Density prediction of aliphatic hydrocarbons using nonlinear group contribution method
کد مقاله : 1122-PHYSCHEM20
منصوره متولی *
چکیده مقاله:
In this work, we propose a quantitative structure property relationship (QSPR) approach in order to model the density of saturated and unsaturated aliphatic hydrocarbons including linear and branched alkanes, substituted and unsubstituted cycloalkanes and cycloalkenes and linear and branched alkenes up to the high temperature, high pressure conditions. The group contribution method was used to select the most important descriptors of compounds structure. Levenberg -Marquardt artificial neural network (ANN) was used to link molecular structures and density data. The data set was randomly divided into three data set: training set (4358 point), validation set (643 point) and test set (643 point). After training and optimization of the ANN parameters, the performance of the model was investigated by the test set. The result indicates that this model can simulate the relationship between the experimental descriptors and the density of the desired molecules accurately.
کلیدواژه ها:
QSPR, Group Contribution Method, Artificial Neural Network, density, hydrocarbons.
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