Periodic Reporting for period 2 - Grinner (ImprovinG waste management through an AI-powered detection system of batteries utilising data from X-Ray detectors and pick-and-place robots.)
Período documentado: 2024-03-01 hasta 2025-08-31
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.
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.
The GRINNER project started in September 2022 and completed in August 2025. All deliverables and milestones have been achieved all set objectives met or exceeded. Within reporting period 2,
- The early X-Ray prototype was deployment and work commenced for building of a database of WEEE scrap and was completed in Reporting Period 2,
- Automation sorting devices have been explored and the team identified suitable solutions for the sorting that are in development for implementation and integration within reporting period 2
- The system conveyor belt as well as the test shield and lead-lined curtains were integrated and assembled to the validation prototype. Additional shielding was also added to ensure that radiation readings were no more that back ground radiation. This allowed the system to be validate around a real working environment.
- The technical team, led by the University of Essex make great progress in the battery detection process where both synthetic and real data was used to train the AI model. This resulted in a 99 percent efficiency in battery detection.
- The coordinator LYNQ completed the development of the Manufacturing Execution system where most of the back end algorithms have been developed and fully integrated with the GRINNER subsystems for integration as per the system architecture developed in Reporting Period 1.
Thanks to the rigorous work ethic of the GRINNER project partners, GRINNER has finished successfully and the technical teams working to put their own resources to develop the prototype to a commercial system post project.
- The early X-Ray prototype was deployment and work commenced for building of a database of WEEE scrap and was completed in Reporting Period 2,
- Automation sorting devices have been explored and the team identified suitable solutions for the sorting that are in development for implementation and integration within reporting period 2
- The system conveyor belt as well as the test shield and lead-lined curtains were integrated and assembled to the validation prototype. Additional shielding was also added to ensure that radiation readings were no more that back ground radiation. This allowed the system to be validate around a real working environment.
- The technical team, led by the University of Essex make great progress in the battery detection process where both synthetic and real data was used to train the AI model. This resulted in a 99 percent efficiency in battery detection.
- The coordinator LYNQ completed the development of the Manufacturing Execution system where most of the back end algorithms have been developed and fully integrated with the GRINNER subsystems for integration as per the system architecture developed in Reporting Period 1.
Thanks to the rigorous work ethic of the GRINNER project partners, GRINNER has finished successfully and the technical teams working to put their own resources to develop the prototype to a commercial system post project.
The GRINNER project is complete and achieved the following progress towards set impacts:
- identified an effective sorting method that can be used to sort the WEEE waste
- produced a proof of concept multi-energy photon counting detector for the X-Ray system
- Produced a proof of concept x-ray system capable of detecting batteries within WEEE waste
- An effective method to eliminate fires associated with batteries
- A stand alone GRINNER system that is cost effective with demonstrable ROI
- Reduction of staff in hazardous environment
- A system that reduces the environmental impact of electrical waste
- A system that will enable proper handling of WEEE waste
- identified an effective sorting method that can be used to sort the WEEE waste
- produced a proof of concept multi-energy photon counting detector for the X-Ray system
- Produced a proof of concept x-ray system capable of detecting batteries within WEEE waste
- An effective method to eliminate fires associated with batteries
- A stand alone GRINNER system that is cost effective with demonstrable ROI
- Reduction of staff in hazardous environment
- A system that reduces the environmental impact of electrical waste
- A system that will enable proper handling of WEEE waste