Patent Abstract:
A mixture of glass pieces can be evaluated by taking an image of an object from the mixture. The object has the possibility of being either a single piece of glass from the mixture or at least two pieces of glass from the mixture. By knowing how many pieces of glass are in each object, the accuracy of the evaluation can be improved. Angles of an outline of the object are determined from the image, and then the angles are evaluated to determine whether the object is at least two pieces. When it is determined that the objection is at least two pieces, it is possible to assign a characteristic, such as color type or material type, for each piece as opposed to assigning the same characteristic to the entire object.

Full Description:
FIELD OF THE INVENTION 
       [0001]    This invention relates generally to a method and system for evaluating a mixture containing colored objects, and more particularly, for evaluating the purity of cullet. 
       INCORPORATION BY REFERENCE 
       [0002]    All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. 
       BACKGROUND 
       [0003]    Glass containers are 100% recyclable and can be recycled endlessly without any loss in purity or quality. Over a ton of natural resources are saved for every ton of glass recycled. Energy costs drop about 2-3% for every 10% cullet (post-consumer glass) used in the manufacturing process. Glass furnace life is increased by 10% when recycled glass is used in the production of new glass containers. One ton of carbon dioxide is reduced for every six tons of recycled container glass used in the manufacturing process. 
         [0004]    Although glass container manufacturers are able to use as much as 95% recycled glass in the manufacturing process, glass container manufacturers often use only about 35% recycled glass. When using recycled glass as feedstock to manufacture new glass containers, energy costs of glass container manufacturers can be 15% less, emissions can be reduced by 20%, and furnace life can be extended by 10%. Glass container manufacturers prefer to use all the recycled furnace-ready cullet they can procure. A significant barrier to this is the quality of recycled glass available and assurance that the cullet receive is furnace-ready cullet, which must be high purity clean and color-sorted. With contaminated cullet, the reject rate in the manufacture of new glass containers increases, which increases the cost of manufacturing new glass containers. Even one small contaminant in a manufactured bottle can result in rejection. 
         [0005]    Consumers place their recyclables by their curb side, which is picked up by hauling companies and taken to a Material Recovery Facility (MRF) where the various recyclables are sorted out and the residual, which is a mixture having high glass content, is sent to Glass Processors to be cleaned, color sorted and then sold as cullet to glass container manufacturers for use in producing new glass containers. 
         [0006]    In years past, consumers had to place recyclables in different bins by their curb side. However, in order to reduce the cost of recycling and increase the amount that is recycled, cities have since transitioned to “single stream collection” whereby all recyclables are placed in a single bin. With single stream collection, Material Recovery Facilities (MRFs) have to deal with co-mingled material. While MRFs remove larger pieces of paper, most of aluminum cans and plastic containers, their residue and what they are unable to remove, comes out of their facilities with a large percentage of broken glass, typically as three-color (clear, brown, green) mixed dirty glass. So glass is part of this residual that MRFs send to glass processors, hence the glass is mixed with a lot of other material which is considered to be contaminants. The glass processors receive this as their raw material feedstock, which can contain as much as 40 to 50% contaminant (non-glass). However, the furnace-ready cullet which glass processors are required to provide to glass container manufacturers must contain less than 0.001% contaminant or almost 100% clean color sorted glass. The quality of cullet is the most important factor for glass container manufacturers. 
