PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning

TL;DR

This study demonstrates the use of PGNAA spectral data combined with machine learning techniques to accurately classify aluminium and copper alloys in real-time, optimizing metal recycling processes.

Contribution

It compares detector types and machine learning methods for spectral classification, highlighting optimal configurations for different measurement durations.

Findings

CeBr₃ detector performs well in short measurements.

HPGe detector yields better accuracy in longer measurements.

MLC and CVAE classifiers achieve high classification accuracy.

Abstract

In this paper, we explore the optimization of metal recycling with a focus on real-time differentiation between alloys of copper and aluminium. Spectral data, obtained through Prompt Gamma Neutron Activation Analysis (PGNAA), is utilized for classification. The study compares data from two detectors, cerium bromide (CeBr$_{3}$) and high purity germanium (HPGe), considering their energy resolution and sensitivity. We test various data generation, preprocessing, and classification methods, with Maximum Likelihood Classifier (MLC) and Conditional Variational Autoencoder (CVAE) yielding the best results. The study also highlights the impact of different detector types on classification accuracy, with CeBr$_{3}$ excelling in short measurement times and HPGe performing better in longer durations. The findings suggest the importance of selecting the appropriate detector and methodology based on specific application requirements.