Predictive dimensionless solubility (pDS) model for solid solutes in supercritical CO2 that requires only pure-component physical properties

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6 Citations (Scopus)

Abstract

Dimensionless analysis was used to develop a correlative dimensionless solubility (DS) model and a predictive dimensionless solubility (pDS) model for estimating solid solubilities in supercritical carbon dioxide. Solubilities of 20 organic compounds made up of 685 data were used in developing the DS model and an additional 10 organic compounds were used for assessing the pDS model. Average relative deviation (ARD) in logarithmic solubility for literature model correlations were: Jouyban (2.06%), Hozhabr (2.09%), Jafarinejad (2.27%), Chrastil (2.49%), and Mendez-Santiago–Teja (3.46%). The proposed DS model had an ARD of 2.35% and the pDS model had an ARD of 10.5%. When the pDS model was optimized to the entire database, an ARD of 10.3% was obtained. The pDS model requires only pure solute properties (melting point, molar volume, entropy-based solubility parameter) and pure CO2 density for predicting solubility of solids in supercritical CO2.

Original languageEnglish
Pages (from-to)251-261
Number of pages11
JournalChemical Engineering Research and Design
Volume136
DOIs
Publication statusPublished - 2018 Aug

Keywords

  • Model
  • Natural products
  • Solubility
  • Supercritical fluid extraction

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)

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