Granular material strength estimation device, granular material strength estimation method, granular material manufacturing method, and coke manufacturing method

Abstract

To provide a strength estimation device and strength estimation method for a granular material which can estimate the strength of a granular material on-line.SOLUTION: A strength estimation device for granular material which estimates the strength of a granular material loaded on a conveyor and conveyed, includes: a line sensor 24 which creates line profile data for the granular material at multiple positions different from each other in the conveyance direction; and an image processing device 26. The image processing device 26 includes: a three-dimensional shape creation part 36 which creates three-dimensional shape data of the granular material by using the multiple pieces of line profile data created by the line sensor 24; and a strength estimation part 38 which uses the three-dimensional shape data to obtain an index of the size of the granular material and uses the index to estimate the strength of the granular material.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a granular material strength estimation device that can estimate online the strength of granular material loaded and transported on a conveyor, a granular material strength estimation method, a granular material manufacturing method that uses the granular material strength estimation method, and a coke manufacturing method. [Background technology]

[0002] Coal plays an important role as a raw material in the production of steel products. Coal is used as a raw material to produce coke in coke ovens. Coke is used as a reducing agent for iron ore. Currently, the caking coal required for coke production is on the verge of depletion worldwide, and using high-quality caking coal to produce coke leads to a significant increase in costs. In order to produce high-strength coke using low-grade coal, the low-grade coal must be molded into briquettes before being charged into the coke oven, and these briquettes must then be charged into the coke oven for carbonization.

[0003] Strength is an important factor when it comes to molded coal. This is because if low-strength molded coal is used, it will easily break before being charged into the coke oven due to vibrations and impacts during transportation, making it impossible to produce high-strength coke. For this reason, in coke production, molded coal is periodically removed from the transport line to the coke oven and its strength is measured to ensure high-strength coke. The strength of molded coal is measured by conducting a drop test in which molded coal is dropped from a specified height, and then sieving the molded coal after the drop. The shutter strength, which is the mass percentage (mass%) of molded coal remaining on the sieve, is used.

[0004] The strength measurement method using the drop test requires a considerable amount of time to obtain the measurement results. Therefore, if the strength of the briquettes decreases due to a change in the manufacturing conditions of the briquettes, a large amount of briquettes with low strength will be produced before the strength decrease can be detected. To quickly detect the decrease in strength of the briquettes, it is necessary to measure the strength of the briquettes online. As a technology for measuring granular raw materials online, Patent Document 1 discloses a particle size measuring device having a particle size calculation means that measures the distance between each particle and an imaging unit in image data of the granular raw materials moving on a transportation route and calculates the particle size of the granular raw materials based on the distance. Patent Document 1 states that the particle size measuring device can be used to measure the particle size of the granular raw materials moving on a transportation route online. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-92494 Summary of the Invention [Problem to be solved by the invention]

[0006] Patent Document 1 discloses a particle size measuring device that measures the particle size of granular raw materials online, but not a device that measures the strength of granular raw materials. Rapid detection of changes in the strength of molded coal requires a device that can detect molded coal strength online, but Patent Document 1 does not disclose such a device, which is a problem. The present invention has been made in consideration of these problems in the prior art, and its object is to provide a granular material strength estimation device and strength estimation method that can estimate the strength of granular materials online, as well as a granular material manufacturing method and coke manufacturing method that use the granular material strength estimation method. [Means for solving the problem]

[0007] The means for solving the above problems are as follows. [1] A granular material strength estimation device that estimates the strength of granular material loaded and transported on a conveyor, comprising: a line sensor that creates line profile data of the granular material at multiple different positions in the transport direction; and an image processing device, wherein the image processing device has a three-dimensional shape creation unit that creates three-dimensional shape data of the granular material using the multiple line profile data created by the line sensor; and a strength estimation unit that uses the three-dimensional shape data to determine an index of the size of the granular material and estimates the strength of the granular material using the index. [2] The strength estimation device for granular material described in [1], wherein the index is the maximum value, minimum value, average value, variance value or standard deviation of at least one of the volume, projected area, perimeter, length, width and particle size of the granular material. [3] The strength estimation device for granular material according to [1] or [2], wherein the three-dimensional shape creation unit complements missing data in the three-dimensional shape data. [4] A granular material intensity estimation device described in any one of [1] to [3], wherein the intensity estimation unit identifies the boundaries of granular materials in the three-dimensional shape data using at least one of a minimum value search method, a Canny edge extraction method, an edge extraction method using a Sobel filter, and an edge extraction method using a Laplacian filter. [5] A granular material intensity estimation device described in any one of [1] to [4], wherein the intensity estimation unit removes noise in the three-dimensional shape data using at least one of expansion processing, contraction processing, median filtering, and averaging processing. [6] The strength estimation device for granular materials described in any one of [1] to [5], wherein the granular materials are molded charcoal. [7] A method for estimating the strength of granular material loaded on a conveyor and transported, comprising: a line profile data creation step for creating line profile data of the granular material at multiple different positions in the transport direction; a three-dimensional shape creation step for creating three-dimensional shape data using multiple pieces of line profile data; and a strength estimation step for using the three-dimensional shape data to determine an index of the size of the granular material and using the index to estimate the strength of the granular material. [8] A method for estimating the strength of a granular material described in [7], wherein the index is the maximum value, minimum value, average value, variance value or standard deviation of at least one of the volume, projected area, perimeter, length, width and particle size of the granular material. [9] A method for estimating the strength of granular material according to [7] or [8], wherein the granular material is transported by the conveyor at a constant speed.

