A new approach to model the influence of stirring intensity on ethanol production by a flocculant yeast grown on cashew apple juice
Bibliographic record
Abstract
Abstract In this work, a rigorous mathematical model was developed, aiming to address a major flaw inherent in most fermentation models found in the literature, which is the inability to accurately account for mass transfer effects. This model was based on the hypothesis of the existence of a stagnant film involving the cell where the mass transfer rate of the substrate flowing from the medium to the cell surface is equal to the rate of substrate consumption by the cells. The model was used to explore the influence of stirring speed, substrate, and initial cell concentrations and temperature on ethanol production by the flocculant yeast Saccharomyces cerevisiae CCA008, grown on cashew apple juice. Model parameters were estimated and validated against experimental data. The experimental data was divided into two sets: one for parameter optimization using non‐linear Marquardt least‐squares method; and the other to validate the final form of the model equations. Results have shown that the model herein proposed was capable of accurately describing the production of ethanol by S. cerevisiae flocculant yeast considering the influence of operational conditions, especially the effect of the stirring speed on the fermentation rate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".