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,

• author = "Ines Traxler and Christian Marschik and Manuel Farthofer and Stephan Laske and Joerg Fischer",
• title = "Application of Mixing Rules for Adjusting the Flowability of Virgin and Post-Consumer Polypropylene as an Approach for Design from Recycling",
• journal = "Polymers",
• year = "2022",
• volume = "14",
• number = "13",
• pages = "Article No. 2699",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2073-4360",
• URL = "https://www.mdpi.com/2073-4360/14/13/2699",
• DOI = "doi:10.3390/polym14132699",
• 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.",
• notes = "also known as \cite{polym14132699}",

}

Genetic Programming entries for Ines Traxler Christian Marschik Manuel Farthofer Stephan Laske Joerg Fischer

Citations