Machine learning technology in biohydrogen production from agriculture waste: Recent advances and future perspectives

Amit Kumar Sharma; Praveen Kumar Ghodke; Nishu Goyal; S Nethaji; Wei-Hsin Chen

doi:10.1016/j.biortech.2022.128076

Machine learning technology in biohydrogen production from agriculture waste: Recent advances and future perspectives

Bioresour Technol. 2022 Nov:364:128076. doi: 10.1016/j.biortech.2022.128076. Epub 2022 Oct 7.

Authors

Amit Kumar Sharma¹, Praveen Kumar Ghodke², Nishu Goyal³, S Nethaji⁴, Wei-Hsin Chen⁵

Affiliations

¹ Department of Chemistry, Applied Sciences Cluster, Centre for Alternate and Renewable Energy Research, R&D, University of Petroleum & Energy Studies (UPES), School of Engineering, Energy Acres Building, Bidholi, Dehradun 248007, Uttarakhand, India.
² Department of Chemical Engineering, National Institute of Technology Calicut, Kozhikode 673601, Kerala, India.
³ School of Health Sciences, University of Petroleum & Energy Studies (UPES), School of Engineering, Energy Acres Building, Bidholi, Dehradun 248007, Uttarakhand, India.
⁴ Department of Chemical Engineering, Manipal Institute of Technology, Manipal Karnataka, 576104 l, India.
⁵ Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407, Taiwan; Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan. Electronic address: weihsinchen@gmail.com.

PMID: 36216286
DOI: 10.1016/j.biortech.2022.128076

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.

Keywords: Agriculture waste; Biochemical and thermochemical; Biohydrogen production; Machine learning.