Fouling assessment in a municipal water reclamation reverse osmosis system as related to concentration factor
Bibliographic record
Abstract
A study was conducted to assess possible formation of inorganic scaling and organic fouling in a municipal wastewater reverse osmosis (MWRO) system with microfiltration membrane treated secondary effluent as feed water. Results indicate that reject from the MWRO process can be supersaturated with sparingly soluble phosphate salts and hydroxide compounds that may form scales on the reverse osmosis (RO) membrane. The degrees of saturation, however, are much less than that in the seawater RO (SWRO) process. Therefore, a concentration factor as high as 10 can be applied to the MWRO, reflecting a recovery of 90%. Such high recovery was demonstrated using RO design software and operating a two 3-elements-in-series RO pilot plant. As the recovery rate increases, organic substances from the feed water concentrate in membrane elements at locations where flowrate is substantially reduced. At CF 5, total organic carbon and total nitrogen in the reject can be greater than 40 g/m3. The challenge of high recovery in the MWRO process then is the design and configuration of an efficient RO process in which organic and bio-fouling can be effectively controlled and advanced technologies can be integrated for fouling minimization. Key words: wastewater reclamation, reverse osmosis, membrane inorganic scaling, organic fouling, concentration factor.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".