Patent Application
20230173543
The present disclosure relates in general to the handling of materials, and in particular, to the classifying and/or sorting of materials from a landfill.
This section is intended to introduce various aspects of the art, which may be associated with exemplary embodiments of the present disclosure. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular 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.
Recycling is the process of collecting and processing materials that would otherwise be thrown away as trash, and turning them into new products. Recycling has benefits for communities and for the environment, since it reduces the amount of waste sent to landfills and incinerators, conserves natural resources, increases economic security by tapping a domestic source of materials, prevents pollution by reducing the need to collect new raw materials, and saves energy. After collection, recyclables are generally sent to a material recovery facility to be sorted, cleaned, and processed into materials that can be used in manufacturing.
As a result, high throughput automated sorting platforms that economically sort highly mixed waste streams would be beneficial throughout various industries. Thus, there is a need for cost-effective sorting platforms that can identify, analyze, and separate mixed industrial or municipal waste streams with high throughput to economically generate higher quality feedstocks (which may also include lower levels of trace contaminants) for subsequent processing. Typically, material recovery facilities are either unable to discriminate between many materials, which limits the scrap to lower quality and lower value markets, or too slow, labor intensive, and inefficient, which limits the amount of material that can be economically recycled or recovered.
Moreover, high throughput technologies for improving liberation of complex scrap/joint streams are needed for all material classes. For example, consumer products often contain both metals and plastics, but with today's technologies, they cannot be effectively and economically recycled for several reasons, including that there are no existing technologies that can rapidly sort these materials for subsequent recovery and processing. Additionally, recycled paper streams (fibers) are often contaminated with ink, adhesives, glass, wood, plastic, shards, flexible films, and organics causing down-grading of waste paper and cardstock. Current sorting processes do not include contaminate removal steps, and contaminated secondary material flows limit the markets and value of the fiber products. Therefore, solutions are needed that can more effectively identify and remove glass, food, and contaminants from paper feedstocks.
In the case of recycling of electronic waste (“e-waste”), separations are generally physical for plastics and chemical for materials. To increase domestic recycling of such e-waste, high throughput approaches for separating e-waste for metals and plastics are needed which are both energy efficient and cost-effective. Additionally, existing sorting technologies have a very limited capability to separate plastics with similar densities. Such complex streams may include both joined and un-joined materials (e.g., plastics, e-waste, auto, etc.). Therefore, more energy-efficient processing methodologies that enable high-resolution sorting of specific complex mixed material streams are needed.
And, there are very few, if any, cost and energy effective recycling technologies for low value waste plastics. As a result, such low value plastics (e.g., carpets and carpet residues, tires, tennis shoes, etc.) have no effective material recovery path. Therefore, technologies for cost-effective and more energy efficient sorting of such low value plastics are needed to generate high value and high purity feedstocks from polymers (carpets, residues, etc.) and natural fibers (cotton/other cellulosic materials).
Scrap metals are often shredded, and thus require sorting to facilitate reuse of the metals. By sorting the scrap metals, metal is reused that may otherwise go to a landfill. Additionally, use of sorted scrap metal leads to reduced pollution and emissions in comparison to refining virgin feedstock from ore. Scrap metals may be used in place of virgin feedstock by manufacturers if the quality of the sorted metal meets certain standards. The scrap metals may include types of ferrous and nonferrous metals, heavy metals, high value metals such as nickel or titanium, cast or wrought metals, and other various alloys.
Various detailed embodiments of the present disclosure are disclosed herein. However, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to employ various embodiments of the present disclosure.
As used herein, “materials” may include any item or object, including but not limited to, metals (ferrous and nonferrous), metal alloys, heavies, Zorba, Twitch, pieces of metal embedded in another different material, plastics (including, but not limited to, any of the plastics disclosed herein, known in the industry, or newly created in the future), rubber, foam, glass (including, but not limited to, borosilicate or soda lime glass, and various colored glass), ceramics, paper, cardboard, Teflon, PE, bundled wires, insulation covered wires, rare earth elements, leaves, wood, plants, parts of plants, textiles, bio-waste, packaging, electronic waste, batteries and accumulators, scrap from end-of-life vehicles, mining, construction, and demolition waste, crop wastes, forest residues, purpose-grown grasses, woody energy crops, microalgae, urban food waste, food waste, hazardous chemical and biomedical wastes, construction debris, farm wastes, biogenic items, non-biogenic items, objects with a specific carbon content, any other objects that may be found within municipal solid waste, and any other objects, items, or materials disclosed herein, including further types or classes of any of the foregoing that can be distinguished from each other, including but not limited to, by one or more sensor systems, including but not limited to, any of the sensor technologies disclosed herein.
