JP7548693B2 - Method for determining heating furnace and heated body - Google Patents

Description

The present invention relates to a heating furnace and a method for determining whether a heated object has been heated.

As described on pages 859-860 of Non-Patent Document 1, used aluminum cans are put into a rotary kiln (roasting furnace) to remove moisture and paint and coating, and these substances are evaporated or decomposed.
Since fine aluminum particles can ignite at high temperatures, a roasting process is carried out in which the aluminum is steamed in a roasting furnace while suppressing the oxygen concentration.

It is extremely difficult to directly measure the internal temperature of a roasting furnace because soot and foreign matter adhere to the thermocouple, and it is also difficult to measure using a radiation thermometer because there is practically no line of sight inside the roasting furnace. Furthermore, it is extremely difficult to measure the temperature of an object being heated in the roasting furnace.
However, it goes without saying that the success or failure of roasting depends on temperature control of the roasted body, and from the standpoint of safety and suppression of oxide generation during melting in the subsequent process, there is a need to develop a technology that enables temperature measurement of the roasted body.

"Recycling of Aluminum Foil and Aluminum Cap": Technical Notes (Recycling Technology): by Makoto Ohga et al.: Applied Physics: Vol. 70, No. 7, pp. 859-860 (2001)

After roasting, aluminum cans are continuously discharged from the roasting furnace, and usually have almost no time to stand still, and are transported to the melting furnace by a belt conveyor or the like. In addition, used aluminum cans discharged from the roasting furnace are usually thin and have a small heat capacity, so that it is difficult to measure the temperature by contact. Furthermore, even if an attempt is made to measure the temperature of used aluminum cans discharged from the roasting furnace by non-contact measurement such as a radiation thermometer, since non-contact measurement is a temperature measurement method that uses radiation, if the measurement position, measurement angle, etc. are not constant, the view factor will change significantly, and it is considered difficult to measure the temperature stably.
Therefore, the inventors of the present application have been conducting research and development into a technology that uses a thermography device that uses emissivity to calculate temperature to accurately measure the temperature of aluminum cans after roasting that are transported on a conveyer belt. This research has demonstrated that it is possible to use a thermography device to measure the temperature of aluminum cans on a conveyer belt with a certain degree of accuracy.

However, if the roasting temperature in the roasting furnace fluctuates significantly, the color of the aluminum can after roasting may change significantly, and the change in color also changes the emissivity. Therefore, when measuring the temperature using a thermography device based on the emissivity, there is a problem that the measurement error becomes large.
Although the color change of aluminum cans after roasting is something that workers can check by visual inspection, it is necessary to quantify the value that is closer to the worker's sense, and there is a need for technology that can determine the color change of aluminum cans after roasting without relying on visual inspection by workers.

The present invention has been developed in consideration of these problems, and aims to provide a technology that makes it possible to determine color changes even in heated objects that are in motion, such as used aluminum cans discharged from a heating furnace such as a roasting furnace.

(1) The heating furnace of the present invention is a heating furnace that heats and roasts a heated object, which is an aluminum can having a paint or coating, while stirring it, and continuously or intermittently conveys the heated object after roasting along a strip-shaped conveying path , the strip-shaped conveying path being configured as a part of a vibrating screen having side walls on both the left and right sides in the width direction of the strip-shaped conveying path, and is equipped with a video camera that continuously or intermittently photographs the heated object being conveyed out by the strip-shaped conveying path and the conveying surface of the strip-shaped conveying path that conveys the heated object, including the inner surface of the side walls, in one image, and the video camera is equipped with a video camera that continuously or intermittently photographs the heated object and the conveying surface, and the video camera is equipped with a video camera that continuously or intermittently photographs the heated object being conveyed out by the strip-shaped conveying path and the conveying surface of the strip-shaped conveying path that conveys the heated object, including the inner surface of the side walls in one image ..., and the video camera is equipped with a video camera that continuously or intermittently photographs the heated object being conveyed out by the strip-shaped conveying path and the conveying surface of the strip-shaped conveying path that conveys the heated object. The apparatus has a function of analyzing an image of the conveying surface of the strip-shaped conveying path and the heated body and the side wall inner surface during conveying by the strip-shaped conveying path taken by an optical camera, and performing a line analysis on the conveying surface of the strip-shaped conveying path and the side wall inner surface , including the side wall inner surface , along a width direction perpendicular to the conveying direction at an arbitrary position in the conveying direction of the conveying surface, the apparatus has a brightness calculation means for calculating the brightness of each pixel of the image corresponding to the line analysis position, a recording means for continuously or intermittently recording the brightness values calculated by the brightness calculation means, and a recording means for calculating the variance of each brightness value corresponding to the line analysis position. and a determination means for determining that the heated body is in an area that exists throughout the entire line analysis position when the variance value calculated by the calculation means exceeds a specific threshold value based on the variance value calculated by the calculation means. The heated body is further provided with a prediction means for monitoring a luminance value in the area determined to be an area that exists throughout the entire line analysis position, determining that roasting is sufficient when the luminance value is within a predetermined range of luminance values of the heated body in a good roasting state, and determining that roasting is sufficient when the luminance value is outside the predetermined range of luminance values of the heated body in a good roasting state . The luminance calculation means, the recording means, the calculation means, and the determination means are provided in a personal computer, the recording means has a function of saving the images taken by the video camera in a bitmap format in a storage means provided in the personal computer, and the calculation means has a function of calculating a luminance value obtained from the RGB values of each pixel in the bitmap format data using the formula 0.299×R+0.587×G+0.114×B.

