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

Abstract

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.

Machine Learning Analysis of Enhanced Biodegradable Phoenix dactylifera L./HDPE Composite Thermograms

“…In Figure 2 a,b, actual weight % is plotted against degradation temperature (°C) for two materials being heated at 10, 20, and 40 °C·min −1 heating rates. There was a general trend of temperature shifting to maxima with increasing heating rate, as substantiated in [ 16 , 20 , 21 ]. As can be seen in Figure 2 a, the actual weight loss of HDPE materials could be divided into three stages: loss of moisture content, decomposition of actual material, and residual content.…”

“…There are other non-linear activation functions, such as hyperbolic tangents (TanH), rectified linear units (ReLU), exponential linear units (ELU), etc. However, the choice of Sigmoid activation functions is based on their ability to converge data points that are between 0 and 1 based on changes in arbitrary constants during training [ 20 , 24 , 26 ]. For this reason, the input and output data points were divided by the maximum value possible for a TGA experiment as part of data preparation before training.…”