Machine learning in biohydrogen production: a review
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high energy yield to replace conventional fossil fuels. However, biohydrogen commercial uptake is mainly hindered by the supply side. As a result, various operating parameters must be optimized to realize biohydrogen commercial uptake on a large-scale. Recently, machine learning algorithms have demonstrated the ability to handle large amounts of data while requiring less in-depth knowledge of the system and being capable of adapting to evolving circumstances. This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production. The accuracy and potential of different machine learning algorithms are reported. Also, the practical implications of machine learning models to realize biohydrogen uptake by the transportation sector are discussed. The review indicates that machine learning algorithms can successfully model non-linear and complex interactions between operational and performance parameters in biohydrogen production. Additionally, machine learning algorithms can help researchers identify the most efficient methods for producing biohydrogen, leading to a more sustainable and cost-effective energy source.
Date Issued
2023-06
Date Acceptance
2023-05-13
Citation
Biofuel Research Journal, 2023, 10 (2), pp.1844-1858
ISSN
2292-8782
Publisher
Green Wave Publishing of Canada
Start Page
1844
End Page
1858
Journal / Book Title
Biofuel Research Journal
Volume
10
Issue
2
Copyright Statement
All materials, including Articles published in Biofuel Research Journal, will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC BY 4.0) Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001008445700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
ARTIFICIAL NEURAL-NETWORKS
Biofuel
Biohydrogen
BIOMASS
CELLULASE
COPPER NANOPARTICLES
DARK FERMENTATION
Energy & Fuels
Fermentation
FERMENTATIVE HYDROGEN-PRODUCTION
Machine learning
MICROALGAE
OPTIMIZATION
Patent landscape
Science & Technology
STRATEGIES
Technology
Waste
WASTE-WATER
Publication Status
Published
Date Publish Online
2023-06-01