Scrap Data Analysis

Abstract

A first sorting system located at a first geographic location, a second sorting system located at a second geographic location, a data center located at a third geographic location, and a wide area network configured to enable data communication between the first sorting system and the data center, and between the second sorting system and the data center. The first sorting system sorts and sorts a first mixture of materials using a sorting / sorting algorithm, whereby information regarding the results of sorting and sorting the first mixture of materials is collected and transmitted to the data center via the wide area network. The data center modifies the sorting / sorting algorithm as a result of the collected information. The modified sorting / sorting algorithm is then made available to the first sorting system and the second sorting system via the wide area network.

Description

[Technical Field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 299,284, which is hereby incorporated by reference.

[0002] The present disclosure relates generally to materials handling, and more particularly to the collection and analysis of data associated with sorting and / or separation of materials. [Background technology]

[0003] This section is intended to introduce various aspects of technology that may be related to exemplary embodiments of the present disclosure. It is believed that the discussion helps to provide a framework to facilitate a better understanding of certain aspects of the present disclosure. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.

[0004] Recycling is the process of collecting and processing materials that would otherwise be discarded as trash, turning them into new products. Recycling benefits local communities and the environment because it reduces the amount of waste sent to landfills and incinerators, conserves natural resources, 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 are usually sent to materials recovery facilities where they are sorted, cleaned, and processed into materials that can be used in manufacturing.

[0005] As a result, a high-throughput automated sorting platform that economically sorts metal alloys and highly mixed waste streams would be beneficial across a variety of industries. Thus, there is a need for a cost-effective sorting platform that can identify, analyze, and separate materials at high throughput to economically produce higher-quality feedstock for subsequent processing. Typically, sorting facilities are unable to distinguish between many materials, limiting scrap to lower-quality, lower-value markets, or are very slow, labor-intensive, and inefficient, limiting the amount of material that can be economically recycled or recovered.

[0006] Because scrap metal is often shredded, sorting is required to facilitate recycling of the metal. Sorting scrap metal recycles metal that would otherwise end up in landfills. Additionally, using sorted scrap metal reduces pollution and emissions compared to refining virgin materials from ore. If the quality of the sorted metal meets certain standards, manufacturers can use it in place of virgin materials. Scrap metal can include ferrous and non-ferrous metals, heavy metals, high-value metals such as nickel or titanium, cast or wrought metals, and various other alloy types. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] U.S. Patent Application Serial No. 17 / 667,397 [Patent Document 2] U.S. Patent No. 10,207,296 [Patent Document 3] U.S. Patent Application Serial No. 18 / 074,110 [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a schematic diagram of a material handling system configured in accordance with an embodiment of the present disclosure; [Figure 2] FIG. 1 illustrates an exemplary representation of a control set of pieces of material used during the training phase in an artificial intelligence (“AI”) system. [Figure 3] FIG. 1 is a flow chart diagram configured in accordance with an embodiment of the present disclosure. [Figure 4] FIG. 1 is a flow chart diagram configured in accordance with an embodiment of the present disclosure. [Figure 5] FIG. 1 is a block diagram of a data processing system configured in accordance with an embodiment of the present disclosure. [Figure 6] FIG. 1 is a schematic diagram of a system configured in accordance with an embodiment of the present disclosure. [Figure 7] FIG. 1 is a flow chart diagram configured in accordance with an embodiment of the present disclosure. [Figure 8] FIG. 1 is a flow chart diagram configured in accordance with an embodiment of the present disclosure. [Figure 9] FIG. 1 is a flow chart diagram configured in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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 figures are not necessarily to scale, and some features may be exaggerated or reduced in size to show details of particular components. Therefore, 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 to use various embodiments of the present disclosure.

[0010] As used herein, "material" means metals (ferrous and non-ferrous), metal alloys, heavy, zorba, twitch, pieces of metal embedded in another, different material, plastics (including, but not limited to, any of the plastics known in the industry, disclosed herein, or newly created in the future), rubber, foam, glass (including, but not limited to, borosilicate or soda lime glass, and various colored glasses), ceramic, paper, cardboard, Teflon, PE, bundles of 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, and demolition waste. This can include any item or object, including but not limited to scrap, crop waste, forest residues, purpose-grown grasses, woody energy crops, microalgae, food waste, hazardous chemical and biomedical waste, construction debris, farm waste, biological items, non-biological items, objects with a particular carbon content, any other object that may be found in municipal solid waste, and any other object, item, or material disclosed herein, including any further types or classes of any of the foregoing that can be distinguished from one another by, including but not limited to, one or more sensor systems, including but not limited to, any of the sensor technologies disclosed herein.

[0011] In a more general sense, "material" can include any item or object composed of a chemical element, a compound or mixture of one or more chemical elements, or a compound or mixture of compounds or mixtures of chemical elements, where the compound or mixture can range in complexity from simple to complex (all of which are sometimes referred to herein as materials having a particular "chemical composition"). "Chemical element" means a chemical element of the periodic table of chemical elements, including chemical elements that may be discovered after the filing date of this application. Within this disclosure, the terms "scrap," "scrap piece," "material," "material piece," and "piece" can be used interchangeably. As used herein, material piece or scrap piece referred to as having a metal alloy composition is a metal alloy having a particular chemical composition that distinguishes it from other metal alloys.

[0012] As used herein, "collective chemical composition" refers to the composition of chemical elements within a collection of material pieces and their relative weight percentages (wt%). (Note that percentage by weight (or weight percentage) is also called mass fraction, which is the ratio of the mass of a particular chemical element within a material or substance to the total mass of the material or substance.) For example, if a collection of metal alloys were melted together, the resulting "melt" would have a chemical composition equivalent to the collective chemical composition. As referred to herein, a "melt" is when selected material pieces are melted together, and a "melt test" is when a compositional analysis is performed on the melted material pieces to determine the proportions (e.g., weight percentages) of the various chemical elements present in the melt.

[0013] As used herein, the term "chemical signature" refers to a unique pattern (e.g., a fingerprint spectrum) that may be generated by one or more analytical instruments that indicates the presence of one or more particular 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 and in U.S. Patent Application No. 17 / 667,397, which is hereby incorporated by reference. According to embodiments of the present disclosure, one or more such sensor systems may be configured to generate a chemical signature of a piece of material.

[0014] 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. A "multilayer polymer film" is a film made up of two or more different compositions, with a thickness of about 7.5 mm. -8 x10 -4 The layers can have a thickness of up to 1 / 4 inch (200 mm). The layers are at least partially continuous and preferably, but optionally, coextensive. As used herein, the terms "plastic," "plastic piece," and "plastic piece" (all of which can be used interchangeably) refer to any object that includes or is composed of one or more polymers and / or polymer compositions of multilayer polymer films.

[0015] As used herein, "fraction" refers to any specified combination of organic and / or inorganic elements or molecules, polymer type, plastic type, polymer composition, chemical signature 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.), and the like, including any and 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 excluding 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 specified plastics without specified contaminants or additives, any type of plastic with a melting point greater than a specified threshold, any thermosetting plastic of multiple specified types, specified plastics without chlorine, combinations of plastics with similar densities, combinations of plastics with similar polarity, and plastic bottles without caps or vice versa.

[0016] As used herein, the term "predetermined" refers to something that is pre-established or determined, for example, by a user of an embodiment of the present disclosure.

[0017] As used herein, "spectral imaging" refers to imaging that uses multiple bands across the electromagnetic spectrum. While a typical camera captures an image composed of light across three wavelength bands in the visible spectrum—red, green, and blue (RGB)—spectral imaging encompasses a variety of techniques that include and extend beyond RGB. For example, spectral imaging can use the infrared, visible, ultraviolet, and / or x-ray spectrums, or some combination of the above. Spectral data, or spectral image data, is a digital data representation of a spectral image. Spectral imaging can 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 in a spectral image.

[0018] As used herein, the term "image data packet" refers to a packet of digital data associated with a captured spectral image of an individual piece of material.

[0019] As used herein, the terms “identify” and “classify,” “identification” and “classification,” and any derivatives of the foregoing, can be used interchangeably. As used herein, to “classify” a piece of material is to determine (i.e., identify) the type or class of material to which the piece of material belongs. For example, according to some embodiments of the present disclosure, a sensor system (as described further herein) can be configured to collect and analyze any type of information to classify materials and distinguish such classified materials from other materials, which classification can be utilized in a sorting system that selectively sorts pieces of material according to a set of one or more physical and / or chemical properties (e.g., which may be user-defined), including, but not limited to, color, texture, hue, shape, brightness, weight, density, chemical composition, size, uniformity, manufacturing type, chemical signature, predetermined fraction, radioactive signature, transmittance to light, sound, or other signals, and response to various field-like stimuli, including emitted and / or reflected electromagnetic radiation (“EM”) of the piece of material.

