Design Mining Microbial Fuel Cell Cascades
This work addresses efficiency improvements in microbial fuel cells for practical wastewater treatment applications, representing an incremental advancement.
The paper tackled the problem of optimizing microbial fuel cell cascades for wastewater treatment and electricity production by using computational intelligence to design and 3-D print conductive inserts, resulting in increased cascade power output and power density.
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.