KR102612850B1 - Method and apparatus for carbon black pellet size distribution analysis - Google Patents

Abstract

According to the concept of the present invention, a carbon black pellet size distribution analysis method includes obtaining a carbon black pellet image from a pelletizer; performing an algorithm for processing the obtained carbon black pellet image; And obtaining weight fraction data of carbon black pellets from the processed carbon black pellet image, wherein performing an algorithm for processing the carbon black pellet image includes: converting the carbon black pellet image into a black and white image. step; Selecting a region of interest from the carbon black pellet image; Converting the brightness of the carbon black pellet image; Removing noise from the carbon black pellet image; Converting the carbon black pellet image into a binarized image; And it may include classifying the carbon black pellets that can be analyzed from the carbon black pellet image.

Description

Carbon black pellet size distribution analysis method and apparatus {Method and apparatus for carbon black pellet size distribution analysis}

The present invention relates to a method and device for analyzing carbon black pellet size distribution, and more specifically, to a method and device for analyzing carbon black pellet size distribution using image processing.

Carbon black is one of the carbon materials produced in excess of 10 million tons annually, and is manufactured in pellet form for ease of transportation. Pellets require additional packaging if they are large, and if they are small in size, a lot of dust is generated during manufacturing and the quality of the product to which the pellets are applied can deteriorate. Therefore, it is important to maintain the appropriate size of the carbon black pellets.

The standard analysis method widely used when analyzing carbon black pellet size distribution is the ASTM D1511 method. The ASTM D1511 method measures the size distribution of carbon black pellets by sampling carbon black pellets, classifying them by size using an assembly composed of sieves of various sizes, and then measuring their weight. . However, the ASTM D1511 method has a problem in that it cannot determine the size distribution of carbon black pellets in real time, and accordingly, it is difficult to control the size of carbon black pellets being manufactured.

The problem to be solved by the present invention is to provide a method and device for analyzing carbon black pellet size distribution using image processing.

According to the concept of the present invention, a carbon black pellet size distribution analysis method includes obtaining a carbon black pellet image from a pelletizer; performing an algorithm for processing the obtained carbon black pellet image; And obtaining weight fraction data of carbon black pellets from the processed carbon black pellet image, wherein performing an algorithm for processing the carbon black pellet image includes: converting the carbon black pellet image into a black and white image. step; Selecting a region of interest from the carbon black pellet image; Converting the brightness of the carbon black pellet image; Removing noise from the carbon black pellet image; Converting the carbon black pellet image into a binarized image; And it may include classifying the carbon black pellets that can be analyzed from the carbon black pellet image.

According to another concept of the present invention, an imaging device for obtaining an image of carbon black pellets from a pelletizer; An illumination device that irradiates light to obtain an image of the carbon black pellet; a control unit that adjusts the direction and angle of each of the photographing device and the lighting device; a lighting controller that controls the lighting device; And a control unit for processing the obtained carbon black pellet image, wherein the control unit includes: an image receiver for receiving the carbon black pellet image from the photographing device; A first unit converting the carbon black pellet image into a black and white image; a second unit selecting a region of interest from the carbon black pellet image; a third unit converting the brightness of the carbon black pellet image; a fourth unit that removes noise from the carbon black pellet image; A fifth unit converting the carbon black pellet image into a binarized image; a sixth unit classifying the carbon black pellets that can be analyzed from the carbon black pellet image; And it may include a data acquisition unit that acquires weight fraction data of the carbon black pellets from the processed carbon black pellet image.

The carbon black pellet size distribution analysis method according to the present invention has the advantage of being able to analyze the carbon black pellet size distribution in close real time because it uses image processing. Accordingly, it is possible to control the size of the carbon black pellets being manufactured in real time.