[0020] The types or classes (i.e., classifications) of materials can be user-definable (e.g., predetermined) and are not limited to any known classification of materials. These types or classes can range in granularity from very coarse to very fine. For example, these types or classes can include relatively coarse-grained plastics, ceramics, glasses, metals, any other materials, and combinations of the foregoing; finer-grained different metals and metal alloys, such as zinc, copper, brass, chrome-plated, and aluminum; or relatively fine-grained specific types of metal alloys or plastics. Thus, these types or classes can be configured to distinguish between materials of widely different chemical compositions, such as plastics and metal alloys, or between materials of nearly identical chemical compositions, such as different types of metal alloys. It should be understood that the methods and systems discussed herein can be applied to accurately identify / classify pieces of material whose chemical composition is completely unknown before classification.

[0021] As used herein, "manufacturing type" refers to the type of manufacturing process by which a piece of material is produced, such as a forged metal part formed by a forging process, cast (including but not limited to expendable die casting, permanent die casting, and powder metallurgy), material removal process, etc.

[0022] As referred to herein, a "conveyor system" can be any known mechanical handling equipment that moves materials from one location to another, including, but not limited to, aero-mechanical conveyors, automotive conveyors, belt conveyors, belt-driven live roller conveyors, bucket conveyors, chain conveyors, chain-driven live roller conveyors, drag conveyors, dust-tight conveyors, powered track vehicle systems, flexible conveyors, gravity conveyors, gravity skate wheel conveyors, line shaft roller conveyors, power-driven roller conveyors, overhead I-beam conveyors, overland conveyors, pharmaceutical conveyors, plastic belt conveyors, pneumatic conveyors, screw or auger conveyors, spiral conveyors, tubular gallery conveyors, vertical conveyors, vibratory conveyors, and wire mesh conveyors.

[0023] Systems and methods described herein in accordance with some embodiments of the present disclosure are configured to receive a mixture of different types of material pieces, where at least one of the material pieces in the mixture has a different chemical composition than one or more other material pieces, and / or is physically distinguishable from the other material pieces, and / or is a different class or type of material from the other material pieces in the mixture, and to identify / classify / separate / sort the one material piece into a group separate from the other material pieces. Embodiments of the present disclosure can be utilized to sort any type or class of material, as defined herein.

[0024] Some embodiments of the present disclosure are described herein as sorting material pieces into separate groups or collections by physically depositing (e.g., ejecting or diverting) the material pieces into separate containers or bins or onto separate conveyor systems according to user-defined or predetermined groupings or collections. As an example, within some embodiments of the present disclosure, material pieces may be sorted to separate material pieces comprised of a particular chemical composition from other material pieces comprised of a different particular chemical composition.

[0025] As used herein, the term "aluminum" refers to aluminum metal and aluminum-based alloys, i.e., alloys containing more than 50% aluminum by weight (including those classified by the Aluminum Association). As defined in the Guidelines for Nonferrous Scrap published by the Institute of Scrap Recycling Industries, Inc., the term "zorba" is a collective term for shredded nonferrous metals, including, but not limited to, those derived from end-of-life vehicles (ELVs) or waste electronic and electrical equipment (WEEE). The Institute of Scrap Recycling Industries, Inc. (ISRI) in the United States developed the specifications for zorba. In zorba, each scrap piece may be composed of a combination of the following nonferrous metals in elemental or alloy (solid) form: aluminum, copper, lead, magnesium, stainless steel, nickel, tin, and zinc. Additionally, the term "twitch" refers to shredded aluminum scrap. Twich can be produced by a float process in which aluminum scrap floats to the top as heavier pieces of scrap metal sink (for example, in some processes, sand may be mixed in to change the density of the water in which the scrap is immersed).

[0026] 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 individual pieces of material 101 through the system 100 so that certain types of pieces of material 101 can be tracked, sorted, differentiated, and / or separated into at least one predetermined desired group. Such a conveyor system 103 may be implemented with one or more conveyor belts along which the pieces of material 101 typically move at a predetermined, constant speed. However, some embodiments of the present disclosure may 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 conveying, capturing, stimulating, detecting, sorting, differentiating, and separating operations or functions may be performed automatically, i.e., without human intervention. For example, in system 100, one or more cameras, one or more stimulus sources, one or more emission detectors, classification modules, sorting devices, and / or other system components may be configured to perform these and other operations automatically.

[0027] 1 depicts a single stream of material pieces 101 on conveyor belt 103, embodiments of the present disclosure can be implemented in which multiple such streams of material pieces pass parallel to one another by various components of system 100. According to some embodiments of the present disclosure, some type of suitable feeder mechanism (e.g., another conveyor system or hopper 102) can be utilized to feed material pieces 101 onto conveyor system 103, which then transports material pieces 101 through various components in system 100. According to some embodiments of the present disclosure, once material pieces 101 are received by conveyor belt 103, a tumbler and / or vibrator can be utilized to separate individual material pieces from a collection (e.g., a physical pile) of material pieces. According to some embodiments of the present disclosure, material pieces can be arranged into one or more singulated (i.e., in-line) streams, which can be performed by an active or passive singulator 106. An example of a passive singulator is further described in U.S. Patent No. 10,207,296. Thus, some embodiments of the present disclosure can simultaneously track, sort, differentiate, and / or separate multiple such parallel-moving streams of material pieces, or pieces of material randomly deposited onto a conveyor system (belt). Alternatively, the conveyor system (e.g., conveyor belt 103) may simply randomly transport a collection of material pieces placed onto conveyor belt 103. However, according to embodiments of the present disclosure, singulation of material pieces 101 is not required to track, sort, differentiate, and / or separate the material pieces.

[0028] Within some 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. Within some embodiments of the present disclosure, control of the conveyor system motor 104 and / or position detector 105 may be performed by an automated control system 108. Such an automated control system 108 may operate under the control of a computer system 107, and / or functionality for performing the automated control may be implemented in software within the computer system 107. If the conveyor system 103 is a conveyor belt, it may be a conventional endless belt conveyor using a conventional drive motor 104 suitable for moving the conveyor belt 103 at the predetermined speed.

[0029] A position detector 105 (e.g., a conventional encoder) can be operatively coupled to the conveyor belt 103 and the automation control system 108 to provide information corresponding to the movement (e.g., speed) of the conveyor belt 103. Thus, as described further herein, through utilizing control over the conveyor belt drive motor 104 and / or the automation control system 108 (or including the position detector 105), once each piece of material 101 moving on the conveyor belt 103 is identified, it can be tracked by location 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 within its vicinity. As a result, the automation control system 108 can track the location of each piece of material 101 as it moves along the conveyor belt 103.

[0030] Referring again to FIG. 1 , some embodiments of the present disclosure may utilize a vision or optical recognition system 110 and / or a piece of material tracking device 111 as a means of tracking each of the pieces of material 101 as they move on the conveyor system 103. The vision system 110 may utilize one or more stationary or live-action cameras 109 to record the position (i.e., location and timing) of each of the pieces 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 a portion 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 of the pieces of material 101. For example, the vision system 110 may be configured (e.g., in an artificial intelligence (“AI”) system) to capture or gather any type of information from the pieces of material that may be utilized within the system 100 to classify and / or selectively sort the pieces of material 101 according to a set of one or more characteristics (e.g., physical and / or chemical and / or radioactive, etc.) as described herein. According to some embodiments of the present disclosure, the vision system 110 can be configured to capture visual images (including one-dimensional, two-dimensional, three-dimensional, or holographic imaging) of each of the pieces of material 101, for example, by using optical sensors such as those utilized in typical digital cameras and video equipment. Such visual images captured by the optical sensors are then stored in a memory device as image data (e.g., formatted as image data packets). According to certain embodiments of the present disclosure, such image data can represent images captured within optical wavelengths of light (i.e., wavelengths of light observable by the typical human eye). However, alternative embodiments of the present disclosure can utilize sensor systems configured to capture images of materials constructed with wavelengths of light outside the visible wavelengths of the human eye.

[0031] According to some embodiments of the present disclosure, the system 100 may be implemented with one or more sensor systems 120, which may be utilized alone or in combination with the vision system 110 to classify / identify / distinguish pieces of material 101. The sensor system 120 can be configured with any type of sensor technology, including sensors that utilize 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, anti-Stokes Raman spectroscopy, gamma spectroscopy, hyperspectral spectroscopy (e.g., any range beyond visible wavelengths), acoustic spectroscopy, NMR spectroscopy, microwave spectroscopy, terahertz spectroscopy, including one-dimensional, two-dimensional, or three-dimensional imaging of any of the foregoing), or any other type of sensor technology, including, but not limited to, chemical or radioactive. Implementations of XRF systems (e.g., for use as sensor system 120 herein) are further described in U.S. Patent No. 10,207,296. XRF can be used within some embodiments of the present disclosure to identify inorganic materials within a piece of plastic (e.g., for inclusion in a chemical signature).

