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The high variety of tailor fitted molecular structures of polyethylene (PE) is very beneficial to fulfill requirements of various applications, however it poses a difficulty in the mechanical recycling of post-consumer PE products. To improve the quality of PE recyclates and increase the amounts of recyclates that can be used in new products, separation of PE waste by density and melt flow rate (MFR) during mechanical sorting is essential. Therefore, 25 virgin PE grades were used to manufacture compression molded plates that were then characterized by means of Attenuated Total Reflection - Fourier transformed IR (ATR-FTIR) and near IR (NIR) spectroscopy, NIR hyperspectral imaging and dual-comb spectroscopy. The results were used to build partial least squares regression (PLS) models to predict MFR and density. ATR-FTIR and laboratory NIR spectroscopy provided sufficient information to predict the density value of PE, whereas the MFR assessments was not possible. The PLS model from the industrial NIR data also only allowed the density-based classification of virgin PE grades. The PLS models built from transmission and reflectance dual comb spectroscopy infrared (DCS-IR) of selected samples clearly showed that density and MFR prediction can be carried out with high accuracy. As DCS-IR could be implemented on plastic sorting systems using a conveyor belt, the addition of this sensor in mechanical sorting line would lead to a significantly higher quality of recycled PE with narrow well-defined density and MFR ranges. Such an improvement would immensely support the targeted recycling rates and amount by the European Union and would make a significant step towards circular plastics.
Bredács et al. (Thu,) studied this question.