Plastic separation

Abstract

To provide: an improvement in processing for separating all types of plastics; a function of separating plastics including PVC, LDPE, PP, PS, multilayer polymers, composite polymers, acrylic, and nylon; a function of separating PVC; and a function of separating plastic mixtures into new classifications or fractions.SOLUTION: There is provided a system and a method for classifying and separating plastic materials by using a vision system and at least one sensor system, the system and method including implementation of a machine learning system for identifying or classifying each material and the materials being separated into different groups on the basis of the identification or classification.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 146,892 and U.S. Provisional Patent Application No. 63 / 173,301. This application is a continuation-in-part of U.S. Patent Application No. 17 / 495,291, which is a continuation-in-part of U.S. Patent Application No. 17 / 380,928, which is a continuation-in-part of U.S. Patent Application No. 17 / 227,245, which is a continuation-in-part of U.S. Patent Application No. 16 / 939,011, which is a continuation-in-part of U.S. Patent Application No. 16 / 375,675 (issued as U.S. Patent No. 10,722,922), which is a continuation-in-part of U.S. Patent Application No. 15 / 9 No. 63,755 (issued as U.S. Patent No. 10,710,119), which claims priority to U.S. Provisional Patent Application No. 62 / 490,219, which is a continuation-in-part of U.S. Patent Application No. 15 / 213,129 (issued as U.S. Patent No. 10,207,296), which claims priority to U.S. Provisional Patent Application No. 62 / 193,332, all of which are incorporated herein by reference. This application is also a continuation-in-part of U.S. patent application Ser. No. 17 / 491,415, which is a continuation-in-part of U.S. patent application Ser. No. 16 / 852,514, which is a divisional application of U.S. patent application Ser. No. 16 / 358,374 (issued as U.S. Patent No. 10,625,304), which is a continuation-in-part of U.S. patent application Ser. No. 15 / 963,755 (issued as U.S. Patent No. 10,710,119).

[0002] Government Licensing Rights This disclosure was made with U.S. government support under Grant No. DE-AR0000422 awarded by the U.S. Department of Energy. The U.S. government may have certain rights in this disclosure.

[0003] FIELD OF THE DISCLOSURE This disclosure relates generally to the separation of solid waste, and more particularly to the separation of plastic debris from municipal or industrial solid waste. [Background technology]

[0004] This section is intended to introduce various aspects of technology that may be related to exemplary embodiments of the present disclosure. The discussion is believed to be helpful in providing a framework to facilitate a better understanding of certain aspects of the present disclosure. As such, it should be understood that this section is to be read in this light, and not necessarily as admissions of prior art.

[0005] Recycling is the process of collecting and treating materials that would otherwise be discarded as trash (e.g., waste streams) to transform them into new products or at least enable more appropriate disposal. Recycling benefits local communities and the environment because it reduces the amount of waste sent to landfills, conserves natural resources such as wood, water, and minerals, increases economic security by utilizing domestic material sources, prevents pollution by reducing the need to collect new raw materials, and saves energy. After collection, recyclables may be sent to a material recovery facility (MRF) to be sorted, cleaned, and processed into materials that can be used in manufacturing. As a result, high-throughput automated sorting platforms that economically separate highly mixed waste streams would be beneficial across various industries. Therefore, there is a need for cost-effective sorting platforms that can identify, analyze, and separate mixed industrial or municipal solid waste streams at high throughput and economically produce high-quality feedstocks (possibly with low levels of trace contaminants) for subsequent processing. Typically, MRFs cannot distinguish between many materials, limiting the market for sorted materials to lower quality and lower prices, or they are too time-consuming, labor-intensive, and inefficient, limiting the amount of material that can be economically recycled or recovered.

[0006] Municipal solid waste (MSW) is a broad term that refers to waste streams including residential, commercial, and industrial sources. Within each of these categories are thousands of different materials and products. The EPA reported that 267.8 million tons of MSW were generated in 2017. 35.37 million tons, or 13.2% of that MSW's total weight, consisted of plastic. Of the 35.37 million tons of plastic, 2.96 million tons (8.4%) were recycled, 5.59 million tons (15.8%) were combusted with energy recovery, and 26.82 million tons (75.8%) were landfilled. The need for more plastic recycling is clear.

[0007] Plastic recycling is the reprocessing of plastic waste to create new, useful products. Recycling is necessary because almost all plastics are non-biodegradable and accumulate in the environment. Currently, almost all recycling is done by remelting used plastics and regenerating them into new items: so-called mechanical recycling. This can cause degradation of the polymer at the chemical level and also requires sorting plastic waste by both color and polymer type before reprocessing, which is complex and expensive. Failure to do so can lead to unstable material properties, making it unattractive for industry. An alternative approach, known as feedstock recycling, involves converting plastic waste back into its original chemical form and then reprocessing it back into new plastic. This promises greater recycling but involves higher energy and capital costs. Plastic waste can also be burned instead of fossil fuels as part of an energy recovery strategy.

[0008] Currently, only some plastics are recyclable. When plastics are recycled, they are typically separated into various types. Recycling rates also vary depending on the type of plastic. Several types are commonly used, each with different chemical and physical properties. This results in differences in the ease of sorting and reprocessing, which affects the value and market size of the recovered material. Plastic packaging and products made from a single material (e.g., polyethylene terephthalate ("PET"), high-density polyethylene ("HDPE"), and polypropylene ("PP")) are more easily recycled. Plastics that are sometimes or rarely recyclable include polyvinyl chloride ("PVC"), low-density polyethylene ("LDPE"), linear low-density polyethylene ("LLDPE"), and polystyrene ("PS"). Furthermore, plastics can only be recycled a limited number of times.

[0009] Modern single-stream MRFs and plastic reclaimers handle large volumes of material, necessitating processing equipment capable of moving and sorting materials at high speeds. At the same time, they ensure the highest value is obtained from the purest, least contaminated stream. To achieve these somewhat contradictory goals, today's single-stream MRFs and reclaimers employ automated devices that sort plastic packaging by its near-infrared ("NIR") signature, either through transmission or reflection. These sensors rely on the reflection of light from an external source and can only see the surface of the material. Furthermore, these sensors only capture polymer information. For example, NIR spectroscopy can distinguish between #1 plastic types—clear and light blue PET—and #2 HDPE, while rejecting other plastics, such as #1 colored PET, #3 PVC, #4 LDPE, #5 PP, #6 PS, and #7 multilayer polymers, composite polymers, acrylic, and nylon. Furthermore, NIR spectroscopy cannot accurately distinguish between black or highly colored plastics, or composite materials such as plastic-coated paper or multilayer packaging (made with polymer multilayer films), potentially resulting in misleading readings. Most black plastics are colored using carbon. Black plastic is widely used in the automotive industry, electronics, food packaging, plastic bags, etc. However, black plastic not only absorbs visible light, but also the near-infrared part of the spectrum, which has the unfortunate side effect of making it invisible to NIR spectroscopy. Therefore, "stealthily" black plastic goes undetected into a "miscellaneous" bin at the end of the conveyor, where it is either burned for energy or dumped in a landfill.

[0010] In closed-loop, or primary, recycling, waste plastics are recycled into new items of similar quality and type (e.g., beverage bottles are recycled back into beverage bottles). However, continuously mechanically recycling plastics without reducing their quality is extremely difficult due to the cumulative degradation of the polymers and the risk of contaminant buildup. Closed-loop recycling has been studied for many polymers, but so far only PET bottle recycling has been industrially successful.

[0011] In open-loop or secondary recycling (also known as downcycling), the quality of plastics decreases each time they are recycled, so the material cannot be recycled permanently and ultimately becomes waste. Recycling plastic bottles into fleece and other fibers is a common example and accounts for the majority of PET recycling. The decrease in polymer quality can be offset by blending recycled plastic with virgin materials or compatibilized plastics when making new products.

[0012] Although thermoset polymers do not melt, techniques have been developed to mechanically recycle them, which typically involves grinding the material and then mixing it with some kind of binder to form a new composite material.

[0013] In raw material or tertiary recycling (also called chemical recycling), polymers are reduced to their chemical building blocks (monomers), which can then be polymerized back into new plastics. Thermal depolymerization and chemical depolymerization are two types of raw material recycling.

[0014] Energy recovery, also known as energy recycling or quaternary recycling, involves burning plastic waste to produce energy instead of fossil fuels.

[0015] Processes have been developed that allow certain types of plastics to be used as a carbon source (instead of coke) in the recycling of scrap steel. Crushed plastics may be used as construction aggregate or filler in certain applications.

[0016] Plastic waste may simply be burned as refuse-derived fuel (RDF) in waste-to-energy processes, or it may first be chemically converted into synthetic fuel. In either approach, PVC must be excluded or compensated for with dichlorination technology, because PVC produces large amounts of hydrogen chloride (HCl) when burned, which can corrode equipment and lead to undesirable chlorination of fuel products.

[0017] Mixed plastic waste can be depolymerized to produce synthetic fuels, which have a higher calorific value than the starting plastics and can be burned more efficiently, but still less efficiently than fossil fuels. Various conversion techniques are being investigated, the most common of which is pyrolysis. The use of catalysts in pyrolysis results in more defined, higher-value products. Compared to widespread incineration, plastic-to-fuel technologies have historically struggled to be economically viable due to the high costs of collecting and sorting plastics and the relatively low value of the fuels produced. [Prior art documents] [Patent documents]

[0018] [Patent Document 1] U.S. Published Patent Application No. 2022 / 0016675 [Non-patent literature]

[0019] [Non-Patent Document 1] Krizhevsky et al., "ImageNet Classification with Deep Convolutional Networks," Proceedings of the 25th International Conference on Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, NV [Non-patent document 2] LeCun et al., "Gradient-Based Learning Applied to Document Recognition," IEEE Proceedings, Institute of Electrical and Electronics Engineers (IEEE), November 1998. Summary of the Invention [Problem to be solved by the invention]

[0020] As a result of the above, there is a desire for improved processes to separate all types of plastics, the ability to separate #3 to #7 types of plastics, the ability to separate PVC, and the ability to separate mixtures of plastics into new classes or fractions so that they can be recycled more efficiently. [Means for solving the problem]

[0021] An aspect of the present disclosure provides a method including capturing a first visual image of a first piece of material, resulting in a first image data packet relating to the first piece of material; capturing a second visual image of a second piece of material, resulting in a second image data packet relating to the second piece of material, the first piece of material having a first chemical signature and the second piece of material having a second chemical signature different from the first chemical signature; processing the first and second image data packets with a machine learning system previously trained to visually distinguish between pieces of material having different chemical signatures; and using the machine learning system to classify the first and second pieces of material into two different classifications in response to the trained visual distinction between the pieces of material having different chemical signatures. The method may further include separating the first piece of material from the second piece of material in response to the classification. The pieces of material may be plastic pieces. The first chemical characteristic may be spectral data measured by a plurality of different sensor systems from at least one sample of plastic pieces of the same type as the first plastic piece, and the second chemical characteristic may be spectral data measured by a plurality of different sensor systems from at least one sample of plastic pieces of the same type as the second plastic piece. The spectral data may relate to the non-visible spectrum. The plurality of different sensor systems may be selected from the group consisting of near-infrared ("NIR"), mid-wavelength infrared ("MWIR"), and X-ray fluorescence ("XRF") systems.The plurality of different sensor systems may be selected from the group consisting of infrared ("IR"), Fourier transform IR ("FTIR"), forward-looking infrared ("FLIR"), very near infrared ("VNIR"), near infrared ("NIR"), short wavelength infrared ("SWIR"), long wavelength infrared ("LWIR"), mid-wavelength infrared ("MWIR" or "MIR"), X-ray transmission ("XRT"), gamma ray, ultraviolet ("UV"), X-ray fluorescence ("XRF"), laser-induced breakdown spectroscopy ("LIBS"), Raman spectroscopy, anti-Stokes Raman spectroscopy, gamma ray spectroscopy, hyperspectral spectroscopy (e.g., 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. The first chemical signature may include measurements of organic and inorganic elements or molecules from at least one sample of plastic pieces of the same type as the first plastic piece, and the second chemical signature may include measurements of organic and inorganic elements or molecules from at least one sample of plastic pieces of the same type as the second plastic piece. The plastic pieces may be selected from the group consisting of Type #1 polyethylene terephthalate ("PET"), Type #2 high-density polyethylene ("HDPE"), Type #3 polyvinyl chloride ("PVC"), Type #4 low-density polyethylene ("LDPE"), Type #5 polypropylene ("PP"), Type #6 polystyrene ("PS"), and Type #7 other polymers. The first material piece may include polyvinyl chloride. The two different classifications may be different fractions.