(2) In the heating furnace of the present invention, it is preferable that the judgment means has a function of examining the line analysis results obtained continuously or intermittently, and if the variance value exceeding the specific threshold value is continuously detected, judging the corresponding region as a region where aluminum cans are present throughout and making it a judgment target, and if the variance value exceeding the specific threshold value is not continuously detected, judging the corresponding region as a region where aluminum cans are not present throughout and excluding it from the judgment target .

(3) In the heating furnace of the present invention, a desired amount of the heated body is transported continuously or intermittently on the transport surface of the belt-shaped transport path.
(4) In the heating furnace of the present invention, it is preferable that the calculation means has a function of calculating the temperature of the heated body from the luminance value.
(5) In the heating furnace of the present invention, it is preferable that the heated body is a roasted used aluminum can.
(6) In the heating furnace of the present invention, it is preferable to provide a display means for displaying a warning when the calculated temperature of the heated body is outside a preset allowable temperature range.

(7) The method for determining a heated object according to the present invention is a heating furnace that heats a heated object, which is an aluminum can having a paint or coating, while stirring it, and continuously or intermittently conveys the heated object after heating along a strip-shaped conveying path, the strip-shaped conveying path being a part of a vibrating sieve having side walls on both the left and right sides of the width direction of the strip-shaped conveying path, and is a method for determining the state of the heated object conveyed out of the heating furnace, the method being applied to a heating furnace that is configured as a part of a vibrating sieve having side walls on both the left and right sides of the width direction of the strip-shaped conveying path, the heated object being conveyed out along the strip-shaped conveying path, and the conveying surface of the strip-shaped conveying path that conveys the heated object are also the inner surfaces of the side walls. The heated body and the conveying surface are continuously or intermittently photographed by a video camera, with the heated body and the conveying surface included in one image, the images of the conveying surface of the strip-shaped conveying path and the heated body and the side wall inner surface during conveying by the strip-shaped conveying path photographed by the video camera are analyzed, and a line analysis is performed on the conveying surface of the strip-shaped conveying path and the side wall inner surface, including the side wall inner surface, along the width direction perpendicular to the conveying direction at any position in the conveying direction of the conveying surface, and a brightness value for each pixel of the image corresponding to these line analysis positions is calculated, and this brightness value is continuously or intermittently calculated. the heated body is determined to be present at the entire line analysis position when the variance value exceeds a specific threshold based on a specific threshold; and the heated body is determined to be present at the entire line analysis position when the variance value exceeds the specific threshold. The heated body is determined to be present at the entire line analysis position when the luminance value is within a predetermined range of luminance values of the heated body in a good roasting state, and is determined to be abnormal when the luminance value is outside the predetermined range of luminance values of the heated body in a good roasting state . The luminance calculation means , the recording means, the calculation means, and the determination means are provided in a personal computer, the images taken by the video camera are stored in a storage means provided in the personal computer by the recording means in a bitmap format, and the calculation means calculates a luminance value obtained from the RGB values of each pixel in the bitmap format data using a formula of 0.299×R+0.587×G+0.114×B.

(8) The heated object judgment method of the present invention is characterized in that it examines the line analysis results obtained continuously or intermittently, and if the variance value exceeding the specific threshold value is continuously detected, the corresponding area is judged to be an area where aluminum cans are present throughout and is made the object of judgment, and if the variance value exceeding the specific threshold value is not continuously detected, the corresponding area is judged to be an area where aluminum cans are not present throughout and is excluded from the object of judgment .
(9) In the method for determining a heated object according to the present invention, the heated object is transported continuously or intermittently on the transport surface of the strip-shaped transport path.

(10) In the method for determining a heated object according to the present invention, the temperature of the heated object can be calculated from the luminance value.
(11) In the method for determining a heated object according to the present invention, a roasted used aluminum can can be provided as the heated object.
(12) In the method for determining a heated body according to the present invention, a display means can be provided for displaying a warning when the calculated temperature of the heated body falls outside a preset allowable temperature range.

The present invention makes it possible to accurately grasp the color change of a heated body that is conveyed in an irregular state in a lump along a belt-shaped conveying path from a heating furnace such as a roasting furnace by using a video camera and a computer without requiring a skilled worker. In addition, it is possible to accurately grasp the color change of the heated body whether it is conveyed continuously or intermittently in an irregular state. Furthermore, by grasping the color change of the heated body, it is possible to grasp the change in the heating state in the heating furnace.
Therefore, when the heated object is, for example, a used aluminum can, by heating the used aluminum can to a target temperature using a heating furnace, it becomes possible to reliably determine whether or not the moisture, paint film, and coating on the surface of the aluminum can have been satisfactorily removed.

