عنوان مقاله [English]
The main purpose of optimal blasting operation is suitable crushing and prevent undesirable and unwanted phenomena arising from the blasting (ground vibration, flyrock and backbreak). Generally, the parameters affecting the blasting operation divided into two main group controllable (blasting pattern) and uncontrollable (geomechanical properties of rocks) parameters. Controllable parameter can be determined using experimental models. Due to variety of obtained values for controllable parameters from experimental models, it is necessary to use methods with high efficiency. The main reason for not achieving good results in experimental models is engaging a large number of parameters, resulting from blasting. The combination of intelligent and mehta heuristic methods to solve suck problems can be useful. In this study, Delkan iron mine as a case study has a side effects of blasting like as flyrock and backbreak. The main purpose of this paper is making a artificial neural network model as a strong predictor and finding a combination of data using ant colony optimization to minimum the unwanted phenomena. After the Modeling the blast pattern Burden 2.8 m, spacing 3.3 m, hole length 10.2 m, stemming 1.5 m and powder factor is 201 gr/ton. Using this model can lead to a reduction of approximately 42 percent and 62 percent in flyrock and backbreak respectively.
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