Enhanced hexavalent chromium removal from aqueous solution using a sepiolite-stabilized zero-valent iron nanocomposite: Impact of operational parameters and artificial neural network modeling

• To synthesize a sepiolite-stabilized zero-valent iron nanocomposite (S-ZVIN).
• Characterization of synthesized S-ZVIN particles.
• To compare the Cr(VI) removal efficiency of bare ZVIN and that of S-ZVIN.
• Assessing effect of operational parameters on Cr(VI) removal efficiency by S-ZVIN.
• To model the Cr(VI) removal data using an artificial neural network approach.

The efficiency of zero-valent iron nanoparticles (ZVINs) for the removal of chromium Cr(VI) from solutions is strongly decreased due to particle agglomeration. To solve this problem, a sepiolite-stabilized ZVIN (S-ZVIN) composite was made using a liquid-phase method and then characterized employing scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometer (EDS). Batch experiments were also conducted to (1) investigate the influence of various experimental variables on the removal efficiency of Cr(VI), (2) compare the removal efficiency of bare ZVIN and S-ZVIN treatments and (3) evaluate the capability of the artificial neural network (ANN) technique to model the Cr(VI) removal. The Cr(VI) removal efficiency was enhanced by increasing S-ZVIN dosage while a considerable decrease was observed by increasing the initial Cr(VI) concentration. The acidic and neutral pH values were appropriate for Cr(VI) removal. The enhancement was observed in Cr(VI) removal by increasing chloride concentration. Additionally, pseudo first-order showed better performance than pseudo second-order kinetic model to fit the experimental data of Cr(VI) removal. The ANN model could predict the experimental data of Cr(VI) removal with a determination coefficient of 0.9803. The relative significance of each input variable on the removal of Cr(VI) was calculated.

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