Leading global sorting solutions provider, TOMRA Recycling, announces a new packaging sorting application for GAINnext, which leverages deep learning – a subset of artificial intelligence (AI) and machine learning – to remove hard to classify materials.

Further expanding its deep-learning-based applications, TOMRA now offers the market a high-throughput solution for used beverage can (UBC) aluminum recovery that delivers 98% purity or higher without manual sorting. This breakthrough technology further automates the sorting line to improve UBC capture efficiency for the material recovery facility (MRF), increasing revenue and decreasing costs.​

TOMRA’s new cutting-edge deep learning solution enables MRFs to maximize recovery and purity of aluminum from metal packaging waste streams. GAINnext leverages sophisticated AI to instantly detect and eject non-UBC materials like aluminum bottles, food cans, trays, UBC metals or plastics, and more, for high-accuracy, automated sorting of aluminum cans. The new solution features automated sorting at high belt speeds to significantly improve operational efficiency with up to 33 times more throughput than manual sorting.

​Developed as an end-of-line solution for MRFs, GAINnext quickly integrates into existing lines to lower overall costs and improve return on investment (ROI). GAINnext uses an RGB camera, trained by thousands of images, to recognize UBC based on shape, size, dimension and more. Its high-throughput processing delivers up to 2,000 ejections per minute, and the deep learning software identifies overlapping objects and calculates positioning for high-precision, above 98% purity sorting. Offering exceptional purity levels, the GAINnext UBC application gives the market an automated process for aluminum can-to-can recycling.

“MRFs typically rely on manual sorters at the end of the line to pick UBC from the metal packaging waste stream,” explains Ty Rhoad, TOMRA Recycling’s vice president of sales for the Americas. “Manual sorting averages approximately 60 picks per minute, but our highly effective GAINnext AI sorting application offers up to 33 times more throughput. Offering high purity, GAINnext is proven to reduce operating costs and increase revenue and productivity, resulting in a quick ROI.”

​Indrajeed Prasad, product manager, deep learning at TOMRA Recycling, adds: “TOMRA’s experience with AI spans decades, as our optical sorting equipment leverages traditional AI to automate sorting lines. GAINnext is trained to see what the human eye can see and detects thousands of objects by visual differences in milliseconds. The deep learning subset of AI creates a hierarchical level of artificial neurons to solve the most complex sorting tasks. We are delighted that our new application focuses on the critical recovery of UBC aluminum cans and offers customers above 98% purity rates.”​

TOMRA adds used beverage can (UBC) application to the GAINnext AI ecosystem to increase purity, capture efficiency and revenue for MRFs

Growing AI ecosystem

​Field proven for years, TOMRA was the first to introduce deep learning AI technology in 2019 with its application to identify and remove polyethylene (PE) silicone cartridges from PE streams. A game-changing second deep learning application focused on wood chip classification, sorting solid wood from wood-based materials like chipboard, plywood and MDF into individual fractions.

Earlier this year, TOMRA announced five new plastics and paper deep learning sorting applications using GAINnext initially for the European market. Three revolutionary applications efficiently separate food-grade from non-food-grade PET, PP and HDPE at high throughput rates with purity levels reaching 95%. Two non-food applications for the GAINnext ecosystem include a PET cleaner application delivering even higher purity PET bottle streams and an application for deinking paper for cleaner paper streams. The new UBC application is TOMRA’s region-specific GAINnext sorting application initially targeting the needs of recyclers in the Americas.