[0022] An aspect of the present disclosure provides a system including: a camera configured to capture a first visual image of a first piece of material resulting in a first image data packet related to the first piece of material, and a second visual image of a second piece of material resulting in a second image data packet related to the second piece of material, the first piece of material having a first chemical characteristic and the second piece of material having a second chemical characteristic different from the first chemical characteristic; a data processing system configured to process the first and second image data packets using a machine learning system previously trained to visually distinguish between pieces of material having different chemical characteristics, the machine learning system classifying the first and second pieces of material into two different fractions in response to the trained visual distinction between the pieces of material having different chemical characteristics; and a sorting device configured to separate the first piece of material from the second piece of material according to the fractions. The pieces of material may be plastic pieces. The first chemical characteristic may be spectral data over the non-visible spectrum measured by a plurality of different sensor systems from at least one sample of plastic piece of the same type as the first plastic piece, and the second chemical characteristic may include spectral data over the non-visible spectrum measured by a plurality of different sensor systems from at least one sample of plastic piece of the same type as the second plastic piece. The plurality of different sensor systems may be from the group consisting of near-infrared ("NIR"), mid-wavelength infrared ("MWIR"), and X-ray fluorescence ("XRF") systems.The plurality of different sensor systems may be from the group consisting of infrared ("IR"), Fourier transform IR ("FTIR"), forward-looking infrared ("FLIR"), very near infrared ("VNIR"), near infrared ("NIR"), short wavelength infrared ("SWIR"), long wavelength infrared ("LWIR"), mid wavelength infrared ("MWIR" or "MIR"), X-ray transmission ("XRT"), gamma ray, ultraviolet ("UV"), X-ray fluorescence ("XRF"), laser-induced breakdown spectroscopy ("LIBS"), Raman spectroscopy, anti-Stokes Raman spectroscopy, gamma ray spectroscopy, hyperspectral spectroscopy (such as 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. The first chemical characteristic may include measurements of organic and inorganic elements or molecules from at least one sample of plastic piece of the same type as the first plastic piece, and the second chemical characteristic may include measurements of organic and inorganic elements or molecules from at least one sample of plastic piece of the same type as the second plastic piece, the plastic piece being selected from the group consisting of Type #1 polyethylene terephthalate ("PET"), Type #2 high density polyethylene ("HDPE"), Type #3 polyvinyl chloride ("PVC"), Type #4 low density polyethylene ("LDPE"), Type #5 polypropylene ("PP"), Type #6 polystyrene ("PS"), and Type #7 other polymers.

[0023] Aspects of the present disclosure provide a method including the steps of: using a plurality of different sensor systems to determine the chemical signature of each of a mixture of different plastic pieces; capturing a visual image of each plastic piece; digitally associating the chemical signature of each plastic piece with the visual image; determining a specific fraction for separating the plastic pieces; using the visual image to identify which plastic pieces in the mixture have the chemical signature corresponding to the specific fraction; and training a machine learning system to visually identify the plastic pieces corresponding to the specific fraction, wherein the training is performed using a control group created from the identified plastic pieces. The control group may be composed of the captured visual image data of each of the identified plastic pieces. The fraction may be composed of a specific combination of organic and inorganic elements or molecules. The plurality of different sensor systems may be selected from the group consisting of near-infrared ("NIR"), mid-wavelength infrared ("MWIR"), and X-ray fluorescence ("XRF") systems. The mixture of different plastic pieces may be selected from the group consisting of Type #1 Polyethylene Terephthalate ("PET"), Type #2 High Density Polyethylene ("HDPE"), Type #3 Polyvinyl Chloride ("PVC"), Type #4 Low Density Polyethylene ("LDPE"), Type #5 Polypropylene ("PP"), Type #6 Polystyrene ("PS"), and Type #7 Other Polymers.The plurality of different sensor systems may be selected from the group consisting of infrared ("IR"), Fourier transform IR ("FTIR"), forward-looking infrared ("FLIR"), very near infrared ("VNIR"), near infrared ("NIR"), short wavelength infrared ("SWIR"), long wavelength infrared ("LWIR"), mid-wavelength infrared ("MWIR" or "MIR"), X-ray transmission ("XRT"), gamma ray, ultraviolet ("UV"), X-ray fluorescence ("XRF"), laser-induced breakdown spectroscopy ("LIBS"), Raman spectroscopy, anti-Stokes Raman spectroscopy, gamma ray spectroscopy, hyperspectral spectroscopy (e.g., 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. [Brief explanation of the drawings]

[0024] [Figure 1] FIG. 1 is a schematic diagram of a sorting system configured in accordance with certain embodiments of the present disclosure. [Figure 2] FIG. 1 illustrates an exemplary representation of a control set of material pieces used in the training phase of a machine learning system. [Figure 3] FIG. 1 illustrates a flowchart configured in accordance with certain embodiments of the present disclosure. [Figure 4] FIG. 1 is a simplified schematic diagram configured in accordance with certain embodiments of the present disclosure. [Figure 5] FIG. 1 illustrates examples of chemical features. [Figure 6] FIG. 1 illustrates examples of chemical features. [Figure 7] FIG. 1 illustrates a flowchart configured in accordance with certain embodiments of the present disclosure. [Figure 8] FIG. 1 illustrates a flowchart configured in accordance with certain embodiments of the present disclosure. [Figure 9] FIG. 1 is a block diagram of a data processing system configured in accordance with certain embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0025] Various detailed embodiments of the present disclosure are disclosed herein. However, it should be understood that the disclosed embodiments are merely exemplary of the present disclosure, which may be embodied in various alternative forms. The numerical values ​​are not necessarily to scale, and some features may be exaggerated or minimized to show details of specific components. Therefore, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching those skilled in the art how to use various embodiments of the present disclosure.

[0026] As used herein, "material" includes metals (ferrous and non-ferrous), alloys, plastics (including, but not limited to, those disclosed herein, known in the industry, or newly created in the future), rubber, foam, glass (including, but not limited to, borosilicate glass or soda lime glass, and various colored glasses), ceramic, paper, cardboard, Teflon, PE, bundled wire, insulated wire, rare earth elements, leaves, wood, plants, plant parts, fibers, biowaste, packaging, electronic waste, batteries and accumulators, end-of-life vehicles, mining, construction, demolition waste, agricultural crop waste, forest residues, cultivated grasses, wood-based energy The term "solid waste" may include any item or object, including, but not limited to, energy crops, microalgae, urban food waste, food waste, hazardous chemical and biomedical waste, construction debris, farm waste, biogenic items, non-biogenic items, objects with a particular carbon content, other objects that may be found in municipal solid waste, and any other object, article, or material disclosed herein, including further types or classes of any of the foregoing that can be distinguished from one another by one or more sensor systems, including, but not limited to, any of the sensor technologies disclosed herein.

[0027] "Material" may include any item or object composed of a chemical element, a compound or mixture of chemical elements, or a compound or mixture of compounds or mixtures of chemical elements, where the compound or mixture may range in complexity from simple to complex. As used herein, "element" means a chemical element of the periodic table of the elements, including elements that may be discovered after the filing date of this application. Within this disclosure, the terms "scrap," "scrap pieces," "material," and "material pieces" may be used interchangeably.

[0028] As is well known in the art, a "polymer" is a substance or material made up of very large molecules or macromolecules made up of many repeating subunits. Polymers can be natural polymers found in nature or synthetic polymers.

[0029] "Multilayer polymer film" is a film made up of two or more different compositions, with a maximum of about 7.5 -8 x10 -4 The layer may have a thickness of 1000 .mu.m. The layer is at least partially continuous, preferably, but optionally coextensive.

[0030] As used herein, the terms "plastic," "plastic piece," and "plastic material piece" (which may all be used interchangeably) refer to any object that includes or is composed of a polymer composition of one or more polymers and / or a multilayer polymer film.

[0031] As used herein, the term "chemical signature" refers to a unique pattern (e.g., a fingerprint spectrum) generated by one or more analytical instruments that indicates the presence of one or more specific elements or molecules (including polymers) in a sample. The elements or molecules may be organic and / or inorganic. Such analytical instruments include any of the sensor systems disclosed herein. According to embodiments of the present disclosure, one or more sensor systems disclosed herein may be configured to generate a chemical signature of a piece of material (e.g., a piece of plastic).

[0032] As used herein, "fraction" refers to a particular combination of organic and / or inorganic elements or molecules, polymer type, plastic type, polymer composition, chemical characteristics of a plastic, physical properties of a plastic piece (e.g., color, clarity, strength, melting point, density, shape, size, manufacturing type, uniformity, response to stimuli, etc.), including all of the various classifications and types of plastics disclosed herein. Non-limiting examples of fractions are LDPE with a relatively high percentage of aluminum; LDPE and PP with a relatively low percentage of iron; PP with zinc; combinations of PE, PET, and HDPE; any type of red LDPE plastic piece; any combination of plastic pieces except PVC; black plastic pieces; combinations of #3-#7 types of plastics containing specified combinations of organic and inorganic molecules; combinations of one or more different types of multilayer polymer films; combinations of certain plastics without certain contaminants or additives; any type of plastic with a melting point above a specified threshold; several certain types of thermosetting plastics; certain plastics without chlorine; combinations of plastics with similar densities; combinations of plastics with similar polarity; and one or more different types of plastic pieces, including plastic bottles without caps or vice versa.

[0033] "Catalytic pyrolysis" involves the decomposition of polymeric materials by heating them in the presence of a catalyst in the absence of oxygen.

[0034] The term "predetermined" refers to something that is established or determined in advance.

[0035] "Spectral imaging" is imaging that uses multiple bands across the electromagnetic spectrum. While a typical camera captures light across three wavelength bands—red, green, and blue (RGB)—of the visible spectrum, spectral imaging encompasses a variety of techniques that include but go beyond RGB. Spectral imaging may use the infrared, visible, ultraviolet, and / or X-ray spectrum, or a combination of the above. Spectral data, or spectral image data, is a digital data representation of a spectral image. Spectral imaging may include simultaneous acquisition of spectral data in visible and non-visible bands, illumination from outside the visible range, or the use of optical filters to capture specific spectral ranges. It is also possible to capture hundreds of wavelength bands for each pixel of a spectral image.

[0036] As used herein, the term "image data packet" refers to a packet of digital data relating to a captured spectral image of an individual piece of material.

[0037] As used herein, the terms “identify” and “classify,” “identification,” and “classification,” and their derivatives, may be used interchangeably. As used herein, “classifying” a piece of material means determining (i.e., identifying) the type or class of material to which the piece of material belongs. For example, according to certain embodiments of the present disclosure, a sensor system (described further herein) can be configured to collect and analyze any type of information to classify materials, and classification can be utilized within a sorting system to selectively separate pieces of material according to a set of one or more physical and / or chemical properties (e.g., user-definable), including, but not limited to, color, texture, hue, shape, brightness, weight, density, composition, size, uniformity, manufacturing type, chemical signature, predetermined percentages, radioactive signature, transmittance of light, sound, or other signals, and response to stimuli such as various fields, including the emitted and / or reflected electromagnetic radiation (“EM”) of the piece of material. As used herein, "manufacturing type" refers to the type of manufacturing process by which a piece of material is produced, such as a metal part formed by a forging process, a cast metal part (including but not limited to expendable die casting, permanent die casting, and powder metallurgy), a forging, a material removal process, etc.

[0038] Material types or classes (i.e., classifications) are user-definable and are not limited to known material classifications. Types or classes range in granularity from very coarse to very fine. For example, types or classes may include relatively coarse-grained types or classes of plastics, ceramics, glass, metals, and other materials; finer-grained types or classes of various metals and metal alloys, such as zinc, copper, brass, chrome plate, and aluminum; or relatively fine-grained types or classes of specific types of plastics. Thus, types or classes may be configured to distinguish between materials of significantly different compositions, such as different types of plastics (e.g., any of types #1 through #7), or between materials of nearly identical composition, such as different subclasses of plastics that may fall within a specific plastic type. It should be understood that the methods and systems discussed herein can be applied to accurately identify / classify materials whose composition is completely unknown before classification.

