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Preen, R.; You, J.; Bull, L.; Ieropoulos, I.
Publisher: Springer Verlag
Languages: English
Types: Preprint
Subjects: Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
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 paper, we investigate the use of computational intelligence to physically explore and optimise (potentially) heterogeneous MFC designs in a cascade, i.e. without simulation. Conductive structures are 3-D 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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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