Archivos suplementarios
Palabras clave
consistencia termodinámica
isobárico
NRTL
Wilson
UNIQUAC
Cómo citar
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Societal impact
Resumen
Los datos de equilibrio vapor–líquido (VLE) son esenciales para el diseño y la optimización de procesos de separación; sin embargo, su confiabilidad sigue siendo un desafío, particularmente para sistemas multicomponentes debido a inconsistencias en los datos experimentales reportados. En este trabajo se analizaron datos VLE isobáricos a 101.33 kPa para los sistemas binarios cloroformo(1) + benceno(2), cloroformo(1) + metanol(2) y metanol(1) + benceno(2), así como para el sistema ternario correspondiente, reportados en las bases de datos Dortmund Data Bank (DDB) y Korean Data Bank (KDB). La consistencia termodinámica se evaluó utilizando la prueba de Herington modificada por Wisniak para los sistemas binarios, y para el sistema ternario se empleó la prueba de Wisniak (L–W); se utilizaron únicamente los conjuntos de datos consistentes para la estimación de los parámetros termodinámicos. Los parámetros de interacción binaria se determinaron para los modelos Wilson, NRTL y UNIQUAC, y se evaluaron mediante la desviación absoluta promedio en temperatura (AADT). Los resultados indicaron una fuerte no idealidad, particularmente para el par cloroformo(1) + metanol(2), lo que sugiere la formación de enlaces de hidrógeno, mientras que el sistema cloroformo(1) + benceno(2) presentó un comportamiento cercano al ideal. Por el contrario, las interacciones metanol(1) + benceno(2) fueron altamente desfavorables en comparación con los componentes puros, a pesar de la posibilidad de asociación mediante interacciones π–dipolo. Los modelos Wilson y UNIQUAC mostraron el mejor desempeño para el modelamiento del sistema ternario (AADT = 0.5849 y 0.6141, respectivamente), mientras que el modelo NRTL presentó desviaciones mayores (AADT = 0.9896). El estudio presenta un marco estructurado de evaluación termodinámica para mezclas ternarias que integra pruebas de consistencia, selección de conjuntos de datos, optimización de parámetros y validación multimodelo, mejorando así la confiabilidad de las predicciones VLE en sistemas multicomponentes no ideales.
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