Graft polymerization of acrylonitrile onto cross-linked (alginate/polyvinyl alcohol) beads initiated by potassium persulfate: synthesis and artificial neural network modeling

Kianfar, Sh.; Keshtkar, Asghar; ZareNezhad, Bahman

doi:10.1007/s00289-020-03106-6

Cited by 2 publications

(1 citation statement)

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“…Their modelling data correlated well with experiments, and a correlation value of R 2 → 0.9999 . Kianfar et al [22] reported a correlation value of R 2 → 0.9913 between experimentally measured and modelled TGA data of alginate/polyvinyl alcohol using the Levenberg-Marquardt algorithm of artificial neural networks (ANN). Using a multilayer perceptron neural network (MLP), Araujo et al [23] assessed the contribution of several kinetic models to accurately describe TGA experimental data for chitosan biopolymers at different heating rates: 2.5, 5.0, 7.5 and 10 • C/min.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Backpropagation Prediction and Analysis of the Thermal Degradation of Poly (Vinyl Alcohol)

Otaru,

Alhulaybi,

Dubdub

2024

Polymers

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Thermogravimetric analysis (TGA) is crucial for describing polymer materials’ thermal behavior as a result of temperature changes. While available TGA data substantiated in the literature significantly focus attention on TGA performed at higher heating rates, this study focuses on the machine learning backpropagation analysis of the thermal degradation of poly (vinyl alcohol), or PVA, at low heating rates, typically 2, 5 and 10 K/min, at temperatures between 25 and 600 °C. Initial TGA analysis showed that a consistent increase in heating rate resulted in an increase in degradation temperature as the resulting thermograms shifted toward a temperature maxima. At degradation temperatures between 205 and 405 °C, significant depths in the characterization of weight losses were reached, which may be attributed to the decomposition and loss of material content. Artificial neural network backpropagation of machine learning algorithms were used for developing mathematical descriptions of the percentage weight loss (output) by these PVA materials as a function of the heating rate (input 1) and degradation temperature (input 2) used in TGA analysis. For all low heating rates, modelling predictions were observably correlated with experiments with a 99.2% correlation coefficient and were used to interpolate TGA data at 3.5 and 7.5 K/min, indicating trends strongly supported by experimental TGA data as well as literature research. Thus, this approach could provide a useful tool for predicting the thermograms of PVA materials at low heating rates and contribute to the development of more advanced PVA/polymer materials for home and industrial applications.

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Section: Introductionmentioning

confidence: 99%