Title :
Artificial neural networks in estimation of hydrocyclone parameter d50c with unusual input variables
Author :
Eren, Halit ; Fung, Chun Che ; Wong, Kok Wai ; Gupta, Ashok
Author_Institution :
Curtin Univ. of Technol., Perth, WA, Australia
fDate :
8/1/1997 12:00:00 AM
Abstract :
The accuracy in the estimation of hydrocyclone parameter, d50c, can substantially be improved by application of artificial neural networks (ANN). With ANN, many nonconventional operational variables such as water and solid split ratios, overflow and underflow densities, apex and spigot flowrates can easily be incorporated as the input parameters in the prediction of d50c. The ANN yields high correlation of data, hence it can be used in automatic control and multiphase operations of hydrocyclones
Keywords :
chemical variables control; chemical variables measurement; computerised instrumentation; flow measurement; neural nets; organic compounds; parameter estimation; suspensions; ANN; apex; artificial neural networks; automatic control; hydrocyclone parameter; multiphase operations; overflow densities; slurry test rig; solids classification; solids separation; spigot flowrates; suspensions; underflow densities; Artificial neural networks; Automatic control; Circuit testing; Data analysis; Input variables; Intelligent networks; Parameter estimation; Reactive power; Slurries; Solids;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
