%0 Journal Article
	%A John Kabuba
	%D 2014
	%J International Journal of Chemical and Molecular Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 92, 2014
	%T Application of Neural Network on the Loading of Copper onto Clinoptilolite
	%U https://publications.waset.org/pdf/9999212
	%V 92
	%X The study investigated the implementation of the
Neural Network (NN) techniques for prediction of the loading of Cu
ions onto clinoptilolite. The experimental design using analysis of
variance (ANOVA) was chosen for testing the adequacy of the
Neural Network and for optimizing of the effective input parameters
(pH, temperature and initial concentration). Feed forward, multi-layer
perceptron (MLP) NN successfully tracked the non-linear behavior of
the adsorption process versus the input parameters with mean squared
error (MSE), correlation coefficient (R) and minimum squared error
(MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed
that NN modeling techniques could effectively predict and simulate
the highly complex system and non-linear process such as ionexchange.

	%P 832 - 835