Membrane filtration for cold regions – impact of cold water on membrane integrity monitoring tests
Bibliographic record
Abstract
Low-pressure water treatment membranes, microfiltration (MF) and ultrafiltration (UF) have enjoyed unprecedented growth during the past decade. These systems are particularly suited for cold region communities because of their operational simplicity, small footprint, and consistent performance despite the fluctuations in raw water characteristics. Effective operation of MF and UF membrane systems in cold regions, however, must account for the impact of low water temperatures on membrane operation. Of particular interest is the impact of cold water on direct membrane integrity monitoring tests. Most regulatory bodies require that regular integrity monitoring tests be performed for low-pressure membranes to ensure the integrity of the membrane systems. Currently, the most widely-used integrity monitoring test is the pressure decay test. Our studies, however, indicated that the pressure-based direct integrity monitoring tests are affected by variations in water temperature, especially in the range of 0 to 5 °C. The considerable drop in diffusive air flow rates and consequent decrease in pressure decay rate for an intact membrane as water temperatures approach 0 may mask the impact of a defect and should be accounted for. It is suggested that the criteria for membrane integrity tests in cold regions shift downward to account for the effect of temperature. This paper presents the results obtained from studies on the effect of water temperature on the pressure-based direct integrity monitoring tests. Key words: membrane integrity monitoring, pressure decay test, diffusive air flow test, low-pressure membranes, microfiltration, ultrafiltration, water treatment, pathogen removal.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".