Method for evaluating the hematite phase, method for estimating the characteristic values ​​of iron-containing ore, and method for producing agglomerate ore.

Abstract

This invention provides a method for accurately evaluating the state of the hematite phase contained in iron-containing ore. [Solution] A method for evaluating the hematite phase contained in an iron-containing ore, comprising: an acquisition step of acquiring distribution information of the crystal orientation of the hematite phase; and a definition step of defining the grain boundaries of the hematite particles constituting the hematite phase based on the distribution information of the crystal orientation.

Description

Technical Field

[0001] The present invention relates to a method for evaluating a hematite phase, a method for estimating characteristic values of an iron-containing ore, and a method for producing a lumped ore.

Background Art

[0002] Lumped ore is an artificial ore produced mainly from iron ore and used in operations such as blast furnaces. The quality of lumped ore is evaluated by characteristics such as strength and reducibility. If the lumped ore has high strength and high reducibility, it leads to an improvement in yield and energy efficiency in blast furnace operations, and an effect of reducing CO2 emissions is expected.

[0003] Iron-containing ores such as lumped ore have a structure containing a plurality of mineral phases, and the structure of the iron-containing ore is one of the factors affecting its characteristics. Therefore, various methods for evaluating the state of the structure have been studied in order to estimate the characteristics of the iron-containing ore.

[0004] For example, Patent Document 1 proposes a method for defining the boundary lines between different phases in the structure of sintered ore and a method for estimating the characteristics of sintered ore based on the boundary lines.

[0005] Patent Document 2 proposes a method for quantifying the phase fraction of a multi-component calcium ferrite having a predetermined crystal arrangement by X-ray diffraction method and Rietveld analysis, and evaluating the quality of sintered ore based on the phase fraction.

[0006] Patent Document 3 proposes a method for evaluating the reducibility of sintered ore based on the phase fractions of an SFCA phase and an SFCA-I phase, which are multi-component calcium ferrite phases, by distinguishing them using Kikuchi patterns obtained by EBSD.

[0007] Patent Document 4 proposes a method for discriminating mineral species using elemental analysis based on characteristic X-rays obtained by irradiating primary rays and mapping the structure image of sintered ore with mineral species.

Prior Art Documents

[0008] [Patent Document 1] Japanese Patent Publication No. 2014-215987 [Patent Document 2] Japanese Patent Publication No. 2023-100537 [Patent Document 3] Japanese Patent Publication No. 2020-169993 [Patent Document 4] Japanese Patent Publication No. 2020-91276 [Overview of the Initiative] [Problems that the invention aims to solve]

[0009] Accurately evaluating the state of the hematite phase, one of the mineral phases contained in iron-containing ore, is important for accurately estimating the properties of iron-containing ore. However, the methods proposed in Patent Documents 1 to 4 have not been able to accurately evaluate the state of the hematite phase contained in iron-containing ore.

[0010] This invention has been made in view of these circumstances, and aims to provide a method for accurately evaluating the state of the hematite phase contained in iron-containing ore. [Means for solving the problem]

[0011] As a result of diligent research, the inventors have found that the above objective can be achieved by adopting the following configuration.

[0012] 1. A method for evaluating the hematite phase contained in iron-containing ore, An acquisition step to acquire information on the distribution of crystal orientations of the hematite phase, A definition step is performed to define the grain boundaries of the hematite particles constituting the hematite phase based on the distribution information of the crystal orientations. A method for evaluating the hematite phase, comprising the following features.

[0013] 2. In the acquisition step, the evaluation method of the hematite phase according to 1 above, wherein distribution information of the crystal orientation of the hematite phase is acquired using any one of a diffraction contrast tomography method, an electron backscatter diffraction method, a polarized light microscopy method, and a birefringence microscopy method.

[0014] 3. The evaluation method of the hematite phase further includes a parameter calculation step of calculating a parameter related to the size of the hematite particles based on the grain boundary, in the evaluation method of the hematite phase according to 1 or 2 above.

