Addressing Reverse Osmosis Fouling within Water Reclamation—A Side‐By‐Side Comparison of Low‐Pressure Membrane Pretreatments
Bibliographic record
Abstract
A tertiary membrane filtration (TMF) pilot operating on secondary effluent and a membrane bioreactor (MBR) were setup in a side-by-side study as pretreatments for two identical reverse osmosis pilot systems. The water quality of the permeate from both low-pressure membrane pretreatment systems and the fouling rate of the reverse osmosis systems were compared to assess the capabilities of the two low-pressure membrane pretreatments to prevent organic fouling of the reverse osmosis systems. Both pretreatment pilots were setup using typical operating conditions (i.e., solids retention time and mixed-liquor suspended solids). A consistent difference in water quality and reverse osmosis performance was demonstrated during the 12-month study. The MBR permeate consistently had significantly lower total organic carbon (TOC) and chemical oxygen demand concentrations, but higher color and specific UV absorbance compared with the permeate from the TMF pretreatment. The pretreatment with the MBR gave an average reverse osmosis fouling rate over the entire study (0.27 Lmh/bar.month) that was less than half of the value found for the reverse osmosis with TMF pretreatment (0.60 Lmh/bar.month). A correlation of reverse osmosis feed TOC concentration with average reverse osmosis fouling rate also was established, independent of the pretreatment method used. Results from a cleaning analysis, energy dispersive spectroscopy, and fourier transformed infrared reflectometry confirmed that the foulants were primarily organic in nature. It is concluded that, for this type of application and setup, MBR systems present an advantage over tertiary membrane polishing of secondary effluent for reverse osmosis pretreatment.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".