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Measurement and correlation study of silymarin solubility in supercritical carbon dioxide with and without a cosolvent using semi-empirical models and back-propagation artificial neural networks

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Graphical Abstract In the present work, the solubility data of silymarin (SM) in pure supercritical carbon dioxide (SCCO2) and SCCO2 with added cosolvent was measured at temperatures ranging from 308… Click to show full abstract

Graphical Abstract In the present work, the solubility data of silymarin (SM) in pure supercritical carbon dioxide (SCCO2) and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models (Chrastil, Bartle and Mendez-Santiago and Teja models) and a back-propagation artificial neural networks (BPANN) model. Unlabelled image

Keywords: models back; semi empirical; propagation artificial; supercritical carbon; carbon dioxide; back propagation

Journal Title: Asian Journal of Pharmaceutical Sciences
Year Published: 2017

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