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HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS Ciênc. Tecnol. Aliment.
STUART,Giane; MACHADO,Ricardo; OLIVEIRA,José V. de; ULLER,Angela C.; LIMA,Enrique L..
This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.
Tipo: Info:eu-repo/semantics/article Palavras-chave: SCFE; Neural Networks; Modeling; Basil oil; Rosemary oil.
Ano: 1997 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20611997000400030
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STATISTICAL ANALYSYS OF THE SCFE OF A BRAZILAN MINERAL COAL Ciênc. Tecnol. Aliment.
DARIVA,Cláudio; OLIVEIRA,José Vladimir; PINTO,José C.; VALE,Maria G.; CARAMÃO,Elina B..
The influence of some process variables on the productivity of the fractions (liquid yield times fraction percent) obtained from SCFE of a Brazilian mineral coal using isopropanol and ethanol as primary solvents is analyzed using statistical techniques. A full factorial 23 experimental design was adopted to investigate the effects of process variables (temperature, pressure and cosolvent concentration) on the extraction products. The extracts were analyzed by the Preparative Liquid Chromatography-8 fractions method (PLC-8), a reliable, non destructive solvent fractionation method, especially developed for coal-derived liquids. Empirical statistical modeling was carried out in order to reproduce the experimental data. Correlations obtained were always...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Statistical analysis; SCFE; Productivity; PLC-8 characterization.
Ano: 1997 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20611997000400032
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