Abstract:
Disclosed herein is a metal sorting device including an X-ray tube, a dual energy detector array, a microprocessor, and an air ejector array. The device senses the presence of samples in the x-ray sensing region and initiates identifying and sorting the samples. After identifying and classifying the category of a sample, at a specific time, the device activates an array of air ejectors located at specific positions in order to place the sample in the proper collection bin.

Description:
This application is a continuation application of co-pending U.S. patent application Ser. No. 12/960,199, filed Dec. 3, 2010, entitled “Method and Apparatus for Sorting Materials According to Relative Composition,” which is hereby incorporated by reference, which is a continuation application of U.S. patent application Ser. No. 12/493,578, filed Jun. 29, 2009, now U.S. Pat. No. 7,848,484, entitled “Method and Apparatus for Sorting Materials According to Relative Composition,” which is hereby incorporated by reference, which is a continuation application of U.S. patent application Ser. No. 11/498,694, filed Aug. 3, 2006, now U.S. Pat. No. 7,564,943, entitled “Method and Apparatus for Sorting Materials According to Relative Composition,” which is hereby incorporated by reference, which is a continuation-in-part application of U.S. patent application Ser. No. 11/069,243, filed Mar. 1, 2005, now U.S. Pat. No. 7,099,433, entitled “Method and Apparatus for Sorting Materials According to Relative Composition,” which is hereby incorporated by reference, which claims benefit of U.S. Provisional Pat. App. Ser. No. 60/549,089, filed Mar. 1, 2004, entitled “High speed non-ferrous metal sorting using XRF ” which is hereby incorporated by reference in its entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     This invention was made with federal grant money under National Science Foundation Small Business Innovation Research program under grant number DMI-0239055.The United States Government has certain rights in this invention. 
    
    
     Be it known that we, Edward J. Sommer, Jr., a citizen of the United States and a resident of Nashville, Tenn., Charles E. Roos, a citizen of the United States and a resident of Nashville, Tenn., David B. Spencer, a citizen of the United States and a resident of Bedford, Mass., and R. Lynn Conley, a citizen of the United States and a resident of Antioch, Tenn. have invented a new and useful “Method and Apparatus for Sorting Materials According to Relative Composition.” 
     BACKGROUND OF THE INVENTION 
     In the scrap metals recycling industry there is a lack of an environmentally clean low cost technology to reliably segregate post-consumer metal scrap into its various metal constituents. Current practice for sorting aluminum metals from other nonmagnetic metals derived from scrap sources such as automobile shredders is to either sort by hand labor or to introduce the mixed metals into a liquid heavy media where the aluminum floats and the heavier nonmagnetic metals sink. Hand labor is far too slow and very expensive. The heavy media process is capital intensive, has high operating costs, and uses a water slurry mixed with chemicals to raise the specific gravity of the liquid to a value above that of aluminum (2.7 gm/cc). The liquid media requires treatment in a wastewater treatment facility. The resulting sludge composed of grease, oil, dirt, and chemicals poses significant disposal issues as do water discharges. Additionally to be cost effective the heavy media process requires a large installation and is normally deployed as a regional facility. This requires the producers of scrap to ship their metals to this regional facility for separation before the metal products can be shipped to market, whereas if sorting could be accomplished locally the scrap producers could ship directly to market. Elimination of the extra shipping requirement would improve the economics of recycling and remove the burden on our environment caused by the shipping of hundreds of thousands of tons of scrap metals annually to regional heavy media plants. 
