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‐CO2) media has advantages over conventional alkali‐catalyzed glycerolysis. However, kinetic data are lacking for such conversions in the presence of SC‐CO2. The objectives of this study were to estimate the rate constants and elucidate the mechanism for the glycerolysis of soybean oil in SC‐CO2 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‐CO2 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".