Evaluating LIBS analysis for improved steel alloy identification in end-of-life vehicle recycling

Securing raw material supply for high-tech products and reducing their ecological-economic footprint has become a pressing challenge for our society due to increasing demands while natural resources deplete. One solution is seen in closing material loops by recycling. But to ensure successful re-entry of secondary resources into the production chain essentially relies on the accurate identification of materials in mixed waste streams in order to allow for precise sorting into as pure as possible material types. A particular relevant, but at the same time particularly challenging, task relates to the identification of metal alloys. A wide variety has been engineered to provide highly specific functionalities of individual metals such as, for example, steel in the automotive industry. Innovation over many years resulted in cars containing multiple high-performance steel alloys. At their end-of-life, car recycling routines can sort out concentrates of steel, but mixing the different alloys prevents the recycling material from meeting the quality criteria needed for new car production, and hence, cause downcycling. Although several sensor-based sorting solutions are available to map qualitative material differences for many waste streams, a precise and quantitative solution is needed to differentiate between steel alloy types. LIBS provides a promising solution as it allows for elemental analysis along with concentration information in a fast and contact-free manner compatible with conveyor-belt operations.

            With this contribution, we highlight the challenges of steel alloy detection using LIBS and point out solutions for analytical workflows and practical applications. This involves especially the detailed investigation of measurement parameters, establishment of calibration models for most relevant elements and discuss potential influences from disturbances such as from surface coating. The results suggest a successful discrimination of automotive-relevant steel alloys. The workflow hence, provides the basis for improved alloy-specific sorting products. Providing such analytical tools and corresponding workflows will help for increasing the quality of recycling and reducing the risk of increasingly complex recycling mixtures after multiple cycles. In this context, accurate quantitative LIBS results provide one cornerstone to future innovations on material recycling by products that at least partially re-enter high-performance product cycles.