1 is a conceptual diagram illustrating a carbon black pellet size distribution analysis device according to embodiments of the present invention.
Figure 2 is a conceptual diagram illustrating a carbon black pellet size distribution analysis device and pelletizer according to embodiments of the present invention.
Figure 3 is a flowchart illustrating a method for analyzing carbon black pellet size distribution according to embodiments of the present invention.
Figure 4 is a conceptual diagram explaining the steps of acquiring a carbon black pellet image.
Figure 5 is a flow chart to explain the steps of processing a carbon black pellet image.
Figure 6 is a conceptual diagram for explaining the steps of processing a carbon black pellet image.
Figure 7 is a flow chart to explain the step of classifying carbon black pellets.
Figures 8 to 11 are conceptual diagrams for explaining the steps of classifying carbon black pellets.
Figure 12 is a flowchart for explaining the steps of acquiring weight fraction data of carbon black pellets.
Figure 13 is an image taken before image processing to convert pixel units to metric units.
Figure 14 is a graph showing weight fraction data obtained according to the carbon black pellet size distribution analysis method according to embodiments of the present invention.
Figure 15 is a graph comparing the results of the carbon black pellet size distribution analysis method according to the embodiments of the present invention and the results of the carbon black pellet size distribution analysis method according to the comparative example of the present invention.
16A to 16D show weight fraction data obtained according to the carbon black pellet size distribution analysis method according to embodiments of the present invention and weight fraction data obtained according to the carbon black pellet size distribution analysis method according to the comparative example of the present invention, respectively. This is a graph comparing data.
Figure 17 is a graph comparing weight fraction data obtained according to the carbon black pellet size distribution analysis method according to embodiments of the present invention and weight fraction data with correction applied thereto.
Figure 18 shows the results of the carbon black pellet size distribution analysis method according to the embodiments of the present invention, the result when correction is applied thereto, and the result of the carbon black pellet size distribution analysis method according to the comparative example of the present invention. This is a graph comparing .
19A to 19D show weight fraction data obtained according to the carbon black pellet size distribution analysis method according to the embodiments of the present invention, weight fraction data with correction applied thereto, and carbon black pellets according to the comparative example of the present invention, respectively. This is a graph comparing the weight fraction data obtained according to the size distribution analysis method.

In order to fully understand the configuration and effects of the present invention, preferred embodiments of the present invention will be described with reference to the attached drawings. However, the present invention is not limited to the embodiments disclosed below, but can be implemented in various forms and various changes can be made. However, the description of the present embodiments is provided to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the present invention of the scope of the invention.

The terminology used herein is for describing embodiments and is not intended to limit the invention. As used herein, singular forms also include plural forms, unless specifically stated otherwise in the context. As used in the specification, 'comprises' and/or 'comprising' does not exclude the presence or addition of one or more other elements.

1 is a conceptual diagram illustrating a carbon black pellet size distribution analysis device according to embodiments of the present invention. Figure 2 is a conceptual diagram illustrating a carbon black pellet size distribution analysis device and pelletizer according to embodiments of the present invention. Figure 1 is a front view of a carbon black pellet size distribution analysis device according to embodiments of the present invention, and Figure 2 is a side view of the carbon black pellet size distribution analysis device according to embodiments of the present invention and a cross-sectional view of the pelletizer. will be.

Referring to FIGS. 1 and 2 , the carbon black pellet size distribution analysis device (CAD) may include an enclosure (ECL) and a support (HLD) disposed on the enclosure (ECL). An imaging device (CAM) and a lighting device (LID) may be disposed between the enclosure (ECL) and the support (HLD).

The imaging device (CAM) can acquire images of carbon black pellets (PET) from the pelletizer (PLZ). As an example, the photographing device (CAM) may be a black-and-white camera or a color camera with 7 million pixels or more. The imaging device (CAM) can have an exposure time of less than 200 μs.

The lighting device (LID) can irradiate light toward the pelletizer (PLZ). The lighting device (LID) can provide sufficient light so that the imaging device (CAM) can acquire images of carbon black pellets (PET) moving at high speed within the pelletizer (PLZ). As an example, the lighting device (LID) may include a light emitting diode (LED). The lighting device (LID) may be controlled by a lighting controller (LCR), which will be described later.

An adjustment unit (MOD) may be disposed at one end of each of the imaging device (CAM) and the lighting device (LID). The control unit (MOD) can adjust the direction and angle of each of the imaging device (CAM) and lighting device (LID). As an example, the adjusting unit (MOD) may be a ball head.

The support (HLD) may support the imaging device (CAM) and the lighting device (LID). The support (HLD) can support the imaging device (CAM) and the lighting device (LID) through the connection portion (CNP). As an example, the connection part (CNP) may connect the control part (MOD) and the support body (HLD) to each other.

Carbon black pellets (PET) can be produced in a pelletizer (PLZ). The pelletizer (PLZ) can produce carbon black pellets (PET) by mixing carbon black with a mixing agent such as molasses. Carbon black pellets (PET) can rotate and move within the pelletizer (PLZ).