[0039] Embodiments of the present disclosure enhance plastic sorting capabilities by fusing multiple sensor technologies with machine learning systems. The limitations of sensor-based sorting technology arise from the use of a single sensor, as each sensor can only detect a narrow range of signals. The most common sorter sensor types are eddy current, visible camera, x-ray transmission, near-infrared, and x-ray fluorescence ("XRF"). These are summarized in the following table:

[0040] [Table 1]

[0041] However, plastic fragments in MSW can be composed of one or more organic polymers and one or more inorganic elements, and come in a variety of colors, shapes, and sizes. Examples of these plastics include potato chip bags, squeezable juice boxes, some beverage containers, and electromagnetically sensitive packaging for electronic devices. Embodiments of the present disclosure create novel fractions from this waste stream using sensor-based technology that can achieve the separation of these different types of plastics into unique classifications that account for their organic polymer composition and / or their inorganic element composition. For example, a conversion chemist with a keen interest in the relative composition of polymers and inorganic elements can select one or more novel fractions and create specific products from recycled plastics separated into such fractions. As a result, a sorting system configured in accordance with embodiments of the present disclosure can create fractions beyond those possible with existing state-of-the-art sorting technologies.

[0042] For example, certain embodiments of the present disclosure may be configured to sort and / or separate predetermined fractions from bales of #3-#7 type plastics to create new products (e.g., through recycling processes) and / or fuels. Examples of end-use applications for such fractions include, but are not limited to, gases (e.g., C1-C4, etc.), fuels (e.g., gasoline, diesel, etc.), and vacuum gas oil. However, sorting #3-#7 type plastics based on organic and inorganic elemental composition has not been successful to date.

[0043] Embodiments of the present disclosure may be configured to sort pieces of plastic material according to a variety of different predetermined fractions or combinations of properties or types as disclosed below and elsewhere within this disclosure.

[0044] Plastics are classified into three types according to their characteristics, such as chemical structure, polarity, and use.

[0045] According to their chemical structure and temperature behavior, plastics can be classified as thermoplastics, thermosets, and elastomers.

[0046] Regarding polarity, when atoms of different properties are present, electrons move toward the most electronegative atom in a covalent bond, creating a dipole. Polymers containing extremely electronegative atoms, such as CI, O, N, and F, are polar compounds, which influence their properties. Increasing polarity improves mechanical resistance, hardness, rigidity, heat resistance, water absorption, and chemical resistance, as well as permeability to polar compounds such as water vapor and adhesion to metals. At the same time, increasing polarity reduces thermal expansion, electrical insulating ability, tendency to accumulate electrostatic charge, and permeability to polar molecules (O2, N2). In this way, different families, such as polyolefins, polyesters, acetals, and halogenated polymers, can be distinguished.

[0047] Depending on the application, a third classification applies to thermoplastic materials. Within this third classification there are four types of plastics:

[0048] Standard plastic or commodity: a plastic that is produced and used in large quantities due to its price and various favorable properties. Examples include polyethylene ("PE"), polypropylene ("PP"), polystyrene ("PS"), polyvinyl chloride ("PVC"), or acrylonitrile butadiene styrene ("ABS") copolymers.

[0049] Engineering plastics: Used where good structure, transparency, self-lubricating properties, and thermal properties are required. Examples include polyamide ("PA"), polyacetal ("POM"), polycarbonate ("PC"), polyethylene terephthalate ("PET"), polyphenylene ether ("PPE"), and polybutylene terephthalate ("PBT").

[0050] Specialty plastics: These have very specific properties, such as polymethyl methacrylate ("PMMA"), which has high transparency and light resistance, and polytetrafluoroethylene (Teflon®), which has excellent resistance to temperature and chemicals.

[0051] High-performance plastics: Primarily thermoplastics with high heat resistance, i.e., with excellent mechanical resistance to high temperatures, especially up to 150°C. Polyimide ("PI"), polysulfone ("PSU"), polyethersulfone ("PES"), polyarylsulfone ("PAS"), polyphenylene sulfide ("PPS"), and liquid crystal polymers ("LCP") are high-performance plastics.

[0052] Many plastic products are marked with a symbol indicating the type of polymer from which they are made. These resin identification codes, often abbreviated as RIC, are used internationally. There are seven codes in total, six of which correspond to the most common commodity plastic types and one that is a catch-all. These types are also referred to herein as polymer types #1 through #7. Polymer type #1 refers to polyethylene terephthalate ("PET"), #2 refers to high-density polyethylene ("HDPE"), #3 refers to polyvinyl chloride ("PVC"), #4 refers to low-density polyethylene ("LDPE"), #5 refers to polypropylene ("PP"), #6 refers to polystyrene ("PS"), and #7 refers to other polymers not included in polymer types #1 through #6 (e.g., acrylic, polycarbonate ("PC"), polylactic acid, nylon, glass fiber, and ABS). The EU maintains a similar nine-code list, which also includes ABS and polyamide.

[0053] PET plastics are used to manufacture many common household items, such as beverage bottles, medicine bottles, rope, clothing, and carpet fibers. HDPE plastics are commonly used to manufacture containers for milk, motor oil, shampoo and conditioner, soap bottles, detergent, and bleach. PVC is used for all types of pipes and tiles, most commonly plumbing pipes. LDPE products include plastic wrap, sandwich bags, squeezable bottles, and plastic grocery bags. PP is used to manufacture lunch boxes, margarine containers, yogurt pots, syrup bottles, medicine bottles, and plastic bottle caps. Polystyrene products include disposable coffee cups, plastic food boxes, plastic cutlery, and packaging foam. Polycarbonate is used for baby bottles, CDs, medical storage containers, and more. Therefore, according to embodiments of the present disclosure, a vision system incorporating a machine learning system can be trained to identify and separate these different types of plastics based on the type of product being manufactured.

[0054] Plastic pieces can be classified according to the types of additives they may contain. Additives are compounds blended into plastics to improve performance, and include stabilizers, fillers, and dyes. Clear plastics are most valuable because they may still be dyed, while black or strongly colored plastics are much less valuable because their presence can discolor the product. Therefore, to obtain material suitable for recycling, it may be necessary to separate plastics by both polymer type and color.

[0055] Plastics can also be classified and separated based on density. Certain polymers have similar density ranges (e.g., PP and PE, or PET, PS, and PVC). If a piece of plastic contains a high percentage of filler, this can affect its density.

[0056] Plastic waste can be broadly divided into two categories: industrial scrap (also known as post-industrial resin) and post-consumer waste.

[0057] Plastic pieces can be sorted / separated depending on how they can be recycled. During mechanical recycling, plastics may be reprocessed at temperatures ranging from 150 to 320°C, depending on the type of polymer, which can lead to undesirable chemical reactions that cause degradation of the polymer. This can reduce the physical properties and overall quality of the plastic, produce volatile low-molecular-weight compounds, and can cause undesirable tastes and odors or thermal discoloration. Therefore, embodiments of the present disclosure can be configured to sort and separate plastic pieces to avoid such undesirable chemical reactions. Additives present in plastics can accelerate this degradation. For example, oxo-biodegradable additives intended to improve the biodegradability of plastics can increase the degree of thermal degradation. Similarly, flame retardants can also have undesirable effects. Therefore, embodiments of the present disclosure can be configured to sort and separate plastic pieces so that plastic pieces containing certain of these additives are discarded.

[0058] Product quality can also depend heavily on how well the plastics are separated. Many polymers are immiscible with each other when melted and undergo phase separation during reprocessing (like oil and water). Products made from such blends contain many boundaries between different types of polymers, resulting in poor cohesion across these boundaries and therefore poor mechanical properties. Therefore, embodiments of the present disclosure can be configured to sort and separate plastic pieces so that certain immiscible plastic pieces are not sorted together in the same group.

[0059] The systems and methods described herein in accordance with certain embodiments of the present disclosure receive a heterogeneous mixture of multiple pieces of material (e.g., any combination of the various plastics disclosed herein), where at least one piece of material within the heterogeneous mixture includes a different elemental composition (e.g., chemical signature) than one or more other pieces of material and / or at least one piece of material within the heterogeneous mixture is distinguishable from the other pieces of material (e.g., by a visually identifiable characteristic or feature, a different chemical signature, etc.), and the systems and methods are configured to identify / classify / separate the piece of material into a group separate from such other pieces of material. Embodiments of the present disclosure may be utilized to separate any type or class of material or fraction as defined herein.

[0060] Embodiments of the present disclosure are described herein as separating pieces of material into separate groups (e.g., material type classifications or fractions) by physically placing (e.g., diverting or discharging) the pieces of material into separate containers or bins according to user-defined groupings (e.g., material type classifications or fractions). By way of example, in certain embodiments of the present disclosure, pieces of material may be separated into separate bins to separate pieces of material having physical characteristics (e.g., visually identifiable properties or characteristics, different chemical characteristics, etc.) that are distinguishable from the physical characteristics of other pieces of material.

[0061] FIG. 1 illustrates an example of a system 100 configured in accordance with various embodiments of the present disclosure. A conveyor system 103 may be implemented to transport one or more streams of individual pieces of material 101 through the system 100, thereby allowing each of the individual pieces of material 101 to be tracked, sorted, and separated into predetermined desired groups. Such a conveyor system 103 may be implemented using one or more conveyor belts along which the pieces of material 101 typically move at a predetermined constant speed. However, certain embodiments of the present disclosure may also be implemented with other types of conveyor systems, including systems in which the pieces of material free-fall through various components of the system 100 (or any other type of vertical sorter), or vibratory conveyor systems. Hereinafter, where applicable, the conveyor system 103 may also be referred to as a conveyor belt 103. In one or more embodiments, some or all of the communicating, stimulating, detecting, classifying, and separating operations may be performed automatically, i.e., without human intervention. For example, in system 100, one or more stimulus sources, one or more radiation detectors, classification modules, fractionators, and / or other system components may be configured to perform these and other operations automatically.

[0062] Additionally, while FIG. 1 depicts a single stream of pieces of material 101 on conveyor system 103, embodiments of the present disclosure may be implemented such that multiple such streams of pieces of material pass through various components of system 100 in parallel with one another. For example, as further described in U.S. Pat. No. 10,207,296, the pieces of material may be distributed into two or more parallel, individualized streams traveling on a single conveyor belt, or a set of parallel conveyor belts. Accordingly, certain embodiments of the present disclosure may simultaneously track, sort, and separate multiple such parallel-moving streams of pieces of material. According to certain embodiments of the present disclosure, the incorporation or use of a singulator is not required. Instead, a conveyor system (e.g., conveyor system 103) may simply transport the mass of pieces of material deposited on conveyor system 103 in a random manner.

[0063] According to certain embodiments of the present disclosure, some suitable feeder mechanism (e.g., another conveyor system or hopper 102) can be utilized to feed the pieces of material 101 onto the conveyor system 103, which can then transport the pieces of material 101 through various components within the system 100. After the pieces of material 101 are received by the conveyor system 103, an optional tumbler / vibrator / singulator 106 can be utilized to separate individual pieces of material from the collection of pieces of material. In certain embodiments of the present disclosure, the conveyor system 103 is operated by a conveyor system motor 104 to move at a predetermined speed. This predetermined speed may be programmable and / or adjustable by an operator in any known manner. Alternatively, monitoring of the predetermined speed of the conveyor system 103 may be performed using a position detector 105. In certain embodiments of the present disclosure, control of the conveyor system motor 104 and / or the position detector 105 may be performed by an automatic control system 108. Such an automated control system 108 may operate under the control of a computer system 107 and / or the functionality for performing the automated control may be implemented in software within the computer system 107 .

[0064] The conveyor system 103 may be a conventional endless belt conveyor that uses a conventional drive motor 104 suitable for moving the belt conveyor at a predetermined speed. The position detector 105 may be a conventional encoder and may be operatively coupled to the conveyor system 103 and the automatic control system 108 to provide information corresponding to the movement (e.g., speed) of the conveyor belt. Thus, as described further herein, through the use of control over the conveyor system drive motor 104 and / or the automatic control system 108 (or including the position detector 105), once each piece of material 101 moving on the conveyor system 103 is identified, it can be tracked by position and time (relative to various components of the system 100) so that various components of the system 100 can be activated / deactivated as each piece of material 101 passes nearby. As a result, the automatic control system 108 can track the position of each piece of material 101 as it moves along the conveyor system 103.

