KEYWORDS: Glasses, Solar cells, Hyperspectral systems, Polymers, Education and training, Photovoltaics, Near infrared, Data modeling, Contamination, Acoustooptic tunable filters
This study investigates the application of hyper-spectral imaging (HSI) for detecting polymer contaminants on glass substrates following photovoltaic (PV) recycling processes. HSI’s non-destructive and real-time capabilities are essential for ensuring the quality of separated PV components such as the glass pane. Experimental results demonstrate the precision of HSI in identifying polymer residues, leading to clean, uncontaminated glass suitable for complete re-use. Moreover, our work examines the challenges and future potential of HSI in contaminant detection, highlighting its importance as an in-line quality control tool in industrial recycling facilities.
The amount of end-of-life photovoltaic modules will increase significantly in the upcoming years. In order to enable a high-quality recycling process, innovative approaches to separate the module laminates layer by layer are required. In this work we use a combination of near-infrared spectroscopy (NIRS) and optical coherence tomography (OCT) to characterize the identity and dimensions of the individual layers within the multi-material composite of a PV module, especially the backsheet. NIRS is used to identify the polymer material types, while OCT measures the respective thickness of the layer. First results show that the combination of both techniques enables a precise qualitative and quantitative description of the layers of a PV module that can be used as an input for subsequent separation and recycling processes.
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