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首页> 外文期刊>International Journal of Rock Mechanics and Mining Sciences >Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks
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Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks

机译:利用人工神经网络同时预测爆破过程中的碎片和飞石

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摘要

Rock blasting is the most commonly used method for rock breakage in the field of mining. The main goal of such an operation is to produce the desired fragment size distribution leading to optimize the overall mine/plant economics [1,2]. Since the rock fragmentation is affected by blasting conditions, there must be some remedial measures to diminish undesirable occurrences such as flyrock in which, a large amount of the explosive energy is exhausted. In the meantime, with the involvement of diverse affecting parameters in the blasting process, the simultaneous optimization of "flyrock" and "rock fragmentation" might not be an easy task to perform [3,4].
机译:爆破是采矿领域中最常用的破岩方法。这种操作的主要目标是产生所需的碎片尺寸分布,从而优化整个矿山/工厂的经济性[1,2]。由于岩石碎裂受爆破条件的影响,因此必须采取一些补救措施以减少不希望发生的事故,例如飞石,在飞石中会消耗大量爆炸能量。同时,由于爆破过程中涉及各种影响参数,“飞石”和“碎石”的同时优化可能不是一件容易的事[3,4]。

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