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
authors Cavalcanti, RN; Oliveira, MB; Meirelles, AJA
nationality International
journal BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING
author keywords Biodiesel systems modeling; Liquid-liquid equilibrium; Artificial neural network; Cubic-Plus-Association Equation of State; Ethylic biodiesel
keywords EQUATION-OF-STATE; TERNARY-SYSTEMS; PLUS ETHANOL; PHASE-EQUILIBRIA; CPA EOS; BIODIESEL; PREDICTION; OIL; MIXTURES; HYDROCARBONS
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.
publisher BRAZILIAN SOC CHEMICAL ENG
issn 0104-6632
year published 2018
volume 35
issue 2
beginning page 819
ending page 834
digital object identifier (doi) 10.1590/0104-6632.20180352s20160267
web of science category Engineering, Chemical
subject category Engineering
unique article identifier WOS:000446318600049
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journal analysis (jcr 2017):
journal impact factor 0.925
5 year journal impact factor 1.600
category normalized journal impact factor percentile 25.182
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