Nanoscale zero-valent iron (nZVI) and sulfidated nZVI (SnZVI) have demonstrated promising treatment performance for individual contaminants in aquatic and terrestrial systems. However, wastewater typically contains suites of contaminants, including metals and nonmetals. These contaminants often interact and would strongly influence the treatment performance of nZVI and SnZVI. Additionally, simulation of wastewater treatment performance is complicated due to the complexity of the matrix and the confounding multiple interactions occurring simultaneously. In this study, we investigated the co-removal of copper (Cu) and phosphate (PO43-) by nZVI and SnZVI (with different S/Fe molar ratios); and probed how each contaminant affect the removal of the other one. Furthermore, artificial neural network (ANN) was used to simulate the remediation performance of both nanoparticles, taking into account six input variables (nanoparticle type, nanoparticle dosage, pH, initial Cu and P concentrations, and treatment time), which were also weighed to determine their relative importance. The adsorption capacities of nZVI were 36 mg Cu/g and 192.6 mg PO43--P/g when each contaminant was present alone. The adsorption capacities of nZVI increased to 149.4 mg Cu/g and 195.2 mg PO43--P/g when both Cu and PO43- were present in media. Sulfidation of nZVI has significant effects on adsorption capacity and kinetics. The highest adsorption capacities (192.6 mg Cu/g and 198 mg PO43--P/g) and reaction rate (0.01387 g/mg-min for Cu and 0.02115 g/mg-min for PO43--P) were achieved using SnZVI (S/Fe molar ration = 0.28) at pH = 5. ANN models were successfully used to simulate both Cu and P removal efficiencies with the six input variables. This study demonstrates the capability of nanotechnology to treat wastewater with multiple contaminants and the potential of using ANN to simulate nanoremediation in complicated systems.

Simultaneous removal copper and phosphate in aqueous media by pristine and sulfidated nanoscale zero-valent iron: Application of artificial neural network

ACS Fall 2021

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