Contribution of biofilm layer to virus removal in gravity-driven membrane systems with passive fouling control
Bibliographic record
Abstract
Passive gravity-driven membrane (PGM) filtration is a type of gravity-driven membrane (GDM) filtration operated with passive physical fouling control measures that add limited to no complexity to the system (e.g. permeate flux interruptions, gravity-driven air scouring, system draining), allowing a greater permeate flux to be sustained. A key aspect of PGM/GDM systems is the development of a structurally loose and permeable biofilm layer on the membrane that enables a sustainable flux to be achieved and has also been associated with improved removal of humic acids, polysaccharides, proteins, assimilable organic carbon and microcystins. The present study investigated if the biofilm layer of PGM systems also contributes to virus removal, for both intact and breached PGM systems. Breach sizes considered ranged from 20 to 180 µm. Challenge tests (CTs) identified an increase of 2.0+ in the log removal value (LRV) for viruses in both intact and breached PGM systems when a biofilm layer was present, suggesting that the biofilm layer is capable of bridging the gap over integrity breaches, acting as a secondary barrier to contaminants that would otherwise bypass treatment by flowing through the breach. Pressure decay tests (PDTs), however, did not identify the same increase in LRVs as that of CTs, suggesting that the standard PDT approach cannot consider the contribution of the biofilm layer to the removal of small material such as viruses. An alternative integrity testing protocol for PGM systems was developed using a modified PDT approach that takes into account the additional removal provided by the biofilm layer. This alternative protocol is also simpler and requires less frequent testing, contributing to the simplification of overall operation of PGM systems in small/remote communities and decentralized applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".