In-line coagulation assessment for ultrafiltration fouling reduction to treat secondary effluent for water reuse
Bibliographic record
Abstract
Abstract Low pressure membranes are attracting attention for their potential to improve secondary effluent quality, but membrane fouling can limit their widespread applicability. In this study, in-line coagulation as pre-treatment to ultrafiltration (UF) was investigated using a bench-scale hollow fiber membrane at a constant flux of 33 L/m2 h. Membrane fouling was monitored by observing change in trans-membrane pressure when the membrane was fed with secondary effluent and in-line coagulated secondary effluent over a 24-h period. The impact of four coagulants at different dosages on reversible and irreversible membrane fouling and permeate quality was studied. It was found that in-line coagulation improved UF performance to varying degrees depending on coagulant type and dosage. Generally, higher reduction of fouling was achieved by increasing coagulant dosage within the 0.5–5.0 mg/L range investigated. Ferric-based coagulants were better than aluminum-based coagulants with respect to improving membrane performance for the secondary effluent investigated, even at low dosages (0.5 mg/L). Further investigations are required to determine how in-line coagulation affects removal of organic compounds through UF membranes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".