CAJ | 학술논문

针对某硫铁矿生产过程中存在的爆破材料消耗大、大块率高、生产能力低等问题，根据爆破参数对爆破效果的影响的原理，选择不同的凿岩参数，在采场进行工业试验。同时以最小抵抗线、孔间距、周边孔距等作为模型的输入因子，单位炸药消耗量和大块率作为模型的输出因子，并以工业实验数据为训练样本，建立BP神经网络预测模型对爆破参数进行优化，得到最优爆破参数为：最小抵抗线0.8 m，孔间距0.9 m，周边孔孔距0.8 m，对应的炸药单耗为0.2223 kg/t，大块率为5.15豫。优化结果表明，所选参数合理，可有效降低矿山的生产成本。
침대모류철광생산과정중존재적폭파재료소모대、대괴솔고、생산능력저등문제，근거폭파삼수대폭파효과적영향적원리，선택불동적착암삼수，재채장진행공업시험。동시이최소저항선、공간거、주변공거등작위모형적수입인자，단위작약소모량화대괴솔작위모형적수출인자，병이공업실험수거위훈련양본，건립BP신경망락예측모형대폭파삼수진행우화，득도최우폭파삼수위：최소저항선0.8 m，공간거0.9 m，주변공공거0.8 m，대응적작약단모위0.2223 kg/t，대괴솔위5.15예。우화결과표명，소선삼수합리，가유효강저광산적생산성본。
Many problems existed in pyrite mine including large consumption of blasting materials,high boulder yield and low production capacity. According to the effect of blasting parameters,different drilling parameters were selected to perform the industrial tests in stopes. At the same time,the prediction model of BP neural network was set up to obtain the optimal blasting parameter by taking minimum burden,hole spaces and peripheral hole space as in-put factors of the model,both specific charge and boulder yield as output factors and data of industrial tests as train-ing sample. The optimal blasting parameters were ultimately determined as following:minimum burden was 0. 8m, hole space was 0. 9m,peripheral hole space was 0. 8 m and the corresponding specific charge was 0. 2223 kg/t,boul-der yield was 5. 15%. The optimizing results show that the selected blasting parameters were reasonable and greatly reduced the production cost for the mine.