TY - JOUR
T1 - Accurate determination of polyethylene (PE) and polypropylene (PP) content in polyolefin blends using machine learning-assisted differential scanning calorimetry (DSC) analysis
AU - Bashirgonbadi, Amir
AU - Ureel, Yannick
AU - Delva, Laurens
AU - Fiorio, Rudinei
AU - Van Geem, Kevin M.
AU - Ragaert, Kim
N1 - Funding Information:
This research has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No 859885 (C-PlaNeT project). Yannick Ureel acknowledges financial support from the Fund for Scientific Research Flanders (FWO Flanders) through the doctoral fellowship grant 1185822 N . We would like to thank dr. Natalie Rudolph and dr. Ekkehard Füglein for their contributions in the discussions. We are also thankful to Andona Dimitrova for her assistance with the experiments. We acknowledge the support of Dow, Sabic, CEFLEX, and Netzsch for providing the materials and consumables.
Publisher Copyright:
© 2024 The Authors
PY - 2024/2
Y1 - 2024/2
N2 - Polyethylene (PE) and polypropylene (PP) are among the most recycled polymers. However, these polymers present similar physicochemical characteristics and cross-contamination between them is commonly observed, affecting the quality of the recyclates. With the increasing demand for recycled plastics, understanding the composition of these materials is crucial. Numerous techniques have been introduced in the literature to determine the composition of recycled plastics. An ideal technique should be accessible, cost-efficient, fast, and accurate. Differential Scanning Calorimetry (DSC) emerges as a suitable technique since it analyzes the thermal behavior of compounds under controlled time and temperature conditions, entitling the quantitative determination of each component, e.g., in PE/PP blends. Nevertheless, the existing predictive methods lack accuracy in estimating the composition of PE/PP blends from DSC analysis since the composition of this blend affects its overall crystallinity. This study advances the state-of-the-art regarding this quantification using DSC by implementing a non-linear calibration curve correlating the evolutions of crystallinity with blend composition. Additionally, a machine-learned (ML) model is introduced and validated, achieving high accuracy for the composition determination, presenting an overall mean absolute error as low as 1.0 wt%. Notably, this ML-assisted approach can also quantify the content of subcategory polymers, enhancing its utility.
AB - Polyethylene (PE) and polypropylene (PP) are among the most recycled polymers. However, these polymers present similar physicochemical characteristics and cross-contamination between them is commonly observed, affecting the quality of the recyclates. With the increasing demand for recycled plastics, understanding the composition of these materials is crucial. Numerous techniques have been introduced in the literature to determine the composition of recycled plastics. An ideal technique should be accessible, cost-efficient, fast, and accurate. Differential Scanning Calorimetry (DSC) emerges as a suitable technique since it analyzes the thermal behavior of compounds under controlled time and temperature conditions, entitling the quantitative determination of each component, e.g., in PE/PP blends. Nevertheless, the existing predictive methods lack accuracy in estimating the composition of PE/PP blends from DSC analysis since the composition of this blend affects its overall crystallinity. This study advances the state-of-the-art regarding this quantification using DSC by implementing a non-linear calibration curve correlating the evolutions of crystallinity with blend composition. Additionally, a machine-learned (ML) model is introduced and validated, achieving high accuracy for the composition determination, presenting an overall mean absolute error as low as 1.0 wt%. Notably, this ML-assisted approach can also quantify the content of subcategory polymers, enhancing its utility.
KW - Composition determination
KW - Differential scanning calorimetry
KW - Machine learning
KW - Plastics recycling
U2 - 10.1016/j.polymertesting.2024.108353
DO - 10.1016/j.polymertesting.2024.108353
M3 - Article
SN - 0142-9418
VL - 131
JO - Polymer Testing
JF - Polymer Testing
M1 - 108353
ER -