Machine learning in biohydrogen production: a review
importance 6/10
ai assisted
Anaerobic Digestion and Biogas Production renewable energy stage: research
Problem
Operating parameters for biohydrogen production must be optimized to enable large-scale commercial uptake.
Why it matters
Biohydrogen is a promising carbon-neutral energy carrier but supply-side hurdles block adoption.
What the AI did
A review of machine learning algorithms for categorizing and predicting biohydrogen production data, reporting their accuracy and potential.
Results
not stated in source
Impact
Review indicates machine learning can model non-linear, complex biohydrogen production data.
Record
| 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 |
Importance rating
6
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.
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