Vapor-liquid equilibria and UNIFAC group interaction parameters for thioamides

1993 ◽  
Vol 32 (12) ◽  
pp. 3068-3071
Author(s):  
Johann Gerdenitsch ◽  
Gerhard Gritzner ◽  
Adolf Schmidt
Keyword(s):  
Group Interaction ◽  
Interaction Parameters ◽  
Vapor Liquid Equilibria ◽  
Vapor Liquid

Thermodynamics of binary mixtures containing a very strongly polar compound — Part 3: DISQUAC characterization of NMP + organic solvent mixtures

2003 ◽  
Vol 81 (12) ◽  
pp. 1451-1461 ◽  
Author(s):  
J A González ◽  
U Domanska ◽  
J Lachwa
Keyword(s):  
Molecular Structure ◽  
Organic Solvent ◽  
Binary Mixtures ◽  
Mean Field Theory ◽  
Interaction Parameters ◽  
Solvent Mixtures ◽  
Excess Enthalpies ◽  
Dipole Interactions ◽  
Vapor Liquid Equilibria ◽  
Vapor Liquid

Binary mixtures of 1-methyl pyrrolidin-2-one (NMP) with alkanes, benzene, toluene, 1-alkanol, or 1-alkyne have been investigated in the framework of the DISQUAC model. The reported interaction parameters change regularly with the molecular structure of the mixture components. The model consistently describes a set of thermodynamic properties, including liquid–liquid equilibria, vapor–liquid equilibria, solid–liquid equilibria, and molar excess enthalpies. A brief comparison of the DISQUAC results and those obtained from the UNIFAC and ERAS models is presented. The experimental excess enthalpies are better represented by DISQUAC than by UNIFAC because this quantity strongly depends on molecular structure. For NMP + alkane mixtures, the liquid–liquid equilibria data are also better represented by DISQUAC, while UNIFAC more accurately describes the vapor–liquid equilibria measurements at temperatures close to the critical point. This result suggests that a mean field theory is not able to represent simultaneously, with the same set of interaction parameters, liquid–liquid and vapor–liquid equilibria at the mentioned temperatures. ERAS fails when treating mixtures with 1-alkanols. This has been attributed to the strong dipole–dipole interactions between NMP molecules, characteristic of the investigated systems. Mixture structure is briefly studied in terms of the concentration–concentration structure factor.Key words: thermodynamics, NMP, organic solvent, self-association, dipole–dipole interactions.

scholarly journals Vapor-Liquid equilibria modeling using gray-box neural networks as binary interaction parameters predictor

DYNA ◽  
2017 ◽  
Vol 84 (203) ◽  
pp. 226-232
Author(s):  
Eduardo Andres Vyhmeister Bastidas ◽  
Jonathan Rodríguez-Pino ◽  
Lorenzo Reyes-Bozo ◽  
Rosa Galleguillos-Pozo ◽  
Héctor Valdés-González ◽  
...  
Keyword(s):  
Neural Networks ◽  
Interaction Parameters ◽  
Binary Interaction ◽  
Binary Interaction Parameters ◽  
Vapor Liquid Equilibria ◽  
Vapor Liquid

Las Simulaciones de Equilibrio Líquido Vapor (VLE) son ampliamente utilizadas dado su impacto en el escalamiento, diseño y extrapolación de diferentes operaciones unitarias. Sin embargo, dado considerable factores, es casi imposible experimentalmente estudiar cada uno de los sistemas de VLE. La simulación de VLE puede ser desarrollada utilizando representaciones que son fuertemente dependientes de la naturaleza e interacción de los compuestos que conforman la mezcla. Un modelo que ayude en la predicción de esas interacciones facilitará el proceso de simulación. Una Red Neuronal Gris (GNM) fue creada como un predictor de parámetros de interacción binaria, los que son estimados utilizando variables de estado e información de componentes puros. Esta información fue utilizada para predecir el comportamiento de VLE en mezclas y rangos no utilizados en la formulación matemática. Las capacidades predictivas del GNM (incluida la dependencia de temperatura) mostraron errores menores al 5% y al 20% para mezclas consideradas y no consideradas en los datos de entrenamiento, respectivamente.

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