Linear polyethers as additives for AOT-based microemulsions: Prediction of percolation temperature changes using artificial neural networks

Óscar Adrían Moldes, Antonio Cid Samamed, I. A. Montoya, Juan Carlos Mejuto

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Predictive models based on artificial neural networks have been developed for the percolation threshold of AOT based microemulsions with addition of either glymes or polyethylene glycols. Models have been built according to the multilayer perceptron architecture, with five input variables (concentration, molecular mass, log P, number of C and O of the additive). Best model for glymes has a topology of five input neurons, five neurons in a single hidden layer and one output neuron. Polyethylene glycol model's architecture consists in five input neurons, three hidden layers with eight neurons in both first two and five in the last, and a neuron in the last output layer. All of them have a good predictive power according to several quality parameters.

Original languageEnglish
Pages (from-to)264-270
Number of pages7
JournalTenside Surfactants Detergents
Volume52
Issue number4
DOIs
Publication statusPublished - 1 Jul 2015

Keywords

  • Artificial neural networks
  • Microemulsion
  • Percolation
  • Polyethers
  • Prediction

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