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Prediction of surface tension of alcohol + water solutions using artificial neural networks

Gonzalo Astray, Oscar A. Moldes, Iago A. Montoya, Juan C. Mejuto

Abstract


 Different Artificial Neural Network architectures have been implemented to predict Surface Tension of aqueous solutions of methanol, ethanol, 1-propanol and 2-propanol in range temperatures of 293.15-323.15 K. Artificial Neural Networks with four entrance variables, Critical Volume, log P, Mole Fraction and Temperature, were used. Best ANN architecture was formed by four input neurons, two middle layers (with eleven and three neurons respectively) and one output neuron. Root Mean Square Errors (RMSEs) are 0.34 mN·m-1 (R2= 0.9995) for the training set and 1.31 mN·m-1 (R2= 0.9955) for the validation set. Those errors correspond with a 0.62% error and 4.37% of error for training and validation set, respectively. For the full data set the Root Mean Square Error is 0.72 mN·m-1 (R2= 0.9976) with a 1.56% error.


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DOI: http://dx.doi.org/10.13171/mjc.1.5.2012.08.03.13

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