Bench-scale study of ultrafiltration membranes for evaluating membrane performance in surface water treatment
Bibliographic record
Abstract
Currently, there is no standard bench-scale dead-end ultrafiltration (UF) testing system. The aim of the present study was to design and build a bench-scale hollow fiber UF system to assess the impact of operational parameters on membrane performance and fouling. A bench-scale hollow fiber UF system was built to operate at a constant flux (±2% of the set-point flux) and included automated backwash cycles. The development of the bench-scale system showed that it is very difficult to maintain a constant flux during the first minute of the filtration cycles, that digital flow meters are problematic, and that the volume of the backwash waste lines should be minimized. The system was evaluated with Ottawa River water, which has a relatively high hydrophobic natural organic matter content and is typical of Northern Canadian waters. The testing using different permeate fluxes, filtration cycle duration and backwash cycle duration showed that this system mimics the performance of larger systems and may be used to assess the impact of operating conditions on membrane fouling and alternative pretreatment options. Modeling the first, middle, and last filtration cycles of the six runs using single and dual blocking mechanisms yielded inconsistent results regarding the controlling fouling mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".