A Nearly Zero-Cost Lot-by-Lot Inspection of Recycled Plastics: Prediction of Mechanical Properties from Viscosity Evolution during Melt Kneading

Driving mechanical recycling with minimal energy consumption
has
become increasingly urgent. However, recycled plastics derived from
household plastic waste, which accounts for approximately half of
all plastic waste, are contaminated with non-plastic substances and
mixed polymers. These contamination levels vary significantly from
lot to lot, limiting their use to low-grade applications, where consistent
quality is less critical. This study highlights that all recycled
plastics undergo melting, kneading, and pelletizing processes. By
predicting the mechanical properties of recycled products based on
melt viscosity (auxiliary data obtained during kneading without additional
costs), we propose a nearly zero-cost, lot-by-lot inspection method.
Pre-production prediction of pellet properties during kneading enables
the classification and extraction of high-quality, uniform recycled
plastics tailored to specific applications. To validate this approach,
we predict the tensile properties and Charpy impact energies of 23
lots of household polypropylene (PP) waste. Leveraging a bidirectional
recurrent neural network, we developed a system to classify pellets
prior to production based on predicted mechanical properties, achieving
over 85% accuracy. This innovative analytical method provides a cost-effective
solution for upcycling household waste, contributing to sustainability
within the circular economy.