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Licensed Unlicensed Requires Authentication Published by De Gruyter October 18, 2013

Percolation Threshold of AOT Microemulsions with n-Alkyl Acids as Additives Prediction by Means of Artificial Neural Networks

Vorhersage der Percolationsschwelle von AOT-Mikroemulsionen mit n-Alkylsäuren als Additive mit Hilfe künstlicher neuraler Netzwerke
Óscar A. Moldes, Gonzalo Astray, Antonio Cid, Manuel Á. Iglesias-Otero, Jorge Morales and Juan C. Mejuto

Abstract

Different artificial neural networks architectures have been assayed to predict percolation temperature of AOT/iC8/H2O microemulsions in the presence of n-alkyl acids with a chain length between 0 and 24 carbons, using a multilayer perceptron with five easy-acquired entrance variables (number of carbons, log P, length of the hydrocarbon chain, pKa and acid concentration). The evaluation of the neural networks was carried out by means of RMSE and IDP, resulting that the architecture with better results consists in five input neurons, two middle layers (with five and ten neuron respectively) and one output neuron. Results prove that Artificial Neural Networks are a useful tool elaborating models to predict percolation temperature of microemulsion systems in the presence of additives.

Kurzfassung

Verschiedene künstliche Netzwerkstrukturen wurden untersucht, um die Percolationstemperatur von AOT/iC8/H2O-Mikroemulsionen in Gegenwart von n-Alkylsäuren, deren Kettenlänge zwischen 0 und 24 Kohlenstoffatomen lag, untersucht. Es wurde ein Mehrschichtpreceptron mit fünf einfach zugänglichen Eingangsvariablen (Anzahl der Kohlenstoffatome, Log P, Länge der Kohlenstoffatomkette, pKa und die Säurekonzentration) verwendet. Die Evaluation der neutralen Netzwerke wurde mit Hilfe von RMSE und IDP durchgeführt, was darin resultierte, dass die Strukturen bessere Ergebnisse in den fünf Eingangsvariablen, den zwei Mittelschichten (mit fünf bzw. zehn Neuronen) und einem Ausgangsneuron lieferten. Die Ergebnisse belegen, dass künstliche neurale Netzwerke (ANN) nützlich für die Erarbeitung von Modellen zur Vorhersage der Perkolationstemperatur von Mikroemulsionen in Anwesenheit von Additiven sind.


4Prof. Dr. Juan C. Mejuto, Physical-Chemistry Department, Faculty of Sciences, University of Vigo, Ourense, 32004-Ourense, Spain, Tel.: 0034988387000, Fax: 0034988387001, E-Mail:

Juan Carlos Mejuto actually is Full Professor in the Physical Chemistry Department of University of Vigo at Ourense Campus. He is the head of the Colloids group at Ourense Campus. His research interest comprises (i) physical organic and physical inorganic chemistry, (ii) reactivity mechanisms in homogeneous and microheterogeneous media, (iii) stability of self-assembly aggregates and (iv) supramolecular chemistry.

Antonio Cid take his PhD at University of Vigo, actually he has got a Post-Doctoral Position at New University of Lisbon. His research interest is focused in porperties of self-assembly colloid aggregates and its applications in biotechnology.

Gonzalo Astray take his PhD at University of Vigo, actually he has got a Post-Doctoral Position at Vigo University. His research interest is focused in the applications of Artificial Neural Networks to chemical and biological problems.

Oscar Adrían Moldes is a PhD student at Colloid Chemistry Group at the Faculty of Sciences at Ourense (University of Vigo)

Manuel Angel Iglesias-Otero is a PhD student at Colloid Chemistry Group at the Faculty of Sciences at Ourense (University of Vigo)

Jorge Morales is a PhD student at Colloid Chemistry Group at the Faculty of Sciences at Ourense (University of Vigo)


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Received: 2013-4-12
Revised: 2013-5-27
Published Online: 2013-10-18
Published in Print: 2013-09-16

© 2013, Carl Hanser Publisher, Munich

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