     There have been recent efforts to develop dry environmentally friendly techniques to sort low atomic number light weight metals and alloys such as magnesium (atomic number Z=12) and aluminum (Z=13) and their alloys from higher atomic number heavier metals such as iron (Z=26), copper (z=29), and zinc (Z=30) and their alloys. One method is to acquire and analyze x-ray fluorescence spectra derived from metals by irradiating metals with excitation x-rays, measuring the resulting x-ray fluorescence emitted from the metals, utilizing spectral information developed from the measurements to identify composition of the metals, and to mechanically sort the metals according to their compositions. This method is exemplified by U.S. Pat. Nos. 6,266,390, 6,519,315, and 6,888,917. Low Z material does not lend itself well to x-ray fluorescence analysis since x-ray photons fluoresced from low Z materials are at low yield and are low energy (˜1-2 kev). Because they are low energy they are easily absorbed in air before reaching the detection system. This method also, by nature of the detection system, requires a significant time interval to build and analyze spectral information for each piece of material analyzed. Consequently systems that operate according to this method are limited in throughput rate of materials. For high throughput rates it is desired to have a faster acting analyses system in order to process materials faster and at greater volumes. 
     Another effort is described in Patent Application Publication No. US2004/0066890 wherein is discussed a process of irradiating materials by x-ray radiation, measuring x-ray transmission values through materials at two different energy levels, and using these measurements to determine the thickness and composition of a material. However, that publication does not reveal how such determinations can be accomplished. That dual energy system, as described, discusses utilizing undisclosed image processing techniques and appears similar to standard security x-ray scanners, such as those used at security checkpoints in airports, which utilize x-ray measurements at two different energy levels to measure thickness and material composition and present on a computer monitor screen a complex image for human visual inspection which is graphically encoded by image intensity and false color mapping to represent thickness and material composition (as described by security x-ray scanner vendor Smith&#39;s Heimann). Some such x-ray scanners utilize a physically stacked dual energy x-ray detector array to measure x-ray transmission values through materials over two energy ranges, the fundamental features of which are described by GE Medical Systems in their U.S. Pat. Nos. 4,626,688 and RE 37,536. A stacked dual energy detector utilizes a physical geometry of having a lower energy detector sandwiched with a higher energy detector with a filter, typically a metal layer such as copper, interposed between the two detectors. X-rays to be measured first enter the detector stack into the lower energy detector. Lower energy photons are absorbed by the lower energy detector as they are measured. Mid-energy and higher energy photons pass through the lower energy detector. Mid-energy photons are absorbed in the filter layer between the two detectors while higher energy photons pass through the filter layer and are measured by the higher energy detector at the back of the stack. Other x-ray scanners utilize other types of dual energy detector arrangements, such as side-by-side arrays, examples of which are disclosed in U.S. Pat. Nos. 5,841,832 and 5,841,833. 
     Still another effort utilizes spectral analysis of plasma evaporated off the surface of metal samples induced by momentarily striking the metals with a focused high power laser beam. This method, referred to as Laser Induced Breakdown Spectroscopy or LIBS, reportedly has been practiced in the U.S. by a metals processing company and is detailed in U.S. Pat. No. 6,545,240 B2. The LIBS process for sorting of metals as they are conveyed in volume through a processing line involves a high level of complexity due in part to requirements to rapidly steer a laser beam to small target points from sample to sample for repeated bursts of laser light and to correspondingly steer spectral acquisition optics from sample to sample in coincidence with the laser beam. This method is very complex and costly. 
     In sorting of many materials, such as nonferrous automobile scrap, it is very advantageous to be able to sort lighter weight materials (such as aluminum and its alloys) from heavier weight materials (such as iron, copper, and zinc and their alloys). To accomplish such a sort it is not necessary to determine both thickness and composition as the method of US2004/0066890 claims to do and it is not necessary to use complex image processing techniques of US2004/0066890 and as practiced using security x-ray scanners. Instead a determination of relative composition, such as relative average atomic number (Z), suffices to make a very valuable sort of the materials. Determination of relative composition, such as in discriminating high Z materials from low Z materials, is simpler for a detection system to accomplish than is determination of thickness and composition which can require high precision detector signals to be able to discern fine differences in measurements from sample to sample, maintenance of comprehensive detection system calibrations, and use of complex pattern matching algorithms such as those used by human visual inspectors in interpreting processed images produced by security x-ray scanners. At this time it has not been technically possible to duplicate by computerized algorithms the complex visual pattern matching skills used by humans in interpreting processed images produced by dual energy x-ray scanner security systems. 
