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Chemistry in Industry : Journal of Chemists and Chemical Engineers of Croatia, Vol. 69 No. 1-2, 2020.

Original scientific paper

https://doi.org/10.15255/KUI.2019.022

QSPR Studies of Carbonyl, Hydroxyl, Polyene Indices, and Viscosity Average Molecular Weight of Polymers under Photostabilization Using ANN and MLR Approaches

Hadjira Maouz ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Quartier Aïn d’Heb, 26000, Algeria
Latifa Khaouane ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Quartier Aïn d’Heb, 26000, Algeria
Salah Hanini ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Quartier Aïn d’Heb, 26000, Algeria
Yamina Ammi ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Quartier Aïn d’Heb, 26000, Algeria
Mabrouk Hamadache ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Quartier Aïn d’Heb, 26000, Algeria
Maamar Laidi orcid id orcid.org/0000-0002-8977-9895 ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, Quartier Aïn d’Heb, 26000, Algeria

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Abstract

One of the main disadvantages of the use of synthetic or semi-synthetic polymeric materials is their degradation and aging. The purpose of this study was to use artificial neural networks (ANN) and multiple linear regressions (MLR) to predict the carbonyl, hydroxyl, and polyene indices (ICO, IOH, and IOP), and viscosity average molecular weight (MV) of poly(vinyl chloride), polystyrene, and poly(methyl methacrylate). These physicochemical properties are considered fundamental during the study of photostabilization of polymers. From the five repeating units of monomers, the structure of the polymer studied is shown. Quantitative structure-property relationship (QSPR) models obtained by using relevant descriptors showed good predictability. Internal validation {R2, RMSE, and Q2LOO}, external validation {R2, RMSE, Q2pred, rm2, Δrm2, k, and k’}, and applicability domain were used to validate these models. The comparison of the results shows that the ANN models are more efficient than those of the MLR models. Accordingly, the QSPR model developed in this study provides excellent predictions, and can be used to predict ICO, IOH, IOP, and MV of polymers, particularly for those that have not been tested.




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

Keywords

QSPR; photostabilization; polymers; artificial neural network; multiple linear regressions

Hrčak ID:

232572

URI

https://hrcak.srce.hr/232572

Publication date:

7.2.2020.

Article data in other languages: croatian

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