Pocheville Ainara, Uria Iratxe, España Paule, Arnaiz Sixto
GAIKER Technology Centre, Basque Research and Technology Alliance (BRTA), Parque Tecnológico, Edificio 202, 48170, Zamudio, Spain.
GAIKER Technology Centre, Basque Research and Technology Alliance (BRTA), Parque Tecnológico, Edificio 202, 48170, Zamudio, Spain.
J Environ Manage. 2025 Jan;373:123897. doi: 10.1016/j.jenvman.2024.123897. Epub 2025 Jan 3.
Current industrial separation and sorting technologies struggle to efficiently identify and classify a large part of Waste of Electric and Electronic Equipment (WEEE) plastics due to their high content of certain additives. In this study, Raman spectroscopy in combination with machine learning methods was assessed to develop classification models that could improve the identification and separation of Polystyrene (PS), Acrylonitrile Butadiene Styrene (ABS), Polycarbonate (PC) and the blend PC/ABS contained in WEEE streams, including black plastics, to increase their recycling rate, and to enhance plastics circularity. Raman spectral analysis was carried out with two lasers of different excitation wavelengths (785 nm and 1064 nm) and varying setting parameters (laser power, integration time, focus distance) with the aim at reducing the fluorescence. Raman spectral data were used to train and test Discriminant Analysis (DA) and Support Vector Machine (SVM) algorithms in an iterative procedure to assess their performance in identifying and classifying real WEEE plastics. Analysis settings were optimized considering industry requirements, such as process productivity (classification rate, short measuring time for fast identification) and product quality (purity of the sorted polymers). Classification models were trained, in a first approach, only on the target WEEE plastics; and in a second approach, on all polymers expected in the WEEE stream, leading to a realistic overview of the potential scalability of the advanced sorting methods and their limitations. The best classification models, based on DA of Raman spectral data obtained with the 1064 nm laser at 500 mW and 1.0 s, led to classify PS and ABS with a purity up to 80 %.
由于电子电气设备废弃物（WEEE）塑料中某些添加剂的含量较高，当前的工业分离和分选技术难以有效地识别和分类其中很大一部分塑料。在本研究中，对拉曼光谱结合机器学习方法进行了评估，以开发分类模型，从而改进对WEEE物流中包含的聚苯乙烯（PS）、丙烯腈-丁二烯-苯乙烯共聚物（ABS）、聚碳酸酯（PC）以及PC/ABS共混物（包括黑色塑料）的识别和分离，提高其回收率，并增强塑料的循环利用。使用两种不同激发波长（785 nm和1064 nm）的激光以及不同的设置参数（激光功率、积分时间、焦距）进行拉曼光谱分析，目的是减少荧光。拉曼光谱数据用于在迭代过程中训练和测试判别分析（DA）和支持向量机（SVM）算法，以评估它们在识别和分类实际WEEE塑料方面的性能。考虑到行业要求，如工艺生产率（分类率、快速识别的短测量时间）和产品质量（分选聚合物的纯度），对分析设置进行了优化。分类模型首先仅在目标WEEE塑料上进行训练；其次在WEEE物流中预期的所有聚合物上进行训练，从而对先进分选方法的潜在可扩展性及其局限性有一个现实的了解。基于在500 mW和1.0 s下用1064 nm激光获得的拉曼光谱数据的判别分析得出的最佳分类模型，能够将PS和ABS的纯度分类到80%。
