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Use of artificial neural networks in estimation of Hydrocyclone parameters with unusual input variables

Eren, H., Fung, C.C., Wong, K.W. and Gupta, A. (1996) Use of artificial neural networks in estimation of Hydrocyclone parameters with unusual input variables. In: Proceedings of the Joint 1996 IEEE Instrumentation and Measurement Technology Conference & IMEKO Technical Committee, 4 - 6 June, Brussels, Belgium pp. 1015-1019.

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Link to Published Version: http://dx.doi.org/10.1109/IMTC.1996.507318
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Abstract

The accuracy of the estimation of the Hydrocyclone parameter, d50 c, can substantially be improved by application of an Artificial Neural Network (ANN). With an ANN, many non-conventional Hydrocyclone variables, such as water and solid split ratios, overflow and underflow densities, apex and spigot flowrates, can easily be incorporated into the prediction of d50c. Selection of training parameters is also shown to affect the accuracy.

Publication Type: Conference Paper
Publisher: IEEE
Copyright: © 1996 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/16272
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