A. de Ram�n-Fern�ndez
Evaluation of artificial neural network algorithms for predicting the effect of the urine flow rate on the power performance of microbial fuel cells
de Ram�n-Fern�ndez, A.; Salar-Garc�a, M. J.; Ruiz Fern�ndez, D.; Greenman, J.; Ieropoulos, I. A.
Authors
M. J. Salar-Garc�a
D. Ruiz Fern�ndez
John Greenman john.greenman@uwe.ac.uk
Yannis Ieropoulos Ioannis2.Ieropoulos@uwe.ac.uk
Professor in Bioenergy & Director of B-B
Abstract
© 2020 The Authors Microbial fuel cell (MFC) power performance strongly depends on the biofilm growth, which in turn is affected by the feed flow rate. In this work, an artificial neural network (ANN) approach has been used to simulate the effect of the flow rate on the power output by ceramic MFCs fed with neat human urine. To this aim, three different second-order algorithms were used to train our network and then compared in terms of prediction accuracy and convergence time: Quasi-Newton, Levenberg-Marquardt, and Conjugate Gradient. The results showed that the three training algorithms were able to accurately simulate power production. Amongst all of them, the Levenberg-Marquardt was the one that presented the highest accuracy (R = 95%) and the fastest convergence (7.8 s). These results show that ANNs are useful and reliable tools for predicting energy harvesting from ceramic-MFCs under changeable flow rate conditions, which will facilitate the practical deployment of this technology.
Citation
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, 213, Article 118806. https://doi.org/10.1016/j.energy.2020.118806
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 6, 2020 |
Online Publication Date | Sep 9, 2020 |
Publication Date | Dec 15, 2020 |
Deposit Date | Oct 30, 2020 |
Publicly Available Date | Mar 29, 2024 |
Journal | Energy |
Print ISSN | 0360-5442 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 213 |
Article Number | 118806 |
DOI | https://doi.org/10.1016/j.energy.2020.118806 |
Keywords | artificial neural networks; modelling; microbial fuel cells; urine; flow rate; bioenergy |
Public URL | https://uwe-repository.worktribe.com/output/6804377 |
Files
Evaluation of artificial neural network algorithms for predicting the effect of the urine flow rate on the power performance of microbial fuel cells
(1.7 Mb)
PDF
Licence
http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
You might also like
Microbial fuel cell compared to a chemostat
(2022)
Journal Article
Microbial fuel cells and their electrified biofilms
(2021)
Journal Article
Electrosynthesis, modulation, and self-driven electroseparation in microbial fuel cells
(2021)
Journal Article
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search