New Amines for CO2 Capture. IV. Degradation, Corrosion, and Quantitative Structure Property Relationship Model

2012 ◽  
Vol 51 (18) ◽  
pp. 6283-6289 ◽  
Author(s):  
Sandrine Martin ◽  
Helene Lepaumier ◽  
Dominique Picq ◽  
Jean Kittel ◽  
Theodorus de Bruin ◽  
...  
Keyword(s):  
Co2 Capture ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Relationship Model

scholarly journals A New Accurate Neural Network Quantitative Structure- Property Relationship for Prediction of θ (Lower Critical Solution Temperature) of Polymer Solutions

e-Polymers ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Farhad Gharagheizi
Keyword(s):  
Neural Network ◽  
Molecular Descriptors ◽  
Polymer Solutions ◽  
Solution Temperature ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Relationship Model

Abstract In this study, a new neural network quantitative structure-property relationship model for prediction of θ (LCST ) of polymer solutions is presented. The parameters of this model are eight molecular descriptors which are calculated only from the chemical structure of polymer and solvent. These eight molecular descriptors were selected from 3328 molecular descriptors of polymer and solvent available in polymer solution by genetic algorithm-based multivariate linear regression (GA-MLR) technique. The obtained neural network model can predict the θ (LCST ) of 169 polymer solutions with mean relative error of 1.67% and squared correlation coefficient of 0.9736.

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