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TOMRA Recycling and PolyPerception Announce New Collaboration

TOMRA Recycling and PolyPerception Announce New Collaboration
TOMRA today announced a new collaboration with PolyPerception to offer AI-powered material analysis technology to waste sorting and recycling facilities.

Focusing on continuous waste flow analysis and offering customers a dedicated analysis solution based on computer vision and deep learning, PolyPerception delivers a complementary solution to TOMRA’s leading sorting technologies for polymer sorting applications, including the deep learning-based GAIN system. TOMRA will support speeding up PolyPerception’s commercial development.

The collaboration further reinforces both companies’ mission, to advance the transition to a circular economy with the use of state-of-the-art technologies.

“It is great”, says Dr. Felix Flemming, SVP, Head of Digital TOMRA Sorting, “to see PolyPerception’s capabilities and innovative approach to the analysis of materials in the different waste streams using deep learning technology. For us, this is the first time we collaborate closely with a start-up in its early stage. We are convinced that this collaboration will bring tangible benefits to the recycling industry by combining material sorting with material analysis and allowing us to optimize the material recovery further.”

Nicolas Braem, CEO & Co-Founder of PolyPerception about the collaboration with TOMRA: “We are thrilled to be collaborating with TOMRA, an industry leader in recycling. Our collaboration will accelerate the development of our material analysis technology. As a mission-driven company, most of all we’re excited to work with like-minded people at TOMRA and to broaden our impact.”

TOMRA Recycling and PolyPerception Announce New Collaboration

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TOMRA Recycling’s pioneering industry expertise continues to result in state-of-the-art machines and exceptional service within the waste and metal recycling industries. Our goal: support our customers to optimize their sustainability and operational value. Our method:...

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