The Closed Loop Foundation collaborated with Greyparrot to analyze the composition of PP recycling streams in partnership with four Material Recovery Facilities (MRFs), based on food-grade and non-food-grade, color, shape, and other key identification indicators. The study analyzed 45 million items, and three main findings are:

1. Transparent and white food-grade PP is abundant: In the study, over 75% of PP is white or colorless, and most is estimated to be food-grade. Additionally, 30% of transparent PP packaging consists of beverage cups. This has significant implications for meeting the growing demand for food-grade recycled PP and considering how to retain their economic value within the system.

AI-enabled technology can reliably and at scale classify and aggregate recyclables, and it has proven to be trustworthy in effectively providing material property data.

3. AI can help measure and trace the operation of factories and equipment: Upgrading the MRF optical sorting speed can have a significant impact on improving material sorting.

"This research provides important data and transparency for MRFAI technology and performance," said Gaspard Duthilleul, COO of Greyparrot. "In just three months, Greyparrot Analyzers analyzed 45 million PP and non-PP materials: if done manually, it would take nearly four years to complete, as manually analyzing 1,000 pounds of material takes a whole day. AI offers new opportunities for data analysis in recycling plants, laying the foundation for improving the recovery of valuable materials in the future."