Simon Van den Eynde
Papers
2
Total Citations
52
H-Index
2
About
Simon Van den Eynde is a researcher at the forefront of sustainable materials engineering, specializing in the circular economy of metals, particularly aluminium. His work bridges advanced machine learning and industrial recycling processes to address critical challenges in scrap metal sorting. Van den Eynde’s most impactful contribution is his pioneering use of deep learning for simultaneous mass estimation and class classification of scrap metals, a method detailed in his highly cited 2022 paper (38 citations). This innovation enables more precise, automated sorting, directly tackling the growing demand for high-quality wrought aluminium alloys driven by sectors like automotive manufacturing. Complementing this technical breakthrough, his techno-economic assessment of robotic aluminium scrap sorting (14 citations) provides a crucial cost-benefit analysis, demonstrating the viability of these technologies at scale. By integrating AI-driven identification with economic modeling, Van den Eynde’s work offers a practical pathway to increase recycling rates and reduce reliance on primary metal production, making him a key voice in the transition toward a more resource-efficient, low-carbon materials economy.
Research Focus
Key Achievements
Top Papers
- 1
- 2Techno-Economic Assessment of Robotic Sorting of Aluminium Scrap14 citations · 2022