Deacidification of Soybean Oil Combining Solvent Extraction and Membrane Technology
Bibliographic record
Abstract
The aim of this work was to study the removal of free fatty acids (FFAs) from soybean oil, combining solvent extraction (liquid-liquid) for the separation of FFAs from the oil and membrane technology to recover the solvent through nanofiltration (NF). Degummed soybean oil containing 1.05 ± 0.10% w/w FFAs was deacidified by extraction with ethanol. Results obtained in the experiences of FFAs extraction from oil show that the optimal operating conditions are the following: 1.8 : 1 w : w ethanol/oil ratio, 30 minutes extraction time and high speed of agitation and 30 minutes repose time after extraction at ambient temperature. As a result of these operations two phases are obtained: deacidified oil phase and ethanol phase (containing the FFAs). The oil from the first extraction is subjected to a second extraction under the same conditions, reducing the FFA concentration in oil to 0.09%. Solvent recovery from the ethanol phase is performed using nanofiltration technology with a commercially available polymeric NF membrane (NF-99-HF, Alfa Laval). From the analysis of the results we can conclude that the optimal operating conditions are pressure of 20 bar and temperature of 35°C, allowing better separation performance: permeate flux of 28.3 L/m 2 ·h and FFA retention of 70%.
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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) | 0.001 | 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".