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Adsorption of Pb(II) and Cd(II) from wastewater utilizing three nano-magnetic materials (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2 Fe2O4, and Cu0.7Zn0.3 Fe2O4) were studied. The nano-magnetic materials were prepared from the Cu Frites powder and then the Cu ions were replaced by Zn ions in three concentrations, these materials were characterized by X-ray diffraction (XRD) which has conformed good crystallinity with spinel structure and particle size in the range (26.5–23.9 nm). Artificial neural networks were applying to model the removal of Pb(II) and Cd(II) on three adsorbents from wastewater. The operating conditions that affect on adsorption process are adsorbent dose (0.1, 0.25, and 0.5) g, pH (3, 7, and 9), and contact time (15, 30, and 45) min. Three Multilayered feedforward neural networks (3:9:2) were successfully used for modeling of removing heavy metals on three adsorbents. The antimicrobial effectiveness of ferrite substances was studied against two types of bacteria. The three adsorbents showed an excellent removal for Cd (II) ions 100% complete removal on Cu0.9Zn0.1 Fe2O4, Cu0.8Zn0.2 Fe2O4, and it was 95% on Cu0.7Zn0.3 Fe2O4, and less removal for Pb (II) ions on Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2 Fe2O4 were 78.4% and 78.8%, and 83.4% on Cu0.7Zn0.3 Fe2O4. ANN models show efficient simulation with a high correlation coefficient (R2 = 0.99) for all three adsorbents, Sensitivity Analysis demonstrated that pH, time, and a dose of the adsorbent have a strong impact on the process of removal.The results for antimicrobial effectiveness showed that Cu0.9Zn0.1 Fe2O4 had the most antibacterial properties against two types of bacteria and the S. aureus killing rate was less than the E. coli killing rate of all ferrite composite nanoparticles.

revista de chimie

Artificial Neural Network Modeling for Removal of Cd (II) and Pb (II) from Wastewater by Using Three Ferrite Nanomaterial (Cu0.9Zn0.1Fe2O4, Cu0.8Zn0.2Fe2O4, and Cu0.7Zn0.3Fe2O4) and Study the Antimicrobial Effectiveness of these Ferrite Substances