In this paper, an optimal artificial neural network determined by a self-adaptive differential evolution approach is applied to model and optimize the removal of copper from wastewater by an ion-exchange process. The Purolite S930 1 resin with iminodiacetic group was used in batch mode for Cu(II) removal from synthetic aqueous solutions in different working conditions (initial solution pH, stirring rate, initial concentration of copper, temperature, contact time and resin amount). The obtained results indicated that the used methodology was able to provide good models for the studied process, the mean squared error in the testing phase obtained by the best network being 0.0034. In addition, the optimal combination of parameters leading to the maximization of removal efficiency determined with the proposed approach was experimentally validated, the prediction being in correlation with the observed data.

New series of Cu(II) and Mn(II) complexes with Schiff base ligands derived from 2-furylmethylketone (Met), 2-furaldehyde (Fur), and 2-hydroxyacetopheneone (Hyd) have been synthesized in situ on SBA-15-NH2, MCM-48-NH2, and MCM-41-NH2 functionalized supports. The hybrid materials were characterized by X-ray diffraction, nitrogen adsorption–desorption, SEM and TEM microscopy, TG analysis, and AAS, FTIR, EPR, and XPS spectroscopies. Catalytic performances were tested in oxidation with the hydrogen peroxide of cyclohexene and of different aromatic and aliphatic alcohols (benzyl alcohol, 2-methylpropan-1-ol, and 1-buten-3-ol). The catalytic activity was correlated with the type of mesoporous silica support, ligand, and metal–ligand interactions. The best catalytic activity of all tested hybrid materials was obtained in the oxidation of cyclohexene on SBA-15-NH2-MetMn as a heterogeneous catalyst. No leaching was evidenced for Cu and Mn complexes, and the Cu catalysts were more stable due to a more covalent interaction of the metallic ions with the immobilized ligands.