[10] A method for producing a granular material, comprising: estimating the strength of the granular material using the method for estimating the strength of the granular material according to any one of [7] to [9]; and adjusting the production conditions of the granular material so that the estimated strength of the granular material is equal to or greater than a predetermined strength.

[11] A method for producing coke, comprising producing molded coal by the method for producing granules according to

[10] , and carbonizing the molded coal to produce coke. [Effects of the Invention]

[0008] By implementing the granular material strength estimation device and strength estimation method according to the present invention, the strength of granular material being loaded and transported on a conveyor can be estimated online. This allows for rapid detection of changes in the strength of the granular material due to fluctuations in manufacturing conditions. When the granular material is molded coal, even if the strength of the molded coal decreases due to fluctuations in the manufacturing conditions of the molded coal, this decrease in strength can be quickly detected and the strength decrease can be quickly addressed. This prevents the decrease in strength of the molded coal, and by using the molded coal to produce coke, it is possible to produce high-strength coke. [Brief explanation of the drawings]

[0009] [Figure 1]FIG. 1 is a schematic diagram showing the process from the production of briquettes to their charging into a coke oven. [Figure 2] FIG. 2 is a schematic diagram showing a granular object strength estimation device 22 according to this embodiment. [Figure 3] FIG. 3 is a schematic diagram showing an example of the configuration of the image processing device 26. As shown in FIG. [Figure 4] FIG. 4 is a schematic diagram illustrating a situation in which missing data occurs. [Figure 5] FIG. 5 is an explanatory diagram showing an example of a method for complementing missing data. [Figure 6] FIG. 6 shows three-dimensional shape images of the molded charcoal 16 before and after the interpolation process. [Figure 7] FIG. 7 is a graph illustrating the process of identifying the boundary of the briquettes by the local minimum search method. [Figure 8] FIG. 8 is an image showing the change in the three-dimensional shape data due to the process of identifying the boundary of the briquettes. [Figure 9] FIG. 9 is a flowchart showing an example of a method for estimating the strength of a particulate matter according to this embodiment. [Figure 10] FIG. 10 is a schematic diagram illustrating the drop test of the molded coal in Example 1. [Figure 11] FIG. 11 is a graph showing the correlation between the index indicating the size of the briquettes and the index indicating the strength of the briquettes. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, a granular material strength estimation device and a granular material strength estimation method according to an embodiment of the present invention will be described with reference to the drawings. Note that in the following embodiments, the granular material will be described as an example in which the granular material is molded charcoal, but the granular material is not limited to molded charcoal, and the present invention can be applied to any granular material whose shape changes due to vibration or impact from a conveying means such as a conveyor that conveys the granular material.

[0011] FIG. 1 is a schematic diagram showing the process from when molded coal is produced until it is charged into a coke oven. Raw coal 10, which is low-grade coal, is mixed with multiple materials including a binder in a mixer 12. The mixed raw materials mixed in the mixer 12 are molded into briquette-shaped molded coal 16 in a molder 14. The molded coal 16 is loaded onto a conveyor 18 and transported toward a coke oven 20. The molded coal 16 is charged through a coal charging hole of the coke oven 20 and carbonized to produce coke. Note that while FIG. 1 shows only one conveyor 18 between the molder 14 and the coke oven 20, in reality, the molded coal 16 is transported to the coke oven 20 via multiple conveyors 18.

[0012] Fig. 2 is a schematic diagram showing a granular material strength estimation device 22 according to this embodiment. As shown in Fig. 2, the granular material strength estimation device 22 is provided near the conveyor 18. The granular material strength estimation device 22 includes a line sensor 24 and an image processing device 26. The line sensor 24 is, for example, a laser range finder.

[0013] The line sensor 24 is provided above the conveyor 18. The line sensor 24 continuously irradiates the molded coal 16, which is being transported at a constant speed by the conveyor 18, with a line-shaped laser beam that runs along the width direction of the conveyor 18, and forms an image of the reflected light on an imaging element such as a CMOS. Using the image data formed on the imaging element, the line sensor 24 creates line profile data that includes height information of the molded coal 16 at multiple positions across the width direction of the conveyor 18 that are different in the transport direction. The line sensor 24 outputs the created multiple line profile data to the image processing device 26.

