TOMRA’s new cutting-edge deep learning solution enables MRFs to maximise the recovery and purity of aluminium from metal packaging waste streams
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) aluminium recovery that delivers 98 per cent 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 maximise the recovery and purity of aluminium from metal packaging waste streams. GAINnext leverages sophisticated AI to instantly detect and eject non-UBC materials like aluminium bottles, food cans, trays, UBC metals or plastics, and more, for high-accuracy, automated sorting of aluminium 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.
