Version 1
: Received: 5 August 2024 / Approved: 5 August 2024 / Online: 5 August 2024 (23:27:20 CEST)
How to cite:
Seifert, L.; Leuchtenberger-Engel, L.; Hopmann, C. Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of Melt Flow Rate and Shear Viscosity. Preprints2024, 2024080359. https://doi.org/10.20944/preprints202408.0359.v1
Seifert, L.; Leuchtenberger-Engel, L.; Hopmann, C. Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of Melt Flow Rate and Shear Viscosity. Preprints 2024, 2024080359. https://doi.org/10.20944/preprints202408.0359.v1
Seifert, L.; Leuchtenberger-Engel, L.; Hopmann, C. Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of Melt Flow Rate and Shear Viscosity. Preprints2024, 2024080359. https://doi.org/10.20944/preprints202408.0359.v1
APA Style
Seifert, L., Leuchtenberger-Engel, L., & Hopmann, C. (2024). Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of Melt Flow Rate and Shear Viscosity. Preprints. https://doi.org/10.20944/preprints202408.0359.v1
Chicago/Turabian Style
Seifert, L., Lisa Leuchtenberger-Engel and Christian Hopmann. 2024 "Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of Melt Flow Rate and Shear Viscosity" Preprints. https://doi.org/10.20944/preprints202408.0359.v1
Abstract
The extensive use of polypropylene (PP) in various industries has heightened interest in devel-oping efficient methods for recycling and optimising its mixtures. This study focuses on formu-lating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The in-vestigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, at-taining R² values over 0.99. Symbolic regression further enhanced these models, offering signif-icant improvements. To mitigate overfitting, empirical noise and variable swapping were in-troduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the de-velopment of recycling technologies and sustainable practices in the polymer industry by opti-mising processing and enhancing the use of recycled materials.
Chemistry and Materials Science, Polymers and Plastics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
