(720h) A "Reference Series" Method for Prediction of Properties of Long-Chain Substances | AIChE

(720h) A "Reference Series" Method for Prediction of Properties of Long-Chain Substances


Shacham, M. - Presenter, Ben Gurion University of the Negev
Paster, I. - Presenter, Ben Gurion University of the Negev
Brauner, N. - Presenter, Tel-Aviv University

      Long chain substances pose special
challenges for property prediction, as many of their properties cannot be
measured because of thermal instability. Nikitin et al. (2001), for example,
noted that critical temperatures (Tc) and pressures (Pc) of n-alkanes have been
measured only up to hexatriacontane (C36H74) and for
1-alkanols up to 1-docosanol (C22H45OH). The critical
constants of the heavier members of the homologous series can only be predicted.

      Current methods used to predict
physical and thermodynamic properties can be classified into group contribution
methods (GC, e.g., Marrero and Gani, 2001), asymptotic behavior correlations
(ABCs, e.g., Marano and Holder, 1997, Nikitin et al., 2005)) and various
quantitative-structure-property relationships (QSPRs, e.g., Brauner et al, 2008).
All of these methods use available experimental data for low carbon number (nC)
compounds in order to obtain either the "group contribution" values
or the QSPR parameter values. The so-obtained group contributions or QSPRs are
used for prediction of properties of long chain members of homologous series by
extrapolation. The ABCs are non-linear correlations in terms of nC
, which use in addition to the experimental property data also an estimation of
the property value at the limit nC → ∞, y
Kontogeorgis and Tassios, 1997 compared several GC methods and methods that
converge to a finite yvalue, for predicting TC
and PC of  heavy alkanes. They concluded that only methods
that converge to finite y values yield reliable
predictions for TCand PCof
heavy alkanes.

      We (Paster et al., 2011) have
recently developed a technique for applying linear QSPRs to prediction of
properties of long chain substances in homologous series. Using this method,
molecular descriptors collinear with a particular property are identified based
on available experimental data. From among these, the descriptors whose asymptotic
behavior is similar to the property behavior are eventually used for prediction.
 For the cases studied in that work the QSPRs developed represented the
available experimental data satisfactorily and converge to theoretically
accepted values for nC → ∞. It has been also found
that the limiting property values for different homologous series are very
close in value to each other, as the effect of the particular functional groups
(e.g., ?CH3, ?COOH, ?CO) diminishes with increasing nC, where
the role of the ?CH2? chain becomes the dominant one. 

      The use of the method of Paster et
al.(2011) can be challenging, as in order to predict the property for a
particular (target) compound its 3D (or 2D) molecular structure (MOL) file must
be available, together with a program for calculating the required molecular
descriptor values. In the present work a new method is presented where the
method of Paster et al. (2011) is applied only to one "reference"
homologous series, thus molecular descriptors need to be calculated only for
members of this series. Typically the n-alkane series, for which the
largest amount of experimental data is available, is used as the reference
series. To predict properties for other ("target") series the
following two general characteristics of homologous series are utilized: 1.The
relationship between the property y at a particular nc
of members of the "reference" series with the same property of the
"target" series can be approximated locally by a straight line
(see Peterson, 2010, for example) and 2. The property value for the
"reference" and "target" series should approach the same (theoretically
accepted) value for nC → ∞.

Using these characteristics a nonlinear
equation with three parameters that converges to y of
the "reference" series (at the limit nC
∞) is fitted to the available property data of the "target"
series vs. the property data of the "reference" series. This
nonlinear equation enables predicting the property value for members of the
"target" series using only nC and the property
value of the members of the "reference" series with the same nC.

The proposed method has been applied to
several "target" series (1-alkenes, aldehydes, 1-alcohols,
n-aliphatic acids) for several properties, with very encouraging results. Detailed
results of these studies and discussions will be provided in the extended
abstract and the presentation.


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