Title :
Use of artificial neural networks in estimation of Hydrocyclone parameters with unusual input variables
Author :
Eren, Halit ; Fung, Chun Che ; Wong, IKok Wai ; Gupta, Ashok
Author_Institution :
Sch. of Electr. & Comput. Eng., Curtin Univ. of Technol., Bentley, WA, Australia
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
Keywords :
backpropagation; chemical technology; neural nets; parameter estimation; particle size; separation; artificial neural networks; backpropagation; correlation coefficient; hydrocyclone control; hydrocyclone parameters estimation; overflow densities; particle size partitioning; prediction error; separation efficiency; spigot flowrates; training parameters selection; underflow densities; unusual input variables; vortex height; water and solid split ratios; Application software; Artificial neural networks; Australia; Industrial training; Intelligent networks; Mathematical model; Parameter estimation; Slurries; Solids; Temperature control;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 1996. IMTC-96. Conference Proceedings. Quality Measurements: The Indispensable Bridge between Theory and Reality., IEEE
Conference_Location :
Brussels
Print_ISBN :
0-7803-3312-8
DOI :
10.1109/IMTC.1996.507318
