A key issue that continues to affect the Waste Electrical and Electronic Equipment (WEEE) management chain is that of battery-caused fires, costing waste management facilities millions of euros every year and acting as a strong barrier to making Europe circular and carbon neutral. Some battery types located inside discarded WEEE, in particular lithium-ion (Li-ion) and nickel-metal hydride (NiMH), can ignite or explode when damaged (e.g. when entered an electronic scrap recycling shredder within a recycling facility).
Increasing occurrences of waste fires that are caused by improperly discarded, mainly Li-ion ones, threaten the whole waste management sector in numerous countries. Ιn recent years, high quantities of these batteries have been found in several municipal solid waste streams of many European countries, including Austria, Germany, France, and Sweden. Recent research showed that high amounts of lithium-ion batteries (LIBs) are improperly discarded in different municipal solid waste streams, such as residual household waste, lightweight packaging waste, or metal packaging waste. While there is a plethora of different electrochemical systems, the average distribution is shifting more and more towards metal lithium and lithium-ion. That shift is accompanied by increased fire hazards and other safety challenges all along the value chain in the batteries’ end-of-life.
The GRINNER project aims to commercialise an autonomous AI-enabled robotic sorting system capable of detecting and removing e-waste containing batteries from current waste streams before they enter machines that crush and consolidate waste, causing damage to batteries, and massively increasing the risk of fires. The system will comprise of the following:
-The fastest energy-resolved X-Ray detectors in the market;
- A Machine Learning-enabled software module that will analyse X-Ray data and effectively detect waste containing batteries while passing through the waste flow
The GRINNER project objectives are to:
- Build an X-Ray data set of e-waste;
- Develop an AI software module for detecting batteries in e-waste;
- Develop a prototype system and install it in an e-waste facility to conduct live trials;
- Explore the potential for exploiting GRINNER as an economically viable, stand-alone product for e-waste treatment plants. passing through the waste flow.