         [0007]    While producing quality cullet is important, the ability to test the cullet is crucial. Today glass processors use manual methods to test the quality of the cullet that is delivered to them. A material sample, usually 50 lbs, is spread on a work table, a quality inspection person manually separates the contents of the sample, weighs each content group, fills out a sheet of paper with data which is compared to the specification of the glass container manufacturer (or other end user) to determine if the cullet delivery passes or fails to meet specifications. This manual process can take up to 45 minutes for each sample. The samples have to be tested several times a day and for each truck shipment, adding high labor cost to the end product. Accordingly, there is a need for efficient and accurate method and system for testing the quality of cullet. 
       SUMMARY 
       [0008]    Described herein are a method and system for glass processing. 
         [0009]    Various aspects of the invention are directed to a method comprises taking an image of an object from the mixture, the object possibly being either a single piece from the mixture or at least two pieces from the mixture. The method further comprises determining, from the image, angles of an outline of the object. The method further comprises evaluating the angles to determine whether the object is at least two pieces, and evaluating a characteristic of the object. 
         [0010]    Various aspects of the invention are directed to a system comprises an imaging device configured to take an image of an object from the mixture, the object possibly being either a single piece from the mixture or at least two pieces from the mixture. The system further comprises a light source configured to direct light toward the imaging device. The system further comprises a processor configured to determining, from an image taken by the imaging device, angles of an outline of the object. The processor is further configured to evaluate the angles to determine whether the object is at least two pieces and to evaluate a characteristic of the object. 
         [0011]    Various aspects of the invention are directed to a non-transitory computer readable medium having a stored computer program embodying instructions, which when executed by a computer, causes the computer to evaluate a mixture including a plurality glass pieces. The computer readable medium comprises instructions to take an image of an object from the mixture, the object possibly being either a single piece from the mixture or at least two pieces from the mixture. The computer readable medium further comprises instructions to determine, from the image, angles of an outline of the object instructions to evaluate the angles to determine whether the object is at least two pieces, and instructions to evaluate a characteristic of the object. 
         [0012]    Various aspects of the invention are directed to a method comprising taking an image of an object from the mixture, the object having the potential of being either a piece of glass with label or a piece of glass without a label. The method further comprises determining, from the image, angles of an outline of the object. The method further comprises detecting, from the image, a light transmittance boundary line within the outline of the object, the light transmittance boundary line having an endpoint on the outline. The method further comprises identifying the object as a piece of glass with a label based, at least, on the angle of the outline at the endpoint of the light transmittance boundary. 
         [0013]    The features and advantages of the invention will be more readily understood from the following detailed description which should be read in conjunction with the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]      FIG. 1  is a schematic view of an exemplary system, showing an imaging device, processor coupled to the imaging device, a light source, and a plurality of objects to be examined. 
           [0015]      FIG. 2  is a simulated image of the objects taken by the imaging device and communicated to the processor. 
           [0016]      FIG. 3  is a block diagram showing an exemplary method. 
           [0017]      FIG. 4  is a diagram showing object outlines obtained from the image of  FIG. 2 . 
           [0018]      FIG. 5  is a detailed view of the object outline of one of the objects in  FIG. 4 . 
           [0019]      FIG. 6  is a schematic view of an exemplary system, showing a glass sorter and quality test modules. 
       