     SUMMARY OF THE INVENTION 
     The present invention discloses a metal sorting device and method of use thereof. The metal sorting device, used to distinguish materials of differing atomic weight, includes an X-ray tube, a dual energy detector array positioned to receive x-rays from the X-ray tube, a microprocessor operationally connected to the dual energy detector array, an air ejector controller operationally connected to the microprocessor, and an air ejector array attached to the air ejector controller. The device may include a conveyor belt disposed between the X-ray tube and the dual energy detector array. Certain embodiments of the invention include an air ejector array having at least two air ejectors. Other embodiments of the invention include an air ejector array having as few as one air ejector and others as many as 128 or more air ejectors. Other embodiments of the invention include a collection bin, or at least two collection bins. In certain embodiments, the device includes a dual energy detector array further including dual energy x-ray detectors and a data acquisition system. In another embodiment the dual energy detector array utilizes stacked dual energy detectors. 
     One embodiment of the invention includes a method of detecting and sorting materials of differing atomic weight, the method includes providing a sample, placing the sample in a sensing region of a dual energy detector array, detecting the sample in the sensing region, reading a high energy sensor value, reading a low energy sensor value, normalizing the high energy sensor value, normalizing the low energy sensor value, computing a ratio of high energy value to low energy value, correlating the ratio with the normalized high energy value, determining whether the correlation is in a high atomic number region or a low atomic number region, transporting the sample to an air ejection array, and energizing at least one air ejector of the air ejection array. 
     Thickness is typically not a factor in quality of sorted materials for users of many sorted materials such as nonferrous automobile scrap. Consequently it would be advantageous to have an automated sorting system to measure the relative composition of materials processed through the system directly without regard to thickness of the materials and without applying complex pattern matching techniques through image processing. Determination of relative composition independent of material thickness simplifies computerized identification and sorting algorithms by reducing complications arising from processing additional dual energy transmission information with regard to thickness. In this way the algorithms can operate rapidly, accurately, and robustly to identify materials by relative composition and reliably provide signals to rapid sorting mechanisms to effect sorting of the materials according to their measured relative compositions at high throughput rates of materials. It is an objective of the present invention to provide such method and apparatus for thickness independent measurement of material relative composition with accompanying high throughput sorting of the materials according to relative composition. 
     The present invention incorporates computerized processing of measurements of amounts of transmission of x-ray photons through materials at two separate energy levels (dual energy detection), as illustrated above by way of example, to distinguish materials of relatively high Z from materials of relatively low Z by comparing results of the processing to an experimentally determined preset threshold level which varies as a function of amount of photon energy transmitted through such materials, and responsive to such determinations activates mechanical sorting mechanisms to segregate the relatively high Z materials from the relatively low Z materials, as is further detailed in the following. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a side view illustration of a schematic showing mechanical arrangement of portions of an embodiment of the invention. 
         FIG. 2  depicts a top view illustration of the schematic of  FIG. 1  showing mechanical arrangement of portions of an embodiment of the invention. 
         FIG. 3  shows a block diagram for an embodiment of the invention illustrating relationships between various portions of the electrical/computer hardware for acquiring and processing x-ray detector signals and for activating selected air valves within an air ejector array responsive to the results of the processing. 
         FIG. 4  shows an example graph of processed x-ray transmission data measured at two different x-ray energy levels for various nonferrous metals derived from an automobile shredder. 
