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

摘要

In the present work, the solubility data of silymarin (SM) in pure supercritical carbon dioxide (SCCO ) and SCCO 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.