摘要
某铁矿地下采场回采中存在生产效率低、炸药单耗大和大块率高的问题.为此,提出了水平炮孔前进式开采的改进方案,进行了以排距、孔距、周边孔距为因素的L9(33)的爆破正交试验,建立了以排距、孔距、周边孔距为输入层因子,炸药单耗和大块率为输出层因子的RBF神经网络模型;从安全和经济的角度提出了爆破综合期望指数公式,结合模型预测结果进行最终优选,优选结果为:排距1 m,孔间距1.4 m,周边孔距1 m.经过现场验证,现生产能力为原来的4倍,增加了可充填的采场数目,顶板暴露时间缩短,生产效率提高约75%,炸药单耗减少62%,大块率降低74%.
The underground stoping at a metal mine was in low production efficiency, large explosives consumption and high boulder yield. To solve these problems, an orthogonal test of L9 （3^3） on blasting parameters was proposed, based on which, the RBF neural network model, using the blasting burden spacing, borehole spacing and surrounding-borehole spacing as input layer, and taking explosives consumption and boulder yield as the output layer, was established. The comprehensive expectation formula of blasting based on safety and economy is given, and it is determined that the best blasting burden spacing, borehole spacing and surrounding-borehole spacing are 1 m, 1. 4m and 1 m, respectively. After the mining process improvements, the stope production capacity is 4 times as before, more shorter, single stope production efficiency is reduced by 62% , large fragment rate is reduced stopes can be filled, exposure time of stope is increased by 75%, explosives consumption is by 74%.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第11期1641-1645,共5页
Journal of Northeastern University(Natural Science)
基金
国家自然科学青年基金资助项目(51404305)
关键词
地下采矿
采场爆破
回采工艺改进
正交试验
RBF神经网络
爆破综合期望指数公式
underground mining
stope blasting
mining technology improvement
orthogonal test
RBF neural network
comprehensive expectation formula of blasting
