Sorting post-consumer waste is one of the key steps towards creating a circular economy for the materials we produce. According to Plastics Europe over 10 million tonnes of post-consumer plastic waste is sent for recycling every year, yet 65% of this plastic waste is still going to energy recovery or to landfill.
Over the last few years there has been an influx of innovative solutions aiming to address this challenge, particularly to differentiate food-grade plastic packaging from the waste stream. Whilst efficient, these solutions have required changes to the packaging or labels and industry has been intensively investigating the pros and cons between the various options.
The lack of consensus around which technology to adopt has slowed progress.
The importance of separating food-grade packaging
Technology experts, Nextek specialise in developing the most carbon-efficient recycling solutions for the world’s most challenging materials. Over a decade ago they had identified the need to recover food-grade PP to boost the plastics circular economy, which led them to researching science-based solutions to accelerate efficient sorting and powerful decontamination of this widely used and versatile resin.
In 2020 Nextek launched NEXTLOOPP, a multi-participant project designed to trial and deploy these innovative technologies. One of the project’s key investigations was to efficiently separate food from non-food packaging for which they trialled UV markers in conjunction with NEXTLOOPP participant, TOMRA.
At the time this was the most effective spectroscopic sorting technology to separate the same polymer into food and non-food fractions.
In the meantime, sensor-based sorting technology leader, TOMRA had introduced the industry’s first AI based deep-learning sorting solution to separate silicone cartridges from PE streams and later for wood sorting (in 2019). In early 2024 TOMRA introduced the food-grade application to address the the industry-wide challenge of food-grade separation.
Brand owners now have the opportunity to signal their pack’s recycling identity without relying on a label.
NEXTLOOPP supported the PP field validations conducted by TOMRA to test GAINnext’s capabilities in industrial conditions, however given that the AI had to be taught and NEXTLOOPP already had their own highly efficient marker technology plug-and-play ready, they believed they would start with markers and then phase in AI.
However they did not count on the incremental speed at which TOMRA grew GAINnext’s capabilities.
By early, 2024, TOMRA had accelerated its GAINnext deep learning technology to separate food and non-food plastics and were using it to identify PP packaging, amongst others. It did not take long to realise this was a real game-changer. The system correctly identified over 95% prior food packaging content, an outstanding result that is poised to enable brands to meet the sorting standards required to deliver the food safety authorities’ stringent requirements.
AI-boosted sorting
Since those initial trials TOMRA and NEXTLOOPP have run a series of ground-breaking trials using TOMRA’s near-infrared, visual spectrometry system AUTOSORT combined with their latest deep-learning technology GAINnext to show how deep learning, a subset of AI, has put markers out of the race and fully resolved the food-grade PP sorting hurdle.
During the latest full-scale trials, AUTOSORT with GAINnext sorted 5 tons an hour of mixed PP plastic packaging and exceeded 97 percent food-grade content in the sorted output.
This development is an invaluable boost to NEXTLOOPP whose participants confirm that TOMRA’s new sorting system has the potential to be rolled out to all PP packaging sorting facilities since it focuses on the packaging design attributes rather than any form of additional markers.
Accelerating recycling’s next step – decontamination
By providing a sorted food-grade PP PCR stream, AUTOSORT with GAINnext can now accelerate the supply of food-grade rPP via the NEXTLOOPP decontamination process in many more recycling operations globally without any further delays associated with new label or marker requirements.
As a consequence this breakthrough will positively impact production of valuable food-grade PP PCR streams.
From markers to AI
Less than 12 months ago NEXTLOOPP’s focus was on packaging design guidelines to facilitate sorting packaging into single-polymer fractions using markers. Now TOMRA’s latest innovation has flipped this element of the design guidelines on its head. Instead of a system that relies on labels featuring specific markers, the neural network of the AI system is trained to identify a wide range of shapes. Through structured training, it learns to separate out food contact from non-food-contact packaging.
Design for Deep Learning
The next step is to revise current design guidelines to take into account how the AI ‘thinks’ to continuously enhance both GAINnext’s and other already existing sorting solutions’ capacity. Certainly the suggested changes to the packaging will be simpler and more cost-effective than relying on labels and markers, if anything the more conventional the packaging, the better.
The principles by which GAINnext recognises a pack are based on object recognition. By segmenting a range of different design factors of the pack the AI gathers the different triggers to build its contextual memory of every pack it is shown.
Using the road sign analogy, whereby the iconic stop sign is internationally recognised, the AI is trained on food pack shapes, sizes, dimensions or other criteria that frequently re-occur. Transparency, opacity, print, shapes and colours alert the system that is designed to aim for accurate recognition of the sorted PP packaging.
The more conventional the pack shape the higher the rate of identification. Given that PP food trays are predominantly transparent and rectangular, these are easily picked out. The likes of ice cream tubs, however, which often are solid white, are likely to be rejected from the food packaging stream as they could just as likely have been dishwasher capsule packs.
This brings us back to NEXTLOOPP’s original suggestion of using colour or design features to signal whether a pack belongs in the food or non-food category. Using colour or design features to identify a pack’s former use would enhance AI capacity to define the pack’s destination.
By making packaging as standard as possible brand owners now have the opportunity to signal their pack’s recycling identity without relying on a label.
GAINnext’s training is such that even in a scenario where the pack is crushed, torn or otherwise damaged, it can pick up enough points of differentiation or similarity to make an effective sorting decision in a split second.
This AI system is rapidly building a wide range of cues to identify and differentiate packaging and this is an ideal opportunity for brand owners to adjust their packaging to align with the way that AI is ‘thinking’. This revolution in high speed and accurate AI sorting, as exemplified by TOMRAs AUTOSORT with GAINnext Deep Learning technology, is already having a profound effect on the recycling industry as a whole. As markers fall by the wayside sustainability will shortly be seamlessly integrated into packaging design, driving both environmental and economic benefits.
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The post New AI recycling solution is good news for the circular economy first appeared on Innovators magazine.