[0065] 1 , certain embodiments of the present disclosure may utilize a vision or optical recognition system 110 and / or a piece of material tracker 111 as a means of tracking each piece of material 101 moving on the conveyor system 103. The vision system 110 may utilize one or more still or live action cameras 109 to record the position (i.e., location and timing) of each piece of material 101 on the moving conveyor system 103. The vision system 110 may additionally or alternatively be configured to perform a particular type of identification (e.g., classification) of all or some of the pieces of material 101, as described further herein. For example, such a vision system 110 may be utilized to capture or obtain information about each piece of material 101. For example, the vision system 110 may be configured to capture or gather any type of information from the pieces of material available within the system 100 (e.g., using a machine learning system) to classify and / or selectively separate the pieces of material 101 according to a set of one or more characteristics (e.g., physical and / or chemical and / or radioactive materials, etc.), as described herein. According to certain embodiments of the present disclosure, the vision system 110 may be configured to capture visual images (including one-dimensional, two-dimensional, three-dimensional, or holographic images) of each of the pieces of material 101, for example, by using optical sensors found in common digital cameras and video equipment. Such visual images captured by the optical sensors are stored in a memory device as image data (e.g., initialized as image data packets). According to certain embodiments of the present disclosure, such image data may represent images captured within optical wavelengths of light (i.e., wavelengths of light observable by a typical human eye). However, alternative embodiments of the present disclosure may utilize sensor systems configured to capture images of materials composed of wavelengths of light outside the visual wavelengths of the human eye.

[0066] According to certain embodiments of the present disclosure, system 100 may be implemented with one or more sensor systems 120 that may be utilized alone or in combination with vision system 110 to classify / identify pieces of material 101. Sensor system 120 may be a sensor system that utilizes emitted or reflected electromagnetic radiation (e.g., infrared (“IR”), Fourier transform IR (“FTIR”), forward-looking infrared (“FLIR”), very near infrared (“VNIR”), near infrared (“NIR”), short wavelength infrared (“SWIR”), long wavelength infrared (“LWIR”), mid-wavelength infrared (“MWIR” or “MIR”), x-ray transmission (“XRT”), gamma ray, ultraviolet (“UV”), x-ray fluorescence (“XRF”), laser-induced breakdown spectroscopy (“LIBS”), Raman spectroscopy, and the like). The sensor system 120 may be configured using any type of sensor technology to determine the chemical signature of the plastic pieces and / or classify the plastic pieces for separation, including, but not limited to, utilizing Stokes Raman spectroscopy, gamma-ray spectroscopy, hyperspectral spectroscopy (e.g., any range above visible wavelengths), acoustic spectroscopy, NMR spectroscopy, microwave spectroscopy, terahertz spectroscopy, including one-dimensional, two-dimensional, or three-dimensional imaging using any of the above), or any other type of sensor technology, including, but not limited to, chemical or radioactive materials. An implementation of an exemplary XRF system (e.g., for use as the sensor system 120 herein) is further described in U.S. Pat. No. 10,207,296. XRF may be used within embodiments of the present disclosure to identify inorganic materials within the plastic pieces (e.g., for inclusion within the chemical signature).

[0067] The following sensor systems may also be used within certain embodiments of the present disclosure to determine the chemical signature of plastic pieces and / or to sort the plastic pieces for separation.

[0068] Various forms of infrared spectroscopy previously disclosed can be used to obtain a unique chemical signature for each plastic piece that provides information about the base polymer of any plastic material and other components present in the material (mineral fillers, copolymers, polymer blends, etc.).

[0069] Differential scanning calorimetry (DSC) is a thermal analysis technique that captures the thermal transitions that are generated during heating of the material being analyzed, which are specific to each material.

[0070] Thermogravimetric analysis ("TGA") is another thermal analysis technique that provides quantitative information about the composition of plastic materials with respect to the proportion of polymers, other organic components, mineral fillers, carbon black, etc.

[0071] Capillary and rotational rheometry can determine the rheological properties of polymeric materials by measuring their creep resistance and resistance to deformation.

[0072] Optical microscopy and scanning electron microscopy ("SEM") can provide information about the structure of the analyzed material with respect to the number and thickness of layers in multilayer materials (e.g., multilayer polymer films), the dispersion size of pigment or filler particles in a polymer matrix, defects in coatings, interfacial morphology between components, etc.

[0073] Chromatography (LC-PDA, LC-MS, LC-LS, GC-MS, GC-FID, HS-GC, etc.) can quantify trace components of plastic materials such as UV stabilizers, antioxidants, plasticizers, and anti-slip agents, as well as residual monomers, residual solvents in inks and adhesives, and degradation products.

[0074] It should be noted that while FIG. 1 is shown with a combination of a vision system 110 and one or more sensor systems 120, embodiments of the present disclosure may be implemented with any combination of sensor systems utilizing any of the sensor technologies disclosed herein or other currently available or future-developed sensor technologies. While FIG. 1 is shown including one or more sensor systems 120, such sensor system implementation is optional within certain embodiments of the present disclosure. In certain embodiments of the present disclosure, a combination of both the vision system 110 and one or more sensor systems 120 may be used to classify the piece of material 101. In certain embodiments of the present disclosure, any combination of one or more different sensor technologies disclosed herein may be used to classify the piece of material 101 without utilizing a vision system 110. Additionally, embodiments of the present disclosure may include any combination of one or more sensor systems and / or vision systems where the output of the sensor / vision systems may be processed within a machine learning system (as further disclosed herein) to classify / identify materials from a heterogeneous mixture of materials, which may then be separated from one another.

[0075] According to alternative embodiments of the present disclosure, the vision system 110 and / or sensor system may be configured to identify which pieces of material 101 are not of the type to be sorted by the system 100 (e.g., plastic pieces containing particular contaminants, additives, or undesirable physical characteristics (e.g., attached container caps made of a different type of plastic than the container)) and transmit a signal to reject such pieces of material. In such a configuration, the identified pieces of material 101 may be diverted / ejected utilizing one of the mechanisms described below for physically diverting sorted pieces of material into individual bins.

[0076] In certain embodiments of the present disclosure, the material piece tracking device 111 and associated control system 112 may be utilized and configured to measure the position (i.e., location and timing) of each material piece 101 on the moving conveyor system 103, as well as the size and / or shape of each material piece 101 passing near the material piece tracking device 111. Exemplary operation of such material piece tracking devices 111 and control systems 112 is further described in U.S. Pat. No. 10,207,296. Alternatively, as previously disclosed, the vision system 110 may be utilized to track the position (i.e., location and timing) of each material piece 101 transported by the conveyor system 103. Accordingly, certain embodiments of the present disclosure may be implemented without a material piece tracking device (e.g., material piece tracking device 111) for tracking material pieces.

[0077] In certain embodiments of the present disclosure implementing one or more sensor systems 120, the sensor systems 120 may be configured to assist the vision system 110 in identifying the chemical composition, relative chemical composition, and / or manufacturing type of each of the pieces of material 101 as they pass near the sensor system 120. The sensor system 120 may include, for example, an energy-emitting source 121 that may be powered by a power source 122 to stimulate a response from each of the pieces of material 101.

[0078] According to certain embodiments of the present disclosure that implement an XRF system as sensor system 120, source 121 may include an in-line X-ray fluorescence ("IL-XRF") tube, as further described in U.S. Pat. No. 10,207,296. Such an IL-XRF tube may include a separate X-ray source dedicated to one or more streams (e.g., singulations) of conveyed material pieces. In such cases, one or more detectors 124 may be implemented as XRF detectors that detect X-ray fluorescence from material pieces 101 within each of the singulated streams. Examples of such XRF detectors are further described in U.S. Pat. No. 10,207,296.

[0079] In certain embodiments of the present disclosure, the sensor system 120 may emit an appropriate sensing signal toward the piece of material 101 as each piece of material 101 passes near the radiation source 121. The one or more detectors 124 may be positioned and configured to sense / detect one or more characteristics from the piece of material 101 in a format appropriate for the type of sensor technology employed. The one or more detectors 124 and associated detector electronics 125 capture these received sensed characteristics and perform signal processing to generate digitized information (e.g., spectral data) representative of the sensed characteristics, which may then be analyzed in accordance with certain embodiments of the present disclosure and used to assist the vision system 110 in classifying each of the pieces of material 101. This classification may be performed within the computer system 107 and then utilized by the automated control system 108 to operate one of the N (N>1) sorting devices 126...129 of the sorting device to sort (e.g., redirect / discharge) the pieces of material 101 according to the determined classification into one or more N (N>1) sorting bins 136...139. Four sorting devices 126...129 and four sorting bins 136...139 associated with these sorting devices are shown in FIG. 1 by way of non-limiting example only.

[0080] Existing sorters for plastics are designed to separate materials in a binary fashion, with air nozzles at the end of a conveyor discharging identified classes of plastic into one of two bins. For example, if four classes of plastic need to be separated, the entire stream must be conveyed through such a binary sorter four separate times, which takes four times longer than removing a single object within the stream. According to embodiments of the present disclosure, system 100 allows for the separation of multiple classes of plastics in a single pass.

[0081] The sorting device may include any known mechanism for diverting selected pieces of material 101 toward a desired location, including, but not limited to, diverting the pieces of material 101 from a conveyor belt system to multiple sorting bins. For example, the sorting device may utilize air jets, with each air jet assigned to one or more classifications. When one of the air jets (e.g., 127) receives a signal from the automatic control system 108, that air jet emits an air stream that redirects / discharges the pieces of material 101 from the conveyor system 103 into the sorting bin (e.g., 137) corresponding to that air jet.

[0082] Other mechanisms can also be used to redirect / eject pieces of material, such as robotically removing the pieces of material from the conveyor belt, pushing the pieces of material off the conveyor belt (e.g., using a paintbrush-type plunger), creating an opening in the conveyor system 103 through which the pieces of material can fall (e.g., a trap door), or using an air jet to redirect the pieces of material into another bin as they fall off the end of the conveyor belt. The term pusher device, as used herein, can refer to any form of device that can be actuated to dynamically move objects onto or off a conveyor system / apparatus using pneumatic, mechanical, or other means, such as an appropriate type of mechanical pushing mechanism (e.g., an ACME screw drive), pneumatic pushing mechanism, or air jet pushing mechanism. Some embodiments can include multiple pusher devices located at different positions along the path of the conveyor system and / or with different redirection path orientations. In various different implementations, these sorting systems described herein can determine which pusher device (if any) to activate depending on the classification of the pieces of material performed by a machine learning system. Additionally, the determination of which pusher device to activate may be based on the detected presence and / or characteristics of other objects that may be in the diversion path of the pusher device simultaneously with the target item. Furthermore, even in installations where singulation along the conveyor system is not perfect, the disclosed sorting system can recognize that multiple objects are not properly separated and dynamically select a pusher device to activate from multiple pusher devices based on the pusher device that provides the optimal diversion path that is likely to separate the closely spaced objects. In some embodiments, objects identified as target objects may represent material that should be diverted from the conveyor system. In other embodiments, objects identified as target objects represent material that should be allowed to remain on the conveyor system so that non-target objects can be diverted instead.

[0083] In addition to the N sorting bins 136...139 into which pieces of material 101 are diverted / discharged, system 100 may also include a container or bin 140 that receives pieces of material 101 that are not sorted / discharged from conveyor system 103 into one of the aforementioned sorting bins 136...139. For example, a piece of material 101 may not be diverted / discharged from conveyor system 103 into one of the N sorting bins 136...139 if the classification of the piece of material 101 has not been determined (or simply because the sorting device failed to properly divert / discharge the piece of material). Thus, bin 140 may serve as a default receptacle into which unsorted pieces of material are dumped. Alternatively, bin 140 may be used to receive one or more classifications of pieces of material that are not intentionally assigned to any of the N sorting bins 136...139. These pieces of material may be further sorted according to other characteristics and / or by another sorting system.

