The Closed Loop Foundation collaborated with Greyparrot and analyzed the composition of the PP recycling stream 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 the three main findings are:

1. A significant amount of transparent and white food-grade PP exists: 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 is highly significant for meeting the growing demand for food-grade recycled PP and considering how to retain their economic value within the system.

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

3. AI can help measure and trace factory and equipment operations: Upgrading the MRF optical sorting at high 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 manual analysis of 1,000 pounds of materials takes a whole day. AI offers new opportunities for data analysis in recycling plants, laying the foundation for future improvements in the recovery of valuable materials."