Application of Mixing Rules for Adjusting the Flowability of Virgin and Post-Consumer Polypropylene as an Approach for Design from Recycling
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- @Article{traxler:2022:Polymers,
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author = "Ines Traxler and Christian Marschik and
Manuel Farthofer and Stephan Laske and Joerg Fischer",
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title = "Application of Mixing Rules for Adjusting the
Flowability of Virgin and Post-Consumer Polypropylene
as an Approach for Design from Recycling",
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journal = "Polymers",
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year = "2022",
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volume = "14",
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number = "13",
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pages = "Article No. 2699",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2073-4360",
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URL = "https://www.mdpi.com/2073-4360/14/13/2699",
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DOI = "doi:10.3390/polym14132699",
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abstract = "To enable the use of recyclates in thermoformed
polypropylene products with acceptable optical
appearance and good mechanical stability, a multilayer
structure of virgin and recycled material can be used.
When producing multilayer films with more than two
layers, the used materials should have similar melt
flow properties to prevent processing instabilities. In
the case of a three-layer film, post-consumer
recyclates are often hidden in the core layer. Due to
the inconsistent melt flow properties of post-consumer
recyclates, the adjustment of the melt flow properties
of the core layer to those of the outer layers has to
be realized by blending with virgin materials. In order
to understand the effect of mixing with a virgin
material with a certain pre-defined melt flow rate
(MFR), material mixtures with different mixing partners
from various sources were realized in this study.
Hence, the pre-defined virgin material was mixed with
(i) virgin materials, (ii) artificial recyclates out of
a mixture of different virgin materials, and (iii)
commercially available recyclates. These blends with
mixing partner contents ranging from 0–100percent
in 10percent increments were prepared by compounding
and the MFR of each mixture was determined. For a
mathematical description of the mixing behaviour and
furthermore for a proper MFR prediction of the material
mix, existing mixing rules were tested on the three
pre-defined sample groups. Therefore, this paper shows
the applicability of different mixing rules for the
prediction of the MFR of material blends. Furthermore,
a new mixing rule was developed using symbolic
regression based on genetic programming, which proved
to be the most accurate predictive model.",
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notes = "also known as \cite{polym14132699}",
- }
Genetic Programming entries for
Ines Traxler
Christian Marschik
Manuel Farthofer
Stephan Laske
Joerg Fischer
Citations