TY - JOUR
T1 - ESTIMATION OF GAS HOLDUP AND INPUT POWER IN FROTH FLOTATION USING ARTIFICIAL NEURAL NETWORK
TT -
JF - IUST
JO - IUST
VL - 12
IS - 1
UR - http://ijmse.iust.ac.ir/article-1-762-en.html
Y1 - 2015
SP - 12
EP - 19
KW - Flotation
KW - Input Power
KW - Gas Holdup
KW - Regression
KW - Artificial Neural Network
N2 - Multivariable regression and artificial neural network procedures were used to modeling of the input power and gas holdup of flotation. The stepwise nonlinear equations have shown greater accuracy than linear ones where they can predict input power, and gas holdup with the correlation coefficients of 0.79 thereby 0.51 in the linear, and R2=0.88 versus 0.52 in the non linear, respectively. For increasing accuracy of predictions, Feed-forward artificial neural network (FANN) was applied. FANNs with 2-2-5-5, and 2-2-3-2-2 arrangements, were capable to estimating of the input power and gas holdup, respectively. They were achieved quite satisfactory correlations of 0.96 in testing stage for input power prediction, and 0.64 for gas holdup prediction
M3 10.22068/ijmse.12.1.12
ER -