Alternative method to predict activity coefficients at infinite dilution of hydrocarbons in aqueous solutions

Authors

  • Gonzalo Astray Colloidal Chemistry Laboratory, Physical Chemistry Department, University of Vigo, As Lagoas, 32004, Ourense, Spain
  • Jorge Morales Colloidal Chemistry Laboratory, Physical Chemistry Department, University of Vigo, As Lagoas, 32004, Ourense, Spain
  • Miriam González-Temes Colloidal Chemistry Laboratory, Physical Chemistry Department, University of Vigo, As Lagoas, 32004, Ourense, Spain
  • Juan C. Mejuto Colloidal Chemistry Laboratory, Physical Chemistry Department, University of Vigo, As Lagoas, 32004, Ourense, Spain
  • Augusto J. Magdalena Colloidal Chemistry Laboratory, Physical Chemistry Department, University of Vigo, As Lagoas, 32004, Ourense, Spain

Abstract

Activity coefficients at infinite dilution are important property in solute-solvent interactions. Experimental techniques show high costs, skilled labour and safety. To solve this, a neural network model with five different topological descriptors to implement different Artificial Neural Network has been implemented.The best Artificial Neural Network (5-11-8-1 topology) presents good fits for the training phase with an Average Percentage Deviation of 1.85%. Similar results have been obtained for the validation phase of the Artificial Neural Network (1.88%). The implemented Neural Networks techniques showed better results than other developed methods, around 30.70% and 24.60% for training and validation phase, respectively. 

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Published

2015-01-02

Issue

Vol. 3 No. 6 (2015)

Section

Physical Chemistry

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