Issue 8, 2023

Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

Abstract

Materials recovery facilities (MRFs) require new automated technologies if growing recycling demands are to be met. Current optical screening devices use visible (VIS) and near-infrared (NIR) wavelengths, frequency ranges that can experience challenges during the characterization of postconsumer plastic waste (PCPW) because of the overly-absorbing spectral bands from dyes and other polymer additives. Technological bottlenecks such as these contribute to 91% of plastic waste never actually being recycled. The mid-infrared (MIR) region has attracted recent attention due to inherent advantages over the VIS and NIR. The fundamental vibrational modes found therein make MIR frequencies promising for high fidelity machine learning (ML) classification. To-date, there are no ML evaluations of extensive MIR spectral datasets reflecting PCPW that would be encountered at MRFs. This study establishes quantifiable metrics, such as model accuracy and prediction time, for classification of a comprehensive MIR database consisting of five PCPW classes that are of economic interest: polyethylene terephthalate (PET #1), high-density polyethylene (HDPE #2), low-density polyethylene (LDPE #4), polypropylene (PP #5), and polystyrene (PS #6). Autoencoders, an unsupervised ML algorithm, were applied to the random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR) models. The RF model achieved accuracies of 100.0% in both the C–H stretching region (2990–2820 cm−1) and molecular fingerprint region (1500–650 cm−1). The C–H stretching region was found to be free from additives that were responsible for misclassification in other regions, making it a fruitful frequency range for future PCPW sorting technologies. The MIR classification of black plastics and polyethylene PCPW using ML autoencoders was also evaluated for the first time.

Graphical abstract: Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

Supplementary files

Article information

Article type
Paper
Submitted
01 may 2023
Accepted
05 iyl 2023
First published
06 iyl 2023
This article is Open Access
Creative Commons BY license

Environ. Sci.: Adv., 2023,2, 1099-1109

Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

N. Stavinski, V. Maheshkar, S. Thomas, K. Dantu and L. Velarde, Environ. Sci.: Adv., 2023, 2, 1099 DOI: 10.1039/D3VA00111C

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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