Digimarc Corporation has released its new Digimarc Recycle sortation software, implementing Graphics Processing Units (GPUs) to almost halve costs compared to compliant hardware and increase accessibility for recycling and waste sortation facilities worldwide.
Digimarc Recycle is intended to increase the precision and accuracy of mechanical sorting for recycled materials. It identifies covert digital watermarks printed onto product packaging and linked with a cloud-based repository of extensible product attributes, such as packaging composition, food or non-food grade plastic, cosmetic and hygiene-grade material, product variant, brand, and SKU.
As such, the software can determine its material composition and boost the level of granularity in the sorting process. Compatible with all form factors, including rigid and flexible packaging, the technology is considered a ‘more holistic and immediate’ step towards circularity, with its specificity on an SKU, product, variant, or item level expected to meet the requirements of the Packaging and Packaging Waste Regulation.
“The problem of plastic pollution is growing exponentially,” says Digimarc CEO Riley McCormack. “We need solutions that are profitable, effective, and can revolutionize the recycling economy now–not in a few years when the problem will be dramatically worse. Digimarc Recycle is ready and available today.”
Digimarc says that current technologies are limited in their sortation parameters and can misidentify the properties of packaging waste, cross-contaminating plastic streams and preventing closed-loop recycling. It believes digital watermarking is the only advanced sortation solution that can accurately detect and sort distinct plastic varieties.
Sorting machines utilize cameras and Digimarc Recycle software to detect recycled materials on moving belts; the latest version of the sortation software works with GPUs, which are growing in popularity among AI applications.
“In redesigning our detector software operations for GPUs, we’re harnessing the parallel processing capabilities and the performance and cost advantages they provide,” Ravi Sharma, vice president for Research and Development at Digimarc, continues. “As a result, we’ve reduced the hardware needed for our detection technology, thereby reducing the compute cost of Digimarc’s detection module by up to 70%, while achieving the same performance.
“It’s a win-win for recycling facilities and the industry, as our latest release increases the ROI for all recycling ecosystem participants.”
At the Hündgen Entsorgnug material recovery facility in Germany, a sorting machine from Pellenc ST is running the latest version of the Digimarc Recycle software. Reportedly, ‘tens of thousands’ of digital watermarked items were identified within a day of the module being functional.
“Achieving such a significant reduction in cost while maintaining efficacy is a key success factor for implementation of the technology in the recycling industry,” concludes Antoine Bourely, co-founder and chief scientific officer at Pellenc ST. “We are eagerly looking forward to the use and validation of this latest version of Digimarc’s Recycle software using GPUs in industrial conditions.”
The HolyGrail 2.0 Initiative has successfully validated digital watermarking technology in combination with NIR for advanced sorting of flexible packaging – especially the separation of hygiene-grade LDPE films and food-grade PP films – at this facility. Assessments conducted in February 2024 saw the results for single-pass sorting reach 89.9% detection efficiency, 75.1% sorting efficiency, and 88.1% purity.
In other news, Wipak is participating in Digimarc’s Center of Expertise programme,which hopes to enable packaging, print, and pre-media companies to integrate digital watermarking into their digital identification and authentication solutions. As well as tackling counterfeiting, this development is set to align with upcoming regulation by improving circularity, boosting recyclability, and minimizing plastic waste.
Pellenc ST has also revealed CNS Brain, which intends to boost detection accuracy, improve sorting performance, and save energy in paper waste streams using artificial intelligence, deep learning, and NIR/VIS technologies.
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