Effect of coagulation and flocculation conditions on water quality in an immersed ultrafiltration process
Bibliographic record
Abstract
The removal of natural organic matter under variable coagulation and flocculation pretreatment conditions was evaluated for three surface waters in an immersed ultrafiltration (UF) process. Coagulation with alum, flocculation and UF treatment were conducted in a bench-scale test apparatus designed to simulate pilot- and full-scale water treatment systems. Variable coagulation and flocculation operating conditions were investigated, including coagulant dose, hydraulic retention time (HRT) and mixing intensity (e.g. velocity gradient). Treatment performance was evaluated by measuring specific water quality parameters in the permeate stream, including dissolved organic carbon (DOC), UV254 and true colour. Coagulant dose was found to be the most important variable for treatment performance with regard to permeate water quality, with significantly lower alum dosages required to achieve enhanced coagulation water quality targets than conventional filtration systems. Experiments conducted to evaluate variable flocculation stage HRT and applied velocity gradient demonstrated that traditional set points for these operating variables, applied in conventional filtration systems, may not be required in UF systems. In particular, optimized UF permeate water quality was found with reduced flocculation retention times (e.g. <10 minutes) and mixing intensities (e.g. < 100 s(-1)). The impact of intermittent air scour, or air sparging, operations in the UF process tank during operation was also evaluated. The use of air scour, tested as an intermittent operation at an applied velocity gradient of 50 s(-1) was found to significantly reduce DOC concentrations and UV254 measurements in the UF permeate stream when compared with UF operations without air scour.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".