[0014] The image processing device 26 creates three-dimensional shape data of the molded coal 16 using the multiple line profile data created by the line sensor 24. If the three-dimensional shape data includes missing data, it is preferable that the image processing device 26 complements the missing data.

[0015] The image processing device 26 calculates a size index of the molded coal 16 using the three-dimensional shape data of the molded coal 16. The size index of the molded coal 16 is the maximum value, minimum value, average value, variance value, or standard deviation of at least one of the volume, projected area, length and width, and particle size of the molded coal 16. Here, the particle size of the molded coal 16 is the Heywood diameter or the average length and width when the size of the molded coal 16 is approximated by a rectangular shape. In calculating the size index of the molded coal 16, the image processing device 26 preferably identifies the boundary of the molded coal 16 in the three-dimensional shape data, binarizes the three-dimensional shape data, and removes noise from the three-dimensional shape data.

[0016] The image processing device 26 estimates the strength of the briquettes 16 using the calculated size index of the briquettes 16 and a correlation equation that indicates the correspondence between the size index of the briquettes 16, which has been obtained in advance through experiments or the like, and the strength of the briquettes 16. In this way, the granular material strength estimation device 22 according to this embodiment estimates the strength of the briquettes 16 that are loaded and transported on the conveyor 18 online.

[0017] 3 is a schematic diagram showing an example configuration of the image processing device 26. The image processing device 26 is, for example, a general-purpose computer such as a workstation or a personal computer. The image processing device 26 has a control unit 28, an input unit 30, an output unit 32, and a storage unit 34. The control unit 28 is, for example, a CPU, and executes a program read from the storage unit 34, causing the control unit 28 to function as a three-dimensional shape creation unit 36 ​​and an intensity estimation unit 38.

[0018] The input unit 30 is, for example, a keyboard, a touch panel integrated with a display, etc. The output unit 32 is, for example, an LCD or CRT display, etc. The storage unit 34 is, for example, an updatable flash memory, a built-in hard disk or a hard disk connected via a data communication terminal, an information recording medium such as a memory card, and a read / write device for the information recording medium. The storage unit 34 stores programs for the control unit 28 to execute each function, data used by the programs, etc.

[0019] Next, the processes executed by the three-dimensional shape creation unit 36 ​​and the intensity estimation unit 38 will be described. The three-dimensional shape creation unit 36 ​​acquires a plurality of line profile data from the line sensor 24. The three-dimensional shape creation unit 36 ​​converts height information contained in the plurality of line profile data into brightness values ​​of 256 grayscale levels, and creates three-dimensional shape data of the molded coal 16 by arranging the plurality of line profile data converted into brightness values ​​in the conveyance direction. It is preferable that the three-dimensional shape creation unit 36 ​​creates the three-dimensional shape data using a number of line profile data such that the length in the conveyance direction is 0.5 m or more and 4.0 m or less.

[0020] The line profile data acquired from the line sensor 24 may contain missing data. FIG. 4 is a schematic diagram illustrating a situation in which missing data occurs. The line sensor 24 creates line profile data by irradiating the molded charcoal 16 with a line-shaped laser beam and forming an image of the light reflected by the molded charcoal 16 on an imaging element. As shown in FIG. 4(a), depending on the surface condition of the molded charcoal 16 (for example, the degree of surface gloss), there may be areas where the light reflected from the molded charcoal 16 is not imaged, and these areas become missing data. Also, as shown in FIG. 4(b), the position where the laser beam is irradiated and the position where the reflected light is received are different in the line sensor 24, so that part of the reflected light is blocked by the molded charcoal 16, resulting in areas where the reflected light is not imaged, and these areas become missing data.

[0021] If the line profile data contains missing data, the three-dimensional shape data created from these data will also contain missing data. If the three-dimensional shape data contains missing data, the measurement accuracy will decrease when the size and number of the molded coals 16 are measured using the three-dimensional shape data. For this reason, it is preferable that the three-dimensional shape creation unit 36 ​​complements the missing data contained in the created three-dimensional shape data.

[0022] FIG. 5 is an explanatory diagram showing an example of missing data complementation processing. The three-dimensional shape creation unit 36, for example, raster scans the three-dimensional shape data from left to right and top to bottom, and when missing data is detected, complements it with the average brightness value of the eight surrounding pixels. In the example shown in FIG. 5, the three-dimensional shape creation unit 36 ​​complements pixel 0, which is missing data, with the average brightness value of pixels 1 to 4, which are not missing data among the eight surrounding pixels (pixels 1 to 8 in FIG. 5). Note that pixel 5 to the right of pixel 0 is also missing data, but the brightness value of pixel 0 complemented with the average brightness value of pixels 1 to 4 is also used to complement the missing data of pixel 5. The three-dimensional shape creation unit 36 ​​performs this type of missing data complementation processing on the three-dimensional image data.

