Sonoco Partners with AMP Robotics to Enhance Paper Can Recycling
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Sonoco (NYSE: SON) announced it has partnered with AMP Robotics, a pioneer in AI, robotics and infrastructure for the waste and recycling industry, to create a new material category within AMP’s neural network specific to rigid paperboard cans. The U.S. partnership will result in increased recycling rates for the spiral wound paper canister with steel bottom produced by Sonoco and other manufacturers.
The use of recycled steel has a 45% lower environmental impact than producing the equivalent amount from virgin material, reducing the need to mine for virgin iron ore. Additionally, when compared to landfilling, recycling the paper container with steel bottom through the steel or other streams has a greater than 40% lower environmental impact than landfilling. Any materials recovery facility with an AMP CortexTM intelligent robotics system can now accurately and efficiently sort Sonoco’s paper can to the desired stream.
“Sonoco is uniquely positioned as a leading recycler to help deliver end-of-life solutions across our consumer and industrial packaging platforms,” said Elizabeth Rhue, Staff Vice President of Sustainability at Sonoco. “This partnership represents another step forward in our growing portfolio of sustainable packaging solutions.”
“Our AI is unique in its strength, precision and flexibility to learn new packaging to the specificity of a manufacturer or brand, and Sonoco was early to recognize the implications of AI in the sorting process,” said Matanya Horowitz, Founder and CEO of AMP Robotics. “Manufacturers like Sonoco are directly influencing what is recoverable in recycling facilities and taking advantage of the ability to capture more of their specific packaging. Recyclers across the world with AMP Cortex gain this sorting capability, as they have many others. And because our AI is continuously learning, we’ll only improve the recovery of this and other materials over time.”
AMP’s AI platform, AMP NeuronTM, encompasses the largest known real-world dataset of recyclable materials for machine learning. With an object recognition rate of more than 10 billion items annually, the company can classify more than 100 different categories and characteristics of recyclables across single-stream recycling, e-scrap and construction and demolition debris. In the U.S., AMP has successfully demonstrated the high-frequency recognition of steel-bottomed paper canisters and updated its software to enable platform-wide recognition of the material for sortation into any target stream.
