Closed Loop Partners publishes new data revealing high volumes of recycled food-grade polypropylene (PP) captured at materials recovery facilities (MRFs). The study, led by the Closed Loop Foundation and Closed Loop Partners鈥� Center for the Circular Economy, in collaboration with technology company Greyparrot and four U.S. MRFs, leveraged AI-powered vision systems to characterize the PP recycling stream with unprecedented detail. These results fill data gaps on the availability of food-grade PP, which can create new opportunities to return this material to foodservice packaging supply chains.
The findings are released amidst growing demand for recycled food-grade PP, driven by policy shifts颅颅鈥撯�搃ncluding recycled content mandates and Extended Producer Responsibility (EPR)鈥撯�揳nd commitments from brands to incorporate more recycled materials in their packaging. Despite increased market demand, there has been a significant lack of data on the available volumes of recycled food-grade PP in the recycling system. With no easy way to track, differentiate and separate food-grade and non-food-grade PP, these materials typically blend together at MRFs, making it challenging to amass the appropriate quantities of food-grade PP to meet growing market demand.
With funding from the NextGen Consortium and the Closed Loop Foundation, the Center for the Circular Economy teamed up with Greyparrot and four MRFs to reveal what was in the PP recycling stream, including the volume of food-grade and non-food-grade items, as well as color, format and other critical identifying features. Nearly 45 million individual PP and non-PP objects were characterized over the course of the study, revealing newfound granular details on the PP comprising the stream.
The study revealed 3 key findings:
1. Clear and white food-grade PP is abundant: On average, more than 75% of the PP captured in the study was white or clear, most of which was also presumed to be food grade. Furthermore, over 30% of clear PP packaging identified were beverage cups. This has important implications for meeting growing food-grade PP demand and how to retain more value in the system.
2. AI-enabled technologies can reliably quantify and classify recyclables with granularity, at scale: AI systems, such as the Greyparrot Analyzer, proved reliable in providing effective material characterization data at previously unavailable scales. This indicates the potential for AI to drive value to MRFs through increased intelligence and data granularity on material flows.
3. AI can help measure and track facility and equipment performance: Upgrades to optical sortation technology at MRFs had a notable impact on improved material sortation. This was progress that the AI technology was able to track and provide critical analytics on, indicating the potential for AI to offer enhanced performance evaluation data for MRF operators.
鈥淭he data captured demonstrates what is possible for the future of recycling and circular materials management, when powered by technology that can enhance transparency in the recycling system and increase high-quality material recovery,鈥� said Kate Daly, Managing Partner and Head of the Center for the Circular Economy at Closed Loop Partners. 鈥淎s we continue our work with many of the world鈥檚 largest retailers and foodservice brands, we look forward to identifying more opportunities to pull valuable food-grade materials back into foodservice packaging supply chains鈥撯�揳 critical step toward recycled content goals and packaging circularity.鈥�
鈥淭his work provides important data and transparency around the performance of AI technology and its capabilities within MRFs. We are proud to contribute critical data on the presence and quantity of food-grade objects within the PP stream,鈥� said Gaspard Duthilleul, COO of Greyparrot. 鈥淚n just three months, Greyparrot Analyzers characterized over 45 million PP and non-PP materials鈥攁 process that would take nearly four years manually, as manually characterizing just 1,000 pounds of material can take an entire day. This scale uncovers new opportunities for data collection at recycling facilities, serving as the foundation for increased recovery of valuable materials.鈥