Excellence in Research and Innovation for Humanity
%0 Journal Article
%A John Kabuba and  Antoine Mulaba-Bafubiandi and  Kim Battle
%D 2012 
%J  International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 68, 2012
%T Binary Mixture of Copper-Cobalt Ions Uptake by Zeolite using Neural Network
%U http://waset.org/publications/3648
%V 68
%X In this study a neural network (NN) was proposed to
predict the sorption of binary mixture of copper-cobalt ions into
clinoptilolite as ion-exchanger. The configuration of the
backpropagation neural network giving the smallest mean square
error was three-layer NN with tangent sigmoid transfer function at
hidden layer with 10 neurons, linear transfer function at output layer
and Levenberg-Marquardt backpropagation training algorithm.
Experiments have been carried out in the batch reactor to obtain
equilibrium data of the individual sorption and the mixture of coppercobalt
ions. The obtained modeling results have shown that the used
of neural network has better adjusted the equilibrium data of the
binary system when compared with the conventional sorption
isotherm models.
%P 727 - 732