A Model of Membrane Fouling by Salt Precipitation from Multicomponent Ionic Mixtures in Crossflow Nanofiltration
Bibliographic record
Abstract
A coupled model of concentration polarization, ion transport in membrane pores, and fouling by salt precipitation is used to predict the permeate flux decline due to scaling during crossflow nanofiltration of a multicomponent ionic mixture. The model considers a fouling layer buildup due to salt precipitation once the solubility product of the sparingly soluble salt in an ionic mixture is exceeded. The precipitated salt deposits on the membrane surface, and reduces the permeate flux through the membrane. The primary novelty of the presented methodology is its ability to predict the local fouling behavior at different axial positions in a crossflow filtration channel. Using the model, we assess the fouling behavior of a ternary mixture of Na2SO4 and CaSO4 for various feed concentrations, pertinent membrane properties, and operating conditions to predict the axial location in a crossflow filtration channel where scaling due to calcium sulfate precipitation will initiate. Simulations for a four-component mixture (Na2SO4/CaCl2) are also performed to depict the fouling behavior in a complex ionic mixture closely resembling feed waters used in membrane treatment operations.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.003 | 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".