         FIG. 5  shows a logic flow diagram representative of a materials identification and sorting algorithm for an embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention uses analyses of x-ray absorptions in a material at differing energy levels in order to determine the relative atomic density (atomic number Z) of the material. X-ray absorption in a material is a function of the atomic density of the material and also a function of the energy of the incident x-rays. A given piece of material will absorb x-rays to differing degrees depending upon the energy of the incident x-rays. Materials of differing atomic numbers will absorb x-rays differently. For instance copper (Z=29) will absorb x-rays much more readily than will aluminum (Z=13). Also the absorption profile of a given piece of copper over a range of x-ray energies will be different than the absorption profile of a given piece of aluminum over that same range of energies. X-ray transmission through a material is given by the equation
 
N (t) =N 0 e −ηρ     t    
 
Where N (t)  is the number of photons remaining from an initial N 0  photons after traveling through thickness t in a material of density ρ. The mass attenuation coefficient η is a property of the given material and has a dependence upon photon energy. The value ηρ is referred to as the mass absorption coefficient (μ) for a given material. Values of the coefficient μ have been established by researchers to high accuracy for most materials and these values are dependent upon the energy of incident x-ray photons. Values of μ/ρ (=η) for most materials can be found at the National Institute of Standards and Technology (NIST) internet website. The lists of values are extensive covering all stable elements for various values of photon energy (kev). The value of ρ for a given material is simply its density in gm/cm 3  and can be found in many textbooks and also at the NIST website. The ratio N (t) /N 0  is the transmittance of photons through a thickness t of material and is often given as a percentage, ie. the percentage of photons transmitted through the material.
 
     The following table, by way of example, gives values of the mass absorption coefficient μ for aluminum and copper over a range of incident x-ray photon energies and the percentage of photons remaining after passing through 0.2 cm of material (% transmission). 
                                                                                 Incident Photon   Mass Absorption                Energy (kev)   Coefficient μ (cm −1 )   % Transmission                        Aluminum   100   0.46   91%           80   0.54   90%           60   0.75   86%           50   0.99   82%           40   1.53   74%           30   3.04   54%       Copper   100   4.11   44%           80   6.84   26%           60   14.27   5.8%            50   23.41   0.93%             40   40.95   0.03%             30   97.84   &lt;0.00%                        
Using the information in the table above we can illustrate how aluminum in this case can be differentiated from copper by comparing ratios of % Transmission (T E ) at two different photon energy levels. For instance:
         Ratios: T 100 /T 50 =1.11 for aluminum, T 100 /T 50 =47.3 for copper
 
The ratio for copper is much higher than that for aluminum. Further, we find that for differing thicknesses of materials it is possible to distinguish between materials of differing Z value by comparing such ratios while correlating to levels of transmission of photon energy through the materials as is discussed in more detail later. This innovative analytical technique allows effectively differentiating between the materials independent of knowing or determining thickness of the materials as is further discussed in reference to  FIG. 4 .
       

       FIG. 1  shows a side view and  FIG. 2  a top view of a schematic of mechanical arrangement of portions of a preferred embodiment of a materials sorting system of the present invention that incorporates a dual energy x-ray detector array  4  positioned below the surface of a conveyor belt  1  used for transporting materials samples  3  into and through a sensing region  4   s  located on conveyor belt  1  between detector array  4  and x-ray tube  15 . Belt  1  moves in a direction as shown by arrow  2  in  FIG. 1  and  FIG. 2 . Detector arrays suitable for this use can be obtained from Elekon Industries, Torrance, Calif. X-ray tubes may be obtained from Lohmann X-ray, Leverkusan, Germany. Materials samples  3  may be a mixture of relatively high Z materials  11  (such as metals copper, iron, and zinc and their alloys—depicted by shaded samples) and relatively low Z materials  9  (such as metals magnesium and aluminum and their alloys—depicted by not shaded samples). The x-ray tube  15  is a broadband source that radiates a sheet of preferably collimated x-rays  16  across the width of conveyor belt  1  along the dual energy x-ray detector array  4  such that x-rays pass through sensing region  4   s  and conveyor belt  1  prior to striking detectors  4 . Such a dual energy x-ray detector array  4  is well-known in the art, an example of which is described in detail in GE Medical Systems