Separation of soybean oil from liquefied n‐butane and liquefied petroleum gas by membrane separation process
Bibliographic record
Abstract
This work investigates the separation of soybean oil/compressed n‐butane and soybean oil/liquefied petroleum gas (LPG) mixtures, using ultra‐ and nanofiltration membranes. For this purpose, soybean oil/n‐butane and soybean oil/LPG in the mass ratio of 1:3 were continuously fed into a flat sheet module without the recycle. The effects of the feed pressure (10, 20, and 30 bar), the pressure difference (1, 5, and 10 bar) and the pre‐treatment with ethanol and n‐propanol on the oil permeate flux and oil retention were investigated. The membranes with best performance (higher oil retention combined with high solvent permeate flux) were Sepa GM (4 kDa) and Sepa HL (98 % MgSO4), previously conditioned in ethanol. The Sepa GM membrane showed oil retention between 95 to 99 %. The Sepa HL membrane showed oil retention between 83 to 97 %. The pre‐treatment with ethanol improved the permeate flux. In most of the experimental conditions severe membrane fouling was observed. A change in the permeate flux and oil retention behaviour was observed in the assays with LPG as solvent due to the different gas composition when compared with n‐butane.
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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 | 0.000 |
| 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".