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Sensors (Basel). 2017 May 13;17(5). pii: E1117. doi: 10.3390/s17051117.

Characterization of Fine Metal Particles Derived from Shredded WEEE Using a Hyperspectral Image System: Preliminary Results.

Author information

1
Institute for the Electromagnetic Sensing of Environment, National Research Council, 20133 Milano, Italy. candiani.g@irea.cnr.it.
2
Institute of Industrial Technologies and Automation, National Research Council, 20133 Milano, Italy. nicoletta.picone@itia.cnr.it.
3
Department of Psychological, Health and Earth Sciences, G. D'Annunzio University, 66031 Chieti, Italy. loredana.pompilio@unich.it.
4
Institute for the Electromagnetic Sensing of Environment, National Research Council, 20133 Milano, Italy. pepe.m@irea.cnr.it.
5
Institute of Industrial Technologies and Automation, National Research Council, 20133 Milano, Italy. marcello.colledani@polimi.it.
6
Department of Mechanical Engineering, Polytechnic of Milan, 20156 Milano, Italy. marcello.colledani@polimi.it.

Abstract

Waste of electric and electronic equipment (WEEE) is the fastest-growing waste stream in Europe. The large amount of electric and electronic products introduced every year in the market makes WEEE disposal a relevant problem. On the other hand, the high abundance of key metals included in WEEE has increased the industrial interest in WEEE recycling. However, the high variability of materials used to produce electric and electronic equipment makes key metals' recovery a complex task: the separation process requires flexible systems, which are not currently implemented in recycling plants. In this context, hyperspectral sensors and imaging systems represent a suitable technology to improve WEEE recycling rates and the quality of the output products. This work introduces the preliminary tests using a hyperspectral system, integrated in an automatic WEEE recycling pilot plant, for the characterization of mixtures of fine particles derived from WEEE shredding. Several combinations of classification algorithms and techniques for signal enhancement of reflectance spectra were implemented and compared. The methodology introduced in this study has shown characterization accuracies greater than 95%.

KEYWORDS:

WEEE recycling; fine metal particles; hyperspectral sensor

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