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TOMRA expands GAINnext ecosystem to include PET Cleaner and Paper Cleaning applications

The two new applications feature an intelligent system that simultaneously integrates multiple sensor data for higher sorting accuracy

TOMRA's new PET Cleaner and Paper Cleaning applications leverage deep learning technology to recognize hard-to-classify objects.
TOMRA's new PET Cleaner and Paper Cleaning applications leverage deep learning technology to recognize hard-to-classify objects. TOMRA

TOMRA Recycling has expanded its GAINnext deep-learning-based AI ecosystem to include two new applications in North America for material recovery facilities (MRFs) and secondary recyclers. The two new applications feature an intelligent system that simultaneously integrates multiple sensor data for higher sorting accuracy than traditional optical sorters alone. 

TOMRA's new PET Cleaner and Paper Cleaning applications leverage deep learning technology to recognize hard-to-classify objects, reducing the need for manual sorting. These new GAINnext applications help recyclers reduce PET and paper bale impurities, enabling operations to create new revenue streams, increase profitability, and decrease costs. 

Together, the new applications complement the TOMRA AUTOSORT sensor-based material identification with deep learning AI visual object recognition offered by GAINnext for high-purity performance. The combination maximizes the recovery and purity of valuable PET and paper materials at high-throughput speeds. 

"Recyclers can integrate GAINnext into existing lines to boost PET and paper recycling recovery and purity without the need for adding lines. This is a significant benefit for operations that are tight on space," explains Ty Rhoad, vice president of sales for the Americas at TOMRA Recycling. "These new GAINnext applications process material at up to 2,000 ejections per minute, depending on the application, and can help to reduce the need for manual sorting at the end of the line. This results in up to 33 times more throughput than manual sorting." 

Cleaner PET recyclates 

By sorting opaque bottles for recycling, GAINnext PET Cleaner's high-accuracy sorting of opaque white packaging, textiles, and foils from PET can create new revenue streams for recyclers. It leverages deep learning AI technology to remove hard-to-classify PET materials that can lead to downstream sorting and recycling challenges. The system instantly identifies and removes over 92 percent of opaque objects with titanium dioxide protection. 

GAINnext PET Cleaner significantly enhances the sorting performance of transparent and colour PET by removing polyester textile waste. It boosts the hit rate for difficult-to-eject and multilayer foils to deliver higher-purity PET fractions. The flexible system allows recyclers to select opaque colours, opaque white, PET blue, light blue, or transparent for the classification stream, enabling recyclers to instantly recover valuable light blue and transparent PET materials from the sorting line. 

The new GAINnext applications help recyclers reduce PET and paper bale impurities. TOMRA

High-purity paper 

The GAINnext Deinking/Paper Cleaning application delivers high-accuracy sorting of office paper, newspapers, and magazines for paper sorting. Capitalizing on multi-sensor integration, the paper cleaning application uses deep learning technology to effectively remove impurities like pizza boxes, egg cartons, and other brown boards from the paper stream. The system also instantly differentiates and removes grey board from the stream at high throughput speeds. 

"Our GAINnext Deinking/Paper Cleaning application can also improve the sorting performance of cardboard-based objects such as frozen food packaging," comments Indrajeed Prasad, product manager, of deep learning at TOMRA Recycling. "By efficiently removing undesired materials like envelopes, wrapping paper, and brown paper grocery bags, the system creates high-quality paper revenue streams." 

AI ecosystem expansion

TOMRA was the first to introduce deep learning AI technology for the recycling industry in 2019, starting with an application to identify and remove polyethylene (PE) silicone cartridges from PE streams. Since then, TOMRA's deep learning engineers have trained the company's artificial neural networks with millions of object images to solve  complex automated sorting tasks, ranging from wood to plastics to used beverage cans (UBCs)

Early this year, the company also introduced three plastics applications initially in the European market to efficiently separate food-grade from non-food-grade PET, PP, and HDPE at high throughput rates with purity levels reaching 95 percent. Simultaneously, TOMRA launched two non-food applications that included the PET cleaner application for higher purity PET bottle streams and the deinking application for cleaner paper streams for the European market.

TOMRA's latest PET Cleaner and Paper Cleaning launch joins the UBC application in expanding the GAINnext ecosystem to address the region-specific needs of recyclers in the Americas.

Company info

Otto-Hahn-StraBe 2-6
56218 Mülheim-Kärlich
DE,

Website:
tomra.com

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