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
       [0020]    Referring now in more detail to the exemplary drawings for purposes of illustrating embodiments of the invention, wherein like reference numerals designate corresponding or like elements among the several views, there is shown in  FIG. 1  a plurality of objects  10 A- 10 D disposed between imaging device  12  and light source  14 . Imaging device  12  is configured to take an image of all objects  10 A- 10 D simultaneously, or optionally take an image of only one or a limited number of the objects. 
         [0021]    Objects  10 A- 10 D are samples obtained from a mixture that includes many pieces of glass and non-glass debris material. The mixture can be residual material from a Material Recovery Facility (MRF). The mixture can be cullet produced by a glass processor which sorts residual material from the MRF. The mixture can be cullet received by a glass manufacturer for making new glass containers. 
         [0022]    The glass pieces in the mixture can belong to various color types. Color types include without limitation clear (or flint), amber (or brown), and green. The non-glass debris material can be ceramic plastic, metal, wood, stone, or rock. The mixture can be a cullet mixture resulting from a prior process which attempted to separate glass pieces by color type. Additionally or alternatively, the mixture can be the result of a prior sorting process which attempted to remove non-glass debris material. The method and system described herein can be used to verify quality of the sorting process. For example, method and system can test whether a cullet mixture satisfies a predetermined requirement, such as 95% amber glass by weight and/or less than 5% non-glass debris material by weight. As another example, the predetermined requirement can be 98% clear glass by weight and/or less than 2% non-glass debris material by weight. Additionally or as an alternative to use after a sorting process, the method and system described herein can be used during the sorting process to help ensure that the resulting cullet mixture satisfies predetermined requirements. 
         [0023]    In  FIG. 1 , four objects ( 10 A- 10 D) are simultaneously within field of view  13  of imaging device  12 . It is possible for there to be a greater or lesser number of objects within field of view  13  than what is illustrated. As discussed below, any one of the four objects ( 10 A- 10 D) can in fact be multiple items. A system and method will be described for determining whether or not any of the objects ( 10 A- 10 D) comprises multiple items. 
         [0024]    Light source  14  is configured to direct light toward objects  10 A- 10 D. The type of light includes visible light. Light source  14  is oriented such that an outline each of the objects can be detected by the imaging device  12 . Light source  14  includes any one or a combination of a mirror, a light guide, and a light generator such as a light bulb or light emitting diode (LED). The light directed by light source  14  allows imaging device  12  to determine the composition and characteristics of objects  10 A- 10 D. Characteristics includes without limitation color type and material type. Color type includes without limitation clear (or flint), amber (or brown), and green. Material type includes without limitation glass versus non-glass debris material. 
         [0025]    Imaging device  12  can include one or more electronic sensors configured to detect the intensity and color of light passing through objects  10 A- 10 D and at the edges of objects  10 A- 10 D. Electronic sensors include without limitation charge-coupled devices (CCD) and complementary metal-oxide-semiconductors (CMOS). Imaging device  12  is coupled to processor  16 . Processor  16  includes one or more integrated circuits for evaluating images from the imaging device  12  and one or more memory components for storing the images. 
         [0026]      FIG. 2  shows image  18  taken by imaging device  12 . Image  18  is stored and evaluated by processor  16 . Each of the objects  10 A- 10 D can actually be one or more pieces of glass and/or non-glass debris material. In the illustration, object  10 A consists of three overlapping pieces of glass, object  10 B consists of a single piece of glass, object  10 C consists of one piece of glass and one piece of non-glass debris material, and object  10 D consists of a single piece of glass with a label. The illustrated composition for the objects is exemplary and is not intended to limit the invention. Other combinations for each object are possible, such as: two pieces of glass with labels; two pieces of glass with labels and one piece of non-glass debris material; one piece of glass with a label, two pieces of glass without a label, and a piece of non-glass debris material; and so on. 
         [0027]    Processor  16  is configured to determine the composition of each one of the objects ( 10 A- 10 D) by analyzing image  18 . The composition refers to the number of items present or contained in each object. By determining the composition of each object, it is possible to characterize the object more accurately and thereby enable a more accurate quality test of whether a cullet mixture meets predetermined requirements or enable more accurate sorting to ensure that the resulting cullet mixture meets the predetermined requirements. Without a determination of composition, object  10 A could be mistakenly characterized as a single piece of amber glass when in fact it consists of one piece of amber glass, one piece of clear glass, and one piece of green glass. As a further example, object  10 C could be mistakenly characterized as single of piece of desired glass when in fact it consists of one piece of desired glass and one piece of undesirable debris material. In yet another example, object  10 D could be mistakenly characterized as a single piece of undesirable debris material when it is a piece of desired glass with a label adhered to it. These potential inaccuracies can be avoided by analyzing image  18  to determine the composition of one or more of the objects. 
         [0028]    In some embodiments, as shown in  FIG. 3 , the determination of composition (block  20 ) of the objects ( 10 A- 10 D) includes determining, from image  18 , angles of an outline of the object (block  22 ). Next, angles are evaluated (block  24 ) to determine whether each of the objects contains at least two pieces. 
         [0029]    Referring to  FIG. 4 , the determination of angles (block  22 ) is performed by obtaining an outline of each of the objects ( 10 A- 10 D). The outline refers to edges of the object which define an area of image  18  occupied by the object. In  FIG. 4 , only outlines  26 A- 26 D of the objects are illustrated. Other details of image  18  in  FIG. 2  (such as indicators of color type, opacity, and where pieces overlap) are omitted from  FIG. 4  to clarify the discussion below. Each of the outlines  26 A- 26 D includes one or more vertices. As discussed below, angles at each of the vertices can be used to determine the number of pieces in each object. 
         [0030]      FIG. 5  shows a detailed view of outline  26 A of object  10 A. Outline  26 A includes vertices V 1  to V 13 . The interior angle Φ at each of the vertices is compared to a threshold value. For example, the threshold value can be 180 degrees, and processor  16  determines whether a vertex has an interior angle Φ greater than 180 degrees. Results of the comparison are shown in TABLE I. 
         [0000]    
       
