Modeling on Hydrocyclone Separation Performance by Neural Network

Feng Qin He; Ping Zhou; Jian Gang Wang

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

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 105-107Modeling on Hydrocyclone Separation Performance by...

Modeling on Hydrocyclone Separation Performance by Neural Network

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 105-107)

Pages:

185-188

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.105-107.185

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Online since:

September 2011

Authors:

Feng Qin He, Ping Zhou, Jian Gang Wang

Keywords:

Hydrocyclone, LZVV, Modeling, Neural Network (NN)

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