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Machine learning in biohydrogen production: a review
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Metadata
Document Title
Machine learning in biohydrogen production: a review
Author
Alagumalai A., Devarajan B., Song H., Wongwises S., Ledesma-Amaro R., Mahian O., Sheremet M., Lichtfouse E.
Affiliations
Department of Mechanical Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam, 532127, India; Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Tamilnadu, Coimbatore, 641407, India; Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada; Fluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangmod,Bangkok, 10140, Thailand; National Science and Technology Development Agency (NSTDA), Pathum Thani, 12120, Thailand; Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, United Kingdom; School of Chemical Engineering and Technology, Xi'an Jiaotong University, Shaanxi, Xi'an, 710049, China; Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, United Kingdom; Laboratory on Convective H eat and Mass Transfer, Tomsk State University, Tomsk, 634050, Russian Federation; State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Shaanxi, Xi'an, 710049, China
Type
Article
Source Title
Biofuel Research Journal
ISSN
22928782
Year
2023
Volume
10
Issue
2
Page
1844-1858
Open Access
All Open Access, Gold
Publisher
Green Wave Publishing of Canada
DOI
10.18331/BRJ2023.10.2.4
Format
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. ? 2023 BRTeam. All rights reserved.
Keyword
Biofuel | Biohydrogen | Fermentation | machine learning | Patent landscape | Waste
License
CC BY
Rights
Authors
Publication Source
WOS