摘要：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.
