Open AccessNumerical simulation of three-dimensional microbial fuel cell
Author(s) -
Kumar Pijush Kataky,
Amaresh Dalal,
Gautam Biswas,
ChinTsan Wang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/463/1/012062
Subject(s) - microbial fuel cell , biochemical engineering , biomass (ecology) , current (fluid) , environmental science , computer science , process engineering , fuel cells , biological system , power (physics) , ecology , electricity generation , engineering , physics , chemical engineering , biology , electrical engineering , thermodynamics
Microbial Fuel Cell (MFC) has various application potential as in generation of bioelectricity, bio-hydrogen production, waste water treatment and it is also used as biosensors. It would not be possible to headway without mentioning that MFCs have quite a many similarities with Chemical Fuel Cells (CFC). It is seen that a lot of research is carried out for CFCs as compared to MFCs. Most of the research works on MFCs include experimental approach while very few computational studies have been carried out for MFCs. So an endeavour is made to create a model which mimics the working by simulating the key physical and biochemical processes occurring. Results imply that variation of current density occurs with change in Reynolds number (Re) and kinetic rate of reaction (k) which lead to the study of effects of variation of flow rates, turbulence and the action of different bacteria in the efficiency of MFCs. The current density achieved computationally is around 512 mA/m 2 for Re=5 and k=10 −3 which is in good agreement with the experimental data. Regions of higher current density are found which can be used to improvise the MFCs. Present mathematical model provides a new perspective in understanding the biomass concentration across the MFC and gives better knowledge of the mechanisms taking place. This simple computational framework provides insight into the fluid dynamics involved during continuous feeding, by overcoming the limitations and technical barriers in monitoring and examining through experiments. By implementing the findings from this model optimization of designs can be achieved leading to higher current generation, increase in efficacy and cost effective production techniques which paves the way for future work.