A center of excellence consortium formed by Gilardoni, Cefriel, and Seval is developing an innovative system to automatically identify lithium batteries prior to WEEE (Waste Electrical and Electronic Equipment) treatment, helping to eliminate fire risks and optimize material recovery.
This initiative is part of the prestigious “Collabora & Innova” call for proposals (first 2024 edition), promoted by the Lombardy Region under the ERDF 2021–2027 Regional Development Program.
Lithium batteries are the invisible heart of our technology: they power smartphones, tablets, and electric vehicles thanks to their high energy density and fast charging capabilities. However, this efficiency hides a danger at the end of the product’s life. If subjected to physical stress (pressure or punctures) during the shredding phases of WEEE, they can trigger a so-called “thermal runaway.” This self-sustaining chain reaction can exceed 400°C, leading to fires and sometimes explosions in recycling facilities.
The Project: Finding an Alternative to Manual Inspection
To address this challenge, a cutting-edge research project was born through the collaboration of three key players: Gilardoni provides its historical technological know-how in the field of X-rays; Cefriel leads the development of advanced algorithms and neural networks for automatic recognition; and Seval offers its experience and infrastructure as a center of excellence for waste disposal.
Until now, the identification of batteries within devices has been entrusted almost exclusively to the sight and experience of human operators. This is a complex task prone to inevitable errors, given the heterogeneity of waste arriving from collection centers. The goal? To create an X-ray machine capable of “seeing” through waste to isolate and separate batteries before they become hazardous.
X-Ray Technology and Neural Networks
The core of the project is the study, development, and creation of a demonstrator based on X-ray analysis and integrated into the treatment line. The process will leverage advanced investigation technologies, including:
- Multi-view Scanning: Waste on conveyor belts passes through multiple X-ray projections to overcome the obstacle of shielding materials.
- Morphological and Atomic Analysis: Thanks to Artificial Intelligence algorithms, the system aims to recognize battery shapes and estimate the atomic number of materials through multi-energy analysis.
- Targeted Intervention: If a battery is detected, the system alerts the operator by providing an X-ray image with the object highlighted, along with an optical photo of the container, allowing for quick and safe extraction.
Project Benefits: Safety and Sustainability
The adoption of this technology will bring concrete benefits on several fronts:
- Safety: A decrease in the risk of explosions and fires caused by hidden batteries.
- Environment: An increase in the percentage of materials recovered and recycled correctly, preventing pollutants from contaminating other supply chains.
- Productivity: Automation that speeds up processes and reduces machine downtime, with the future possibility of implementing automatic waste rejection systems for “alarmed” items.
This synergy between AI, materials physics, and industrial logistics represents a fundamental step toward a safer and more efficient circular economy, transforming a potential environmental risk into a valuable resource for the future.
