摘要
爆破块度是岩石性质、爆破参数和炸药性质等组合作用结果,其准确预测是实现矿山精细爆破与采选能耗协同优化的关键.为了提升预测精度,构建了基于蚁群优化(ACO)算法和BP神经网络的爆破块度预测模型,并确定了主要影响因素.以司家营露天矿为例,为提升预测精度,通过建立爆破案例样本库来训练模型,分析了各影响因素权重.结果表明:ACO显著提高了模型的预测能力;在爆破块度影响因素中,孔距权重最大,最小抵抗线权重最小;爆破参数对爆破块度的影响存在最优范围,单一调节某参数难以持续改善爆破块度.该模型为根据爆破效果要求反向优化爆破设计提供了有效手段和理论依据.
Blasting fragmentation is governed by the combined interplay of rock properties,blasting parameters,and explosive characteristics.Its accurate prediction is the key to achieving the coordinated optimization of precision blasting in mines and energy consumption during mining and processing.To improve prediction accuracy,a blasting fragmentation prediction model was constructed using ant colony optimization(ACO)and a back propagation(BP)neural network,and the key influencing factors of blasting fragmentation were identified.Based on the Sijiaying open pit mine,a blasting case database was established to train the model and improve prediction accuracy,and the weights of the influencing factors were analyzed.Results show that ACO significantly improves model performance.Among the influencing factors of blasting fragmentation,blasthole spacing has the highest weight,and minimum burden has the lowest weight.Blasting parameters exhibit optimal ranges for blasting fragmentation,but singleparameter adjustments cannot sustainably improve blasting fragmentation.This model provides an effective means and theoretical basis for inverse optimization of blasting design based on blasting effect requirements.
作者
于庆磊
吴嘉伟
李友
蒲江涌
YU Qing-lei;WU Jia-wei;LI You;PU Jiang-yong(School of Resources&Civil Engineering,Northeastern University,Shenyang 110819,China)
出处
《东北大学学报(自然科学版)》
北大核心
2025年第12期116-123,共8页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金联合基金资助项目(U21A20106)。
关键词
ACO-BP神经网络
精细爆破
爆破块度
影响因素权重
反向优化
ACO-BP neural network
precision blasting
blasting fragmentation
weight of influencing factor
inverse optimization