         
               
               
               
             
           
               
                   
                 TABLE I 
               
               
                   
                   
               
               
                   
                   
                 Does interior angle Φ 
               
               
                   
                 Vertex 
                 violate threshold? 
               
               
                   
                   
               
             
             
               
                   
                 V1 
                 No 
               
               
                   
                 V2 
                 No 
               
               
                   
                 V3 
                 YES (suspect vertex) 
               
               
                   
                 V4 
                 No 
               
               
                   
                 V5 
                 YES (suspect vertex) 
               
               
                   
                 V6 
                 No 
               
               
                   
                 V7 
                 No 
               
               
                   
                 V8 
                 No 
               
               
                   
                 V9 
                 No 
               
               
                   
                 V10 
                 YES (suspect vertex) 
               
               
                   
                 V11 
                 No 
               
               
                   
                 V12 
                 YES (suspect vertex) 
               
               
                   
                 V13 
                 No 
               
               
                   
                   
               
             
          
         
       
     
         [0031]    The interior angle Φ of outline  26 A is greater than the threshold value of 180 degrees at vertices V 3 , V 5 , V 9 , and V 12 . A vertex which violates the threshold (e.g., has an interior angle Φ greater than 180 degrees) represents either a stress concentration that could result in the piece of glass breaking apart into more pieces or an intersection between two pieces of glass. For convenience of discussion, a vertex which violates the threshold is referred to as a suspect vertex. The threshold value is selected such that a suspect vertex is more likely to represent an intersection between two separate pieces. The threshold value of 180 degrees is selected to provide confidence that a suspect vertex most probably represents an intersection between two separate pieces, as opposed to representing a single piece of glass with a stress concentration. Other threshold values for the interior angle Φ can be selected, such as 185 degrees, 190 degrees, 195 degrees, 200 degrees, 205 degrees, and so on. The threshold value can be a value in the range of 180 degrees to 205 degrees, for example. In general, a greater threshold value can provide greater confidence that a suspect vertex truly represents an intersection between two separate pieces. 
         [0032]    In some embodiments, the angle which is determined in block  22  of  FIG. 3  is the exterior angle θ. For the exterior angle θ, the threshold angle is used in reverse. That is, a vertex is identified as a suspect vertex when its exterior angle θ is less than the threshold of 180 degrees. Other threshold values for the exterior angle θ can be selected, such as 185 degrees, 190 degrees, 195 degrees, 200 degrees, 205 degrees, and so on. 
         [0033]    In some embodiments, a suspect vertex is paired with another suspect vertex as a condition to concluding that the two suspect vertices represent an intersection between two separate pieces. In  FIG. 5 , processor  16  determines that leg L 5  of vertex V 5  is aligned with leg L 10  of vertex V 10 . Due to alignment of legs, processor  16  identifies vertices V 5  and V 10  as a suspect vertex pair and as an intersection between two separate pieces  28  and  30 . Alignment is found when legs L 5  and L 10  are on the same imaginary line (i.e., legs L 5  and L 10  are collinear) or when legs L 5  and L 10  form an angle that is less than an alignment threshold angle. The alignment threshold angle can be, for example, any one of 2 degrees, 4 degrees, 6 degrees, 8 degrees, 10 degrees, and so on. 
         [0034]    Additionally or alternatively, processor  16  determines vertices V 5  and V 10  are in sufficient proximity to each other as a condition to concluding that the two suspect vertices represent an intersection between two separate pieces. Due to sufficient proximity, processor identifies vertices V 5  and V 10  as a suspect vertex pair and as an intersection between two separate pieces  28  and  30 . Sufficient proximity is found when the distance D between vertices V 5  and V 10  is within a threshold distance. The threshold for distance D can be an absolute distance. The absolute distance can be, for example, any one of 5 mm, 1 cm, 2 cm, 3 cm, 4 cm, and so on. The threshold for distance D can be a percentage of another dimension taken from outline  26 A. For example, the threshold for distance D can be a percentage (such as 50%, 100%, 150%, or 200%) of a leg (such as L 5  or L 10 ) adjacent to a suspect vertex. As a further example, the threshold for distance D can be a percentage (such as 50%, 25%, 10%, or 5%) of an overall length L or width W of the object. In the foregoing examples, pairing of suspect vertices V 5  and V 10  is based on a predetermined criteria, such alignment and/or proximity. In addition or alternatively, other criteria can be used, such as similarity in the curvature of legs adjacent to the suspect vertices (e.g., legs L 5  and L 10 ), similarity in image pixel color of legs adjacent to the suspect vertices, and/or presence of a light transmittance boundary line at the suspect vertices. Light transmittance boundary lines  40  are described below. 
