PGNAA Spectral Classification of Metal with Density Estimations
This addresses a critical bottleneck for the copper and aluminium recycling industries by enabling real-time material analysis, though it is incremental as it builds on existing density estimation methods.
The paper tackled the problem of non-destructive online classification of metal alloys using PGNAA spectral data, which is challenging due to small, noisy datasets from short-term measurements, and achieved near-perfect classification of pure aluminium alloys in under 0.25 seconds.
For environmental, sustainable economic and political reasons, recycling processes are becoming increasingly important, aiming at a much higher use of secondary raw materials. Currently, for the copper and aluminium industries, no method for the non-destructive online analysis of heterogeneous materials are available. The Prompt Gamma Neutron Activation Analysis (PGNAA) has the potential to overcome this challenge. A difficulty when using PGNAA for online classification arises from the small amount of noisy data, due to short-term measurements. In this case, classical evaluation methods using detailed peak by peak analysis fail. Therefore, we propose to view spectral data as probability distributions. Then, we can classify material using maximum log-likelihood with respect to kernel density estimation and use discrete sampling to optimize hyperparameters. For measurements of pure aluminium alloys we achieve near perfect classification of aluminium alloys under 0.25 second.