The pelletizer (PLZ) may include an inspection unit (IP). The inspection part (IP) may be a part that exposes the inside of the pelletizer (PLZ). The imaging device (CAM) can acquire carbon black pellet (PET) images from the inspection part (IP) of the pelletizer (PLZ).

The enclosure (ECL) may include a lighting controller (LCR) and a control unit (CPU) therein. A lighting controller (LCR) can control a lighting device (LID). As an example, the lighting controller (LCR) may remotely control the lighting device (LID), but is not limited thereto. The control unit (CPU) may receive the carbon black pellet image acquired by the imaging device (CAM), process it, and then obtain weight fraction data of the carbon black pellet. As an example, a control unit (CPU) may be a component included within a computer.

The control unit (CPU) includes an image receiving unit (IMR), a data acquisition unit (DAA), a first unit (UN1), a second unit (UN2), a third unit (UN3), a fourth unit (UN4), and a fifth unit ( UN5) and a sixth unit (UN6).

The image receiver (IMR) may receive the acquired carbon black pellet image from the imaging device (CAM). The first to sixth units (UN1-UN6) may process carbon black pellet images. The first unit UN1 can convert the received carbon black pellet image into a black and white image. The second unit UN2 may select a region of interest in the carbon black pellet image converted to a black and white image. The third unit (UN3) can convert the brightness of the carbon black pellet image cut out by selecting the region of interest. The fourth unit UN4 can remove noise from the brightness-converted carbon black pellet image. The fifth unit (UN5) can convert the carbon black pellet image from which noise has been removed into a binary image. The sixth unit (UN6) can classify carbon black pellets that can be analyzed. The data acquisition unit (DAA) may acquire weight fraction data of carbon black pellets from the processed carbon black pellet image. Although not shown, the control unit (CPU) may further include a seventh unit (not shown). A seventh unit (not shown) may analyze the weight fraction data derived by the data acquisition unit (DAA). The seventh unit (not shown) may control the pelletizer (PLZ) so that the weight fraction of carbon black pellets (PET) between the first and second sizes is greater than or equal to a preset weight fraction.

Figure 3 is a flowchart illustrating a method for analyzing carbon black pellet size distribution according to embodiments of the present invention.

Referring to FIG. 3, the carbon black pellet size distribution analysis method includes obtaining a carbon black pellet image from a pelletizer (S100), processing the carbon black pellet image (S200), and weight fraction data of the carbon black pellet. It may include a step of acquiring (S300). Hereinafter, Figures 1 and 2 will be referred to together to explain the carbon black pellet size distribution analysis method.

Figure 4 is a conceptual diagram explaining the steps of acquiring a carbon black pellet image. Referring to FIG. 4, the imaging device (CAM) can acquire an image of a carbon black pellet. A lighting device (LID) may be activated to acquire images of carbon black pellets. The photographing device (CAM) may transmit a trigger signal to the lighting controller (LCR) before starting filming. The lighting controller (LCR) that receives the trigger signal can operate the lighting device (LID). After filming is completed, the photographing device (CAM) may transmit a stop signal to the lighting controller (LCR). The lighting controller (LCR) that receives the stop signal may stop the operation of the lighting device (LID). That is, the operating time of the lighting device (LID) may correspond to the operating time of the imaging device (CAM). As a result, heat generated when the lighting device (LID) operates for a long time can be suppressed. As a result, the power consumption of the lighting device (LID) can be reduced and its lifespan can be increased. Additionally, it is possible to prevent the imaging device (CAM) from being affected by the heat of the lighting device (LID).

Before processing, the carbon black pellet image may include a pelletizer (PLZ) and carbon black pellets (PET) inside the pelletizer (PLZ) (see Figure 4). The carbon black pellet image is made up of pixels, and if the number of image model channels is m (ex. m = 3 for RGB model, m = 4 for CMYK model), the carbon black pellet image is (number of horizontal pixels It can be composed of a matrix of number If n is the number of color depths in the carbon black pellet image, the elements of the matrix have values ranging from 0 to 2 ⁿ -1. For example, when n=8, the elements of the matrix have a value between 0 and 255.

The imaging device (CAM) can acquire a plurality of carbon black pellet images. As an example, the photographing device (CAM) can continuously photograph 300 carbon black pellet images and transmit them to the control unit (CPU).

Figure 5 is a flow chart to explain the steps of processing a carbon black pellet image. Referring to Figure 5, the step of processing the carbon black pellet image (S200) includes converting the carbon black pellet image into a black and white image (S210), selecting a region of interest in the carbon black pellet image (S220), and carbon black pellet image processing (S200). Converting the brightness of the pellet image (S230), removing noise from the carbon black pellet image (S240), converting the carbon black pellet image into a binarized image (S250), and classifying carbon black pellets that can be analyzed. It may include step S260.

The image reception unit (IMR) of the control unit (CPU) can receive the carbon black pellet image from the imaging device (CAM). As an example, the image receiver (IMR) may receive a plurality of carbon black pellet images from the imaging device (CAM).

The first unit (UN1) of the control unit (CPU) can convert the carbon black pellet image received by the image receiver (IMR) into a black and white image (S210). The carbon black pellet image has m values for each pixel coordinate by a matrix (number of horizontal pixels The carbon black pellet image can be converted to a black and white image by multiplying and adding m values by m constants that add up to 1. The second unit (UN2) of the control unit (CPU) may select the region of interest (IR) of the carbon black pellet image converted into a black and white image by the first unit (UN1) (S220).

Figure 6 is a conceptual diagram for explaining the steps of processing a carbon black pellet image. Referring again to FIG. 4, a portion of the pelletizer (PLZ) may be photographed by the imaging device (CAM), and a problem of blurring of focus toward the periphery of the image may occur. To solve this problem, the second unit (UN2) of the control unit (CPU) can select and crop only the region of interest (IR) in the center of the image (see FIG. 6). Carbon black pellets Carbon black pellets (PET) in the image may include empty space (HO) therein.

The third unit (UN3) of the control unit (CPU) can convert the brightness of the carbon black pellet image cut out by selecting the region of interest (IR) (S230). In the binarization image conversion step, which will be described later, a step of converting the image brightness may be preemptively performed so that the processing results of carbon black pellets (PET) with the same brightness do not come out differently.

First, find the minimum and maximum values of brightness for each pixel of the current black and white image. If the number of color depths is n, the minimum value of brightness per pixel that an image can have is 0 and the maximum value is 2 ⁿ -1. The brightness of the image can be converted by replacing the minimum value of the brightness of each pixel of a black and white image with 0 and the maximum value with 2 ⁿ -1, and replacing the brightness values in between while maintaining the ratio.

The fourth unit (UN4) of the control unit (CPU) can remove noise from the brightness-converted carbon black pellet image (S240). As an example, noise removal may be performed using a Non-local means filter (NLM) algorithm.

The fifth unit (UN5) of the control unit (CPU) can convert the noise-removed carbon black pellet image into a binary image (S250). Based on a specific brightness value, pixels with a larger value are converted to 1 or 0, and pixels with a smaller value are converted to 0 or 1, thereby converting the carbon black pellet image into a binarized image. As an example, binarization image conversion can be performed using Otsu's method algorithm.

The sixth unit (UN6) of the control unit (CPU) can classify carbon black pellets that can be analyzed from the carbon black pellet image converted into a binarized image (S260). Carbon black pellets may clump together in the pelletizer to form clumps. In this case, the error in the size value of the carbon black pellets may increase when analyzing the image. To prevent this, the agglomerated carbon black pellets are removed and the carbon black pellets that can be analyzed are classified.

Figure 7 is a flow chart to explain the step of classifying carbon black pellets. Figures 8 to 11 are conceptual diagrams for explaining the steps of classifying carbon black pellets.

Referring to FIG. 7, the step of classifying the carbon black pellets that can be analyzed (S260) includes the step of filling the empty space surrounded by the carbon black pellets (S261), and the step of removing the carbon black pellets in contact with the boundary of the carbon black pellet image (S261). S262), a step of removing carbon black pellets smaller than the preset size (S263), and a step of removing agglomerated carbon black pellets using circularity (S264).

Referring to FIG. 8, the sixth unit (UN6) of the control unit (CPU) can fill the empty space surrounded by carbon black pellets (PET) on the carbon black pellet image (S261). Referring again to FIG. 6, carbon black pellets (PET) may include empty space (HO) therein. For more precise size distribution analysis, pixels belonging to empty space (HO) can be converted and filled with pixels of the same brightness as carbon black pellets (PET).

