Design Mining Microbial Fuel Cell Cascades

TL;DR

This paper explores the use of computational intelligence and 3D-printed conductive structures to optimize microbial fuel cell cascades, enhancing power output and density without relying on simulations.

Contribution

It introduces a design mining approach to physically explore and optimize heterogeneous MFC cascade designs using 3D-printed conductive inserts.

Findings

Optimized conductive structures increase power output.

Design mining identifies effective MFC configurations.

Heterogeneous MFC designs outperform uniform ones.

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 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.