A discrimination model in waste plastics sorting using NIR hyperspectral imaging system

Yan, Zheng; Bai, Jiarui; Xu, Jia; Li, Xiayang; Zhang, Yimin

doi:10.1016/j.wasman.2017.10.015

Cited by 123 publications

(63 citation statements)

References 21 publications

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“…Knowing that the separation of plastics for recycling purposes is based mainly on near infrared spectroscopy (NIR) 34 , we compared the polymers synthesized from the petro-derived monomers DET and DEI with the bio-derivable polyesters obtained from DEF (furan-based) and PD24, PD25, and PD26 (pyridine-based). Interestingly, using FT-IR, polymers containing DET and PD25 are in our case easily distinguishable by observing the 1614–1548 cm −1 region, where polymers containing DET have a single representative peak at 1578 cm −1 (see Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

Enzymatic synthesis of lignin derivable pyridine based polyesters for the substitution of petroleum derived plastics

et al. 2019

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Following concerns over increasing global plastic pollution, interest in the production and characterization of bio-based and biodegradable alternatives is rising. In the present work, the synthesis of a series of fully bio-based alternatives based on 2,4-, 2,5-, and 2,6-pyridinedicarboxylic acid-derived polymers produced via enzymatic catalysis are reported. A similar series of aromatic-aliphatic polyesters based on diethyl-2,5-furandicarboxylate and of the petroleum-based diethyl terephthalate and diethyl isophthalate were also synthesized. Here we show that the enzymatic synthesis starting from 2,4-diethyl pyridinedicarboxylate leads to the best polymers in terms of molecular weights (M n = 14.3 and M w of 32.1 kDa when combined with 1,8-octanediol) when polymerized in diphenyl ether. Polymerization in solventless conditions were also successful leading to the synthesis of bio-based oligoesters that can be further functionalized. DSC analysis show a clear similarity in the thermal behavior between 2,4-diethyl pyridinedicarboxylate and diethyl isophthalate (amorphous polymers) and between 2,5-diethyl pyridinedicarboxylate and diethyl terephthalate (crystalline polymers).

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Section: Resultsmentioning

confidence: 99%

Enzymatic synthesis of lignin derivable pyridine based polyesters for the substitution of petroleum derived plastics

et al. 2019

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“…This requires the density of the floatation medium to be adjusted by adding saline solutions or organic solvents [7]. Nowadays, a few advanced sorting technologies are recognized of being promising for the fast industrial identification and separation of solid plastic wastes [8]. Among them, separation processes exploiting the potential of the hyperspectral analysis are gaining increasing attention.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, these are considered to be alternatives for the classical methods, which require large amounts of chemical reagents and are both time consuming and more expensive [9]. In particular, Near InfraRed (NIR) spectroscopy presents several advantages such as remote high-speed measurements, high penetration depth of the NIR radiation and high signal-to-noise ratio [8].…”

Section: Introductionmentioning

confidence: 99%

Characterization and Separation of Traditional and Bio-Plastics by Hyperspectral Devices

Moroni

Mei

2020

Applied Sciences

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Nowadays, bio-plastics can contaminate conventional plastics sent to recycling. Furthermore, the low volume of bio-plastics currently in use has discourage the development of new technologies for their identification and separation. Technologies based on hyperspectral data detection may be profitably employed to separate the bio-plastics from traditional ones and to increase the quality of recycled products. In fact, sensing devices make it possible to accomplish the essential requirement of a mechanical recycling technology, i.e., end products which comply with specific standards determined by industrial applications. This paper presents the results of the hyperspectral analysis conducted on two different plastic polymers (PolyEthylene Terephthalate and PolyStyrene) and one bio-based and biodegradable plastic material (PolyLactic Acid) in different phases of their life cycle (primary raw materials and urban waste). The reflectance analysis is focused on the near-infrared region (900–1700 nm) and data are detected with a linear-spectrometer apparatus and a spectroradiometer. A rapid and reliable identification of three investigated polymers is achieved by using simple two near-infrared wavelength operators employing key wavelengths.

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“…With the emergence of hyperspectral imaging, some industrial firms have designed sorting devices able to discriminate several categories of plastics based on their absorption or transmittance spectra. The sorting process is generally performed with supervised classification that has been well developed with the emergence of computer sciences and data science [17,38,22]. The classification performance might be affected by several issues such as noises or overlapping regions in the feature space [34,21].…”

Section: Introductionmentioning

confidence: 99%

Evidential Classification of Incomplete Data via Imprecise Relabelling: Application to Plastic Sorting

Jacquin

Imoussaten

Trousset

et al. 2019

Lecture Notes in Computer Science

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Besides the ecological issues, recycling of plastics involves economical matters that encourage industrial firms to invest in the field. Part of them have focused on the waste sorting phase by designing optical device able to discriminate on line among plastics categories. For achieving ecological and economical objectives, sorting errors must be minimized to avoid serious recycling problems and significant quality degradation of the final recycled product. Even with the most recent acquisition technologies based on spectra imaging, plastic recognition remains a tough task due to the presence of imprecision and uncertainty, e.g., variability in the measurement due to atmospheric disturbances, ageing of plastics, dark or black coloured materials etc. The enhancement of the recent sorting techniques based on classification algorithms leads to rather good performance results, however for such applications, the remaining errors have serious consequences. In this article, we propose an imprecise classification algorithm to minimize sorting errors of standard classifiers when dealing with incomplete data by both integrating the processing of classification's doubt and hesitation in the decision process and improving the classification performances. To this aim, we propose a relabelling procedure that allows to better represent the imprecision of the learning data and we introduce the belief functions framework to represent the posterior probability provided by a classifier. Finally, the performances of our approach compared to existing imprecise classifiers is illustrated on the sorting problem of four plastics categories from mid-wavelength infra-red spectra acquired in an industrial context.

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A discrimination model in waste plastics sorting using NIR hyperspectral imaging system

Cited by 123 publications

References 21 publications

Enzymatic synthesis of lignin derivable pyridine based polyesters for the substitution of petroleum derived plastics

Enzymatic synthesis of lignin derivable pyridine based polyesters for the substitution of petroleum derived plastics

Characterization and Separation of Traditional and Bio-Plastics by Hyperspectral Devices

Evidential Classification of Incomplete Data via Imprecise Relabelling: Application to Plastic Sorting

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