Synthesis of Zeolite-Supported Magnesia/Magnetite Nanoparticles, and its Efficient Application in Ultrasound-Assisted Adsorption of Dairy Wastewater: Optimization and Modeling of the Process by RSM and ANN

Background Aims: Dairy industries are among the major sectors consuming huge amounts of water converting it into effluents during various processes. Developing an efficient method to treat such effluents is inevitable. In this research, magnesium oxide iron oxide (MgO-Fe3O4)-zeolite was used in the ultrasound-assisted adsorption of dairy wastewater. Materials and Methods: The MgO-Fe3O4-zeolite composite was prepared via a facile method. Batch adsorption experiments were conducted on a laboratory scale. Typically, 200 mL of the effluent was poured into an Erlenmeyer flask and sonicated in the presence of an adsorbent. The effects of four factors (i.e., initial chemical oxygen demand [COD], adsorbent dosage, pH, and time) at five different levels were surveyed and optimized by response surface methodology (RSM) and artificial neural network (ANN). Finally, the analysis of variance was performed to assess the accuracy and validity of the models. Results: Using RSM, the optimization of the adsorption process led to a swift reduction in COD (83.22%) within 20 minutes at an initial COD of 300 mgL^-1, an adsorbent dosage of 4 gL^-1, and a pH of 9. The modeling of the process by the ANN demonstrated that the adsorbent dosage was the most important factor with a 38.30% impact on the COD extent. The isothermal and kinetics studies indicated that the process could be attributed to chemical adsorption. Studies associated with thermodynamics revealed that this process was endothermic and spontaneous. Conclusion: Based on the findings, it is concluded that the MgO-Fe3O4-zeolite has considerable potential in reducing the COD of dairy wastewater as a magnetically separable adsorbent.