Process Design for Very-high-gravity Ethanol Fermentation
Bibliographic record
Abstract
Metabolic flux distribution may be altered by manipulating intracellular reducing equivalents. To favour ethanol synthesis by Saccharomyces cerevisiae , a reduced cytosolic environment is desired, otherwise biomass formation is favoured. Direct variation of intracellular NADH/NAD + is difficult, however, indirect control through measurement of fermentation redox potential is applicable. To utilize fermentation redox potential into designing an ethanol fermentation process under very-high-gravity (VHG) conditions, correlations between yeast growth pattern and fermentation redox potential profile were established. Under VHG conditions, S. cerevisiae initially encounters osmotic stress resulting in a lengthy lag phase. As fermentation proceeds, the built-up of ethanol inhibits yeast propagation, resulting in sudden cell death and incomplete sugar conversion. Additionally, an operational scheduling for a continuous VHG ethanol fermentation, consisting of a chemostat device and an ageing vessel, was proposed and compared to the equivalent batch operation. Results show that the proposed operational scheduling is superior to the batch counterpart. Process design criteria for a chemostat device connecting to several equal-size ageing vessels were developed in an attempt to increase annual ethanol productivity.
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 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".