         [0035]    Optionally, other suspect vertices can be paired by a process of elimination. For example, after suspect vertices V 5  and V 10  are paired in object  10 A, the only remaining suspect vertices are V 3  and V 12 . In this situation where there are exactly two remaining suspect vertices, suspect vertices V 3  and V 12  are automatically identified by processor  16  as a suspect vertex pair and as an intersection between two separate pieces  30  and  32  ( FIG. 2 ). 
         [0036]    Referring to  FIG. 4 , outline  26 B of object  10 B has only one suspect vertex. That is, outline  26 B has only one vertex (V 3 ) having an interior angle Φ that is greater than the threshold or an exterior angle θ that is less than the threshold. In some embodiments, since the total number of suspect vertices for object  10 B is exactly one, processor  16  identifies that vertex (V 3 ) as not being an intersection between two separate pieces. Processor  16  concludes that object  10  consists of a single piece  34  ( FIG. 2 ). 
         [0037]    On the other hand, outline  26 C of object  10 C has exactly two suspect vertices. That is, vertices V 1  and V 5  each have an interior angle Φ that is greater than the threshold or an exterior angle θ that is less than the threshold. In some embodiments, since the total number of suspect vertices for object  10 C is exactly two, processor  16  identifies those vertices (V 1  and V 6 ) as a suspect vertex pair and as an intersection between two separate pieces  36  and  38  ( FIG. 2 ). 
         [0038]    In object  10 C, piece  36  is actually a piece of translucent glass, and piece  38  is actually a piece of opaque, non-glass debris material. This composition can be determined by processor  16  as follows. The opacity of piece  38  results in light transmittance boundary line  40  ( FIG. 4 ) within outline  26 C. The light transmittance boundary line  40  can arise when, for example, no light or very little light passes through the opaque, non-glass debris material. Processor  16  detects light transmittance boundary line  40  as an abrupt change in light intensity passing through object  10 C captured in image  18 . Light transmittance boundary line  40  has endpoints  40 E at suspect vertices V 1  and V 5 . Additionally or alternatively, since endpoints  40 E are located at suspect vertices V 1  and V 5 , processor  16  identifies those vertices (V 1  and V 5 ) as a suspect vertex pair and as an intersection between two separate pieces  36  and  38  ( FIG. 2 ) and does not mistakenly identify piece  36  as a piece of glass with a label. Further evaluation by processor  16 , as described below, will reveal piece  36  as a piece of glass and piece  38  as non-glass debris material. 
         [0039]    Referring again  FIG. 4 , outline  26 D of object  10 D has no suspect vertex. That is, none of vertices V 1  to V 5  have an interior angle Φ that is greater than the threshold or an exterior angle θ that is less than the threshold. In some embodiments, since the total number of suspect vertices for object  10 D is exactly zero, processor  16  concludes that object  10 D consists of a single piece  42  ( FIG. 2 ). Piece  42  is actually a single piece of glass with a label L, as shown in  FIG. 2 . This can be determined by processor  16  as follows. Label L is a piece of paper, opaque plastic, or foil which is adhered on the surface of piece  42 . Label L results in light transmittance boundary line  40 . The light transmittance boundary line  40  can arise when, for example, no light or very little light passes through label L. Processor  16  determines that endpoints  40 E of light transmittance boundary line  40  are not located at any suspect vertex. Additionally or alternatively, since endpoints  40 E are not located at any suspect vertex, processor  16  concludes that object  10 D consists of a single piece of glass  42  with label L and does not mistakenly conclude that object  10 D contains a piece of non-glass debris material. 
         [0040]    Referring again to  FIG. 3 , the characteristic of the object can be determined (block  50 ) after the composition of the object has been determined (block  20 ). As described above, when processor  12  concludes that the object is a single piece of material, it can deduce that the single piece is a piece of glass with a label based, at least, on the presence of a light transmittance boundary line within the outline of the object. To evaluate other characteristics, processor  12  analyzes image  18  to determine, for each piece contained within the object, color type, material type, and/or whether the piece is a piece of glass with a label. 
