Artificial neural network (ANN) approach for modeling of Cr(VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA)

In this present work, artificial neural networks (ANN) are applied for prediction of percentage adsorption efficiency for the removal of Cr(VI) ions from aqueous solution by zeolite (ZFA) prepared from raw fly ash (RFA). The off operational parameters such as initial pH, adsorbent dosage, contact time and temperature is studied to optimize the conditions for maximum removal of Cr(VI) ions. Three equations, i.e. Morris–Weber, Lagergren, and pseudo second order have been tested to track the kinetics of removal process. The Langmuir, Freundlich, Redlich–Peterson, Temkin, and D-R are subjected to sorption data to estimate sorption capacity. Thermodynamic parameters showed that the adsorption of Cr(VI) onto ZFA was feasible, spontaneous and endothermic. Artificial neural networks are effective in modeling and simulation of highly non-liner multivariable relationships. The comparison of the removal efficiencies of Cr(VI) using ANN model and experimental results showed that ANN model can estimate the behavior of the Cr(VI) removal process under different conditions.