Machine learning in biohydrogen production: a review

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. (c) 2023 BRTeam. All rights reserved.

Automated summary

Biohydrogen, a carbon-neutral and sustainable energy carrier, shows promise as a replacement for conventional fossil fuels with its high energy yield. However, the commercial uptake of biohydrogen is hindered by supply-side issues. To address this, optimizing operating parameters is necessary for large-scale biohydrogen production. This review highlights the role of machine learning in categorizing and predicting biohydrogen production data, assessing the accuracy and potential of different algorithms, and discussing their practical implications for the transportation sector's adoption of biohydrogen. Machine learning algorithms have proven effective in modeling complex interactions and identifying efficient biohydrogen production methods, contributing to a more sustainable and cost-effective energy source.