Asker, Norway-based Tomra Sorting Recycling has launched Gain, a deep learning-based sorting technology to further enhance the performance of its sensor-based sorting machines, according to a Tomra news release.
The gain technology will be made available as an add-on option for the company’s autosort machines. By classifying objects from sensor data, gain enables the sorting of objects, which could previously not be separated with high levels of purity and without compromising the throughput speed of the autosort.
Tomra’s gain technology officially launched at Ecomondo Nov. 5 in Rimini, Italy.
“By bringing deep learning to our sorting technologies, Tomra is adding further sophistication and effectiveness to its market-leading autosort sorting machines,” says Alessandro Granziera, sales manager for Tomra Sorting Recycling in Italy. “The gain technology will also help sorting machines adapt to new waste streams, which will be increasingly important as we move towards a circular economy.”
Deep learning, as a method of artificial intelligence (AI), enables computers to imitate human learning. Humans make associations with what they have seen before and what they are seeing now to identify various objects or materials. Machines are taught to do the same, but much faster. Tomra machines have deployed AI since the early days of sorting, but this technology has continually evolved and now Gain Technology takes it to a new level with algorithms out of the area of deep learning, Tomra says.
Classical machine learning requires features engineered by a domain expert, whereas deep learning, which is a subset of machine learning, does not. It learns from thousands of images that particular types, which should be separated in the sorting task. Deep learning mimics the activity of large numbers of layers of neurons in the human brain to learn complex tasks. This way, during machine learning, gain learns how to connect the artificial neurons to classify objects.
The first version of the gain technology to be released by Tomra is specifically developed to eject silicon cartridges from a polyethylene (PE) stream by using camera information. On grounds of silicon remaining in the cartridges, separating those cartridges from the wanted PE material is necessary in order to purify the sorting result.
In addition to detecting common forms of silicon cartridges, gain can also detect smaller double cartridges, mostly used for two-component adhesives, as well as deformed or partly destroyed cartridges.
The new technology was trained for this task with thousands of images and achieves an overall ejection of 99 percent of the cartridges using two systems in a sequence.