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Improving waste management through an artificial intelligence (AI) powered detection system of batteries utilising data from x-ray detectors and pick-and-place robots.

The project’s mission is to improve the management chain of Waste from Electrical and Electronic Equipment (WEEE).  Batteries are the main focus of GRINNER because of the careless discarding of them in recycling bins, or in rubbish bags which can cause fire incidents due to damage by sorting equipment.  Subsequently, such battery-caused fires can have a negative ecological impact, harmful effects on people, and bring about financial damage (from €190.000 up to €1.3m per fire incident).  So, even though the severity of the problem has been widely realised, there is no universal solution for it as yet.

GRINNER aspires to become the first autonomous, AI-enabled, robotic sorting system to offer an ideal method for detecting and removing waste that contains batteries from waste streams, and thus shields them from the machines that crush and consolidate waste.  To do so, the fastest energy-resolved x-ray detectors, an ML-enabled software module and vision-based, pick-and-place robots will be combined to respectively detect WEEE, analyse x-ray data and then remove the WEEE from the waste flow.  In this way, toxic fluoride gas emissions will decrease, additional waste will not be generated and burned battery substances will not contaminate nearby water sources.

TWI Hellas's contribution to the project is the development of the x-ray system, the design of the edge AI hardware and the manufacturing and integration of x-ray subsystems.

Partners: Lynq, WEEE Forum, Direct Conversion, GreenWeee International, Erion, University of Essex and TWI Hellas.

GRINNER is funded by the European Union under the Horizon Europe programme.