Persulfate enhanced electrocoagulation of paint production industry wastewater: Process optimization, energy consumption, and sludge analysis

•Box Behnken design was applied to optimize the EC-PS process.•The COD and color number removal efficiencies were 64% and 98% under optimum conditions.•Specific energy consumption under optimum conditions was 20.4 kWh/kg COD.•SIR and ORSR values were calculated as 2.7 L/mole and 11.1 g/L, respectively. [Display omitted] This study aimed to investigate the treatment of paint production industry (PPI) wastewater, which is characterized by low biodegradability and high concentrations of resistant organic matter, by persulfate enhanced electrocoagulation process (EC-PS). A regression quadratic model was developed to describe the removal of chemical oxygen demand (COD) and color number (CN) from PPI wastewater. The effects of independent variables (initial pH, PS dose, current density, and reaction time) on system responses and the interaction between the parameters were determined. Validation experiments were carried out under the optimum conditions determined by the quadratic model (initial pH: 5, PS dose: 5.6 g/L, current density: 21 mA/cm2, and reaction time: 35 min) and 64% COD and 98.1% CN removal were obtained. Pollutant removal efficiencies increased with the increase of PS dose, current density, and reaction time while the highest removal efficiencies were achieved at acidic pH values. The scavenging studies indicated that although the sulfate radicals were the dominant radical type, both hydroxyl and sulfate radicals were involved in the process. In the synergistic effect studies performed under optimum conditions, the highest reaction rate was obtained in the EC-PS process with a value of 0.074 1/min. Specific energy consumption under optimum conditions was calculated as 20.4 kWh/kg COD. The results of the study showed that the EC-PS is an effective process for the treatment of PPI wastewater and response surface methodology is an applicable technique for the optimization of the variables.