[0032] The following sensor systems can also be used in some embodiments of the present disclosure to determine the chemical signature of plastic pieces and / or classify plastic pieces for sorting. Various forms of infrared spectroscopy, as previously disclosed, can be utilized to obtain a chemical signature unique to each plastic piece that provides information about the base polymer of any plastic material, as well as other components present in the material (mineral fillers, copolymers, polymer blends, etc.). Differential scanning calorimetry ("DSC") is a thermal analysis technique that obtains thermal transitions produced during heating of an analyzed material that are unique to each material. 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. Capillary and rotational rheometry can determine the rheological properties of polymeric materials by measuring their creep and resistance to deformation. Optical 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 within a polymer matrix, defects in coatings, interfacial morphology between components, etc. Chromatography (e.g., LC-PDA, LC-MS, LC-LS, GC-MS, GC-FID, HS-GC) can quantify minor components of plastic materials such as UV stabilizers, antioxidants, plasticizers, anti-slip agents, etc., as well as residual monomers, residual solvents from inks or adhesives, decomposition products, etc.

[0033] While FIG. 1 is shown with a combination of vision system 110 and one or more sensor systems 120, it should be noted that embodiments of the present disclosure can be implemented with any combination of sensor systems utilizing any of the sensor technologies disclosed herein or any other sensor technologies currently available or developed in the future. While FIG. 1 is shown including one or more sensor systems 120, implementing such a sensor system is optional within some embodiments of the present disclosure. Within some embodiments of the present disclosure, a combination of both vision system 110 and one or more sensor systems 120 can be used to classify the piece of material 101. Within some embodiments of the present disclosure, any combination of one or more of the different sensor technologies disclosed herein can be used to classify the piece of material 101 without utilizing vision system 110. Furthermore, embodiments of the present disclosure can include any combination of one or more sensor systems and / or vision systems, and the output of such sensor / vision systems can be processed within an AI system (as further disclosed herein) to classify / identify / distinguish materials from a heterogeneous mixture of materials, which can then be separated from one another.

[0034] According to some embodiments of the present disclosure, the vision system 110 and / or sensor system can be configured to identify which pieces of material 101 contain contaminants (e.g., steel or iron pieces containing copper, or plastic pieces containing certain 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 separate (sort) such pieces of material (e.g., from those that do not contain contaminants). In such a configuration, the identified pieces of material 101 can be diverted / ejected using one of the mechanisms described below for physically diverting the sorted pieces of material to individual containers.

[0035] Within some embodiments of the present disclosure, material piece tracking devices 111 and associated control systems 112 may be utilized and configured to measure the size and / or shape of each of the material pieces 101 as they pass near the material piece tracking devices 111, as well as the position (i.e., location and timing) of each of the material pieces 101 on the moving conveyor system 103. Exemplary operation of such material piece tracking devices 111 and control systems 112 is further described in U.S. Patent No. 10,207,296. Alternatively, as previously disclosed, a vision system 110 may be utilized to track the position (i.e., location and timing) of each of the material pieces 101 as they are transported by the conveyor system 103. In this manner, some embodiments of the present disclosure may be implemented without a material piece tracking device (e.g., material piece tracking device 111) that tracks the material pieces.

[0036] Within some embodiments of the present disclosure that implement 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 an energy-emitting source 121, which may be powered by a power source 122, for example, to stimulate a response from each of the pieces of material 101.

[0037] Within some embodiments of the present disclosure, as each piece of material 101 passes near the emission source 121, the sensor system 120 can emit an appropriate sensing signal toward the piece of material 101. One or more detectors 124 can 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 and perform signal processing on these received sensed characteristics to generate digital information (e.g., spectral data) representative of the sensed characteristics, which can then be analyzed and used to classify each of the pieces of material 101 according to some embodiments of the present disclosure. This classification can be performed within the computer system 107, which is then utilized by the automated control system 108 to activate one of the N (N≧1) sorting devices 126...129 of the sorting apparatus to sort (e.g., divert / discard) the piece of material 101 into one or more N (N≧1) sorting bins 136...139 according to the determined classification. Four sorting devices 126...129 and four sorting receptacles 136...139 associated with these sorting devices are shown in Figure 1 by way of non-limiting example only.

[0038] The sorting device may include any known mechanism for redirecting selected pieces of material 101 to a desired location, including, but not limited to, diverting the pieces of material 101 from the conveyor belt system to multiple sorting bins. For example, the sorting device may utilize air jets, each of which is assigned to one or more of the classifications. When one of the air jets (e.g., 127) receives a signal from the automated control system 108, that air jet emits an air stream that diverts / exits the pieces of material 101 from the conveyor system 103 into the sorting bin (e.g., 137) corresponding to that air jet.

[0039] 1 uses air jets to deflect / eject the pieces of material, other mechanisms can be used to deflect / eject the pieces of material, such as robotically removing the pieces from the conveyor belt, pushing the pieces of material off the conveyor belt (e.g., with a paintbrush-type plunger), opening an opening (e.g., a trap door) in the conveyor system 103 through which the pieces of material can fall, or using air jets to separate the pieces of material into separate bins as they fall off the edge of the conveyor belt. Pusher device, as the term is used herein, refers to any type of device that can be actuated to dynamically displace objects on or from the conveyor system / device, and can use pneumatic, mechanical, or other means of doing so, such as any suitable type of mechanical pushing mechanism (e.g., ACME screw drive), pneumatic pushing mechanism, or air jet pushing mechanism.

[0040] In addition to the N sorting bins 136...139 into which pieces of material 101 are diverted / discharged, system 100 may also include bin 140 to receive pieces of material 101 that are not diverted / discharged from conveyor system 103 into any of the aforementioned sorting bins 136...139. For example, if the classification of a piece of material 101 cannot be determined (or simply because a sorting device fails to properly divert / discharge the piece of material), that piece of material 101 may not be diverted / discharged from conveyor system 103 into one of the N sorting bins 136...139. Thus, bin 140 can serve as a default bin into which unsorted or unsorted pieces of material are discarded. Alternatively, bin 140 can be used to receive pieces of material of one or more classifications that are not intentionally assigned to any of the N sorting bins 136...139. These such pieces of material can then be further sorted according to other characteristics and / or by another sorting system.

[0041] Depending on the variety of desired material classifications, multiple classifications can be mapped to a single sorting device and associated sorting bin. In other words, there need not be a one-to-one correlation between classifications and sorting bins. For example, a user may desire that certain classifications of material be sorted into the same sorting bin. To achieve this sorting, once material pieces 101 are classified as falling into a predetermined grouping classification, the same sorting device can be activated to sort them into the same sorting bin. Such combinatorial sorting can be applied to generate any desired combination of sorted material pieces. The classification mapping can be programmed by the user (e.g., using a sorting algorithm (e.g., see Figures 3-4) operated by the computer system 107) to generate such desired combinations. Additionally, the classifications of material pieces are user-definable and are not limited to any particular known classification of material pieces.

[0042] The systems and methods described herein can be applied to sort and / or separate individual pieces of material of various small sizes, such as ¼ inch in diameter or less. While the systems and methods described herein are primarily described in connection with sorting individual pieces of material one by one in a singulated stream, the systems and methods described herein are not limited thereto. Such systems and methods can be used to stimulate and / or detect emissions from multiple materials simultaneously. For example, rather than singulated streams of material being transported serially along one or more conveyor belts, multiple singulated streams can be transported in parallel. Each stream can be on the same belt or on a different belt arranged in parallel. Furthermore, pieces of material can be randomly distributed on (e.g., across and along) one or more conveyor belts. Thus, the systems and methods described herein can be used to stimulate and / or detect emissions from multiple these small pieces of material simultaneously. In other words, rather than considering each small piece of material individually, multiple small pieces of material can be treated as a single piece of material. Thus, multiple small pieces of material can be sorted and separated (e.g., diverted / discharged from the conveyor system) together. It should be understood that multiple larger pieces of material can also be treated as a single piece of material.

[0043] As previously mentioned, some embodiments of the present disclosure may implement one or more vision systems (e.g., vision system 110) to identify, track, sort, and / or distinguish 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 a sensor system (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 solely with such vision system 110, sensor system 120 may be omitted from system 100 (or simply disabled).

[0044] Such a vision system can be configured with one or more devices for capturing or acquiring images of the pieces of material as they pass through the conveyor system. These devices can be configured to capture or acquire any desired wavelength range emitted or reflected by the pieces of material, including, but not limited to, visible light, infrared (IR) light, and ultraviolet (UV) light. For example, the vision system can be configured with one or more cameras (still and / or video, any of which can be configured to capture two-dimensional, three-dimensional, and / or holographic images) positioned near (e.g., above) the conveyor system to capture images of the pieces of material as they pass by the sensor system. According to alternative embodiments of the present disclosure, data captured by the sensor system 120 can be processed (converted) into data to be used (alone or in combination with image data captured by the vision system 110) for sorting / separating the pieces of material. Such implementations can be performed instead of or in combination with utilizing the sensor system 120 to sort the pieces of material.

