Effect of aeration strategy on the performance of a very high gravity continuous fuel ethanol fermentation process
Bibliographic record
Abstract
The fuel ethanol industry is now making use of a very efficient process where virtually all sugar substrates are converted to ethanol. Nevertheless, some metabolic by-products excreted from Saccharomyces cerevisiae tend to reduce the ethanol yield. Of such, glycerol is the major one, accounting for about 5-10% relative to the amount of ethanol produced. Glycerol plays an important role in maintaining the redox balance within the cells by oxidizing the cytosolic NADH under anaerobic conditions. It is also believed that it acts as an osmoprotectant and would be favourably produced in high osmotic pressure conditions. In order to mitigate the production of glycerol, various aeration strategies were investigated in a single-stage continuous fermentation system. Oxygen dissolved in the fermentation medium put the yeast in aerobiosis, acted as an oxidizing agent and hence minimised the specific glycerol production by 36% as compared to a completely anaerobic fermentation. This has hardly been reproduced in a more industrially relevant system using a multi-stage continuous fermentation process. Indeed, oscillations in the concentrations of the various metabolites over time made difficult the assessment of significant changes. Nevertheless, these findings open the door to further investigations in order to understand the effect of oxygen in continuous fermentations using very high gravity feeds, such as in the fuel ethanol industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".