High-Gravity Fermentation for Bioethanol Production from Industrial Spent Black Cherry Brine Supplemented with Whey
Bibliographic record
Abstract
By-products from different industries could represent an available source of carbon and nitrogen which could be used for bioethanol production using conventional Saccharomyces cerevisiae yeast. Spent cherry brine and whey are acid food by-products which have a high organic matter content and toxic compounds, and their discharges represent significant environmental and economic challenges. In this study, different combinations of urea, yeast concentrations, and whey as a nutrient source were tested for bioethanol production scale-up using 96-well microplates as well as 7.5 L to 100 L bioreactors. For bioethanol production in vials, the addition of urea allowed increasing the bioethanol yield by about 10%. Bioethanol production in the 7.5 L and 100 L bioreactors was 73.2 g·L−1 and 103.5 g·L−1 with a sugar consumption of 81.5% and 94.8%, respectively, using spent cherry brine diluted into whey (200 g·L−1 of total sugars) supplemented with 0.5 g·L−1 urea and 0.5 g·L−1 yeast at 30 °C and a pH of 5.0 after 96 h of fermentation for both systems. The results allow these by-products to be considered low-economic-value alternatives for fuel- or food-grade bioethanol production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".