JFET_V8_N4_RP1 D. Krishna Padma Sree R. Journal on Future Engineering and Technology 2230 – 7184 8 4 9 15 Artificial Neural Network, Biosorption, Custard Apple Peel Powder, Cu (II), Genetic Algorithm In this paper, an artificial neural network (ANN) model was developed to predict the removal efficiency of Cu (II) from aqueous solution using a custard apple peel powder as adsorbent. The effect of operational parameters such as pH, adsorbent dosage, and initial Cu (II) concentration are studied to optimize the conditions for the maximum removal of Cu (II) ions. Experimentally it was found that adsorption equilibrium is obtained in 60 minutes. The ANN model was developed using 40 experimental data points for training and 14 data points for testing by a single layer feed forward back propagation network with 10 neurons to obtain minimum mean squared error (MSE). A tansigmoid was used as transfer function for input and purelin for output layers. The high correlation coefficient (R2average-ANN =0.989) between the model and the experimental data showed that the model was able to predict the removal of Cu (II) from aqueous solution using custard apple peel powder efficiently. Pattern search method in genetic algorithm was applied to get optimum values of input parameters for the maximum removal of Cu (II). May - July 2013 Copyright © 2013 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=2356