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作 者:刘国栋 于鹏辉 刘伟军[2] 张双侠 LIU Guodong;YU Penghui;LIU Weijun;ZHANG Shuangxia(Sanshandao gold mine,Shandong Gold Mining(Laizhou)Co.,Ltd.;School of Resources and Safety Engineering,Central South University)
机构地区:[1]山东黄金矿业(莱州)有限公司三山岛金矿,山东省烟台市261400 [2]中南大学资源与安全工程学院
出 处:《现代矿业》2025年第2期57-61,共5页Modern Mining
摘 要:为了获得三山岛金矿中深孔采矿的最佳爆破技术参数,提出了89和102 mm 2种炮孔直径的多组孔网参数爆破技术方案,用40组爆破技术参数为学习样本,建立了三山岛金矿中深孔爆破BP神经网络模型,用训练后的BP神经网络模型和混沌优化原理对爆破参数进行反分析,获得了最佳的爆破技术参数。研究表明:三山岛智能开采爆破技术方案的神经网络模型预测值与试验值误差均在4.61%以内,以炸药单耗0.40 kg/t为优化目标,爆破成本最低为优化原则,上向中深孔爆破选取孔径为89 mm时,混沌优化反演分析获得最佳孔网参数为底距2.4 m,排距1.6 m,爆破成本11.96元/t;上向中深孔孔径为102 mm时,获得最佳孔网参数为底距2.6 m,排距1.8 m,爆破成本9.68元/t。In order to obtain the optimal blasting technical parameters for medium-deep hole mining in Sanshandao Gold Mine,multiple sets of hole network parameters with diameters of 89 mm and 102 mm are proposed.A BP neural network model for medium-deep hole blasting in Sanshandao Gold Mine is es-tablished using 40 sets of blasting technical parameters as learning samples.The trained BP neural network model and chaotic optimization principle are used for reverse analysis of blasting parameters,yielding the optimal blasting technical parameters.The study shows that the error between the predicted values and ex-perimental values of the neural network model for the intelligent mining blasting scheme in Sanshandao is within 4.61%.With an optimization goal of an explosive consumption of 0.40 kg/t and the principle of mini-mizing blasting cost,the optimal hole network parameters for upward medium-deep hole blasting with a hole diameter of 89 mm are obtained through chaotic optimization inversion analysis,bottom hole distance is 2.4 m,row spacing is 1.6 m,and blasting cost is 11.96 yuan/t.For a hole diameter of 102 mm,the opti-mal hole network parameters were bottom hole distance of 2.6 m,row spacing of 1.8 m,and blasting cost of 9.68 yuan/t.
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