AMP Robotics’ artificial intelligence (AI) platform, AMP Neuron, encompasses the largest known real-world dataset of recyclable materials, with the ability to classify more than 50 different categories of recyclables across single-stream recycling, e-scrap and construction and demolition debris with an object recognition run rate of more than 10 billion items annually.
This combination of scalable accuracy and classification creates a step-level solution for data collection and measurement that material recovery facilities can use to optimize their operations. Reclaimers, mills and manufacturers can use the data to validate that incoming feedstock meets specifications and standards for chemically compliant bales. Brand owners and government stakeholders can use the data to measure the quality, flow and recovery of recyclable materials.
AMP’s AI platform precisely identifies and captures plastics, including polyethylene terephthalate (PET or PETE), high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP) and polystyrene (PS). The plastics can be sorted further by color, clarity and opacity, as well as by different form factors, such as lids, tubs, thermoform, cups and more. AMP’s technology also recovers cardboard, paper, cans, cartons and many other containers and packaging types that can be reclaimed for raw material processing. It can quickly adapt to container packaging introduced into the recycling stream with recognition capabilities to the brand level. This is increasingly critical as demand for sufficient quantities of high-quality recycled materials grows to meet consumer packaged goods companies’ commitments to the use of postconsumer recycled content.
AMP has deployed six AI-guided robotic sorting systems with Evergreen, one of the largest recyclers of PET bottles in the United States, at its Ohio processing facility. AMP’s technology identifies and sorts green and clear PET from postconsumer bales of plastic soft drink bottles, which Evergreen recycles into reusable flakes or pellets (rPET) and sells to end markets as feedstock for new containers and packaging. If plastics are not properly separated during the sorting phase and different materials get processed together, it produces a lower quality resin. This might not meet manufacturers’ product standards and the stringent requirements for food-grade and beverage containers. AI-guided sortation can deliver scientifically calibrated mixes of material to meet reclaimers’ specifications and those of end-market buyers.
With robots focused on refining the quality of material, separating plastics more precisely by color, Evergreen has seen a notable improvement in purity. Moreover, the addition of AI and robotics technology has brought consistency to the operation by helping an area in the facility that was always challenging to staff run autonomously, allowing Evergreen to focus on and drive improvements in other areas of the operation.
Read the full Evergreen case study here.