Étude du colmatage des membranes en ultrafiltration et en coagulationultrafiltration d'eau de surface
Bibliographic record
Abstract
Ultrafiltration (UF) and coagulationultrafiltration (CUF) of raw water from the Des Roches Lake were performed and compared in terms of natural organic matter (NOM) removal and in terms of membrane permeability variation. The study was carried out at a laboratory scale with a membrane having a nominal pore size of 0.035 μμm. The removal of NOM by UF was significant (10% to more than 50% for UV absorbance at 254 nm and 0% to more than 30% for TOC) but was also very dependent on operating conditions. Membrane fouling occurred rapidly and was strongly accelerated as the permeate flux increased. Fouling also induced a decrease of NOM removal. Coagulation before UF brought a strong increase of NOM removal (TOC removal higher than 60% and reduction of UV absorbance at 254 nm higher than 80%). It also largely reduced membrane permeability loss even for permeation flux higher than 150 L·h1·m2. In the absence of coagulation, and operating with full recirculation of the concentrate, there was a reduction of the SUVA ratio (UV254/TOC) and iron concentration in the feed water. This indicates a preferential deposit of humic substances onto the membranes and shows that iron either plays a role in membrane fouling or could be an indicator of the presence of fouling substances. Key words: ultrafiltration, coagulation, surface water, fouling, organic matter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".