Prediction of the Hildebrand parameter of various solvents using linear and nonlinear approaches

Fluid Phase Equilibria

Volumn 293, Issue 2, 2010, Pages 130-136

  Goodarzi, Mohammad ^a,c   Duchowicz, Pablo R ^a   Freitas, Matheus P ^b   Fernández, Francisco M ^a  

^a INSTITUTO DE INVESTIGACIONES FISICOQUÍMICAS TEÓRICAS Y APLICADAS INIFTA   (Argentina)

^b FEDERAL UNIVERSITY OF LAVRAS   (Brazil)

^c ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Artificial neural networks; Fullerene; Hildebrand parameter; QSPR

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; BUCKMINSTERFULLERENES; CARBON ALLOTROPES; DEGREE OF INTERACTION; DRAGON DESCRIPTORS; HILDEBRAND; HILDEBRAND SOLUBILITY; MULTIPLE LINEAR REGRESSIONS; NON-LINEAR REGRESSION; NONLINEAR APPROACH; PREDICTION PERFORMANCE; SOLUBILITY STUDIES; STATISTICAL PARAMETERS; THERMODYNAMIC PARAMETER;

FULLERENES; LINEAR REGRESSION; ORGANIC SOLVENTS; SOLUBILITY;

NEURAL NETWORKS;

EID: 77953322454     PISSN: 03783812     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fluid.2010.02.025     Document Type: Article

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