Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy
Fei Long, Shengli Jiang, Adeyinka Gbenga Adekunle, Víctor M. Zavala, Ezra Bar‐Ziv
Abstract
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.
Topics & Concepts
Characterization (materials science)Convolutional neural networkPlastic wasteSpectroscopyInfraredInfrared spectroscopyComputer scienceThroughputProcess engineeringEnvironmental scienceArtificial intelligenceMaterials scienceNanotechnologyWaste managementChemistryEngineeringOpticsTelecommunicationsOrganic chemistryPhysicsWirelessQuantum mechanicsSpectroscopy and Chemometric AnalysesAdvanced Chemical Sensor TechnologiesSpectroscopy Techniques in Biomedical and Chemical Research