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Design mining microbial fuel cell cascades

Preen, Richard; You, Jiseon; Bull, Larry; Ieropoulos, Ioannis A.

Authors

Richard Preen Richard2.Preen@uwe.ac.uk
Research Fellow - Deep Evolutionary Learning

Jiseon You Jiseon.You@uwe.ac.uk
Research Fellow - Bristol BioEnergy Centre

Lawrence Bull Larry.Bull@uwe.ac.uk
AHOD Research and Scholarship and Prof



Abstract

Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms. For practical applications, it has been suggested that greater efficiency can be achieved by arranging multiple MFC units into physical stacks in a cascade with feedstock flowing sequentially between units. In this article, we investigate the use of cooperative coevolution to physically explore and optimise (potentially) heterogeneous MFC designs in a cascade, i.e., without simulation. Conductive structures are 3D printed and inserted into the anodic chamber of each MFC unit, augmenting a carbon fibre veil anode and affecting the hydrodynamics, including the feedstock volume and hydraulic retention time, as well as providing unique habitats for microbial colonisation. We show that it is possible to use design mining to identify new conductive inserts that increase both the cascade power output and power density.

Citation

Preen, R., You, J., Bull, L., & Ieropoulos, I. (2019). Design mining microbial fuel cell cascades. Soft Computing, 23(13), 4673-7643. https://doi.org/10.1007/s00500-018-3117-x

Journal Article Type Article
Acceptance Date Feb 28, 2018
Online Publication Date Mar 7, 2018
Publication Date Jul 31, 2019
Journal Soft Computing
Print ISSN 1432-7643
Electronic ISSN 1433-7479
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 23
Issue 13
Pages 4673-7643
DOI https://doi.org/10.1007/s00500-018-3117-x
Keywords 3D printing, cascade stacks, cooperative coevolution, microbial fuel cell, shape optimisation
Public URL https://uwe-repository.worktribe.com/output/871163
Publisher URL https://doi.org/10.1007/s00500-018-3117-x
Additional Information Additional Information : The dataset for this study is available from the UWE Research Data Repository: http://researchdata.uwe.ac.uk/217

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