In a more general sense, a “material” may 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 a compound or mixture of chemical elements, wherein the complexity of a compound or mixture may range from being simple to complex (all of which may also be referred to herein as a material having a specific “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 pieces,” “materials,” and “material pieces” may be used interchangeably.
As used herein, the term “chemical signature” refers to a unique pattern (e.g., fingerprint spectrum), as would be produced by one or more analytical instruments, indicating the presence of one or more specific elements or molecules (including polymers) in a sample. The elements or molecules may be organic and/or inorganic. Such analytical instruments include any of the sensor systems disclosed herein, and also disclosed in U.S. patent application Ser. No. 17/667,397, which is hereby incorporated by reference herein. In accordance with embodiments of the present disclosure, one or more such sensor systems may be configured to produce a chemical signature of a material piece.
As well known in the industry, a “polymer” is a substance or material composed of very large molecules, or macromolecules, composed of many repeating subunits. A polymer may be a natural polymer found in nature or a synthetic polymer. “Multilayer polymer films” are composed of two or more different compositions and may possess a thickness of up to about 7.5−8×10−4 m. The layers are at least partially contiguous and preferably, but optionally, coextensive. As used herein, the terms “plastic,” “plastic piece,” and “piece of plastic material” (all of which may be used interchangeably) refer to any object that includes or is composed of a polymer composition of one or more polymers and/or multilayer polymer films.
As used herein, a “fraction” refers to any specified combination of organic and/or inorganic elements or molecules, polymer types, plastic types, polymer compositions, chemical signatures of plastics, physical characteristics of the plastic piece (e.g., color, transparency, strength, melting point, density, shape, size, manufacturing type, uniformity, reaction to stimuli, etc.), etc., including any and all of the various classifications and types of plastics disclosed herein. Non-limiting examples of fractions are one or more different types of plastic pieces that contain: LDPE plus a relatively high percentage of aluminum; LDPE and PP plus a relatively low percentage of iron; PP plus zinc; combinations of PE, PET, and HDPE; any type of red-colored LDPE plastic pieces; any combination of plastic pieces excluding PVC; black-colored plastic pieces; combinations of #3-#7 type plastics that contain a specified combination of organic and inorganic molecules; combinations of one or more different types of multi-layer polymer films; combinations of specified plastics that do not contain a specified contaminant or additive; any types of plastics with a melting point greater than a specified threshold; any thermoset plastic of a plurality of specified types; specified plastics that do not contain chlorine; combinations of plastics having similar densities; combinations of plastics having similar polarities; plastic bottles without attached caps or vice versa.
As used herein, the term “predetermined” refers to something that has been established or decided in advance, such as by a user of embodiments of the present disclosure.
As used herein, “spectral imaging” is imaging that uses multiple bands across the electromagnetic spectrum. While a typical camera captures images composed of light across three wavelength bands in the visible spectrum, red, green, and blue (RGB), spectral imaging encompasses a wide variety of techniques that include and go beyond RGB. For example, spectral imaging may 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 may include the acquisition of spectral data in visible and non-visible bands simultaneously, illumination from outside the visible range, or the use of optical filters to capture a specific spectral range. It is also possible to capture hundreds of wavelength bands for each pixel in a spectral image.
As used herein, the term “image data packet” refers to a packet of digital data pertaining to a captured spectral image of an individual material piece.
As used herein, the terms “identify” and “classify,” the terms “identification” and “classification,” and any derivatives of the foregoing, may be utilized interchangeably. As used herein, to “classify” a piece of material is to determine (i.e., identify) a type or class of materials to which the piece of material belongs. For example, in accordance with certain embodiments of the present disclosure, a sensor system (as further described herein) may be configured to collect and analyze any type of information for classifying materials and distinguishing such classified materials from other materials, which classifications can be utilized within a sorting system to selectively sort material pieces as a function of a set of one or more physical and/or chemical characteristics (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, transmissivity to light, sound, or other signals, and reaction to stimuli such as various fields, including emitted and/or reflected electromagnetic radiation (“EM”) of the material pieces.