[0084] Depending on the various classifications of desired pieces of material, multiple classifications can be mapped to a single sorting device and associated sorting bins. In other words, there need not be a one-to-one correlation between classifications and sorting bins. For example, a user may desire to separate certain classifications of material into the same sorting bin (e.g., different plastic types contained in fractions). To achieve this separation, when pieces of material 101 are classified as falling into a predetermined classification group (e.g., fraction), the same sorting device can operate to separate them into the same sorting bin. Such combinatorial separations can be applied to generate any desired combination of separated pieces of material. The classification mapping may be programmed by a user (e.g., using a separation algorithm (e.g., see FIG. 7 ) operated by the computer system 107) to generate such desired combinations. Furthermore, the classifications of the pieces of material are user-definable and are not limited to any particular known classifications of pieces of material (e.g., fractions as disclosed herein).

[0085] Conveyor system 103 may include a carousel (not shown) so that unsorted pieces of material are returned to the beginning of system 100 and passed through system 100 again. Additionally, because system 100 can specifically track each piece of material 101 moving on conveyor system 103, some sorting device (e.g., sorting device 129) can be implemented to guide / eject the pieces of material 101 so that system 100 does not fail to sort after a predetermined number of cycles by system 100 (or after the pieces of material 101 have collected in bins 140).

[0086] In certain embodiments of the present disclosure, the conveyor system 103 may be divided into multiple belts configured in series, e.g., two belts, where a first belt carries pieces of material past the vision system 110 and a second belt carries a specific sorted piece of material past a sensor system 120 implemented for a second sorting. Furthermore, such a second conveyor belt may be at a lower height than the first conveyor belt such that the pieces of material fall from the first belt onto the second belt.

[0087] In certain embodiments of the present disclosure implementing sensor system 120, light emission source 121 may be located above the detection area (i.e., above conveyor system 103), although certain embodiments of the present disclosure may place light emission source 121 and / or detector 124 in other locations that still produce an acceptable sensed / detected physical characteristic.

[0088] The systems and methods described herein can be applied to sorting and / or separating individual pieces of material having any of a variety of sizes and shapes. While the systems and methods described herein are primarily described in connection with separating individual pieces of material, the systems and methods described herein are not limited thereto. Such systems and methods can be used to simultaneously stimulate and / or detect emissions from multiple materials. For example, as opposed to singulated material streams being transported serially along one or more conveyor belts, multiple singulated streams may be transported in parallel. Each stream may be on the same belt or on a different belt arranged in parallel. Furthermore, the pieces of material may be randomly dispersed on (e.g., across and along) one or more conveyor belts. Thus, the systems and methods described herein can be used to simultaneously stimulate and / or detect emissions from multiple pieces of material. In other words, multiple pieces of material can be treated as a single component, rather than each piece being considered individually. Thus, multiple pieces of material can be sorted and separated together (e.g., diverted / discharged from a conveyor system).

[0089] Although the systems and methods described herein are primarily described in connection with separating pieces of material, such systems and methods are not limited to that application and may be used for other applications, such as identifying elements (e.g., contaminants) within a piece of material or determining the composition of a piece of material.

[0090] As previously mentioned, certain embodiments of the present disclosure may implement one or more vision systems (e.g., vision system 110) to identify, track, and / or sort pieces of material. According to embodiments of the present disclosure, such vision systems may operate alone to identify and / or sort and separate pieces of material, or may operate in combination with one or more sensor systems (e.g., sensor system 120) to identify and / or sort and separate pieces of material. If a sorting system (e.g., system 100) is configured to operate with only such vision system 110, sensor system 120 may be omitted from system 100 (or may simply be deactivated).

[0091] Regardless of the sensed characteristics / type of information captured of the piece of material, the information (e.g., image data packets) may then be transmitted to a computer system (e.g., computer system 107) and processed by a machine learning system to identify and / or classify each piece of material. Such machine learning systems may implement any well-known machine learning system, including those that implement neural networks (e.g., artificial neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, reinforcement learning, etc.), fuzzy logic, artificial intelligence ("AI"), deep learning algorithms, deep structured learning hierarchical learning algorithms, support vector machines ("SVMs") (e.g., linear SVMs, nonlinear SVMs, SVM regression, etc.), decision tree learning (e.g., classification and regression trees ("CART")), ensemble methods (e.g., ensemble learning, random forests, bagging and pasting, patch and subspace, boosting, stacking, etc.), dimensionality reduction (e.g., projection, manifold learning, principal component analysis, etc.) and / or deep machine learning algorithms, such as those described and published at the deeplearning.net website (including hyperlinks to all software, publications, and available software referenced within this website), which is incorporated herein by reference.Non-limiting examples of publicly available machine learning software and libraries that can be utilized within embodiments of the present disclosure include Python, OpenCV, Inception, Theano, Torch, PyTorch, Pylearn2, Numpy, Blocks, TensorFlow, MXNet, Caffe, Lasagne, Keras, Chainer, Matlab Deep Learning, CNTK, MatConvNet (a MATLAB® toolbox that implements convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for deep learning (by Rasmus Berg Palm)), BigDL, Cuda-Convnet (a fast C++ / CUDA implementation of convolutional (more generally feedforward) neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factorized RBM and mcRBM, mPoT (Python code to train a model for natural images using CUDAMat and Gnumpy), ConvNet, Elektronn, OpenNN, NeuralDesigner, Theano generalized Hebbian learning, Apache Singa, Lightnet, and SimpleDNN.

[0092] According to certain embodiments of the present disclosure, machine learning may be performed in two stages. For example, training occurs first, which may be performed offline in that system 100 is not utilized to perform the actual classification / separation of material pieces (see, e.g., FIGS. 3-4). System 100 may be utilized to train the machine learning system such that a homogenous set (also referred to herein as a control sample) of material pieces (i.e., having the same type or class of material or falling within the same predetermined fraction) is passed through system 100 (e.g., by conveyor system 103), and all such material pieces may not be separated but may be collected in a common bin (e.g., bin 140). Alternatively, training may be performed at a separate location remote from system 100, including using some other mechanism for collecting sensory information (characteristics) of a control set of material pieces. During this training phase, algorithms within the machine learning system extract features from the captured information (e.g., using image processing techniques well known in the art). Non-limiting examples of training algorithms include, but are not limited to, linear regression, gradient descent, feedforward, polynomial regression, learning curve, regularized learning model, and logistic regression. It is during this training phase that the algorithms within the machine learning system learn the relationships between materials and their features / characteristics (e.g., as captured by a vision system and / or sensor system) to create a knowledge base for later classifying heterogeneous mixtures of material pieces received by the system 100 and subsequently separated by a desired classification. Such a knowledge base may include one or more libraries, each library containing parameters (e.g., neural network parameters) utilized by the machine learning system in classifying the material pieces. For example, one particular library may contain parameters configured by the training phase to recognize and classify one or more materials of a particular type or class of material, or a predetermined fraction.According to certain embodiments of the present disclosure, such a library can be input into a machine learning system, and a user of the system 100 may be able to adjust certain of the parameters to tune the operation of the system 100 (e.g., adjusting a threshold effectiveness for how well the machine learning system recognizes a particular piece of material from a heterogeneous mixture of materials).

[0093] As shown in FIG. 2, during the training phase, multiple pieces of material 201 of one or more specific types, classes, or fractions of material that are control samples may be delivered (e.g., by conveyor system 203) through a vision system and / or one or more sensor systems so that an algorithm within the machine learning system detects, extracts, and learns features representative of such types or classes of material. For example, each of the pieces of material 201 may be an individual piece of plastic of a particular type, class, or predetermined fraction that has undergone such a training phase so that an algorithm within the machine learning system "learns" (trains) how to detect, recognize, and classify such plastic pieces. When training a vision system (e.g., vision system 110), it is trained to visually identify the pieces of material. This creates a library of parameters specific to one or more specific types, classes, or fractions of plastic material. The same process can then be performed on different types, classes, or fractions of plastic pieces to create libraries of parameters specific to those types, classes, or fractions, and so on. For each type, class, or fraction of plastic classified by the machine learning system, any number of exemplary plastic pieces of that type, class, or fraction of plastic may be passed through the system. Given the captured sensory information as input data, the algorithm within the machine learning system uses N classifiers, each testing one of N different material types, classes, or fractions. Note that the machine learning system may be "taught" (trained) to detect any type, class, or fraction of material, including any type, class, or fraction of material found within MSW, or any other material disclosed herein.

[0094] After the algorithms are established and the machine learning system has sufficiently learned (trained) the differences (e.g., visually discernible differences) between material classifications (e.g., within a user-defined statistical confidence level), the library of different material classifications may then be implemented into a material classification / separation system (e.g., system 100) that is used to identify and / or classify material pieces from a heterogeneous mixture of material pieces (e.g., as contained within MSW) and, if separation is performed, to separate such classified material pieces.

[0095] Techniques for building, optimizing, and utilizing machine learning systems are known to those skilled in the art, as described in the relevant literature, examples of which include the following publications: Krizhevsky et al., "ImageNet Classification with Deep Convolutional Networks," Proceedings of the 25th International Conference on Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, NV, and LeCun et al., "Gradient-Based Learning Applied to Document Recognition," IEEE Transactions on Machine Learning, Institute of Electrical and Electronics Engineers (IEEE), November 1998, both of which are incorporated herein by reference in their entireties.

[0096] In one exemplary technique, data captured by a vision or sensor system regarding a particular piece of material may be processed (within a data processing system (configured) to implement a machine learning system (e.g., data processing system 3400 of FIG. 9 )) as an array of data values. For example, the data may be spectral data captured by a digital camera or other type of sensor system regarding a particular piece of material and processed as an array of data values ​​(e.g., image data packets). Each data value may be represented by a single number or a series of numbers representing a value. These values ​​may be multiplied by neuron weight parameters (e.g., using a neural network) and a bias may be added, which may affect the neuron's nonlinearity. The resulting number output by a neuron may be treated like a value by multiplying this output by the weight value of the subsequent neuron, optionally adding a bias, and again affecting the neuron's nonlinearity. Each iteration of such processing is known as a "layer" of the neural network. The final output of the final layer may be interpreted as a probability of the presence or absence of material in the captured data associated with the piece of material. Examples of such processing are detailed in both the aforementioned references, "ImageNet Classification with Deep Convolutional Networks" and "Gradient-Based Learning Applied to Document Recognition."

[0097] According to certain embodiments of the present disclosure in which a neural network is implemented as the final layer ("classification layer"), a final set of neuron outputs is trained to represent the likelihood that a piece of material is associated with the captured data. In operation, if the likelihood that a piece of material is associated with the captured data exceeds a user-specified threshold, it is determined that the piece of material is indeed associated with the captured data. These techniques can be extended to determine not only the presence of a certain type of material associated with particular captured data, but also whether a subregion of particular captured data belongs to one type of material or another. This process is known as segmentation, and techniques using neural networks exist in the literature, including neural networks known as "fully convolutional" neural networks and networks that are not fully convolutional but include convolutional portions (i.e., are partially convolutional). This allows the location and size of the material to be determined.

[0098] It should be understood that the present disclosure is not limited solely to machine learning techniques. Other common approaches for material classification / identification can also be used. For example, a sensor system may utilize spectroscopy techniques using a multispectral or hyperspectral camera to examine the spectral emissions (i.e., spectral imaging) of a material to provide a signal indicating the presence or absence of a certain type, class, or fraction of material. Spectral images of a piece of material may also be used in a template matching algorithm, in which a database of spectral images is compared to the acquired spectral image to detect the presence or absence of a particular type of material. A histogram of a captured spectral image can also be compared to a database of histograms. Similarly, a bag-of-words model can be used in conjunction with feature extraction techniques such as scale-invariant feature transform ("SIFT") to compare extracted features between the captured spectral image and those in the database.

[0099] Thus, as disclosed herein, certain embodiments of the present disclosure provide for the identification / classification of one or more different types, classes, or fractions of material to determine which pieces of material should be diverted from the conveyor system within defined groups. According to certain embodiments, machine learning techniques are utilized to train (i.e., configure) a neural network to identify one or more different types, classes, or fractions of material. Spectral images or other types of sensory information are captured from materials (e.g., moving on a conveyor system), and based on such material identification / classification, the systems described herein can determine which pieces of material should remain on the conveyor system and which pieces of material should be diverted / removed from the conveyor system (e.g., either placed in a collection bin or diverted to another conveyor system).

[0100] According to certain embodiments of the present disclosure, a machine learning system for an existing facility (e.g., system 100) can be dynamically reconfigured to identify / classify the properties of new types, classes, or fractions of material by replacing the current set of neural network parameters with a new set of neural network parameters.

