Nanoscale zero-valent iron (nZVI) particles were investigated for the removal of methylene blue (MB) from aqueous solutions and the treatment of textile industry effluents. The nZVI material was characterized by XRD, TEM, EDS, FTIR, and SEM. It was demonstrated that several functional groups such as C–H, C = C, C–C, and C–O contributed to MB reduction. At initial MB concentration of 70 mg/L, the optimum pH was 6, achieving a removal efficiency of 72.1% using an nZVI dosage of 10 g/L, stirring rate of 150 rpm, and temperature of 30 °C within 30 min. The adsorption isotherm was described by the Langmuir model with monolayer coverage of 5.53 mg/g, and the Freundlich equation with multilayer adsorption capacity of 1.59 (mg/g)·(L/mg)1/n. The removal mechanisms of MB included reduction into colorless leuco-MB, precipitation as Fe(II)-MB, adsorption as ZVI-MB or FeOOH-MB, and/or degradation using •OH radicals. The synthesized nZVI particles were applied to reduce various organic and inorganic compounds, as well as heavy metal ions from real textile wastewater samples. The removal efficiencies of COD, BOD, TN, TP, Cu2+, Zn2+, and Pb2+ reached up to 91.9%, 87.5%, 65.2%, 78.1%, 100.0%, 29.6%, and 99.0%, respectively. The treatment cost of 1 m3 of textile wastewater was estimated as 1.66 $USD.

This study investigated the adsorption of PO₄³⁻ onto encapsulated nanoscale zero-valent iron (nZVI). At initial PO₄^3–: 10 mg · L⁻¹, the optimum condition was initial pH: 6.5, nZVI dosage: 20 g · L⁻¹, stirring-rate: 100 rpm, and time: 30 min, achieving PO₄³⁻ removal of 42%. The effect of pH and time on the PO₄³⁻ removal efficiency was quadratic-linear concave up, whereas the curve of nZVI dosage was quadratic-convex. Artificial neural network with a structure of 5−7−1 adequately predicted PO₄³⁻ removal (R²: 97.6%), and the sensitivity analysis demonstrated that pH was the most influential input. The cost of the adsorption unit was 3.15 $USD · m⁻³.

This study aims to investigate the efficiency of a pilot prototype system comprising coagulation/flocculation, filtration, and nano-bimetallic iron/copper (Fe/Cu) degradation and adsorption units for the removal of chemical oxygen demand (COD), biological oxygen demand (BOD), color, total nitrogen (TN), total phosphorus (TP), and TSS from real textile wastewater. The total removal efficiencies of the system were 96, 98, 82, 69, 88, and 97%, respectively, using 0.5 g/L ferric chlorides as a coagulant under an optimum adsorption condition of pH 6.0, nano-dosage 1.4 g/L, contact time 80 min, and stirring rate 250 r/min at room temperature. Adsorption isotherms indicated that the removal of COD and TP obeys both Koble–Corrigan and Freundlich adsorption models, removal of color obeys both Koble–Corrigan and Hill adsorption models, and removal of TN and TSS obeys Koble–Corrigan and Khan models, respectively. Avrami kinetic models adequately describe the adsorption data for COD, BOD, TN, and TSS, while pseudo-second-order and intraparticle models described the removal mechanism of color and TSS, respectively. An artificial neural network (ANN) with r²-value exceeding 0.98 is accurate and can be used with confidence in predicting removal efficiencies of the targeted parameters. Sensitivity analysis results showed that the initial concentration was the most influential parameter for TSS removal with relative importance greater than 25%, while the bimetallic Fe/Cu dosage was the most influential factor for all other studied parameters with relative importance greater than 40%. The total treatment cost of the proposed system per m³ after scaling up was found to be US$4.5 for reuse of the treated water for the irrigation of forest trees.

Predicting the heavy metals adsorption performance from contaminated water is a major environment-associated topic, demanding information on different machine learning and artificial intelligence techniques. In this research, nano zero-valent aluminum (nZVAl) was tested to eliminate Cu(II) ions from aqueous solutions, modeling and predicting the Cu(II) removal efficiency (R%) using the adsorption factors. The prepared nZVAl was characterized for elemental composition and surface morphology and texture. It was depicted that, at an initial Cu(II) level (C_o) 50 mg/L, nZVAl dose 1.0 g/L, pH 5, mixing speed 150 rpm, and 30 °C, the R% was 53.2 ± 2.4% within 10 min. The adsorption data were well defined by the Langmuir isotherm model (R²: 0.925) and pseudo-second-order (PSO) kinetic model (R²: 0.9957). The best modeling technique used to predict R% was artificial neural network (ANN), followed by support vector regression (SVR) and linear regression (LR). The high accuracy of ANN, with MSE < 10⁻⁵, suggested its applicability to maximize the nZVAl performance for removing Cu(II) from contaminated water at large scale and under different operational conditions.