U.S. Pat. Nos. 6,266,390 and 6,519,315. As materials samples  3  pass through the sheet of x-rays in sensing region  4   s  x-rays transmitted through them are detected by the dual energy x-ray detector array  4  at two different energy levels. The detection signals are transmitted to computer system  12  over electrical connections  13  and the signals analyzed by a software algorithm  40  ( FIG. 5 ) executing within computer system  12  to determine relative composition of samples  3  with respect to a preset relative composition level  35  ( FIG. 4 ), as will be discussed in more detail later. In the example shown computerized algorithm  40  processes measurements of transmission levels of x-rays through materials at two energy levels using data from detector array  4  and makes a classification of each material sample  3  as being a relatively low Z material  9  or as being a relatively high Z material  11  with respect to preset relative composition level  35  and selects either low Z materials  9  or high Z materials  11  for ejection from the stream. Downstream from the sensing region  4   s , located just off the discharge end of the conveyor belt  1  and positioned across the width of the trajectory paths  6 , 7  of materials discharged off the end of conveyor belt  1 , is an array of high speed air ejectors  5 , such as the L2 series supplied by Numatics, Highland, Mich., which are controlled by computer system  12 , responsive to the algorithm  40  computations, by signaling air ejectors controller  17  through connections  14  to selectively energize through connections  18  appropriate air ejectors within air ejectors array  5  to deflect by short air blasts  5   a  selected materials from the flow. In the example shown relatively high Z metals  11  are selected for ejection along trajectories  7  into the metal group  1  bin  10  and relatively low Z metals  9  pass unejected along trajectories  6  into metal group  2  bin  8 . It is noted that the system can just as easily be configured by the user through a standard control interface (not shown) to eject low Z materials into group  1  bin  10  and let high Z materials pass unejected into group  2  bin  8 . It will be apparent to those skilled in the art that three or more bins could be utilized using multi-directional air ejectors or other sorting means. The sequence of sensing, selection, and ejection can happen simultaneously in multiple paths along the width of the conveyor belt  1  so that multiple metal samples  3  can be analyzed and sorted coincidentally as indicated in  FIG. 2 . 
       FIG. 3  shows a block diagram for an embodiment of the invention illustrating relationships between various portions of the electrical/computer system for acquiring and processing x-ray detector signals and for activating selected air valves within air ejector array  5  responsive to the results of the processing. The dual energy detector array  4  in this embodiment includes within its circuitry the dual energy x-ray detectors  4   a  and a data acquisition system (DAQ) with analog to digital (A/D) circuitry  4   b  for acquiring analog signals from the detectors over connections  4   c  and converting these signals to digital signals. The digital signals are transmitted over connections  13   a , which are part of the electrical connections  13  between computer system  12  and dual energy array  4 , to digital input/output (DIO) module  20 . For this data transfer the input function of module  20  is utilized. Internal to computer system  12  DIO module  20  passes the digital data to microprocessor system  21 . Microprocessor system  21  may be a single microprocessor or a system of multiple microprocessors linked together to share computational tasks to enable high speed data processing as is the case for this preferred embodiment. A suitable multiple microprocessor system is the Barcelona-HS available from Spectrum Signal Processing, Burnaby, Canada. Microprocessor  21  provides control signals to dual energy array  4  through serial controller  23  over electrical connection  13   b . Materials classification and sorting algorithm  40  ( FIG. 5 ), which is discussed in more detail later, executes within microprocessor system  21  processing digital data received from dual energy array  4  and utilizes computer memory  22  for storing data and accessing data during execution. According to results derived through executing of algorithm  40  microprocessor system  21  signals air ejectors controller  17 , for example a bank of solid state relays such as those supplied by Opto  22 , Temecula, Calif., through DIO module  24  to energize selected air ejectors within air ejector array  5  over connections  18  so to eject from the flow of materials  3  selected materials  11  according to computed relative composition as the materials are discharged off the discharge end of conveyor  1 . The user of the sorting system may chose through a standard control interface (not shown) for ejected materials  11  to be relatively high Z materials or relatively low Z materials, compared to preset relative composition level  35  (stored in memory  22 ) as determined by algorithm  40 . 
     The x-ray technology of the present invention measures changes in amount of x-ray transmission through an object as a function of energy. This technology can evaluate the entire object and looks through the entire object taking into consideration exterior and interior variations. The technology evaluates how the quantities of transmitted x-rays at various energy levels change as a function of the incident x-ray energy. One embodiment may be a multi-energy cadmium zinc telluride (CZT) pixel detection system arranged into a linear detector array of very small size is suitable to collect x-ray transmission information at each detector site and transmit it to an on-board computer system to collect data from multiple sensors simultaneously. Another embodiment may be an arrangement of multiple individual multi-energy detection systems such as those provided by Amptek, Bedford, Mass. Such systems could provide a greater number of energy bins. Multiple sub-systems could cover a wide conveyor belt. The data from the multi-detector array will provide multi-energy readings from each detector to provide an energy dispersive x-ray transmission profile of an object for assessing composition of a broad range of matter. Such a multi-energy CZT linear detector array having 32 CZT detectors at 0.5 mm pitch is available with supporting electronics from Nova R&amp;D, Riverside, Calif. Each detector in the Nova R&amp;D detection system can read and report x-ray transmission levels at up to five energy bands simultaneously at high rates of data acquisition and this capability is expected to expand to more energy bands as the technology is further developed. Further, the detectors have a spatial resolution of 0.02 inches per pixel in the array allowing detailed high resolution multi-energy profiling of x-ray transmission through an object under inspection. In effect one can build a high resolution multi-energy image of an object under inspection as the object is conveyed through the inspection region as well as simultaneously measuring the relative average atomic number of bits of matter within the image. 
     Such a system is functionally analogous to a line-scan camera commonly found in industrial inspection processes. Whereas a line-scan camera detects multiple “colors” within the visible spectrum, the system of the present invention detects “colors” within the x-ray spectrum. Thus, the system may be characterized as a multi-spectral, x-ray camera providing a much richer data set than the dual energy techniques described in more detail herein. This multi-energy data set allows expanded imaging and material identification capabilities as described in general terms below. 
     While the new system provides data that can be represented by an x-ray image, an intelligent interpretation of that image is essential to identification and sortation of material. The presence of any atomic element is manifest by spectral peaks (from fluorescence) or discontinuities (from transmission) that result from electron-state transitions unique to that element. Since these peaks or edges occur in spectrally narrow regions (on the order of eV), detection of an element only requires monitoring a small portion of the spectrum. Unfortunately, the absorption edges of “interesting” elements span a wide energy range, from less than 1 keV to more than 80 keV. Additionally, material morphology and composition, processing rates and environment, and sensor response renders peak or edge detection as the exclusive method of sorting a wide variety of materials impractical. Peaks or edges may be used to discriminate among a subset of elements, but it is thought that interrogating a material&#39;s spectrum over a shorter energy range (shorter than 1 keV→80 keV) will divulge information sufficient for recycling purposes although a range double that (up to 160 keV) could be useful. In particular, applying derivatives, tangential intersections and spectral correlation to the absorption curve of a material could provide adequate discrimination among categories of recyclables. 
     When compared to the simple discriminators of difference or ratio, the proposed operations are, in general, more susceptible to noise within the response curve of a material. Thus, to generate meaningful descriptors, mitigating all forms of “noise” is advantageous. Since in one embodiment the system of the present invention measures individual photons, the inherent noise from this method of detection is described by a Poisson distribution and can be reduced by collecting more photons. The nature of the CZT detectors in the Nova R&amp;D linear array system limits the photon counting rate to approximately 50 million counts per second (MCount/Sec): the ensuing electrons further limit this rate to approximately 1 MCount/Sec. New systems under development could extend the counting rate by an order of magnitude (up to 500 MCount/Sec). Since sufficiently “smooth” curves may require thousands of counts per acquisition, noise reduction through increased photon counts can result in decreased processing rates. 
