Design mining microbial fuel cell cascades

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

doi:10.1007/s00500-018-3117-x

Authors

Richard Preen Richard2.Preen@uwe.ac.uk
Senior Research Fellow

Jiseon You Jiseon.You@uwe.ac.uk
Lecturer in Engineering/ Project Management

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

Yannis Ieropoulos Ioannis2.Ieropoulos@uwe.ac.uk
Professor in Bioenergy & Director of B-B

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