[0101] One point worth mentioning here is that, according to certain embodiments of the present disclosure, the detected / captured features / characteristics (e.g., spectral images) of the pieces of material do not necessarily have to be simply particularly identifiable or distinguishable physical properties; they may be abstract formulas that can only be expressed mathematically, or may not be expressible mathematically at all; nevertheless, the machine learning system may be configured to analyze the spectral data and look for patterns that can classify the control samples during the training phase. Furthermore, the machine learning system may take subsections of the captured information (e.g., spectral images, etc.) of the pieces of material and try to find correlations between predefined classifications.

[0102] According to certain embodiments of the present disclosure, instead of utilizing a training phase in which control samples of material pieces are passed through a vision and / or sensor system, training of the machine learning system can be performed using labeling / annotation techniques, whereby data / information about the material pieces is captured by the vision / sensor system and a user inputs labels or annotations that identify each material piece and are used to create a library that the machine learning system uses in classifying material pieces within a heterogeneous mix of material pieces.

[0103] With reference to FIGS. 3-6 , embodiments of the present disclosure combine or fuse multiple sensor technologies (e.g., any combination of visual ("VIS"), XRF, NIR, and MWIR) in a manner that uniquely identifies various types, classes, or fractions of plastic so that they can be separated by their organic and inorganic chemical composition. However, because these plastic pieces in MSW come in a variety of sizes and shapes, there can be significant variance in the signals generated from these various sensors. Therefore, combining machine learning with the fusion of various sensor technologies improves the classification accuracy of these signals, even in the presence of such large variance. Because implementing multiple different sensors in a system can increase the system's cost and also reduce the sorting speed, certain embodiments of the present disclosure implement a system (e.g., system 100) with fewer sensor systems (and consequently lower capital and operating costs) to increase economic viability while still being able to sufficiently separate materials.

[0104] 4 shows a simplified schematic diagram of a system (e.g., system 100) in which pieces of material (e.g., plastic pieces) 401 are transported by a conveyor system 403 past a sensor system that captures spectral data from each piece of material 401. In this non-limiting example, the sensor systems are a camera 410 (e.g., vision system 110) that captures visible image data of each piece of material 401, an XRF system 411, an NIR system 412, and an MWIR system 413. However, it should be noted that any of the other sensor systems disclosed herein may be utilized in any combination.

[0105] 3 and 4, the chemical signature of a material is determined in process block 301 using one or more sensor systems. The sensed / detected / captured signals from the sensor systems are combined (e.g., in a multidimensional data array) for each material to create a chemical signature. Recall that an XRF sensor system can determine the presence of inorganic elements or molecules within a plastic piece, while combining one or more other sensor systems, such as NIR or MWIR, can determine the presence of organic elements or molecules within a plastic piece. In process block 302, a visible image of each material piece is captured. In process block 303, the captured visible image of each material piece (i.e., its associated image data) is associated with its determined chemical signature (i.e., spectral image data). FIGS. 5 and 6 show non-limiting, exemplary representations of the chemical signatures and associated image data of two different types of plastic materials (a potato chip bag and an electronics package). As can be readily appreciated, different types or classes of plastic fragments have different (unique) chemical signatures that are utilized within embodiments of the present disclosure to generate a separation and / or classification (which may be user-defined) of plastic waste. According to embodiments of the present disclosure, a control group of plastic fragments of a particular type or class can be run through the system shown in FIG. 4 to train a machine learning system to associate specific chemical signatures with specific types or classes of plastic fragments.

[0106] For example, with respect to the example shown in FIG. 5, images captured from multiple bags of potato chips (which may include bags of different physical conditions or orientations, or bags associated with a brand and / or manufacturer of chips) may be processed to train a machine learning system.

[0107] Processing block 304 may include separating the plastic pieces into one or more fractions. There are various ways to create these fractions. One method is to create a first layer based on major elements, then a second and even a third layer based on minor elements. For example, fractions can be determined first by polymer type, then branched out by inorganic elements such as aluminum and zinc. Other exemplary fractions can then be created for blends of polymers, branched out by their inorganic element composition. There are also computational techniques, such as principal component analysis, k-means clustering, unsupervised learning, and semi-supervised learning, to perform this type of clustering and determine fractions. Fractions are further defined herein.

[0108] In processing block 305, after the fractions are determined, the plastic pieces associated with the fractions can be separated (e.g., manually) to create a control group for each fraction. Because each fraction was measured with a sensor system, each control group contains chemical information about the pieces. A vision system (e.g., vision system 110) can be used to train a machine learning system to identify these fractions. Using this method, chemical data in the plastics is converted into visual features that the machine learning system can learn and classify. Using system 100 to perform classification based on visual images also separates plastics by chemical composition. This method works when two objects look different and have different chemical compositions. When two objects are the same or very similar but have different chemical compositions, two or more sensor systems (e.g., VIS and XRF) can be used to perform the classification.

[0109] Because the determined fractions may comprise any desired variety of specific organic and / or inorganic elements or molecules, process 300 may be utilized to train a machine learning system implemented within a sorting system to be configured to separate a heterogeneous mixture of different plastic pieces to produce at least one fraction comprising one or more different types or classes of plastic pieces. For example, if the machine learning system is trained to identify any plastic pieces comprising a specified combination of organic and / or inorganic elements or molecules, once sorting is complete, the separated fractions may contain plastic pieces that are not all identical (i.e., multiple plastic chip bags associated with different brands of chips, because each plastic chip bag is composed of organic and / or inorganic elements or molecules defined by a predetermined fraction).

[0110] FIG. 7 shows a flowchart diagram illustrating an exemplary embodiment of a process 3500 for sorting / separating material pieces utilizing a vision system and / or one or more sensor systems, according to certain embodiments of the present disclosure. Process 3500 may be performed to sort a heterogeneous mixture of plastic pieces into any combination of predetermined types, classes, and / or fractions. Process 3500 may be configured to operate within any embodiment of the present disclosure described herein, including system 100 of FIG. 1. The operations of process 3500 may be performed by hardware and / or software, including within a computer system (e.g., computer system 3400 of FIG. 9) that controls a system (e.g., computer system 107, vision system 110, and / or sensor system 120 of FIG. 1). In process block 3501, material pieces may be deposited on a conveyor system. In process block 3502, the position of each material piece on the conveyor system is detected to track each material piece as it moves through system 100. This may be performed by the vision system 110 (e.g., by distinguishing the piece of material from the underlying conveyor system material while communicating with a conveyor system position detector (e.g., position detector 105)). Alternatively, the material tracker 111 may be used to track the piece of material. Alternatively, a system with a detector capable of generating a light source (including, but not limited to, visible light, UV, and IR) and using it to locate the piece of material may be used. In processing block 3503, sensed information / characteristics of the piece of material are captured / obtained as the piece of material moves near one or more of the vision system and / or sensor system. In processing block 3504, the vision system (e.g., implemented in the computer system 107) as described above may perform preprocessing of the captured information, which may be used to detect (extract) information about each piece of material (e.g., from the background (e.g., conveyor belt)); in other words, preprocessing may be used to identify the difference between the piece of material and the background). Well-known image processing techniques, such as dilation, thresholding, and contouring, may be used to identify the piece of material as distinguishable from the background.In processing block 3505, segmentation may be performed. For example, the captured information may include information about one or more pieces of material. Furthermore, when the image is captured, a particular piece of material may be located at a seam in a conveyor belt. Therefore, in such cases, it may be desirable to separate the image of the individual piece of material from the background of the image. In an exemplary technique for processing block 3505, the first step is to apply high contrast to the image, thereby reducing background pixels to substantially all black pixels and brightening at least some pixels related to the piece of material to substantially all white pixels. The white image pixels of the piece of material are expanded to cover the size of the entire piece of material. Upon completing this step, the location of the piece of material results in a high-contrast image of all white pixels on a black background. Next, a contour algorithm may be used to detect the boundary of the piece of material. The boundary information is saved, and the boundary location is transferred to the original image. Next, segmentation is performed on an area of ​​the original image that is larger than the previously defined boundary. In this way, the piece of material is identified and separated from the background.

[0111] In optional processing block 3506, the material pieces may be transported along a conveyor system near a material piece tracking device and / or sensor system to track each material piece and / or determine the size and / or shape of the material piece, which may be useful if an XRF system or other spectroscopic sensor is also implemented in the sorting system. In processing block 3507, post-processing may be performed. Post-processing may include resizing the captured information / data in preparation for use by a machine learning system. This post-processing may also include modifying certain characteristics (e.g., enhancing image contrast, changing the image background, applying filters, etc.) in a manner that enhances the machine learning system's ability to classify the material pieces. In processing block 3509, the data may be resized. Under certain circumstances, it may be desirable to resize the data to match the data input requirements of a particular machine learning system, such as a neural network. For example, a neural network may require an image size (e.g., 225 x 255 pixels or 299 x 299 pixels) that is much smaller than the size of images captured by a typical digital camera. Furthermore, the smaller the size of the input data, the less processing time is required to perform the classification. Therefore, smaller data sizes may ultimately increase the throughput of the system 100, making it more valuable.

[0112] In processing blocks 3510 and 3511, each piece of material is identified / classified based on the sensed / detected features. For example, processing block 3510 may consist of a neural network using one or more machine learning algorithms that compare the extracted features to features stored in a previously generated knowledge base (e.g., generated during a training phase) and, based on such comparison, assigns the best-matching classification to each piece of material. The algorithms of the machine learning system may process the captured information / data hierarchically using automatically trained filters. The filter responses are successfully combined at the next level of the algorithm until a probability is obtained in the final step. In processing block 3511, these probabilities can be used for each of the N classifications to determine which of the N sorting bins each piece of material should be sorted into. For example, each of the N classifications can be assigned to one sorting bin, and the piece of material under consideration is sorted into the bin corresponding to the classification that returns a probability greater than a predefined threshold. In embodiments of the present disclosure, such a predetermined threshold can be preset by the user. If neither probability is greater than a predetermined threshold, the particular piece of material may be classified in an outlier bin (eg, sorting bin 140).

[0113] Next, in process block 3512, a sorting device corresponding to the classification of the material piece is activated. Between the time the image of the material piece was captured and the time the sorting device was activated, the material piece moved from near the vision system and / or sensor system to a position downstream of the conveyor system (e.g., at the conveying speed of the conveyor system). In an embodiment of the present disclosure, activation of the sorting device is timed so that the sorting device activates when the material piece passes a sorting device mapped to the material piece's classification, and the material piece is diverted / discharged from the conveyor system into an associated sorting bin. In an embodiment of the present disclosure, activation of the sorting device may be timed by a respective position detector that detects when the material piece passes in front of the sorting device and sends a signal enabling activation of the sorting device. In process block 3513, the sorting bin corresponding to the activated sorting device receives the diverted / discharged material.

[0114] 8 shows a flowchart diagram illustrating an example embodiment of a process 800 for separating pieces of material, according to certain embodiments of the present disclosure. Process 800 may be configured to operate within any embodiment of the present disclosure described herein, including system 100 of FIG. 1. Process 800 may also be configured to operate in conjunction with process 3500. For example, according to certain embodiments of the present disclosure, process blocks 803 and 804 may be incorporated into process 3500 (e.g., operating in series or parallel with process blocks 3503-3510) to combine the efforts of vision system 110, which is implemented in conjunction with a machine learning system, with a sensor system (e.g., sensor system 120) that is not implemented in conjunction with a machine learning system, to classify and / or separate pieces of material.

[0115] The operations of process 800 may be performed by hardware and / or software, including within a computer system (e.g., computer system 3400 of FIG. 9) that controls a system (e.g., computer system 107 of FIG. 1). In process block 801, pieces of material may be deposited on a conveyor system. Then, in optional process block 802, the pieces of material may be transported along the conveyor system within the vicinity of a piece of material tracking device and / or an optical imaging system to track each piece of material and / or determine the size and / or shape of the piece of material. In process block 803, as the pieces of material move near the sensor system, they may be interrogated or stimulated with EM energy (waves) or other types of stimuli appropriate for the particular type of sensor technology utilized in the sensor system. In process block 804, physical properties of the pieces of material are sensed / detected and captured by the sensor system. In process block 805, for at least some of the pieces of material, the type of material is identified / classified based (at least in part) on the captured properties, which may be combined with classification by a machine learning system in conjunction with the vision system 110.

