Determination of Vapor Pressure of Chemical Compounds: A Group Contribution Model for an Extremely Large Database

In the present study, a group contribution model is developed
for
determination of the vapor pressure of pure chemical compounds at
temperatures from 55 to 3040 K. About 42 000 vapor pressure
values belonging to around 1400 chemical compounds (mostly organic
ones) at different temperatures are treated to propose a reliable
and predictive model. A three-layer artificial neural network is optimized
using the Levenberg–Marquardt (LM) optimization algorithm to
establish the final relationship between the functional groups and
the vapor pressure values. The obtained results indicate the average absolute
relative deviation (AARD%) of the calculations/estimations
from the applied data to be about 6% and a squared correlation coefficient
of 0.994. Furthermore, the outliers of the model are detected using
the leverage value statistics method.