[0045] AI systems include any well-known AI systems (e.g., artificial narrow intelligence ("ANI"), artificial general intelligence ("AGI"), and artificial superintelligence ("ASI")), machine learning systems including those implementing neural networks (e.g., artificial neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, reinforcement learning, etc.), supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, robot learning, association rule learning, fuzzy logic, deep learning algorithms, deep structured learning hierarchical learning algorithms, support vector machines ("SVM") (e.g., linear A machine learning system may be implemented that implements algorithms such as SVM, nonlinear SVM, 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 publicly available at the deeplearning.net website (including hyperlinks to all software, publications, and available software referenced within this website), which is hereby incorporated by reference.Non-limiting examples of publicly available AI software and libraries that may 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 (from Rasmus Berg Palm)), BigDL, Cuda-Convnet (a fast C++ / CUDA implementation of convolutional (or more generally, feedforward) neural networks), Deep Belief Networks, including RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factor 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.

[0046] According to some embodiments of the present disclosure, some types of machine learning can be performed in two stages. For example, first, training occurs, which can be performed offline, and system 100 is not utilized to perform the actual classification / sorting of material pieces. System 100 can be utilized to train the machine learning system by passing a homogeneous set of material pieces (also referred to herein as control samples) (i.e., having the same type or class of material or falling within the same predetermined fraction) through system 100 (e.g., via conveyor system 103), and not all such material pieces can be sorted but collected in a common container (e.g., container 140). Alternatively, training can be performed at a separate location, remote from system 100, including using some other mechanism for collecting sensory information (characteristics) of the 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) and create a knowledge base for later classifying heterogeneous mixtures of material pieces received by the system 100 that can then be sorted according to a desired classification. Such a knowledge base can include one or more libraries, each containing parameters (e.g., neural network parameters) for the machine learning system to utilize in classifying material pieces. For example, one particular library can contain parameters configured by the training phase to recognize and classify a particular type or class of material, or one or more materials falling into a predetermined fraction.According to some embodiments of the present disclosure, such a library can be input into a machine learning system, and a user of system 100 may then be able to adjust some of the parameters to tune the operation of system 100 (e.g., adjust the effectiveness of a threshold for how well the machine learning system recognizes a particular piece of material from a heterogeneous mixture of materials).

[0047] Additionally, the inclusion of certain materials in a piece of material results in identifiable physical characteristics (e.g., visually distinguishable properties) of the material. As a result, when multiple pieces of material containing such specific compositions are put through the aforementioned training phase, the machine learning system can learn how to distinguish such pieces of material from one another. Thus, a machine learning system configured according to some embodiments of the present disclosure can be configured to sort between pieces of material according to their respective material / chemical compositions.

[0048] During the training phase, a control sample of multiple pieces of material of one or more specific types, classes, or fractions of material can be conveyed (e.g., by a conveyor system) through the vision system and / or one or more sensor systems, and an algorithm in the machine learning system can detect, extract, and learn features representative of such types or classes of material. For example, each of the pieces of material in the control sample (e.g., see FIG. 2) can first be passed through such a training phase so that an algorithm in the machine learning system "learns" (is trained) how to detect, recognize, and classify such pieces of material. When a vision system (e.g., vision system 110) is trained, it is trained to visually distinguish between pieces of material. This creates a library of parameters specific to such homogeneous classes of pieces of material. The same process can be performed with images of any classification of pieces of material to create a library of parameters specific to such classifications of pieces of material. For each type of material to be classified by the vision system, any number of example pieces of material of that classification of material can be passed through the vision system. Given the captured sensory information as input data, an algorithm within the machine learning system can employ N classifiers, each of which tests one of N different material types. Note that the machine learning system can be "taught" (trained) to detect any type, class, or fraction of material, including any of the material types, classes, or fractions disclosed herein.

[0049] After the algorithms are established and the machine learning system has sufficiently learned (trained) the differences (e.g., visually recognizable differences) for material classification (e.g., within a user-defined statistical confidence level), the library of different material classifications can then be implemented into a material classification / sorting system (e.g., system 100) to be used to identify, distinguish, and / or classify material pieces from a heterogeneous mixture of material pieces, and then, alternatively, to sort such classified material pieces if sorting is to be performed.

[0050] It should be understood that the present disclosure is not exclusively limited to AI techniques. Other common techniques for material classification / identification can also be used. For example, a sensor system can utilize optical spectroscopy techniques using multi- or hyperspectral cameras to examine the spectral emissions (i.e., spectral imaging) of materials to provide a signal that can indicate the presence or absence of a certain type, class, or fraction of material. The spectral image of a piece of material can also be used in a template matching algorithm, comparing the acquired spectral image with a database of spectral images to find the presence or absence of a particular type of material from the database. The histogram of the captured spectral image can also be compared with 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.

[0051] According to some embodiments of the present disclosure, instead of utilizing a training phase in which control (homogeneous) samples of material pieces are passed through a vision and / or sensor system, the training of the AI ​​system can be performed using labeling / annotation techniques (or any other supervised learning techniques) whereby as the data / information for the material pieces is captured by the vision / sensor system, a user inputs labels or annotations that identify each material piece, which are then used to create a library that is used by the AI ​​system when classifying material pieces within a heterogeneous mixture of material pieces.

[0052] According to certain embodiments of the present disclosure, any sensed characteristic output by any of the sensor systems 120 disclosed herein can be input into an AI system for classifying and / or sorting materials. For example, in an AI system implementing supervised learning, the output of the sensor system 120 that uniquely characterizes a particular type or composition of material can be used to train the AI ​​system.

[0053] FIG. 3 shows a flowchart diagram illustrating an example embodiment of a process 3500 for sorting / separating material pieces utilizing a vision system and / or one or more sensor systems according to some embodiments of the present disclosure. Process 3500 can be performed to sort a heterogeneous mixture of material pieces into any combination of predetermined types, classes, and / or fractions. Process 3500 can be configured to operate within any of the embodiments of the present disclosure described herein, including system 100 of FIG. 1 or system 601 of FIGS. 6A-6B. Operations of process 3500 can be performed by hardware and / or software, including within a computer system (e.g., computer system 3400 of FIG. 5) that controls a system (e.g., computer system 107, vision system 110, and / or sensor system 120 of FIG. 1). At process block 3501, material pieces can be deposited onto a conveyor system. At process block 3502, the location of each material piece on the conveyor system is detected to track each material piece as it moves through system 100. This may be done by the vision system 110 (e.g., by distinguishing the piece of material from the material on the conveyor system below it while communicating with a position detector (e.g., position detector 105) of the conveyor system). Alternatively, the piece of material may be tracked using a piece of material tracking device 111. Alternatively, any system may be used that can produce a light source (including, but not limited to, visible light, UV, and IR) and have a detector that can be used to identify the location of the piece of material. In process block 3503, sensory information / characteristics of the piece of material are captured / obtained as the piece of material approaches one or more of the vision system and / or sensor system.At process block 3504, a vision system (e.g., implemented in computer system 107) as previously disclosed can perform preprocessing of the captured information, which can be used to detect (extract) information about each of the pieces of material (e.g., from a background (e.g., a conveyor belt)); in other words, preprocessing can be used to identify differences between the pieces of material and the background. Well-known image processing techniques, such as dilation, thresholding, and contouring, can be used to identify the pieces of material as distinct from the background. At process block 3505, segmentation can be performed. For example, the captured information can include information about one or more pieces of material. Additionally, a particular piece of material may be located on a seam of a conveyor belt when its image is captured. Therefore, in such cases, it may be desirable to isolate the image of the individual piece of material from the background of the image. In an exemplary technique for process block 3505, the first step is to apply high contrast to the image, such that background pixels are reduced to substantially all black pixels and at least some of the pixels related to the piece of material are brightened to substantially all white pixels. The image pixels of the white piece are then dilated to cover the entire size of the piece. After this step, the piece location is a high-contrast image of all white pixels on a black background. A contour algorithm can then be used to detect the boundary of the piece. The boundary information is saved, and the boundary location is then transferred to the original image. A segmentation is then performed on the original image on areas larger than the previously defined boundary. In this way, the piece is identified and separated from the background.

