QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR

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.

A Smart Model for the Prediction of Heat Transfer Coefficient during Flow Boiling of Nanofluids in Horizontal Tube

“…The technique of artificial neural networks (ANN) is widely used to solve boiling heat transfer problems [30][31][32][33][34]. This technique is also useful for modeling flow boiling heat transfer of nanofluids [35][36][37], and it is also used for diverse application areas [38][39][40][41].…”

A Grey Wolf Optimizer-based Fractional Calculus in Studies on Solar Drying

“…1 Lately, fractional calculus modelling in drying has been shown as a potential tool to generalise mathematical models providing a description of the process by differential equations of arbitrary order. 5 The prediction of the drying characteristics and behaviours using mathematical models, multiple linear regression (MLR) or artificial intelligence methods, such as artificial neural network (ANN), [6][7][8] Bayesian extreme learning machine (BELM), extreme learning machine (ELM), support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), fuzzy inference system (FIS), and response surface methodology (RSM), have been reported in numerous research papers such as [9][10][11][12][13][14][15] . Results from these papers show that ANFIS is the most convenient approach to model drying kinetics of agricultural products.…”

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

“…1 In addition to different computational techniques like artificial neural networks (ANNs), 7,8 adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) have been successfully used to estimate these properties of different components from descriptors. 1,[9][10][11][12] A well-known intelligent learning method, such as SVM, is used as a powerful predictive tool to fit the non-linear behaviour of any system. 13 This approach has great modelling capability when large datasets are available and the relationships of parameters are complicated.…”