Key Highlights:
- TOMRA Recycling, announced the launch of three 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 AI.
- By combining its traditional near-infrared, visual spectrometry or other sensors with deep learning technology, TOMRA has achieved purity levels of about 95%.
TOMRA
AUTOSORT with GAINnext combines object recognition with traditional sensor-based sorting.
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 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 to GAINnext in tribute to the product’s significant evolution – can now resolve all of 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.
Purity levels of over 95%
TOMRA
Food-grade sorting is now possible for PP, PET and HDPE.
By combining its traditional near-infrared, visual spectrometry or other sensors with deep learning technology, TOMRA has developed an accurate solution that's now available on the market. And the degrees of purity that this solution is achieving – upwards of 95% for the packaging applications in customers’ plants across UK and Europe – will open up opportunities for new revenue streams for TOMRA’s customers.
TOMRA is also launching two non-food applications that complement the company’s existing GAINnext ecosystem: an application for deinking paper for cleaner paper streams, and a PET cleaner application for even higher purity PET bottle streams.
Bottle-to-bottle quality
Dr. Volker Rehrmann, EVP, head of TOMRA Recycling, commented: “We have used AI technology to improve sorting performance for decades, but this latest groundbreaking application marks another industry first for us. AI has the power to transform resource recovery as we know it, and our latest sophisticated applications of deep learning and AI reinforce our position as a pioneer in this field.
"With its sophisticated use of deep learning, GAINnext enables food-grade sorting and bottle-to-bottle quality, tasks that have posed significant challenges for our industry for many years. The use of AI is driving material circularity at a time when it is needed most, with tightening regulations and increasing customer demand for technologically advanced solutions. At TOMRA, we're proud to be driving the change in sorting.”
Solving the most complex sorting tasks
Indrajeed Prasad, product manager Deep Learning at TOMRA Recycling, added: “The use of deep learning technology not only automates manual sorting but also enables the industry to achieve high-quality recyclates through more granular sorting. Thanks to its ability to detect thousands of objects by material and shape in milliseconds, GAINnext solves even the most complex sorting tasks. Plus, with its integrated deep learning software, it offers the opportunity to adapt to future demands.
"We are delighted to be able to launch these innovative and much-needed solutions to meet the ever more stringent quality requirements for sorting outputs, driven by the increasing demand from consumer brands for more high purity recycled content.”
Field-proven technology
GAINnext'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 in 2022. To date, more than 100 AUTOSORT units with GAINnext are installed at material recovery facilities across the globe.
Among the early adopters of the brand new applications are market-leading plants such as Berry Circular Polymers’ flagship facility in Leamington Spa, Viridor Avonmouth in Bristol - one of the UK’s largest multi-polymer facility – and the French Nord Pal Plast plant, which is owned by the European Dentis Group.
Feedback from the market on the latest GAINnext developments has also been positive.
Professor Edward Kosior, founder and CEO of Nextek Ltd and its NEXTLOOPP initiative that aims to create food-grade recycled polymer from advanced mechanical recycling, was among the most recent visitors to TOMRA’s test centre and confirmed: “TOMRA’s ground-breaking AI system, GAINnext, has propelled the recycling industry to an exciting pivotal juncture in plastic packaging sorting and creates new opportunities for closing the loop on many plastics in food-grade applications. GAINnext is poised to accelerate the most simplified, cost-effective and highly efficient sorting system on the market. We’re immensely proud to see our industry moving forward on this transformational journey.”