Avinash Alagumalai, Balaji Devarajan, Huan-zhi Song, S. Wongwises, R. Ledesma-Amaro, O. Mahian, M. Sheremet, E. Lichtfouse
{"title":"Machine learning in biohydrogen production: a review","authors":"Avinash Alagumalai, Balaji Devarajan, Huan-zhi Song, S. Wongwises, R. Ledesma-Amaro, O. Mahian, M. Sheremet, E. Lichtfouse","doi":"10.18331/brj2023.10.2.4","DOIUrl":null,"url":null,"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.","PeriodicalId":46938,"journal":{"name":"Biofuel Research Journal-BRJ","volume":null,"pages":null},"PeriodicalIF":14.4000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biofuel Research Journal-BRJ","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18331/brj2023.10.2.4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 8
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.
期刊介绍:
Biofuel Research Journal (BRJ) is a leading, peer-reviewed academic journal that focuses on high-quality research in the field of biofuels, bioproducts, and biomass-derived materials and technologies. The journal's primary goal is to contribute to the advancement of knowledge and understanding in the areas of sustainable energy solutions, environmental protection, and the circular economy. BRJ accepts various types of articles, including original research papers, review papers, case studies, short communications, and hypotheses. The specific areas covered by the journal include Biofuels and Bioproducts, Biomass Valorization, Biomass-Derived Materials for Energy and Storage Systems, Techno-Economic and Environmental Assessments, Climate Change and Sustainability, and Biofuels and Bioproducts in Circular Economy, among others. BRJ actively encourages interdisciplinary collaborations among researchers, engineers, scientists, policymakers, and industry experts to facilitate the adoption of sustainable energy solutions and promote a greener future. The journal maintains rigorous standards of peer review and editorial integrity to ensure that only impactful and high-quality research is published. Currently, BRJ is indexed by several prominent databases such as Web of Science, CAS Databases, Directory of Open Access Journals, Scimago Journal Rank, Scopus, Google Scholar, Elektronische Zeitschriftenbibliothek EZB, et al.