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

Abstract

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.

A new group contribution-based model for estimation of lower flammability limit of pure compounds

“…As reported by Albahri [7], application of this method for estimation of 454 pure compounds respectively shows average deviation, maximum deviation, average error, and squared correlation coefficient of 1.35 (vol%), 14.02 (vol%), 12.3 (vol%), and 0.83. Jones presented another method for estimation of LFL of pure compounds based on the concentration of the flammable product for complete combustion in air (C est ).…”

“…This type of neural networks has been used by one of the authors in his previous works, therefore, the detail explanations about the three layer feed forward used in this study can be found, elsewhere [12][13][14][15][16][17][18][19]. The simplified form of the relationship between input parameters and output of a three-layer FFNN can be shown as Eq.…”

Prediction of upper flammability limit percent of pure compounds from their molecular structures

“…This methodology has been extensively presented in the previous works of the author and the results are satisfactory [12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Overview and Computational Approach for Studying the Physicochemical Characterization of High-Boiling-Point Petroleum Fractions (350°C+)

“…It is used as an approximate index of the paraffinicity of a petroleum cut, thus a high value for this index indicates a high percent of saturated pure components and paraffin components (Gharagheizi and Fazeli, 2008;Watson and Nelson, 1933).…”