The Combined Effects of Oxygen Supply Strategy, Inoculum Size and Temperature Profile on Very-High-Gravity Beer Fermentation by Saccharomyces cerevisiae
Bibliographic record
Abstract
The timing and concentration of oxygen supply to wort are of particular relevance in industrial beer brewing where tank volumes exceed brewhouse capacity, thereby necessitating fermenter filling in a multiple-brew fashion. A simple technique for accurately controlling dissolved oxygen concentration is presented to model industrial, multi-brew fermentations at bench and pilot scales. This method was employed to identify an effective oxygen supply strategy for batch fermentations conducted with very-high-gravity (VHG) wort. Addition of 25 ppm dissolved oxygen to the fermenting wort, 12 h after inoculation, was the most effective oxygenation strategy and reduced fermentation time by 33% compared to the control conditions. Pilot-scale trials were subsequently conducted to further optimize VHG batch fermentation performance through simultaneous manipulation of key fermentation process parameters, including increased yeast inoculum size, early and increased free-rise timing and temperature, and optimized oxygenation strategy. This approach reduced the time to achieve end of fermentation targets by 34% compared to trials conducted under control conditions. The improved fermentation profile was consistent over three successive inoculations and minimal impact was observed on key flavour volatiles. Employing the optimized process for VHG batch beer production would be industrially desirable due to the potential for improved process efficiency and cost-savings.
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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 | 0.000 | 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 | 0.000 | 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 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".