         [0041]    After concluding that object  10 A consists of three pieces, processor  12  determines areas of possible overlap that might lead of inaccurate analysis. The areas of possible overlap are areas between suspect vertex pair V 5 , V 10  and between suspect vertex pair V 3 , V 12  ( FIG. 5 ). In some embodiments, processor  12  analyzes areas of image  18  adjacent to a vertex which is not a suspect vertex. For example, processor can analyze image areas adjacent to vertices V 6 , V 4 , and V 2  to determine the color type of each of glass pieces  28 ,  30 , and  32  respectively. 
         [0042]    Additionally or alternatively, after concluding that object  10 C consists of two pieces, processor  12  analyzes areas of image  18  adjacent to points P on the outline and located at a distance away from suspect vertex pair V 1 , V 5  ( FIG. 4 ). Selection of areas at a distance away from suspect vertices V 1  and V 5  can help avoid areas of possible overlap that could lead of inaccurate analysis. 
         [0043]    After the composition (i.e., number of items) of each object is determined, the characteristics (e.g., color type, presence of non-glass debris, and presence of labels adhered to glass) of individual pieces in each object can be determined using system and methods known in the art, in addition to or as alternatives to the methods described above. See, for example, U.S. Pat. No. 5,314,071 issued to Christian et al., entitled “Glass Sorter.” 
         [0044]    Imaging device  12 , processor  16 , and light source  14  can be implemented to perform quality tests on the cullet output of a glass sorter. 
         [0045]    In  FIG. 6 , glass sorter  44  is configured to sort a mixture of glass pieces  45  by color type and eject a separate cullet output stream  46 A, B, C for each glass color type. For example cullet output stream  46 A can be a stream of green cullet, output stream  46 B can be a stream of amber (or brown) cullet, and output stream  46 C can be a stream of clear (or flint) cullet. Glass sorter  44  includes conveyor belt  48  which transports the mixture of glass pieces  45  to sorting assembly  50 . 
         [0046]    Sorting assembly  50  produces output streams  46 A, B, C. Sorting assembly  50  includes sensor modules  52  and light modules  54  directed toward sensor modules  52 . Sensor modules  52  are used to determine the color type of the glass pieces which fall from the edge of conveyor belt  48 . Sorting assembly  50  includes actuators  56  controlled by control module  60  which is communicatively coupled to conveyor belt  48 , sensor modules  52 , and light modules  54 . Actuators  56  can be pneumatic blowers, mechanical gates, or electrostatic plates. Actuators  56  are configured to push or guide selected glass pieces into a selected one of the output streams  46 A, B, C based upon analysis of data from sensor modules  52  by control module  60 . In the illustrated embodiment, sensor modules  52  are located above free-fall trajectory  58  of mixed glass material  45 . The number, arrangement and orientation of sensor modules  52 , light modules  54 , and actuators  56  can be different from what is illustrated. The sensor modules, light modules, and actuators can be as described in U.S. Pat. No. 7,351,929, U.S. Pat. No. 7,355,140, or U.S. Pat. No. 8,436,268. The entirety or a portion of glass sorter  44  can be as described in U.S. Pat. No. 5,314,071. 
         [0047]    Tests on the quality of the cullet output of glass sorter  44  ( FIG. 6 ) can be performed using quality test modules  62  which are configured to determine the composition and characteristics of objects as described in connection with  FIGS. 1-5 . Transporters  64  move cullet from each of output streams  46 A, B, C to quality test modules  62 . Each transporter  64  can be a conveyor belt, a rotating feed wheel, a pivoting diverter plate, or similar device. Each quality test module  62  includes imaging device  12 , processor  16 , and light source  14  previously described (see  FIGS. 1-5 ). Each transporter  64  slides or drops cullet pieces (for example, objects  10 A- 10 D in  FIG. 1 ) between imaging device  12  and light source  14 . Processor  16  provides an indication of the purity of the cullet. For example, processor  16  can indicate that the amber cullet output from glass sorter  44  satisfies or fails to satisfy a predetermined quality requirement, such as at least 95% amber glass by weight. As another example, the predetermined quality requirement can be that any of the cullet output must be less than 2% non-glass debris material by weight with the remainder being at least 95% by weight of the desired color type. Each quality test module  62  optionally includes output module  66  which provides the indication of purity. Output module  66  can be a display screen or printer that shows the purity level. Alternatively, output module  66  can be an audio or visual alarm configured to automatically alert the person who is operating glass sorter  44 . 
         [0048]    In the illustrated embodiment, there are three quality test modules  62 . One quality test module  62  is dedicated for each cullet collection area  47 A, B, C. In other embodiments, sorter  44  is configured to sort more than three color types and output a corresponding number of cullet output streams. There can be a separate quality test module for each cullet collection area. Alternatively, there can be only one quality test module which is movable between various cullet collection areas. 
         [0049]    In  FIG. 6 , the quality test modules  62  are shown at fixed locations at the end of each cullet output streams. In other embodiments, there is no quality test module at a fixed location. For example, there can be a quality test module that is stored at a location away from the cullet output streams. When needed, a sample quantity can be taken from a cullet output stream and then carried by a person to quality test module so that a quality test can be performed. 
