Paper Title:
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.

  Info
Periodical
Chapter
Chapter 1: Vibration Engineering
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, P. Zhou, J. G. Wang, "Modeling on Hydrocyclone Separation Performance by Neural Network", Applied Mechanics and Materials, Vols. 105-107, pp. 185-188, 2012
Online since
September 2011
Export
Price
$32.00
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