Prediction of Alternative Gasoline Sorption in a Semicrystalline Poly(ethylene)

In this work, we first report the acquisition of new experimental data and then the development of quantitative structure-property relationships on the basis of sorption values for neat compounds and up to quinary mixtures of some hydrocarbons, alcohols, and ethers, in a semicrystalline poly(ethylene). Two machine learning methods (i.e., genetic function approximation and support vector machines) and two families of descriptors (i.e., functional group counts and substructural molecular fragments) were used to derive predictive models. Models were then used to predict sorption variations when increasing the number of carbon atoms in a series of hydrocarbons and for n-alkan-1-ols. In addition to the performed internal/external validations, the model was further tested for surrogate gasolines containing ca. 300 compounds, and predicted sorption values were in excellent agreement with experimental data (R(2) = 0.940).