Factors affecting coagulation as a pretreatment to ultrafiltration membranes
Bibliographic record
Abstract
Low pressure membranes can be effective in treating various types of water, but are subject to fouling. In this work, chemical coagulation was studied as a pretreatment to ultrafiltration (UF), with the goal of mitigating fouling while maintaining high permeate water quality. Alum and ferric chloride were evaluated, as well as two polyaluminum chloride (PACl) products of different basicities and compositions. A bench-scale hollow fiber UF unit was developed to study the treatment of raw and pretreated water from two southern Ontario drinking water sources. The four coagulants were compared at optimal dosages, as well as at lower dosages. The roles of mixing and pH conditions were also evaluated. Membrane fouling was evaluated by observing changes in trans-membrane pressure (TMP) over 3-day experiments. Under optimal dose conditions, all four coagulants were able to reduce the rate at which the membrane fouled to varying degrees for each water source. Total organic carbon (TOC) removal by the membrane was also enhanced with coagulation pretreatment as would be expected. Experiments conducted with low coagulant dosages displayed under-dosing and a subsequent increase in membrane fouling. Experiments conducted with modified raw water pH improved both membrane performance and TOC removal, while experiments with modified mixing intensities proved ineffective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".