Classification of wrought aluminum alloys by Artificial Neural Networks evaluation of Laser Induced Breakdown Spectroscopy spectra from aluminum scrap samples

Every year throughout the world > 50 million vehicles reach the end of their life, producing millions of tons of automotive waste. The current strategies for the separation of the non-ferrous waste fraction, contain mainly aluminum, magnesium, zinc and copper alloys, involve high investment and operational costs, and pose environmental concerns. The European project SHREDDERSORT, in which our research group was actively involved, aimed to overcome this issue by developing a new dry sorting technology for the shredding of non-ferrous automotive wastes. This work represents one step of the complex SHREDDERSORT project, dedicated to the development of a strategy based on Laser Induced Breakdown Spectroscopy (LIBS) for the sorting of light alloys.

LIBS was here applied in laboratory for the analysis of stationary aluminum shredder samples. To process the LIBS spectra a methodological approach based on artificial neural networks was used. Although separation could in principle be based on simple emission line ratios, the neural networks approach enables more reproducible results, which can accommodate the unavoidable signal variations due to the low intrinsic reproducibility of the LIBS systems. The neural network separated samples into different clusters and estimates their elemental concentrations.