Open AccessUsing hybrid neural models to describe supercritical fluid extraction processes
Author(s) -
A.L.A Fonseca,
G. Stuart,
J. Vladimir Oliveira,
Enrique Luis Lima
Publication year - 1999
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/s0104-66321999000300005
Subject(s) - artificial neural network , computer science , training set , experimental data , supercritical fluid extraction , set (abstract data type) , supercritical fluid , data set , data mining , artificial intelligence , mathematics , statistics , chemistry , organic chemistry , programming language
This work presents the results of a hybrid neural model (HNM) technique as applied to modeling supercritical fluid extraction (SCFE) curves obtained from two Brazilian vegetable matrices. The serial HNM employed uses a neural network to estimate parameters of a phenomenological model. A small set of SCFE data for each vegetable was used to generate a semi-empirical extended data set, large enough for efficient network training, using three different approaches. Afterwards, other sets of experimental data, not used during the training procedure, were used to validate each approach. The HNM correlates well withthe experimental data, and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes