Statistical Optimization of Medium Components to Enhance Bioelectricity Generation in Microbial Fuel Cell

Abstract

In this work, sequential optimization strategy, based on statistical designs, was employed to enhance the generation of electricity in microbial fuel cell. For screening of growth medium composition significantly influencing electricity generation, the two-level Plackett–Burman design was used. Under our experimental conditions, glucose, KCl, and NaHCO3 were found to be the major factors of the electricity generation. A near optimum medium formulation was obtained using this method with increased voltage yield by 5.3 %. Response surface methodology was adopted to acquire the best process conditions. In this respect, the three-level Box–Behnken design was applied. A polynomial model was created to correlate the relationship between the three variables (glucose, KCl, and NaHCO3) and voltage yield. Estimated optimum levels of optimized variables for the generation of electricity were glucose 8.5 g/l, KCl 0.8 g/l, and NaHCO3 0.2 g/l. The optimum voltage yield was 738.72 mV which was 8.0 % than the basal medium.