In the face of increasing global demand for food-grade polypropylene (PP) used in packaging, a crucial study has been spearheaded by Closed Loop Partners (CLP) in collaboration with Greyparrot. This research aims to explore the efficiency of AI-enabled materials recovery facilities (MRFs) in enhancing the recovery rates of food-grade PP. The study meticulously analyzed over 650 tons of materials across various MRFs in the United States, providing a detailed insight into the potential of AI in boosting at-scale material recovery.
The Driving Force Behind the Research
The escalating need for food-grade polypropylene is largely driven by recent policy shifts and the commitments of major brands to increase the use of recycled content in their packaging. These policy changes have created a pressing need to secure more high-quality recycled PP to ensure that these commitments are met. Despite these efforts, the supply of high-quality recycled polypropylene remains limited, posing a significant challenge. The report suggests that using AI-enabled equipment can substantially enhance material characterization. This improvement is crucial for meeting the rising demand and ensuring that the quality of the recovered polypropylene remains consistently high.
AI technology offers precise identification and separation capabilities, which are instrumental in efficiently sorting food-grade PP from other materials. The equipment can analyze the visual and physical properties of items on the conveyor belts, leading to more accurate and efficient sorting processes. This makes AI essential in tackling the current shortage of high-quality recycled PP, aligning with sustainability goals and regulatory requirements.
Key Findings of the Study
The study’s key findings revealed that food-grade polypropylene, including clear and white beverage cups and yogurt tubs, is predominant in the recycling streams of the participating MRFs. These items constitute over 75% of all PP captured, indicating a considerable opportunity for recovery. The use of Greyparrot’s AI-enabled analyzer technology enabled the collection of meticulous data from four U.S. MRFs, illustrating intricate details about material flow and recovery efficiency at scale.
The analyzers, strategically installed above moving belts, captured images of every object moving along the belts. These images were then analyzed based on mass, area, and the likelihood of being food-grade PP. By utilizing AI, the system achieved a highly refined classification of PP items, improving the granularity and accuracy of material characterization. This advanced data collection method allowed for a deeper understanding of the types and quantities of materials processed, setting the foundation for improved sorting and recovery strategies.
Deployment in MRFs
The deployment of analyzer units in key MRFs across Texas, Pennsylvania, Ohio, and Minnesota was critical for precise data collection. Balcones Recycling, Cougles Recycling, Rumpke Waste & Recycling, and Eureka Recycling were among the facilities that participated in this study. The analyzers captured detailed images of every object passing on the belts.
Each image was meticulously classified by criteria such as mass, area, and probability of being food-grade polypropylene. Greyparrot’s system utilized an intricate classification scheme for 20 different types of PP items, ranging from various containers to lids. This level of detail in classification allowed for a more nuanced understanding of the material composition in recycling streams, significantly aiding in the identification and separation processes.
By leveraging AI, these facilities were able to improve their sorting accuracy, enhancing the overall efficiency of the recycling process. The substantial volume of data collected provided valuable insights into operational performance, enabling targeted improvements and optimizing recovery rates of food-grade PP.
Enhanced Transparency and Value Creation
AI-enabled material characterization has brought about remarkable transparency and value creation within MRF operations. The technology allows MRF operators to more accurately identify and separate food-grade PP, facilitating the creation of dedicated PP bales. This enhanced sorting accuracy can significantly improve the quality of recycled materials, increasing their market value and making the recycling process more efficient and profitable.
Improved sorting efficiency achieved through AI not only maximizes recovery rates but also reduces contamination levels in recycled bales. By producing high-purity food-grade PP bales, MRFs can cater to the stringent quality requirements of manufacturers, thereby commanding better prices in the market. This technological advancement supports a more sustainable recycling system, incentivizing facilities to adopt innovative solutions for material recovery.
Detailed PP Distribution
The analysis conducted by the researchers also shed light on the distribution of different types of PP found in the MRFs. The study found that white PP constituted nearly one-third of the material, while clear PP accounted for approximately half. Among the clear PP items, beverage cups were the most prevalent, followed by lids, pots, and tubs. Virtually all clear and white PP was identified as food-grade, whereas around 90% of colored PP was non-food-grade.
These findings underscore the potential for improved sorting processes that specifically target high-value food-grade PP materials. By focusing on the prevalent types of PP in the recycling stream, MRFs can optimize their operations and increase the efficiency of their recovery efforts. This targeted approach helps address the supply constraints of high-quality recycled PP, aligning with sustainability goals and market demands.
Future Goals and Broader Implications
The detailed material characterization achieved through this study represents an initial step towards a broader goal of optimizing the PP recycling chain. By uncovering critical data at this stage of the recycling process, the next logical progression involves addressing downstream bottlenecks that hinder food-grade materials from reaching their highest-value end markets. Utilizing the data to refine logistics and processing practices can enhance the recyclability and utility of food-grade PP.
Kate Daly, managing partner, and head of the Center for the Circular Economy at CLP, emphasized that this study lays the groundwork for future research that goes beyond food-grade PP. The ultimate aim is to develop a more efficient and circular packaging system that reintegrates high-value recycled materials back into the supply chain. Such efforts will be instrumental in meeting recycled content goals and fostering a more sustainable and circular economy.
Corporate Backing and Research Impact
In response to the growing global demand for food-grade polypropylene (PP) used in packaging, Closed Loop Partners (CLP) has teamed up with Greyparrot to conduct a pivotal study. The aim is to evaluate the effectiveness of AI-powered materials recovery facilities (MRFs) in improving recovery rates of food-grade PP. This research is significant given the increasing emphasis on sustainability and efficient recycling. By leveraging the capabilities of artificial intelligence, the study meticulously examined over 650 tons of materials across various MRFs in the United States. This comprehensive analysis offers valuable insights into the potential of AI to enhance large-scale material recovery operations. The initiative not only underscores the importance of advanced technology in recycling but also paves the way for potentially more efficient and sustainable waste management solutions. As the world grapples with environmental concerns, such innovative approaches are crucial in managing resources responsibly.
