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Evaluation of effect of blast design parameters on flyrock using artificial neural networks

机译:利用人工神经网络评估爆破设计参数对飞石的影响

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Flyrock, the propelled rock fragments beyond a specific limit, can be considered as one of the most crucial and hazardous events in the open pit blasting operations. Involvement of various effective parameters has made the problem so complicated, and the available empirical methods are not proficient to predict the flyrock. To achieve more accurate results, employment of new approaches, such as artificial neural network (ANN) can be very helpful. In this paper, an attempt has been made to apply the ANN method to predict the flyrock in the blasting operations of Sungun copper mine, Iran. Number of ANN models was tried using various permutation and combinations, and it was observed that a model trained with back-propagation algorithm having 9-5-2-1 architecture is the best optimum. Flyrock were also computed from various available empirical models suggested by Lundborg. Statistical modeling has also been done to compare the prediction capability of ANN over other methods. Comparison of the results showed absolute superiority of the ANN modeling over the empirical as well as statistical models. Sensitivity analysis was also performed to identify the most influential inputs on the output results. It was observed that powder factor, hole diameter, stemming and charge per delay are the most effective parameters on the flyrock.
机译:飞石是超过特定极限的推进岩石碎片,可以认为是露天爆破作业中最关键和最危险的事件之一。各种有效参数的参与使问题变得如此复杂,并且可用的经验方法不足以预测飞石。为了获得更准确的结果,采用新方法(例如人工神经网络(ANN))可能会非常有帮助。本文尝试将ANN方法用于预测伊朗Sungun铜矿爆破作业中的飞石。使用各种置换和组合尝试了多种ANN模型,并且观察到,采用具有9-5-2-1架构的反向传播算法训练的模型是最佳的。 Flyrock也是根据Lundborg建议的各种经验模型计算得出的。还进行了统计建模以比较ANN与其他方法的预测能力。结果的比较表明,ANN建模绝对优于经验模型和统计模型。还进行了敏感性分析,以识别对输出结果影响最大的输入。据观察,粉末系数,孔直径,杆和每次延迟的电荷是飞石上最有效的参数。

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