Process efficiency of casein separation from milk using polymeric spiral-wound microfiltration membranes
Bibliographic record
Abstract
Microfiltration is largely used to separate casein micelles from milk serum proteins (SP) to produce a casein-enriched retentate for cheese making and a permeate enriched in native SP. Skim milk microfiltration is typically performed with ceramic membranes and little information is available about the efficiency of spiral-wound (SW) membranes. We determined the effect of SW membrane pore size (0.1 and 0.2 µm) on milk protein separation in total recirculation mode with a transmembrane pressure gradient to evaluate the separation efficiency of milk proteins and energy consumption after repeated concentration and diafiltration (DF). Results obtained in total recirculation mode demonstrated that pore size diameter had no effect on the permeate flux, but a drastic loss of casein was observed in permeate for the 0.2-µm SW membrane. Concentration-DF experiments (concentration factor of 3.0× with 2 sequential DF) were performed with the optimal 0.1-µm SW membrane. We compared these results to previous data we generated with the 0.1-µm graded permeability (GP) membrane. Whereas casein rejection was similar for both membranes, SP rejection was higher for the 0.1-µm SW membrane (rejection coefficient of 0.75 to 0.79 for the 0.1-µm SW membrane versus 0.46 to 0.49 for the GP membrane). The 0.1-µm SW membrane consumed less energy (0.015-0.024 kWh/kg of permeate collected) than the GP membrane (0.077-0.143 kWh/kg of permeate collected). A techno-economic evaluation led us to conclude that the 0.1-µm SW membranes may represent a better option to concentrate casein for cheese milk; however, the GP membrane has greater permeability and its longer lifetime (about 10 yr) potentially makes it an interesting option.
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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) | 0.000 | 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 | 0.001 |
| 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 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".