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Optimal Data-Driven Modelling of a Microbial Fuel Cell

Author

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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
    These authors contributed equally to this work.)

  • Abdullah Alharbi

    (Department of Accounting and Finance, KFUPM Business School (KBS), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    These authors contributed equally to this work.)

  • Mujahed Aldhaifallah

    (Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    These authors contributed equally to this work.)

  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Mojeed Opeyemi Oyedeji & Abdullah Alharbi & Mujahed Aldhaifallah & Hegazy Rezk, 2023. "Optimal Data-Driven Modelling of a Microbial Fuel Cell," Energies, MDPI, vol. 16(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4740-:d:1172162
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    References listed on IDEAS

    as
    1. de Ramón-Fernández, A. & Salar-García, M.J. & Ruiz Fernández, D. & Greenman, J. & Ieropoulos, I.A., 2020. "Evaluation of artificial neural network algorithms for predicting the effect of the urine flow rate on the power performance of microbial fuel cells," Energy, Elsevier, vol. 213(C).
    2. de Ramón-Fernández, Alberto & Salar-García, M.J. & Ruiz-Fernández, Daniel & Greenman, J. & Ieropoulos, I., 2019. "Modelling the energy harvesting from ceramic-based microbial fuel cells by using a fuzzy logic approach," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Garg, A. & Lam, Jasmine Siu Lee, 2017. "Design of explicit models for estimating efficiency characteristics of microbial fuel cells," Energy, Elsevier, vol. 134(C), pages 136-156.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    ANN; Bayesian; fuel cell; GPR; SVR;
    All these keywords.

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