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.
|Publication status||Published - 2023 Feb|
Bibliographical noteFunding Information:
The research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science & ICT) (No. NRF-2022M3I3A1094215 ).
© 2022 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Renewable Energy, Sustainability and the Environment
- Waste Management and Disposal