Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications. (February 2023)

• Record Type: Journal Article
• Authors: Pandey, Ashutosh Kumar; Park, Jungsu; Ko, Jeun; Joo, Hwan-Hong; Raj, Tirath; Singh, Lalit Kumar; Singh, Noopur; Kim, Sang-Hyoun
• Is Part Of: Bioresource technology. Volume 370(2023)
• Publication Date: 2023-02
• Subjects: Biohydrogen -- Machine learning -- Optimization -- Microbial shift -- Reinforced machine learning; 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.2022.128502
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: Biohydrogen data are highly complex and non-linear. Multiple ML approach are revised and compared for biohydrogen production. The reinforced machine learning method exhibited precise state prediction. Guidelines are proposed for the use of machine learning in biohydrogen production. 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.