Roles of various mixed liquor constituents in membrane filtration of activated sludge
Bibliographic record
Abstract
The roles of various mixed liquor constituents in the membrane filtration of activated sludge were investigated. Bench-scale filtration tests with four mixed liquor fractions (whole mixed liquor, mixed liquor supernatant, filtrate I (<8.0 μm), and filtrate II (<0.45 μm) or the soluble fraction) were carried out at different permeate fluxes. It was found that due to its high contents of organic carbon compounds, macromolecules and small supra-dissolved particles, which were most likely to adsorb onto and/or clog in the membrane structure, the soluble fraction (<0.45 μm) of mixed liquor ranked first in terms of its contribution to the total mixed liquor fouling, followed by the colloidal fraction (0.45–8.0 μm) and the unsettleable microfloc fraction (>8.0 μm). In particular, the study revealed that large flocs were able to exert dual effects on membrane filtration. Depending on the hydrodynamic conditions, they could either act as membrane foulants causing fouling via particle deposition and cake formation, or serve as “moving filters” entrapping soluble and colloidal substances and thus alleviating the fouling. A “steric hindrance effect” mechanism was postulated to explain the positive effect of large sludge flocs on membrane filtration.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".