Modelling Microbial Fuel Cells Using Lattice Boltzmann Methods

Abstract

An accurate modelling of bio-electrochemical processes that govern Microbial Fuel Cells (MFCs) and mapping their behaviour according to several parameters will enhance the development of MFC technology and enable their successful implementation in well defined applications. The geometry of the electrodes is among key parameters determining efficiency of MFCs due to the formation of a biofilm of anodophilic bacteria on the anode electrode, which is a decisive factor for the functionality of the device. We simulate the bio-electrochemical processes in an MFC while taking into account the geometry of the electrodes. Namely, lattice Boltzmann methods are used to simulate the fluid dynamics and the advection-diffusion phenomena in the anode compartment. The model is verified on voltage and current outputs of a single MFC derived from laboratory experiments under continuous flow. Conclusions can be obtained from a parametric analysis of the model concerning the design of the geometry of the anode compartment, the positioning and microstructure of the anode electrode, in order to achieve more efficient overall performance of the system. An example of such a parametric analysis is presented here, taking into account the positioning of the electrode in the anode compartment.

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

“…temperature). While there are several works that simulate the behaviour of MFCs ( Picioreanu et al, 2008 ; Pinto et al, 2010 ; Tsompanas et al, 2017a ; Tsompanas et al, 2018 ), a more dynamic modelling tool is required to tackle the non linear behaviour observed in MFCs. Inspired by this, we propose the training and utilization of Nonlinear Autoregressive Networks with exogenous inputs (NARX) ( Lin et al, 1996 ; Menezes and Barreto, 2008 ) to predict a time series of the outputs of MFCs that can be used on biodegradable soft robotics.…”

Artificial neural network simulating microbial fuel cells with different membrane materials and electrode configurations

“…The task of optimizing MFC technology is heavily based on laboratory tests [1,2,3] that usually involve changing one parameter at a time. However, due to high costs and long time intervals required in laboratory tests, mathematical modelling and simulated optimisation of MFCs has been proposed as a viable alternative [4,5,6,7,8,9,10,11], even though such modelling is subject to some level of abstraction.…”