[0023] FIG. 6 shows three-dimensional shape images of the molded charcoal 16 before and after the interpolation process. FIG. 6(a) shows the three-dimensional shape image of the molded charcoal 16 before the missing data interpolation process. FIG. 6(b) shows the three-dimensional shape image of the molded charcoal 16 after the missing data interpolation process. By the interpolation process, the missing data scattered in the three-dimensional shape image of FIG. 6(a) can be interpolated in a natural way, and three-dimensional shape data without missing data as shown in FIG. 6(b) can be obtained.

[0024] In the example shown in FIG. 5, the missing data is interpolated using the average luminance value of the non-missing pixels among the eight pixels surrounding the missing data. However, this is not limited to this example. The 3D shape creation unit 36 ​​may interpolate using the median luminance value, minimum luminance value, or maximum luminance value of the eight pixels surrounding the missing data, or may interpolate using the average luminance value or median luminance value excluding the maximum and minimum luminance values ​​of the eight pixels surrounding the missing data. Furthermore, in order to eliminate abnormal values, a luminance range may be preset based on past performance, and the missing data may be interpolated using the average or median value of pixels within the set range. Furthermore, the raster scan order is not limited to left-to-right or top-to-bottom, and may be any of four combinations of right-to-left and top-to-bottom scan orders.

[0025] The three-dimensional shape creation unit 36 ​​outputs the created three-dimensional shape data of the briquettes 16 to the strength estimation unit 38. The strength estimation unit 38 measures the size and number of the briquettes 16 using the three-dimensional shape data and calculates an index indicating the size of the briquettes 16 using the measured size and number. However, before that, it is preferable to identify the boundaries of the briquettes 16, binarize the three-dimensional shape data, and remove noise from the three-dimensional shape data. This improves the measurement accuracy when measuring the size and number of the briquettes 16 using the three-dimensional shape data. These processes are described below.

[0026] First, we will explain the boundary identification process for identifying the boundaries of the molded charcoal 16. Figure 7 is a graph illustrating the process for identifying the boundaries of the molded charcoal using a minimum search method. Figure 7(a) is a graph illustrating the valley detection method, and Figure 7(b) is a graph illustrating the step detection method. As shown in Figure 7(a), the intensity estimation unit 38 detects a target pixel in the 3D shape data as a valley when the luminance value of the target pixel satisfies "right tilt ≥ 0, left tilt ≤ 0" and "(right tilt - left tilt) ≥ threshold." Furthermore, the intensity estimation unit 38 detects a target pixel as a step when the difference in luminance values ​​between the target pixel and its neighboring pixels is greater than a threshold. The intensity estimation unit 38 identifies the target pixel of the detected valley or step as a boundary pixel of the molded charcoal 16 and changes the luminance value of the pixel to "0." Hereinafter, the process of changing the luminance value of the boundary pixel of the molded charcoal 16 to "0" will be referred to as "zero filling."

[0027] FIG. 8 is an image showing the change in the three-dimensional shape data due to the process of identifying the boundary of the molded coal. The intensity estimation unit 38 vertically scans the three-dimensional shape data corresponding to the pre-processing image shown in FIG. 8(a) to detect the boundary pixels of the molded coal 16 and performs zero-filling on the pixels. The three-dimensional shape image in which the vertical boundary pixels have been zero-filled is shown in FIG. 8(b). Similarly, the intensity estimation unit 38 horizontally scans the three-dimensional shape data corresponding to the pre-processing image shown in FIG. 8(a) to detect the boundary pixels of the molded coal 16 and performs zero-filling on the pixels. The three-dimensional shape image in which the horizontal boundary pixels have been zero-filled is shown in FIG. 8(c).

[0028] The intensity estimation unit 38 combines the images of Figures 8(b) and (c) using an "OR operation" to create 3D shape data in which boundary pixels in the vertical and horizontal directions are zero-filled. The 3D shape image corresponding to this 3D shape data is shown in Figure 8(d). By performing a boundary identification process to identify the boundaries of the briquettes 16 in this manner, the boundaries of the briquettes 16 become clear, improving the accuracy of subsequent measurements of the size and number of briquettes 16. In the granular material strength estimation device 22 according to this embodiment, the intensity estimation unit 38 identifies the boundaries of the briquettes 16 using a minimum search method, but this is not limiting. The intensity estimation unit 38 may identify the boundaries of the briquettes 16 using at least one of a minimum search method, a Canny edge extraction method, an edge extraction method using a Sobel filter, and an edge extraction method using a Laplacian filter.

[0029] For example, when detecting the boundary of the briquettes 16 using the Canny edge extraction method, the three-dimensional shape creation unit 36 ​​performs differentiation processing on the three-dimensional shape data smoothed by a Gaussian filter using a Sobel filter or the like, and then determines whether or not the boundary is the boundary of the briquettes 16 by threshold processing using hysteresis. Alternatively, the three-dimensional shape creation unit 36 ​​may process the three-dimensional shape data using a Sobel filter or a Laplacian filter, and then identify the boundary of the briquettes 16 by threshold processing. In this way, by identifying the boundary of the briquettes 16 and padding the boundary pixels with zero, the accuracy of subsequent measurements of the size and number of briquettes 16 is improved.

