Sorting of plastics
Systems and methods for classifying and sorting of plastic materials utilizing a vision system and one or more sensor systems, which may implement a machine learning system in order to identify or classify each of the materials, which may then be sorted into separate groups based on such an identification or classification.
1. A method comprising:
capturing a first visual image of a first material piece resulting in a first image data packet pertaining to the first material piece;
capturing a second visual image of a second material piece resulting in a second image data packet pertaining to the second material piece, wherein the first material piece has a first chemical signature, and wherein the second material piece has a second chemical signature different than the first chemical signature;
processing the first and second image data packets with a machine learning system that has previously learned to visually discern between material pieces having the different chemical signatures; and
classifying with the machine learning system the first and second material pieces into two different classifications as a function of the learned visual discernment between material pieces having the different chemical signatures.
2. The method as recited in claim 1 , further comprising sorting the first material piece from the second material piece as a function of the classifications.
3. The method as recited in claim 2 , wherein the material pieces are plastic pieces.
4. The method as recited in claim 3 , wherein the first chemical signature comprises spectral data measured by a plurality of different sensor systems from at least one sample of a plastic piece of a same type as the first plastic piece, and wherein the second chemical signature comprises spectral data measured by the plurality of different sensor systems from at least one sample of a plastic piece of a same type as the second plastic piece.
5. The method as recited in claim 4 , wherein the spectral data pertains to the non-visible spectrum.
6. The method as recited in claim 4 , wherein the plurality of different sensor systems is selected from a group consisting of near infrared (“NIR”), medium wavelength Infrared (“MWIR”), and x-ray fluorescence (“XRF”) systems.
7. The method as recited in claim 4 , wherein the plurality of different sensor systems is selected from a group consisting of infrared (“IR”), Fourier Transform IR (“FTIR”), Forward-looking Infrared (“FUR”), Very Near Infrared (“VNIR”), Near Infrared (“NIR”), Short Wavelength Infrared (“SWIR”), Long Wavelength Infrared (“LWIR”), Medium Wavelength Infrared (“MWIR” or “MIR”), X-Ray Transmission (“XRT”), Gamma Ray, Ultraviolet (“UV”), X-Ray Fluorescence (“XRF”), Laser Induced Breakdown Spectroscopy (“LIB S”), Raman Spectroscopy, Anti-stokes Raman Spectroscopy, Gamma Spectroscopy, Hyperspectral Spectroscopy (e.g., any range beyond visible wavelengths), Acoustic Spectroscopy, NMR Spectroscopy, Microwave Spectroscopy, Terahertz Spectroscopy, Differential Scanning calorimetry (“DSC”), Thermogravimetric analysis (“TGA”), Capillary and rotational rheometry, Optical and scanning electron microscopy (“SEM”), and Chromatography.
8. The method as recited in claim 3 , wherein the first chemical signature comprises measurements of organic and inorganic elements or molecules from at least one sample of a plastic piece of a same type as the first plastic piece, and wherein the second chemical signature comprises measurements of organic and inorganic elements or molecules from at least one sample of a plastic piece of a same type as the second plastic piece.
9. The method as recited in claim 3 , wherein the plastic pieces are selected from the group consisting of type #1 polyethylene terephthalate (“PET”), type #2 high-density polyethylene (“HDPE”), type #3 polyvinylchloride (“PVC”), type #4 low-density polyethylene (“LDPE”), type #5 polypropylene (“PP”), type #6 polystyrene (“PS”), and type #7 other polymers.
10. The method as recited in claim 3 , wherein the first material piece comprises polyvinyl chloride.
11. The method as recited in claim 1 , wherein the two different classifications are different fractions.
12. A system comprising:
a camera configured to capture a first visual image of a first material piece resulting in a first image data packet pertaining to the first material piece, and a second visual image of a second material piece resulting in a second image data packet pertaining to the second material piece, wherein the first material piece has a first chemical signature, and wherein the second material piece has a second chemical signature different than the first chemical signature;
a data processing system configured to process the first and second image data packets with a machine learning system that has previously learned to visually discern between material pieces having the different chemical signatures, wherein the machine learning system classifies the first and second material pieces into two different fractions as a function of the learned visual discernment between material pieces having the different chemical signatures; and
a sorting apparatus configured to sort the first material piece from the second material piece as a function of the fractions.
13. The system as recited in claim 12 , wherein the material pieces are plastic pieces.
14. The system as recited in claim 13 , wherein the first chemical signature comprises spectral data pertaining to the non-visible spectrum measured by a plurality of different sensor systems from at least one sample of a plastic piece of a same type as the first plastic piece, and wherein the second chemical signature comprises spectral data pertaining to the non-visible spectrum measured by the plurality of different sensor systems from at least one sample of a plastic piece of a same type as the second plastic piece.
