Machine learning in biohydrogen production: a review

Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high yield to replace conventional fossil fuels. However, biohydrogen commercial uptake mainly hindered by the supply side. As result, various operating parameters must be optimized realize on large-scale. Recently, machine learning algorithms have demonstrated ability handle large amounts of data while requiring less in-depth knowledge system being capable adapting evolving circumstances. This review critically reviews role in categorizing predicting related production. The accuracy potential different are reported. Also, practical implications models transportation sector discussed. indicates that can successfully model non-linear complex interactions between operational performance Additionally, help researchers identify most efficient methods for producing biohydrogen, leading more cost-effective source.