Fouling of low-pressure membranes during drinking water treatment: effect of NOM components and biofiltration pretreatment
Bibliographic record
Abstract
Fouling is a major challenge for low-pressure membrane drinking water treatment systems. Previous research has demonstrated that under the right conditions, biofiltration is an effective method to reduce fouling of low-pressure polymeric membranes. This study provides additional insight into the effect of biofiltration as a pretreatment for fouling reduction by using river water with different raw water quality characteristics than has been examined in previous studies. Two parallel pilot-scale dual media (sand/anthracite) biological filters were operated continuously over a period of 14 months. Liquid chromatography–organic carbon detection analysis confirmed that the parallel biofilters performed similarly with both averaging on 21% biopolymer removal. Raw and treated water biopolymer concentrations were correlated, with increased absolute removals occurring at higher raw water concentrations. Ultrafiltration (UF) membrane fouling experiments showed substantial improvement in performance following biofiltration pretreatment by reducing hydraulically irreversible and reversible fouling rates by 14–68% and 8–55%, respectively. The results also reaffirm the importance of biopolymers at concentrations as low as ∼0.1 mg/L on irreversible and reversible UF membrane fouling and a minimal impact of humic substances.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".