Valorization of Concentrated Dairy White Wastewater by Reverse Osmosis in Model Cheese Production
Bibliographic record
Abstract
Treatment of dairy white wastewater (WW) by reverse osmosis (RO) is usually performed to generate process water and to reclaim dairy components for their valorization. For this study, a mixture of pasteurized milk and WW from a dairy plant was concentrated by RO to achieve a protein concentration similar to that of skimmed milk. Retentates, which are concentrated WW, were used in the preparation of cheese milk. The effect of using model concentrated WW was evaluated on (1) the soluble–colloidal equilibrium between protein and salt, (2) the milk-coagulation kinetics, and (3) the cheese composition and yield. An economic assessment was also carried out to support the decision-making process for implementing a new RO system in a dairy plant for the valorization of dairy WW. The results showed that substituting more than 50% of the amount of cheese milk with model pasteurized WW concentrates decreased the moisture-adjusted cheese yield and impaired the coagulation kinetics. Excessive cheese moisture was observed in cheeses that were made from 50% and 100% model WW concentrates, correlating with a change in the soluble–colloidal equilibrium of salts, especially in calcium. To achieve sustainable and economic benefits, the ratio of added WW concentrates to cheese milk must be less than 50%. However, for such an investment to be profitable to a dairy plant within 0.54 years, a large-size plant must generate 200 m3 of WW per day with at least 0.5% of total solids, as the economic analysis specific to our case suggests.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".