Optimisation of Microbial Electrolysis Cells Through Computational Modelling

Wastewater treatment accounts for 21 billion kWh of electrical consumption per year, yet wastewater typical contains 7.6 kJ/L of energy. Developments of strategies and technologies to effectively capture energy from the wastewater treatment (WWT) process is a fundamental step to obtaining a low carbon future for the water industry. Microbial electrolysis cells (MECs) could be a feasible method to reduce energy demand at WWT plants, recapturing energy from the wastewater either directly as electricity or as products such as hydrogen. These MECs have similar structures to classical electrochemical systems containing parts such as anodes and cathodes, however major differences lie in how the systems operate, and the scale at which they would need to do so. Furthermore rather than being purely a chemical system MECs rely on microorganisms to act as a biocatalyst to convert chemical energy into electricity or other usable resources forming a bioelectrochemical system. Currently these systems have been developed and tested for small scales and show promising results but have not been built at scales comparable to treatment plants, mainly due to financial costs and unpredictability of microbial communities.

Although a number of models for the simulation of MECs have been produced, these are mostly focused on replication and modelling of lab scale experiments. While production of small scale models is fundamental to aid with design and overall understanding, there is still need for larger scale development to assess and optimise MECs implementation at a scale comparable to that of a treatment plant. The main aim and overall goal of this project is to develop a large scale mathematical model that will accurately simulate MECs in a wastewater treatment setting. Once this has been developed focus can then be shifted into optimisation of this implementation, including adjustment of cell layout and size while considering optimisation of design parameters. This would primarily focus on maximisation of substrate removal alongside current production via optimal control or other optimisation algorithms. Development of such a model will allow for quick and easy testing of potential designs, removing the need to build and test un-optimised layouts, further reducing the cost overhead of testing these systems. While a model cannot remove the need for physical testing, it can be used as a highly effective tool throughout the design process.