Applications to Separate Food-grade From Non-food-grade Plastics
from Waste Industry ProductsTOMRA Recycling has announced the launch of three revolutionary applications to separate food-grade from non-food-grade plastics for PET, PP and HDPE. The breakthrough was made possible by the company’s intensive research and development in deep learning, a subset of artificial intelligence (AI). Thanks to TOMRA’s continued investment in GAIN—the company’s deep learning-based sorting add-on for its world-renowned AUTOSORT™ units—it is now possible for the first time to quickly and efficiently separate food-grade from non-food-grade plastics for PET, PP, and HDPE on a large scale. Until now, food-grade sorting has proved a real challenge for the industry as food and non-food packaging are often made of the same material and visually very similar, which makes it difficult for any traditional sorting system on the market today to differentiate and separate. Hygiene concerns and increasingly stringent industry regulations add a further layer of complexity to handling food waste in recycling. However, TOMRA’s GAIN technology—rebranded GAINnext™ to pay tribute to the product’s significant evolution—resolves these challenges by further enhancing the sorting performance of the company’s AUTOSORT™ units, so they are capable of identifying objects that are hard and, in some cases, even impossible to classify using traditional optical waste sensors. By combining its traditional near-infrared, visual spectrometry or other sensors with deep learning technology, TOMRA has developed the most accurate solution available on the market today. The degrees of purity that this solution is achieving—upwards of 95 percent for the packaging applications in customers’ plants—will expand opportunities for new revenue streams for TOMRA’s customers. TOMRA is also launching two non-food applications that complement the company’s existing GAINnextTM ecosystem: a PET cleaner application for even higher purity PET bottle streams and an application for deinking paper for cleaner paper streams. GAINnextTM ’s deep learning technology has been proven in the field for many years. TOMRA was the first in the industry to introduce deep learning technology in 2019 with an application to identify and remove PE-silicon cartridges from polyethylene (PE) streams. An application for wood chip classification soon followed. To date, more than 100 AUTOSORTTM units with GAINnextTM are installed at material recovery facilities across the globe. For more information, visit www.tomra.com/en/gainnext.
