A new blasting approach of combined blastholes with different diameters is proposed to solve the problems of oversize boulders and rock toes in open-pit mine. A non-ideal detonation model and a statistical damage cons...A new blasting approach of combined blastholes with different diameters is proposed to solve the problems of oversize boulders and rock toes in open-pit mine. A non-ideal detonation model and a statistical damage constitutive model are implemented in dynamic finite element analysis to investigate the formation mechanism of oversize boulders and toes. The damage distribution and evolution process of rock blasting fragmentation is simulated, and the scheme is further optimized. Numerical analysis results showed that pocket charges and satellite blastholes can only improve bench top fragmentation, but they cannot reduce the oversize in the middle and bottom of bench as well as the toe problem. The new blasting approach of combined blastholes with different diameters can effectively reduce the oversize boulders and toes as well as the production costs.展开更多
Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapti...Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapting blast design parameters to geological conditions to optimize the utilization of explosive energy for rock fragmentation.To achieve this,data on fifty geo-blast design parameters were collected and used to train machine learning algorithms.The objective was to develop predictive models for estimating the blast oversize percentage,incorporating seven controlled components and one uncontrollable index.The study employed a combination of hybrid long-short-term memory(LSTM),support vector regression,and random forest algorithms.Among these,the LSTM model enhanced with the tree seed algorithm(LSTM-TSA)demonstrated the highest prediction accuracy when handling large datasets.The LSTM-TSA soft computing model was specifically leveraged to optimize various blast parameters such as burden,spacing,stemming length,drill hole length,charge length,powder factor,and joint set number.The estimated percentage oversize values for these parameters were determined as 0.7 m,0.9 m,0.65 m,1.4 m,0.7 m,1.03 kg/m^(3),35%,and 2,respectively.Application of the LSTM-TSA model resulted in a significant 28.1%increase in the crusher's production rate,showcasing its effectiveness in improving blasting operations.展开更多
基金supported by Chinese National Natural Science Foundation (No. 51809016 and No. 51979152)Chongqing Municipal Natural Science Foundation (No. cstc2019jcyjmsxmX0645)
文摘A new blasting approach of combined blastholes with different diameters is proposed to solve the problems of oversize boulders and rock toes in open-pit mine. A non-ideal detonation model and a statistical damage constitutive model are implemented in dynamic finite element analysis to investigate the formation mechanism of oversize boulders and toes. The damage distribution and evolution process of rock blasting fragmentation is simulated, and the scheme is further optimized. Numerical analysis results showed that pocket charges and satellite blastholes can only improve bench top fragmentation, but they cannot reduce the oversize in the middle and bottom of bench as well as the toe problem. The new blasting approach of combined blastholes with different diameters can effectively reduce the oversize boulders and toes as well as the production costs.
基金funded by China Scholarship Council (No.202006370006).
文摘Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapting blast design parameters to geological conditions to optimize the utilization of explosive energy for rock fragmentation.To achieve this,data on fifty geo-blast design parameters were collected and used to train machine learning algorithms.The objective was to develop predictive models for estimating the blast oversize percentage,incorporating seven controlled components and one uncontrollable index.The study employed a combination of hybrid long-short-term memory(LSTM),support vector regression,and random forest algorithms.Among these,the LSTM model enhanced with the tree seed algorithm(LSTM-TSA)demonstrated the highest prediction accuracy when handling large datasets.The LSTM-TSA soft computing model was specifically leveraged to optimize various blast parameters such as burden,spacing,stemming length,drill hole length,charge length,powder factor,and joint set number.The estimated percentage oversize values for these parameters were determined as 0.7 m,0.9 m,0.65 m,1.4 m,0.7 m,1.03 kg/m^(3),35%,and 2,respectively.Application of the LSTM-TSA model resulted in a significant 28.1%increase in the crusher's production rate,showcasing its effectiveness in improving blasting operations.
