Supercritical extraction (SE) is a separation technique utilizes near or above critical properties of the
solvents. In this technique, modeling of yield and solubility of materials are crucial points in supercritical
fluid extraction processes. Generally, mathematical modeling of the supercritical oil extraction is a very
difficulttask since a highly nonlinear relation exists between process variables and solubility. Considering
these facts, in the present study, a trainable cascade-forward back-propagation network (CFBPN) was
proposed to correlate the yield of spearmint oil extracted by supercritical carbon dioxide. The results
revealed the applicability of the proposed model to correlate the yield of spearmint oil extraction with
an acceptable level of accuracy. Finally, the obtained results were compared to mathematical models
namely Goodarznia & Eikani and Kim & Hong. The comparison between the results of proposed network
and mathematical models demonstrated a better predictive capability of the proposed network
کلید واژگان :
SC-CO2 Spearmint Extraction Packed bed Artificial neural network modeling
