QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods

This work aimed to predict the normal boiling point temperature (Tb) and relative liquid density (d20) of petroleum fractions and pure hydrocarbons, through a multi-layer perceptron artificial neural network (MLP-ANN) based on the molecular descriptors. A set of 223 and 222 diverse data points for Tb and d20 were respectively used to build two quantitative structure property relationships-artificial neural network (QSPR-ANN) models. For each model, the total database was divided respectively into two subsets: 80% for the training set and 20% for the test set. A total of 1666 descriptors were calculated, and the statistical reduction methodology, based on the Multiple Linear Regression (MLR) method, has been adopted. The Quasi-Newton back propagation (BFGS) algorithm was applied in order to train the ANN. A comparison was made between the outcomes of obtained QSPR-ANN models and other well-known correlations for each property. The two best QSPR-ANN models result showed a good accuracy confirmed by the high determination coefficient (R-2) values and the low mean absolute percentage error (MAPE) values ranging from 0.9999 to 0.9931 and from 0.5797 to 0.2600%, respectively for both best models (Tb and d20 models). Furthermore, the comparison between our models and the other quantitative structure property relationships (QSPR) models shows that the QSPR-ANN models provided better results. This computational approach can be applied in the petroleum engineering for an accurate determination of Tb and d20 of pure hydrocarbons. (C) 2018 Elsevier Inc. All rights reserved.