     A material&#39;s absorption curve could prove sufficient for identification and sortation. However, certainty during the identification process may be augmented by fluorescence information. When x-rays pass through a material, some x-rays with energies greater than the electron excitation energy of constituent elements are absorbed and re-emitted as fluoresced photons. This process of absorption and re-emission is characterized in the transmission spectrum as an “absorption edge” and a “fluorescence peak,” where the peak is always near, but at a slightly lower energy than the edge. In a traditional absorption curve, the fluorescent peak is negligible. However, as a detector is gradually removed from the primary path of x-ray transmission, the signal contribution from primary x-rays are reduced and the contribution from secondary x-rays, such as fluorescence and scatter, are increased. Understandably, fluorescence is considered a “surface” phenomenon, but perhaps this information could enhance identification under certain conditions. 
       FIG. 4  shows an example graph  30  of processed x-ray transmission data measured for two different x-ray energy levels through various pieces of nonferrous metals derived from an automobile shredder. X-axis  31  of the graph represents normalized values of percentage transmission of x-rays (ie. transmittance values) through each metal piece as measured by the high energy detectors (item  43 ,  FIG. 5 ) of array  4 . Y-axis  32  of the graph represents values of the ratio (item  46 ,  FIG. 5 ) of normalized values of percentage transmission of x-rays through each metal piece as measured by the high energy detectors of array  4  to the percentage transmission of x-rays through a material sample  3  as measured by the low energy detectors of array  4 . In graph  30  data points  34  for the various metal samples are plotted according to their X-axis and Y-axis values. Legend  33  identifies each type data point as being for a brass, copper, zinc, stainless steel, aluminum alloy, or aluminum sample. Brass, copper, zinc, and stainless steel are considered to be relatively high Z metals and are represented by shaded data points in graph  30 . Aluminum and aluminum alloys are considered to be relatively low Z metals and are represented by not shaded data points in graph  30 . As can be seen in graph  30  data points for relatively high Z metals generally fall into a region  36  which resides above a region  37  into which fall data points for relatively low Z metals. A discriminator curve  35  has been drawn through the graph separating high Z region  36  from low Z region  37 . This curve  35  in effect represents a preset relative composition level against which values ( 43 , 46 ) derived for a material sample can be compared to classify the sample as being either a relatively high Z material or as being a relatively low Z material. Other treatments of the x-ray transmission data can be utilized as well, for example locating paired logarithmic transmittance data points from the detectors in a two dimensional space with the logarithm of transmittance from the low energy detector being one axis of the space and the logarithm of transmittance from the high energy detector being the other axis. In this case a discriminator curve such as curve  35  may be found which will separate the two dimensional space into relatively high Z materials and relatively low Z materials independent of thickness of the materials. Those skilled in the art will recognize that there are numerous other methods of varying complexity for correlating data from the detectors so that regions of relative composition, such as high Z regions, low Z regions, and other Z regions can be reliably distinguished. 
