Regression Models Analysis for the Degradation of Polystyrene Waste by Thermogravimetric Data

Abstract

Plastics are known for their beneficial properties, such as lightness, strength, and low cost, and they are used in different applications such as construction, electronics, and packaging. However, plastics do not degrade naturally, accumulating in soils and affecting the environment. Recycling techniques have been developed to minimize plastic waste; chemical recycling through pyrolysis has been cataloged as an effective method for transforming plastic waste into high-value products in the chemical and petrochemical industry. Previous studies of feedstock degradation through thermogravimetry analysis (TGA) have been essential to estimate the optimal temperature ranges to evaluate the pyrolysis process. The present work aims to study regression models to estimate the temperature degradation of polystyrene (PS) through thermogravimetry data, which can be applied before the pyrolysis process. In addition, this work compares linear and polynomial regression models to estimate the best-fitting model and to determine the maximum temperature of degradation of PS by different heating rates. Relative errors were calculated by comparing them with experimental values from the literature not included in the models. As a result, a polynomial model of a fourth-order obtained a better fit with an r2=70.45 % compared to the linear models, where the best fit was obtained with r2= 69.71 %. However, a higher relative error was obtained, with the polynomial models being the lowest, 7.35 and 0.50 % for 15 and 60 °C min-1; for the linear models, 7.05 and 0.39 % were obtained for heating rates of 15 and 60 °C min-1, respectively.