Prediction of the Watson Characterization Factor of Hydrocarbon Components from Molecular Properties

In the present work, a Quantitative Structure–Property Relationship (QSPR) study was performed to predict the Watson characterization factor of hydrocarbon components. A Genetic Algorithm‐based Multivariate Linear Regression (GA‐MLR) was applied to select the most statistically effective molecular descriptors of the Watson characterization factor. Then, based on the selected molecular descriptors by GA‐MLR, a three‐layer Feed Forward Neural Network (FFNN) was constructed to predict the Watson characterization factor. The obtained results showed that the constructed FFNN can accurately predict the Watson characterization factor of hydrocarbon components.