     In an embodiment of the present invention a classification and sorting algorithm  40 , represented in  FIG. 5 , utilizes the above described type of data interpretation to classify samples as being composed of relatively high Z materials or relatively low Z materials and effects sorting of the samples accordingly. For this example a material sample  3  enters the sensing region  4   s  and the presence of the sample is detected by a drop in x-ray radiation received by the detectors beneath the sample at the detector array  4 . This drop in radiation results in a drop in signal level from the detectors  4   a . The measured drop in signal level is noted by microprocessor system  21  which is monitoring the signal levels and causes microprocessor system  21  to start  41  execution of identification and sorting algorithm  40 . During execution of algorithm  40  the value E H  of a high energy sensor is read  42  and the value E L  of a corresponding low energy sensor is read  44 . The values are normalized  43  and  45 , for instance by subtracting out pre-measured detector noise and then scaling the readings to the detector readings when no materials are in region  4   s  over the detectors. These subtracting and scaling operations convert the sensor readings to transmittance values. Normalized value  43 , (transmittance of the high energy region photons) is then divided by normalized value  45  (transmittance of low energy region photons) to compute a ratio E R    46  of high energy transmittance to low energy transmittance. Ratio  46  is then correlated with normalized high energy transmittance  43  using a correlation function  47  which is electronically equivalent to plotting a data point ( 43 , 46 ) onto a graph such as that of  FIG. 4 . Step  48  in the algorithm then computes whether correlated data ( 43 , 46 ) electronically lies within a relatively high Z region  36  or a relatively low Z region  37 . In the example shown, if the correlated data ( 43 , 46 ) electronically is in a high Z region  36  algorithm  40  returns YES determination  49  and the material is categorized as a high Z material  50 . In the example shown the algorithm continues along path  51 , calculates  52  position and timing information for arrival of sample  3  at the ejection array  5  needed to accurately energize downstream ejector mechanisms in array  5  and issues the necessary commands  53  at the right time to energize the appropriate ejectors to eject high Z material  50  from the flow  2  of materials  3 . In this case materials determined to be low Z materials  55  by algorithm  40  returning a NO determination  54  will not be ejected by ejection array  5 . Alternatively, the algorithm can be configured by the user through a standard user interface to the computer system  12  to not follow path  51  and to instead follow alternate path  56  so that materials that are determined to be low Z materials  55  are ejected by ejection array  5  and materials determined to be high Z materials  50  are not ejected by ejection array  5 . Those skilled in the art will recognize that other similar algorithms can be applied according to the method selected for treatment of the detector data. 
     All references, publications, and patents disclosed herein are expressly incorporated by reference. 
     For the convenience of the reader, the following is a listing of the reference numbers used in the figures:
       1 . Conveyor belt     2 . Arrow showing flow of materials on conveyor belt     3 . Materials samples     4 . X-ray detector array     4   a . Dual x-ray detector     4   b . Data acquisition with A/D     4   c . Electrical connection     4   s . Sensing region on conveyor belt  1       5 . Air ejectors array     5   a . Air blast     6 . Trajectory corresponding to unejected sample     7 . Trajectory corresponding to ejected sample     8 . Group  2  bin (for unejected samples)     9 . Unejected metal sample     10 . Group  1  bin (for ejected samples)     11 . Selected materials for ejection by air ejectors     12 . Computer system     13 . Electrical connection     13   a . Electrical connection     13   b . Electrical connection     14 . Electrical connection     15 . X-ray tube     16 . Sheet of collimated x-rays     17 . Air ejectors controller     18 . Electrical connection     20 . Digital input/output (DIO) module     21 . Microprocessor system     22 . Computer memory     23 . Serial controller     24 . Digital input/output (DIO) module     30 . Graph     31 . X-axis of graph     32 . Y-axis of graph     33 . Legend to graph     34 . Data points for various metal samples     35 . Discriminator curve     36 . High Z region of graph     37 . Low Z region of graph     40 . Identification and sorting algorithm     41 . Start of execution of algorithm     42 . High energy sensor is read     43 . Normalizing value of  42       44 . Low energy sensor is read     45 . Normalizing value of  44       46 . Computing ratio of normalized value  43  to normalized value  45       47 . Correlation function     48 . Determining step in algorithm  40       49 . YES determination in algorithm  40       50 . High Z material     51 . Path in algorithm  40       52 . Calculation step in algorithm  40       53 . Command step in algorithm  40       54 . NO determination in algorithm  40       55 . Low Z material     56 . Alternate path in algorithm  40     

     Thus, it is seen that the system and method of the present invention readily achieves the ends and advantages mentioned as well as those inherent therein. While certain preferred embodiments of the invention have been illustrated and described for purposes of the present disclosure, numerous changes in the arrangement and construction of parts may be made by those skilled in the art, which changes are encompassed within the scope and spirit of the present invention as defined by the following claims.