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@PHDTHESIS{Kroell:974799,
author = {Kroell, Nils},
othercontributors = {Greiff, Kathrin and Pomberger, Roland},
title = {{S}ensor-based characterization of anthropogenic material
systems: developing characterization methods and novel
applications for optimizing the mechanical recycling of
lightweight packaging waste},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2023-11638},
pages = {1 Online-Ressource : Illustrationen, Diagramme},
year = {2023},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2024; Dissertation, RWTH Aachen University, 2023,
Kumulative Dissertation},
abstract = {Mechanical recycling of post-consumer plastic packaging is
characterized by a high lack of transparency due to the high
effort of manual material flow characterization. As a
consequence, optimizing collection processes in a targeted
manner as well as adaptively designing and operating sorting
and processing plants are often not possible, and confidence
in secondary raw materials is often missing. This
dissertation demonstrates how sensor technology in
mechanical recycling can evolve from a sorting technology
towards a key technology for enabling value-chain-wide
transparency, and what optimization potentials can be
derived from this enabled transparency. In the first part of
the dissertation, a systematic literature review was
conducted, which introduces a unified terminology, provides
a comprehensive overview of the current state of research
and identifies ten essential future research potentials. In
the second part, novel characterization methods for
extracting mass-based material flow compositions from
area/volume-based sensor data were developed. Machine
learning models were successfully trained to determine
binary compositions of plastic flakes and post-consumer
plastic packaging with a measurement uncertainty of 1.2
$vol\%$ and 2.4 $wt\%,$ respectively, across different
material flow presentations and compositions using
near-infrared (NIR) sensors. Based on the developed
characterization methods, two novel sensor technology
applications were demonstrated at industrial scale in the
third part. First, a sensor-based quality monitoring of
plastic pre-concentrates from sorting plants was developed
using inline NIR sensors. The results showed that for a PET
tray fraction as an example, sensor-based quality control of
mass-based product purities is possible with a measurement
uncertainity of 0.31 $wt\%.$ In comparison with
state-of-the-art sampling-based quality analyses, it was
shown that more than 350 kg of a 600 kg PET tray
pre-concentrate bale would need to be analyzed manually to
achieve a comparable measurement accuracy. Second, inline
NIR sensors were used for sensor-based process monitoring of
an industrial sensor-based sorting (SBS) unit. Using
artificial neural networks, the process data was used to
develop a process model that can predict the sorting
behavior of the SBS unit across different sorting scenarios
with a mean absolute error of $3.0\%.$ In summary, the
dissertation demonstrates the promising potentials of sensor
technology in optimizing the circular economy. Based on the
generated transparency, future process improvements can be
implemented in individual process stages and across value
chains, thus increasing the quantity and quality of recycled
materials and the resulting ecological and economic
benefits.},
cin = {512110 / 510000},
ddc = {620},
cid = {$I:(DE-82)512110_20140620$ / $I:(DE-82)510000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2023-11638},
url = {https://publications.rwth-aachen.de/record/974799},
}
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