Referring to FIG. 9, the sixth unit (UN6) of the control unit (CPU) can remove the carbon black pellet (PET) contacting the boundary of the carbon black pellet image (S262). Carbon black pellets (PET) that come into contact with the border of the carbon black pellet image are removed because their size cannot be measured accurately.

Referring to FIG. 10, the sixth unit (UN6) of the control unit (CPU) can remove carbon black pellets (PET) of a size smaller than the preset size (S263). For example, the preset size input to the sixth unit (UN6) of the control unit (CPU) may be 50 μm, and carbon black pellets (PET) having a size smaller than this may be removed.

Referring to FIG. 11, the sixth unit UN6 of the control unit (CPU) can remove the agglomerated carbon black pellets using circularity (S264). In order to measure the circularity of each carbon black pellet (PET), the sixth unit (UN6) of the control unit (CPU) can measure the area and perimeter of each carbon black pellet (PET). Here, the circularity of carbon black pellets (PET) can be expressed by the following equation.

Here, c is the circularity of the carbon black pellet (PET), A is the area of the carbon black pellet (PET), and l is the perimeter of the carbon black pellet (PET).

After measuring the circularity of the carbon black pellets (PET), the sixth unit (UN6) of the control unit (CPU) removes the carbon black pellets (PET) whose circularity is less than the first value or exceeds the second value. can do. The first value may be 1 - critical circularity, and the second value may be 1 + critical circularity. The critical circularity may be a value arbitrarily input to the sixth unit (UN6) of the control unit (CPU). For example, when the critical circularity is input as 0.3, the first value may be 0.7 and the second value may be 1.3. In this case, among the carbon black pellets (PET), only those with a circularity of 0.7 to 1.3 are classified and the rest can be removed from the image.

Figure 12 is a flowchart for explaining the steps of acquiring weight fraction data of carbon black pellets.

Referring to Figure 12, the step of acquiring the weight fraction data of the carbon black pellets (S300), the step of calculating the diameter of the classified carbon black pellets (S310), and the step of calculating the volume of the classified carbon black pellets (S320). , and classifying the classified carbon black pellets by size to obtain weight fraction data (S330).

The data acquisition unit (DAA) can calculate the diameter of carbon black pellets (PET) that can be analyzed from the processed carbon black pellet image. First, the area of the carbon black pellets (PET) can be calculated and the diameter of the carbon black pellets (PET) can be calculated using the equation below.

Here, d is the diameter of the carbon black pellet (PET), and A is the area of the carbon black pellet (PET).

Since the calculated diameter of carbon black pellets (PET) has pixel units, it is necessary to convert it to metric units.

Figure 13 is an image taken before image processing to convert pixel units to metric units. Referring to FIG. 13, before image processing, laser lines (LL) were emitted from two laser pointers, respectively, and a laser line (LL) image was acquired through an imaging device (CAM). The distance between the actual laser lines (LL) (metric units) and the distance between the laser lines on the image (pixel units) can be measured and then input into the data acquisition unit (DAA). The data acquisition unit (DAA) can convert the diameter units of carbon black pellets (PET) into metric units using the equation below.

The data acquisition unit (DAA) can calculate the volume of the carbon black pellet (PET) from the diameter of the carbon black pellet (PET) using the equation below.

Here, V is the volume of the carbon black pellet (PET), and d is the diameter of the carbon black pellet (PET).

After classifying the carbon black pellets (PET) by size using the calculated volume of the carbon black pellets (PET), weight fraction data of the carbon black pellets (PET) can be obtained. Here, the size of carbon black pellets (PET) can be classified according to ASTM standards (see Table 1).

Standard Mesh Number Sieve Opening Size (μm) No. 120 125 No. 60 250 No. 35 500 No. 18 1000 No. 14 1400 No. 12 1700 No. 10 2000

Figure 14 is a graph showing weight fraction data obtained according to the carbon black pellet size distribution analysis method according to embodiments of the present invention.

When the control unit (CPU) receives a plurality of carbon black pellet images, the weight fraction data is derived by integrating the analysis results of the plurality of carbon black pellet images. For example, when the control unit (CPU) receives 300 carbon black pellet images from the imaging device (CAM), it calculates the volume of all carbon black pellets in the 300 images and uses this to derive the final weight fraction data. .

According to the weight fraction data, the control unit (CPU) can control the pelletizer (PLZ). Although not shown, the control unit (CPU) may further include a seventh unit (not shown). As an example, the seventh unit (not shown) may be connected to a network with the pelletizer (PLZ) to control the operation of the pelletizer (PLZ).

A seventh unit (not shown) may analyze weight fraction data. The seventh unit (not shown) may control the pelletizer (PLZ) so that the weight fraction of carbon black pellets (PET) between the first and second sizes is greater than or equal to a preset weight fraction.

As an example, the first size input to the 7th unit (not shown) is Mesh No. 35, the second size is Mesh No. It may be 18, and the preset weight fraction may be 70%. The 7th unit (not shown) is Mesh No. 18 and no. The pelletizer (PLZ) can be controlled so that the weight fraction of carbon black pellets (PET) of size 35 is more than 70%. For example, the 7th unit (not shown) has Mesh No. 18 and no. If the weight fraction of carbon black pellets (PET) of size 35 is 65%, the mixing agent input amount and operating speed of the pelletizer (PLZ) are controlled to control Mesh No. 18 and no. The weight fraction of carbon black pellets (PET) of size 35 can be controlled to exceed 70%.

The carbon black pellet size distribution analysis method according to an embodiment of the present invention uses image processing, so it is possible to obtain weight fraction data of carbon black pellets in close real time. Accordingly, it is possible to control the size of the carbon black pellets being manufactured in real time.

Figure 15 is a graph comparing the results of the carbon black pellet size distribution analysis method according to the embodiments of the present invention and the results of the carbon black pellet size distribution analysis method according to the comparative example of the present invention. 16A to 16D show weight fraction data obtained according to the carbon black pellet size distribution analysis method according to embodiments of the present invention and weight fraction data obtained according to the carbon black pellet size distribution analysis method according to the comparative example of the present invention, respectively. This is a graph comparing data. Figures 16a to 16d show weight fraction data acquired on different shooting dates, respectively.

Referring to FIGS. 15 and 16A to 16D, it was confirmed that the weight fraction data according to the embodiments of the present invention and the weight fraction data according to the comparative example showed a similar tendency, but some differences were observed. The weight fraction data according to the comparative example is data obtained using the conventional ASTM D1511 method.

Figure 17 is a graph comparing weight fraction data obtained according to the carbon black pellet size distribution analysis method according to embodiments of the present invention and weight fraction data with correction applied thereto. Figure 18 shows the results of the carbon black pellet size distribution analysis method according to the embodiments of the present invention, the result when correction is applied thereto, and the result of the carbon black pellet size distribution analysis method according to the comparative example of the present invention. This is a graph comparing . 19A to 19D show weight fraction data obtained according to the carbon black pellet size distribution analysis method according to the embodiments of the present invention, weight fraction data with correction applied thereto, and carbon black pellets according to the comparative example of the present invention, respectively. This is a graph comparing the weight fraction data obtained according to the size distribution analysis method. Figures 19a to 19d show weight fraction data acquired on different shooting dates, respectively.

In order to increase the accuracy of the carbon black pellet size distribution analysis method according to the embodiments of the present invention, correction of the analysis results was performed. A linear model is calculated under the assumption that there is a linear relationship between the analysis results according to the embodiments of the present invention and the comparative examples, and the derived correction coefficient is used as the result for the carbon black pellet size distribution analysis method according to the embodiments of the present invention. multiplied by . The weight fraction data according to the comparative example is data obtained using the conventional ASTM D1511 method.

Referring to FIGS. 17, 18, and 19A to 19D, it can be seen that when correction is applied to the weight fraction data according to the embodiments of the present invention, results are more similar to the weight fraction data according to the comparative example. .

As a result, it can be seen that the carbon black pellet size distribution analysis method using image processing also produced similar results to the ASTM D1511 method, which is a physical analysis method. That is, according to embodiments of the present invention, it is possible to analyze the size distribution of carbon black pellets in close real time and simultaneously derive analysis results with high accuracy.

