https://doi.org/10.15255/KUI.2020.063
Published: Kem. Ind. 70 (7-8) (2021) 375–386
Paper reference number: KUI-63/2020
Paper type: Original scientific paper
Download paper:  PDF

Critical Properties and Acentric Factors of Pure Compounds Modelling Based on QSPR-SVM with Dragonfly Algorithm

M. Moussaoui, M. Laidi, S. Hanini, A. E. H. Abdallah and M. Hentabli

Abstract

This work aimed to model the critical pressure, temperature, volume properties, and acentric factors of 6700 pure compounds based on five relevant descriptors and two thermodynamic properties. To that end, four methods were used, namely, multi-linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs) using sequential minimal optimisation (SMO), and hybrid SVM with Dragonfly optimisation algorithm (SVM-DA) to model each property. The results suggested that hybrid SVM-DA had better prediction performance compared to the other models in terms of average absolute relative deviation (AARD%) of {0.7551, 1.962, 1.929, and 2.173} and R² of {0.9699, 0.9673, 0.9856, and 0.9766} for critical temperature, critical pressure, critical volume, and acentric factor, respectively. The developed models can be used to estimate the property of newly designed compounds only from their molecular structure.

This work is licensed under a Creative Commons Attribution 4.0 International License

Keywords

support vector machine, critical properties, Dragonfly optimisation algorithm, quantitative structure-property relationship