Prediction of Thermochemical Properties of Long-Chain Alkanes Using Linear Regression: Application to Hydroisomerization

Linear regression (LR) is used to predict thermochemical
properties
of alkanes at temperatures (0–1000) K to study chemical reaction
equilibria inside zeolites. The thermochemical properties of C1 until C10 isomers reported by Scott are used as
training data sets in the LR model which is used to predict these
properties for alkanes longer than C10 isomers. Second-order
groups are used as independent variables which account for the interactions
between the neighboring groups of atoms. This model accurately predicts
Gibbs free energies, enthalpies, Gibbs free energies of formation,
and enthalpies of formation for alkanes which exceeds the chemical
accuracy of 1 kcal/mol and outperforms the group contribution methods
developed by Benson et al., Joback and Reid, and Constantinou and
Gani. Predictions from our model are used to compute the reaction
equilibrium distribution of hydroisomerization of C10 and
C14 isomers in MTW-type zeolite. Calculation of reaction
equilibrium distribution inside zeolites also requires Henry coefficients
of the isomers which can be computed using classical force field-based
molecular simulations using the RASPA2 software for which we created
an automated workflow. The reaction equilibrium distribution for C10 isomers obtained using the LR model and the training data
set for this model are in very good agreement. The tools developed
in this study will enable the computational study of hydroisomerization
of long-chain alkanes (>C10).