Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy

Fei Long, Shengli Jiang, Adeyinka Gbenga Adekunle, Victor M Zavala, Ezra Bar-Ziv

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 languageEnglish
Pages (from-to)16064-16069
Number of pages6
JournalACS Sustainable Chemistry and Engineering
Volume10
Issue number48
DOIs
StatePublished - 5 Dec 2022
Externally publishedYes

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

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