工程爆破

2018, v.24(06) 18-22

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基于深度神经网络的露天矿岩石爆破效果预测
Rock blasting effect forecast based on deep neural network in open pit mines

王赟;薛大伟;汤万钧;
WANG Yun;XUE Da-wei;TANG Wan-jun;China Coal Science and Engineering Group Taiyuan Research Institute Co.Ltd.;North Anhui Coal and Electricity Group;College of Mining,China University of Mining and Technology;

摘要(Abstract):

装载和运输成本占到露天矿运营总成本的50%以上,而爆破后岩石破碎程度直接影响到露天矿装载和运输成本。为了得到爆破参数和岩石破碎度之间的关系,收集了平朔东露天矿爆破参数和岩石破碎度数据,使用图像识别技术大规模收集分析爆破后岩块粒径分布,基于深度神经网络研究分析爆破参数和岩石破碎度之间的关系,建立了适用于东露天矿的爆破参数和破碎度的预测模型,并对爆破参数做敏感性分析,确立了炸药单耗和孔距是主要影响因素,并分别建立了炸药单耗和孔距与岩石破碎度之间的变化关系。研究结果可以指导矿山确定合理的穿孔和爆破参数以得到最佳的岩石破碎度,从而减少露天矿运营总成本。
Loading and transport costs constitute up to 50% of the total operational costs in open pit mines. Fragmentation of the rock after blasting is an important determinant of the cost associated with these two components of mine development.By collecting the data of blasting parameters and rock breakage of Pingshuo east open pit, based on the study of the relationship between blasting parameters and rock breakage based on deep neural network, a prediction model for blasting parameters and breakage suitable for east open pit mine is established, and the sensitivity analysis of blasting parameters is made and the single consumption of explosives is established. The distance between holes is the main factor, and then the relationship between explosive consumption and pore size and rock fragmentation is established according to the single factor analysis method. The results obtained in this study and the methodology introduced, can assist the mining design engineer to decide on a drilling and blasting pattern that produces the most suitable fragmentation of the blasted ore and hence minimize the total cost of the mining operations.

关键词(KeyWords): 深度神经网络;爆破效果;破碎度;露天矿
deep neural network;blasting effect;fragmentation;open pit mine

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作者(Author): 王赟;薛大伟;汤万钧;
WANG Yun;XUE Da-wei;TANG Wan-jun;China Coal Science and Engineering Group Taiyuan Research Institute Co.Ltd.;North Anhui Coal and Electricity Group;College of Mining,China University of Mining and Technology;

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