Grinding Technical Efficiency Prediction Model Based on Matlab and Particle Swarm Optimization

doi:10.12068/j.issn.1005-3026.2019.04.018

Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (4): 548-552.DOI: 10.12068/j.issn.1005-3026.2019.04.018

• Resources & Civil Engineering • Previous Articles Next Articles

Grinding Technical Efficiency Prediction Model Based on Matlab and Particle Swarm Optimization

ZHOU Wen-tao¹， HAN Yue-xin¹， LI Yan-jun¹， YANG Jin-lin²

1. School of Resources & Civil Engineering， Northeastern University， Shenyang 110819， China; 2. School of Resources Environment and Materials， Guangxi University， Nanning 530004， China.

Received:2018-03-05 Revised:2018-03-05 Online:2019-04-15 Published:2019-04-26
Contact: LI Yan-jun
About author:-
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Abstract

Abstract: 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 8min， 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 network(GRNN) program of Matlab. The model parameters were optimized by the particle swarm optimization. The applicability and reliability of the model were verified by experiments.

Key words: cassiterite polymetallic sulfide ore, grinding optimization, grinding technical efficiency, particle swarm optimization, GRNN(generalized regression neural network)

CLC Number:

TD921

ZHOU Wen-tao， HAN Yue-xin， LI Yan-jun， YANG Jin-lin. Grinding Technical Efficiency Prediction Model Based on Matlab and Particle Swarm Optimization[J]. Journal of Northeastern University Natural Science, 2019, 40(4): 548-552.

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Grinding Technical Efficiency Prediction Model Based on Matlab and Particle Swarm Optimization

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