Near critical carbon dioxide extraction of Anise (Pimpinella Anisum L.) seed: Mathematical and artificial neural network modeling

In the current study, two models for estimating essential oil extraction yield from Anise, at high pressure condition, were used: mathematical modeling and artificial neural network (ANN) modeling. The extractor modeled mathematically using material balance in both fluid and solid phases. The model was solved numerically and validated with experimental data. Since the potential of near critical extraction is of consider able economic significance, a multi-layer feed forward ANN has been presented for accurate prediction of the mass of extract at this region of extraction. According to the network's training, validation and testing results, a three layer neural network with fifteen neurons in the hidden layer is selected as the best architecture for accurate prediction of mass of extract from Anise seed. Finally, the influence of pressure and solvent flow rate on the extraction kinetics was studied using ANN model and the optimum pressure range has been determined.

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► Applying a mathematical modeling for supercritical fluid extraction (SFE) process.
► Applying an artificial neural network modeling for SFE process.
► Comparison between mathematical modeling and artificial neural network modeling of SFE process.
► Optimization of the SFE process and determining the optimum pressure.