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Journal of the Southern African Institute of Mining and Metallurgy

On-line version ISSN 2411-9717
Print version ISSN 0038-223X

J. S. Afr. Inst. Min. Metall. vol.110 n.5 Johannesburg May. 2010

 

TRANSACTION PAPERS

 

Prediction of hydrocyclone performance using artificial neural networks

 

 

M. Karimi; A. Dehghani; A. Nezamalhosseini; Sh. Talebi

Department of Mining and Metalllurgical Engineering, Yazd University, Yazd, Iran

 

 


SYNOPSIS

Artificial neural networks (ANNs) have found their applications in the modelling of unit operations of mineral processing plants. In this research, laboratory-scale tests were conducted, using a three-inch diameter Mozley hydrocyclone. Main parameters included pressure drop at inlet, solid per cent, vortex and apex diameter were adjusted. The corrected cut size (d50c) and the flow rates of underflow and overflow were determined. Multi layers perceptron (MLP) feed forward network architectures were designed to predict the responses. The results showed a good correlation between experimental and network output, for corrected cut size and flow rates.

Keywords: hydrocyclone, artificial neural network, corrected cut size, flow rates


 

 

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References

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SVAROVSKY, L. Hydrocyclones. 1984, Lancaster, PA, USA Technomic.         [ Links ]

NAPIER-MUNN, T.J., MORRELL, S., MORRISON, R.D., and KOJOVIC, T. Mineral Comminution Circuits: Their Operation and Optimization. 1996, Australia, JKMRC.         [ Links ]

WANDER WALT, T.J. and BERNARD, E. Neural nets for the simulation of mineral processing operations, 1993, South Africa, Elsevier Scientific Publishing.         [ Links ]

WILLS, B.A. Mineral Processing Technology, An Introduction to the practical aspects of ore treatment and mineral processing, 6th edition, 1992, Pergamon Press, Oxford.         [ Links ]

 

 

Paper received Jun. 2009
Revised paper received Dec. 2009

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