[0116] Next, if sorting of the material pieces is to be performed, a sorting device corresponding to the material piece's classification is activated in process block 806. Between sensing the material piece and activating the sorting device, the material piece has moved from near the sensor system to a position downstream of the conveyor system at the conveying speed of the conveyor system. In certain embodiments of the present disclosure, activation of the sorting device is timed so that when the material piece passes a sorting device mapped to the material piece's classification, the sorting device is activated and the material piece is diverted / discharged from the conveyor system into its associated sorting bin. In certain embodiments of the present disclosure, activation of the sorting device may be timed by a respective position detector that detects when the material piece passes in front of the sorting device and sends a signal enabling activation of the sorting device. In process block 807, the sorting bin corresponding to the activated sorting device receives the diverted / discharged material pieces.

[0117] According to certain embodiments of the present disclosure, at least some of the systems 100 may be linked together in series to perform multiple iterations or layers of sorting. For example, when two or more systems 100 are coordinated in this manner, the conveyor system may be implemented with a single conveyor belt or multiple conveyor belts to transport material pieces past a first vision system (and, according to certain embodiments, a sensor system) configured to separate a first set of material pieces of a heterogeneous mixture of material into a first set of one or more containers (e.g., sorting bins 136...139) by a sorting device (e.g., a first automated control system 108 and associated one or more sorting devices 126...129), and then transport the material pieces past a second vision system (and, according to certain embodiments, another sensor system) configured to separate a second set of material pieces of a heterogeneous mixture of material into a second set of one or more sorting bins by a second sorting device. Further discussion of such multi-stage sorting is found in U.S. Patent Application Publication No. 2022 / 0016675, which is incorporated herein by reference.

[0118] Such a series of systems 100 may include any number of such systems linked together in such a manner, and according to certain embodiments of the present disclosure, each successive system may be configured to separate a different category or type of material than the previous system.

[0119] According to various embodiments of the present disclosure, different types, classes, or fractions of material can be classified by different types of sensors, each for use in a machine learning system, and combined to classify pieces of material in a scrap or waste stream.

[0120] According to various embodiments of the present disclosure, data (e.g., spectral data) from two or more sensors can be combined using a single or multiple machine learning systems to perform classification of the material pieces.

[0121] According to various embodiments of the present disclosure, multiple sensor systems can be attached to a single conveyor system, and each sensor system can utilize a different machine learning system. According to various embodiments of the present disclosure, multiple sensor systems can be attached to different conveyor systems, and each sensor system can utilize a different machine learning system.

[0122] Certain embodiments of the present disclosure may be configured to produce chunks of material that have less than a predetermined weight or volume percent content of a particular element or material after fractionation.

[0123] According to various embodiments of the present disclosure, any combination of different types of sensor systems can be utilized to identify / classify, and possibly separate, the materials disclosed herein. For example, each imaging or spectroscopic sensor disclosed herein can be used to generate data from information / properties sensed from a piece of material to be processed by a machine learning system specific to that sensor system. Alternatively, any sensor system can be used without processing by a machine learning system, with processing by a machine learning system, or a combination of both.

[0124] According to various embodiments of the present disclosure, different types, classes, and / or fractions of material may be classified by different types of sensor systems, each for use in a machine learning system, and combined to classify material pieces in a waste stream.

[0125] Referring now to FIG. 9 , a block diagram illustrates a data processing (“computer”) system 3400 in which aspects of an embodiment of the present disclosure may be implemented. (The terms “computer,” “system,” “computer system,” and “data processing system” may be used interchangeably herein.) The computer system 107, the automatic control system 108, aspects of the sensor system 120, and / or the vision system 110 may be configured similarly to the computer system 3400. The computer system 3400 may use a local bus 3405 (e.g., a peripheral component interconnect (“PCI”) local bus architecture). Any suitable bus architecture may be utilized, such as Accelerated Graphics Port (“AGP”) or Industry Standard Architecture (“ISA”), among others. One or more processors 3415, volatile memory 3420, and non-volatile memory 3435 may be connected to the local bus 3405 (e.g., via a PCI bridge (not shown)). An integrated memory controller and cache memory may be coupled to the one or more processors 3415. The one or more processors 3415 may include one or more central processor units and / or one or more graphics processor units 3401 and / or one or more tensor processing units. Additional connections to the local bus 3405 may be made through direct component interconnections or add-in boards. In the illustrated example, a communications (e.g., network (LAN)) adapter 3425, an I / O (e.g., small computer system interface ("SCSI") host bus) adapter 3430, and an expansion bus interface (not shown) may be connected to the local bus 3405 by direct component connections. An audio adapter (not shown), a graphics adapter (not shown), and a display adapter 3416 (coupled to a display 3440) may be connected to the local bus 3405 (e.g., by an add-in board inserted into an expansion slot).

[0126] User interface adapter 3412 may provide connections for a keyboard 3413 and mouse 3414, a modem (not shown), and additional memory (not shown). I / O adapter 3430 may provide connections for a hard disk drive 3431, a tape drive 3432, and a CD-ROM drive (not shown).

[0127] An operating system may run on one or more processors 3415 and be used to coordinate and control various components within computer system 3400. In Figure 9, the operating system may be a commercially available operating system. An object-oriented programming system (e.g., Java, Python, etc.) may run in conjunction with the operating system and provide calls to the operating system from programs (e.g., Java, Python, etc.) running on system 3400. Instructions for the operating system, object-oriented operating system, and programs may be located on non-volatile memory 3435 storage devices, such as hard disk drive 3431, and may be loaded into volatile memory 3420 for execution by processor 3415.

[0128] Those skilled in the art will appreciate that the hardware in Figure 9 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash ROM (or equivalent non-volatile memory) or optical disk drives, may be used in addition to or in place of the hardware depicted in Figure 9. Also, any of the processes disclosed herein may be applied to a multi-processor computer system or performed by multiple such systems 3400. For example, training of a machine learning system may be performed by a first computer system 3400, while the operations of system 100 for classification may be performed by a second computer system 3400.

[0129] As another example, computer system 3400 may be a standalone system configured to be bootable independent of some type of network communication interface, regardless of whether computer system 3400 includes any type of network communication interface. As a further example, computer system 3400 may be an embedded controller configured with ROM and / or flash ROM that provides non-volatile memory for storing operating system files or user-generated data.

[0130] 9 and above-described examples are not meant to imply architectural limitations. Moreover, the computer program format of the aspects of the present disclosure may reside on any computer-readable storage medium (i.e., floppy disk, compact disc, hard disk, tape, ROM, RAM, etc.) used by a computer system.

[0131] As described herein, embodiments of the present disclosure may be implemented to perform various functions described for identifying, tracking, sorting, and / or separating pieces of material. Such functions may be implemented in hardware and / or software, such as in one or more data processing systems (e.g., data processing system 3400 of FIG. 9 ), such as aspects of the computer system 107, vision system 110, sensor system 120, and / or automated control system 108 described above. However, the functions described herein are not limited to implementation on any particular hardware / software platform.

[0132] As will be appreciated by those skilled in the art, aspects of the present disclosure may be embodied as a system, process, method, and / or computer program product. Accordingly, various aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, generally referred to herein as a "circuit," "circuitry," "module," or "system." Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable storage medium(s) having computer-readable program code embodied therein. (Although, any combination of one or more computer-readable medium(s) may be utilized; the computer-readable medium(s) may be a computer-readable signal medium or a computer-readable storage medium.)

[0133] A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, biological, atomic, or semiconductor system, apparatus, controller, or device, or any suitable combination thereof, and the computer-readable storage medium itself is not a transitory signal. More specific examples (non-exhaustive list) of computer-readable storage media may include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (“RAM”) (e.g., RAM 3420 in FIG. 9), a read-only memory (“ROM”) (e.g., ROM 3435 in FIG. 9), an erasable programmable read-only memory (“EPROM” or flash memory), an optical fiber, a portable compact disk read-only memory (“CD-ROM”), an optical storage device, a magnetic storage device (e.g., hard drive 3431 in FIG. 9), or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, controller, or device. The program code embodied on a computer readable signal medium may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

[0134] A computer-readable signal medium may include a propagated data signal having computer-readable program code embodied therein, for example, as part of baseband or a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or a suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium but may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, controller, or device.

[0135] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, processes, and program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of code, which includes one or more executable program instructions for implementing the specified logical function(s). It should also be noted that, depending on the implementation, the functions shown in the blocks may be executed in a different order than shown in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may be executed in the reverse order, depending on the functionality involved.

[0136] In the description herein, flowcharted techniques may be described as a series of sequential operations. The order of operations and the parties performing the operations may be freely modified without departing from the scope of the teachings. Operations may be added, deleted, or modified in several ways. Similarly, the order of operations may be changed or looped. Furthermore, while processes, methods, algorithms, etc. may be described in a sequential order, such processes, methods, algorithms, or any combination thereof, may be operable to be performed in another order. Furthermore, some actions within a process, method, or algorithm may be performed simultaneously, at least at some point (e.g., actions performed in parallel), or may be performed in whole, in part, or in any combination thereof.

[0137] Modules implemented in software executed by various types of processors (e.g., GPU 3401, CPU 3415) may include, for example, one or more physical or logical blocks of computer instructions, which may be organized as, for example, an object, a procedure, or a function. However, the executable files of identified modules need not be physically located together and may include disparate instructions stored in different locations that, when logically combined, incorporate the module and achieve the module's specified purpose. In fact, a module of executable code may be a single instruction or many instructions, and may be distributed across multiple different code segments, different programs, and multiple memory devices. Similarly, operational data (e.g., a material classification library described herein) may be identified and illustrated in modules herein and may be embodied in any suitable format and organized within any suitable type of data structure. Operational data may be collected as a single data set or distributed across different locations, including different storage devices. The data may provide electronic signals over a system or network.

[0138] These program instructions can be provided to one or more processors and / or controllers of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus (e.g., controller) such that the instructions, which execute via a processor (e.g., GPU 3401, CPU 3415) of the computer or other programmable data processing apparatus, create circuits or means for implementing the functions / acts specified in the flowchart and / or block diagram blocks to produce a machine.

[0139] It should also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by a dedicated hardware-based system (which may include, for example, one or more graphics processing units (such as GPU 3401)) that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions. For example, a module can be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, controllers, or other discrete components. A module can also be implemented with programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, etc.

[0140] Computer program code, i.e., instructions, for carrying out operations of aspects of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, Python, C++, etc., the "C" programming language or similar programming languages, or traditional procedural programming languages ​​such as any of the machine learning software disclosed herein. The program code may run entirely on the user's computer system as a standalone software package, partially on the user's computer system, partially on the user's computer system (e.g., a computer system used for classification), partially on a remote computer system (e.g., a computer system used to train a sensor system), or entirely on a remote computer system or server. In the latter scenario, the remote computer system may be connected to the user's computer system via any type of network, including a local area network ("LAN") or wide area network ("WAN"), or may be connected to an external computer system (e.g., via the Internet using an Internet Service Provider).

[0141] These program instructions may also be stored on a computer-readable storage medium that can cause a computer system, other programmable data processing device, controller, or other device to function in a particular manner, such that the instructions stored on the computer-readable medium produce an article of manufacture that includes instructions that implement the functions / acts specified in the flowchart and / or block diagram blocks.

[0142] The program instructions may also be loaded into a computer, other programmable data processing device, controller, or other device to cause the computer, other programmable device, or other device to perform a series of operational steps to generate a computer-implemented process, such that the instructions, which execute on the computer or other programmable device, provide a process for implementing the functions / operations specified in the flowchart and / or block diagram blocks.

[0143] A host may include one or more databases for storing and providing access to data in various implementations. Those skilled in the art will also understand that, for security reasons, any database, system, or component of the present disclosure may include any combination of databases or components in a single location or multiple locations, and each database or system may include any of a variety of appropriate security features, such as firewalls, access codes, encryption, and decryption. Databases may be any type of database, such as relational, hierarchical, or object-oriented. Common database products that can be used to implement a database include IBM's DB2, database products available from Oracle Corporation, Microsoft Access from Microsoft Corporation, or other database products. Databases may be organized in any suitable manner, such as data tables and lookup tables.

