Optimizing chemical oxygen demand removal from synthesized wastewater containing lignin by catalytic wet-air oxidation over CuO/Al2O3 catalysts

UNLABELLED: In this study, 10% CuO/Al2O3 catalyst was used in a catalytic wet-air oxidation process to remove chemical oxygen demand (COD) and color from experimentally designed wastewater containing lignin. The catalyst was prepared using an impregnation method and was characterized by X-ray diffraction (XRD), atomic absorption spectroscopy (AAS), and Brunauer-Emmett-Teller method (BET) for surface area before use. A series of Box-Behnken design (BBD) experiments were used to identify the conditions (temperature, pressure, reaction time, and catalysts) necessary for the COD removal process. The predicted model had R2 and R2adj correlation coefficients of 0.98 and 0.97, respectively. Pressure only and the interaction effect between temperature and pressure were found to have a significant effect on COD removal (both confidence interval [CI] 95%). Finally, response surface methodology (RSM)-optimized results suggested that 92% of COD could be removed in 1 L of experimental wastewater with a lignin concentration 350 g/L in 120 min under the following conditions: a reaction temperature of 185 °C, a pressure of 10 bars, and catalyst loading of 1 mg/L. The experiment, performed in triplicate, yielded a COD removal of 90±2%. The results are believed to be of importance to pulp and paper industrial wastewater treatment and other similar applications. IMPLICATIONS: Catalytic wet-air oxidation (CWAO) has been used as an alternative to overcome problems related to the high temperatures and pressures required by the traditional wet-air oxidation. CWAO has been widely applied to treat various industrial wastewaters. To reduce the overall operational cost, it is necessary to identify the optimal condition required when designing wastewater treatment plant processes. In this work, the authors had successfully demonstrated the application of response surface methodology (RSM) with the Box-Behnken design (BBD) as a means of elucidating the complicated interaction effects between parameters.