GRINNER: Improving waste management through an AI-powered detection system of batteries utilising data from x-ray detectors and pick-and-place robots
GRINNER’s mission is to improve the management chain of Waste from Electrical and Electronic Equipment (WEEE). Batteries are the main focus of the project because discarding them, such as in recycling bins or rubbish bags, can lead to fire incidents when sorting equipment causes damage to them. Lithium-ion (Li-ion) and nickel-metal hydride (NiMH) batteries in particular can ignite or explode when damaged. As well as being costly for the waste management chain, these battery-caused fires can have a negative ecological impact and harmful effects on people, and act as a strong barrier to making Europe circular and carbon neutral. But even though the severity of the problem has been widely realised, there is no universal solution to it as yet.
GRINNER aspires to become the first autonomous, artificial intelligence (AI)-enabled robotic sorting system to offer an ideal method for detection and removal of waste containing batteries from waste streams, and thus shield them from the machines that crush and consolidate waste. To do this, the fastest, energy-resolved x-ray detectors, a machine learning -enabled software module, and vision-based pick-and-place robots will be combined to respectively detect, analyse x-ray data and remove WEEE from the waste flow. In this way, toxic fluoride gas emissions will decrease, additional waste will not be generated and burnt battery substances will not contaminate nearby water sources.
Visit the GRINNER website to find out more about the project.
Partners: Direct Conversion, Erion, GreenWeee International, Lynq, WEEE Forum and the Essex Innovation Centre.
The GRINNER project is funded by the European Union under the Horizon Europe programme.