Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy
Document Type
Article
Publication Date
11-18-2022
Department
Department of Mechanical Engineering-Engineering Mechanics
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.
Publication Title
ACS Sustainable Chemistry and Engineering
Recommended Citation
Long, F.,
Jiang, S.,
Adekunle, A.,
M Zavala, V.,
&
Bar Ziv, E.
(2022).
Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy.
ACS Sustainable Chemistry and Engineering,
10(48), 16064-16069.
http://doi.org/10.1021/acssuschemeng.2c06052
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16736