Prediction and simulation of Chromium (VI) ions removal efficiency by riverbed sand adsorbent using Artificial Neural Networks
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 3, Issue 5
Abstract
In the present work removal of chromium from aqueous solution using riverbed sand as adsorbent was studied. The initial Cr (VI) concentration was varied from 10 mg/L to 100 mg/L with varying amount of riverbed sand (0.025 – 0.2 gm) in laboratory batch adsorption experiment. The maximum adsorption efficiency was found at initial Cr (VI) concentration of 10 mg/L, adsorption dose of 0.2 g/L and pH of the solution of 2.0. The equilibrium contact time was found at 90 min. A three layer feed forward artificial neural network (ANN) with back propagation training algorithm was developed to model the adsorption process of Cr (VI) in aqueous solution using riverbed sand as adsorbent. The neural network architecture consisted of tangent sigmoid transfer function (tansig) at hidden layer with 10 hidden neurons, linear transfer function (purelin) at output layer and Lavenberg-Marquardt (LM) backpropagation training algorithm. The neural network model predicted values are found in close agreement with the batch experiment result with correlation coefficient (R) of 0.995 and mean squared error (MSE) 0.0043975.
Authors and Affiliations
Dr. D. Sarala Thambavani
Keywords
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