Journal article
Artificial neural networks in estimation of hydrocyclone parameter d50c with unusual input variables
IEEE Transactions on Instrumentation and Measurement, Vol.46(4), pp.908-912
1997
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.
Details
- Title
- Artificial neural networks in estimation of hydrocyclone parameter d50c with unusual input variables
- Authors/Creators
- H. Eren (Author/Creator) - Curtin UniversityC.C. Fung (Author/Creator)K.W. Wong (Author/Creator)A. Gupta (Author/Creator)
- Publication Details
- IEEE Transactions on Instrumentation and Measurement, Vol.46(4), pp.908-912
- Publisher
- IEEE
- Identifiers
- 991005541331707891
- Copyright
- © 1997 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 7 Engineering & Materials Science
- 7.139 Energy & Fuels
- 7.139.1755 Cyclone Separators
- Web Of Science research areas
- Engineering, Electrical & Electronic
- Instruments & Instrumentation
- ESI research areas
- Engineering