Machine learning technology in biohydrogen production from agriculture waste: Recent advances and future perspectives. (November 2022)

• Record Type: Journal Article
• Authors: Kumar Sharma, Amit; Kumar Ghodke, Praveen; Goyal, Nishu; Nethaji, S.; Chen, Wei-Hsin
• Is Part Of: Bioresource technology. Volume 364(2022)
• Publication Date: 2022-11
• Subjects: Biohydrogen production -- Machine learning -- Agriculture waste -- Biochemical and thermochemical; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.128076
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: This study reviews machine learning (ML) methods for biohydrogen (BioH2 ) production. Agriculture waste shows great potential for biohydrogen production. Techno-economic and scientific obstacles to ML application are summarized in brief. Advanced ML methods should be encouraged to predict large-scale BioH2 production. Abstract: Agricultural waste biomass has shown great potential to deliver green energy produced by biochemical and thermochemical conversion processes to mitigate future energy crises. Biohydrogen has become more interested in carbon-free and high-energy dense fuels among different biofuels. However, it is challenging to develop models based on experience or theory for precise predictions due to the complexity of biohydrogen production systems and the limitations of human perception. Recent advancements in machine learning (ML) may open up new possibilities. For this reason, this critical study offers a thorough understanding of ML's use in biohydrogen production. The most recent developments in ML-assisted biohydrogen technologies, including biochemical and thermochemical processes, are examined in depth. This review paper also discusses the prediction of biohydrogen production from agricultural waste. Finally, the techno-economic and scientific obstacles to ML application in agriculture waste biomass-based biohydrogen production are summarized.