[0054] At optional process block 3506, the material pieces can be transported along a conveyor system near a piece tracking device and / or sensor system to track each piece and / or determine the size and / or shape of the material pieces, which may be useful if an XRF system or some other spectroscopic sensor is also implemented in the sorting system. At process block 3507, post-processing can be performed. Post-processing can involve resizing the captured information / data to prepare it for use in a neural network. This can also include modifying certain properties (e.g., enhancing image contrast, changing the image background, or applying a filter) that will enhance the AI ​​system's ability to classify the material pieces. At process block 3509, the data can be resized. Data resizing may be desirable in some situations to match the data input requirements for some AI systems, such as neural networks. For example, neural networks may require image sizes (e.g., 225 x 255 pixels or 299 x 299 pixels) that are much smaller than the size of images captured by a typical digital camera. Additionally, the smaller the input data size, the less processing time is required to perform the classification, and therefore, reducing data size ultimately increases the throughput of the system 100, increasing its value.

[0055] At process blocks 3510 and 3511, each piece of material is identified / classified based on the sensed / detected features. For example, process block 3510 can be comprised of a neural network that uses one or more algorithms to compare the extracted features with those stored in a previously generated knowledge base (e.g., generated during a training phase) and assign each piece of material a classification that best matches the comparison. The algorithm can process the captured information / data hierarchically by using automatically trained filters. The filter responses are then successfully combined at the next level of the algorithm until a probability is obtained in the final step. At process 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 the highest probability, greater than a predefined threshold. In embodiments of the present disclosure, such predefined threshold can be preset by the user. If none of the probabilities are greater than a predetermined threshold, the particular piece of material may be sorted into an outlier bin (eg, sort bin 140).

[0056] Next, at process block 3512, a sorting device corresponding to the classification of the material is activated (e.g., instructions to perform sorting are sent to the sorting device). Between the time the image of the material piece is captured and the time the sorting device is activated, the material piece is moving from near the vision system and / or sensor system to a downstream location on the conveyor system (e.g., at the conveying speed of the conveyor system). In an embodiment of the present disclosure, the operation 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 to its associated sorting bin. Within an embodiment of the present disclosure, the operation of the sorting device can be timed by respective position detectors that detect when the material piece passes in front of the sorting device and send a signal enabling the sorting device to be activated. At process block 3513, the sorting bin corresponding to the activated sorting device receives the diverted / discharged material.

[0057] FIG. 4 shows a flowchart diagram illustrating an example embodiment of a process 400 for sorting material pieces according to some embodiments of the present disclosure. Process 400 may be configured to operate within any of the embodiments of the present disclosure described herein, including system 100 of FIG. 1. Process 400 may be configured to operate in conjunction with process 3500. For example, according to some embodiments of the present disclosure, process blocks 403 and 404 may be incorporated into process 3500 (e.g., operating serially or in parallel with process blocks 3503-3510) to combine the efforts of a sensor system (e.g., sensor system 120) that is not implemented in conjunction with an AI system and a vision system 110 that is implemented in conjunction with an AI system to sort and / or separate material pieces 101.

[0058] The operations of process 400 may be performed by hardware and / or software, including within a computer system (e.g., computer system 3400 of FIG. 5) that controls a system (e.g., computer system 107 of FIG. 1). At process block 401, pieces of material 101 may be deposited onto conveyor system 103. Then, at optional process block 402, pieces of material 101 may be transported along conveyor system 103 near piece of material tracking devices 111 and / or optical imaging systems to track each piece of material and / or determine the size and / or shape of the piece of material 101. At process block 403, when the pieces of material 101 approach sensor system 120, the pieces of material 101 may be interrogated or stimulated with EM energy (waves) or some other type of stimulus appropriate for the particular type of sensor technology utilized by sensor system 120. At process block 404, physical properties of the pieces of material 101 are sensed / detected and captured by sensor system 120. At process block 405, for at least some of the pieces of material 101, the type of material is identified / classified based (at least in part) on the captured characteristics, which can be combined with classification by an AI system in conjunction with the vision system 110.

[0059] Next, if sorting of the piece of material 101 is to be performed, then at process block 406, the sorting device 126...129 corresponding to the classification of the piece of material 101 is activated. Between the time the piece of material 101 is sensed and the time the sorting device 126...129 is activated, the piece of material 101 is moving from near the sensor system 120 to a downstream location on the conveyor system 103 at the conveying speed of the conveyor system. In some embodiments of the present disclosure, the operation of the sorting device 126...129 is timed so that when the piece of material 101 passes the sorting device 126...129 mapped to the classification of the piece of material 101, the sorting device 126...129 is activated and the piece of material 101 is diverted / ejected from the conveyor system 103 into its associated sorting bin 136...139. Within some embodiments of the present disclosure, actuation of the sorting devices 126...129 may be timed by respective position detectors that detect when a piece of material 101 passes in front of the sorting device 126...129 and send a signal to enable actuation of the sorting device 126...129. At process block 407, the sorting receptacle 136...139 corresponding to the actuated sorting device 126...129 receives the diverted / discarded piece of material. According to some embodiments of the present disclosure, multiple at least portions of the system 100 may be linked in series to perform multiple iterations or layers of sorting. For example, when two or more systems 100 are linked in this manner, the conveyor system may be implemented with a single conveyor belt, or multiple conveyor belts, that transport a first set of material pieces of a heterogeneous mixture of materials past a first vision system (and, according to some embodiments, a sensor system) configured to sort a first set of material pieces of a heterogeneous mixture of materials by a sorter (e.g., a first automated control system 108 and one or more associated sorting devices 126...129) into a first set of one or more receptacles (e.g., sorting receptacles 136...139), and then transport a second set of material pieces of a heterogeneous mixture of materials past a second vision system (and, according to some embodiments, another sensor system) configured to sort a second set of material pieces of a heterogeneous mixture of materials by a second sorter into a second set of one or more sorting receptacles.

[0060] Such a series of systems 100 may include any number of such systems linked together in this manner. According to some embodiments of the present disclosure, each successive vision system may be configured to sort a different category or type of material than the previous vision system.

[0061] According to various embodiments of the present disclosure, different types or classes of material can be classified by different types of sensors, each for use with an AI system, and combined to separate pieces of material in a scrap or waste stream.

[0062] According to various embodiments of the present disclosure, data from two or more sensors can be combined using a single or multiple AI systems to perform classification of the material pieces.

[0063] According to various embodiments of the present disclosure, multiple sensor systems can be mounted on a single conveyor system, with each sensor system utilizing a different AI system. According to various embodiments of the present disclosure, multiple sensor systems can be mounted on different conveyor systems, with each sensor system utilizing a different AI system.

[0064] Some embodiments of the present disclosure can be configured to produce a mass of material having a content that is less than a predetermined weight or volume percent of a particular element or material after sorting.

[0065] Referring now to FIG. 5, a block diagram is depicted illustrating 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 automation 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”) and 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., through 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 processing units and / or one or more graphics processing units and / or one or more tensor processing units. Additional connections to the local bus 3405 may be made through direct component interconnects or through 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).

[0066] 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).

[0067] An operating system may run on one or more processors 3415 and be used to coordinate and provide control of various components within computer system 3400. In Figure 5, the operating system may be a commercially available operating system. An object-oriented programming system (e.g., Java, Python, etc.) may work in conjunction with the operating system to 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 a non-volatile memory 3435 storage device, such as hard disk drive 3431, and loaded into volatile memory 3420 for execution by processor 3415.

[0068] Those skilled in the art will appreciate that the hardware in Figure 5 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 5. Also, any of the processes of this disclosure may be applied to a multi-processor computer system or performed by multiple such systems 3400. For example, training of vision system 110 may be performed by a first computer system 3400, while the operation of vision system 110 to sort may be performed by a second computer system 3400.

[0069] As another example, computer system 3400 may be a standalone system configured to be bootable without relying on any 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 to provide non-volatile memory for storing operating system files or user-generated data.

[0070] 5 and above-described examples are not meant to imply architectural limitations. Moreover, the computer program format of the embodiments of the present disclosure may reside on any computer-readable storage medium for use by a computer system (i.e., floppy disk, compact disc, hard disk, tape, ROM, RAM, etc.).

[0071] FIG. 6 shows a schematic diagram of a system 600 that enables various devices configured to analyze, classify, and / or sort materials to communicate with one another via a cloud 601 and a centralized data center 602. The cloud 601 can be comprised of the Internet and / or one or more intranets implemented within an organization. The data center 602 can include one or more data processing systems operated by one or more data scientist experts trained to analyze various forms of data being received from the remote devices. The remote devices can include one or more sorting facilities 603, 604 (e.g., located in different geographic locations, e.g., cities), each of which can include one or more sorting devices as described with respect to FIG. 1. For example, the first sorting facility 603 can include N sorting devices or systems, where N≧1. Similarly, the system 600 can include one or more additional sorting facilities, such as the sorting facility 604 (e.g., located in different geographic locations, e.g., cities), which can include M sorting devices or systems, where M≧1. System 600 may further communicate with one or more Z point-of-use analyzers 605 (where Z≧1), which may be located at various locations around the world (e.g., landfills, ports of entry, airports, etc.). Data center 602, sorting facilities 603, 604, and point-of-use analyzers 605 are all located at various locations around the world that are remote from one another, but may be in data communication with at least data center 602 and possibly each other via cloud 601. Point-of-use analyzers (also called scrap analyzers) are further described in U.S. Patent Application No. 18 / 074,110, which is hereby incorporated by reference.

