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

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dc.contributor.authorVyhmeister, Eduardo
dc.contributor.authorRodríguez-Pino, Jonathan
dc.contributor.authorReyes-Bozo, Lorenzo
dc.contributor.authorGalleguillos-Pozo, Rosa
dc.contributor.authorValdés González, Héctor
dc.contributor.authorRodríguez-Maecker, Roman
dc.date.accessioned2022-03-10T16:48:59Z
dc.date.available2022-03-10T16:48:59Z
dc.date.issued2017
dc.description.abstractSimulations of vapor-liquid equilibrium (VLE) are widely used given their impact on the scale, design, and extrapolation of different operational units. However, due to a number of factors, it is almost impossible to experimentally study each of the VLE systems. VLE simulations can be developed using representations that are strongly dependent on the nature and interactions of the compounds forming mixtures. A model that helps in predicting these interactions would facilitate simulation processes. A Gray Box Neural Network Model (GNM) was created as Binary Interaction Parameters predictors (BIP), which are estimated using state variables and information from pure components. This information was used to predict VLE behavior in mixtures and ranges not used in the mathematical formulation. The GNM prediction capabilities (including temperature dependency) showed an error level lower than 5% and 20% for mixtures considered and not considered in the training data, respectively.es
dc.identifier.citationVyhmeister, E., Rodríguez-Pino, J., Reyes-Bozo, L., Galleguillos-Pozo, R., Valdés-González, H. and Rodríguez-Maecker, R., Vapor-Liquid equilibria modeling using gray-box neural networks as binary interaction parameters predictor DYNA, 84(203), pp. 226-232, December, 2017.es
dc.identifier.urihttp://dx.doi.org/10.15446/dyna.v84n203.56364es
dc.identifier.urihttp://hdl.handle.net/11447/5693
dc.language.isoenes
dc.subjectAcetone-Alcohol Systemes
dc.subjectPeng-Robinsones
dc.subjectNon-Linear Evaluationses
dc.subjectANN predictiones
dc.titleVapor-Liquid equilibria modeling using gray-box neural networks as binary interaction parameters predictores
dc.title.alternativeModelamiento de equilibrio Líquido-Vapor usando redes neuronales grises como predictor de parámetros de interacción binariaes
dc.typeArticlees
dcterms.sourceDynaes

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