An adaptive, continuous substrate feeding strategy based on evolved gas to improve fed-batch ethanol fermentation
Bibliographic record
Abstract
Advances in the ethanol fermentation process are essential to improving the performance of bioethanol production. Fed-batch fermentation is a promising approach to increase the final ethanol titer, which benefits the recovery in the bioethanol industry's downstream process. However, the development of feeding strategies, a crucial control variable in the fed-batch approach, is limited. Thus, in the present work, different modes of substrate delivery-fixed feeding, adapted feeding-were investigated in fed-batch cultures of Saccharomyces cerevisiae in a 5-L bioreactor. Evolved gas production, which was positively correlated with glucose consumption, was used to adjust the sugar feed rate in fed-batch fermentations under an adapted feeding strategy. The adapted feeding strategy enhanced ethanol productivity by 21% compared to the fixed feeding strategy, in which the sugar feed rate was stable, and the ethanol titer reached 91 g/L (~ 11.5%, v/v) at the end of fermentation. Moreover, cell biomass accumulation and cell growth rate were significantly improved when using the adapted feeding strategy. The effect of nitrogen availability on the performance of the adapted feeding strategy was further explored using a low-nitrogen content medium. The results showed that, even under low nitrogen feeding conditions (N/C = 0.046:10), the adapted feeding strategy maintained the same ethanol productivity as nitrogen-rich medium feeding. Overall, these results suggest that sugar delivery with low nitrogen content using the adapted feeding strategy could help reduce medium costs and improve the productivity of current facilities in the ethanol industry.Future work will integrate adapted feeding strategies with other fermentation approaches to improve ethanol production. KEY POINTS: • Novel continuous sugar delivery was developed for fed-batch ethanol fermentation. • The adapted feeding strategy improved ethanol productivity by 21%. • The final ethanol concentration reached 91 g/L (11.5%, v/v) with no residual sugar.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".