Optimal Separation of Glycerol and Methyl Oleate via Liquid−Liquid Extraction
Bibliographic record
Abstract
To meet the ASTM D-6751-02 Standard for biodiesel, impurities, particularly glycerol, must be reduced to acceptable levels. Liquid−liquid extraction (i.e., solvent partitioning) is commonly used to achieve this standard. Evaluation of the technical feasibility of a single-stage mixer−settler liquid−liquid equilibrium process to separate glycerol and methyl oleate (as a model for biodiesel) was carried out based on the following criteria: (a) a volume ratio of the liquid phases close to 1:1, (b) a high recovery of biodiesel, (c) a short residence time of the liquid phases in the settler, and (d) achievement of the ASTM Standard for glycerol mass fraction of 0.0002 or less. Eight liquid−liquid extraction processes using different combinations of three potential solvents, hexane, methanol, and water, were studied. All data for the optimal compositions of each solvent system were obtained by calculations using the UNIFAC activity coefficient model, and no experimental measurements were done. Extraction using multisolvent systems containing hexane, methanol, and water were found to be technically feasible and gave the best results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".