Optimizing chromium (VI) adsorption onto superheated steam activated granular carbon through response surface methodology and artificial neural network

The adsorptive removal of hexavalent chromium from aqueous medium by superheated steam activated granular carbon (GAC) prepared indigenously from non‐usable coconut shell was investigated by a series of batch sorption experiments. The removal efficiency of the process is optimized using response surface methodology and artificial neural networking. The influence of five different parameters viz. pH, contact time, GAC dose, initial concentration, and temperature on percent removal was observed through the model equations developed by a 25 full factorial central composite rota table design in shake‐flask system to find optimum condition. Maximum chromium removal is observed to be 96.33% at initial concentration 100 mg/L, pH 4, GAC dose 0.8 g/L, contact time 60 min, and temperature 15°C. Second‐order regression model was developed to study analysis of variance showing the relative significance of process parameters in removal process. From the results of this study it is revealed that GAC from waste coconut shell with surface area of 989 m2/g could be a cost effective and efficient adsorbent for the removal of Cr (VI).