Optimization of supercritical fluid extraction of isoflavone from soybean meal
Bibliographic record
Abstract
Abstract This study aims at developing a mathematical model to predict the yield of isoflavone from soybean meal in a supercritical extraction process using carbon dioxide and aqueous methanol as a co‐solvent and to optimize the process using a genetic algorithm. In the model, a partial differential equation based conservation of mass was solved to predict the yield of isoflavone extraction. The model parameters such as densities of carbon dioxide and co‐solvent methanol, the mixture viscosity, the binary diffusion coefficient of isoflavone in the supercritical solvents, the film mass transfer coefficient, effective diffusivity, and axial dispersion coefficient were estimated using available correlations, and the solubility was estimated using the Mohsen‐Nia‐Moddaress‐Mansoori equation of state. The model was successfully validated with experimental data. In the optimization, the operating conditions of the isoflavone extraction process were identified as decision variables and a profit function was maximized. The optimum was found under the condition in which the carbon dioxide flow rate was 5.88 kg/h and the particle diameter was 0.68 mm, when the temperature was 323.15 K, the pressure was 59.45 MPa, and the extraction time was 283 min. The maximum profit found under these optimum conditions was 46.18 $ per batch.
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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".