A review of advanced optimization strategies for fermentative biohydrogen production processes
The inability of statistical optimization to represent the dynamic interaction of the biohydrogen process, which is highly non-linear and complicated, has been identified. However, incorporating a data-driven black-box model could overcome the limitations of conventional methods to provide correct r...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
ELSEVIER
2022
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/38298/1/Mohd%20Asrul%20et%20al.%20%282022%29-1.pdf http://ir.unimas.my/id/eprint/38298/ https://www.sciencedirect.com/science/article/pii/S0360319922013301?dgcid=author https://doi.org/10.1016/j.ijhydene.2022.03.197 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The inability of statistical optimization to represent the dynamic interaction of the biohydrogen process, which is highly non-linear and complicated, has been identified. However, incorporating a data-driven black-box model could overcome the limitations of conventional methods to provide correct responses rapidly and cost-effective modeling. Despite significant reports on the optimization of hydrogen production from fermentation, fewer studies have been made for the case using artificial intelligence algorithms. As a result, critical and extensive analyses of previous works are conducted to develop a general methodological framework for advanced response optimization. |
|---|