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Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy
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Researchers developed a machine learning framework combining convolutional neural networks and mid-infrared spectroscopy to identify mixed plastic waste compositions in real time, achieving near-100% accuracy at 100 Hz on a moving platform, paving the way for high-throughput industrial plastic sorting and recycling.
To recycle the mixed plastic wastes (MPW), it is important to obtain the compositional information online in real time. We present a sensing framework based on a convolutional neural network (CNN) and mid-infrared spectroscopy (MIR) for the rapid and accurate characterization of MPW. The MPW samples are placed on a moving platform to mimic the industrial environment. The MIR spectra are collected at the rate of 100 Hz, and the proposed CNN architecture can reach an overall prediction accuracy close to 100%. Therefore, the proposed method paves the way toward the online MPW characterization in industrial applications where high throughput is needed.
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