新桥矿爆破工艺与参数优化

为解决新桥矿大块率高、炸药单耗高及爆破效率低等问题,在对爆破工艺改进的基础上设计有限的爆破试验(13组试验)获取样本,并建立BP神经网络预测模型(隐含层节点数取9),以最小抵抗线W、孔间距a、周边孔距Z作为输入因子,以炸药单耗、大块率作为输出因子预测、优选爆破参数.优化推荐W=0.8 m、a=1 m、Z=0.8 m,对应的炸药单耗为0.2001 kg/t,仅为原工艺的50%;大块率为5.2091%,仅为原工艺的20%;生产效率提高了约65%.该方法采用有限的试验与智能预测相结合,实现低成本获取真实样本,并提高了预测精度.

Optimization of Blasting Process and Parameters in Xin-Qiao Mine

In order to solve the problem of high block rate,high unit explosive consumption and low blasting effi-ciency in Xin-Qiao mine,13 samples were obtained from limited blasting tests on the basis of improved blasting te-chology.The blasting parameters BP neural networks pediction model with 9 hidden layer nodes' was established. Taking the minimum burden W,hole spacing a,contour hole distance Z as the input factors and the unit explosive consumption,block rate as the output factor.The recommended parameters were W=0.8 m,a=1 m,Z=0.8 m,and the explosive specific charge was 0.2001 kg/t,which is 50% of the original process;the boulder yield was 5. 2091%,only took up 20% of the original process;the production efficiency was increased by 65%.Combined with finite test and intelligent prediction,the method can achieve better samples with low cost.In addition,the prediction accuracy was improved.