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

Abstract

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 m3, 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, R2, adjusted R2, and predicted R2 were 0.839, 0.807, and 0.703, respectively. The fact that there is only a 3.8% error in the actual and adjusted R2 demonstrates that the proposed model is statistically significant.

Boosting the power density of two‐chamber microbial fuel cell: Modeling and optimization

“…The coefficient of determination values are 1.00 and 0.9883, respectively, for training and testing. For prediction, the coefficient of determination is increased from 0.703 using RSM 40 to 0.993 using ANFIS. So it is boosted by 41.25%.…”

“…In sum, the optimization phase aims to define the optimal values of acetate concentration, fuel feed flow rate and oxygen concentration to boost the output power density of TCMFCs. Therefore, next to creating a consistent ANFIS model of power density, HHO has been employed to determine the best values for the three controlling parameters, using the available data set in Reference 40. The problem argument of can be stated as 40 : where x is the set of input variables and y is the power density.…”

“…Therefore, next to creating a consistent ANFIS model of power density, HHO has been employed to determine the best values for the three controlling parameters, using the available data set in Reference 40. The problem argument of can be stated as 40 : where x is the set of input variables and y is the power density.…”

“…Using measured data, 40 ANFIF‐based model of TCMFCs has been established to simulate the power density of TCMFC. The measured data are distributed into two portions: 70% (to train) and 30% (to test).…”

“…It was found that decreasing these parameters by 0.8% reduced the power output by 10.57%, 0.12% and 78.42, respectively 39 . Moreover, the analytical model was developed via response surface methodology (RSM) to show the consolidated impact of these parameters on power generation 40 …”