Reducibility evaluation method

Abstract

To evaluate reducibility of a material having the reducibility with precision without performing a reducibility test.SOLUTION: The present invention is a method for evaluating reducibility of a material having the reducibility, comprising: an image acquisition step of acquiring an X-ray CT image representing a three-dimensional shape of the material; a porosity calculating step of identifying a pore region, which is a region in which pores exist in the material, from a grey scale image of the X-ray CT image to calculate the porosity that is a volume fraction of the pore region; and a reducibility evaluation step for evaluating the reducibility of the material, based on the porosity, in which in the porosity calculation step, only an open pore region that is a pore region connected with the outside of the material is identified, and the open pore rate that is a volume fraction of the open pore region is taken as the porosity.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for evaluating reducibility. [Background technology]

[0002] When evaluating the quality of a porous material, information about the pores of the porous material is one of the important items. In particular, when considering the reactivity of a porous material, the size of the contact area with a reacting substance is important. Therefore, various methods have been proposed to measure the porosity, which is the proportion of pores in a porous material.

[0003] Conventional methods for measuring the porosity of porous materials include the so-called "water method" based on JIS K2151:2004 and the "PAC method" using a vacuum packer (Kasama et al., Iron and Steel, 83 (1997) p.109-114). However, these measurement methods require sample crushing, so they can only be applied to materials for which multiple samples in a similar state can be obtained. In the case of highly heterogeneous porous materials, it is difficult to obtain multiple samples in a similar state, so even if the porosity can be calculated using the "water method" or "PAC method," it is thought that there are many errors superimposed in the correlation between the obtained porosity and reactivity such as reducibility. Therefore, a porosity measurement method that can suppress errors in the correlation between porosity and reactivity such as reducibility has been desired, even for highly heterogeneous porous materials.

[0004] From this perspective, for example, Patent Document 1 listed below proposes a method for measuring the porosity of a porous material using X-ray CT measurement, which can obtain an image showing the three-dimensional shape of the material without destroying the material. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2019-82388 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, even if the method proposed in Patent Document 1 is used, there is still room for improvement in terms of porosity measurement error, and a method capable of measuring porosity with higher accuracy is desired.

[0007] On the other hand, sintered ore used in the steel industry for reducing iron ore is a porous material having reducibility. However, since such sintered ore is a highly heterogeneous material, a method capable of accurately evaluating the reducibility of sintered ore without conducting a reduction test has not yet been established.

[0008] Under these circumstances, the present inventors came up with the idea that if the porosity of a reducible porous material could be measured with higher accuracy, it would be possible to accurately evaluate the reducibility of such a material.

[0009] Therefore, the present invention has been made in consideration of the above idea, and an object of the present invention is to provide a method for evaluating reducibility that can accurately evaluate the reducibility of a material having reducibility without conducting a reduction test. [Means for solving the problem]

[0010] As a result of further investigations based on the above-mentioned idea, the inventors discovered that by identifying only the pores that contribute to reducibility and using the porosity based on such pores, it is possible to accurately evaluate the reducibility of a material of interest without conducting a reduction test. The gist of the present invention, which was completed based on these findings, is as follows.

[0011] (1) A method for evaluating the reducibility of a material having reducibility, comprising: an image acquiring step of acquiring an X-ray CT image showing a three-dimensional shape of the material; a porosity calculating step of identifying pore regions, which are regions in the material where pores exist, from a grayscale image of the X-ray CT image, and calculating a porosity, which is the volume fraction of the pore regions; and a reducibility evaluating step of evaluating the reducibility of the material based on the porosity, wherein in the porosity calculating step, only open pore regions, which are pore regions connected to the outside of the material, are identified, and the open porosity, which is the volume fraction of the open pore regions, is taken as the porosity. (2) The reducibility evaluation method according to (1), wherein in the porosity calculation step, a closing process is performed on the grayscale image, and a portion having a pore diameter equal to or smaller than a predetermined maximum pore diameter is identified as the open pore region. (3) The reducibility evaluation method according to (1), wherein in the porosity calculation step, an ambient occlusion process is performed on the grayscale image, and a portion having a pixel value within a predetermined range is identified as the open pore region. (4) The reducibility evaluation method according to any one of (1) to (3), wherein in the reducibility evaluation step, the reducibility of the material is evaluated by comparing with a group of porosity data calculated based on common processing conditions. (5) The reducibility evaluation method according to any one of (1) to (4), wherein in the reducibility evaluation step, the reduction rate of the material of interest is calculated from the porosity calculated in the porosity calculation step based on a relational equation between the porosity and the reduction rate of the material that has been obtained in advance. (6) The method for evaluating reducibility according to any one of (1) to (5), wherein the material is iron ore, iron ore pellets, or sintered ore. Effect of the Invention