[0072] Each sorting facility 603, 604 can include one or more sorting devices, each configured to sort different types of materials using various combinations of sensor systems as described herein. For example, sorting facility 603 can include a first sorting device configured to sort twich and a second sorting device configured to sort plastics. In this manner, each sorting device within a particular sorting facility can be configured differently from other sorting devices within that facility, e.g., with a different combination of sensor systems.

[0073] A result of the aforementioned variability in sorting devices is that a particular sorting device can be configured to utilize different specific AI algorithms than those utilized in other sorting devices within the sorting facility or in any other sorting devices elsewhere in system 600. This may be due to the fact that the particular material being sorted and / or separated by a particular sorting device may have been received from a particular source where the material was processed differently than material processed by another source (e.g., shredding facility). As a result, the AI ​​algorithms used by such different sorting devices may be configured differently and / or utilize different neural network parameters to classify the material.

[0074] Similarly, each of the various point-of-use analyzers 605 may be located in different locations around the world and may be configured to analyze materials that are very or drastically different from the materials being analyzed by other point-of-use analyzers coupled to the system 600. As such, each point-of-use analyzer may require different AI algorithms and / or neural network parameters than the other point-of-use analyzers.

[0075] As each sorting device and / or point-of-use analyzer processes material, that data can be delivered via cloud 601 to data center 602 for collection and analysis. Such data can include, if a vision system is utilized, visual images of each piece of material (and information associated with such image files), and / or data associated with each piece of material, parameters of the particular sorting device or point-of-use analyzer associated with those materials, location data, data associating the material piece with its source, data associating the sorted material piece with the end product that will utilize the sorted material piece, the chemical composition of each analyzed and / or classified material piece, AI algorithm and neural network parameters utilized to analyze / classify each material piece within the particular sorting device or point-of-use analyzer, etc. Data center 602 can then serve as a repository for collecting and storing all of this data. Such data can also include the end-of-life type (e.g., vehicle, aircraft, appliance), sorting system location, material type, alloy composition (if known), sorting system identification, sensor technology used in the sorting system, software version, and customer identification for the sorted material.

[0076] FIG. 7 shows a flowchart diagram of a system and process 700 configured in accordance with some embodiments of the present disclosure. At process block 701, any one or more of the sorting devices and / or point-of-use analyzers 605 at the various sorting facilities 603, 604 can perform material classification, analysis, and / or sorting. At process block 702, in some circumstances, the material sorted by a particular sorting device can be tested to determine the accuracy of the algorithms (e.g., AI, XRF, etc.) utilized within the sorting device. For example, a melt test can be performed on all or a portion of the sorted material, and the chemical composition of the melt can be determined using some other analytical device, such as a LIBS or XRF device. At process block 703, data related to the processes performed in process blocks 701 and / or 702 is communicated via the cloud 601 to the data center 602. At process block 704, the data center 602 can make improvements to the classification / analysis algorithms utilized within the sorting devices and / or point-of-use analyzers. For example, the algorithms and / or neural network parameters utilized by the various sorting devices and / or point-of-use analyzers are also communicated to and stored in the data center 602 so that data scientists can then refine the algorithms and / or neural network parameters to further improve the analysis / classification of these particular or specific types of materials. These improvements are then made available to the various sorting devices and / or point-of-use analyzers from the data center 602 via the cloud 601 in process block 705. As a result, a particular sorting device and / or point-of-use analyzer can be configured to download such improvements, or the entire algorithms and / or neural network parameters utilized to analyze / classify the particular materials for which the improvements were designed, in process block 706. Process 700 can then be repeated indefinitely.

[0077] FIG. 8 illustrates a system and process 800 configured in accordance with some embodiments of the present disclosure. The system and process 800 can be configured to collect various information and data described with respect to the system and process 700 for analysis. At process block 801, various remote devices analyze, classify, and / or sort materials according to their particular design. In some circumstances, as indicated by process block 802, some materials sorted by a particular sorting device can also be tested as described with respect to process block 702. At process block 803, all of the various data described with respect to the system and process 700 can be sent to the data center 602. At process block 804, the data can be analyzed according to various methodologies. For example, a life cycle assessment (“LCA”) can be performed on various data sets associated with a particular analysis or classification of materials by a particular remote device. In some circumstances, some type of scoring (e.g., sustainability-related) can be performed on the analyzed / classified materials, as indicated by process block 805. In process block 806, such scoring can produce several results or scores, such as carbon tax credits, carbon reduction values, verified carbon units ("VCUs"), or greenhouse gas ("GHG") emission reductions and removals based on ISO 14064-2:2006 and ISO 14064-3:2006 as defined in the Verified Carbon Standard ("VCS"), also referred to as "sustainability-related scores." For example, a set of sorted materials (e.g., aluminum alloys sorted into separate specific aluminum alloy classifications) can be assigned a score based on how many carbon tax credits, carbon reduction values, VCUs, or GHG emission reductions are achieved as a result of the sorting. As a non-limiting example, such scoring can be based on the prior knowledge that such a set of aluminum alloys can be utilized to manufacture products without using a certain amount of virgin aluminum.

[0078] At process block 807, the results can be sent to various users, such as automobile manufacturers. At process block 808, users can modify existing manufacturing processes in light of these results. At process block 809, benefits achieved from modifying such processes, such as the granting of carbon tax credits, VCUs, etc., can be obtained by such users.

[0079] The results generated in process block 806 can be used to create benchmarks that can then be analyzed and refined to create alloys of specific qualities and / or produce manufactured vehicles with specific associated carbon reductions. Such benchmarks can be made available on a subscription basis to automakers and the like. The data can include not only the specific chemical compositions for such alloys, but also which sources of scrap and other additive metals provide ideal, or at least better / improved, alloy compositions and / or carbon reductions. Benchmarks can also be established on a country-by-country basis, as different countries may have different carbon reduction requirements.

[0080] Such benchmarks may be certified in some manner (e.g., by some governmental or quasi-governmental agency) so that consumers of the benchmarked alloys may claim carbon credits and / or receive carbon tax reductions (i.e., carbon tax credits) when they utilize the benchmarked alloys to manufacture goods (e.g., automobiles, etc.).

[0081] Such benchmarks could also be valuable for use by the military, aerospace companies, NASA, and others.

[0082] Such certification may designate certain selected materials having chemical compositions that comply with such certification as certified materials (also referred to herein as "certified products").

[0083] A company that manufactures certified products may have an online marketplace where buyers of such products (e.g., some select materials) can log on and bid on certified products, each of which may also include an associated certified carbon offset. Consequently, the company can certify and / or guarantee the chemical composition of the products sold on such a marketplace.

[0084] According to some embodiments of the present disclosure, a load of mixed scrap material (e.g., a truckload of mixed scrap metal) may be received and passed through a vision system 110 (e.g., on a conveyor belt). The vision system captures and stores an image of each scrap piece. Additionally, a distance measuring device 111 may associate an approximate mass of each scrap piece with each image.

[0085] As a result, a certificate (e.g., a digital certificate) can be assigned to each shipment, i.e., the company can guarantee the composition of each shipment along with images of all scrap pieces (approximately 1.3 million images per truck). Further, each certificate can include the amount of "negative" material in each shipment (e.g., "negative" material is scrap piece that is not useful to a particular potential buyer). Additionally, shipments can be sorted according to any one or more of the techniques disclosed herein to produce sorted material having a certified composition, which can also include certified carbon offsets.

[0086] For some metals (e.g., aluminum), each scrap piece in a sorted collection can be associated with such a certification. For example, each scrap piece of aluminum in a sorted collection that is certified to have a particular carbon offset can be associated with that certified carbon offset. Some consumers of aluminum scrap may want to know that the aluminum scrap they purchase has such a certified carbon offset. For example, automobile manufacturers may be required to guarantee that the vehicles they manufacture were made using aluminum with a particular carbon offset.

[0087] A vision system such as described herein can be configured to capture and store image data and associated chemical composition, mass, carbon offset, etc. for each scrap piece processed, so that a company shredding end-of-life vehicles, appliances, aircraft, etc. can be instructed to include only certain types of material in a particular load of scrap pieces, and consequently not include certain types of sources for shredding, in order to obtain a particular chemical composition, carbon offset, certification, etc. For example, in the case of shredded aluminum, the shredding company can be instructed not to include certain appliances, or not to shred older model vehicles along with newer model vehicles.

