Engineered biofiltration for ultrafiltration fouling mitigation and disinfection by-product precursor control
Bibliographic record
Abstract
A pilot-scale study was conducted to evaluate the impact of several biofiltration enhancement strategies in terms of organic removal to reduce disinfection by-product (DBP) formation potential and mitigate ultrafiltration (UF) fouling. Strategies included nutrient addition (nitrogen and phosphorus) to optimize metabolic degradation of organics, use of hydrogen peroxide (H2O2, peroxide) to improve filter run times, and the application of in-line aluminum sulphate (alum) for biopolymer removal. The impact of media type on performance was also examined (anthracite versus granular activated carbon (GAC)). Passive biofiltration (without enhancement) reduced dissolved organic carbon (∼5%), biopolymers (∼20%), and trihalomethane and haloacetic acid precursors (∼20% and ∼12%, respectively) while mitigating UF irreversible fouling (∼60%). Nutrient addition was not observed to enhance biological performance. Addition of 0.5 mg/L hydrogen peroxide decreased head loss by up to 45% without affecting organic removal; however at a dosage of 1 mg/L, it negatively impacted both UF fouling and DBP precursor removal. In-line alum addition prior to biofiltration (<0.5 mg/L) improved UF fouling control by up to 40%, without sacrificing head loss. Overall, GAC provided superior performance when compared to anthracite.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".