[0012] As described above, according to the present invention, it is possible to accurately evaluate the reducibility of a material having reducibility without carrying out a reduction test. [Brief description of the drawings]

[0013] [Figure 1] 1 is a flow chart showing an example of the flow of a reducibility evaluation method according to an embodiment of the present invention. [Diagram 2] 11 is a flowchart showing an example of a flow of a porosity calculation step according to the embodiment. [Diagram 3] FIG. 11 is an explanatory diagram for explaining a closed pore region elimination process performed on a grayscale image of an X-ray CT image. [Figure 4] FIG. 11 is an explanatory diagram for explaining a closing process performed in a porosity calculation step according to the embodiment. [Diagram 5] FIG. 11 is an explanatory diagram for explaining an ambient occlusion process performed in a porosity calculation step according to the embodiment. [Figure 6] FIG. 2 is an explanatory diagram illustrating a correlation between porosity and reduction rate. [Figure 7] 1 is a table showing the verification results using a model sintered tablet sample. [Figure 8A] FIG. 13 is a graph showing verification results using a model sintered tablet sample. [Figure 8B] FIG. 13 is a graph showing verification results using a model sintered tablet sample. [Figure 9] 13 is a table showing verification results using sintered ore. [Figure 10] FIG. 13 is a graph showing verification results using sintered ore. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0015] (About the reducibility evaluation method) Hereinafter, the reducibility evaluation method according to the embodiment of the present invention will be described in detail with reference to FIGS.

[0016] FIG. 1 is a flow chart showing an example of the flow of the reducibility evaluation method according to this embodiment. The reducibility evaluation method according to this embodiment focuses on a material having reducibility and evaluates the reducibility of such a material.

[0017] <About the target materials> Here, the material having reducibility (hereinafter, sometimes simply referred to as "material") is not particularly limited, and the reducibility evaluation method according to the present embodiment can be applied to various porous materials used as reducing materials. The reducibility evaluation method according to the present embodiment is particularly useful when focusing on highly heterogeneous porous materials for which it is difficult to obtain multiple samples in the same state. Examples of such highly heterogeneous porous materials include iron ore, iron ore pellets, sintered ore, etc., used in the iron making process. Here, the iron ore pellets are made by adding water, auxiliary materials (limestone, dolomite, etc.), and if necessary, binders (bentonite, etc.) to iron ore in a fine powder state (fine ore), forming them into spheres with a diameter of 10 to 30 mm, and baking them. Sintered ore, which accounts for the majority of the iron source used in the iron making process, is a material having reducibility, and contains Fe2O3, CaCO3, AlOOH, SiO2, etc. as its chemical components, as main components, and further contains impurities. The size of the material of interest is not particularly limited, but is preferably, for example, about 10 to 20 mm in diameter.

[0018] As shown in FIG. 1, the reducibility evaluation method according to this embodiment includes an image acquisition step S11, a porosity calculation step S13, and a reducibility evaluation step S15.

[0019] <Image acquisition step S11> The image acquisition step S11 is a step of acquiring an X-ray CT image that represents the three-dimensional shape of a material of interest.

[0020] Here, an X-ray CT image is an image obtained by detecting the intensity of X-rays transmitted through a sample of interest. Therefore, such an X-ray CT image has pixel values ​​that reflect not only the surface shape of the sample but also the three-dimensional structure including the internal state of the sample. In other words, suppose there are two samples with exactly the same outer shape, one of which is solid and the other of which contains bubbles inside. In this case, when X-ray CT images of the two samples are obtained, the pixel values ​​constituting each image will be different from each other, even though the outer shapes of the samples are the same.

