Studying beer dealcoholization by reverse osmosis
Bibliographic record
Abstract
The production of low-alcohol beer (LAB) or alcohol-free beer (AFB) is important because of several reasons. In this research, pale lager beer samples were dealcoholized by reverse osmosis (RO) at a temperature of 15 ± 1 °C. Alfa Laval RO99 membrane with 0.05 m2 active surface was used for dealcoholization processes. Flux values were measured during the separations. Dynamic viscosity values and ethanol content of beer and permeate samples were measured. Initial flux values were determined by linear regression. Initial ethanol flux (JEtOH 0) values were calculated from initial flux values and ethanol content values. 2P type full factorial experimental design was applied, the two factors were the following: Transmembrane Pressure (TMP): 10, 20, 30 bar and Retentate Flow Rate (Q): 120, 180, 240 L hr-1 . JEtOH 0 was considered as a response of the full factorial experimental design. The effect sizes of the significant parameters were calculated. The global maximum of the objective function was found with self-developed Grid Search code. The best fitted linear function was as follows: JEtOH 0 = 80.871 + 41.094 × xTMP The effect size of the significant parameter was the following: TMP: 1.20. The optimal value of the factor amounted to TMP = 30 bar. The predicted JEtOH 0 under the above condition was 121.965 g m-2 hr-1 . The detailed method in this study can be implemented by breweries.
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 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) | 0.004 | 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".