The types or classes (i.e., classification) of materials may be user-definable (e.g., predetermined) and not limited to any known classification of materials. The granularity of the types or classes may range from very coarse to very fine. For example, the types or classes may include plastics, ceramics, glasses, metals, and other materials, where the granularity of such types or classes is relatively coarse; different metals and metal alloys such as, for example, zinc, copper, brass, chrome plate, and aluminum, where the granularity of such types or classes is finer; or between specific types of plastic, where the granularity of such types or classes is relatively fine. Thus, the types or classes may be configured to distinguish between materials of significantly different chemical compositions such as, for example, plastics and metal alloys, or to distinguish between materials of almost identical chemical compositions such as, for example, different types of metal alloys. It should be appreciated that the methods and systems discussed herein may be applied to accurately identify/classify pieces of material for which the chemical composition is completely unknown before being classified.
As used herein, “manufacturing type” refers to the type of manufacturing process by which the material piece was manufactured, such as a metal part having been formed by a wrought process, having been cast (including, but not limited to, expendable mold casting, permanent mold casting, and powder metallurgy), having been forged, a material removal process, etc.
As referred to herein, a “conveyor system” may be any known piece of mechanical handling equipment that moves materials from one location to another, including, but not limited to, an aero-mechanical conveyor, automotive conveyor, belt conveyor, belt-driven live roller conveyor, bucket conveyor, chain conveyor, chain-driven live roller conveyor, drag conveyor, dust-proof conveyor, electric track vehicle system, flexible conveyor, gravity conveyor, gravity skatewheel conveyor, lineshaft roller conveyor, motorized-drive roller conveyor, overhead I-beam conveyor, overland conveyor, pharmaceutical conveyor, plastic belt conveyor, pneumatic conveyor, screw or auger conveyor, spiral conveyor, tubular gallery conveyor, vertical conveyor, vibrating conveyor, and wire mesh conveyor.
The systems and methods described herein according to certain embodiments of the present disclosure receive a mixture of different types of material pieces, wherein at least one material piece within this mixture includes a chemical composition different from one or more other material pieces and/or at least one material piece within this mixture is physically distinguishable from other material pieces, and/or at least one material piece within this mixture is of a class or type of material different from the other material pieces within the mixture, and the systems and methods are configured to identify/classify/distinguish/sort this one material piece into a group separate from such other material pieces. Embodiments of the present disclosure may be utilized to sort any types or classes of materials as defined herein.
As used herein, a “pile” of materials refers to a heap of things laid on or lying one on top of another. As used herein, a “heap” of things is usually untidy, and often has the shape of a hill or mound.
Certain embodiments of the present disclosure will be described herein as sorting material pieces into such separate groups or collections by physically depositing (e.g., ejecting or diverting) the material pieces into separate receptacles or bins, or onto another conveyor system, as a function of user-defined or predetermined groupings or collections. As an example, within certain embodiments of the present disclosure, material pieces may be sorted in order to separate material pieces composed of a specific chemical composition, or compositions, from other material pieces composed of a different specific chemical composition.
Furthermore, though the illustration in
Within certain embodiments of the present disclosure, the conveyor system 103 is operated to travel at a predetermined speed by a conveyor system motor 104. This predetermined speed may be programmable and/or adjustable by the operator in any well-known manner. Within certain embodiments of the present disclosure, control of the conveyor system motor 104 and/or the position detector 105 may be performed by an automation control system 108. Such an automation control system 108 may be operated under the control of a computer system 107, and/or the functions for performing the automation control may be implemented in software within the computer system 107. If the conveyor system 103 is a conveyor belt, then it may be a conventional endless belt conveyor employing a conventional drive motor 104 suitable to move the conveyor belt 103 at the predetermined speeds.
A position detector 105 (e.g., a conventional encoder) may 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 will be further described herein, through the utilization of the controls to the conveyor belt drive motor 104 and/or the automation control system 108 (and alternatively including the position detector 105), as each of the material pieces 101 travelling on the conveyor belt 103 are identified, they can be tracked by location and time (relative to the various components of the system 100) so that the various components of the system 100 can be activated/deactivated as each material piece 101 passes within their vicinity. As a result, the automation control system 108 is able to track the location of each of the material pieces 101 while they travel along the conveyor belt 103.
Referring again to
In accordance with certain embodiments of the present disclosure, the system 100 may be implemented with one or more sensor systems 120, which may be utilized solely or in combination with the vision system 110 to classify/identify/distinguish material pieces 101. A sensor system 120 may be configured with any type of sensor technology, including sensors utilizing irradiated or reflected electromagnetic radiation (e.g., utilizing 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”), Medium 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 with any of the foregoing), or by any other type of sensor technology, including but not limited to, chemical or radioactive. Implementation of an XRF system (e.g., for use as a sensor system 120 herein) is further described in U.S. Pat. No. 10,207,296. XRF can be used within certain embodiments of the present disclosure to identify inorganic materials within a plastic piece (e.g., for inclusion within a chemical signature).
