MODELLING AND OPTIMIZATION OF A MULTI-POPULATION, SINGLE-CHAMBER MICROBIAL FUEL CELL OPERATING IN A CONTINUOUS FLOW MODE AT STEADY-STATE

Abstract

There is no doubt that energy demand has been increased in the last decade due to the economic revolution. However, this revolution is based on the fossils fuel which are going to be depleted. Microbial fuel cells are one the proposed technologies to provide renewable energy and treat wastewater simultaneously. The stumbling block for this technology is the low power production compared to conventional fuel cells. Modelling and optimization is carried out on a multi-population, single chamber microbial fuel cell operating in a continuous flow under steady state conditions for the first time in the literature. Microsoft Excel and Matlab computational tools were used to optimize three objective functions which are: power density, current density and substrate removal efficiency. Five parameters were varied around specified ranges for the optimization which are: dilution rate, external resistance, anodophilic and methanogenic bacteria concentration and substrate concentration. Results showed that all the objective functions converged to an optimum point at which power density, current density and substrate removal efficiency were 157 mW/L, 251 mA/L and 99%, respectively. The optimum point was at 2 day-1 dilution rate, 25 ? external resistance, anodophilic and methanogenic bacteria concentration of 510.5 and 2 mg/L, respectively and 0.01 mg/L substrate concentration. From the local sensitivity analysis on power density, the most impacting factors of the studied parameters were external resistance and anodophilic bacteria concentration. This was agreed with the response surface methodology contour plots using Minitab software. Finally, variance-based global sensitivity analysis was carried out studying six parameters which are maximum specific anodophilic growth rate, number of electrons transferred per mole of mediator, decay rate constant of anodophilics, half rate constant of both mediator and anodophilic bacteria. Following this analysis, first and total indices of each parameter were computed using Matlab and was coupled with Monte Carlo simulations. Maximum specific anodophilic growth rate and number of electrons transferred per mole of mediator were the most impacting factors on power density having total indices of 0.74 and 0.624, respectively. The findings herein are critical in understanding and further model improvement of microbial fuel cells