[0030] Next, the binarization process will be described. The intensity estimation unit 38 preferably binarizes the three-dimensional shape data corresponding to the zero-filled image shown in FIG. 8(d). The threshold value used for binarization is predetermined and stored in the storage unit 34. By binarizing the three-dimensional shape data in this way, the accuracy of the subsequent measurement of the size and number of molded coals 16 is improved.

[0031] Next, the noise removal process will be described. The intensity estimation unit 38 preferably performs at least one of the following processes once or more: expansion, contraction, median filtering, and averaging to remove noise from the 3D shape data after the binarization process. By removing noise in this way, the accuracy of the subsequent measurement of the size and number of molded coals 16 is improved.

[0032] The strength estimation unit 38 performs watershed region division on the 3D shape data after the noise-removing binarization process. This makes it possible to eliminate overlaps between the briquettes 16 and measure the size of each briquette 16. The strength estimation unit 38 uses the 3D shape data after region division to measure the size and number of the briquettes 16 included in the 3D shape data. The strength estimation unit 38 then calculates an index indicating the size of the briquettes 16 using the measured size and number of the briquettes 16. In this embodiment, the index indicating the size of the briquettes 16 is the maximum value, minimum value, average value, variance value, or standard deviation of at least one of the volume, projected area, perimeter, length, width, and particle size of the briquettes 16.

[0033] After calculating the index indicating the size of the briquettes 16, the strength estimation unit 38 reads out from the storage unit 34 a correlation equation indicating the correspondence between the index and the strength of the briquettes 16. The correlation equation between the index indicating the size of the briquettes 16 and the strength of the briquettes 16 is obtained in advance by an experiment or the like and stored in the storage unit 34.

[0034] The strength estimation unit 38 calculates the strength of the molded coal 16 using the calculated index and the correlation equation read from the storage unit 34. The strength of the molded coal 16 calculated in this way becomes an estimated value of the strength of the molded coal 16. The strength estimation unit 38 displays the estimated strength of the molded coal 16 on the output unit 32. The operator can grasp the estimated value of the strength of the molded coal 16 by visually checking the strength of the molded coal 16 displayed on the output unit 32.

[0035] When two or more indices indicating the size of the briquettes 16 are used, the combined index may be calculated using the following formula (1).

[0036] Briquette size index = a1 × T1 + a2 × T2 + + an × Tn (1) In the above formula (1), T1, T2, ..., Tn are indices indicating the size of the briquettes 16, and a1, a2, ..., an are parameters. These parameters can be calculated using a data set of past performance data of the indices indicating the size of the briquettes 16, similar to the parameters of the multiple regression equation.

[0037] Furthermore, when two indices indicating the size of the briquettes 16 are used, the indices may be calculated using the following formula (2).

[0038] Index showing the size of briquettes = (1 - α) × T1 + α × T2 (2) In the above formula (2), T1 and T2 are indices indicating the size of the coal briquette 16, and α is a value within the range of 0<α<1. 2 ) is set to the highest value.

[0039] When calculating the index showing the size of briquettes using the above equations (1) and (2), the values ​​of T1, T2, ..., Tn are normalized using the following equation (3) to make the contributions of T1, T2, ..., Tn the same.

[0040] V norm =(VV min ) / (V max -V min )···(3)

[0041] In the above formula (3), V norm is the normalized value, V is the average value of the index indicating the size of the coal briquette 16, and V min is the minimum value of the index, and V max is the maximum value of the index.

[0042] In the above formula (2), the optimum value of α may be determined as an inherent value. However, for example, when T1 is the average value of the volume of the briquettes and T2 is the average value of the projected area of ​​the briquettes, the previously measured average height is normalized using the above formula (3) to obtain the value h norm Using the above, α may be calculated as a function such as the following equation (4).

[0043] α=β×h norm ···(4) When T1 is the average volume of the briquettes and T2 is the average projected area of ​​the briquettes, the index showing the size of the briquettes may be calculated using the following formula (5) instead of the formula (2). Note that β is the coefficient of determination of α (R 2 ) is set to the highest value.

[0044] Index showing the size of briquettes = αT1 + (1 - α) × T2 (5) Of the equations (2) and (5), the coefficient of determination (R 2 ) is higher.

[0045] Fig. 9 is a flow diagram showing an example of a method for estimating the strength of granular material according to this embodiment. Next, the method for estimating the strength of granular material according to this embodiment will be described with reference to Fig. 9. The flow shown in Fig. 9 starts, for example, when the granular material strength estimation device 22 is started and an input instructing the start of estimation of the strength of the molded coal 16 is received from the input unit 30 of the image processing device 26.

[0046] The line sensor 24 executes a line profile data creation step, irradiating the molded coal 16 with a line-shaped laser beam along the width direction of the conveyor 18, and creates line profile data including height information of the molded coal 16 at a plurality of different positions in the conveying direction (step S101). The line sensor 24 outputs the created line profile data to the three-dimensional shape creation unit 36 ​​of the image processing device 26.

