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 Craft and Parameters in Xin-qiao Mine

In order to solve the problem of high boulder yield,high explosive specific charge and low blasting ef-ficiency of Xin-qiao Mine,13 samples were obtained from limited blasting tests on the basis of improved blasting craft. The blasting parameters were predicted by 9 hidden layer nodes′ BP neural networks with the minimum burden W,hole spacing a,peripheral hole distance Z as the input factors and with the explosive cost,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,only 50% of the original process;the block rate was 5. 2091%,only 20% of the original process;the production efficiency was promoted about 65%. Combined with finite test and intelligent prediction,the method a-chieved low cost and real samples,in addition,the prediction accuracy was improved.