한빛사논문
- 조회201
연세대학교
Ashutosh Kumar Pandey a,1, Jungsu Park a,1, Jeun Ko a, Hwan-Hong Joo a, Tirath Raj a, Lalit Kumar Singh b, Noopur Singh c, Sang-Hyoun Kim a
aDepartment of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
bDepartment of Biochemical Engineering, Harcourt Butler Technical University, Kanpur 208002, Uttar Pradesh (UP), India
cDr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh (UP), India
1Ashutosh Kumar Pandey and Jungsu Park contributed equally to this study and shared the first authorship.
Corresponding author: Sang-Hyoun Kim
Abstract
Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.
논문정보
- Review Paper
- 2023년 02월 (BRIC 등록일 2023-03-08)
- 국내(교신)+국외 연구진
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