect.In addition,the smaller the particle size of coal dust,the larger the amount of rock dust.展开更多
基金Projects(42177164,52474121)supported by the National Science Foundation of ChinaProject(PBSKL2023A12)supported by the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China。
文摘In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.
文摘The size distribution of a muck pile depends not on only the blasting standard but also on the mechanical properties,joint system,and crack density of the rock mass. As,the cracks in the rock masses are especially heavily developed at the limestone quar- ries in Japan,they,along with the joints,have a large impact on the effects of blasting, such as the size of the muck pile.Therefore,if the joint system and/or crack density in a rock mass can be determined and quantitatively evaluated,the blasting operation can be conducted more effectively,efficiently and safely.However,guidelines for designing ap- propriate blasting standards based on the rock mass conditions have not yet been scien- tifically developed.Therefore,blasting tests were conducted on different mines and faces, under different geological conditions and blasting standards,in order to determine the im- pacts of each factor on the effects of blasting.Summarized the results of a series of blast- ing tests and described the impacts of geological conditions on the size of the muck pile produced by blast.
文摘Studied forecasting and controlling the blasting fragmentation by using artifi- cial neural network for multi-ingredients. At the same time, according to the characteris- tic of multi-parameters input to network model, the gray correlation theory was employed to find out key factors, which can not only save time of computation and parameters in- put, but improve the stability of the model.
基金Special Foundation for Platform Base and Outstanding Talent of Shanxi Province(No.201705D211002)National Natural Science Foundation of China(No.11802272)
文摘The effect of solid inertants like rock dust on explosion suppression was experimentally tested.By adding solid inertants with different concentrations into three kinds of coal dust,the maximum explosion pressure P max and the rate of explosion pressure rise(d p/d t)max were acquired.Based on this,the suppression effect of rock dust on coal dust explosion was analyzed.The experimental and analytical results show that there are two major factors that play an important role in explosion suppression:composition of solid inertant and particle size of solid inertant.The higher the concentration of solid inertant and the smaller the particle size of solid inertant,the better the suppression effect.In addition,the smaller the particle size of coal dust,the larger the amount of rock dust.