FIG. 1 is an explanatory diagram showing an example of an aluminum can recycling process using a roasting furnace. FIG. 2 is a block diagram showing a main part of a computer provided in the example of the recycling process shown in FIG. 1 . FIG. 2 is a configuration diagram showing an example of a roasting furnace provided in the example of the recycling process shown in FIG. 1 . FIG. 13 is a perspective view showing an example of a state in which heated bodies transported along a strip-shaped transport path are continuously discharged from a roasting furnace. FIG. 13 is a perspective view showing an example of a state in which heated bodies transported from a roasting furnace along a strip-shaped transport path are intermittently discharged. FIG. 4 is a perspective view showing an example of a video camera installed in the vicinity of the strip-shaped transport path. 7 is a diagram showing an example of a state in which a video image captured by the video camera shown in FIG. 6 is displayed on a display device of a computer. 8 is a diagram showing an example of an apparatus that is connected to the computer shown in FIG. 7 and displays a warning depending on the state of the heated body. FIG. 13 is an explanatory diagram showing positions at which line analysis is performed on an image captured by a video camera. FIG. 13 is a perspective view showing an example of a case where a video camera captures images including a position where lumps of heated bodies are densely packed together being transported along a strip-shaped transport path. FIG. 13 is a perspective view showing an example in which a mass of heated material being transported along a strip-shaped transport path is distributed to the front and rear, and the area between them is photographed by a video camera. 13 is a perspective view showing an example of a case where a video camera captures a location where the lumps of heated bodies being transported along a strip-shaped transport path are dispersed and few in number. FIG.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A first embodiment of a heating furnace according to the present invention will be described below in detail with reference to the accompanying drawings.
In addition, the drawings used in the following description may show characteristic parts in an enlarged scale for convenience in order to make the characteristics easier to understand, and the dimensional ratios of each component are not limited to the examples shown.
The recovery and recycling facility shown in Fig. 1 includes a receiving facility 1 for receiving recovered materials, mainly consisting of block-shaped used aluminum cans (heated bodies), carried in by a transport means such as a truck. The recovery and recycling facility also includes a crusher (breaker) 2 for crushing the recovered materials, a sorting means 3 for separating non-aluminum materials from the recovered materials crushed by the crusher 2 using magnetic force and wind force, and a rotary kiln (heating furnace: roasting furnace) 4 for roasting the aluminum cans (heated bodies) that have passed through the sorting means 3.

The sorting means 3 includes a magnetic separator 3a and a wind separator 3b, and the aluminum cans are transported to the roasting furnace 4 via a primary stock hopper 11. Belt conveyors 10a to 10d are provided between each piece of equipment from the crusher 2 to the roasting furnace 4, and transport used aluminum cans that have been processed in each piece of equipment.
Furthermore, the recovery and recycling facility is roughly composed of a melting furnace 5 that melts the aluminum cans discharged from the roasting furnace 4, a holding furnace 6 that keeps the aluminum obtained in the melting furnace 5 in a molten state and adjusts the alloy, and a casting machine 7 that obtains slabs from the molten metal whose alloy has been adjusted in the holding furnace 6, and these and other devices are arranged on the same site 8.

A vibrating sieve 12 is provided downstream of the roasting furnace 4, which forms a belt-shaped transport path 12A via a belt conveyor 10e. Aluminum cans that have passed through the vibrating sieve 12 are introduced into the melting furnace 5 by the belt conveyor 10f. A storage furnace 14 is provided on the discharge side of the melting furnace 5 via a trough 13, and the discharge side of the storage furnace 14 is connected to the input side of the holding furnace 6 by a trough 15a. As shown in FIG. 3, a re-burning furnace 4A is provided near the roasting furnace 4, and the internal temperature of the roasting furnace 4 can be adjusted by adjusting the temperature of the hot air sent from the re-burning furnace 4A to the roasting furnace 4.

In the recovery and recycling equipment shown in Figure 1, the roasting furnace 4 has an internal stirring device (not shown) that stirs used aluminum cans (heated objects), and a vibrating sieve 12 connected via a conveyor 10e shown in Figures 4 and 5 that continuously or intermittently conveys out block aluminum cans S after heating and roasting to the desired temperature. A video camera 20, which will be described below with reference to Figures 1 and 6, is installed near the vibrating sieve 12, for example, diagonally above the front side of the vibrating sieve 12 so as to be supported by a gate-shaped frame or pillars erected around the vibrating sieve 12.

The video camera 20 comprises a camera body 21 and a transmitting device 22, and the transmitting device 22 can transmit the captured data to a data analyzing device 23 consisting of a personal computer or the like.
6 shows an overview of the camera body 21, which is a typical video shooting device equipped with a zoom lens 21b at the front of a cylindrical, grippable body portion 21a and an image sensor inside the body portion 21a. A transmission device 22 is incorporated inside the body portion 21a, and the shooting data captured by the image sensor can be sequentially transmitted to a data analysis device 23. Note that the configuration of the camera body 21 is not limited to the configuration shown in FIG. 6, and any video camera configuration may be used as long as it is a device capable of shooting moving images in color.

A base 21c is provided on the bottom side of the camera body 21. This base 21c is attached to the aforementioned gate-shaped frame or the like, and the orientation of the lens 21b is adjusted, and the magnification of the zoom lens 21b is adjusted while viewing the captured image using a small monitor 21d provided on the side of the body 21a. In this way, the camera body 21 is set up so that the entire tip of the vibrating sieve 12 can be enlarged and displayed in the center of the screen of the display device 30, as shown in FIG.
In addition, the lighting device that illuminates the area photographed by the video camera 20 with respect to the strip-shaped conveying path 12A of the vibrating sieve 12 is preferably an LED lighting device, whose illuminance does not deteriorate much over time, rather than a fluorescent lamp, which deteriorates rapidly over time.

