Open AccessNeural networks for predicting mass transfer parameters in supercritical extraction
Author(s) -
A.L.A Fonseca,
J. Vladimir Oliveira,
Enrique Luis Lima
Publication year - 2000
Publication title -
brazilian journal of chemical engineering/brazilian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.313
H-Index - 52
eISSN - 1678-4383
pISSN - 0104-6632
DOI - 10.1590/s0104-66322000000400016
Subject(s) - supercritical fluid , artificial neural network , mass transfer , extraction (chemistry) , mass transfer coefficient , correlation coefficient , set (abstract data type) , supercritical carbon dioxide , data set , data correlation , computer science , data mining , artificial intelligence , chemistry , machine learning , chromatography , thermodynamics , physics , programming language
Neural networks have been investigated for predicting mass transfer coefficients from supercritical Carbon Dioxide/Ethanol/Water system. To avoid the difficulties associated with reduce experimental data set available for supercritical extraction in question, it was chosen to use a technique to generate new semi-empirical data. It combines experimental mass transfer coefficient with those obtained from correlation available in literature, producing an extended data set enough for efficient neural network identification. With respect to available experimental data, the results obtained to benefit neural networks in comparing with empirical correlations for predicting mass transfer parameters