Coagulation/Flocculation-Ultrafiltration Optimization in Drinking Water Treatment
Bibliographic record
Abstract
Coagulation/flocculation is applied prior to ultrafiltration (UF) to reduce fouling and increase natural organic matter (NOM) removal. A lack of knowledge exists regarding the selection of optimal conditions to satisfy these treatment objectives. The present research includes three studies which provide guidance for the design and operation of coagulation/flocculation-UF by: i) development of a bench-scale approach for process evaluation, ii) elucidating impacts of coagulation mechanisms and coagulation/flocculation configurations on UF performance, and iii) evaluating the retention of microplastics (MPs) and release by cleaning during coagulation/flocculation-UF treatment. Bench-scale continuous-flow systems allow evaluation of coagulation/flocculation-UF over consecutive permeation cycles, but require high flowrates to achieve hydraulic retention times (HRTs) typical of full-scale rapid mixing. To reduce flowrates, the present research evaluated a typical 2 min HRT vs. 20 min in terms of impacts on particle properties and UF performance; 20 min represents HRTs previously considered during bench-scale investigations. Increases in particle size/concentration and reduced UF fouling resistance at a 20 min HRT, suggest that HRTs equivalent to those typically applied during full-scale rapid mixing must be considered during bench-scale studies in order to produce results relevant to full-scale. In the subsequent study, previous knowledge regarding alum dosages and pH values which promote specific coagulation mechanisms was utilized as a framework to select coagulation conditions applied during coagulation/flocculation-UF. In cases where fouling control is required, conditions that promote adsorption destabilization are optimal, whereas in cases where NOM removal is required, conditions that promote sweep are optimal. Inclusion of flocculation (vs. coagulation alone) increased NOM removal while reducing irreversible fouling resistance, despite increased NOM retention by the membrane. In the final study, when compared to raw water, alum addition increased hydraulically irreversible accumulation of MPs on the membrane from 50% to 80% of those present in UF feed water. Chemical cleaning released 20% to 60% of MPs which had accumulated on the membrane during previous permeation cycles. While positive correlations were observed between the release of MPs and foulants, the release of MPs was consistently lower. Accumulation of MPs on the membrane may increase UF fouling over extended operating periods.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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".