Machine learning in biohydrogen production: a review

IF 14.4 Q1 ENERGY & FUELS
Avinash Alagumalai, Balaji Devarajan, Huan-zhi Song, S. Wongwises, R. Ledesma-Amaro, O. Mahian, M. Sheremet, E. Lichtfouse
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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.
生物制氢中的机器学习:综述
生物氢作为一种极具潜力的碳中和可持续能源载体,正以其高能源产量取代传统的化石燃料。然而,生物氢的商业利用主要受到供应方的阻碍。因此,为了实现生物氢的大规模商业利用,必须优化各种操作参数。最近,机器学习算法已经证明了处理大量数据的能力,同时不需要对系统有深入的了解,并且能够适应不断变化的环境。这篇综述批判性地回顾了机器学习在分类和预测与生物氢生产相关的数据中的作用。报告了不同机器学习算法的准确性和潜力。此外,还讨论了机器学习模型对交通部门实现生物氢吸收的实际意义。该综述表明,机器学习算法可以成功地模拟生物制氢过程中操作参数和性能参数之间的非线性和复杂相互作用。此外,机器学习算法可以帮助研究人员确定生产生物氢的最有效方法,从而开发出更可持续、更具成本效益的能源。
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来源期刊
CiteScore
22.10
自引率
1.50%
发文量
15
审稿时长
8 weeks
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.
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