Direct biofiltration pretreatment for fouling control of ultrafiltration membranes
Bibliographic record
Abstract
Broader application of ultrafiltration (UF) membranes in drinking water treatment is limited by particulate and organic fouling. This pilot‐scale study demonstrated the ability of rapid biofiltration (without prior coagulation or ozonation) to reduce fouling of a UF membrane treating impacted river water over a two‐year period. Three biofilter empty bed contact times (EBCTs; 5, 10, and 15 min) were investigated, using parallel dual‐media filters followed by a hollow‐fiber membrane unit. Utilizing essentially conventional rapid filtration operating conditions, biofiltration pretreatment enabled stable membrane operation with respect to hydraulically reversible and irreversible fouling. Increased biofiltration EBCTs led to lower rates of irreversible fouling. The results were reproducible, and biofiltration was able to provide effective pretreatment during all seasons (T = 1‐25°C). The biofilters also contributed to effective turbidity removal. Overall, biofiltration pretreatment for UF is a promising chemical‐free alternative to coagulation pretreatment. A particular niche application would be in small or remote systems.
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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 | 0.000 |
| 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 | 0.000 |
| 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".