Application of multistage continuous fermentation for production of fuel alcohol by very-high-gravity fermentation technology
Bibliographic record
Abstract
A fermentation system to test the merging of very-high-gravity (VHG) and multistage continuous culture fermentation (MCCF) technologies was constructed and evaluated for fuel ethanol production. Simulated mashes ranging from 15% to 32% w/v glucose were fermented by Saccharomyces cerevisiae and the dilution rates were adjusted for each glucose concentration to provide an effluent containing less than 0.3% w/v glucose (greater than 99% consumption of glucose). The MCCF can be operated with glucose concentrations up to 32% w/v, which indicates that the system can successfully operate under VHG conditions. With 32% w/v glucose in the medium reservoir, a maximum of 16.73% v/v ethanol was produced in the MCCF. The introduction of VHG fermentation into continuous culture technology allows an improvement in ethanol productivity while producing ethanol continuously. In comparing the viability of yeast by methylene blue and plate count procedures, the results in this work indicate that the methylene blue procedure may overestimate the proportion of dead cells in the population. Ethanol productivity (Yps) increased from the first to the last fermentor in the sequence at all glucose concentrations used. This indicated that ethanol is more effectively produced in later fermentors in the MCCF, and that the notion of a constant Yps is not a valid assumption for use in mathematical modeling of MCCFs.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".