Modeling of Chromium (VI) adsorption on limonia acidissima hull powder using Artificial Neural Network (ANN) approach

Abstract

Batch experiment was carried out for the removal of Chromium (VI) from aqueous solution to get experimental data to which an Artificial Neural Network (ANN) model was developed using 16 experimental data points (for testing) and 36 experimental data points (for training). A single layer feed forward back propagation was used to get minimum mean square error with eleven neurons in hidden layer. To develop ANN model, a tan sigmoid transfer function for input and purelin for output layers were used. The optimized process parameters viz., adsorbent dosage, pH and initial concentration of chromium (VI) were obtained along with the maximum percentage removal of Chromium (VI). The predicted ANN model data were in perfect match with the experimental data based on the regression coefficient R2 of 0.995. The optimized parameters along with maximum percentage removal of chromium (VI) were achieved by means of pattern search method in Genetic Algorithm.