How electrodialysis configuration influences acid whey deacidification and membrane scaling
Bibliographic record
Abstract
With the rising popularity of Greek-style yogurts in the past few years, the production of acid whey has drastically increased. If sweet whey is usually further processed, the acid whey valorization comes with challenges because its drying is jeopardized by its high mineral and organic acid contents. For this reason, prior demineralization and deacidification are usually performed at industrial scale using a combination of ion exchange resins and electrodialysis. This whole process represents large amounts of resources and energy consumption as well as an important production of effluents. The optimization of the electrodialysis technique, currently the focus of a few studies, could result in the replacement of the serial processes and would provide a cost-effective and eco-efficient alternative. In this work, the demineralization and deacidification of acid whey were compared via 2 electrodialysis configurations: one conventional and one using bipolar membranes. Both configurations allowed to reach interesting demineralization (67%) and deacidification (44%) rates. However, even though the appearance of fouling or scaling has never been reported, scalings of different natures were observed on membranes using both configurations. Amorphous calcium phosphate was identified on the anion exchange membranes for both configurations while calcite and brucite were identified on cation exchange ones using the bipolar membrane configuration. These scaling formations were linked to the migration of divalent ions and water splitting phenomenon caused by a high demineralization rate or by an already formed significant scaling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".