At this year’s Plastics Recycling Show Europe (PRSE), our team noticed a change. Plastic recyclers weren’t asking what waste intelligence was. Instead, they were asking how they could start applying it in their own facilities.
Since attending our first PRSE, we’ve seen AI waste analytics technology evolve from innovation to indispensable survival tool. Recently, policies like the EU’s PPWR have accelerated that evolution. Recovery facilities are turning to automation to sample more material and meet rising demands for recycled content — without shrinking their margins.
Thankfully, we had plenty of plastic recovery innovation to share with a record number of visitors to our PRSE stand, and beyond.
PRSE 2025 came in the wake of a landmark report on food-grade polypropylene (PP), which used Greyparrot Analyzer data to assess over 45 million PP objects in just three months — and confirm AI’s plastic recognition capabilities on a massive scale.
We took plastic recognition a step further at this year’s conference, previewing an upcoming product and a major AI update that will help facility operators target valuable plastics in even more detail:
We showcased all the latest features in a live demo of Greyparrot Analyzer portal including ability to set advance Alerts, schedule reports based on shifts and facility wide reporting for set KPIs.
Hand-in-hand with our portal demonstration was a demo of recent expansion to our waste recognition library from 89 classes to 111 classes, which you can learn about here, making it possible for plastic recovery facility operators to:
At PRSE, innovators like Greenback Recycling Technologies spoke about using granular polymer identification to target and remove PVC from their pyrolysis streams — eliminating chlorine byproducts in the process of creating high-quality recycled polymer oil.
While we were in the Netherlands, we co-hosted an open day with KSI Recycling to share the results of facility-wide AI deployment.
KSI as part of the Omrin who already use Greyparrot Analyzer deploy the same technology on 7 lines for KSI, where mixed plastic is separated into high-quality recycled polymer bales. The remaining waste is digested anaerobically, creating the methane Omrin uses to fuel its collections vehicles.
A key theme that emerged throughout the day was that customers like KSI are using AI waste analytics is to optimise processes, not just to track composition. It was inspiring to see facility operators from different businesses compare their applications for waste intelligence, pooling their knowledge to create a more efficient plastic recycling system.
After the success of PRSE and our open day with Omrin, we’re continuing to use collaboration to transform plastic recovery. Back in the UK, we hosted an “AI sampling Collaboration Day” between policy experts and leaders from the country’s largest waste management businesses.
Recent (and upcoming) increases in waste sampling requirements have proved costly for many facilities relying on manual data collection. Last year, however, the EA announced that it would accept sampling data gathered by AI waste analytics systems. Our team has already submitted an AI reporting methodology to speed adoption in real-world facilities. It’s the first methodology of its kind, and pilot projects are already underway.
Our collaboration day focused on using automated sampling to bridge the gap between regulators and recyclers. Both plastic recyclers and policymakers want transparency. For the former, waste data a vital means of maximising yield and product purity. For the latter, it’s essential for implementing policies like EPR.
Waste data, and the waste intelligence insights that emerge from it, is helping deliver that transparency without sacrificing regulatory impact, or the profitability of plastic recycling. Products like AnalyzerWave and ongoing taxonomy expansions are helping us ensure that data is as granular and accurate as possible.