4 and 5 show an outline of a block of aluminum cans (heated bodies) S being conveyed along the belt-shaped conveying path 12A of the vibrating sieve 12. FIG.
The vibrating sieve 12 constantly vibrates and transports the lump aluminum cans S along the belt-shaped transport path 12A at a high speed, for example, a speed of several meters/second, and the shape and amount of the lump aluminum cans S discharged from the roasting furnace 4 change from moment to moment.
The lump aluminum cans S are formed by a number of crushed aluminum cans individually or a number of crushed aluminum cans joined together to form an integrated unit, each of which has an indefinite shape, and appear irregularly and randomly in two states: a state in which the lump aluminum cans S are discharged in a continuous connected state as shown in Fig. 4, and a state in which the lump aluminum cans S are discharged discontinuously and intermittently as shown in Fig. 5. In this embodiment, the irregular lump aluminum cans S that are vibrating and moving are treated as objects for color judgment.

If the internal temperature of the roasting furnace 4 is too high, some of the lump aluminum cans S may burn, and if the temperature is too low, the paint or coating will not be sufficiently removed, and in some cases the paint or coating will remain attached to the lump aluminum cans S as char when discharged. If the amount of char attached increases, the color of the lump aluminum cans S will become closer to black. If the paint or coating is sufficiently removed, the color of the lump aluminum cans S will become closer to white.

FIG. 2 is a block diagram showing an example of the main parts of the data analysis device 23. The data analysis device 23 in this example is a so-called computer, and mainly comprises an input means 24, a control unit 25, a memory means 26, and an output means 27.
The input means 24 is, for example, a keyboard for inputting letters and numbers, and various information can be input to the storage means 26 or the control unit 25 .
The control unit 25 is composed of a so-called CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), etc., and can perform various numerical calculations, information processing, device control, etc. using programs.

The storage means 26 is, for example, an information recording medium such as an HDD (hard disk drive) or SSD (solid state drive), and can store and read out necessary programs, various information necessary for executing the calculation means 28 and the prediction means 29, such as processing and calculations using screen capture software or spreadsheet software and the results thereof, and the results obtained thereby, as needed.
The output means 27 is, for example, a monitor or a printer, and can display various information obtained from the necessary programs as well as information on char adhesion, which will be described later, as necessary.
The data analysis device 23 shown in FIGS. 2 and 7 has a display device 30 of a display monitor type as the output means 27.

In addition to a basic operating system (OS), the storage means 26 stores programs such as screen capture software and spreadsheet software, as well as various information required for executing the calculation means 28 and prediction means 29 described below, and stores necessary information before the execution of the programs. These information and programs can be read and operated as desired. Also, calculation results obtained by executing the programs can be stored and read as necessary.
In the data analysis device 23 of this embodiment, screen capture software and spreadsheet software are installed in the storage means 26 .

The video camera 20 shown in Figure 6 sends image information of the tip of the vibrating sieve 12 captured by the camera body 21 to a data analysis device 23, and the data analysis device 23 displays the image information of the tip of the vibrating sieve 12 on a display device 30 as shown in Figure 7.
A screen capture software and a spreadsheet software are installed in the storage means 26 of the data analysis device 23. For example, the free software winshot created by woodybells can be used as the screen capture software, and for example, Excel (product name) made by Microsoft can be used as the spreadsheet software. Note that the screen capture software and spreadsheet software shown here are examples that can be applied to this embodiment, and are not limited to these software.

Using this screen capture software and spreadsheet software, a position a predetermined distance (for example, approximately 100 mm behind the tip) from the tip of the strip-shaped transport path 12A displayed on the display device 30 is subjected to a line analysis along the width direction of the strip-shaped transport path 12A as described below.
The screen capture software is set so that a rectangular area including the line analysis range can be periodically or continuously sent and stored in the storage means 26 in bitmap format. The screen information captured by the screen capture software is preferably in 24-bit color.

In order to reduce a portion of the amount of information stored in the storage means 26, it is preferable to set it so that the RGB values of pixels in a rectangular range including the line analysis range are stored in bitmap format at intervals of 3 seconds. The 3-second interval used here is just one example, and other time intervals are not limited to 3 seconds, and may be used, such as 1 to 5 seconds, and the RGB values constituting the pixels may be recorded at short intervals within a range that allows data processing to keep up, depending on the performance of the computer and the transmission speed between the camera and computer, etc.

The lens 21b of the camera body 21 is adjusted to adjust the width of the leading end of the strip-shaped transport path 12A to be displayed on the screen of the display device 30, and the rectangular width is set to, for example, about (70 pixels x 134 pixels), and the binary data is stored as directly readable bitmap data. The above-mentioned number of pixels for the rectangular width is an example, and the number of pixels is not limited to the above number. A range of several tens of pixels to several hundreds of pixels may be appropriately selected.
This rectangular range is set by the screen capture software recording still image information (70 pixels x 134 pixels) continuously at 3 second intervals in the storage means 26. The binary data of the still image recorded in the storage means 26 in this bitmap format data is regarded as a matrix, and the data analysis for each row is performed as follows, which is the line analysis in this embodiment.

