Statistical Modeling and Performance Optimization of a Two-Chamber Microbial Fuel Cell by Response Surface Methodology

Microbial fuel cell, as a promising technology for simultaneous power production and waste treatment, has received a great deal of attention in recent years; however, generation of a relatively low power density is the main limitation towards its commercial application. This study contributes toward the optimization, in terms of maximization, of the power density of a microbial fuel cell by employing response surface methodology, coupled with central composite design. For this optimization study, the interactive effect of three independent parameters, namely (i) acetate concentration in the influent of anodic chamber; (ii) fuel feed flow rate in anodic chamber; and (iii) oxygen concentration in the influent of cathodic chamber, have been analyzed for a two-chamber microbial fuel cell, and the optimum conditions have been identified. The optimum value of power density was observed at an acetate concentration, a fuel feed flow rate, and an oxygen concentration value of 2.60 mol m(-3), 0.0 m(3), and 1.00 mol m(-3), respectively. The results show the achievement of a power density of 3.425 W m(-2), which is significant considering the available literature. Additionally, a statistical model has also been developed that correlates the three independent factors to the power density. For this model, R-2, adjusted R-2, and predicted R-2 were 0.839, 0.807, and 0.703, respectively. The fact that there is only a 3.8% error in the actual and adjusted R-2 demonstrates that the proposed model is statistically significant.

Automated summary

This study optimized the power density of a microbial fuel cell by using response surface methodology and central composite design. The study identified the optimal conditions for maximizing power density and achieved a significant power density of 3.425 W m(-2). Moreover, a statistical model was developed to correlate the independent factors with power density, demonstrating its statistical significance.