AOC reduction by biologically active filtration
Bibliographic record
Abstract
Biological treatment was examined for production of biologically stable water, increase disinfectant stability, and reduced formation of disinfection by products. Monitoring of assimilable organic carbon (AOC) levels in the effluent of the Swimming River Treatment Plant (SRTP) showed that values >100 µg/L could be related to the occurrence of coliform bacteria in the distribution system. A treatment goal of <100 µg/L was established for biologically active treatment processes. Granular activated carbon (GAC) filters were found to support a larger bacterial population, and thus, provide better biological removal of AOC and total organic carton (TOC). All biologically active filters showed good performance relative to effluent turbidity levels, and headloss development. Preozonation of raw water increased AOC levels an average of 2.3 fold, and always increased filter effluent AOC levels relative to nonozonated water. Application of free chlorine to GAC filters did not inhibit biological activity. Application of chloramines to GAC filters showed a slight inhibitory affect relative to free chlorine. Effluent AOC levels averaged 82 µg/L at an EBCT of 5 min, and decreased to an average of 57 µg/L at 20 min EBCT. EBCT did affect TOC removals, with efficiencies averaging 29, 33, 42, and 51 % removal at EBCTs of 5, 10, 15 and 20 min, respectively. Trihalomethane formation potentials (THMFP) were related to TOC levels. Processes Chat decreased TOC levels also decreased THMFP. A preozonated GAC/sand filter (EBCT 10 min) achieved an annual average 54 % removal of THMFP precursors. Post disinfection of biologically treated effluents reduced HPC bacterial counts by 2-2.5log10. Post chlorination or chloramination of prechlorinated GAC/sand effluents resulted in a 20 %, or a 44 % (respectively) increase in AOC levels. Post disinfection of preozonated water resulted in small (<8%) AOC increases. Despite increases in AOC levels, prechlorinated water had lower AOC levels than preozonated water, even after post disinfection.
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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 | 0.001 |
| 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) | 0.001 | 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".