In this paper, the extract of Citrus aurantium (CA) was used as a green approach for the preparation of Fe₃O₄ nanoparticles. The green Fe₃O₄ (Fe₃O₄/CA) was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy analysis (EDX), Fourier-transform infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller (BET) surface area measurement, and vibrating sample magnetometry (VSM). The synthesized Fe₃O₄/CA was used to remove methylene blue (MB) dye from an aqueous solution. A four-factor central composite design (CCD), combined with response surface modeling (RSM), was used to maximize the MB dye removal. The four independent variables, which were initial dye concentration (10–50 mg/L), solution pH (3–9), adsorbent dose (ranging from 200–1000 mg/L), and contact time (30–90 min), were used as inputs to the model of the perecentage dye removal. The results yielded by an analysis of variance (ANOVA) confirmed the high significance of the regression model. The predicted values of the MB dye removal were in agreement with the corresponding experimental values. Optimized conditions for the maximum MB dye removal (93.14%) by Fe₃O₄/CA were the initial dye concentration (10.02 mg/L), pH (8.98), adsorbent mass (997.99 mg/L), and contact time (43.71 min). The validity of the quadratic model was examined, and good agreement was found between the experimental and predicted values. Our findings demonstrated that green Fe₃O₄NPs is a good adsorbent for MB removal.

Green synthesis methods using plants have many advantages such as time-saving, chemical-free, and negative effects on the environment. So, extracted Citrus aurantium juice was used to synthesize green CuO/TiO2 and(G- CuO/TiO2) nanocatalyst which was characterized by XRD, SEM, EDX, FTIR, BET, and ZP and utilized in the degradation of methylene blue (MB) under UV lamps and dark environments. The ANOVA program was used to maximize the photodegradation efficiency (%) of (G-CuO/TiO2) on the MB dye. The four independent variables: Initial dye concentration (10-50 mg/L), pH (3-9), adsorbent dose (200-1000 mg/L), and contact time (30-90 min) served to the model of the photodegradation efficiency (%). The ANOVA results confirmed the high significance of the regression model while the predicted values of the photodegradation efficiency (%) of MB were in good agreement with the corresponding experimental ones. Optimized conditions for the maximum photodegradation efficiency (98.6%) by (G- CuO/TiO2) NPs were the initial dye concentration (10.93 mg/L), pH (8.87), adsorbent mass (986.43 mg/L), and contact time (89.08 min). The validity of the quadratic model was examined, and found in good agreement with the experimental values. Results demonstrated that (G-CuO/TiO2) could be a promising photocatalyst in the degradation of MB dye. Copyright © 2023 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Conventional treatment methods such as chlorination and ozonation have been proven not to be effective in eliminating and degrading contaminants such as Bisphenol A (BPA) from wastewater. Hence, the degradation of BPA using a photocatalytic reactor has received a lot of attention recently. In this study, a model-based approach using a multilayer perceptron neural network (MLPNN) coupled with back-propagation, as well as support vector machine regression coupled with cubic kernel function (CSVMR) and Gaussian process regression (EQGPR) coupled with exponential quadratic kernel function, were employed to model the relationship between the textural properties such as pore volume (V_p), pore diameter (V_d), crystallite size, and specific surface area (S_BET) of erbium- and iron-modified TiO₂ photocatalysts in degrading BPA. Parametric analysis revealed that effective degradation of the Bisphenol up to 90% could be achieved using photocatalysts having textural properties of 150 m²/g, 8 nm, 7 nm, and 0.36 cm³/g for S_BET, crystallite size, particle diameter, and pore volume, respectively. Fifteen architectures of the MPLNN models were tested to determine the best in terms of predictability of BPA degradation. The performance of each of the MLPNN models was measured using the coefficient of determination (R²) and root mean squared errors (RMSE). The MLPNN architecture comprised of 4 input layers, 14 hidden neurons, and 3 output layers displayed the best performance with R² of 0.902 and 0.996 for training and testing. The 4-14-3 MLPNN robustly predicted the BPA degradation with an R² of 0.921 and RMSE of 4.02, which is an indication that a nonlinear relationship exists between the textural properties of the modified TiO₂ and the degradation of the BPA. The CSVRM did not show impressive performance as indicated by the R² of 0.397. Therefore, appropriately modifying the textural properties of the TiO₂ will significantly influence the BPA degradability.