Impact of Operating Conditions on Fouling Probability and Cake Height in Ultrafiltration of Latex Solution
Bibliographic record
Abstract
The aim of the present study was to investigate the effects of operating conditions (transmembrane pressure, feed flow rate, and feed concentration) on the fouling attachment probabilities, mass of fouling, and cake height. Polycarbonate flat membrane with a pore size of 0.05 µm was used under a constant feed flow rate and cross-flow mode in ultrafiltration of a latex paint solution. The results obtained indicate that increasing transmembrane pressure from 15 to 45 psi lead to an increase in the particle-to-particle (αpp) and particle-to-membrane (αpm) attachment probabilities from 0.4 to 0.76 and 0.55 to 0.8, respectively. It was observed that both attachment probabilities were significantly decreased when the feed flow rate was increased from 1 to 6 LPM (cross flow velocity from 10.4 to 62.5 cm/s). As a consequence, mass of fouling and cake height were reduced. Increasing the feed concentration from 0.78 to 1.82 kg/m3 resulted in a substantial raise in the cake height from 4.3 to 18.5 µm. Response Surface Methodology (RSM) was used to set up the experimental design. According to regression analysis, two correlation models were obtained in order to predict the fouling attachment probabilities at different operation conditions. Estimated attachment probabilities were used to predict mass of fouling retained by membrane.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".