Effect of transmembrane pressure control on energy efficiency during skim milk concentration by ultrafiltration at 10 and 50°C
Bibliographic record
Abstract
The efficiency of the ultrafiltration process during skim milk concentration was studied using both dynamic and constant (465 or 672kPa) transmembrane pressure experiments at refrigerated temperature (10°C) and high temperature (50°C). The pilot-scale module was equipped with a 10-kDa polyethersulfone spiral-wound membrane element with a surface area of 2.04m 2 . Permeation flux, resistance-in-series model, mineral and protein rejection, and energy consumption were studied as a function of temperature and transmembrane pressure applied. Higher permeation flux values were systematically obtained at 50°C. Also, a significant temperature effect was found for calcium rejection, which was lower at 10°C compared with 50°C. Total hydraulic resistance and reversible fouling resistance were higher at 50°C than at 10°C. No change in protein rejection was observed, depending on the operating mode studied. Permeation flux, which was higher at 50°C, had lower pumping energy consumption compared with ultrafiltration at the colder temperature. Also, the low ultrafiltration temperature required a higher total energy consumption to reach the 3.6× retentate compared with ultrafiltration at 50°C. Overall, our study shows that the operating parameters and temperature can be optimized using an energy efficiency ratio.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".