[0047] The three-dimensional shape creation unit 36 ​​executes a three-dimensional shape creation step to create three-dimensional shape data of the molded coal 16 using line profile data at multiple different positions in the conveying direction (step S102). It is preferable that the three-dimensional shape creation unit 36 ​​complements missing data included in the created three-dimensional shape data. The three-dimensional shape creation unit 36 ​​outputs the created three-dimensional shape data to the strength estimation unit 38.

[0048] The strength estimation unit 38 uses the three-dimensional shape data to calculate the maximum value, minimum value, average value, variance value, or standard deviation of at least one of the volume, projected area, perimeter, length and width, and particle size of the molded coal 16 as an index of the size of the molded coal 16 (step S103). Note that, when calculating the index of the size of the molded coal 16, the strength estimation unit 38 preferably performs a boundary identification process to identify the boundary of the molded coal 16, a binarization process, and a noise removal process.

[0049] After calculating the size index of the briquettes 16, the strength estimation unit 38 reads out a correlation equation that indicates a correspondence relationship between the size index of the briquettes 16 and the strength of the briquettes 16, which is stored in advance in the storage unit 34. The strength estimation unit 38 estimates the strength of the briquettes 16 using the size index of the briquettes 16 and the correlation equation (step S104). The processes in steps S103 and S104 correspond to the strength estimation step in the strength estimation method for granular material.

[0050] The strength estimation unit 38 displays the estimated strength of the molded coal 16 on the output unit 32 (step S15). This allows the operator to visually confirm the strength of the molded coal 16 being loaded and transported on the conveyor. Thereafter, the strength estimation unit 38 determines whether or not an input instructing to end the estimation of the strength of the molded coal 16 has been received via the input unit 30 (step S106). If the input has been received, the strength estimation unit 38 determines that an end instruction has been received (step S106: Yes) and ends the flow shown in FIG. 9. On the other hand, if an input instructing to end the estimation of the strength of the molded coal 16 has not been received, the strength estimation unit 38 determines that an end instruction has not been received (step S106: No), returns the process to step S101, and repeats the process from step S101 again.

[0051] As described above, by using the granular material strength estimation device 22 and the granular material strength estimation method according to this embodiment, it is possible to estimate the strength of the molded coal 16 loaded and transported on the conveyor 18 online. This allows for rapid detection of a decrease in the strength of the molded coal due to a change in some manufacturing conditions, and allows adjustment of the molded coal manufacturing conditions so that the molded coal strength is equal to or exceeds a predetermined target value. For example, the molded coal manufacturing conditions can be adjusted by increasing the amount of binder added to the molded coal material. This allows the strength of the molded coal 16 to be maintained equal to or exceeds a predetermined target value. Then, by using this molded coal 16 to manufacture coke, high-strength coke can be produced.

[0052] The granular material strength estimation device and granular material strength estimation method according to this embodiment finds a correlation between the change in size of a granular material due to vibrations and impacts caused by a conveying means such as a conveyor 18 and the strength of the granular material, and estimates the strength of the granular material by utilizing this correlation. Therefore, the granular material is not limited to molded coal 16, and the granular material strength estimation device and granular material strength estimation method according to this embodiment can be applied to any granular material whose shape changes due to vibrations and impacts caused by a conveying means that conveys the granular material.

[0053] Furthermore, it is preferable that the molded coal 16 is transported at a constant speed by the conveyor 18, which is a transport means. If the molded coal 16 is transported at a constant speed, line profile data can be created at regular intervals in the transport direction by creating line profile data at regular intervals. Creating line profile data at regular intervals in this way improves the accuracy of creating the three-dimensional shape data of the molded coal 16 created by the three-dimensional shape creation unit 36. [Example]

[0054] Example 1 Example 1 will be described, in which the number of briquettes after a drop test was measured using the granular material strength estimation device 22 shown in Figure 2. Figure 10 is a schematic diagram illustrating the drop test of briquettes in Example 1. As shown in Figures 10(a) and 10(b), one to three drop tests were conducted from a height of 2 m. The briquettes were then sieved using a sieve (mesh diameter 15 x 15 mm). The sieved briquettes were placed on a moving stage simulating a conveyor, and the number of briquettes was measured using the granular material strength estimation device 22. The imaging resolution of the line sensor used in the width direction was 0.3 mm / pixel, the imaging resolution in the conveying direction was 0.4 mm / pixel, and the moving speed of the moving stage was 200 mm / sec. The created 3D image data was also subjected to missing data completion, boundary identification, binarization, and noise reduction. Table 1 below shows the measurement results for the number of briquettes.

