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A neural network approach to predict activity coefficients
Author(s) -
RamírezBeltrán Nazario D.,
Vallés Harry Rodríguez,
Estévez L. Antonio,
Duarte Horacio
Publication year - 2009
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20212
Subject(s) - unifac , artificial neural network , group contribution method , binary number , activity coefficient , representation (politics) , computer science , mathematics , phase (matter) , artificial intelligence , phase equilibrium , chemistry , organic chemistry , arithmetic , politics , aqueous solution , political science , law
Artificial neural networks (ANNs) and a group‐contribution approach were used to develop an algorithm to predict activity coefficients for binary solutions. The Levenberg–Marquardt algorithm was used to train the ANN and to predict the parameters of the Margules equation. The ANN was trained using phase‐equilibrium database from DECHEMA. The selected systems include alcohols, phenols, aldehydes, ketones, and ethers. The trim mean based on 20% data elimination was selected as the best representation of the Margules‐equation parameters. The algorithm was validated with 121 VLE systems and results show that the ANN provides a relative improvement over the UNIFAC method.