Abstract
The accuracy and prediction capability of the linear double log-log (LDL-L), mixture response-surface (MR-S) and the combined nearly ideal binary solvent/Redlich-Kister (CNIBS/R-K) solubility equations have been compared using the model parameters calculated from either the whole data or a minimum number of data in an experimental set. The CNIBS/R-K model produced better prediction for some experimental sets than the other two models when the parameters obtained from the whole data in a set were employed, whereas the LDL-L model was superior to the other models when the parameters calculated from a minimum number of data were used, indicating its greatest prediction capability.
Published Version
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