How Overlimiting Current Condition Influences Lactic Acid Recovery and Demineralization by Electrodialysis with Nanofiltration Membrane: Comparison with Conventional Electrodialysis
Bibliographic record
Abstract
Acid whey is the main co-product resulting from the production of fresh cheeses and Greek-type yogurts. It generally goes through a spray-drying process prior to valorization, but it needs to be deacidified (lactic acid recovery) and demineralized beforehand to obtain a powder of quality with all the preserved compounds of interest such as lactose and proteins. Electrodialysis (ED) is a process actually used for acid whey treatment, but scaling formation at the surface of the ion-exchange membrane is still a major problem. In this work, a combination of two new avenues of ED treatment has been studied. First, the integration of a nanofiltration (NF) membrane in an ED conventional stack was compared to a classical ED stack with an anion-exchange membrane in a standard current condition. Secondly, both configurations were tested in the overlimiting current condition to study the impact of electroconvective vortices on process efficiency. The combined effects of the NF membrane and overlimiting current condition led to a higher lactic acid recovery rate of acid whey (40%), while the conventional ED stack in the overlimiting current condition led to a higher demineralization (87% based on the total cation concentration). Those effects were related to the conductivity, pH, global resistance, and energy consumption of each treatment that are influenced by water splitting phenomenon, which was decreased in the overlimiting condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".