# Cellular non-nonlinear network model of microbial fuel cell

## TL;DR

This paper presents a cellular non-linear network model to simulate and analyze the spatial and temporal dynamics of microbial fuel cells, capturing bacterial populations, charge production, and nutrient distribution.

## Contribution

It introduces a novel CNN-based modeling approach for microbial fuel cells, enabling detailed study of spatial disturbances and biofilm configurations.

## Key findings

- Model effectively simulates bacterial population dynamics.
- Allows analysis of inhomogeneous biofilm configurations.
- Facilitates study of temporal responses to spatial disturbances.

## Abstract

A cellular non-linear network (CNN) is a uniform regular array of locally connected continuous-state machines, or nodes, which update their states simultaneously in discrete time. A microbial fuel cell (MFC) is an electro-chemical reactor using the metabolism of bacteria to drive an electrical current. In a CNN model of the MFC, each node takes a vector of states which represent geometrical characteristics of the cell, like the electrodes or impermeable borders, and quantify measurable properties like bacterial population, charges produced and hydrogen ions concentrations. The model allows the study of integral reaction of the MFC, including temporal outputs, to spatial disturbances of the bacterial population and supply of nutrients. The model can also be used to evaluate inhomogeneous configurations of bacterial populations attached on the electrode biofilms.