Trainable cascade-forward back-propagation network modeling of spearmint oil extraction in a packed bed using SC-CO2

Abstract

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 difficult task 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.