A <scp>bench‐scale</scp> study of potable reuse impacts on surface water treatment
Bibliographic record
Abstract
Abstract Water utilities are considering raw water augmentation schemes to blend potable reuse water directly into the raw water for existing water treatment plants (WTPs). In this study, bench‐scale testing evaluated the impacts of introducing advanced‐treated reuse water (treated by ozonation, biological active carbon, ultrafiltration, reverse osmosis, and advanced oxidation) into the raw water supply of an existing WTP. To determine whether treatment would remain effective, blends of raw water and reuse water were coagulated, flocculated, and settled in a jar test apparatus matching the flocculator energy dissipation rate of the full‐scale WTP and were tested for filterability, defined as positive removal of turbidity through 5‐μm filter paper. The testing demonstrated that blends were treatable across a range of conditions, and alkalinity was the main observed limitation for treatability. Conditioning the advanced‐treated reuse water to add both hardness and alkalinity buffered against extreme pH drops during coagulation. This also achieved pH and calcium carbonate indexes after treatment that matched the current finished water, but some stability indexes shifted in a more corrosive direction, suggesting a topic for future research. Overall, this study demonstrated that the coagulation, flocculation, settling, and filtration processes of an existing WTP can treat potable reuse blends provided alkalinity is sufficient, and this is an important finding for the viability of raw water augmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".