  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.
