Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks

Artificial
neural networks (ANNs) were developed to accurately
predict the self-diffusion constants for individual components in
binary fluid mixtures. The ANNs were tested on an experimental database
of 4328 self-diffusion constants from 131 mixtures containing 75 unique
compounds. The presence of strong hydrogen bonding molecules may lead
to clustering or dimerization resulting in non-linear diffusive behavior.
To address this, self- and binary association energies were calculated
for each molecule and mixture to provide information on intermolecular
interaction strength and were used as input features to the ANN. An
accurate, generalized ANN model was developed with an overall average
absolute deviation of 4.1%. Forward input feature selection reveals
the importance of critical properties and self-association energies
along with other fluid properties. Additional ANNs were developed
with subsets of the full input feature set to further investigate
the impact of various properties on model performance. The results
from two specific mixtures are discussed in additional detail: one
providing an example of strong hydrogen bonding and the other an example
of extreme pressure changes, with the ANN models predicting self-diffusion
well in both cases.