Microbial fuel cell is a clean technology in which waste treatment and energy production continue simultaneously. Process performance depends on the independent variables and their interaction with each other specific to the waste to be treated. In this study, Microbial Fuel Cell (MFC), in which yeast production process wastewater is oxidized in the anolyte, and azo dye is reduced in the catholyte under abiotic conditions, was optimized using the Response Surface Methodology for maximum waste treatment and energy production. The independent variables were wastewater type, electrode array, and amount of sulfate-reducing inhibitor, while the dependent variables were azo dye removal, maximum power density, chemical oxygen demand (COD) removal, and coulomb efficiency. The effect of variables on all responses was determined using Analysis of Variance (ANOVA) and 3-dimensional (3D) graphs. The suitability of the models was tested by verification experiments under the determined optimum conditions (OPT 1, 2 and 3). Optimum experiments were ranked with the PROMETHEE approach according to azo dye removal (mg/L), maximum power density (mW/m2), COD removal (g/L), coulombic efficiency (%), and electrode cost (€/m2) criteria. The preference order of the optimum experiments was determined as OPT 1>OPT 2>OPT 3. With OPT 1, azo dye removal of 16.8 mg/L, maximum power density of 95.34 mW/m2, COD removal of 5.8 g/L and coulombic efficiency of 0.23 % were achieved. With a 1000 Ω external resistor, the maximum power density and coulombic efficiency increased to 147 mW/m2 and 1.5 %, respectively. In addition, the determined optimum experiments were examined using polarization curve and Electrochemical Impedance Spectroscopy.
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