Prediction and simulation of Chromium (VI) ions removal efficiency by riverbed sand adsorbent using Artificial Neural Networks

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.