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Modelling the energy harvesting from ceramic-based microbial fuel cells by using a fuzzy logic approach

Author

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  • de Ramón-Fernández, Alberto
  • Salar-García, M.J.
  • Ruiz-Fernández, Daniel
  • Greenman, J.
  • Ieropoulos, I.

Abstract

Microbial fuel cells (MFCs) is a promising technology that is able to simultaneously produce bioenergy and treat wastewater. Their potential large-scale application is still limited by the need of optimising their power density. The aim of this study is to simulate the absolute power output by ceramic-based MFCs fed with human urine by using a fuzzy inference system in order to maximise the energy harvesting. For this purpose, membrane thickness, anode area and external resistance, were varied by running a 27-parameter combination in triplicate with a total number of 81 assays performed. Performance indices such as R2 and variance account for (VAF) were employed in order to compare the accuracy of the fuzzy inference system designed with that obtained by using nonlinear multivariable regression. R2 and VAF were calculated as 94.85% and 94.41% for the fuzzy inference system and 79.72% and 65.19% for the nonlinear multivariable regression model, respectively. As a result, these indices revealed that the prediction of the absolute power output by ceramic-based MFCs of the fuzzy-based systems is more reliable than the nonlinear multivariable regression approach. The analysis of the response surface obtained by the fuzzy inference system determines that the maximum absolute power output by the air-breathing set-up studied is 450 μW when the anode area ranged from 160 to 200 cm2, the external loading is approximately 900 Ω and a membrane thickness of 1.6 mm, taking into account that the results also confirm that the latter parameter does not show a significant effect on the power output in the range of values studied.

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  • 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.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:40
    DOI: 10.1016/j.apenergy.2019.113321
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    1. 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.

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