Dynamic characterization of a FeCl3-dosed anaerobic membrane bioreactor (AnMBR) treating municipal wastewater
Bibliographic record
Abstract
Abstract A transient study was conducted at pilot scale to assess the impact of Fe dosage on the dynamics of biological and membrane performance of an anaerobic membrane bioreactor (AnMBR) treating authentic municipal wastewater. A transient model of the AnMBR system was employed to assist with interpretation of the observed responses in the mixed liquor under different FeCl3 dosages. A high dosage (43 mg FeCl3/LSewage) resulted in a significant accumulation of fixed suspended solids and volatile suspended solids (VSS) and reduction of colloidal COD in the mixed liquor. The elevated dosages appeared to reduce the biodegradability of VSS that was present in the raw wastewater. Intermediate dosages of FeCl3 (21–12 mg/L) had less effect on these responses and did not appear to affect VSS biodegradation. Membrane performance was significantly affected by FeCl3 dosage as indicated by reversible resistance (RR) and physically irreversible resistance (IR). RR was closely related to the colloidal COD in the mixed liquor, thus responded quickly to Fe dosage. Physically, IR had a delayed response to changes in the colloidal COD concentrations in the mixed liquor and this was attributed to the effect of slow mass transfer of colloidal matter between the mixed liquor and the membrane.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".