Development of a novel method for Cu(II) sorption from aqueous solution and modeling by artificial neural networks (ANN)

In this study, coal Fly Ash (FA) was used as a low-cost source of Si and Al to synthesize the Zeolite A (ZA) via a hydrothermal reaction. ZA and FA were characterized by XRD, XRF and SEM/EDX. A series of batch sorption experiments was carried out for the sorption of Cu(II) from aqueous solutions on ZA and FA. The obtained data revealed that maximum removal efficiencies were similar to 98% (with 0.3 g, 5 min, 5 of adsorbent dose, contact time and pH respectively for ZA) and 96% (with 0.3 g, 4 h, 5 of adsorbent dose, contact time and pH respectively for FA). Studying the kinetic of Cu(II) adsorption onto FA indicated the adsorption process was controlled by pseudo-second-order and intra-particle diffusion models. Isotherm models subjected to experimental data at T = 25 degrees C demonstrated that Cu(II) adsorption onto ZA and FA were both favorable with good adsorption characteristics with maximum sorption capacity of 54.9 and 16.6 mg g(-1) for ZA and FA respectively. The calculated amounts of thermodynamic parameters such as Delta H degrees, Delta S degrees and Delta G degrees showed that the adsorption of Cu(II) onto both FA and ZA was feasible, spontaneous and endothermic. Moreover, a three-layer artificial neural network was constructed to model the experimental data by matlab software. Levenberg-Marquardt back-propagation training function with 6 neurons in the hidden layer was found to be the most proper network.