15. The system as recited in claim 14 , wherein the plurality of different sensor systems is selected from a group consisting of near infrared (“NIR”), medium wavelength Infrared (“MWIR”), and x-ray fluorescence (“XRF”) systems.
16. The system as recited in claim 14 , wherein the plurality of different sensor systems is selected from a group consisting of infrared (“IR”), Fourier Transform IR (“FTIR”), Forward-looking Infrared (“FUR”), Very Near Infrared (“VNIR”), Near Infrared (“NIR”), Short Wavelength Infrared (“SWIR”), Long Wavelength Infrared (“LWIR”), Medium Wavelength Infrared (“MWIR” or “MIR”), X-Ray Transmission (“XRT”), Gamma Ray, Ultraviolet (“UV”), X-Ray Fluorescence (“XRF”), Laser Induced Breakdown Spectroscopy (“LIB S”), Raman Spectroscopy, Anti-stokes Raman Spectroscopy, Gamma Spectroscopy, Hyperspectral Spectroscopy (e.g., any range beyond visible wavelengths), Acoustic Spectroscopy, NMR Spectroscopy, Microwave Spectroscopy, Terahertz Spectroscopy, Differential Scanning calorimetry (“DSC”), Thermogravimetric analysis (“TGA”), Capillary and rotational rheometry, Optical and scanning electron microscopy (“SEM”), and Chromatography.
17. The system as recited in claim 13 , wherein the first chemical signature comprises measurements of organic and inorganic elements or molecules from at least one sample of a plastic piece of a same type as the first plastic piece, and wherein the second chemical signature comprises measurements of organic and inorganic elements or molecules from at least one sample of a plastic piece of a same type as the second plastic piece, wherein the plastic pieces are selected from the group consisting of type #1 polyethylene terephthalate (“PET”), type #2 high-density polyethylene (“HDPE”), type #3 polyvinylchloride (“PVC”), type #4 low-density polyethylene (“LDPE”), type #5 polypropylene (“PP”), type #6 polystyrene (“PS”), and type #7 other polymers.
18. A method comprising:
determining a chemical signature of each one of a mixture of different plastic pieces with a plurality of different sensor systems;
capturing visual images for each of the plastic pieces;
digitally associating the visual images with the chemical signature for each plastic piece;
determining a specific fraction for sorting of plastic pieces;
using the visual images to identifying which of the plastic pieces within the mixture have a chemical signature that falls within the specific fraction; and
training a machine learning system to visually identify plastic pieces that fall within the specific fractions, wherein the training is performed with a control group produced from the identified plastic pieces.
19. The method as recited in claim 18 , wherein the control group is composed of captured visual image data of each of the identified plastic pieces.
20. The method as recited in claim 18 , wherein the fraction is composed of a specific combination of organic and inorganic elements or molecules.
21. The method as recited in claim 18 , wherein the plurality of different sensor systems is selected from a group consisting of near infrared (“NIR”), medium wavelength Infrared (“MWIR”), and x-ray fluorescence (“XRF”) systems.
22. The method as recited in claim 21 , wherein the mixture of different plastic pieces is selected from the group consisting of type #1 polyethylene terephthalate (“PET”), type #2 high-density polyethylene (“HDPE”), type #3 polyvinylchloride (“PVC”), type #4 low-density polyethylene (“LDPE”), type #5 polypropylene (“PP”), type #6 polystyrene (“PS”), and type #7 other polymers.
23. The method as recited in claim 18 , wherein the plurality of different sensor systems is selected from a group consisting of infrared (“IR”), Fourier Transform IR (“FTIR”), Forward-looking Infrared (“FUR”), Very Near Infrared (“VNIR”), Near Infrared (“NIR”), Short Wavelength Infrared (“SWIR”), Long Wavelength Infrared (“LWIR”), Medium Wavelength Infrared (“MWIR” or “MIR”), X-Ray Transmission (“XRT”), Gamma Ray, Ultraviolet (“UV”), X-Ray Fluorescence (“XRF”), Laser Induced Breakdown Spectroscopy (“LIB S”), Raman Spectroscopy, Anti-stokes Raman Spectroscopy, Gamma Spectroscopy, Hyperspectral Spectroscopy (e.g., any range beyond visible wavelengths), Acoustic Spectroscopy, NMR Spectroscopy, Microwave Spectroscopy, Terahertz Spectroscopy, Differential Scanning calorimetry (“DSC”), Thermogravimetric analysis (“TGA”), Capillary and rotational rheometry, Optical and scanning electron microscopy (“SEM”), and Chromatography.