Purifying Anaerobically Treated Municipal Secondary Wastewater Effluent by a Reverse Osmosis-Based Potable Reuse Treatment Train
Bibliographic record
Abstract
As a pretreatment to potable reuse trains, anaerobic secondary treatment could reduce the energy demand and footprint compared to aerobic secondary treatment. Long-term pilot tests linked a reverse osmosis (RO)-based potable reuse treatment system to a pilot-scale staged anaerobic fluidized membrane bioreactor (SAF-MBR). A membrane-aerated bioreactor removed sulfide in SAF-MBR effluent prior to RO. The RO operated for ∼120 days at 15 LMH and 67–83% water recovery, with a final feed pressure during each cycle of ∼9–10 bar. When the final pressure increased to ∼12 bar, chemical cleaning reestablished membrane performance, and a membrane autopsy indicated reversible fouling by biomass and phosphate-based minerals. MS2 bacteriophage spiking tests indicated at least 5–6-log removal each by RO and UV/H 2 O 2 advanced oxidation process (AOP) treatment at ∼730 mJ/cm 2 average UV fluence. A 1,100 mJ/cm 2 average UV fluence met treatment goals for 1,4-dioxane and indicators for other organic contaminants. Halogenated DBPs in the chlorinated final effluent were ∼5-fold lower than potable reuse trains fed by aerobic secondary effluent. N -Nitrosodimethylamine was well below California’s 10 ng/L Notification Limit. An operating cost comparison indicated that a potable reuse train fed by SAF-MBR effluent ($0.69/m 3 ) is cost-competitive to that fed by aerobic secondary effluent ($0.69/m 3 ).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".