Billen, Pieter

Brancart, Joost

Cunha, Ana

Gani, Rafiqul

Kov\ue1cs, Attila

Mansouri, Seyed Soheil

Nimmegeers, Philippe

Abstract: Chemical recycling of polymers is taking off as a circular technology, typically targeting pure recyclates. However, this is often not achieved efficiently due to high energy demand of separation and purification steps. In addition, many polymer applications have complex formulations that may be sensitive to impure feedstocks. Substitution of virgin feedstocks by complex recyclates (often containing impurities) requires a good knowledge of the structure/composition\u2013property relations of polymer formulations. As this is often not the case, current practice relies on costly and rather inefficient enumeration experiments, or, at best, classical design-of-experiments approaches. We review the state-of-the art in structure\u2013property modeling, present an example for polyurethane formulations, and propose a hybrid model-based framework. This involves a machine learning workflow for substitution problems in complex polymer formulations, combining existing data, novel reaction kinetics, structure\u2013property models, molecular dynamics, and a minimum of experimental\u2013analytical data where necessary, to build and validate the model

Hybrid substitution workflows should accelerate the uptake of chemical recyclates in polymer formulations

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