Data analysis and prediction of pure component properties of long-chain substances is considered. The emphasis is on homologous series and properties for which insufficient data are available. A two-stage procedure is recommended, whereby a linear (or nonlinear) quantitative structure–property relationship (QSPR) is fitted to a “reference” series, for which an adequate amount of precise data is available. This QSPR should represent correctly both the available data and the asymptotic behavior of the property. In the second stage a quantitative property-property relationship (QPPR) is derived to represent the predicted property values of a “target” series in terms of the property values of the reference series. The procedure is applied for properties which are highly correlated with the number methylene groups in homologous series: and . It is shown that the method is very useful for consistency analysis of property data and enables a reliable prediction of and , and, thus, also of for long-chain substances. © 2012 American Institute of Chemical Engineers AIChE J, 59: 420–428, 2013
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