CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

Author:

Laidi Maamar1,Abdallah el1,Si-Moussa Cherif1,Benkortebi Othmane1,Hentabli Mohamed1,Hanini Salah1

Affiliation:

1. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Medea, Algeria

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 (R?) of 0.9971 for the global dataset.

Publisher

National Library of Serbia

Subject

General Chemical Engineering

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