A process efficiency assessment of serum protein removal from milk using ceramic graded permeability microfiltration membrane
Bibliographic record
Abstract
Microfiltration (MF) is a well-known process that can be used in the dairy industry to separate caseins from serum proteins (SP) in skim milk using membranes with a pore diameter of 0.1μm. Graded permeability ceramic membranes have been studied widely as means of improving milk fractionation by overcoming problems encountered with other MF membranes. The ideal operating parameters for process efficiency in terms of membrane selectivity, permeate flux, casein loss, SP transmission, energy consumption, and dilution with water remain to be determined for this membrane. Our objective was to evaluate the effects of transmembrane pressure (TMP), volumetric concentration factor (VCF), and diafiltration on overall process efficiency. Skim milk was processed using a pilot-scale MF system equipped with 0.72-m(2) graded permeability membranes with a pore size of 0.1μm. In the first experiment, in full recycle mode, TMP was set at 124, 152, 179, or 207 kPa by adjusting the permeate pressure at the outlet. Whereas TMP had no significant effect on permeate and retentate composition, 152 kPa was found to be optimal for SP removal during concentration and concentration or diafiltration experiments. When VCF was increased to 3×, SP rejection coefficient increased along with energy consumption and total casein loss, whereas SP removal rate decreased. Diafiltering twice allowed an increase in total SP removal but resulted in a substantial increase in energy consumption and casein loss. It also reduced the SP removal rate by diluting permeate. The membrane surface area required for producing cheese milk by blending whole milk, cream, and MF retentate (at different VCF) was estimated for different cheese milk casein concentrations. For a given casein concentration, the same quantity of permeate and SP would be produced, but less membrane surface area would be needed at a lower retentate VCF. Microfiltration has great potential as a process of adding value to conventional cheesemaking processes, but its cost-effectiveness at a large scale remains to be demonstrated.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".