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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.
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kCARTA: a fast pseudo line-by-line radiative transfer algorithm with analytic Jacobians, fluxes, nonlocal thermodynamic equilibrium, and scattering for the infrared
Abstract. A fast pseudo-monochromatic radiative transfer package using asingular value decomposition (SVD) compressed atmosphericoptical depth database has been developed, primarily for simulatingradiances from hyperspectral sounding instruments (resolution ≥0.1 cm−1). The package has been tested extensively for clear-skyradiative transfer cases, using field campaign data and satelliteinstrument data. The current database uses HITRAN 2016 lineparameters and is primed for use in the spectral region spanning 605 to 2830 cm−1. Optical depths for other spectral regions (15–605 and 2830–45 000 cm−1) can also be generated for use by kCARTA. Theclear-sky radiative transfer model computes the background thermalradiation quickly and accurately using a layer-varying diffusivityangle at each spectral point; it takes less than 30 s (on a2.8 GHz core using four threads) to complete a radiance calculationspanning the infrared. The code can also compute non-localthermodynamic equilibrium effects for the 4 µm CO2 region, as wellas analytic temperature, gas and surface Jacobians. The package alsoincludes flux and heating rate calculations and an interface to aninfrared scattering model.
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- Award ID(s):
- 1726023
- PAR ID:
- 10200866
- Date Published:
- Journal Name:
- Atmospheric Measurement Techniques
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 1867-8548
- Page Range / eLocation ID:
- 323 to 339
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/amt-13-323-2020
