Prediction of Density and Viscosity of Biofuel Compounds Using Machine Learning Methods

In the present work, temperature dependent models for
the prediction
of densities and dynamic viscosities of pure compounds within the
range of possible alternative fuel mixture components are presented.
The proposed models have been derived using machine learning methods
including Artificial Neural Networks and Support Vector Machines.
Experimental data used to train and validate the models was extracted
from the DIPPR database. A comparison between models using an ample
range of molecular descriptors and models using only functional group
count descriptors as inputs was performed, and consensus models were
created by testing different combinations of the individual models.
The resulting consensus models’ predictions were in agreement
with the available experimental data. Comparisons were also made between
predictions of our models and correlations validated by the DIPPR
staff. Our models were used to predict densities and dynamic viscosities
of compounds for which no experimental data exists. Our models were
also used to estimate other properties such as kinematic viscosities,
critical temperatures, and critical pressures for compounds in the
database. Finally, predictions were used to study the main trends
of density and viscosity at the aforementioned temperatures as a function
of the number of carbon atoms for chemical families of interest.