TY - JOUR
T1 - Machine learning technology in biohydrogen production from agriculture waste
T2 - Recent advances and future perspectives
AU - Kumar Sharma, Amit
AU - Kumar Ghodke, Praveen
AU - Goyal, Nishu
AU - Nethaji, S.
AU - Chen, Wei Hsin
N1 - Funding Information:
The authors thank NIT Calicut and UPES, Dehradun, Uttarakhand, India. The authors also thanks to the financial support of the National Science and Technology Council, Taiwan , R.O.C., under contracts MOST 109-2221-E-006-040-MY3, MOST 110-2622-E-006-001-CC1, and MOST 110-3116-F-006-003- for this research. This research is partly supported by the Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University (NCKU). The authors are also thankful to Dr. D K Avasthi (R & D, Dean) for his continuous support and motivation during this work.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
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U2 - 10.1016/j.biortech.2022.128076
DO - 10.1016/j.biortech.2022.128076
M3 - Article
C2 - 36216286
AN - SCOPUS:85139726534
SN - 0960-8524
VL - 364
JO - Bioresource Technology
JF - Bioresource Technology
M1 - 128076
ER -
