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Licensed Unlicensed Requires Authentication Published by De Gruyter March 3, 2017

Approach of Different Properties of Alkylammonium Surfactants using Artificial Intelligence and Response Surface Methodology

Bewertung der Eigenschaften von Alkylammoniumtensiden mittels künstlicher Intelligenz und Response-Surface-Methoden
Gonzalo Astray and Juan Carlos Mejuto

Abstract

Response surface methodology (RSM) and artificial neural networks (ANNs) architectures to predict the density, speed of sound, kinematic viscosity, and surface tension of aqueous solutions were developed. All models implemented using the root mean square error (RMSE) for training and validation phase were evaluated. The ANN models implemented show good values of R2 (upper than 0.974) and low errors in terms of average percentage deviation (APD) (lower than 2.92 %). Nevertheless, RSM models present low APD values for density and speed of sound prediction (lower than 0.31 %) and higher APD values around 5.18 % for kinematic viscosity and 14.73 % for surface tension. The results show that the different individual artificial neural networks implemented are a useful tool to predict the density, speed of sound, kinematic viscosity, and surface tension with reasonably accuracy.

Kurzfassung

Es wurden Response-Surface-Methoden (RSM) und künstliche neuronale Netzwerke (ANNs) zur Vorhersage der Dichte, der Schallgeschwindigkeit, der kinematischen Viskosität und der Oberflächenspannung von wässrigen Lösungen entwickelt. Alle umgesetzten Modelle wurden in der Trainings- und Validierungsphase mit der Methode der mittleren quadratischen Fehler (RMSE) bewertet. Die umgesetzten ANN-Modelle lieferten gute R2-Werte (größer als 0,974) und hinsichtlich der durchschnittlichen prozentualen Abweichung (APD) geringe Fehler (geringer als 2,92 %). Dennoch erreichten die RSM-Modelle niedrige APD-Werte für die Dichte und die Schallgeschwindigkeit (kleiner als 0,31 %) und höhere APD-Werte von etwa 5,18 % für die kinematische Viskosität und von 14,73 % für die Oberflächenspannung. Die Ergebnisse machen deutlich, dass die verschiedenen individuellen künstlichen neuronalen Netzwerke nützlich sind, um die Dichte, die Schallgeschwindigkeit, die kinematischen Viskosität und die Oberflächenspannung mit einer hinreichenden Genauigkeit vorherzusagen.


*Correspondence address, Dr. Gonzalo Astray, University of Vigo, Faculty of Science, Physical Chemistry Department, Ourense, Spain, E-Mail:

Juan Carlos Mejuto currently 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 micro heterogeneous media, (iii) stability of self-assembly aggregates and (iv) supramolecular chemistry.

Gonzalo Astray take his PhD at University of Vigo. His research interest is focused in the applications of Artificial Neural Networks to chemical and biological problems.


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Received: 2016-11-15
Accepted: 2016-11-25
Published Online: 2017-03-03
Published in Print: 2017-03-15

© 2017, Carl Hanser Publisher, Munich

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