Hybrid membrane process: Performance evaluation of biological PAC
Bibliographic record
Abstract
This study investigated the use of biological powdered activated carbon (PAC) for the removal of natural organic matter (NOM) and ammonia from drinking water. The impact of solids retention time (SRT), hydraulic retention time (HRT), PAC diameter and PAC concentration on the process efficiency was evaluated. Five bioreactors were filled with a slurry using two PAC concentrations (5 or 25 g l-1) two PAC mean diameters (25 or 200 μm) and two SRTs (30 or 100-160 days). The bioreactors were operated during 161 days using post-ozonated water as influent. It was determined that the PAC concentration in the bioreactors was a key parameter for the improvement of biological removal. The higher PAC concentration (25 g l-1) was more efficient for the removal of ammonia, dissolved organic carbon (DOC) and biodegradable dissolved organic carbon (BDOC). Full nitrification was observed after 90 days in bioreactors with 25 g l- of PAC. The PAC diameter (25 vs. 200 μm) did not significantly influence BDOC, DOC and N-NH4 removals under stable conditions, although nitrification was initiated faster using a 25-μm diameter PAC. Increasing HRT from 15 to 30 minutes improved NOM and ammonia removals. Reducing SRT from 100-161 to 30 days improved DOC removals but reduced BDOC and ammonia removals. The overall performances observed during this study demonstrate the efficiency of biological PAC. Its combination with ultrafiltration in a hybrid membrane process appears promising but the feasibility from an operational standpoint still has to be demonstrated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".