Evaluation of improvements in the separation of monolayer and multilayer films via measurements in transflection and application of machine learning approaches

  • Small plastic packaging films make up a quarter of all packaging waste generated annually in Austria. As many plastic packaging films are multilayered to give barrier properties and strength, this fraction is considered hardly recyclable and recovered thermally. Besides, they can not be separated from recyclable monolayer films using near-infrared spectroscopy in material recovery facilities. In this paper, an experimental sensor-based sorting setup is used to demonstrate the effect of adapting a near-infrared sorting rig to enable measurement in transflection. This adaptation effectively circumvents problems caused by low material thickness and improves the sorting success when separating monolayer and multilayer film materials. Additionally, machine learning approaches are discussed to separate monolayer and multilayer materials without requiring the near-infrared sorter to explicitly learn the material fingerprint of each possible combination of layered materials. Last, a fastSmall plastic packaging films make up a quarter of all packaging waste generated annually in Austria. As many plastic packaging films are multilayered to give barrier properties and strength, this fraction is considered hardly recyclable and recovered thermally. Besides, they can not be separated from recyclable monolayer films using near-infrared spectroscopy in material recovery facilities. In this paper, an experimental sensor-based sorting setup is used to demonstrate the effect of adapting a near-infrared sorting rig to enable measurement in transflection. This adaptation effectively circumvents problems caused by low material thickness and improves the sorting success when separating monolayer and multilayer film materials. Additionally, machine learning approaches are discussed to separate monolayer and multilayer materials without requiring the near-infrared sorter to explicitly learn the material fingerprint of each possible combination of layered materials. Last, a fast Fourier transform is shown to reduce destructive interference overlaying the spectral information. Through this, it is possible to automatically find the Fourier component at which to place the filter to regain the most spectral information possible.show moreshow less

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Metadaten
Author:Gerald Koinig, Nikolai Kuhn, Chiara Barretta, Karl Friedrich, Daniel VollprechtORCiDGND
URN:urn:nbn:de:bvb:384-opus4-984234
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/98423
ISSN:2073-4360OPAC
Parent Title (English):Polymers
Publisher:MDPI AG
Type:Article
Language:English
Year of first Publication:2022
Publishing Institution:Universität Augsburg
Release Date:2022/10/05
Tag:Polymers and Plastics; General Chemistry
Volume:14
Issue:19
First Page:3926
DOI:https://doi.org/10.3390/polym14193926
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management / Lehrstuhl für Resource and Chemical Engineering
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)