CMC OF DIVERSE GEMINI SURFACTANTS MODELING USING A HYBRID APPROACH COMBINING SVR-DA

Original scientific paper

Authors

  • MAAMAR LAIDI Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Medea, Algeria
  • ABDALLAH ABDALLAH EL HADJ Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Medea, Algeria
  • CHERIF SI-MOUSSA Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Medea, Algeria
  • OTHMANE BENKORTEBI Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Medea, Algeria
  • MOHAMED HENTABLI Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Medea, Algeria
  • SALAH HANINI Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Medea, Algeria

DOI:

https://doi.org/10.2298/CICEQ200907048L

Keywords:

quantitative structure–property relationship, surfactants, critical micelle concentration, modelling, machine learning

Abstract

Quantitative structure-property relationship (QSPR) technique provides a suitable tool to predict the critical micelle concentration (CMC) of Gemini surf­actants from their structure descriptors. In this study, a comparative work was conducted to model the CMC property of 211 diverse Gemini surfactants based on their structural characteristics using linear and non-linear quantitative structure–property relationship models. Least squares model (OLS) and partial least squares (PLS) against k-nearest neighbours regression model (KNN), artificial neural network (ANN) and support vector regression (SVR) have been developed to model the CMC. Molecular descriptors were calculated and screened to remove unsuitable descriptors and improve the learning. Results indicate that the improved performance of support vector regression when the hyper-parameters are optimized using Dragonfly algorithm (SVR-DA) was highly capable of predicting the pCMC (-log CMC) values with an average absolute relative deviation (AARD) of 0.666 and coefficient of determination (R²) of 0.9971 for the global dataset.

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Published

26.10.2021

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How to Cite

CMC OF DIVERSE GEMINI SURFACTANTS MODELING USING A HYBRID APPROACH COMBINING SVR-DA: Original scientific paper. (2021). Chemical Industry & Chemical Engineering Quarterly, 27(3), 299-312. https://doi.org/10.2298/CICEQ200907048L

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