Although embodiments of the present invention have been described above with reference to the attached drawings, those skilled in the art will understand that the present invention can be implemented in other specific forms without changing its technical idea or essential features. You will understand that it exists. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

Claims (13)

Obtaining an image of carbon black pellets from a pelletizer;
performing an algorithm for processing the obtained carbon black pellet image; and
Obtaining weight fraction data of carbon black pellets from the processed carbon black pellet image,
The steps for performing the algorithm for processing the carbon black pellet image are:
Converting the carbon black pellet image into a black and white image;
Selecting a region of interest from the carbon black pellet image;
Converting the brightness of the carbon black pellet image;
Removing noise from the carbon black pellet image;
Converting the carbon black pellet image into a binarized image; and
A carbon black pellet size distribution analysis method comprising classifying the carbon black pellets that can be analyzed from the carbon black pellet image.
According to paragraph 1,
The step of classifying the analyzable carbon black pellets is:
filling empty spaces within the carbon black pellets of the carbon black pellet image;
removing the carbon black pellets in contact with a boundary of the carbon black pellet image;
Removing the carbon black pellets of a size smaller than a preset size; and
A carbon black pellet size distribution analysis method including the step of removing agglomerated carbon black pellet lumps using circularity.
According to paragraph 2,
The steps for removing the agglomerated carbon black pellets are:
Measuring the circularity of the carbon black pellets; and
A carbon black pellet size distribution analysis method comprising the step of removing carbon black pellets whose circularity is less than a first value or more than a second value.
According to paragraph 1,
After acquiring the weight fraction data,
A carbon black pellet size distribution analysis method further comprising controlling the pelletizer.
According to paragraph 4,
Controlling the pelletizer is:
A carbon black pellet size distribution analysis method comprising controlling the pelletizer so that the weight fraction of the carbon black pellets between the first size and the second size is more than a preset weight fraction.
According to paragraph 1,
The steps for acquiring the weight fraction data are:
measuring the diameter of the carbon black pellets that can be analyzed;
measuring the volume of the carbon black pellets available for analysis; and
A carbon black pellet size distribution analysis method comprising the step of classifying the analyzeable carbon black pellets by size and obtaining the weight fraction data.
According to paragraph 1,
The step of acquiring the carbon black pellet image is,
Irradiating light from a lighting device to the pelletizer; and
Including acquiring an image of the carbon black pellet through an imaging device,
A carbon black pellet size distribution analysis method in which the operating time of the lighting device corresponds to the operating time of the imaging device.
delete An imaging device that acquires images of carbon black pellets from the pelletizer;
An illumination device that irradiates light to obtain an image of the carbon black pellet;
a control unit that adjusts the direction and angle of each of the photographing device and the lighting device;
a lighting controller that controls the lighting device; and
Including a control unit for processing the obtained carbon black pellet image,
The control unit:
an image receiving unit that receives the carbon black pellet image from the photographing device;
A first unit converting the carbon black pellet image into a black and white image;
a second unit selecting a region of interest from the carbon black pellet image;
a third unit converting the brightness of the carbon black pellet image;
a fourth unit that removes noise from the carbon black pellet image;
A fifth unit converting the carbon black pellet image into a binarized image;
a sixth unit classifying the carbon black pellets that can be analyzed from the carbon black pellet image; and
A carbon black pellet size distribution analysis device comprising a data acquisition unit that acquires weight fraction data of the carbon black pellets from the processed carbon black pellet image.
According to clause 9,
The sixth unit is,
Filling empty spaces within the carbon black pellets of the carbon black pellet image,
Remove the carbon black pellets in contact with the boundary of the carbon black pellet image,
Remove the carbon black pellets of a size smaller than the preset size,
Measure the circularity of the carbon black pellets,
A carbon black pellet size distribution analysis device that performs an algorithm to remove carbon black pellets whose circularity is less than a first value or exceeds a second value.
According to clause 9,
The control unit,
After acquiring the weight fraction data, a carbon black pellet size distribution analysis device for controlling the pelletizer so that the weight fraction of the carbon black pellets between the first size and the second size is more than a preset weight fraction.
According to clause 9,
The data acquisition unit,
Measure the diameter of the carbon black pellets that can be analyzed,
Measure the volume of the carbon black pellets available for analysis,
A carbon black pellet size distribution analysis device that classifies the carbon black pellets that can be analyzed by size and performs an algorithm to obtain the weight fraction data.
According to clause 9,
The lighting controller is,
A carbon black pellet size distribution analysis device that controls the lighting device so that the operating time of the lighting device corresponds to the operating time of the imaging device.

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