摘要
研究了磨矿时间、干矿质量分数和充填率对锡石多金属硫化矿磨矿技术效率的影响.结果表明,在最优的磨矿参数条件下,即磨矿时间为8 min、干矿质量分数为65%、充填率为42%时,锡石和硫化矿二元结构所对应的磨矿技术效率最佳.通过Matlab的广义回归神经网络(GRNN)计算程序建立了一种磨矿技术效率预测模型,利用粒子群算法对模型参数进行优化,并通过试验验证了模型的适用性和可靠性.
The effect of grinding time,dry ore mass fraction and filling rate on the grinding technical efficiency of cassiterite polymetallic sulfide ore were studied. The results showed that the grinding efficiency corresponding to the binary structure of cassiterite and sulfide ore is the best when the grinding time is 8 min,the dry ore mass fraction is 65%,and the filling rate is 42%. A grinding technical efficiency prediction model was established by using the generalized regression neural netw ork( GRNN) program of M atlab. The model parameters were optimized by the particle sw arm optimization. The applicability and reliability of the model were verified by experiments.
作者
周文涛
韩跃新
李艳军
杨金林
ZHOU Wen-tao;HAN Yue-xin;LI Yan-jun;YANG Jin-lin(School of Resources&Civil Engineering,Northeastern University,Shenyang 110819,China;School of Resources Environment and Materials,Guangxi University,Nanning 530004,China.)
出处
《东北大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2019年第4期548-551,556,共5页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(51741401
51264001
51874105
51734005)
关键词
锡石多金属硫化矿
磨矿优化
磨矿技术效率
粒子群算法
GRNN模型优化
cassiterite polymetallic sulfide ore
grinding optimization
grinding technical efficiency
particle sw arm optimization
GRNN(generalized regression neural network)