Supercritical fluid extraction of Drimys angustifolia Miers: Experimental data and identification of the dynamic behavior of extraction curves using neural networks based on wavelets

• Extraction of Drimys augustifolia Miers by SFE on a wide range of operational condition was studied.
• SFE of Drimys angustifolia Miers were provided in significant sampling during the transient.
• A methodology for SFE curves identification using a neural network based on wavelets was proposed.
• The neural network based on wavelets was able to predict the Drimys SFE curve behavior with success.

Drimys angustifolia Miers is a tree species native to and found in southern Brazil. The extract of this plant is rich with active compounds that show medicinal potential, its uses being prospected as phytotherapy. In this study, yield data from supercritical extraction of D. angustifolia Miers are provided at different pressure and temperature conditions, and for various process operation times. Additionally, with the view to allowing a scale-up process, a methodology for identifying the extraction curves using neural networks based on wavelets was proposed. This showed good prediction performance provided that a sufficient number of extraction curves are used during training. The identification method proposed is robust, fast and optimal, in the sense that the best neural network structure and respective associated weights can be determined, thus optimizing a quadratic approximation criterion.

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