Figures 9(A) to (D) are explanatory diagrams illustrating patterns in which heated block aluminum cans S pass through the tip of the strip-shaped conveying path 12A in various states, with the line analysis position set at a position that crosses the center of each figure.
Fig. 9(A) shows a state where the center of the densely packed heated objects passing through the strip-shaped transport path 12A is the line analysis position, and Fig. 9(B) shows a state where the densely packed heated objects are at the line analysis position immediately after passing through the strip-shaped transport path 12A. In Fig. 9(B), after block aluminum cans S as densely packed heated objects have passed, there are almost no heated objects present, and the strip-shaped transport path 12A is exposed.
Fig. 9(C) shows a state in which the heated bodies are densely packed and pass through the strip-shaped transport path 12A, and the next dense portion of the heated bodies does not reach the line analysis position, and the strip-shaped transport path 12A becomes the line analysis position. Fig. 9(D) shows a state in which a small number of heated bodies are randomly distributed on the strip-shaped transport path 12A, and the position that crosses them becomes the line analysis position.

9(A) to 9(D), when the line analysis position is defined so as to be perpendicular to the direction in which the heated objects move along the strip-shaped transport path 12A, i.e., along the width direction of the strip-shaped transport path 12A, a difference in dispersion in the direction along the line dispersion position (the row direction in which pixel data is accumulated) occurs in either case. In the area where no aluminum cans S are present, the upper surface of the strip-shaped transport path 12A is exposed, so that the brightness in this case is a generally uniform brightness shown by the upper surface of the strip-shaped transport path 12A illuminated by the lighting device.
Taking the variance of data for each line from the binary data of a bitmap image recorded by the screen capture software is equivalent to performing line analysis on a pixel-by-pixel basis along the width direction of the strip-shaped transport path 12A and taking the variance at the line analysis position.

More specifically, the screen capture software continuously records RGB values according to the pixels corresponding to the above-mentioned rectangular range every three seconds so as to fill the cells of a table prepared in the storage means 26. Since the bitmap binary data consists of a header and RGB values for each pixel, these values can be stored in the storage means 26.
Tables 1 to 5 below show examples of some of the character strings stored in storage means 26. The examples in Tables 1 to 5 show data in which pixel information equivalent to 33 pixels (rows) x 110 pixels (columns) is recorded. Note that in this embodiment, the screen capture software records information of 55 pixels x 110 pixels, but Tables 1 to 5 show data in which the bitmap binary data equivalent to 33 pixels x 110 pixels is converted to luminance using the formula 0.299 x R + 0.587 x G + 0.114 x B.

Next, the spreadsheet software has a function of calculating luminance according to the following formula (1) for the RGB values stored in the aforementioned storage means 26. This luminance calculation may be performed continuously within an allowable range, or may be performed periodically, such as every 5 seconds, by using the timer function of the spreadsheet software.
Furthermore, the coefficients multiplied by RGB in the following formula (1) are sensory coefficients that are considered to be close to the intervals between luminance levels experienced by humans, and although it is preferable to use values close to these values, it is not essential to use these values.

The spreadsheet software records the luminance thus calculated as a numerical value so as to fill the cells of the table prepared in the storage means 26. Next, the spreadsheet software has a function of periodically reading the bitmap binary data recorded by the screen capture software periodically capturing the screen, calculating the luminance from the above-mentioned formula (1), continuously storing the read luminance per time in the storage means 26, calculating the average luminance for each row, and storing the average luminance in the storage means 26. The spreadsheet software also has a function of calculating the variance for each row shown in Tables 1 to 6.
The variance can be expressed by the following formula (2): In formula (2), ^s2 is the variance, n is the number of measured values, _x1 , _x2 , _x3 , ... _xn are each individual measured value (the subscript of x indicates the number of the measured data), and the overbar of x indicates the average of the measured values.

Tables 1 to 5 show some of the input data, and Table 6 shows an example of the calculation results.
Table 6 shows the results of calculating the variance for all rows in the rectangular area, finding the minimum variance value, and inputting that value into "row variance min." This is done for each captured image, so if it is done at 3 second intervals, data will be recorded once every 3 seconds. If the value is above the threshold, it will be entered in the presence/absence luminance column, and if it is below the threshold, the corresponding column in Table 6 will be left blank. One row of data in Table 6 can be obtained from all the input data in Tables 1 to 5.
When Microsoft Excel (trade name) is used as an example of spreadsheet software, calculations can be performed using the VARPA function provided in Microsoft Excel (trade name), with each row regarded as an independent population.

The calculation results in Table 6 were performed on multiple columns together.
The brightness column in Table 6 displays the calculated brightness, the row variance min displays the minimum variance calculated for each column, and the presence/absence brightness displays only those whose variance is greater than a threshold value. If the variance is less than the threshold value, a blank is entered.

Tables 7 to 12 below show other input examples, and Table 13 shows a calculation example.

For example, the spreadsheet software verifies the variance value, and in the case of the data shown in Table 13, if a threshold value within the range of 400 to 600, for example 500, is input, the spreadsheet software has the function of determining what type of area the corresponding area is, with the threshold value of 500 as the boundary.
Similarly, the spreadsheet software has a function of judging that rows with a variance value less than the threshold value of 500 are areas where no aluminum cans exist, and judging that an area with successive rows with a variance value less than 500 is an area where no aluminum cans exist.
Furthermore, the spreadsheet software has a function of judging that rows with variance values exceeding a threshold value of 500 are areas containing aluminum cans, and judging that the entire area containing consecutive rows with variance values exceeding 500 is made up of aluminum cans.

