Prediction of Alternative Gasoline Sorption in a Semicrystalline Poly(ethylene)
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- 作者：Nicolas Villanueva ; Bruno Flaconn猫che ; Benoit Creton
- 刊名：ACS Combinatorial Science
- 出版年：2015
- 出版时间：October 12, 2015
- 年：2015
- 卷：17
- 期：10
- 页码：631-640
- 全文大小：449K
- ISSN：2156-8944
文摘
In this work, we first report the acquisition of new experimental data and then the development of quantitative structure鈥損roperty 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 (R2 = 0.940).