The following sensor systems may also be used within certain embodiments of the present disclosure for determining the chemical signatures of plastic pieces and/or classifying plastic pieces for sorting. The previously disclosed various forms of infrared spectroscopy may be utilized to obtain a chemical signature specific of 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 the thermal transitions produced during the heating of the analyzed material specific for each material. Thermogravimetric analysis (“TGA”) is another thermal analysis technique resulting in quantitative information about the composition of a plastic material regarding polymer percentages, 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 deformation resistance. Optical and scanning electron microscopy (“SEM”) can provide information about the structure of the materials analyzed regarding the number and thickness of layers in multilayer materials (e.g., multilayer polymer films), dispersion size of pigment or filler particles in the polymeric matrix, coating defects, interphase 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, degradation substances, etc.
It should be noted that though
In accordance with certain embodiments of the present disclosure, a vision system 110 and/or sensor system(s) may be configured to identify which of the material pieces 101 contain a contaminant (e.g., steel or iron pieces containing copper; plastic pieces containing a specific contaminant, additive, or undesirable physical feature (e.g., an attached container cap formed of a different type of plastic than the container)), and send a signal to separate (sort) such material pieces (e.g., from those not containing the contaminant). In such a configuration, the identified material pieces 101 may be diverted/ejected utilizing one of the mechanisms as described hereinafter for physically diverting sorted material pieces into individual receptacles.
Within certain embodiments of the present disclosure, the material piece tracking device 111 and accompanying control system 112 may be utilized and configured to measure the sizes and/or shapes of each of the material pieces 101 as they pass within proximity of the material piece tracking device 111, along with the position (i.e., location and timing) of each of the material pieces 101 on the moving conveyor system 103. An exemplary operation of such a material piece tracking device 111 and control system 112 is further described in U.S. Pat. No. 10,207,296. Alternatively, as previously disclosed, the vision system 110 may be utilized to track the position (i.e., location and timing) of each of the material pieces 101 as they are transported by the conveyor system 103. As such, certain embodiments of the present disclosure may be implemented without a material piece tracking device (e.g., the material piece tracking device 111) to track the material pieces.
Within certain embodiments of the present disclosure that implement one or more sensor systems 120, the sensor system(s) 120 may be configured to assist the vision system 110 to identify the chemical composition, relative chemical compositions, and/or manufacturing types of each of the material pieces 101 as they pass within proximity of the sensor system(s) 120. The sensor system(s) 120 may include an energy emitting source 121, which may be powered by a power supply 122, for example, in order to stimulate a response from each of the material pieces 101.
Within certain embodiments of the present disclosure, as each material piece 101 passes within proximity to the emitting source 121, the sensor system 120 may emit an appropriate sensing signal towards the material piece 101. One or more detectors 124 may be positioned and configured to sense/detect one or more characteristics from the material piece 101 in a form appropriate for the type of utilized sensor technology. The one or more detectors 124 and the associated detector electronics 125 capture these received sensed characteristics to perform signal processing thereon and produce digitized information representing the sensed characteristics (e.g., spectral data), which is then analyzed in accordance with certain embodiments of the present disclosure, which may be used to classify each of the material pieces 101. This classification, which may be performed within the computer system 107, may then be utilized by the automation control system 108 to activate one of the N (N≥1) sorting devices 126 . . . 129 of a sorting apparatus for sorting (e.g., diverting/ejecting) the material pieces 101 into one or more N (N≥1) sorting receptacles 136 . . . 139 according to the determined classifications. Four sorting devices 126 . . . 129 and four sorting receptacles 136 . . . 139 associated with the sorting devices are illustrated in
The sorting devices may include any well-known mechanisms for redirecting selected material pieces 101 towards a desired location, including, but not limited to, diverting the material pieces 101 from the conveyor belt system into the plurality of sorting receptacles. For example, a sorting device may utilize air jets, with each of the air jets assigned to one or more of the classifications. When one of the air jets (e.g., 127) receives a signal from the automation control system 108, that air jet emits a stream of air that causes a material piece 101 to be diverted/ejected from the conveyor system 103 into a sorting receptacle (e.g., 137) corresponding to that air jet.
