Optimal Data-Driven Modelling of a Microbial Fuel Cell

Abstract

Microbial fuel cells (MFCs) are biocells that use microorganisms as biocatalysts to break down organic matter and convert chemical energy into electrical energy. Presently, the application of MFCs as alternative energy sources is limited by their low power attribute. Optimization of MFCs is very important to harness optimum energy. In this study, we develop optimal data-driven models for a typical MFC synthesized from polymethylmethacrylate and two graphite plates using machine learning algorithms including support vector regression (SVR), artificial neural networks (ANNs), Gaussian process regression (GPR), and ensemble learners. Power density and output voltage were modeled from two different datasets; the first dataset has current density and anolyte concentration as features, while the second dataset considers current density and chemical oxygen demand as features. Hyperparameter optimization was carried out on each of the considered machine learning-based models using Bayesian optimization, grid search, and random search to arrive at the best possible models for the MFC. A model was derived for power density and output voltage having 99% accuracy on testing set evaluations.

ANN-Based Reliability Enhancement of SMPS Aluminum Electrolytic Capacitors in Cold Environments

“…This research utilizes machine learning techniques, encompassing support vector regression, artificial neural networks, and Gaussian process regression, to establish ideal data-informed models for MFCs. Fine-tuning hyperparameters through Bayesian, grid, and random exploration yields models with 99% precision for forecasting power density and output voltage, facilitating enhanced MFC optimization [28]. This paper introduces a synthetic neural network-centered (SNN) energy management approach (SMA) for a hybrid AC/DC microgrid.…”