Prediction of some important physical properties of sulfur compounds using quantitative structure-properties relationships.
In this work, physical properties of sulfur compounds (critical temperature (Tc), critical pressure (Pc), and Pitzer's acentric factor (omega)) are predicted using quantitative structure-property relationship technique. Sulfur compounds present in petroleum cuts are considered environmental hazards. Genetic algorithm based multivariate linear regression (GA-MLR) is used to select most statistically effective molecular descriptors on the properties. Using the selected molecular descriptors, feed forward neural networks (FFNNs) are applied to develop some molecular-based models to predict the properties. The presented models are quite accurate and can be used to predict the properties of sulfur compounds.
Gharagheizi
Farhad
F
Department of Chemical Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365-4563, Tehran, Iran. fghara@ut.ac.ir
Mehrpooya
Mehdi
M
D000465:Algorithms; D002627:Chemistry, Physical; D004785:Environmental Pollutants / Q000737:chemistry; D016014:Linear Models; D015394:Molecular Structure; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010578:Petroleum / Q000032:analysis; D011312:Pressure; D013438:Sulfhydryl Compounds / Q000737:chemistry; D013457:Sulfur Compounds / Q000737:chemistry*; D013696:Temperature
D004785:Environmental Pollutants; D010578:Petroleum; D013438:Sulfhydryl Compounds; D013457:Sulfur Compounds