Although the example illustrated in
In addition to the N sorting receptacles 136 . . . 139 into which material pieces 101 are diverted/ejected, the system 100 may also include a receptacle 140 that receives material pieces 101 not diverted/ejected from the conveyor system 103 into any of the aforementioned sorting receptacles 136 . . . 139. For example, a material piece 101 may not be diverted/ejected from the conveyor system 103 into one of the N sorting receptacles 136 . . . 139 when the classification of the material piece 101 is not determined (or simply because the sorting devices failed to adequately divert/eject a piece). Thus, the receptacle 140 may serve as a default receptacle into which unclassified or unsorted material pieces are dumped. Alternatively, the receptacle 140 may be used to receive one or more classifications of material pieces that have deliberately not been assigned to any of the N sorting receptacles 136 . . . 139. These such material pieces may then be further sorted in accordance with other characteristics and/or by another sorting system.
Depending upon the variety of classifications of material pieces desired, multiple classifications may be mapped to a single sorting device and associated sorting receptacle. In other words, there need not be a one-to-one correlation between classifications and sorting receptacles. For example, it may be desired by the user to sort certain classifications of materials into the same sorting receptacle. To accomplish this sort, when a material piece 101 is classified as falling into a predetermined grouping of classifications, the same sorting device may be activated to sort these into the same sorting receptacle. Such combination sorting may be applied to produce any desired combination of sorted material pieces. The mapping of classifications may be programmed by the user (e.g., using the sorting algorithm (e.g., see
The systems and methods described herein may be applied to classify and/or sort individual material pieces having any of a variety of sizes as small as a ¼ inch in diameter or less. Even though the systems and methods described herein are described primarily in relation to sorting individual material pieces of a singulated stream one at a time, the systems and methods described herein are not limited thereto. Such systems and methods may be used to stimulate and/or detect emissions from a plurality of materials concurrently. For example, as opposed to a singulated stream of materials being conveyed along one or more conveyor belts in series, multiple singulated streams may be conveyed in parallel. Each stream may be on a same belt or on different belts arranged in parallel. Further, pieces may be randomly distributed on (e.g., across and along) one or more conveyor belts. Accordingly, the systems and methods described herein may be used to stimulate, and/or detect emissions from, a plurality of these small pieces at the same time. In other words, a plurality of small pieces may be treated as a single piece as opposed to each small piece being considered individually. Accordingly, the plurality of small pieces of material may be classified and sorted (e.g., diverted/ejected from the conveyor system) together. It should be appreciated that a plurality of larger material pieces also may be treated as a single material piece.
As previously noted, certain embodiments of the present disclosure may implement one or more vision systems (e.g., vision system 110) in order to identify, track, classify, and/or distinguish material pieces. In accordance with embodiments of the present disclosure, such a vision system(s) may operate alone to identify and/or classify and sort material pieces, or may operate in combination with a sensor system (e.g., sensor system 120) to identify and/or classify and sort material pieces. If a sorting system (e.g., system 100) is configured to operate solely with such a vision system(s) 110, then the sensor system 120 may be omitted from the system 100 (or simply deactivated).
Such a vision system may be configured with one or more devices for capturing or acquiring images of the material pieces as they pass by on a conveyor system. The devices may be configured to capture or acquire any desired range of wavelengths irradiated or reflected by the material pieces, including, but not limited to, visible, infrared (“IR”), ultraviolet (“UV”) light. For example, the vision system may be configured with one or more cameras (still and/or video, either of which may be configured to capture two-dimensional, three-dimensional, and/or holographical images) positioned in proximity (e.g., above) the conveyor system so that images of the material pieces are captured as they pass by the sensor system(s). In accordance with alternative embodiments of the present disclosure, data captured by a sensor system 120 may be processed (converted) into data to be utilized (either solely or in combination with the image data captured by the vision system 110) for classifying/sorting of the material pieces. Such an implementation may be in lieu of, or in combination with, utilizing the sensor system 120 for classifying material pieces.
An AI system may implement any well-known AI system (e.g., Artificial Narrow Intelligence (“ANI”), Artificial General Intelligence (“AGI”), and Artificial Super Intelligence (“ASI”)), a machine learning system including one that implements a neural network (e.g., artificial neural network, deep neural network, convolutional neural network, recurrent neural network, autoencoders, reinforcement learning, etc.), a machine learning system implementing 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 machine (“SVM”) (e.g., linear SVM, nonlinear SVM, SVM regression, etc.), decision tree learning (e.g., classification and regression tree (“CART”), ensemble methods (e.g., ensemble learning, Random Forests, Bagging and Pasting, Patches and Subspaces, Boosting, Stacking, etc.), dimensionality reduction (e.g., Projection, Manifold Learning, Principal Components Analysis, etc.), and/or deep machine learning algorithms, such as those described in and publicly available at the deeplearning.net website (including all software, publications, and hyperlinks to available software referenced within this website), which is hereby incorporated by reference herein. Non-limiting examples of publicly available machine learning software and libraries that could 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 implementing 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, feed-forward) neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPoT (Python code using CUDAMat and Gnumpy to train models of natural images), ConvNet, Elektronn, OpenNN, NeuralDesigner, Theano Generalized Hebbian Learning, Apache Singa, Lightnet, and SimpleDNN.
