Optimal Data-Driven Modelling of a Microbial Fuel Cell

Mojeed Opeyemi Oyedeji, Abdullah Alharbi, Mujahed Aldhaifallah () and Hegazy Rezk
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Mojeed Opeyemi Oyedeji: SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Abdullah Alharbi: Department of Accounting and Finance, KFUPM Business School (KBS), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Mujahed Aldhaifallah: Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Hegazy Rezk: Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia

Energies, 2023, vol. 16, issue 12, 1-21

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.

Keywords: ANN; Bayesian; fuel cell; GPR; SVR (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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