Nanofiltration and Tight Ultrafiltration Membranes for Natural Organic Matter Removal—Contribution of Fouling and Concentration Polarization to Filtration Resistance
Bibliographic record
Abstract
Nanofiltration (NF) and tight ultrafiltration (tight UF) membranes are a viable treatment option for high quality drinking water production from sources with high concentrations of contaminants. To date, there is limited knowledge regarding the contribution of concentration polarization (CP) and fouling to the increase in resistance during filtration of natural organic matter (NOM) with NF and tight UF. Filtration tests were conducted with NF and tight UF membranes with molecular weight cut offs (MWCOs) of 300, 2000 and 8000 Da, and model raw waters containing different constituents of NOM. When filtering model raw waters containing high concentrations of polysaccharides (i.e., higher molecular weight NOM), the increase in resistance was dominated by fouling. When filtering model raw waters containing humic substances (i.e., lower molecular weight NOM), the increase in filtration resistance was dominated by CP. The results indicate that low MWCO membranes are better suited for NOM removal, because most of the NOM in surface waters consist mainly of humic substances, which were only effectively rejected by the lower MWCO membranes. However, when humic substances are effectively rejected, CP can become extensive, leading to a significant increase in filtration resistance by the formation of a cake/gel layer at the membrane surface. For this reason, cross-flow operation, which reduces CP, is recommended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".