[0144] Association of specific data (e.g., for each piece of material processed by the sorting system described herein) can be achieved through any data association technique known and practiced in the art. For example, association can be performed manually or automatically. Automatic association techniques can include, for example, database lookup, database merge, GREP, AGREP, SQL, etc. The association step can be achieved, for example, by a database merge function using key fields in the manufacturer's and retailer's respective data tables. The key fields divide the database according to high-level classes of objects defined in the key fields. For example, a specific class can be designated as a key field in both the first and second data tables, and the two data tables can be merged based on the class data in the key fields. In these embodiments, the data corresponding to the key fields in each of the merged data tables is preferably the same. However, data tables that are not identical but have similar data in the key fields can also be merged using, for example, AGREP.

[0145] As used herein, reference is made to "configuring" a device or a device being "configured" to perform some function. It should be understood that this may include selecting and logically associating predefined logic blocks to provide a particular logical function, including a monitoring or control function. It may also include programming computer software-based logic in a custom controller, wiring individual hardware components, or a combination of any or all of the foregoing.

[0146] In the description herein, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, controllers, etc., to provide a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will recognize that the present disclosure may be practiced without one or more of the specific details or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations may not be shown or described in detail to avoid obscuring aspects of the present disclosure.

[0147] Those skilled in the art will understand that the various settings and parameters of the components of system 100 (including neural network parameters) can be customized, optimized, and reconfigured over time based on the type of material being sorted and separated, the desired sorting and separation results, the type of equipment being used, empirical results of previous sorting, data that becomes available, and other factors.

[0148] References throughout this specification to "one embodiment," "an embodiment," or similar terms mean that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of "in one embodiment," "in an embodiment," "an embodiment," "a particular embodiment," "various embodiments," and similar terms throughout this specification may all refer to the same embodiment, although not necessarily the same. Furthermore, the described features, structures, aspects, and / or characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. Accordingly, in some cases, one or more features may be deleted from a claimed combination, and the claimed combination may be directed to subcombinations or variations of subcombinations, even if the functions are initially claimed to function in a particular combination.

[0149] Benefits, advantages, and solutions to problems have been described herein with respect to specific embodiments. However, the benefits, advantages, solutions to problems, and elements by which the benefits, advantages, or solutions occur or may become more pronounced, may not be construed as critical, necessary, or essential features or elements of some or all of the claims. Furthermore, no element described herein is required for the practice of the present disclosure unless expressly described as essential or critical.

[0150] While this specification contains many details, these should not be construed as limiting the scope of the disclosure or the claims, but rather as descriptions of features specific to particular implementations of the disclosure. Headings herein may not be intended to limit the disclosure, the embodiments of the disclosure, or other matters disclosed thereunder.

[0151] As used herein, the term "or" is intended to be inclusive, such that "A or B" includes A or B, and also includes both A and B. As used herein, the term "and / or," when used in the context of a list of entities, refers to the entities present alone or in combination. Thus, for example, the phrase "A, B, C, and / or D" includes A, B, C, and D individually, but also all combinations and subcombinations of A, B, C, and D.

[0152] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0153] Corresponding structure, material, acts, and equivalents of all means- or step-plus-function elements in the following claims may be intended to include any structure, material, or act for performing a function in combination with other claim elements that are specifically claimed.

[0154] As used herein, the terms "controller," "processor," "memory," "neural network," "interface," "sorter," "device," "pushing mechanism," "pusher device," "image sensor," "bin," "receptacle," "system," "circuit," etc. each refer to a non-common device element recognized and understood by one of ordinary skill in the art, and are not used herein as orthogonal or orthogonal terms for purposes of invoking 35 U.S.C. 112(f).

[0155] As used herein with respect to a specified characteristic or circumstance, "substantially" refers to a degree of deviation that is small enough so as not to measurably impair the specified characteristic or circumstance. The exact degree of deviation that is acceptable may depend, in some cases, on the particular circumstances.

[0156] As used herein, for convenience, a plurality of items, structural elements, components, exemplary fractions, and / or materials may be presented in common lists. However, these lists should be construed as if each member of the list were individually identified as a separate and unique member. Accordingly, individual members of such lists should not be construed as being effectively equivalent to other members of the same list solely based on their appearance within a common grouping, absent a statement to the contrary.

[0157] Unless otherwise defined, all technical and scientific terms used herein (such as acronyms used for polymers or chemical elements in the periodic table) have the same meaning as commonly understood by one of ordinary skill in the art to which the subject matter disclosed herein belongs. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety unless a specific text is cited. In the event of a conflict, the present specification, including definitions, controls. Furthermore, materials, methods, and examples (e.g., listed fractions, plastics) are for illustrative purposes only and are not intended to be limiting.

[0158] Unless otherwise described herein, many details regarding specific materials, processing operations, and circuits are conventional and can be found in computing, electronics, and software technology textbooks and other sources.

[0159] Unless otherwise indicated, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about." Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and appended claims are approximations that may vary depending upon the desired properties sought to be obtained by the presently disclosed subject matter. [Explanation of symbols]

[0160] 100 systems 101 Material pieces 102 Hopper 103 Conveyor systems, conveyor belts 104 Conveyor system motor 105 Position detector 106 Tumbler / Vibrator / Singulator 107 Computer Systems 108 Automatic Control System 109 Live-action camera 110 Optical Recognition Systems, Visual Systems 111 Material piece tracking device, material tracking device 112 Control System 120 Sensor System 121 Radiation source 122 Power supply 124 detectors 125 Detector Electronics 126…129 Sorting device 136...139 Sorting bins 140 bottles, sorting containers 201 Material pieces 203 Conveyor System 401 Plastic Pieces 403 Conveyor System 410 VIS 411 XRF 412 NIR 413 MWIR 3400 Data processing systems, computer systems 3401 Graphics Processor Unit, GPU 3405 Local Bus 3412 User Interface Adapter 3413 Keyboard 3414 Mouse 3415 Processor (CPU) 3416 Display Adapter 3420 Volatile Memory, RAM 3425 Network (LAN) Adapter 3430 I / O Adapter 3431 hard disk drive 3432 tape drive 3435 Non-volatile memory, ROM 3440 Display

Claims

1. capturing a first visual image of a first piece of material, resulting in a first image data packet relating to said first piece of material; capturing a second visual image of a second piece of material, resulting in a second image data packet relating to the second piece of material, the first piece of material having a first chemical characteristic and the second piece of material having a second chemical characteristic different from the first chemical characteristic; processing the first and second image data packets with a machine learning system that has been pre-trained to visually identify pieces of material having the different chemical signatures; using the machine learning system to classify the first and second pieces of material into two different classes in response to the learned visual discrimination between the pieces of material having different chemical characteristics; Including, the classifying step is performed by training the machine learning system to visually identify plastic pieces that fall into the specific fraction, and the training is performed using a control group created from the identified plastic pieces. method.

2. The method of claim 1 , further comprising the step of separating the first pieces of material from the second pieces of material in response to the classification.

3. The method of claim 2 wherein the piece of material is a piece of plastic.

4. 4. The method of claim 3, wherein the first chemical characteristic comprises spectral data measured by a plurality of different sensor systems from at least one sample of plastic piece of the same type as the first plastic piece, and the second chemical characteristic comprises spectral data measured by the plurality of different sensor systems from at least one sample of plastic piece of the same type as the second plastic piece.

5. The method of claim 4 , wherein the spectral data pertains to the non-visible spectrum.

6. 5. The method of claim 4, wherein the plurality of different sensor systems are selected from the group consisting of near-infrared ("NIR"), mid-wave infrared ("MWIR"), and X-ray fluorescence ("XRF") systems.

7. The plurality of different sensor systems may include infrared ("IR"), Fourier transform IR ("FTIR"), forward-looking infrared ("FLIR"), very near infrared ("VNIR"), near infrared ("NIR"), short wavelength infrared ("SWIR"), long wavelength infrared ("LWIR"), mid-wavelength infrared ("MWIR" or "MIR"), x-ray transmission ("XRT"), gamma ray, ultraviolet ("UV"), x-ray fluorescence ("XRF"), laser-induced breakdown spectroscopy ("LIBS"). "), Raman spectroscopy, anti-Stokes Raman spectroscopy, gamma ray spectroscopy, hyperspectral spectroscopy (e.g., beyond visible wavelengths), acoustic spectroscopy, NMR spectroscopy, microwave spectroscopy, terahertz spectroscopy, differential scanning calorimetry ("DSC"), thermogravimetric analysis ("TGA"), capillary and rotational rheometry, optical microscopy and scanning electron microscopy ("SEM"), and chromatography.

8. 4. The method of claim 3, wherein the first chemical characteristic comprises measurements of organic and inorganic elements or molecules from at least one sample of plastic piece of the same type as the first plastic piece, and the second chemical characteristic comprises measurements of organic and inorganic elements or molecules from at least one sample of plastic piece of the same type as the second plastic piece.

9. 4. The method of 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 Polyvinyl Chloride ("PVC"), Type #4 Low Density Polyethylene ("LDPE"), Type #5 Polypropylene ("PP"), Type #6 Polystyrene ("PS"), and Type #7 Other polymers.

10. The method of claim 3 , wherein the first piece of material comprises polyvinyl chloride.

11. The method of claim 1 , wherein the two different classifications are different fractions.

12. a camera configured to capture a first visual image of a first piece of material resulting in a first image data packet for the first piece of material and a second visual image of a second piece of material resulting in a second image data packet for a second piece of material, the first piece of material having a first chemical characteristic and the second piece of material having a second chemical characteristic different from the first chemical characteristic; a data processing system configured to process the first and second image data packets with a machine learning system previously trained to visually distinguish between pieces of material having the different chemical characteristics, the machine learning system classifying the first and second pieces of material into two different fractions in response to the trained visual distinction between the pieces of material having the different chemical characteristics; and a sorting device configured to separate the first pieces of material from the second pieces of material according to the fraction; Equipped with the sorting device is configured to perform the sorting by training the machine learning system to visually identify plastic pieces that fall into the specific fraction, the training being performed using a control group created from the identified plastic pieces. A system.

13. The system of claim 12 , wherein the piece of material is a piece of plastic.

14. 14. The system of claim 13, wherein the first chemical characteristic comprises spectral data for the non-visible spectrum measured by a plurality of different sensor systems from at least one sample of plastic pieces of the same type as the first plastic piece, and the second chemical characteristic comprises spectral data for the non-visible spectrum measured by the plurality of different sensor systems from at least one sample of plastic pieces of the same type as the second plastic piece.

15. 15. The system of claim 14, wherein the plurality of different sensor systems are selected from the group consisting of near-infrared ("NIR"), mid-wavelength infrared ("MWIR"), and X-ray fluorescence ("XRF") systems.

16. The plurality of different sensor systems may include infrared ("IR"), Fourier transform IR ("FTIR"), forward-looking infrared ("FLIR"), very near infrared ("VNIR"), near infrared ("NIR"), short wavelength infrared ("SWIR"), long wavelength infrared ("LWIR"), mid-wavelength infrared ("MWIR" or "MIR"), x-ray transmission ("XRT"), gamma ray, ultraviolet ("UV"), x-ray fluorescence ("XRF"), laser-induced breakdown spectroscopy ("LIBS"), and the like. ), Raman spectroscopy, anti-Stokes Raman spectroscopy, gamma ray spectroscopy, hyperspectral spectroscopy (e.g., beyond visible wavelengths), acoustic spectroscopy, NMR spectroscopy, microwave spectroscopy, terahertz spectroscopy, differential scanning calorimetry ("DSC"), thermogravimetric analysis ("TGA"), capillary and rotational rheometry, optical microscopy and scanning electron microscopy ("SEM"), and chromatography.

17. 14. The system of claim 13, wherein the first chemical signature comprises measurements of organic and inorganic elements or molecules from at least one sample of plastic piece of the same type as the first plastic piece, and the second chemical signature comprises measurements of organic and inorganic elements or molecules from at least one sample of plastic piece of the same type as the second plastic piece, the plastic piece being selected from the group consisting of Type #1 Polyethylene Terephthalate ("PET"), Type #2 High Density Polyethylene ("HDPE"), Type #3 Polyvinyl Chloride ("PVC"), Type #4 Low Density Polyethylene ("LDPE"), Type #5 Polypropylene ("PP"), Type #6 Polystyrene ("PS"), and Type #7 Other Polymers.

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