LAUSR

Abstract The aim of this research was optimization of supercritical fluid extraction (SFE) of sage herbal dust obtained as by-product from filter tea factory. Extraction kinetics modelling and artificial neural network (ANN) simulation were used for that purpose. Experiments were performed within expanded Box-Behnken experimental design on three levels and three variables. Influence of pressure (100–300 bar), temperature (40–60 °C) and CO2 flow rate (0.2–0.4 kg/h) on total extraction yield was determined. In order to determine initial slope, extraction curves were fitted with five modified empirical models. Since Sovova model provided the best accordance with experimental data, initial slope obtained by this model was used as response variable for optimization with ANN and multivariable models (linear, exponential, logarithmic I and logarithmic II). Optimized SFE parameters for maximized initial slope were pressure of 283 bar, temperature of 60 °C and CO2 flow rate of 0.4 kg/h.