Premium
Hybrid first‐principles/neural networks model for column flotation
Author(s) -
Gupta Sanjay,
Liu PiHsin,
Svoronos Spyros A.,
Sharma Rajesh,
AbdelKhalek N. A.,
Cheng Yahui,
ElShall Hassan
Publication year - 1999
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690450312
Subject(s) - artificial neural network , column (typography) , volumetric flow rate , flow (mathematics) , particle (ecology) , process engineering , biological system , chromatography , chemistry , computer science , engineering , mechanics , geology , artificial intelligence , mechanical engineering , physics , oceanography , connection (principal bundle) , biology
A new model for phosphate column flotation is presented which for the first time relates the effects of operating variables such as frother concentration on column performance. This is a hybrid model that combines a first‐principles model with artificial neural networks. The first‐principles model is obtained from material balances on both phosphate particles and gangue (undesired material containing mostly silica). First‐order rates of net attachment are assumed for both. Artificial neural networks relate the attachment rate constants to the operating variables. Experiments were conducted in a 6‐in.‐dia. (152‐mm‐dia.) laboratory column to provide data for neural network training and model validation. The model successfully predicts the effects of frother concentration, particle size, air flow rate and bubble diameter on grade and recovery.