Modeling on Hydrocyclone Separation Performance by Neural Network

Abstract:

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A 17-27-5 type BP neural network model was built, whose sampled data was got by hydrocyclone separation experiments; another 6-30-5 type BP neural network was also built, whose sampled data came from the simulation results of the LZVV of a hydrocyclone with CFD code FLUENT. The two neural network models also have good predictive validity aimed at hydrocyclone separation performance. It demonstrates LZVV structural parameters can embody hydrocyclone separation performance and reduce input parameter numbers of neural network model. It also indicates that the predictive model of hydrocyclone separation performance can be built by neural network.

Info:

Periodical:

Edited by:

Paul P. Lin and Chunliang Zhang

Pages:

185-188

DOI:

10.4028/www.scientific.net/AMM.105-107.185

Citation:

F. Q. He et al., "Modeling on Hydrocyclone Separation Performance by Neural Network", Applied Mechanics and Materials, Vols. 105-107, pp. 185-188, 2012

Online since:

September 2011

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Price:

$35.00

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