Conference paper
Use of artificial neural networks in estimation of Hydrocyclone parameters with unusual input variables
Quality Measurement: The Indispensable Bridge between Theory and Reality (No Measurements? No Science! Joint Conference - 1996: IEEE Instrumentation and Measurement Technology Conference and IMEKO Technical Committee 7. Conference Proceedings, Vol.2, pp.1015-1019
IEEE
Proceedings of the Joint 1996 IEEE Instrumentation and Measurement Technology Conference & IMEKO Technical Committee (Brussels, Belgium, 04/06/1996–06/06/1996)
1996
Abstract
The accuracy of the estimation of the Hydrocyclone parameter, d50 c, can substantially be improved by application of an Artificial Neural Network (ANN). With an ANN, many non-conventional Hydrocyclone variables, such as water and solid split ratios, overflow and underflow densities, apex and spigot flowrates, can easily be incorporated into the prediction of d50c. Selection of training parameters is also shown to affect the accuracy.
Details
- Title
- Use of artificial neural networks in estimation of Hydrocyclone parameters with unusual input variables
- Authors/Creators
- H. Eren (Author/Creator) - Curtin UniversityC.C. Fung (Author/Creator) - Curtin UniversityK.W. Wong (Author/Creator) - Curtin UniversityA. Gupta (Author/Creator) - Curtin University
- Publication Details
- Quality Measurement: The Indispensable Bridge between Theory and Reality (No Measurements? No Science! Joint Conference - 1996: IEEE Instrumentation and Measurement Technology Conference and IMEKO Technical Committee 7. Conference Proceedings, Vol.2, pp.1015-1019
- Conference
- Proceedings of the Joint 1996 IEEE Instrumentation and Measurement Technology Conference & IMEKO Technical Committee (Brussels, Belgium, 04/06/1996–06/06/1996)
- Publisher
- IEEE
- Identifiers
- 991005542986007891
- Copyright
- © 1996 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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