A novel magnetic biocomposite adsorbent, denoted as AgZnFe₂O₄@Ch, was utilized for the degradation of Metronidazole (MNZ) from water. Various analytical techniques, including vibrating sample magnetometer (VSM), X-ray diffraction (XRD), Brunauer–Emmett–Teller, Fourier transform infrared spectroscopy, and field emission scanning electron microscopy (FESEM), were applied to investigate the characteristics of the magnetic biocomposite adsorbent. XRD examination confirmed the formation of spinel ferrites phases. FESEM assessment indicated a notable reduction in sample aggregation. The ferromagnetic character of the adsorbent was well demonstrated by VSM analysis. The saturation magnetization value for straightforward separating by the outside magnetic fields was 14.64 emu/g. An analytical modeling approach was used to evaluate and analyze the impacts of factors including MNZ initial concentration, temperature, contact time, adsorbent dosage, and pH. Optimized conditions involved an adsorbent dosage of 0.9 g/L, pH of 7, MNZ initial concentration of 10 mg/L, and a contact time of 50 min, resulting in a peak adsorption efficiency of 65.53% under favorable circumstances. A good degree of fit was achieved with the linear model. The experimental equilibrium data fitting to the Langmuir, Freundlich, and Temkin isotherm models demonstrates that the Langmuir model was an effective and appropriate model for evaluating adsorption. Intraparticle kinetic modeling was also shown to be better suitable for characterizing the MNZ adsorption onto the adsorbent. The thermodynamic analysis indicated that the process of MNZ adsorption by AgZnFe₂O₄@Ch was characterized by exothermicity and lacked spontaneity.

Metal–organic frameworks (MOFs) have attracted considerable attention in wastewater treatment due to their high porosity and tunable structures. In this study, UiO-66 materials were synthesized using different molar ratios of zirconium tetrachloride to terephthalic acid (metal-to-ligand ratios) to evaluate their performance in greywater treatment. Structural and physicochemical properties of the synthesized materials were characterized using XRD, BET, FTIR, SEM, and EDS analyses. Among the prepared samples, UiO-66 with a metal-to-ligand ratio of 1:0.75 exhibited improved crystallinity, a higher surface area (1387 m²/g), and a larger total pore volume (1.66 cm³/g), indicating favorable structural properties for adsorption applications. Batch adsorption experiments were conducted to investigate the effects of initial COD concentration, adsorbent dosage, pH, and contact time on greywater treatment efficiency. To model the adsorption process and analyze the interactions between operational parameters, an artificial neural network (ANN) model was developed and combined with a genetic algorithm (GA) for process optimization. The model demonstrated high predictive capability and enabled the identification of optimal operating conditions, namely an initial COD concentration of 236 ppm, adsorbent dosage of 500 ppm, pH 2, and contact time of 3 h, yielding a maximum COD removal efficiency of 88.4%. Adsorption equilibrium analysis indicated that the Langmuir model provided the best fit to the experimental data, with a maximum adsorption capacity of 722 mg/g. Kinetic analysis revealed that the adsorption process followed a pseudo-second-order model. The results demonstrate that the optimized UiO-66 adsorbent exhibits promising potential for greywater treatment and that the combined experimental–modeling approach provides valuable insights for optimizing MOF-based adsorption processes.