Effect of pressure and cross-flow velocity on membrane behaviour in red wine nanofiltration
Bibliographic record
Abstract
Abstract Nanofiltration has found numerous applications in winemaking industry as an effective separation technology, allowing for the recovery and concentration of valuable bioactive compounds from wine/grape by-products, as well as for wine dealcoholization. However, the assessment of membrane fouling remains essential for a stable and sustainable membrane operation. Because of its complex nature, wine components (colloids and solutes such as polysaccharides, polyphenols, proteins) contribute to different mechanisms of fouling. The increase of fouling propensity as well as the conditions that contribute to it - transmembrane pressure and cross-flow velocity – are studied in the present study. Quantitative characteristics as fouling indices are discussed. Results with red wine (Mavrud) nanofiltration are obtained using two NF membranes - Nadir® NP030 P (asymmetric polyethersulfone (PES), MWCO 500 Da) and Alfa Laval NF99HF (thin film composite polyester, MWCO 200 Da). Nanofiltration runs were carried out by a constant-pressure cross-flow filtration system MaxiMem, Prozesstechnik GmbH with a rectangular flat-sheet membrane of 215 cm 2 active area. Effect of transmembrane pressure (TMP) (10 to 50 bar) and cross-flow rate (1-3 l/min) on the permeate flux are discussed in view of membrane fouling. Higher operating pressures enhance the permeate flux but may also lead to increased fouling. The two membranes show very different permeate fluxes, the ones measured with NF99HF being 10-20 times higher. Increase in cross-flow velocity improves the hydrodynamic conditions such as shear stress field close to the membrane. The expected positive effect on permeate flux is the more noticeable the more pronounced the fouling. This was shown by two polymer membranes of different molecular weight cut-off exhibiting different susceptibility to fouling.
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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 | 0.000 | 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 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".