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

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 surfactan...

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Autores principales:	Laidi Maamar, Abdallah el Hadj Abdallah, Si-Moussa Cherif, Benkortebi Othmane, Hentabli Mohamed, Hanini Salah
Formato:	article
Lenguaje:	EN
Publicado:	Association of the Chemical Engineers of Serbia 2021
Materias:	quantitative structure-property relationship surfactants critical micelle concentration modelling machine learning Chemical engineering TP155-156 Chemical industries HD9650-9663
Acceso en línea:	https://doaj.org/article/72b2ae903f4d4b029d33db1174d15f5e
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spelling	oai:doaj.org-article:72b2ae903f4d4b029d33db1174d15f5e2021-11-10T07:27:43ZCMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA1451-93722217-743410.2298/CICEQ200907048Lhttps://doaj.org/article/72b2ae903f4d4b029d33db1174d15f5e2021-01-01T00:00:00Zhttp://www.doiserbia.nb.rs/img/doi/1451-9372/2021/1451-93722000048L.pdfhttps://doaj.org/toc/1451-9372https://doaj.org/toc/2217-7434Quantitative 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.Laidi MaamarAbdallah el Hadj AbdallahSi-Moussa CherifBenkortebi OthmaneHentabli MohamedHanini SalahAssociation of the Chemical Engineers of Serbiaarticlequantitative structure-property relationshipsurfactantscritical micelle concentrationmodellingmachine learningChemical engineeringTP155-156Chemical industriesHD9650-9663ENChemical Industry and Chemical Engineering Quarterly, Vol 27, Iss 3, Pp 299-312 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	quantitative structure-property relationship surfactants critical micelle concentration modelling machine learning Chemical engineering TP155-156 Chemical industries HD9650-9663
spellingShingle	quantitative structure-property relationship surfactants critical micelle concentration modelling machine learning Chemical engineering TP155-156 Chemical industries HD9650-9663 Laidi Maamar Abdallah el Hadj Abdallah Si-Moussa Cherif Benkortebi Othmane Hentabli Mohamed Hanini Salah CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA
description	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.
format	article
author	Laidi Maamar Abdallah el Hadj Abdallah Si-Moussa Cherif Benkortebi Othmane Hentabli Mohamed Hanini Salah
author_facet	Laidi Maamar Abdallah el Hadj Abdallah Si-Moussa Cherif Benkortebi Othmane Hentabli Mohamed Hanini Salah
author_sort	Laidi Maamar
title	CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA
title_short	CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA
title_full	CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA
title_fullStr	CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA
title_full_unstemmed	CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA
title_sort	cmc of diverse gemini surfactants modelling using a hybrid approach combining svr-da
publisher	Association of the Chemical Engineers of Serbia
publishDate	2021
url	https://doaj.org/article/72b2ae903f4d4b029d33db1174d15f5e
work_keys_str_mv	AT laidimaamar cmcofdiversegeminisurfactantsmodellingusingahybridapproachcombiningsvrda AT abdallahelhadjabdallah cmcofdiversegeminisurfactantsmodellingusingahybridapproachcombiningsvrda AT simoussacherif cmcofdiversegeminisurfactantsmodellingusingahybridapproachcombiningsvrda AT benkortebiothmane cmcofdiversegeminisurfactantsmodellingusingahybridapproachcombiningsvrda AT hentablimohamed cmcofdiversegeminisurfactantsmodellingusingahybridapproachcombiningsvrda AT haninisalah cmcofdiversegeminisurfactantsmodellingusingahybridapproachcombiningsvrda
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CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

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