A 2D Modelling Approach for Predicting the Response of a Two-Chamber Microbial Fuel Cell to Substrate Concentration and Electrolyte Conductivity Changes

Theofilos Kamperidis, Asimina Tremouli, Antonis Peppas and Gerasimos Lyberatos
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Theofilos Kamperidis: School of Chemical Engineering, National Technical University of Athens, 15780 Athens, Greece
Asimina Tremouli: School of Chemical Engineering, National Technical University of Athens, 15780 Athens, Greece
Antonis Peppas: School of Chemical Engineering, National Technical University of Athens, 15780 Athens, Greece
Gerasimos Lyberatos: School of Chemical Engineering, National Technical University of Athens, 15780 Athens, Greece

Energies, 2022, vol. 15, issue 4, 1-15

Abstract: Bioelectrochemical systems have been the focus of extensive research due to their unique advantages of converting the chemical energy stored in waste to electricity. To acquire a better understanding and optimize these systems, modelling has been employed. A 2D microbial fuel cell (MFC) model was developed using the finite element software Comsol Multiphysics ® (version 5.2), simulating a two-chamber MFC operating in batch mode. By solving mass and charge balance equations along with Monod–Butler–Volmer kinetics, the operation of the MFC was simulated. The model accurately describes voltage output and substrate consumption in the MFC. The computational results were compared with experimental data, thus validating the model. The voltage output and substrate consumption originating from the model were in agreement with the experimental data for two different cases (100 Ω, 1000 Ω external resistances). A polarization curve was extracted from the model by shifting the external resistance gradually, calculating a similar maximum power (47 mW/m 2 ) to the observed experimental one (49 mW/m 2 ). The validated model was used to predict the MFC response to varying initial substrate concentrations (0.125–4 g COD/L) and electrolyte conductivity (0.04–100 S/m) in order to determine the optimum operating conditions.

Keywords: MFC; modelling; BES; bioelectricity; conductivity; concentration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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