[0055] [Table 1]

[0056] In Table 1 above, rows 1-1 to 1-5, 2-1 to 2-5, and 3-1 to 3-5 show the results of five measurements performed by rearranging the samples on the moving stage. The "Number of Briquettes" column shows the number of briquettes measured by the granular material strength estimation device 22. The "Measured Number" column shows the number of briquettes measured visually. As shown in Table 1, the number of briquettes and the measured number are almost the same, confirming that the granular material strength estimation device 22 according to this embodiment can measure the number of briquettes with high accuracy.

[0057] <Example 2> Next, Example 2 will be described, in which the strength of molded coal was estimated on a conveyor of a molded coal production line using the granular material strength estimation device 22 shown in Figure 2. Using a line sensor with an imaging resolution of 0.3 mm / pixel in both the vertical and horizontal directions, line profile data was created for molded coal piled in about three layers on a conveyor transporting at a speed of 50 m / min. Note that the zero level of height was set by creating profile data for a conveyor without molded coal before measurement.

[0058] Three-dimensional shape data was created using the created line profile data. The created 3D shape data was subjected to missing data completion, boundary identification, binarization, and noise removal processes, and then the size and number of the briquettes were measured. These measurement results were used to calculate the average volume, average diameter, average projected area, and average perimeter of the briquettes as indices of briquettes size. Furthermore, a drop test was performed on the briquettes for which the above indices were calculated. The briquettes after the drop test were sieved through a sieve (15 mm × 15 mm mesh), and the average mass per briquettes sieved on the sieve (the total mass of the remaining briquettes divided by the number of briquettes) was used as an index of briquettes strength. This briquettes strength index, like the conventionally used shutter strength index, uses the mass of the briquettes sieved on the sieve after the drop test and shows the same tendency as shutter strength. The correlation between this briquettes strength index and an index indicating briquettes size was confirmed.

[0059] FIG. 11 is a graph showing the correlation between an index indicating the size of a briquette and an index indicating the strength of the briquette. FIG. 11(a) is a graph showing the correlation between the average volume of a briquette and its strength, and FIG. 11(b) is a graph showing the correlation between the average diameter of a briquette and its strength. FIG. 11(c) is a graph showing the correlation between the average projected area of ​​a briquette and its strength, and FIG. 11(d) is a graph showing the correlation between the average perimeter of a briquette and its strength. The horizontal axis of these graphs is the average mass (g) per briquette, which is an index indicating the strength of the briquette. The vertical axis of the graph is an index (-) indicating the size of the briquette normalized by the maximum value. The unit (-) indicates that the values ​​are dimensionless.

[0060] As shown in Figure 11(a) to (d), the coefficient of determination (R 2) were all 0.89 or more, confirming a high correlation with the strength of the briquettes. These results show that if the correlation equations shown in Figures 11(a) to 11(d) are calculated in advance and stored in the memory unit 34, the strength of the briquettes can be estimated with high accuracy by using the index indicating the size of the briquettes and the correlation equations.

[0061] On the other hand, in Comparative Example 1, image data captured by an area camera with an imaging resolution of 0.25 mm / pixel in both the vertical and horizontal directions instead of the line sensor was analyzed to measure the size and number of briquettes, and the average value of the projected area of ​​the briquettes was calculated using these measurement results. The coefficient of determination (R 2 ) was calculated to be 0.08. It is thought that the boundary of the briquettes is difficult to identify in the image data taken by the area camera, which reduces the accuracy of measuring the size and number of the briquettes, and as a result, the coefficient of determination between the projected area of ​​the briquettes, which is an index of the briquettes' size, and their strength is significantly small.

[0062] Example 3 Next, Example 3 will be described, in which the strength of molded coal was estimated on a conveyor of a molded coal production line using the granular material strength estimation device 22 shown in Figure 2. Using a line sensor with an imaging resolution of 0.3 mm / pixel in both the vertical and horizontal directions, line profile data of molded coal stacked in about 4 to 5 layers on a conveyor transporting at a speed of 50 m / min was acquired. Note that the zero level of height was set by acquiring profile data of a conveyor on which no molded coal was stacked before measurement.

[0063] Three-dimensional shape data was created using the acquired line profile data. The created three-dimensional shape data was subjected to missing data completion, boundary identification, binarization, and noise removal processes, and then the size and number of briquettes were measured. These measurement results were used to calculate the volume and projected area of ​​the briquettes. In Example 3, the average value of the briquettes' volumes normalized using the above formula (3) and the average value of the briquettes' projected areas normalized using the above formula (3) were used, β was set to 1 in the above formula (4), and the value combined using the above formula (5) was used as an index indicating the size of the briquettes.

[0064] The coefficient of determination (R 2 The coefficient of determination (R 2 ) was calculated to be 0.87. On the other hand, the coefficient of determination (R 2 ) was calculated to be 0.88. In this way, it was confirmed that by combining the two indices in an appropriate ratio, the correlation with the strength of the molded coal can be increased more than with each individual indices.

[0065] In Example 3, when a decrease in the strength of the molded coal was detected by the granular material strength estimation device 22, the amount of binder added to the raw coal was increased. As a result, molded coal having a strength equal to or greater than the target was produced for 85% or more of the total time.

