A review of the impact of chemical pretreatment on low-pressure water treatment membranes
Bibliographic record
Abstract
Historically, microfiltration (MF) and ultrafiltration (UF) membranes have been used to remove turbidity, particulate matter, and pathogens. Chemical and physical pretreatment, however, can greatly expand the use of MF and UF membrane systems beyond turbidity and microorganism removal. Both MF and UF membrane systems may be used to remove a variety of chemical contaminants such as arsenic, pesticides, taste and odour, iron, and manganese, provided that the proper water chemistry is attained to convert the contaminants to a particulate form. In addition to enhancing the removal of dissolved contaminants, chemical pretreatment processes such as coagulation have been shown to improve membrane performance by reducing the rate of membrane fouling. Several issues, however, still remain to be resolved before chemical pretreatment can be applied optimally in the water treatment membrane field. These issues include the impact of chemical pretreatment on the performance of membrane systems (i.e., membrane reversible fouling, chemical cleaning frequency), the compatibility of these chemicals with membrane materials, the optimum conditions for chemical pretreatment, and overall cost and benefits of chemical pretreatment to MF and UF membrane systems. Key words: microfiltration, ultrafiltration, chemical pretreatment, membrane fouling, natural organic matter, coagulation, clarification, oxidation, contaminant removal, process optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".