Author/Authors :
Shahbazi، B. نويسنده Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. , , Rezai، B. نويسنده Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran. , , Chehreh Chelgani، S. نويسنده Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. , , Koleini، S. M. J. نويسنده Department of Mining Engineering, Tarbiat Modares University, Tehran, Iran. , , Noaparast4، M. نويسنده Department of Mining Engineering, University of Tehran, Tehran, Iran ,
Abstract :
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.
