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Innovative technologies as enabler for sorting of black plastics

: Nüßler, Dirk; Gruna, R.; Brandt, C.; Küter, A.; Längle, T.; Kieninger, M.; Pohl, N.

Volltext urn:nbn:de:0011-n-4087297 (916 KByte PDF)
MD5 Fingerprint: d76fd0650147da3fdf3ae8d2c242beb9
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Erstellt am: 9.8.2016

International Committee for Non-Destructive Testing -ICNDT-; Deutsche Gesellschaft für Zerstörungsfreie Prüfung e.V. -DGZfP-, Berlin:
19th World Conference on Non-Destructive Testing, WCNDT 2016 : Munich, Gemany, 13-17 June 2016; Proceedings; USB-Stick
Berlin: DGZfP, 2016
ISBN: 978-3-940283-78-8
8 S.
World Conference on Non-Destructive Testing (WCNDT) <19, 2016, Munich>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
Fraunhofer FHR ()
Fraunhofer IOSB ()
sorting; recycling; black plastic; line camera; THz

Thermal recycling of plastics is no longer seasonable. More modern recycling techniques require pure fractions containing only a single variety of polymer. A large portion of the plastic waste contains black or multilayer materials that are not sortable with todays sorting technologies. A number of means to analyse and sort mixed plastic waste based on the specific mechanical, electrical, and chemical properties of its components such as density, conductivity and melting point have been developed. The most promising electromagnetic principles like XRay imaging employs ionizing radiation that requires special safety measures, while infrared and visible light is absorbed by the carbon in black plastics. Publications in the last few years show the feasibility of identifying and then separating different types of plastics based on their specific frequency response in the millimetre-wave and terahertz region. From an economical point of view, a line camera radar system operating between 30 GHz and 300 GHz offers an acceptable trade-off between cost, resolution and enough information to reliably identify different materials. The system approach uses data-driven statistical machine-learning methods for classification. The use of deep neural networks in combination with very large training-datasets with thousands of samples improves the predicted sorting purity between 90% and 99.9% for common use-cases. Finally the THz-camera and the classification methods have to be integrated in a sorting solution that meets the realtime requirements of recycling systems. Due to the modular app roach, it is also possible to upgrade existing sorting systems.