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More Cleanly Sorting of PE and PET Waste

Greater Sorting Reliability thanks to AI-Supported Upgrades

  • UniSort PR Evo 5.0 is the fifth evolutionary stage of the NIR sorting machine with HSI camera technology (© Steinert)

  • Alongside a whole host of detail improvements, the latest iteration features a design which is much easier to maintain and at the same time provides the basis for AI-supported object detection (© Steinert)

  • Silicone-residue-free PE material flow: the first application of AI-supported object detection is specialized in the detection of silicone cartridges to achieve silicone-residue-free PE products (© Steinert)

  • The training of the algorithms is based on extensive and detailed data as generated by Steinert since the introduction of HSI technology (hyperspectral imaging) in 2012 (© Steinert)

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Some sorting applications are far more complex than others and cannot be handled using conventional methods – for example sorting silicone cartridges. Since they comprise a polyethylene (PE) outer wall, they are valuable materials for recycling. Silicone residues inside the cartridge can, however, contaminate pure PE products, even rendering them unusable, and meaning that they have to be removed from the material flow.

Improving Sorting Results

The Intelligent Object Identifier has been developed for precisely this initial application; an Artificial Intelligence (AI)-supported object detection system for sorting machines that detects and isolates these cartridges using optically detectable characteristics. These additional distinguishing features make for a more stable sorting process, while also improving sorting results.

In the future, this technology will also support other sorting tasks or indeed make them possible for the first time. For example, Steinert GmbH, Cologne, Germany, is working on an addition to the sorting program to separate polyethylene terephthalate (PET) bottles and trays that will further improve sorting reliability thanks to a new object detection feature.

What makes the solution so special reportedly is the fact that it can be integrated without any additional sensorics and is downward compatible with UniSort machines dating back to 2018 with a combination of near-infrared (NIR) and color cameras.

Detailed Data for Machine Learning

This improvement has come about through software and the latest developments in the field of machine learning, especially in artificial neural networks. The precondition for this training is comprehensive and detailed data. Since hyperspectral imaging (HSI) technology was introduced in 2012, Steinert has been generating this data and uses it today to create the training conditions for algorithms. On this way users do not have to configure parameters themselves or deal with programming.

This is exactly how the latest stage of expansion of the UniSort PR, the UniSort PR EVO 5.0, came about. Dynamic calibration monitors the spectrum of the belt lighting, which is changing all the time in response to external factors, and does so without interrupting the sorting process. Revamped light boxes ensure improved detection in the long term while also simplifying maintenance. An optional automatic white balance reduces the intensity of maintenance, eliminating further manual stages.

Company profile

RTT Steinert GmbH

Hirschfelder Ring 9
DE 02763 Zittau
Tel.: 03583 54084-0

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