Recycleye, a waste and recycling technology based in London, has announced it will showcase its artificial intelligence (AI)-powered robotic pickers at IFAT 2022 in Munich. The company says the equipment, called Recycleye Robotics, can identify and sort materials faster than humans.

In response to the high operating expenditures associated with manual picking in material recovery facilities, Recycleye developed an AI-powered computer vision system, called Recycleye Vision, which identifies every item on a waste stream using a low-cost camera and machine learning algorithms. This system is integrated with the robot, a picker developed in partnership with Japan-based manufacturer Fanuc, which automates the physical task of separating waste items into pure material streams.

The machine has several applications, including negative sorting. It can sort materials like polyethylene terephthalate (PET), high-density polyethylene (HDPE), polyethylene/polypropylene (PE/PP), aluminum and paper.

The company says it installed the first wave of waste-sorting robots of their kind in material recovery facilities in the UK, France and Italy. These installations have achieved up to 55 successful picks per minute, less than 1 percent contamination and an increase in output volume of the target material by 12 percent at some sites.

The company says through this technology, the global waste sorting processes could sort to higher granularities with more efficiency, extracting things like food-grade PET from residual lines. This would lower the cost of waste sorting and increase profit margins on resale bales due to the higher purity of the material.

“By rendering recycling a more economically attractive proposition, our technology will prevent more of Europe’s valuable recyclable materials being lost to landfills, proving that waste doesn’t exist, only materials in the wrong place,” Recycleye CEO Victor DeWulf says.

Recycleye is exhibiting at IFAT 2022 in Munich from May 30 to June 3 and will be in the startup area in Hall B4 on Stand 34.