Kinetic modeling of the glycerolysis reaction for soybean oils in supercritical carbon dioxide media
Bibliographic record
Abstract
Abstract Production of MAG by glycerolysis is important for food, pharmaceutical, and cosmetic industries. Conducting glycerolysis in supercritical carbon dioxide (SC‐CO 2 ) media has advantages over conventional alkali‐catalyzed glycerolysis. However, kinetic data are lacking for such conversions in the presence of SC‐CO 2 . The objectives of this study were to estimate the rate constants and elucidate the mechanism for the glycerolysis of soybean oil in SC‐CO 2 using previously reported data. The data were taken from experiments using soybean oil, glycerol (glycerol/oil molar ratios of 15–25) and water (3–8% w/w) in SC‐CO 2 at 20.7–62.1 MPa and 250°C for a 4 h period. Rate constants for the parallel glycerolysis and hydrolysis reactions were estimated for each processing parameter (glycerol/oil, water content, pressure) by minimizing the summed squared error between the values calculated from the experimental data and those obtained from the kinetic model. The results suggested that water and pressure had an effect on rate constants but the glycerol/oil ratio did not. Findings provide the kinetic modeling data necessary for the optimization of supercritical processes involving glycerolysis reactions for the production of MAG from vegetable oils.
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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".