Prediction of Thermochemical\nProperties of Long-Chain\nAlkanes Using Linear Regression: Application to Hydroisomerization

Linear regression (LR) is used to predict thermochemical\nproperties\nof alkanes at temperatures (0–1000) K to study chemical reaction\nequilibria inside zeolites. The thermochemical properties of C<sub>1</sub> until C<sub>10</sub> isomers reported by Scott are used as\ntraining data sets in the LR model which is used to predict these\nproperties for alkanes longer than C<sub>10</sub> isomers. Second-order\ngroups are used as independent variables which account for the interactions\nbetween the neighboring groups of atoms. This model accurately predicts\nGibbs free energies, enthalpies, Gibbs free energies of formation,\nand enthalpies of formation for alkanes which exceeds the chemical\naccuracy of 1 kcal/mol and outperforms the group contribution methods\ndeveloped by Benson et al., Joback and Reid, and Constantinou and\nGani. Predictions from our model are used to compute the reaction\nequilibrium distribution of hydroisomerization of C<sub>10</sub> and\nC<sub>14</sub> isomers in MTW-type zeolite. Calculation of reaction\nequilibrium distribution inside zeolites also requires Henry coefficients\nof the isomers which can be computed using classical force field-based\nmolecular simulations using the RASPA2 software for which we created\nan automated workflow. The reaction equilibrium distribution for C<sub>10</sub> isomers obtained using the LR model and the training data\nset for this model are in very good agreement. The tools developed\nin this study will enable the computational study of hydroisomerization\nof long-chain alkanes (>C<sub>10</sub>).