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Article Dans Une Revue Biofuel Research Journal Année : 2023

Machine learning in biohydrogen production: a review

Avinash Alagumalai
  • Fonction : Auteur
Balaji Devarajan
  • Fonction : Auteur
Hua Song
Somchai Wongwises
  • Fonction : Auteur
Rodrigo Ledesma-Amaro
  • Fonction : Auteur
Omid Mahian
  • Fonction : Auteur
  • PersonId : 1282078
Mikhail Sheremet
  • Fonction : Auteur
Eric Lichtfouse

Résumé

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.
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Dates et versions

hal-04201233 , version 1 (09-09-2023)

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Citer

Avinash Alagumalai, Balaji Devarajan, Hua Song, Somchai Wongwises, Rodrigo Ledesma-Amaro, et al.. Machine learning in biohydrogen production: a review. Biofuel Research Journal, 2023, 38, pp.1844 - 1858. ⟨10.18331/brj2023.10.2.4⟩. ⟨hal-04201233⟩
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