In accordance with certain embodiments of the present disclosure, certain types of machine learning may be performed in two stages. For example, first, training occurs, which may be performed offline in that the system 100 is not being utilized to perform actual classifying/sorting of material pieces. The system 100 may be utilized to train the machine learning system in that homogenous sets (also referred to herein as control samples) of material pieces (i.e., having the same types or classes of materials, or falling within the same predetermined fraction) are passed through the system 100 (e.g., by a conveyor system 103); and all such material pieces may not be sorted, but may be collected in a common receptacle (e.g., receptacle 140). Alternatively, the training may be performed at another location remote from the system 100, including using some other mechanism for collecting sensed information (characteristics) of control sets of material pieces. During this training stage, 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, feed forward, polynomial regression, learning curves, regularized learning models, and logistic regression. It is during this training stage that the algorithms within the machine learning system learn the relationships between materials and their features/characteristics (e.g., as captured by the vision system and/or sensor system(s)), creating a knowledge base for later classification of a heterogeneous mixture of material pieces received by the system 100, which may then be sorted by desired classifications. Such a knowledge base may include one or more libraries, wherein each library includes parameters (e.g., neural network parameters) for utilization by the machine learning system in classifying material pieces. For example, one particular library may include parameters configured by the training stage to recognize and classify a particular type or class of material, or one or more material that fall with a predetermined fraction. In accordance with certain embodiments of the present disclosure, such libraries may be inputted into the machine learning system and then the user of the system 100 may be able to adjust certain ones of the parameters in order to adjust an operation of the system 100 (for example, adjusting the threshold effectiveness of how well the machine learning system recognizes a particular material piece from a heterogeneous mixture of materials).
Additionally, the inclusion of certain materials in material pieces result in identifiable physical features (e.g., visually discernible characteristics) in materials. As a result, when a plurality of material pieces containing such a particular composition are passed through the aforementioned training stage, the machine learning system can learn how to distinguish such material pieces from others. Consequently, a machine learning system configured in accordance with certain embodiments of the present disclosure may be configured to sort between material pieces as a function of their respective material/chemical compositions.
During the training stage, a plurality of material pieces of one or more specific types, classifications, or fractions of material(s), which are the control samples, may be delivered past the vision system and/or one or more sensor systems(s) (e.g., by a conveyor system) so that the algorithms within the machine learning system detect, extract, and learn what features represent such a type or class of material. For example, each of the material pieces in the control sample (e.g., see
After the algorithms have been established and the machine learning system has sufficiently learned (been trained) the differences (e.g., visually discernible differences) for the material classifications (e.g., within a user-defined level of statistical confidence), the libraries for the different material classifications are then implemented into a material classifying/sorting system (e.g., system 100) to be used for identifying, distinguishing, and/or classifying material pieces from a heterogeneous mixture of material pieces, and then possibly sorting such classified material pieces if sorting is to be performed.
It should be understood that the present disclosure is not exclusively limited to AI techniques. Other common techniques for material classification/identification may also be used. For instance, a sensor system may utilize optical spectrometric techniques using multi- or hyperspectral cameras to provide a signal that may indicate the presence or absence of a type, class, or fraction of material by examining the spectral emissions (i.e., spectral imaging) of the material. Spectral images of a material piece may also be used in a template-matching algorithm, wherein a database of spectral images is compared against an acquired spectral image to find the presence or absence of certain types of materials from that database. A histogram of the captured spectral image may also be compared against a database of histograms. Similarly, a bag of words model may be used with a feature extraction technique, such as scale-invariant feature transform (“SIFT”), to compare extracted features between a captured spectral image and those in a database.
In accordance with certain embodiments of the present disclosure, instead of utilizing a training stage whereby control (homogenous) samples of material pieces are passed by the vision system and/or sensor system(s), training of the AI system may be performed utilizing a labeling/annotation technique (or any other supervised learning technique) whereby as data/information of material pieces are captured by a vision/sensor system, a user inputs a label or annotation that identifies each material piece, which is then used to create the library for use by the AI system when classifying material pieces within a heterogenous mixture of material pieces.