[0088] According to some embodiments of the present disclosure, a scrap analyzer utilizing one or more of the sensor systems 120 described herein can be implemented at various locations around the world where containers / shipments of scrap material are transported, imported, exported, purchased, and / or sold, etc. A vision system can essentially tag each scrap piece and track its location as it is transported around the world. Scrap analyzers are further described in U.S. Patent Application No. 18 / 074,110, which is hereby incorporated by reference.

[0089] According to some embodiments of the present disclosure, a vision system can be utilized to process and analyze all or substantially all scrap pieces exiting a shredder. For example, such a vision system can be implemented to process and analyze scrap pieces from an entire shredded vehicle. As a result, the aggregate chemical composition of any particular make or model of vehicle can be determined (and authenticated). Because the various classes of scrap produced from a particular shredded vehicle make or model each have value on the open market, a value can be assigned to the scrap from a particular end-of-life vehicle. With these assigned values, a sort of "blue book" can be established for vehicle scrap, allowing buyers and sellers of end-of-life vehicles to look up the value of scrap from any particular make or model of vehicle.

[0090] Referring to FIG. 9, a flowchart diagram of several process blocks that may be implemented within several embodiments of the present disclosure, as previously described, is shown. At process block 901, analysis results are generated, such as within process block 806. At process block 902, these analysis results may be compared to one or more previously established known benchmarks and / or certifications. At process block 903, if the analysis results from the processed scrap pieces are within specified thresholds for any previously established benchmarks and / or certifications, such benchmarks and / or certifications may be assigned to each scrap piece processed within the collection. Additionally, at process block 904, each image of the scrap piece may be tagged with the location where the scrap piece was analyzed.

[0091] As described herein, embodiments of the present disclosure can be implemented to perform various described functions for identifying, tracking, sorting, and / or separating pieces of material. Such functionality can be implemented in hardware and / or software, such as in one or more data processing systems (e.g., data processing system 3400 of FIG. 5 ), such as aspects of the aforementioned computer system 107, vision system 110, sensor system 120, and / or automation control system 108. However, the functionality described herein should not be limited to implementation on any particular hardware / software platform.

[0092] 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 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, which may be generally referred to herein as a "circuit," "circuitry," "module," or "system." Furthermore, aspects of the present disclosure may take the form of a program product embodied in one or more computer-readable storage medium(s) having computer-readable program code embodied therein. (However, 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.)

[0093] A computer-readable storage medium can 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 of the foregoing, and the computer-readable storage medium is not itself a transitory signal. More specific examples (non-exhaustive list) of computer-readable storage media can include the following: 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. 5), a read-only memory (“ROM”) (e.g., ROM 3435 in FIG. 5), 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. 5), or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium can be any tangible medium that can store or preserve 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, wireline, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

[0094] A computer-readable signal medium may include, for example, a propagated data signal with computer-readable program code embodied therein, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium but may be any computer-readable medium that can convey, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, controller, or device.

[0095] 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 comprises one or more executable program instructions for implementing the specified logical function(s). It should also be noted that in some implementations, the functions noted in the blocks may be executed in a different order than noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved.

[0096] Modules implemented in software for execution by various types of processors (e.g., GPU 3401, CPU 3415) can include, for example, one or more physical or logical blocks of computer instructions, which can be organized, for example, as objects, procedures, or functions. However, the executable files of identified modules need not be physically located together but can include different instructions stored in different locations that, when logically coupled together, comprise the module and achieve the specified purpose for the module. In fact, a module of executable code can be a single instruction, or many instructions, and can be distributed across several different code segments, among different programs, and across several memory devices. Similarly, operational data (e.g., material classification libraries and neural network parameters described herein) can be identified and illustrated in modules herein and can be embodied in any suitable form and organized in any suitable type of data structure. Operational data can be collected as a single data set or distributed across different locations, including different storage devices. The data can provide electronic signals over a system or network.

[0097] 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., a controller) to produce a machine, such that the instructions, executed via the 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.

[0098] It will 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 special purpose hardware-based systems (e.g., including one or more graphics processing units (e.g., GPU 3401)) that perform the specified functions or operations, or by a combination of special purpose 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 in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, etc.

[0099] In the description herein, flowchart techniques may be described as a series of sequential operations. The order of operations, and the elements performing the operations, may be freely changed without departing from the scope of the present teachings. Operations may be added, deleted, or modified in several ways. Likewise, these operations may be reordered 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 an alternate order. Furthermore, some operations within a process, method, or algorithm may be performed simultaneously (e.g., operations performed in parallel), at least for some time, and may also be performed in whole, in part, or any combination thereof.

[0100] As used herein, reference is made to "configuring" a device or a device "configured to" perform some function. This should be understood to include selecting and logically associating predefined logic blocks to provide a particular logic function, including monitoring or control functions. It may also include programming computer software-based logic in a retrofit control device, wiring discrete hardware components, or a combination of any or all of the foregoing. A device configured in this manner is physically designed to perform a specified function.

[0101] Unless described herein, many details regarding specific materials, process acts, and circuits are conventional and can be found in textbooks and other sources in the computing, electronics, and software arts.

[0102] Computer program code, i.e., instructions, for carrying out operations for 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.; traditional procedural programming languages ​​such as the “C” programming language or similar programming languages; programming languages ​​such as MATLAB or LabVIEW; or any of the AI ​​software disclosed herein. The program code can execute entirely on a user's computer system, partially on a user's computer system, as a standalone software package, partially on a user's computer system (e.g., a computer system utilized for sorting) and partially on a remote computer system (e.g., a computer system utilized to train an AI system), or entirely on a remote computer system or server. In the latter scenario, the remote computer system can be connected to the user's computer system through any type of network, including a local area network (“LAN”) or a wide area network (“WAN”), or can be connected to an external computer system (e.g., through the Internet using an Internet Service Provider). As an example of the foregoing, various aspects of the present disclosure may be configured to execute on one or more aspects of computer system 107, automation control system 108, vision system 110, and sensor system 120.

[0103] These program instructions may be stored on a computer-readable storage medium that can direct a computer system, other programmable data processing apparatus, 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.

[0104] The program instructions are loaded onto a computer, other programmable data processing apparatus, controller, or other device and a series of operational steps are executed on the computer, other programmable apparatus, or other device to generate a computer-implemented process, such that the instructions executing on the computer or other programmable apparatus provide a process for implementing the functions / acts specified in the flowchart and / or block diagram blocks.

[0105] One or more databases may be included on the host to store and provide access to data for various implementations. Those skilled in the art will also understand that for security reasons, the databases, systems, or components of the present disclosure may include any combination of databases or components at a single location or at multiple locations, and each database or system may include any of a variety of appropriate security features, such as firewalls, access codes, encryption, decryption, etc. The databases may be any type of database, such as relational, hierarchical, object-oriented, etc. Common database products that can be used to implement the database include DB2 by IBM, any of the database products available from Oracle, Microsoft Access by Microsoft, or any other database product. The databases may be organized in any suitable manner, including as data tables or lookup tables.

[0106] Association of specific data (e.g., between a classified piece of material and its known chemical composition) can be achieved through any data association technique known and practiced in the art. For example, association can be achieved 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 each of the manufacturer and retailer data tables. The key fields divide the database according to high-level classes of objects defined by the key fields. For example, a particular class can be designated as a key field in both a first data table and a second data table, and then the two data tables can be merged based on the class data in the key field. In these embodiments, the data corresponding to the key field in each of the merged data tables is preferably the same. However, data tables having similar, but not identical, data in the key field can also be merged, for example, by using AGREP.

[0107] 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 relevant art will recognize that the present disclosure can be practiced without one or more of these 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.

[0108] References throughout this specification to "one embodiment," "an embodiment," or similar language 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 the phrases "in one embodiment," "in an embodiment," "an embodiment," "some embodiments," "various embodiments," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment. 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. Correspondingly, even if several features are initially claimed as acting in a particular combination, one or more features from the claimed combination may in some cases be deleted from the combination, and the claimed combination may be directed to a subcombination or a variation of the subcombination.

[0109] Benefits, advantages, and solutions to problems have been described above with reference to specific embodiments. However, the benefits, advantages, solutions to problems, and any elements that may provide or make more pronounced the benefits, advantages, or solutions should not be construed as critical, necessary, or essential features or elements of any or all claims. Furthermore, no element described herein is required to practice the present disclosure unless expressly described as essential or critical.

[0110] Those skilled in the art who have read this disclosure will recognize that changes and modifications can be made to the embodiments without departing from the scope of the present disclosure. It should be understood that the specific implementations shown and described herein can be illustrative of the disclosure and its best mode, and are in no way intended to otherwise limit the scope of the disclosure. Other variations may be within the scope of the following claims.

