Together with better performance, a crucial element for the economic viability of a sorting facility is simplified maintenance. Through new software and the fifth generation of its tried-and-tested UniSort PR, Steinert offers improvements in both respects and is paving the way to the next era of waste sorting technology.
Some sorting applications are far more complex than others and cannot be handled using conventional methods – for example processing silicone cartridges. Since they comprise a polyethylene (PE) outer wall, they are valuable materials for recycling. But silicone residues inside the cartridge can contaminate pure PE products, even rendering them unusable and requiring removal from the material flow.
Going beyond what is possible
The Intelligent Object Identifier has been developed for precisely this application: an Artificial Intelligence (AI)-supported system that detects and isolates these cartridges during sorting using optically detectable characteristics. These additional distinguishing features ensure a more stable sorting process while also improving sorting results.
In the future, this technology will also support other sorting tasks or indeed make others possible for the first time. For example, Steinert is working on an addition to the sorting program to separate polyethylene terephthalate (PET) bottles and trays, improving sorting reliability thanks to a new object detection feature.
What makes the Steinert solution so special is that it can be integrated without any additional sensors and is compatible with UniSort machines dating back to 2018 with a combination of near-infrared (NIR) and colour cameras.
Learning from machines
This improvement has come about through enhanced software and the latest developments in the field of machine learning, especially artificial neural networks.
Machines learn through the use of algorithms processing information without being explicitly programmed to do so. In its most basic form, data can therefore be analysed by a machine to learn recognition and distinguishing features and then – in the case in question – come to a conclusion about any silicone cartridges potentially present.
The optimum precondition for this training is comprehensive and detailed data. Since hyper spectral imaging (HSI) technology was introduced in 2012, Steinert has been generating this data and uses it today to create the training conditions for algorithms that set the bar in this industry.
The data offers better sorting and, in the long term, allows users to develop digital strategies, for example in collaboration with customers and suppliers. Users can then be provided with the best possible sorting result without having to configure machinery themselves or master programming.
Taking the all-rounder to its next level
This is exactly how the latest version of the UniSort PR, the UniSort PR EVO 5.0, came about.
The model has been put through its paces in practical trials since the start of 2019 and is the next logical step in the evolution of sorting machines. It is also a showpiece of modern technology and robust engineering work. Alongside a whole host of detail improvements, the latest iteration features a design that is much easier to maintain and delivers advanced sorting results.
Dynamic calibration monitors the spectrum of the belt lighting, which changes constantly 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.
Coupled with software updates that are being developed continuously, optimised valve blocks guarantee a consistently precise separation of the waste flow. An optional automatic white balance reduces the intensity of maintenance, eliminating further manual stages, thereby improving staff management and making new levels of flexibility possible.
Fit for the future
The UniSort PR EVO 5.0 reflects Steinert’s years of experience gained through the UniSort generations and the huge amounts of data that have been processed. This forms the basis for further advances in sorting performance and for optimising processes in the value-added chain. This in turn enables users to respond flexibly to changing material flows and, most importantly, to tap into new opportunities.
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