Monster Machines: Self-Teaching Battery Sifter Sorts Cells By Sight

Monster Machines: Self-Teaching Battery Sifter Sorts Cells By Sight


Nickel-cadmium and lithium-ion batteries can’t simply be thrown away — their dangerous heavy metal contents could leak and contaminate the environment. Problem is, batteries have to be sorted by size and type before being recycled, which is both labour-intensive and time-consuming. Luckily, this high-speed battery separating system is anything but manual.

It’s called the Optisort. Developed by Claes Strannegård, an AI researcher at the University of Gothenburg, in conjunction with Renova, a Swedish recycling company, it can identify nearly 2000 different types of battery — from alkaline button cells to Li-ion cell phone packs — and sort them all based on their individual size and chemistry. This system is even able to differentiate within a battery family, separating Ni-MH from Ni-Cd with ease. What’s more, the Optisort is as accurate as it is fast, separating up to a tonne of dead cells every hour — that’s 10 batteries per second — with 98 per cent accuracy.

Incoming piles of mixed batteries arrive via a high-speed conveyor belt. As they pass under the Optisort, each one is photographed by the machine’s camera and matched to a verified example from the system’s extensive image database based on various physical characteristics like shape, colour temperature, and logo. By utilising a neural network architecture to enhance its recognition speed, the system can positively identify batteries even if they are damaged. Once identified, the battery is then pushed off the belt into a sorting bin by a jet of compressed air and its image is added to the database. The more batteries that the system sees, the more accurate it becomes.


GizmagOptisortUniversity of Gothenburg