[0015] 4. The evaluation method of the hematite phase further includes a classification step of classifying the hematite particles into two or more hematite phases based on the parameter, in the evaluation method of the hematite phase according to 3 above.

[0016] 5. A method for estimating a characteristic value of an iron-containing ore containing a hematite phase, comprising a fraction calculation step of calculating the fraction of each of the two or more hematite phases classified using the evaluation method of the hematite phase according to 4 above, and the fraction of each of a magnetite phase, a calcium ferrite phase, slag, and pores that may be contained in the iron-containing ore; and an estimation step of estimating the characteristic value of the iron-containing ore based on the fractions calculated in the fraction calculation step. A method for estimating a characteristic value of an iron-containing ore.

[0017] 6. A method for producing agglomerated ore, comprising estimating the characteristic value of the agglomerated ore using the method for estimating the characteristic value of the iron-containing ore according to 5 above; and changing the production conditions of the agglomerated ore based on the estimated characteristic value. A method for producing agglomerated ore.

Advantages of the Invention

[0018] According to the present invention, the state of the hematite phase contained in the iron-containing ore can be accurately evaluated.

Best Mode for Carrying Out the Invention

[0019] The present invention will be described below. The following description shows preferred embodiments of the present invention, and the present invention is not limited by the following description in any way.

[0020] <Method for Evaluating Hematite Phase> The method for evaluating the hematite phase according to an embodiment of the present invention is a method for evaluating the hematite phase contained in an iron-containing ore, and includes an acquisition step and a definition step.

[0021] [Iron-containing ore] In this embodiment, the hematite phase contained in the iron-containing ore is the object of evaluation. The iron-containing ore is not particularly limited, and may be, for example, massive ore, iron ore, etc. Examples of iron ore include lumpy ore. Examples of massive ore include sintered ore and pellets. Sintered ore is produced by sintering raw materials containing iron ore (especially powdered ore). On the other hand, pellets are produced by granulating raw materials containing iron ore (especially powdered ore) and binding and solidifying them thermally or cold, and shaping them into, for example, spherical shapes. By changing the production conditions when making iron ore into massive ore, the particle size, properties, etc. can be adjusted.

[0022] [Hematite phase] The above iron-containing ore contains a hematite phase as a mineral phase.

[0023] The hematite phase contained in iron-containing ore can be classified into two or more types of hematite phases (e.g., primary hematite and secondary hematite). Primary hematite is the hematite phase derived from the original ore that has not undergone melting, while secondary hematite is the hematite phase that crystallized after melting and has a skeletal crystal structure. If the hematite phase can be classified into two or more types, the properties of iron-containing ore can be estimated with high accuracy. However, conventional methods have either been unable to further classify the hematite phase or the accuracy of the classification has been insufficient. For example, since primary and secondary hematite have the same crystal structure, classification using X-ray diffraction was difficult. Similarly, it was difficult to classify primary and secondary hematite by brightness in optical microscope images. However, by utilizing the fact that secondary hematite differs from primary hematite in having a skeletal crystal structure, it is possible to classify primary and secondary hematite by observing the shape of the crystal with an optical microscope. However, the criteria for judgment lack objectivity, and the accuracy of the classification is insufficient. Therefore, in this invention, information on the distribution of crystal orientations of the hematite phase is used to accurately evaluate the state of the hematite phase (for example, to accurately classify the hematite phase into two or more types of hematite phases).

[0024] The hematite phase is composed of multiple hematite particles. These hematite particles are crystal grains with the same crystal orientation, and their grain boundaries can be defined using information about the distribution of crystal orientations within the hematite phase. Furthermore, unlike secondary hematite, primary hematite is considered to be an aggregate of smaller hematite particles. In this way, by utilizing the differences in the size of hematite particles between two or more hematite phases, hematite particles can be classified into two or more hematite phases (for example, a phase corresponding to primary hematite and a phase corresponding to secondary hematite).

[0025] The microstructure of iron-containing ore other than the hematite phase is not particularly limited, but other mineral phases may include the magnetite phase, calcium ferrite phase, and slag. The mineral phases of iron-containing ore may consist of the hematite phase, magnetite phase, calcium ferrite phase, and slag. In addition, iron-containing ore generally contains pores. The microstructure of iron-containing ore may consist of mineral phases and pores.

[0026] [Acquisition process] In the acquisition process, information on the distribution of crystal orientations of the hematite phase contained in the iron-containing ore is obtained. The distribution information of crystal orientations may be obtained as a 2D map such as image data, or as a 3D map.

[0027] When acquiring information on the distribution of crystal orientations, it is also possible to acquire either or both information on the distribution of the hematite phase and information on the distribution of microstructures other than the hematite phase at the same time.

[0028] Information on the distribution of crystal orientations can be obtained, for example, by measuring the distribution of crystal orientations.

[0029] The measurement may be performed by pre-treating the iron-containing ore to create an observation surface, and then measuring the observation surface. The observation surface may be a cross-section of the iron-containing ore. That is, information on the distribution of crystal orientations in the cross-sectional structure of the iron-containing ore may be obtained.

[0030] Pretreatment can be performed by processing the iron-containing ore with polishing, a cutter, or a laser. In pretreatment, it is preferable to mirror-polish the observation surface. Mirror polishing prevents diffuse reflection of electron beams or light, allowing for more accurate measurements. Furthermore, since iron-containing ore is generally irregular in shape and easily crumbles, it is preferable to embed the iron-containing ore in resin beforehand. The size of the iron-containing ore after pretreatment is not particularly limited and may be any size that can be measured by the measurement method described later, for example, a size that can be loaded into the measuring device.

[0031] Methods for measuring the distribution of crystal orientations may include diffraction contrast tomography, EBSD (electron beam backscatter diffraction), polarized light microscopy, and birefringence microscopy. EBSD is preferred because it allows for detailed measurement of the crystal orientation distribution. On the other hand, methods using microscopes (such as polarized light microscopy and birefringence microscopy) can measure a wide area in a single measurement and provide easy access to average information. Therefore, from the standpoint of ease of use, methods using microscopes are preferred.

[0032] (Diffraction contrast tomography) In diffraction contrast tomography, by irradiating a sample with X-rays while rotating it, the diffraction pattern of crystal grains irradiated at an angle satisfying Bragg's law is projected onto the detector. In this way, the distribution information of the crystal orientation of the entire sample can be obtained as a three-dimensional map. The sample should be cylindrical in shape and of a size that allows X-rays to pass through it for the energy of the X-ray generator.

[0033] (Electron backscatter diffraction (EBSD)) In EBSD, by irradiating the observation surface with an electron beam while it is tilted (for example, at a 70° angle), the diffracted electron beam appears as a Kikuchi pattern. By analyzing the Kikuchi pattern, information about the distribution of crystal orientations can be obtained.

[0034] (Polarization microscopy) Polarized light microscopy uses a polarized light microscope to obtain information on the crystal orientation distribution of the hematite phase. In images taken using a polarized light microscope (polarized light microscope images), differences in brightness are observed when the crystal orientation of the hematite phase differs. Therefore, information on the crystal orientation distribution of the hematite phase can be obtained based on the brightness distribution of the polarized light microscope images.

[0035] Furthermore, when determining the distribution information of the crystal orientation of the hematite phase, it is preferable to use the distribution information of the hematite phase to mask regions other than the hematite phase in the polarized light microscope image. The distribution information of the hematite phase can be obtained using images taken with a bright-field microscope (bright-field microscope image). It is also possible to obtain the distribution information of structures other than the hematite phase using the bright-field microscope image.

[0036] The following describes a specific example of polarized light microscopy.

[0037] First, bright-field and polarized light microscope images are acquired as optical microscope images. These images may be acquired using the same optical microscope. To acquire images, the optical microscope may have an imaging function, and it is preferable that an imaging device such as a camera is attached to the eyepiece. Bright-field microscope images can be acquired using the bright-field mode of the optical microscope. Polarized light microscope images can be acquired using the polarized light observation mode of the optical microscope. In this case, the polarizer and analyzer should be in a crossed nicol state or nearly crossed nicol state. It is preferable to observe with the polarizer shifted a few degrees from the crossed nicol state so that the contrast in the hematite phase is clear. To easily identify the region of the hematite phase in the polarized light microscope image, a polarized light microscope image of the same field of view as the bright-field microscope image may be acquired.

[0038] There are no particular upper or lower limits to the magnification of optical microscope images. However, increasing the magnification allows for more detailed images of individual regions, thus enabling more accurate observation. For this reason, a magnification of 50x or higher is preferable, and 100x or higher is more preferable. On the other hand, if the magnification is excessively high, the measurement time required to acquire image information increases, resulting in poor efficiency. Therefore, from the viewpoint of measurement efficiency, a magnification of 200x or lower is preferable.

[0039] There are no particular upper or lower limits to the size of the area from which optical microscope images are acquired, but to ensure tissue representativeness, it is preferable that the observation area of ​​the optical microscope image covers the entire observation surface. Similarly, from the same viewpoint, the size of the observation area of ​​the optical microscope image should be 25 mm. 2 It is preferable to have a minimum of 100 mm 2 It is more preferable to use the above. The upper limit of the size of the observation area of ​​the optical microscope image is, for example, 225 mm. 2 That is acceptable.

[0040] Optical microscope images can be captured while continuously changing the field of view. More specifically, it is preferable to capture images while continuously changing the field of view of the optical microscope. By acquiring multiple images while changing the field of view in this way, it is possible to easily obtain optical microscope images that combine high magnification and a wide observation area.

[0041] Next, distribution information of the hematite phase is obtained using the obtained bright-field microscope images. Specifically, the hematite phase region in the bright-field microscope image is identified based on its color tone. Image processing is preferably used to identify the hematite phase region. The image processing may, for example, involve setting a threshold for brightness values ​​and identifying phases with high brightness values ​​as the hematite phase. Alternatively, segmentation may be performed using a machine learning model. The machine learning model may be a model generated by supervised learning using training images. Segmentation is preferably performed using TWS (Trainable Weka Segmentation), a plugin for ImageJ. At the same time, distribution information of tissues other than the hematite phase can be obtained.

[0042] Next, using the distribution information of the hematite phase, the region of the hematite phase is identified in the polarized light microscope image, and the regions other than the hematite phase are masked to obtain a polarized light microscope image of only the hematite phase. Based on the brightness of the obtained polarized light microscope image of only the hematite phase, the distribution information of the crystal orientation can be obtained.

[0043] The method for obtaining information on the distribution of crystal orientations based on the brightness of a polarizing microscope image is not particularly limited. However, it is preferable to obtain information on the distribution of crystal orientations by classifying the brightness into multiple levels and assigning regions with the same brightness level to the same crystal orientation. The number of brightness levels is not limited, but five or more levels are preferable for more accurate evaluation. On the other hand, if the brightness classification is excessively large, the accuracy may actually decrease, so 15 levels or less is preferable. The number of brightness gradations in the polarizing microscope image to be classified is not limited, but for example, it may be 256 gradations.

[0044] (Birefringence microscopy) In birefringence microscopy, information on the crystal orientation distribution of the hematite phase is obtained using a birefringent microscope. In images taken using a birefringent microscope (birefringence microscope images), differences in brightness are observed when the crystal orientation of the hematite phase differs. Therefore, information on the crystal orientation distribution of the hematite phase can be obtained based on the brightness distribution of the birefringence microscope images.

[0045] The specific techniques for birefringence microscopy can be the same as those for polarized light microscopy. The optical microscope used should be one equipped with a polarizer and analyzer capable of receiving circularly polarized light, and capable of birefringence microscopy observation.

[0046] [Definition Process] In the definition process, the grain boundaries of the hematite particles constituting the hematite phase are defined based on the distribution information of crystal orientations. The defined grain boundaries of the hematite particles represent the shape of each hematite particle.

[0047] By defining the grain boundaries of hematite particles based on the distribution information of crystal orientations, the state of the hematite phase can be accurately evaluated. For example, hematite particles can be accurately classified into two or more hematite phases. Furthermore, when iron-containing ore is reduced, the reduction reaction proceeds for each crystal grain having the same crystal orientation. Therefore, defining the grain boundaries of hematite particles is important for quality evaluation when using iron-containing ore in blast furnace operation.

[0048] First, the outer edges of consecutive regions having the same crystal orientation are defined as the apparent grain boundaries of the hematite particles. Here, the obtained apparent grain boundaries may be defined as the grain boundaries of the hematite particles. That is, each of the consecutive regions having the same crystal orientation may be considered as a single hematite particle, and the outer edges of these regions may be defined as the grain boundaries of the hematite particles.

[0049] However, for example, two or more hematite particles that appear separated in image information may actually be a single hematite particle when viewed in three dimensions. Therefore, in order to evaluate the state of the hematite phase more accurately, it is preferable to treat two or more hematite particles that appear to be separated at the apparent grain boundary as a single hematite particle.

[0050] In particular, secondary hematite grains have a skeletal structure, which can cause a single hematite particle to appear as if it were divided into multiple parts. Therefore, by identifying particles corresponding to secondary hematite grains and treating two or more identified particles that satisfy predetermined conditions as a single hematite particle, the grain boundaries of hematite particles can be defined with greater precision.

[0051] Specifically, the grain boundaries of hematite particles can be defined based on apparent grain boundaries by performing the following steps (1) to (3). (1) A process of classifying hematite particles defined by apparent grain boundaries into aggregate hematite particles that exist as aggregates and discrete hematite particles that exist discretely. (2) A step of determining that two or more discrete hematite particles are a single hematite particle based on the results of comparing the proximity and crystal orientation of discrete hematite particles. (3) A step to correct apparent grain boundaries based on the judgment result of (2).

[0052] (1) Classification First, hematite particles defined by apparent grain boundaries are classified into aggregate hematite particles, which exist as aggregates, and discrete hematite particles, which exist discretely. Here, aggregate hematite particles correspond to primary hematite crystal grains, and discrete hematite particles correspond to secondary hematite crystal grains. This classification should be performed for all hematite particles. If multiple hematite particles (e.g., three or more) are in contact, they should be classified as aggregate hematite particles. Discrete hematite particles should be any hematite particles other than aggregate hematite particles.

[0053] (2) Judgment Next, based on a comparison of the proximity and crystal orientation of the discrete hematite particles, it is determined that two or more discrete hematite particles constitute a single hematite particle. The criteria for this determination can be proximity (e.g., the distance between the centers of the discrete hematite particles) and crystal orientation. For example, when considering a specific discrete hematite particle, discrete hematite particles located within the square root of the perimeter of the target hematite particle and having the same crystal orientation can be determined to be the same hematite particle as the target hematite particle. This determination can be performed on all discrete hematite particles, for example, starting with those with the largest perimeter. Furthermore, this can result in a chain reaction where three or more discrete hematite particles are determined to be a single hematite particle. For example, regarding discrete hematite particles A to C, it may be determined that A and B are the same hematite particle, and that B and C are the same hematite particle. In that case, regardless of whether A and C are determined to be the same hematite particle or not, A to C can all be determined to be the same hematite particle.

[0054] (3) Correction Next, the apparent grain boundaries are modified based on the determination result in (2). For aggregate hematite particles, the apparent grain boundaries may be used as they are. Similarly, for discrete hematite particles that were not determined to be the same hematite particle as other hematite particles in (2), the apparent grain boundaries may be used as they are. On the other hand, for two or more discrete hematite particles that were determined to be a single hematite particle in (2), the figure circumscribing the two or more discrete hematite particles (for example, a circumscribing rectangle) may be defined as the grain boundary of that single hematite particle.

[0055] [Parameter calculation process] The hematite phase evaluation method according to this embodiment may further include a parameter calculation step. In the parameter calculation step, parameters related to the size of hematite particles are calculated based on grain boundaries. Since the grain boundaries of hematite particles represent the shape of each hematite particle, parameters related to the particle shape, particularly size, can be calculated using the grain boundaries.

[0056] Parameters related to the size of hematite particles include, but are not limited to, ellipsoidal size, Ferret diameter, equivalent circle diameter, and perimeter. The ellipsoidal size can be calculated from an ellipsoid obtained by approximating the shape of the hematite particle with an ellipse, and may be at least one of the major axis, minor axis, and aspect ratio of the ellipsoid. The Ferret diameter can be calculated as the distance between two parallel lines drawn in a predetermined direction so as to sandwich the hematite particle. The equivalent circle diameter can be calculated as the diameter of a circle having the same area as the hematite particle, which can be determined from the shape of the hematite particle. The perimeter can be calculated as the length of the grain boundary of the hematite particle.

[0057] [Classification process] The hematite phase evaluation method according to this embodiment may further include a classification step. In the classification step, hematite particles are classified into two or more hematite phases based on parameters relating to the size of the hematite particles.

[0058] For example, when classifying hematite particles into two types of hematite phases, one threshold can be set, and the particles can be classified into two phases: one consisting of hematite particles whose parameters are below (or less than) the threshold, and another consisting of hematite particles whose parameters are above (or greater than) the threshold. Here, the hematite phase consisting of smaller hematite particles corresponds to primary hematite, and the hematite phase consisting of larger hematite particles corresponds to secondary hematite. In this case, the frequency distribution of the above parameters can be plotted, and the inflection point can be set as the threshold. Furthermore, when classifying into three or more types of hematite phases, a threshold for the above parameters can be set similarly, or the shape of the hematite particles can be used as a criterion in addition to the threshold for the above parameters.

[0059] <Method for estimating the characteristic values ​​of iron-containing ore> A method for estimating the characteristic values ​​of an iron-containing ore according to one embodiment of the present invention is a method for estimating the characteristic values ​​of an iron-containing ore containing a hematite phase, and comprises a fraction calculation step and an estimation step.

[0060] [Fraction calculation process] First, the fractions of two or more hematite phases classified using the hematite phase evaluation method described above, as well as the fractions of magnetite phase, calcium ferrite phase, slag, and pores that may be contained in iron-containing ore, are calculated.

[0061] For the fraction, either area fraction or volume fraction may be used. If the distribution information of crystal orientation is obtained as a 2D map, the area fraction should be calculated. In this case, the fraction of two or more hematite phases can be calculated using the sum of the areas of the hematite particles constituting each hematite phase. If the distribution information of crystal orientation is obtained as a 3D map, the volume fraction should be calculated. In this case, the fraction of two or more hematite phases can be calculated using the sum of the volumes of the hematite particles constituting each hematite phase.

[0062] The fraction of the magnetite phase can be calculated using the distribution information of the microstructure other than the hematite phase in the region where the distribution information of the crystal orientation of the hematite phase has been obtained. This distribution information can be calculated by classifying the phases other than the hematite phase in the region where the distribution information of the crystal orientation of the hematite phase has been obtained, and may be obtained simultaneously when obtaining the distribution information of the crystal orientation. The classification method is not particularly limited, but it is preferable to perform segmentation using a machine learning model. It is preferable to perform segmentation using TWS, an ImageJ plugin.

[0063] [Estimation process] In the estimation process, the characteristic values ​​of the iron-containing ore are estimated based on the calculated fractions of two or more hematite phases, magnetite phases, calcium ferrite phases, slag, and pores.

[0064] The characteristic values ​​of iron-containing ore include its reducibility, reducibility into pulverized powder, and strength. Strength can be determined, for example, based on JIS M8712 (Iron ore - Rotational strength test method). Reducibility can be determined, for example, based on JIS M8713 (Iron ore - Reducibility test method). Reducibility into pulverized powder can be determined, for example, based on JIS M8720 (Iron ore - Low-temperature reducibility into pulverized powder test method).

[0065] Here, it is preferable to at least estimate the reducibility. Among the hematite phases, primary hematite and secondary hematite have different forms of metallic iron formed upon reduction. In other words, primary hematite is generally difficult to reduce because a metallic iron shell is formed around it during reduction, while secondary hematite is easily reduced because no metallic iron shell is formed. Thus, since reducibility differs greatly depending on the type of hematite phase, the effect of improving the accuracy of characteristic value estimation is greater.

[0066] The specific estimation method is not particularly limited. However, characteristic values ​​can be estimated by inputting data containing fractions of two or more hematite phases, magnetite phases, calcium ferrite phases, slag, and pores into a machine learning model. This machine learning model can be generated by machine learning using training data in which the above fractions are explanatory variables and characteristic rates are the target variable.

[0067] <Method for producing agglomerated ore> The method for producing agglomerate ore according to this embodiment includes estimating the characteristic values ​​of agglomerate ore using the method for estimating the characteristic values ​​of iron-containing ore described above, and changing the production conditions of the agglomerate ore based on the estimated characteristic values. According to this embodiment, agglomerate ore with superior characteristics can be produced, and yield and energy efficiency can be improved in the operation of the blast furnace.

[0068] The manufacturing conditions that may be changed are not limited to these, but examples include the amount and type of fuel, and whether or not oxygen is blown into the sintering machine.

[0069] Here, it is preferable to assume multiple agglomerates with different fractions, estimate the characteristic values ​​of each agglomerate, investigate the trend of changes in characteristic values ​​when the fraction of each phase contained in the agglomerate is changed based on the estimated characteristic values, and change the manufacturing conditions based on that trend.

[0070] For example, the manufacturing conditions can be changed as follows. First, assuming agglomerated ore with a high content of hematite particles (small hematite particles) and agglomerated ore with a low content of hematite particles (small hematite particles) whose size parameter is below a predetermined threshold, the characteristic values ​​are estimated using the method described above. Based on the estimated characteristic values, the trend of changes in characteristic values ​​when the content of small hematite particles differs is investigated. As described above, agglomerated ore with a high content of small hematite particles is thought to contain a large amount of primary hematite. Therefore, if it is found that the properties are better the more small hematite particles there are, the amount of heat during sintering should be reduced from the perspective of increasing the phase fraction of primary hematite. Specifically, the amount of fuel should be reduced. On the other hand, if it is found that the properties are better the less small hematite particles there are, the amount of heat during sintering should be increased to create an oxidizing atmosphere from the perspective of lowering the phase fraction of primary hematite. Specifically, the amount of fuel should be increased and oxygen should be blown into the sintering machine. By manufacturing agglomerated ore under these modified conditions, it is possible to manufacture agglomerated ore with superior properties. [Examples]

[0071] The present invention will be described below based on examples.

[0072] First, five types of sintered ore (sintered ore No. A to E) with different sintering conditions were produced using laboratory-scale sintering pots.

[0073] The obtained sintered ore samples No. A to E were each crushed to a particle size of 1 to 2 mm using a crusher. After crushing, each of the sintered ore samples No. A to E was embedded in resin, and the surface to be evaluated (observation surface) was mirror-polished. Polishing was performed using SiC paper from #120 to #400, followed by polishing with diamonds of 9 μm, 3 μm, 1 μm, and 0.25 μm to obtain evaluation samples No. A to E.

[0074] Bright-field and polarized light microscope images were acquired for each observation surface of evaluation samples No. A to E using an optical microscope. These images were acquired using an optical microscope equipped with imaging capabilities at a magnification of 100x, capturing the entire observation surface of the evaluation sample while continuously changing the field of view. Here, the bright-field and polarized light microscope images were acquired using bright-field mode and polarized light mode, respectively, for the same field of view.

[0075] The obtained bright-field microscope images were segmented based on color tone using the ImageJ plugin TWS, and classified into hematite phase, magnetite phase, calcium ferrite phase, slag, and pores. The area percentage of each tissue after classification was calculated using the ImageJ Measure function. The obtained area percentages of magnetite phase, calcium ferrite phase, slag, and pores are shown in Table 1.

[0076] Next, using the segmentation results performed on the bright-field microscope images, the hematite phase region was identified in the polarized light microscope image. Regions other than the hematite phase were masked to obtain a polarized light microscope image of only the hematite phase. The brightness of the above polarized light microscope image of only the hematite phase was classified into 10 levels to represent the distribution information of the crystal orientation.

[0077] Next, in the distribution of crystal orientations, the outer edges of regions with continuous luminance levels were defined as apparent grain boundaries of hematite particles. Then, the hematite particles defined by these apparent grain boundaries were classified into aggregate hematite particles, which exist as aggregates of three or more hematite particles in contact with each other, and discrete hematite particles. Next, using the distance between the centers of the discrete hematite particles and their crystal orientations, the determination that two or more discrete hematite particles constitute a single hematite particle was made for all discrete hematite particles, starting with those with the largest perimeters. The determination criterion used was that, when a specific discrete hematite particle was targeted, discrete hematite particles located within the square root of the perimeter of the target hematite particle and having the same crystal orientation were considered to be the same hematite particle as the target hematite particle. Next, for two or more discrete hematite particles that were determined to be a single hematite particle, the rectangle circumscribing the two or more discrete hematite particles was defined as the grain boundary of that single hematite particle. For the remaining hematite particles, the apparent grain boundaries were used to define the grain boundaries. From the defined grain boundaries of the hematite particles, the Ferret diameter and area of ​​each hematite particle were calculated.

[0078] The frequency distribution of the obtained Ferret diameters was plotted, and the hematite phases were classified into two types using 50 μm, the inflection point, as the threshold. The area fraction was calculated from the area of ​​the hematite particles constituting each hematite phase. The obtained area fractions are shown in Table 1.

[0079] [Table 1]

[0080] Thus, by defining grain boundaries using information on the distribution of crystal orientations, the state of the hematite phase contained in iron-containing ore can be accurately evaluated. Therefore, the characteristic values ​​of iron-containing ore can be accurately estimated using the above results. Furthermore, by changing the manufacturing conditions of agglomerate ore based on the estimated characteristic values, high-quality agglomerate ore can be produced.

Claims

1. A method for evaluating the hematite phase contained in iron-containing ore, An acquisition step to acquire information on the distribution of crystal orientations of the hematite phase, A definition step is performed to define the grain boundaries of the hematite particles constituting the hematite phase based on the distribution information of the crystal orientations. A method for evaluating the hematite phase, comprising the following features.

2. The method for evaluating a hematite phase according to claim 1, wherein in the acquisition step, information on the crystal orientation distribution of the hematite phase is acquired using one of diffraction contrast tomography, electron backscatter diffraction, polarizing microscopy, and birefringence microscopy.

3. The method for evaluating the hematite phase further includes, A method for evaluating a hematite phase according to claim 1 or 2, comprising a parameter calculation step for calculating parameters relating to the size of the hematite particles based on the grain boundaries.

4. The method for evaluating the hematite phase further includes, The method for evaluating hematite phases according to claim 3, comprising a classification step of classifying the hematite particles into two or more hematite phases based on the aforementioned parameters.

5. A method for estimating the characteristic values ​​of iron-containing ore containing a hematite phase, A fraction calculation step for calculating the fractions of the two or more hematite phases classified using the hematite phase evaluation method described in claim 4, as well as the fractions of the magnetite phase, calcium ferrite phase, slag, and pores that may be contained in the iron-containing ore, An estimation step is performed to estimate the characteristic values ​​of the iron-containing ore based on the fraction calculated in the fraction calculation step. A method for estimating the characteristic values ​​of iron-containing ore, comprising the following features.

6. A method for producing agglomerated ore, The method for estimating the characteristic values ​​of iron-containing ore described in claim 5 is used to estimate the characteristic values ​​of the agglomerated ore, Based on the estimated characteristic values, the manufacturing conditions for the agglomerated ore are changed. A method for producing agglomerate ore, including the method described above.

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