[0111] While this specification contains many details, these should not be construed as limitations on the scope of the disclosure or what may be claimed, but rather as descriptions of features inherent in particular implementations of the disclosure. Headings herein are not intended to limit the disclosure, embodiments of the disclosure, or other matter disclosed under that heading.

[0112] As used herein, the term "or" may be 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 whether the entities are 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 any and all combinations and subcombinations of A, B, C, and D.

[0113] 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 dictates otherwise.

[0114] The 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 that function in combination with other claimed elements as specifically claimed.

[0115] As used herein with respect to a particular property or circumstance, "substantially" refers to a degree of deviation small enough to not measurably impair that particular property or circumstance. The exact degree of deviation that is permitted may in some cases depend on the specific context.

[0116] As used herein, a plurality of items, structural elements, components, and / or materials may be presented in common lists for convenience. However, these lists should be construed as though each member of the list is individually identified as a separate and unique member. Accordingly, no individual member of such list should be construed as a de facto equivalent of any other member of the same list solely based on their presentation in a common group, absent indication to the contrary.

[0117] Unless defined otherwise, all technical and scientific terms used herein (such as acronyms used for chemical elements in the periodic table) have the same meaning as commonly understood by one of ordinary skill in the art to which the presently disclosed subject matter belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently disclosed subject matter, representative methods, devices, and materials are described herein.

[0118] Unless otherwise indicated, all numbers expressing quantities of ingredients, reaction conditions, and the like used in the specification and claims are to be understood as 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. As used herein, the term "about," when referring to a value or amount of mass, weight, time, volume, concentration, or percentage, is meant to encompass variations from the specified amount of, in some embodiments, ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1%, as such variations are appropriate for carrying out the disclosed methods.

[0119] The term "coupled," as used herein, is not intended to be limited to a direct or mechanical coupling. Unless otherwise noted, terms such as "first" and "second" are used to arbitrarily distinguish between the elements described by such terms. As such, these terms are not necessarily intended to indicate a temporal or other priority of such elements. [Explanation of symbols]

[0120] 100 systems 101 Material pieces 102 Hopper 103 Conveyor System 104 Conveyor system motor 105 Position detector 106 Singulator 107 Computer Systems 108 Automation Control Systems 109 Camera 110 Vision System 111 Material piece tracking device 112 Control System 120 Sensor System 121 Energy Emission Source 122 Power supply 124 detectors 125 Detector Electronics 126...129 Sorting Device 136...139 Sorting container 140 Container 600 System 601 Cloud 602 Data Center 603 Sorting Equipment 604 Sorting Equipment 605 Point-of-Use Analyzer 3400 Computer Systems 3405 Local Bus 3412 User Interface Adapter 3413 Keyboard 3414 Mouse 3415 processor 3416 Display Adapter 3420 Volatile Memory, RAM 3425 Communication Adapter 3430 I / O Adapter 3431 Hard Disk Drive, Hard Drive 3432 tape drive 3435 Non-volatile memory, ROM 3440 Display

Claims

1. 1. A system including a first sorting system located at a first geographic location, a second sorting system located at a second geographic location, a data center located at a third geographic location, and a wide area network configured to enable data communication between the first sorting system and the data center, and between the second sorting system and the data center, sorting and separating the first mixture of materials with the first sorting system using a sorting / separation algorithm; collecting information regarding the results of said sorting and screening of said first mixture of materials; transmitting the collected information from the first sorting system to the data center via the wide area network; the data center modifying the classification / sorting algorithm as a result of the collected information transmitted from the first sorting system; downloading the modified sorting / screening algorithm to the first sorting system over the wide area network; sorting and separating a second mixture of materials with the first sorting system using the modified sorting / separation algorithm; The method includes:

2. downloading the modified sorting / screening algorithm to the second sorting system over the wide area network; sorting and separating a third mixture of materials with the second sorting system using the modified sorting / separation algorithm; The method of claim 1, further comprising:

3. performing tests on the classified and separated first mixture of materials to determine how accurate the classification and separation of the first mixture of materials was; transmitting results of the testing to the data center over the wide area network, the modification of the classification / sorting algorithm being based at least in part on the results of the testing; and The method of claim 1, further comprising:

4. The method of claim 1 , wherein the first sorting system, the second sorting system, and the data center are each located in a different population center.

5. The method of claim 1 , wherein the classification / sorting algorithm is an artificial intelligence algorithm.

6. Each of the first and second sorting systems comprises: an image capture device configured to generate image data of the first mixture of materials; a conveyor system configured to transport the first mixture of material past the image capture device; and a data processing system including an artificial intelligence system configured to classify the ones of the first mixture of materials based on the image data of the first mixture of materials, the classification / sorting algorithm utilizing a knowledge base including a previously generated library of observed properties captured from a homogenous set of samples of the ones of the first mixture of materials; a sorter configured to sort the classified ones of the first mixture of materials from the first mixture of materials in response to a classification of the ones of the first mixture of materials; 2. The method of claim 1, comprising:

7. said step of sorting and sorting said first mixture of materials by said first sorting system using said sorting / separation algorithm comprises: generating image data of the first mixture of materials; assigning a classification to the ones of the first mixtures of materials with an artificial intelligence system based on the image data of the first mixtures of materials, the classification being based on a knowledge base including a previously generated library of observed properties captured from a homogenous set of samples of the ones of the first mixtures of materials; separating some of the first mixture of materials from the first mixture in response to the classification; 2. The method of claim 1, comprising:

8. said step of sorting and separating said third mixture of materials by said second sorting system using said modified sorting / separation algorithm further comprising: generating image data of the third mixture of materials; assigning a classification to some of the third mixture of materials with an artificial intelligence system based on the image data of the third mixture of materials, the artificial intelligence system being configured with the modified classification / sorting algorithm; separating some of the third mixture of materials from the third mixture in response to the classification; 3. The method of claim 2, comprising:

9. The method of claim 1 , wherein the first mixture of materials comprises cast and forged aluminum scrap pieces.

10. The method of claim 1 , wherein the first mixture of materials comprises plastic pieces of different types of polymer compositions.

11. 2. The method of claim 1, wherein the sorting / sorting algorithm is configured to sort and sort solvers using a combination of one or more vision systems and one or more XRF systems implemented within the first sorting system.

12. 2. The method of claim 1, further comprising the step of: the data center uploading the modified classification / sorting algorithm to the wide area network for download by the first and second sorting systems.

13. 2. The method of claim 1, further comprising the step of the data center associating a sustainability-related score with the classified and sorted second mixture of materials, the sustainability-related score being based on the information collected from the first sorting system.

14. 14. The method of claim 13, wherein the sustainability-related score is selected from the group consisting of carbon tax credits, carbon reduction values, verified carbon units ("VCUs"), and greenhouse gas ("GHG") emission reductions.

15. The system further includes a point-of-use analyzer disposed at a fourth geographic location, the point-of-use analyzer configured to analyze a sample of material taken from the shipping container to ascertain a composition of the material contained within the shipping container; The method of claim 1 , further comprising transmitting the analysis of the material of the shipping container from the point-of-use analyzer to the data center over the wide area network.

16. a first sorting system disposed at a first geographic location, the first sorting system configured to sort and sort a first mixture of materials by using a sorting / sorting algorithm, the sorting / sorting algorithm being an artificial intelligence algorithm, and the first sorting system configured to collect information regarding results of the sorting and sorting of the first mixture of materials; a data center located in a second geographic location; a wide area network configured to enable data communication between the first sorting system and the data center; circuitry configured to transmit the collected information from the first sorting system to the data center over the wide area network, the data center configured to modify the classification / sorting algorithm as a result of the collected information transmitted from the first sorting system; a circuit configured to communicate the modified sorting / screening algorithm over the wide area network to the first sorting system, the first sorting system configured to sort and screen a second mixture of materials by the first sorting system using the modified sorting / screening algorithm; and A system including:

17. a second sorting system disposed at a second geographic location; and a circuit configured to communicate the modified sorting / screening algorithm over the wide area network to the second sorting system, the second sorting system configured to sort and screen a third mixture of materials by the second sorting system using the modified sorting / screening algorithm; and 17. The system of claim 16, further comprising:

18. 17. The system of claim 16, wherein the first mixture of materials comprises cast and forged aluminum scrap pieces.

19. 17. The system of claim 16, wherein the sorting / sorting algorithm is configured to sort and sort solvers using a combination of one or more vision systems and one or more XRF systems implemented within the first sorting system.

20. 17. The system of claim 16, further comprising the data center configured to assign a sustainability-related score to the classified and sorted second mixture of materials, the sustainability-related score being based on the information collected from the first sorting system, the sustainability-related score being selected from the group consisting of carbon tax credits, carbon reduction values, verified carbon units ("VCUs"), and greenhouse gas ("GHG") emission reductions.

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