[0021] As an image acquisition device for acquiring such an X-ray CT image, for example, an X-ray CT device as disclosed in the above-mentioned Patent Document 1 can be used.

[0022] The pores to be measured in the present invention are relatively large pores, generally ranging in size from 5 μm to several mm, although this depends on the resolution of the X-ray CT device used.

[0023] <Porosity calculation step S13> The porosity calculation step S13 is a step performed by various image analysis applications (e.g., 3D visualization and analysis application Avizo, etc.) using various information processing devices such as various computers and servers. The porosity calculation step S13 is a step of identifying pore regions, which are regions in which pores exist in the material of interest, from the grayscale image of the X-ray CT image acquired in the image acquisition step S11, and calculating the porosity, which is the volume fraction of the pore regions. More specifically, in the porosity calculation step S13, only open pore regions, which are pore regions connected to the outside of the material, are identified, and the open porosity, which is the volume fraction of the open pore regions, is taken as the porosity.

[0024] When evaluating the reducibility of a material that has reducibility, it is believed that what contributes to reducibility are pores connected to the outside of the material, through which raw materials (e.g., CO gas in the case of sintered ore) can enter from the outside in order for the reduction reaction to proceed. Therefore, it is believed that pores that are closed only inside the material contribute less to the reduction reaction than pores that are connected to the outside. Therefore, in order to properly evaluate the reducibility of a material, it was conceived that it is important to distinguish only the pores that are connected to the outside from the two types of pores that can exist in the material (i.e., pores that are connected to the outside and pores that are closed only inside).

[0025] Hereinafter, pores that are connected to the outside will be referred to as "open pores," and pores that are closed only internally will be referred to as "closed pores." Furthermore, in the material of interest, a region in which "open pores" exist will be referred to as an "open pore region," and a region in which "closed pores" exist will be referred to as a "closed pore region."

[0026] In the porosity calculation step S13 according to this embodiment, only the open pore regions as described above are identified by performing specific image processing on the grayscale image of the X-ray CT image.

[0027] The porosity calculation step S13 according to this embodiment will be described in detail below with reference to FIGS. Fig. 2 is a flow chart showing an example of the flow of the porosity calculation step according to this embodiment. Fig. 3 is an explanatory diagram for explaining the closed pore region excluding process performed on the grayscale image of the X-ray CT image. Fig. 4 is an explanatory diagram for explaining the closing process performed in the porosity calculation step according to this embodiment. Fig. 5 is an explanatory diagram for explaining the ambient occlusion process performed in the porosity calculation step according to this embodiment.

[0028] 2, the porosity calculation step S13 according to this embodiment includes a closed pore region exclusion step S101 for excluding closed pore regions from the grayscale image of the X-ray CT image, an open pore region identification step S103 for identifying open pore regions by performing a closing process or an ambient occlusion process on the grayscale image after excluding the closed pore regions, and an open porosity calculation step S105 for calculating the open porosity, which is the volume fraction of the open pore regions, as the porosity. Note that the closed pore region exclusion step S101 described below may be performed after the open pore region identification step S103.

[0029] A specific description will be given below with reference to Figs. 3 to 5. Figs. 3 to 5 show sintered ore as an example of a material. For convenience in creating the drawings, Figs. 3 to 5 show cross-sectional images of grayscale images of X-ray CT images. However, actual processing is performed on the grayscale images of X-ray CT images that represent a three-dimensional shape.

[0030] [Closed pore region exclusion step S101] The closed pore region excluding step S101 is a step of performing various processes on the grayscale image of the X-ray CT image to exclude closed pore regions from the grayscale image of the X-ray CT image of interest. As shown in FIG. 3(a), recesses that are candidates for open pores or closed pores exist in the grayscale image of the X-ray CT image before processing. In the example shown in FIG. 3(a), a cross-sectional image is shown for convenience, so that such recesses that are candidates for open pores or closed pores exist as holes. In the closed pore region excluding step S101, portions corresponding to closed pore regions are excluded from such regions that are candidates for open pores or closed pores.

[0031] Specifically, as shown in Fig. 3(b), the pixel values ​​of the pixels constituting the grayscale image are first threshold-judged to identify the edge corresponding to the outline of the material of interest, thereby making it possible to identify the area occupied by the material in the grayscale image (hereinafter also referred to as the "substrate area").

[0032] Next, a process for identifying closed pore regions is performed on the image shown in Figure 3(b). Such a process is not particularly limited, and for example, the "Fill Hole" function of the 3D visualization and analysis application Avizo can be used to remove closed pore regions (more specifically, to erase the parts corresponding to closed pore regions). In addition, the method is not limited to this, and for example, it is possible to identify closed pore regions by actually analyzing the interconnected structure of pores and remove them from the grayscale image. By such a process, an image from which closed pore regions have been removed can be obtained, as shown in Figure 3(c). It may appear that closed pores remain in Figure 3(c), but this is because it is displayed in two dimensions. When viewed in three dimensions, it can be confirmed that only open pores exist.

[0033] [Open pore area identification step S103] The grayscale image after the closed pore region elimination process described above is subjected to the open pore region identification step S103. The open pore region identification step S103 is a step of identifying the open pore region by performing a closing process or an ambient occlusion process on the grayscale image after the closed pore region has been excluded.

[0034] Here, the closing process is an image processing method that performs dilation processing on the image of interest and then compression (erosion) processing. Meanwhile, the ambient occlusion process is an image processing method that assumes that light is applied to the image of interest from all directions and generates a new image that takes into account information on the shadows that are generated by applying light. In the open pore region identification step S103 according to this embodiment, the open pore regions that exist in the grayscale image after the closed pore region exclusion process are identified by the closing process or the ambient occlusion process.

[0035] <Closing process> First, the process of identifying an open pore region using the closing process will be specifically described with reference to FIG. As described above, the closing process is a process in which the image of interest is first expanded by a specified size and then compressed by a specified size. Consider a case in which a hole of a certain size exists in an image, as shown in Fig. 4(a). When the closing process is performed on such a hole, it can be seen that, depending on the size of the expansion / compression (this size will be referred to as the "kernel size" below), the hole that originally existed in the image may or may not be reproduced as a hole after expansion / compression. Therefore, when identifying open pore regions by the closing process, how to set this kernel size is important.

[0036] Now, suppose that we want to identify as open pores those pores whose size is equal to or smaller than the area enclosed by the dashed line in the image shown in Fig. 4(a). In this case, we specify the maximum distance of the area enclosed by the dashed line in Fig. 4(a). The size of the pixels that make up the image (pixel size) gives the spatial resolution for identifying open pores. If the maximum distance in Fig. 4(a) is 4 mm and the pixel size is 23 μm / pixel, the kernel size is 4 mm / (23 μm / pixel×2)=87 pixels. Here, the pixel size is doubled in the above calculation because expansion occurs from both ends of the area enclosed by the dashed line during expansion. By setting the kernel size to 87 pixels or more, pores whose diameter is 4 mm or less will be reproduced as pores after expansion and compression. As described above, when identifying open pore regions, the maximum size to be identified (i.e., the maximum pore diameter) is determined in advance through prior verification of the material of interest, and the minimum kernel size is determined by the above calculation according to the pixel size of the grayscale image of the X-ray CT image. If it is difficult to determine the above, the maximum pore diameter may be calculated as 1 / 2 the sample radius.

[0037] According to the verification by the present inventors, when analyzing iron ore, iron ore pellets, and sintered ore having a diameter of 10 to 20 mm, if the kernel size is set to 30 pixels or more, it is possible to perform a sufficient analysis according to the desired maximum pore diameter. However, it is preferable to set the kernel size to a large value, for example, a value of about 100 pixels. In addition, it is preferable not to change such kernel size and pixel size during the evaluation. This makes it possible to calculate the porosity more accurately, and thus to evaluate the reducibility more accurately.

[0038] In this way, in the closing process, first, the process conditions are set as shown in Fig. 4(a). Note that, when analyzing samples to which common process conditions can be applied, such process conditions only need to be set once, and do not need to be changed for each analysis.

[0039] Once the processing conditions have been set in this manner, the image to be processed (i.e., the image from which the closed pore regions have been removed) is subjected to expansion and compression processing using the set kernel size. Figure 4(b) shows the result of performing closing processing with a kernel size of 100 pixels on the image shown in Figure 4(a). By setting this kernel size, holes up to approximately 4 mm in diameter are reproduced as holes after processing. After that, by subtracting the image shown in Figure 4(b) from the image shown in Figure 4(a), the open pore regions can be identified as shown in Figure 4(c).

[0040] Ambient Occlusion Processing Next, the process of identifying open hole regions using ambient occlusion processing will be specifically described with reference to FIG. As described above, Ambient Occlusion processing is an image processing method that generates a new image by assuming that light is applied from all directions to the image of interest and adding information about shadows that occur when light is applied. Now, Fig. 5(a) shows an image obtained by applying Ambient Occlusion processing to the image shown in Fig. 3(c) from which the closed pore regions have been removed. As shown in Fig. 5(a), this processing adds shadows to the image that are caused by the shape of the target object and are generated by the light hitting it.

[0041] In such Ambient Occlusion processing, the main processing conditions are the maximum distance to a virtually placed light source (Maximum Distance) and the number of light sources (Number of Rays). These processing conditions may be set appropriately depending on the size and shape of the material of interest, but it is preferable to set both to as large a value as possible in order to obtain good accuracy. As a result of verification by the present inventors, for example, good accuracy can be obtained by setting the Maximum Distance to 34.8437 mm. In addition, it was found that the open pore region could be well identified by setting the Number of Rays to 6 or more, but it is preferable to set it to a value of about 50, for example.

[0042] Other processing conditions that can be set in the Ambient Occlusion process include, for example, the number of random rotations and the smoothing kernel radius. As a result of verification by the present inventors, it was found that although the value of the number of random rotations may be set to 0, setting it to a larger value enables more accurate processing. It is preferable to set the value of the number of random rotations to, for example, about 100. It was also found that the value of the smoothing kernel radius should be set to a value equal to or greater than 1 (for example, about 2).

[0043] For example, by performing processing under the above conditions, it is possible to obtain an image as shown in FIG. 5(a).

[0044] Then, the pixel value of each pixel constituting the obtained image is subjected to a threshold judgment, thereby making it possible to identify the open pore region. For example, when a pixel value between 0 and 1 is assigned to each pixel, it has been revealed that in order to identify the open pore region present in iron ore, iron ore pellets, and sintered ore, the minimum pixel value should be set to a range of 0.60 to 0.85, and the maximum pixel value should be set to 1. The region identified in this way is the open pore region. The open pore region obtained by performing the above-mentioned threshold judgment on the image obtained in FIG. 5(a) is shown in FIG. 5(b).

[0045] It is preferable that the above-mentioned processing conditions are not changed during the evaluation. This allows for a more accurate calculation of the porosity, and therefore a more accurate evaluation of the reducibility. Furthermore, when analyzing samples to which common processing conditions can be applied, it is sufficient to carry out the processing conditions once, and it is not necessary to change the processing conditions for each analysis.

[0046] As described above, in the open pore region identifying step S103 according to this embodiment, the open pore region is identified from the pre-processed grayscale image.

[0047] [Open porosity calculation step S105] When the open pore region is identified from the grayscale image of the X-ray CT image in the above manner, the volume of the open pore region can be specified. The volume can be specified by using one function of the application used for the analysis, or by analyzing the connection structure of the specified open pore region. Similarly to the volume of the open pore region, the volume of the substrate region is also specified for the grayscale image of interest. In this embodiment, the volume of the substrate region corresponds to the volume of FIG. 3(b), for example. In this case, it is necessary to specify the volume of the closed pore region as well. Since the volume of FIG. 3(c) is the total value of the substrate volume and the volume of the closed pore region, this total value is used. By specifying these three or two volumes, the open porosity, which is the volume fraction of the open pore region, can be calculated based on the following formula. In the reducibility evaluation method according to this embodiment, the open porosity obtained in this manner is treated as the porosity, which is the volume fraction of the pore region in the material of interest.

[0048] Open porosity [%] = {volume of open pore region / (volume of substrate region + volume of open pore region + volume of closed pore region)} x 100

[0049] The porosity calculation step S13 according to this embodiment has been described in detail above.

[0050] <Reducibility evaluation step S15> The reducibility evaluation step S15 may be performed by an information processing device such as various computers or servers, or may be performed by a human such as a production manager of the material of interest. The reducibility evaluation step S15 according to this embodiment is a step of evaluating the reducibility of the material of interest based on the porosity calculated in the porosity calculation step S13.

[0051] FIG. 6 is an explanatory diagram that illustrates the correlation between the porosity and the reduction rate. As shown in Fig. 6 and described below with specific examples, it has become clear that there is a high correlation between the porosity of a material having reducibility and the reducibility of such a material (for example, the reduction rate obtained by the reduction test method (JIS-RI) specified in JIS M8719:2009). Based on this correlation between the porosity (more specifically, the open porosity treated as the porosity) and the reduction rate, the reducibility of the material of interest can be evaluated from its porosity.

[0052] Here, such evaluation may be a qualitative evaluation such as "reducibility is poor / medium / good" based on the obtained porosity. Alternatively, a quantitative evaluation may be performed by calculating a specific value of the reduction rate from the obtained porosity using a linear equation (i.e., a so-called calibration curve) that represents the correlation as exemplified in FIG. 6. However, the higher the reducibility is (approximately 85% or more), the relationship between the open porosity and the reducibility tends to be closer to a logarithmic function. In this way, the so-called calibration curve does not need to be a straight line, and it is sufficient to set a curve or straight line that can appropriately represent the correlation.

[0053] Here, when evaluating the reducibility of a material, it is preferable to compare it with a data group of porosity calculated based on common processing conditions. Specifically, when evaluating based on porosity calculated based on a closing process, for example, it is preferable to compare it with a data group of porosity obtained from a closing process performed under common processing conditions (for example, a data group as shown in FIG. 6). By performing the evaluation in this manner, it is possible to more accurately evaluate the reducibility of the material of interest.

[0054] By carrying out such a process, it becomes possible to appropriately evaluate the reducibility of the material of interest.

[0055] The reducibility evaluation method according to this embodiment has been described in detail above.

[0056] (Specific example of reducibility evaluation method 1 - Evaluation of model sintered tablet sample) In the following, a model sintered tablet sample simulating sintered ore was produced by mixing standard reagents Fe2O3, CaCO3, AlOOH, and SiO2, adding polyethylene spheres, forming them into a tablet shape (diameter 13 mm x height 7 mm), and firing them at a maximum temperature of 1300°C. The porosity and pore shape of the model sintered ore tablet sample can be adjusted by adjusting the size and amount of the polyethylene spheres added. The average particle size of the polyethylene spheres used is two types, 20 μm and 1 mm. The polyethylene spheres disappear when fired at a maximum temperature of 1300°C, becoming pores. Since the composition of the above standard reagents was constant, the difference in the reducibility of the model sintered tablet reagents obtained can be considered to be the difference in the pore structure derived from the polyethylene spheres.

[0057] In this verification example, four model sintered tablet samples of each of 10 levels with controlled pores were manufactured. After that, they were used for the reducibility evaluation method using X-ray CT images according to this embodiment, the water method, the PAC method, and the reduction ratio measurement. The water method was performed in accordance with JIS K2141:2004, and the PAC method was performed in accordance with the method described in Tetsu-to-Haganen, 83 (1997) p.109-114. The reduction ratio was measured by performing reduction at 900°C for 180 minutes in accordance with the reduction test method (JIS-RI) specified in JIS M8719:2009.

[0058] The open porosity (Ambient Occlusion method was used) obtained by the reducibility evaluation method using X-ray CT images according to the present embodiment, the closed porosity obtained for comparison, the porosity obtained by the water method and the PAC method, and the reduction rate obtained by the reduction test method are shown in Fig. 7. Fig. 8A and Fig. 8B show the results shown in Fig. 7.

[0059] As shown in Fig. 8A, the relationship between the open porosity and the reduction ratio obtained from the grayscale image of the X-ray CT image is R 2 The value was 0.8735, while the relationship between the closed porosity and the reduction rate was R 2The value was 0.3508. From these results, it was confirmed that the pores that contribute to reducibility are not closed pores but open pores.

[0060] As shown in Fig. 8B, the relationship between the porosity and reduction rate obtained by the water method is R 2 The value was 0.8696, and the relationship between the porosity and reduction rate obtained by the PAC method was R 2 The value was 0.7291. These results confirmed that the relationship between the open porosity and reduction degree obtained from the grayscale image of the X-ray CT image has a higher correlation with the reduction degree than the porosity obtained by the water method and the PAC method.

[0061] In addition, as shown in FIG. 8B, the porosity values ​​obtained by the water method and the PAC method are highly correlated overall, but within the range of reduction rates of 60 to 80%, there is variation in the values, and the tendency that "the higher the porosity, the higher the reduction rate" is not observed. On the other hand, as is clear from FIG. 8A, when open porosity is used, a good correlation is obtained even within the range of reduction rates of 60 to 80%. From the above results, it was confirmed that the 900°C reducibility of the sample can be evaluated with high accuracy by using the open porosity from the grayscale images of the X-ray CT images.

[0062] (Specific example of reducibility evaluation method 2 - Evaluation of sintered ore) In this verification example, three sintered ore grains with a diameter of 20 mm were taken from the sintered ore fired in an actual sintering pot test and used as samples. The taken samples were measured using an X-ray CT device to determine the open porosity based on the reducibility evaluation method according to this embodiment, and the reduction rate was measured by performing reduction at 900°C for 180 minutes in accordance with the reduction test method (JIS-RI) specified in JIS M8719:2009.

[0063] The obtained results are shown in Fig. 9 and Fig. 10. From Fig. 10, it can be seen that an extremely high correlation is obtained between the open porosity and the reduction rate even in actual sintered ore. Furthermore, when the formula of the calibration curve was obtained from the results shown in Fig. 10, the formula (reduction rate [%]) = 49.385 × (open porosity [%]) + 55.788 was obtained. By using such a calibration curve, it is expected that even for sintered ore whose other reduction rates are unknown, it is possible to accurately estimate the reduction rate by measuring the open porosity using the reducibility evaluation method according to this embodiment.

[0064] The reducibility evaluation method according to this embodiment has been specifically described above with reference to FIGS. 8A to 10. FIG.

[0065] Although the preferred embodiment of the present invention has been described in detail above with reference to the accompanying drawings, the present invention is not limited to such an example. It is clear that a person having ordinary knowledge in the technical field to which the present invention pertains can conceive of various modified or altered examples within the scope of the technical ideas described in the claims, and it is understood that these also naturally belong to the technical scope of the present invention.

Claims

1. A method for evaluating the reducibility of a material having reducibility, comprising the steps of: an image acquisition step of acquiring an X-ray CT image representing a three-dimensional shape of the material; a porosity calculation step of identifying a pore region, which is a region in which pores exist in the material, from the grayscale image of the X-ray CT image, and calculating a porosity, which is a volume fraction of the pore region; A reducibility evaluation step of evaluating the reducibility of the material based on the porosity; Including, In the porosity calculation step, only open pore regions, which are pore regions connected to the outside of the material, are identified, and the open porosity, which is the volume fraction of the open pore regions, is taken as the porosity.

2. 2. The reducibility evaluation method according to claim 1, wherein in the porosity calculation step, a closing process is performed on the grayscale image, and a portion having a pore diameter equal to or smaller than a predetermined maximum pore diameter is identified as the open pore region.

3. 2. The reducibility evaluation method according to claim 1, wherein in the porosity calculation step, an ambient occlusion process is performed on the grayscale image, and a portion having a pixel value within a predetermined range is identified as the open pore region.

4. In the reducibility evaluation step, the reducibility of the material is evaluated by comparing with a data group of the porosity calculated based on common processing conditions. The reducibility evaluation method according to any one of claims 1 to 3.

5. In the reducibility evaluation step, the reduction rate of the material of interest is calculated from the porosity calculated in the porosity calculation step based on a relational equation between the porosity and the reduction rate of the material obtained in advance. The reducibility evaluation method according to any one of claims 1 to 4.

6. The reducibility evaluation method according to any one of claims 1 to 5, wherein the material is iron ore, iron ore pellets, or sintered ore.

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