Pervaporation and vacuum membrane distillation processes: Modeling and experiments
Bibliographic record
Abstract
Abstract Two separation processes, pervaporation (PV) and vacuum membrane distillation (VMD), were studied using polyvinylidene fluoride (PVDF) flat‐sheet membranes for the separation of chloroform–water mixtures. Both PV and VMD membranes were prepared using the phase‐inversion method and the same polymer material. VMD membranes with different pore sizes were prepared using pure water as a pore‐forming additive in the PVDF/dimethylacetamide casting solution, whereas PV membranes were obtained with higher polymer concentration, without nonsolvent additives and with solvent evaporation before gelation. The mean pore size, porosity, and pore size distributions of the VMD membranes were determined. Water and formamide advancing and receding contact angles of PV membranes were measured. The swelling degree, the solubility parameter of PV membranes, and the interaction of the permeants with the PVDF polymer were calculated. In the VMD process, a more general theoretical model that considers the pore size distribution, the solution–diffusion contribution through nonporous membrane portion, and the gas transport mechanisms through membrane pores was developed based on the kinetic theory of gases. The contribution of each mechanism was analyzed. A comparative study was made between both membrane separation technologies. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1697–1712, 2004
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".