Artificial Neural Network (ANN) Approach for Modeling Chromium (VI) Adsorption From Aqueous Solution Using a Borasus Flabellifer Coir Powder

Abstract

An artificial neural network (ANN) model was developed to predict the removal efficiency of chromium (VI) from aqueous solution using a Borasus flabellifer coir powder as adsorbent. The effect of operational parameters such as pH, adsorbent dosage, and initial chromium (VI) concentration are studied to optimize the conditions for the maximum removal of chromium (VI) ions. The ANN model was developed using 54 experimental data points for training and 16 data points for testing by a single layer feed forward back propagation network with 18 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 (R^2_(average-ANN)=0.992) between the model and the experimental data showed that the model was able to predict the removal of chromium (VI) from aqueous solution using Borasus flabellifer coir powder efficiently. Pattern search method in genetic algorithm was applied to get optimum values of input parameters for the maximum removal of chromium (VI).