In the case of an area where there are consecutive rows exceeding the threshold value of 500, such as rows 1 to 25 in Table 13, the corresponding area is determined to be an area where aluminum cans are present throughout.
In the case of an area where there are consecutive rows with values below the threshold value of 500, such as rows 26-27 and rows 31-32 in Table 13, the corresponding area is determined to be an area where no aluminum cans exist.

As described above, for rows where the variance value falls below the threshold value of 500 and the row is deemed to be an area where no aluminum cans exist, the prediction means 29 of the control unit 25 excludes these rows from the judgment in this embodiment, and no judgment is made using the brightness values of these rows.
The prediction means 29 of the control unit 25 has the function of determining the state of the lump aluminum cans S only from the brightness values of areas that have a variance value exceeding the threshold value of 500 as described above and are deemed to be areas where aluminum cans exist.
For example, the brightness value is monitored, and if the brightness value is within the range that a block of aluminum can S that has been roasted well is supposed to have, it is determined that the roasting is sufficient, and the prediction means recognizes that the state is normal.
On the other hand, if the brightness value falls outside the desired range, it is determined that the number of partially burnt aluminum cans in the roaster is increasing, whereas if the brightness value rises, it is determined that the risk of fire is approaching.
When judging based on the range of brightness values, as an example in this embodiment, a brightness value in the range of 170 to 190 is judged to be good. Note that brightness values below 170 and above 190 are judged to be problematic. For example, with 180 as the median, ±10 is judged to be good, ±20 is caution, ±30 is warning, and anything other than these is judged to require emergency action.

In this embodiment, the screen capture software periodically extracts bitmap images from the video screen including the strip-shaped transport path 12A captured by the camera body 21, calculates the brightness from the RGB values for each row contained in the binary data of the bitmap image, and performs a line analysis to determine the variance of the brightness for each row and grasp the state of the lump aluminum cans S. By performing this line analysis, it was possible to determine the state in which the lump aluminum cans S were passing the analysis position.

Figure 10 shows a state in which a lump aluminum can 35 passing through the tip of the strip-shaped conveying path 12A and a rectangular area K1 located behind the lump aluminum can 35 and including the strip-shaped conveying path 12A where no lump aluminum can 35 is present are photographed by the camera body 21.
If the entire area K1 is photographed with the camera body 21 and turned into a video, the brightness from the pixels corresponding to the area of the lump aluminum cans 35 and the brightness from the pixels corresponding to the surface area of the strip-shaped transport path 12A will be mixed together, making it impossible to determine the state of the lump aluminum cans 35.

Figure 11 shows a rectangular area K2 including the strip-shaped transport path 12A photographed by the camera body 21, in which there is a lump aluminum can 36A passing through the tip of the strip-shaped transport path 12A, another lump aluminum can 36B positioned at a distance behind the lump aluminum can 36A, and other lump aluminum cans 36C present at a low density dispersed between the lump aluminum can 36A and the lump aluminum can 36B.
If the entire area K2 is photographed with the camera body 21 and turned into a video, the brightness from the pixels corresponding to the areas of the lump aluminum cans 36A, 36B, and 36C will be mixed with the brightness from the pixels corresponding to the surface area of the strip-shaped transport path 12A, making it impossible to determine the state of the lump aluminum cans 36A, 36B, and 36C.

Figure 12 shows a rectangular area K3 including the strip-shaped transport path 12A photographed by the camera body 21 in a state in which there are a small number of lump aluminum cans 37A passing at the end of the strip-shaped transport path 12A, a small number of other lump aluminum cans 37B positioned at a distance behind the lump aluminum cans 37A, and there is a strip-shaped transport path 12A with almost no other lump aluminum cans between the lump aluminum cans 37A and the lump aluminum cans 37B.
If the entire area K3 is photographed with the camera body 21 and turned into a video, the brightness from the pixels corresponding to the areas of the lump aluminum cans 37A, 37B and the brightness from the pixels corresponding to the surface area of the strip-shaped transport path 12A will be mixed together, making it impossible to determine the state of the lump aluminum cans 37A, 37B.

Considering the above-mentioned conditions, unless the area photographed by the camera body 21 is set so that only lump aluminum cans are occupied, there is a problem that lump aluminum cans cannot be accurately identified. However, as long as the area photographed by the camera body 21 is set to be rectangular, it is considered difficult to accurately identify the brightness of lump aluminum cans. In addition, the brightness differs from position to position in the depth direction of the strip-shaped transport path 12A depending on the irradiation state of light from the lighting device that illuminates the strip-shaped transport path 12A, so the brightness becomes non-uniform.
Moreover, the distribution of the aluminum cans passing through the belt-like transport path 12A is not uniform, but varies as described above.