         [0050]    In some embodiments, one or more of the quality test modules  62  are communicatively coupled to control module  60  of glass sorter  44 . Control module  60  is configured to alter the operation of glass sorter  44  based on output signals from quality test module  62 . For example, if the predetermined quality requirement is not met, processor  16  within quality test module  66  automatically causes control module  60  to stop conveyor belt  48  and other machinery in glass sorter  44  to allow for maintenance or adjustments to the machinery. As another example, if the predetermined quality requirement is not met, processor  16  within quality test module  66  automatically causes control module  60  to change one or more glass sorter parameters to increase the purity level of the cullet. Glass sorter parameters include without limitation the speed of conveyor belt  48 , the rate at which mixed glass is place onto conveyor belt  48 , and settings for suctioning and/or filtering out non-glass debris from the mixture of glass material  45 . 
         [0051]    In some embodiments, one or more of the quality test modules  62  are configured or programmed to performed the above-described tests on the quality of cullet at random times or at fixed time intervals. In the case of fixed time intervals, tests can be performed every 5 minutes, or every 10 minutes, or every 30 minutes, or other time duration. In the case of testing at random times, the time interval between tests is not fixed, and the time intervals of many tests can be specified to provide an average time interval. For example, tests can be performed randomly such a first time interval is 32 minutes, followed by a second time interval of 15 minutes, and followed by a third time interval of 18 minutes, and so on, such that all time intervals result in an average time interval. The average time interval between tests is 5 minutes, 10 minutes, 30 minutes, or other time duration. Optionally, one or more of the quality test modules  62  are further configured or programmed such that, when the predetermined quality requirement is not met, the quality test modules  62  automatically increase the frequency of testing. For example, the quality test module  62  may automatically reduce the fixed time interval or the average time interval. Increasing the frequency of testing provides a greater number of data points for characterizing the quality of the cullet. 
         [0052]    Additionally or alternatively, one or more of the quality test modules  62  are configured or programmed such that, when the predetermined quality requirement has been met and exceeded, the quality test module  62  automatically decreases the frequency of testing. For example, the quality test module  62  may automatically increases the fixed time interval or the average time interval. 
         [0053]    In some embodiments, sorting assembly  50  can determine the composition and characteristics of objects as described in connection with  FIGS. 1-5 . For example, imaging device  12 , processor  16 , and light source  14  (all of which were described in connection with  FIGS. 1-5 ) can be implemented to perform sorting within sorting assembly  50  of glass sorter  44 . Within sorting assembly  50 , sensor modules  52  can include or be replaced with imaging devices  12 , light modules  52  can include or be replaced with light sources  14 , and control module  60  can include or be replaced with processor  16 . 
         [0054]    In some embodiments, there are one or more memory components which form a computer readable medium. The computer readable medium may be volatile or non-volatile. Examples of a computer readable medium include without limitation a magnetic storage device (e.g., computer hard drives), an optical storage device (e.g., a CD-ROM and DVD-ROM), or a flash memory device (e.g., memory cards and USB flash drives). Processor  16  and/or control module  60  may include the computer readable medium. Alternatively, processor  16  and/or control module  60  may be communicatively coupled to another device capable of reading the computer readable medium. 
         [0055]    The computer readable medium has a stored computer program embodying instructions, which when executed by a computer (e.g., processor  16  and/or control module  60 , or other computer) causes the computer to evaluate a mixture of glass pieces according to the process steps described herein, including process steps described in connection with any of  FIGS. 1-6 . The computer readable medium includes instructions for performing the process steps described herein, including process steps described in connection with any of  FIGS. 1-6 . 
         [0056]    While several particular forms of the invention have been illustrated and described, it will also be apparent that various modifications can be made without departing from the scope of the invention. It is also contemplated that various combinations or subcombinations of the specific features and aspects of the disclosed embodiments can be combined with or substituted for one another in order to form varying modes of the invention. Accordingly, it is not intended that the invention be limited, except as by the appended claims.

Technology Classification (CPC): 1