Abstract
This work was carried out to develop a hyperspectral imaging system in the near infrared (NIR) range (1000–1700nm) to classify polyolefin particles from complex waste streams in order to improve their recovery, producing high purity polypropylene (PP) and polyethylene (PE) granulates, according to market requirements. In particular, hyperspectral images were acquired for polyolefins coming from building & construction waste (B&CW), divided into 9 different density fractions, ranging from <0.88g/cm3 up to 0.96g/cm3 and in different color classes. Spectral data were analyzed using principal component analysis (PCA) to reduce the high dimensionality of data and for selecting some effective wavelengths. Results showed that it was possible to recognize PP and PE waste particles and to define the “real cut density” between PP and PE from B&CW, to be utilized in the recycling process based on magnetic density separation (MDS). The results revealed the potentiality of NIR hyperspectral imaging as an objective and non-destructive method for classification and quality control purposes in the recycling chain of polyolefins.
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