Impact of operational parameters on biofiltration performance: organic carbon removal and effluent turbidity
Bibliographic record
Abstract
The objectives of this pilot-scale study were to optimize backwash frequency and empty bed contact time (EBCT) of biofilters treating ozonated surface water from Lake Ontario. Performance was benchmarked in terms of the reduction of turbidity, dissolved organic carbon (DOC), disinfection by-product (DBP) precursors, and ultrafiltration foulants (biopolymers). Increasing the EBCT from 4 to 8 min resulted in a higher reduction of DOC (5%), trihalomethane (THM4) and haloacetic acid (HAA9) precursors (∼12%) without negatively impacting effluent turbidity (consistently below 0.4 NTU), while biopolymer removal remained unaffected (2%). The impact of varying backwash frequency (5, 10, and 25 day intervals) was also compared for biofilters operated at an EBCT of 4 min. Results showed no impact of extended run times (up to 25 days) on DOC or DBP precursor removal; however turbidity removal was affected beyond 15 days of operation. Backwashing biofilters at 10 vs 5 day intervals would result in a reduction of backwash water, energy consumption and amount to nearly $17,000 in savings for the utility.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".