[0066] On the other hand, in Comparative Example 2, the strength of the molded coal was evaluated by a random drop test. When the strength of the molded coal was evaluated by a drop test, the detection of a decrease in the strength of the molded coal was delayed. Similarly, even if the amount of binder added to the raw coal was increased when a decrease in the strength of the molded coal was detected, molded coal with a strength equal to or greater than the target could only be produced in 65% or more of the total period. [Explanation of symbols]

[0067] 10 Coking coal 12 Mixer 14 Molding machine 16 Molded coal 18 Conveyor 20 Coke oven 22 Granular strength estimation device 24 line sensor 26 Image Processing Device 28 Control Unit 30 Input section 32 Output section 34 Storage section 36 3D Shape Creation Section 38 Intensity estimation part

Claims

1. A granular material strength estimation device that estimates the strength of granular materials loaded and transported on a conveyor, a line sensor that creates line profile data of the granular object at a plurality of different positions in a conveying direction; an image processing device, the image processing device includes a three-dimensional shape creation unit that creates three-dimensional shape data of the granular object using a plurality of line profile data created by the line sensor; a strength estimation unit that calculates a size index of the granular object using the three-dimensional shape data and estimates the strength of the granular object using the size index and a correlation equation between the size index and a strength index of the granular object; and the granular material is a granular material whose shape changes due to vibration or impact of a conveying means that conveys the granular material, The index of the size of the granular material is the average value of the volume, the average value of the diameter, the average value of the projected area, the average value of the perimeter, the average value of the vertical length, or the average value of the horizontal length of the granular material, A granular material strength estimation device, in which an indicator of the strength of the granular material is obtained by performing a drop test on the granular material, sieving the granular material after the drop test through a sieve, and measuring the average mass per piece of the granular material sieved on the sieve.

2. The granular material strength estimation device according to claim 1 , wherein the three-dimensional shape creation unit complements missing data in the three-dimensional shape data.

3. The granular material intensity estimation device according to claim 1, wherein the intensity estimation unit identifies the boundaries of the granular material in the three-dimensional shape data using at least one of a minimum value search method, a Canny edge extraction method, an edge extraction method using a Sobel filter, and an edge extraction method using a Laplacian filter.

4. The granular material intensity estimation device according to claim 2, wherein the intensity estimation unit identifies the boundaries of the granular material in the three-dimensional shape data using at least one of a minimum value search method, a Canny edge extraction method, an edge extraction method using a Sobel filter, and an edge extraction method using a Laplacian filter.

5. The apparatus for estimating the intensity of a granular object according to claim 1 , wherein the intensity estimating unit removes noise from the three-dimensional shape data using at least one of an expansion process, an erosion process, a median filter, and an averaging process.

6. The apparatus for estimating the strength of a granular object according to claim 2 , wherein the intensity estimating unit removes noise from the three-dimensional shape data using at least one of an expansion process, an erosion process, a median filter, and an averaging process.

7. The apparatus for estimating the strength of a granular object according to claim 3 , wherein the intensity estimating unit removes noise from the three-dimensional shape data using at least one of an expansion process, an erosion process, a median filter, and an averaging process.

8. The apparatus for estimating the strength of a granular object according to claim 4 , wherein the intensity estimating unit removes noise from the three-dimensional shape data using at least one of an expansion process, an erosion process, a median filter, and an averaging process.

9. The apparatus for estimating strength of granular material according to claim 1 , wherein the granular material is molded charcoal.

10. A method for estimating the strength of granular objects loaded and transported on a conveyor, comprising: a line profile data creation step of creating line profile data of the granular object at a plurality of different positions in the conveying direction; a three-dimensional shape creating step of creating three-dimensional shape data using the plurality of line profile data; a strength estimating step of estimating the strength of the granular object using a size index of the granular object obtained from the three-dimensional shape data and a correlation equation between the size index and a strength index of the granular object; and the granular material is a granular material whose shape changes due to vibration or impact of a conveying means that conveys the granular material, The index of the size of the granular material is the average value of the volume, the average value of the diameter, the average value of the projected area, the average value of the perimeter, the average value of the vertical length, or the average value of the horizontal length of the granular material, A method for estimating the strength of a granular material, in which an index of the strength of the granular material is performed by performing a drop test on the granular material, sieving the granular material after the drop test through a sieve, and measuring the average mass per piece of the granular material sieved on the sieve.

11. The method for estimating strength of a granular material according to claim 10 , wherein the granular material is transported by the conveyor at a constant speed.

12. A method for manufacturing granular material, comprising estimating the strength of the granular material using the granular material strength estimation method described in claim 10 or 11, and adjusting the manufacturing conditions of the granular material so that the estimated strength of the granular material is equal to or greater than a predetermined target value.

13. A method for producing coke, comprising producing molded coal by the method for producing granules according to claim 12, and carbonizing the molded coal to produce coke.

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