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.

Realistic porous structures typical of those made by replication of packed beds of spherical particles have been produced by a novel modelling method. Fluid dynamics simulation of the permeability of these structures agrees well with experimental measurements and similar modelling of structures derived from X-ray tomographic images. By varying the model structures the “bottleneck” flow concept proposed by analytical models in the literature was substantiated, confirming the high dependence of permeability on the size of the windows connecting the pores but also highlighting the need for accurate determination of the connectivity of the pores for these models to be accurate.