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.
Original language | English |
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Pages (from-to) | 16064-16069 |
Number of pages | 6 |
Journal | ACS Sustainable Chemistry and Engineering |
Volume | 10 |
Issue number | 48 |
DOIs | |
State | Published - 5 Dec 2022 |
Externally published | Yes |
Keywords
- MIR spectra
- classification
- machine learning
- mixed plastic waste
- real-time
ASJC Scopus subject areas
- General Chemistry
- Environmental Chemistry
- General Chemical Engineering
- Renewable Energy, Sustainability and the Environment