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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".