Taking the above into consideration, the present embodiment employs the line analysis described above.
Using the line analysis described above, the brightness of each line is found, and the variance of these is calculated. If the variance exceeds a certain threshold, it is determined that what is passing through the line analysis position is mainly solid aluminum cans.
In a state in which these lump aluminum cans are the majority, by determining whether the corresponding brightness is an increase or decrease from the aforementioned desirable range, it is possible to determine whether the distribution is dominated by lump aluminum cans that are close to white, or whether the distribution is one in which in addition to the lump aluminum cans that are close to white, there are also lump aluminum cans that are close to black.
By grasping the increase or decrease in brightness of the aforementioned regions, the prediction device 29 of the data analysis device 23 displays warnings such as caution, warning, and warning that urgent action is required on the screen of the display device 30. For example, an increase in the amount of nearly black blocky aluminum cans is determined to be an increase in the amount of char adhesion, and if the brightness is lower than a preset brightness range, it is interpreted that the increase in the amount of char adhesion has become a problem, and a warning such as caution, warning, and warning that urgent action is required can be displayed depending on the severity.

In response to the above display, an operator monitoring the roasting furnace 4 can control the state of the roasting furnace 4 by means of adjusting the re-burning furnace 4A attached to the roasting furnace 4 and adjusting the temperature of the hot air sent to the roasting furnace 4, thereby adjusting the heating state of the aluminum can blocks S discharged from the roasting furnace 4.
The above process can reduce the labor required for the step of judging the color, which has conventionally been performed by a skilled worker visually observing the color of the lump aluminum cans S, grasping the roasting state of the lump aluminum cans according to the change in color, and controlling the operating state of the roasting furnace 4. Therefore, the worker only needs to keep his or her eyes on the lump aluminum cans displayed on the display device 30 by the data analysis device 23, and perform the control operation of the roasting furnace 4 in accordance with the warnings, such as caution, warning, and warnings requiring urgent action, displayed on the display device 30.
In this embodiment, the data analysis device 23 judges the color of the block aluminum cans, which reduces the burden on the worker. In addition, the worker need only adjust the roasting furnace in accordance with the display on the display device 30, so that the worker can quickly and accurately begin adjusting the roasting furnace in response to changes in color.

FIG. 8 is a perspective view showing an example of a display device connected to the data analysis device 23. As shown in FIG.
The display device 40 of this example has a disk-shaped base 41 and a rod-shaped display unit 42 standing on the center of the upper surface of the base 41. A lower display unit 43, a central display unit 44, and an upper display unit 45, all equipped with LED light-emitting elements, are provided on the upper side of the display unit 42. The lower display unit 43 incorporates an LED light-emitting element that emits green or blue light, the central display unit 44 incorporates an LED light-emitting element that emits yellow light, and the upper display unit 45 incorporates an LED light-emitting element that emits red light.

These display devices 40 are connected to the output means 27 of the data analysis device 23, and are configured so that when the control unit 25 operates the output means 27 to issue a warning as described above, the lower display unit 43, central display unit 44, and upper display unit 45 can be illuminated according to the content of the warning.
When the control unit 25 operates the output means 27 to issue a warning, if an abnormality occurs, the upper display unit 45 is illuminated to display red, if a caution is required, the central display unit 44 is illuminated to display yellow, and if normal, the lower display unit 43 is illuminated to display blue or green.

By using the display device 40 to select red, yellow, blue, or green, the worker operating the roasting furnace can easily grasp the roasting status of the aluminum can blocks according to the color of light emitted by the display device 40. If the display device 40 displays red, this is deemed to be an abnormality in the roasting furnace, and the worker can take measures such as changing the heating conditions of the roasting furnace to prevent the non-production of poorly roasted aluminum cans.

Moreover, the temperature of the heated body can be calculated by inverse calculation from the brightness described above.
When performing the calculation, the relational equation expressed as "temperature=525.64-0.5185×luminance" can be used.

In the present embodiment, a bitmap image is recorded using a specific screen capture software and a spreadsheet software that are commercially available or free software, and the brightness is calculated from the binary data of the bitmap image, but the software for calculating the brightness is not limited to the example used in the embodiment. For example, when the leading end of the strip-shaped transport path 12A is displayed on the screen of the display device 30, a dedicated software equipped with a means for sequentially measuring the RGB values of pixels corresponding to the line analysis position for the leading end of the strip-shaped transport path 12A on the display screen and recording them in the storage means 26 every time, and a means for calculating the brightness for each row may be developed and applied to the present embodiment. In addition, the software for calculating the variance for each row is not limited to a spreadsheet software, and a dedicated software equipped with a means for calculating the variance for each row may be developed and applied to the present embodiment.

S...lump aluminum can (heated body), 4...roasting furnace (rotary kiln: heating furnace), 4A...reburning furnace, 10e...conveyor, 12...vibrating sieve, 12A...strip-shaped transport path, 20...video camera, 21...camera body, 22...transmitting device, 23...data analysis device (personal computer), 24...input means, 25...control unit, 26...storage means, 27...output means, 28...calculation means, 29...prediction means, 30...display device, 40...display device, 43...lower display unit, 44...central display unit, 45...upper display unit.