In accordance with certain embodiments of the present disclosure, any sensed characteristics output by any of the sensor systems 120 disclosed herein may be input into an AI system in order to classify and/or sort materials. For example, in an AI system implementing supervised learning, sensor system 120 outputs that uniquely characterize a particular type or composition of material may be used to train the AI system.
In the optional process block 3506, the material pieces may be conveyed along the conveyor system within proximity of a material piece tracking device and/or a sensor system in order to track each of the material pieces and/or determine a size and/or shape of the material pieces, which may be useful if an XRF system or some other spectroscopy sensor is also implemented within the sorting system. In the process block 3507, post processing may be performed. Post processing may involve resizing the captured information/data to prepare it for use in the neural networks. This may also include modifying certain properties (e.g., enhancing image contrast, changing the image background, or applying filters) in a manner that will yield an enhancement to the capability of the AI system to classify the material pieces. In the process block 3509, the data may be resized. Data resizing may be desired under certain circumstances to match the data input requirements for certain AI systems, such as neural networks. For example, neural networks may require much smaller image sizes (e.g., 225×255 pixels or 299×299 pixels) than the sizes of the images captured by typical digital cameras. Moreover, the smaller the input data size, the less processing time is needed to perform the classification. Thus, smaller data sizes can ultimately increase the throughput of the system 100 and increase its value.
In the process blocks 3510 and 3511, each material piece is identified/classified based on the sensed/detected features. For example, the process block 3510 may be configured with a neural network employing one or more algorithms, which compare the extracted features with those stored in a previously generated knowledge base (e.g., generated during a training stage), and assigns the classification with the highest match to each of the material pieces based on such a comparison. The algorithms may process the captured information/data in a hierarchical manner by using automatically trained filters. The filter responses are then successfully combined in the next levels of the algorithms until a probability is obtained in the final step. In the process block 3511, these probabilities may be used for each of the N classifications to decide into which of the N sorting receptacles the respective material pieces should be sorted. For example, each of the N classifications may be assigned to one sorting receptacle, and the material piece under consideration is sorted into that receptacle that corresponds to the classification returning the highest probability larger than a predefined threshold. Within embodiments of the present disclosure, such predefined thresholds may be preset by the user. A particular material piece may be sorted into an outlier receptacle (e.g., sorting receptacle 140) if none of the probabilities is larger than the predetermined threshold.
Next, in the process block 3512, a sorting device corresponding to the classification, or classifications, of the material piece is activated (e.g., instructions sent to the sorting device to sort). Between the time at which the image of the material piece was captured and the time at which the sorting device is activated, the material piece has moved from the proximity of the vision system and/or sensor system(s) to a location downstream on the conveyor system (e.g., at the rate of conveying of a conveyor system). In embodiments of the present disclosure, the activation of the sorting device is timed such that as the material piece passes the sorting device mapped to the classification of the material piece, the sorting device is activated, and the material piece is diverted/ejected from the conveyor system into its associated sorting receptacle. Within embodiments of the present disclosure, the activation of a sorting device may be timed by a respective position detector that detects when a material piece is passing before the sorting device and sends a signal to enable the activation of the sorting device. In the process block 3513, the sorting receptacle corresponding to the sorting device that was activated receives the diverted/ejected material piece.
Operation of the process 400 may be performed by hardware and/or software, including within a computer system (e.g., computer system 3400 of
Next, if sorting of the material pieces is to be performed, in the process block 406, a sorting device corresponding to the classification, or classifications, of the material piece is activated. Between the time at which the material piece was sensed and the time at which the sorting device is activated, the material piece has moved from the proximity of the sensor system to a location downstream on the conveyor system, at the rate of conveying of the conveyor system. In certain embodiments of the present disclosure, the activation of the sorting device is timed such that as the material piece passes the sorting device mapped to the classification of the material piece, the sorting device is activated, and the material piece is diverted/ejected from the conveyor system into its associated sorting receptacle. Within certain embodiments of the present disclosure, the activation of a sorting device may be timed by a respective position detector that detects when a material piece is passing before the sorting device and sends a signal to enable the activation of the sorting device. In the process block 407, the sorting receptacle corresponding to the sorting device that was activated receives the diverted/ejected material piece.
With reference now to
The user interface adapter 3412 may provide a connection for a keyboard 3413 and a mouse 3414, modem (not shown), and additional memory (not shown). The I/O adapter 3430 may provide a connection for a hard disk drive 3431, a tape drive 3432, and a CD-ROM drive (not shown).
