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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




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




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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Paper received Jun. 2009
Revised paper received Dec. 2009

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