Relative Permittivity of Carbon Dioxide + Ethanol Mixtures prediction by means of Artificial Neural Networks

Authors

  • Gonzalo Astray Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004, Ourense, Spain.
  • Manuel A. Iglesias-Otero Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004, Ourense, Spain.
  • Jorge Morales Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004, Ourense, Spain.
  • Juan C. Mejuto Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004, Ourense, Spain.

DOI:

https://doi.org/10.13171/mjc.2.1.2012.10.09.09

Abstract

CO2 + ethanol mixtures have a huge scientific interest and enormous relevance for many industrial processes. Obtaining of their chemical and physical properties is a fundamental task. Relative permittivity (ï¥r) of these mixtures is a key property because allows a better knowledge of the structure and the interactions in other media. In this work predictive values of relative permittivity (ï¥r) of carbon dioxide + ethanol mixtures were obtained implementing artificial neural networks (ANNs). They are used successfully in very different fields; therefore it is a very useful tool. In this case the obtained results enhance the ones from the usual multiple linear regression analysis. In both cases mass fraction, pressure and temperature experimental data from a direct capacitance method were used.

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Published

2012-09-10

Issue

Vol. 2 No. 1 (2012)

Section

Physical Chemistry

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