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Modelling microbial fuel cells using Lattice Boltzmann methods

Tsompanas, Michail Antisthenis; Adamatzky, Andrew; Ieropoulos, Ioannis; Phillips, Neil; Sirakoulis, Georgios; Greenman, John

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Yannis Ieropoulos
Professor in Bioenergy & Director of B-B

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Dr Neil Phillips
Research Fellow in Fungal Analog Electronics

Georgios Sirakoulis

John Greenman


An accurate modelling of bio-electrochemical processes that govern Microbial Fuel Cells (MFCs) and mapping their behaviour according to several parameters will enhance the development of MFC technology and enable their successful implementation in well defined applications. The geometry of the electrodes is among key parameters determining efficiency of MFCs due to the formation of a biofilm of anodophilic bacteria on the anode electrode, which is a decisive factor for the functionality of the device. We simulate the bioelectrochemical processes in an MFC while taking into account the geometry of the electrodes. Namely, lattice Boltzmann methods are used to simulate the fluid dynamics and the advection-diffusion phenomena in the anode compartment. The model is verified on voltage and current outputs of a single MFC derived from laboratory experiments under continuous flow. Conclusions can be obtained from a parametric analysis of the model concerning the design of the geometry of the anode compartment, the positioning and microstructure of the anode electrode, in order to achieve more efficient overall performance of the system. An example of such a parametric analysis is presented here, taking into account the positioning of the electrode in the anode compartment.


Tsompanas, M. A., Adamatzky, A., Ieropoulos, I., Phillips, N., Sirakoulis, G., & Greenman, J. (2019). Modelling microbial fuel cells using Lattice Boltzmann methods. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6(16), 2035-2045.

Journal Article Type Article
Acceptance Date Apr 25, 2018
Online Publication Date Apr 30, 2018
Publication Date 2019-12
Deposit Date Apr 30, 2018
Publicly Available Date Apr 30, 2018
Journal IEEE/ACM Transactions on Computational Biology and Bioinformatics
Print ISSN 1545-5963
Electronic ISSN 1557-9964
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 6
Issue 16
Pages 2035-2045
Public URL
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Additional Information Additional Information : (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.


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