Characterization and chemometric modelling of mechanically recycled polypropylene for automotive manufacturing

In order to successfully apply recycled plastics for car manufacturing, it is necessary to identify chemical and physical changes during the recycling process to ensure quality and assess potential risks of material deterioration and failure. For a common automotive plastic material such as polypropylene (PP), expected consequences of repeated processing are polymer chain scission due to thermal and mechanical stress, oxidation, contamination, and changes in the composition of additives. In this study, we describe a systematic chemometric approach towards quantitative prediction models for a target value, exemplified by the recyclate content in PP. Based on a multimodal material analysis, a feature matrix is composed from large, heterogeneous datasets through dimensionality reduction. Analytical methods include infrared spectroscopy, Raman microscopy, combined thermodesorption-gas chromatography-mass spectrometry, as well as size exclusion and high performance liquid chromatography. Analytical features and prediction models are then selected and scored. We show that the weight content of recyclate can be predicted with an accuracy ∼2% using simple linear models. We present an outlook for model deployment to the factory floor by using fast, non-destructive analytics such as IR spectroscopy.