Optimization of greywater treatment using UiO-66 nanomaterial: artificial neural network modeling

Abstract

Abstract 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.