Machine learning in biohydrogen production: a review

Avinash Alagumalai, D. Balaji, Hua Song, Somchai Wongwises, Rodrigo Ledesma‐Amaro, Omid Mahian, Mikhail А. Sheremet, Eric A. Lichtfouse

Biofuel Research Journal 2023 article cited by 76

importance 6/10 ai assisted Anaerobic Digestion and Biogas Production renewable energy stage: research

Operating parameters for biohydrogen production must be optimized to enable large-scale commercial uptake.

Biohydrogen is a promising carbon-neutral energy carrier but supply-side hurdles block adoption.

A review of machine learning algorithms for categorizing and predicting biohydrogen production data, reporting their accuracy and potential.

not stated in source

Review indicates machine learning can model non-linear, complex biohydrogen production data.

Venue Biofuel Research Journal
Published 2023
Work type article
Authors Avinash Alagumalai, D. Balaji, Hua Song, Somchai Wongwises, Rodrigo Ledesma‐Amaro, Omid Mahian, Mikhail А. Sheremet, Eric A. Lichtfouse
Citations 76 (OpenAlex, at import time)
References 95
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.18331/brj2023.10.2.4
Open access diamond
Keywords biohydrogen, machine learning, optimization, energy, review
6 / 10

significant — thousands to millions affected

Abstract frames biohydrogen as a carbon-neutral replacement for fossil fuels with large-scale commercial uptake potential, but no scale is quantified and this is a review of ML methods.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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