Neural networks for predicting mass transfer parameters in supercritical extraction

• Main Authors: A.P. Fonseca, J.V. Oliveira, E.L. Lima
• Format: Article
• Language: English
• Published: Brazilian Society of Chemical Engineering 2000-12-01
• Series: Brazilian Journal of Chemical Engineering
• Subjects: Neural network; Mass transfer coefficients; Supercritical carbon dioxide; Ethanol
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322000000400016

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.