Utjecaj temperature i tlaka na protok permeata kod koncentriranja soka aronije nanofiltracijom
Bibliographic record
Abstract
The aim of this study was to concentrate aronia juice by NF using membrane type Alfa Laval-NF and to investigate the influence of process parameters of pressure and temperature on the permeate flow of aronia juice at pressures of 50 and 55 bar, with or without the use of cooling. Concentration was conducted on a laboratory device for nanofiltration Lab Unit M20 with panels and mountings, using composite membrane type Alfa Laval-NF. Samples of permeate and retentate were taken at regular time intervals, every 12 minutes. Experiments showed that the increase of dry matter in the retentate decreased flow in all processes. By increasing the proportion of dry matter the index of concentration magnifies in the permeate, which is especially noticed in the use of cooling in both of the pressures tested. By rising the index of concentration in the retentate an increase in volume relation retentate appears. Permeability and concentration of dry matter in the permeate were lower in experiments with the use of cooling. During concentration by NF, the temperature of aronia juice is getting bigger and bigger, from initial 10 ⁰C to 45 °C in process without the use of cooling, or to 24°C int he process with the use of cooling.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".