An operating system may be run on the one or more processors 3415 and used to coordinate and provide control of various components within the computer system 3400. In
Those of ordinary skill in the art will appreciate that the hardware in
As another example, the computer system 3400 may be a stand-alone system configured to be bootable without relying on some type of network communication interface, whether or not the computer system 3400 includes some type of network communication interface. As a further example, the computer system 3400 may be an embedded controller, which is configured with ROM and/or flash ROM providing non-volatile memory storing operating system files or user-generated data.
The depicted example in
In an exemplary configuration, the mobile sorter 601 may be backed up to a location within a landfill 604. Then some sort of device 605 appropriate for collecting or extracting materials from the landfill (e.g., a creep feeder with a discharge chute) may be utilized to gather materials from the landfill 604 to be transported up a conveyor system 606 to be deposited onto another conveyor system 607 for depositing on the conveyor system 608 so that the materials may be transported through the classification system 609, and eventually to the sorting apparatus 610 whereby one or more specifically identified materials can be sorted and separated from each other. For example, landfill materials containing a specific type of material may be sorted for depositing into the receptacle 612 via the conveyor system 611, while the remaining landfill materials may be deposited into the receptacle 614 via the conveyor system 613. These remaining landfill materials received in the receptacle 614 may then be re-deposited into the landfill 604.
The device 605 may be a creep feeder with a discharge chute, a long conveyor system onto which the materials can be deposited from the landfill, such as from a vacuum system, a litter pick-up mechanism centered around a header pick-up belt, etc.
The classification system 609 may be implemented with a vision system 110 and/or one or more sensor systems 120, while the sorting apparatus 610 may be implemented with any of the various sorting devices described herein or otherwise commercially available.
The flowchart and block diagrams in the figures illustrate architecture, functionality, and operation of possible implementations of systems, methods, processes, and program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of code, which includes one or more executable program instructions for implementing the specified logical function(s). It should also be noted that, in some implementations, the functions noted in the blocks may occur out of the order 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 upon the functionality involved.
Reference is made herein to “configuring” a device or a device “configured to” perform some function. It should be understood that this may include selecting predefined logic blocks and logically associating them, such that they provide particular logic functions, which includes monitoring or control functions. It may also include programming computer software-based logic of a retrofit control device, wiring discrete hardware components, or a combination of any or all of the foregoing. Such configured devises are physically designed to perform the specified function or functions.
To the extent not described herein, many details regarding specific materials, processing acts, and circuits are conventional, and may be found in textbooks and other sources within the computing, electronics, and software arts.
Reference throughout this specification to “an embodiment,” “embodiments,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” “embodiments,” “certain 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 disclosure may be combined in any suitable manner in one or more embodiments. Correspondingly, even if features may be initially claimed as acting in certain combinations, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination can be directed to a sub-combination or variation of a sub-combination.
Benefits, advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced may be not to be construed as critical, required, or essential features or elements of any or all the claims. Further, no component described herein is required for the practice of the disclosure unless expressly described as essential or critical.
Those skilled in the art having read this disclosure will recognize that changes and modifications may be made to the embodiments without departing from the scope of the present disclosure. It should be appreciated that the particular implementations shown and described herein may be illustrative of the disclosure and its best mode and may be not intended to otherwise limit the scope of the present disclosure in any way. Other variations may be within the scope of the following claims.
Herein, the term “or” may be intended to be inclusive, wherein “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 listing of entities, refers to the entities being present singly or in combination. Thus, for example, the phrase “A, B, C, and/or D” includes A, B, C, and D individually, but also includes any and all combinations and subcombinations of A, B, C, and D.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an,” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise.
The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below may be intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed.
As used herein with respect to an identified property or circumstance, “substantially” refers to a degree of deviation that is sufficiently small so as to not measurably detract from the identified property or circumstance. The exact degree of deviation allowable may in some cases depend on the specific context.
As used herein, a plurality of items, structural elements, compositional elements, and/or materials may be presented in a common list for convenience. However, these lists should be construed as though each member of the list is individually identified as a separate and unique member. Thus, no individual member of such list should be construed as a defacto equivalent of any other member of the same list solely based on their presentation in a common group without indications to the contrary.
The term “coupled,” as used herein, is not intended to be limited to a direct coupling or a mechanical coupling. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements.
This application claims priority to U.S. Provisional Patent Application Ser. No. 63/285,971, which is hereby incorporated by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 63285971 | Dec 2021 | US |
