Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/347233
Type: Artigo
Title: Liquid-liquid equilibria for systems containing fatty acid ethyl esters, ethanol and glycerol at 333.15 and 343.15 K: experimental data, thermodynamic and artificial neural network modeling
Author: Cavalcanti, Rodrigo N.
Oliveira, Mariana B.
Meirelles, Antonio J. A.
Abstract: In this study, the liquid-liquid equilibrium (LLE) data of systems containing ethyl linoleate/oleate/palmitate/laurate, ethanol and glycerol at temperatures ranging from 323.15 to 353.15 K were used to evaluate the performance of the NRTL, UNIFAC, Cubic-Plus-Association Equation of State (CPA EoS), and artificial neural network (ANN) models. The systems evaluated correspond to the most important components formed at the end of the ethanolysis reaction of soybean, palm and coconut oils. The temperature range selected is very important for heterogeneous catalysts, especially for high-pressure systems. The accuracy of the models was evaluated by average global deviation. UNIFAC, UNIFAC-LLE and CPA EoS models showed lower accuracy with deviations of 10.1, 8.01 and 5.95%, respectively. In spite of this predictive limitation, these models show high extrapolation capability for the description of LLE behavior when few experimental data are available in the literature. The ANN model shows the best agreement between experimental and predicted data with an average deviation of 1.12%. In this regard, ANN is offered in this work as an alternative to equations of state and activity coefficient models to be used in a more reliable and less cumbersome way for process simulators of biodiesel production and separation equipment design
Subject: Equilíbrio líquido-líquido
Country: Alemanha
Editor: Springer
Rights: Aberto
Identifier DOI: 10.1590/0104-6632.20180352s20160267
Address: https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322018000200819
Date Issue: 2018
Appears in Collections:FEA - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
S0104-66322018000200819.pdf1.39 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.