关键词: 台阶爆破, 爆破参数优化, 神经网络运算, 大块率

Abstract: An open-pit molybdenum mine bench blasting are poor rock-breaking effect,blasting effect is not ideal,large pieces of phenomenon happens,along the wall,foundation,shovel loading efficiency is low,the subsequent grinding equipment efficiency and capacity release,optimum blasting parameters is obtained in this paper,through the BP neural network arithmetic,after the adjustment final hole net parameter and explosive area of 6 m×4 m,and medium difficulty blasting network parameters for 5 m×4 m.Study results show that the optimization of blasting parameters after although before the specific charge is a little increase,but obviously improve the blasting effect,rock from the large rate can be controlled within 2%,basic no root,meet the requirements of mine on the blasting effect,production efficiency is improved,the follow-up production release,relevant broken grinding equipment spare parts replacement cycle extended,energy consumption is reduced,compared to the mine selecting achieved good economic benefit.

Key words: Step blasting, Optimization of blasting parameters, Neural network operation, Large rate