Phase distributions of alcohol, glycerol, and catalyst in the transesterification of soybean oil
Bibliographic record
Abstract
Abstract Two‐phase base‐catalyzed transesterification of vegetable oils is the most common method for making biodiesel. The reaction starts as separate oil and alcohol phases. At the end of the reaction, the mixture, if allowed to settle, consists of an upper ester‐rich layer and a lower glycerol‐rich layer. The compositions of these layers from the methanolysis and ethanolysis of soybean oil were measured. Synthetic mixtures and actual reaction mixtures were used either to represent or generate steadystate reaction mixtures resulting from the initial condition of 6∶1 alcohol/oil molar ratio and catalyst concentration (1.0 wt% sodium methoxide or 1.26 wt% sodium ethoxide). At 23°C, for methanolysis, 42.0% of the alcohol, 2.3% of the glycerol, and 5.9% of the catalyst were in the ester‐rich phase at steady state. In ethanolysis, 75.4% of the ethanol, 19.3% of the glycerol, and 7.5% of the catalyst were in the ester‐rich phase. The volume of the glycerol‐rich phase decreased from methanolysis to ethanolysis to propanolysis; butanolysis remained monophasic throughout. The results explain some of the general kinetic behavior observed in transesterifications and provide useful information for alcohol recovery and product purification.
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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) | 0.001 | 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".