Claims (12)

A heating furnace in which a heated object, which is an aluminum can having a paint or coating, is heated and roasted while being stirred, and the heated object after roasting is continuously or intermittently conveyed along a belt-shaped conveying path , the belt-shaped conveying path being configured as a part of a vibrating sieve having side walls on both the left and right sides in a width direction of the belt-shaped conveying path,
A video camera is provided for continuously or intermittently photographing the heated body and the conveying surface of the belt-shaped conveying path that conveys the heated body , including the inner surface of the side wall, in one image, and the heated body and the conveying surface,
The image of the conveying surface of the strip-shaped conveying path and the heated body and the side wall inner surface during conveying by the strip-shaped conveying path captured by the video camera is analyzed, and a line analysis is performed on the conveying surface of the strip-shaped conveying path and the side wall inner surface along a width direction perpendicular to the conveying direction, including the side wall inner surface, at any position in the conveying direction of the conveying surface,
The apparatus includes a brightness calculation means for calculating the brightness of each pixel of the image corresponding to the line analysis position, a recording means for continuously or intermittently recording the brightness values calculated by the brightness calculation means, a calculation means for calculating the variance of each brightness value corresponding to the line analysis position, and a determination means for determining that the heated body is an area that exists throughout the entire line analysis position when the variance value calculated by the calculation means exceeds a specific threshold value,
Further, a prediction means is provided for monitoring a brightness value in a region determined to be present in the entire line analysis position of the heated body, and determining that roasting is sufficient when the brightness value is within a predetermined range of brightness values of the heated body in a good roasting state, and determining that roasting is abnormal when the brightness value is out of the predetermined range of brightness values of the heated body in a good roasting state ,
said brightness calculation means, said recording means, said calculation means and said determination means are provided in a personal computer, said recording means has a function of saving said images captured by said video camera in a bitmap format in a memory means provided in said personal computer, and said calculation means has a function of calculating as brightness a value found from RGB values for each pixel in said bitmap format data using the formula 0.299×R+0.587×G+0.114×B. examining the line analysis results obtained continuously or intermittently, and when the variance value exceeding the specific threshold value is continuously detected, determining that the corresponding region is a region in which aluminum cans are entirely present and making it a judgment target;
The heating furnace according to claim 1, characterized in that the judgment means has a function of judging the corresponding area as an area where aluminum cans are not entirely present and excluding it from the judgment target when the variance value exceeding the specific threshold value is not continuously detected. The heating furnace according to claim 1 or 2, characterized in that the heated body is transported continuously or intermittently in any amount to the transport surface of the belt-shaped transport path. The heating furnace according to any one of claims 1 to 3, characterized in that the calculation means has a function for calculating the temperature of the heated body from the brightness value. The heating furnace according to any one of claims 1 to 4, characterized in that the heated body is a roasted used aluminum can. A heating furnace as described in claim 4 or claim 5 which derives from claim 4, characterized in that it is provided with a display means for displaying a warning when the calculated temperature of the heated body falls outside a preset allowable temperature range. A method for determining the state of a heated object discharged from a heating furnace, the heated object being an aluminum can having a paint or coating, while being heated and then continuously or intermittently discharged along a strip-shaped conveying path, the strip-shaped conveying path being a part of a vibrating sieve having side walls on both the left and right sides in a width direction of the strip-shaped conveying path, the method comprising:
The heated body being transported along the strip-shaped transport path and the transport surface of the strip-shaped transport path transporting the heated body are captured in one image, including the inner surface of the side wall, and the heated body and the transport surface are continuously or intermittently photographed by a video camera;
Analyzing images of the conveying surface of the strip-shaped conveying path and the heated body and the side wall inner surface during conveying by the strip-shaped conveying path captured by the video camera, and performing line analysis on the conveying surface of the strip-shaped conveying path and the side wall inner surface at any position in the conveying direction of the conveying surface, including the side wall inner surface, along a width direction perpendicular to the conveying direction, and calculating brightness values for each pixel of the image corresponding to these line analysis positions;
The brightness values are recorded continuously or intermittently, and a variance value of each brightness value corresponding to the line analysis position is calculated. When the variance value exceeds a specific threshold value based on the variance value, it is determined that the heated body is present throughout the entire line analysis position;
Furthermore, the luminance value in the area determined to be present in the entire line analysis position of the heated body is monitored, and when the luminance value is within a predetermined range of luminance values of the heated body in a good roasting state, the roasting is determined to be sufficient, and when the luminance value is out of the predetermined range of luminance values of the heated body in a good roasting state, the roasting is determined to be abnormal;
A method for determining a heated body, characterized in that a luminance calculation means, a recording means , a calculation means , and a determination means are provided in a personal computer, the images captured by the video camera are stored in a memory means provided in the personal computer by the recording means in bitmap format, and the calculation means calculates the luminance as a value obtained from the RGB values of each pixel in the bitmap format data using the formula 0.299 x R + 0.587 x G + 0.114 x B. examining the line analysis results obtained continuously or intermittently, and when the variance value exceeding the specific threshold value is continuously detected, determining that the corresponding region is a region in which aluminum cans are entirely present and making it a judgment target;
A method for determining a heated object as described in claim 7, characterized in that if the variance value exceeding the specific threshold value is not continuously detected, the corresponding area is judged to be an area where aluminum cans are not present in its entirety and is excluded from the judgment target . The method for determining a heated object according to claim 7 or 8, characterized in that the heated object is transported continuously or intermittently to the transport surface of the belt-shaped transport path. The method for determining a heated object according to any one of claims 7 to 9, characterized in that the temperature of the heated object is calculated from the brightness value. The method for determining a heated object according to any one of claims 7 to 10, characterized in that the heated object is a roasted used aluminum can. A method for determining a heated body as described in claim 10 or claim 11 which derives from claim 10, characterized in that the method is provided with a display means for displaying a warning when the calculated temperature of the heated body falls outside a predetermined allowable temperature range.

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