摘要
为了开采阿舍勒铜矿上下中段大体积充填体之间的隔离中段矿体,对隔离中段的采场结构参数进行了研究。结合矿山实际情况,对采场结构参数进行了多种方案的FLAC3D数值模拟,采用神经网络和遗传算法对模拟结果进行选择、优化,确定了最佳的采场结构参数。结果表明:将神经网络和遗传算法结合起来,利用FLAC3D数值模拟的计算结果,以充填体的破坏率为目标函数,取得了理想的优化效果,实现了采场结构参数值的连续不间断优化,很好地弥补了数值模拟的缺点。
For mining the insulating-level ore body between large volume backfill body in the upper and lower levels in Ashele copper mine,the optimization of structural parameters of the stope in insulating-level was carried out.Combined with the actual situation of the mine,the numerical simulation of variant schemes about the structural parameters of stope was made by FLAC3D.After selecting and optimizing the different results of numerical simulation by neural networks and genetic algorithm,the optimal structural parameters of stope were determined.The results showed that desired optimization effects were achieved by the combination of neural networks and genetic algorithm,making use of the results of FLAC3D numerical simulation and taking the failure rate of backfill as the objective function,the continuous optimization of values of stope structure parameters was,realizedalso.This method can overcome the defect of numerical simulation.
出处
《矿业研究与开发》
CAS
北大核心
2012年第2期8-11,57,共5页
Mining Research and Development
关键词
隔离中段矿体
大体积充填体
采场结构参数
优化选择
神经网络
遗传算法
Insulating-level ore body
Large volume backfill
Structural parameters of stope
Optimal selection
Neural network
Genetic algorithm
