Modelling development for ultrafiltration membrane fouling of heterogeneous membranes with non‐uniform pore size
Bibliographic record
Abstract
Abstract The aim of the present study is to develop a mathematical model for a better understanding and prediction of the Ultrafiltration membrane fouling, which can be applied to the heterogeneous membrane with non‐uniform pore size. Polysulfone flat membrane with a molecular weight cut‐off (MWCO) of 60 000 (nominal size of 0.05 µm) was used with a constant feed flow rate and a cross‐flow mode in ultrafiltration of a latex paint solution. The pore size distribution of the heterogeneous membrane was estimated using ImageJ software. The model was developed based on analyses of particle attachments. All possible depositional and coagulation fouling attachments were included in the model with the evidence derived from the SEM images. Monodisperse particles with sizes of 50 nm and 100 nm, as well as the latex effluent with a wide range of particle size distribution, were utilized in order to test the validity of the model. The transmembrane pressure estimated from the mathematical model agreed with the transmembrane pressure experimentally measured mostly within 3.3–10.0 % error and up to 13.0 % error, using the monodisperse particles and latex effluent, respectively.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".