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Researchers developed optimal data-driven models for a microbial fuel cell (MFC) using machine learning algorithms, achieving 99% accuracy in modeling power density and output voltage.

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Microfluidic MFCRepresentative model
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Representative microfluidic MFC — matched on the paper’s reported configuration, not its exact geometry.

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What they did

System
MFC
Substrate
synthetic mixture
Inoculum
mixed-culture biofilm acclimated on acetate medium; not taxonomically identified

What worked

No outcome metrics extracted yet.

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.

Key findings

  • Optimal data-driven models for MFCs were developed using SVR, ANNs, GPR, and ensemble learners.
  • Hyperparameter optimization was carried out using Bayesian optimization, grid search, and random search to arrive at the best possible models.
  • The models achieved 99% accuracy in modeling power density and output voltage.

Keywords

Microbial fuel cellKrigingHyperparameterSupport vector machineHyperparameter optimizationGaussian process

Identifiers

DOI
10.3390/en16124740
Journal
Energies
Year
2023