SciELO - Scientific Electronic Library Online

 
vol.84 número203Productive development model of the fisheries chain SpanishFast estimation of chlorophyll content on plant leaves using the light sensor of a smartphone índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Artigo

Indicadores

Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Não possue artigos similaresSimilares em SciELO
  • Em processo de indexaçãoSimilares em Google

Compartilhar


DYNA

versão impressa ISSN 0012-7353

Resumo

VYHMEISTER, Eduardo et al. Vapor-Liquid equilibria modeling using gray-box neural networks as binary interaction parameters predictor. Dyna rev.fac.nac.minas [online]. 2017, vol.84, n.203, pp.226-232. ISSN 0012-7353.  http://dx.doi.org/10.15446/dyna.v84n203.56364.

Simulations 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.

Palavras-chave : Acetone-Alcohol System; Peng-Robinson; Non-Linear Evaluations; ANN prediction..

        · resumo em Espanhol     · texto em Inglês     · Inglês ( pdf )