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.

翻译：本文探索金属回收的优化，重点关注铜合金与铝合金的实时区分。研究利用瞬发伽马中子活化分析（PGNAA）获取的能谱数据进行分类，对比了溴化铈（CeBr$_{3}$）和高纯锗（HPGe）两种探测器，评估其能量分辨率与灵敏度。通过测试多种数据生成、预处理及分类方法，最大似然分类器（MLC）和条件变分自编码器（CVAE）取得了最优结果。研究还揭示了不同探测器类型对分类准确率的影响：CeBr$_{3}$在短测量时间内表现优异，而HPGe在长时测量中性能更佳。结果表明，需根据具体应用需求选择适当的探测器与方法。