Membrane Fouling Test: Apparatus Evaluation
Bibliographic record
Abstract
Flux decline with time is one of the most serious shortcomings of microfiltration and ultrafiltration membranes. It is highly desirable to have a membrane (fouling) testing procedure that is short in duration, utilizes a minimum amount of test solution, only requires a small membrane area, and is representative of the large-scale process. The objective of this study was to compare the results of the testing of a given membrane using a number of different test units (reverse osmosis, ultrafiltration, dead-end, and cross-flow cells) and testing procedures. It was of particular interest to determine if smaller cells used in the literature perform similarly to the Sepa CF cell, as it is a standard. During six-day runs the flux decline of the polyethersulfone membrane tested was mainly caused by membrane compaction and much less due to fouling. As various membrane materials compact to a different extent, studies into the fouling characteristics of different types of membranes should incorporate precompaction and pure water testing to quantify the contribution of membrane compaction and true fouling to the overall flux decline. The dead-end cell performed very differently from continuous cells, so their use is not recommended. The six-day continuous flow tests showed that the reverse osmosis (RO), ultrafiltration (UF), and cross-flow (CF) cells yielded very similar dissolved organic carbon removals and flux decline, despite UF and RO cells using membrane coupons eight times smaller than CF cells.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".