Artificial neural networks approach for modeling of Cr(VI) adsorption from aqueous solution by MR, MAC, MS

The adsorption ability of Dowex Optipore L493 resin modified with Aliquat 336 (MR), activated carbon modified with Aliquat 336 (MAC), and sawdust modified with Aliquat 336 (MS) for removal of Cr(VI) from aqueous solution in batch system is investigated. The effects of operational parameters such as adsorbent dosage, initial concentration of Cr(VI) ions, pH, temperature, and contact time are studied. An artificial neural network (ANN) model is developed to predict the efficiency of Cr(VI) ions removal. The results reveal that the Langmuir isotherm fits better than the Freundlich isotherm. The rate of adsorption shows the best fit with the pseudo-second order model. Thermodynamic parameters show that the adsorption of Cr(VI) adsorption is feasible, spontaneous, and exothermic. The comparison of the removal efficiencies of Cr(VI) using ANN model and experimental results show that the ANN model can estimate the behavior of the Cr(VI) removal process under different conditions.