Use of Artificial Neural Networks for Recycled Pellets Identification: Polypropylene-Based Composites.

ABSTRACT: Polymer recycling is challenging due to practical classification difficulties. Even when the polymer matrix is identified, the presence of various polymeric composites complicates their accurate classification. In this study, Fourier-transform infrared spectroscopy (ATR-FTIR) was used in combination with artificial neural networks (ANNs) to quantitatively predict the mineral filler content in polypropylene (PP) composites. Calibration curves were developed to correlate ATR-FTIR spectral features (600-1700 cm^-1) with the concentration (wt.%) of three mineral fillers: talc (PP-Talc), calcium carbonate (PP-CaCO₃), and glass fiber (PP-GF). ANN models developed in MATLAB 2024a achieved prediction errors below 7.5% and regression coefficients (R²) above 0.98 for

SUBMITTER: Gomez-Bacab MT 

PROVIDER: S-EPMC12431023 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature