JP7601079B2 - Sintered ore yield prediction method, sintered ore manufacturing method, and sintered ore yield prediction device - Google Patents

Description

The present invention relates to a method for predicting the yield of sintered ore, a method for producing sintered ore, and a yield prediction device.

Conventional methods for measuring the yield of sintered ore involve actually dropping the sintered ore to crush it, and then measuring the yield from the particle size after crushing. As a result, the sintered cake before crushing cannot be used for other analyses. Also, it may be unclear in which area of the sintered cake the crushed sintered ore was located before crushing, making it difficult to quantify the yield for any area of the sintered cake.

To address these problems, Patent Document 1 discloses a non-destructive method for evaluating the yield of sintered cake. According to Patent Document 1, a dedicated pot testing device and a CT device are used to take CT images during firing, and the area of pores in the sintered cake whose maximum width is 10 mm or more and the change in the area of these pores whose maximum width is 10 mm or more during the firing process are obtained. The productivity of the entire sintered cake can be calculated non-destructively by using the area of the pores whose maximum width is 10 mm or more, the time during which the area of the pores changes to 10 mm or more due to firing, and a specified formula.

Japanese Patent Application Publication No. 7-270344

However, the technology in Patent Document 1 uses the time over which the area of the pores changes to determine the yield of the entire sintered cake, and is not capable of evaluating the yield of any region. This poses the problem that it is not possible to grasp the yield of any region of the sintered cake, such as the upper part of the sintered cake. The present invention has been made in consideration of such problems, and its purpose is to provide a method and device for predicting the yield of sintered ore that is non-destructive and capable of predicting not only the yield of the entire sintered cake, but also the yield of any region.

The gist of the present invention to solve the above problems is as follows.
[1] A photographing step of granulating and firing a sintered cake containing an iron-containing raw material, an auxiliary raw material, and a carbonaceous material, and photographing the inside of the sintered cake using an X-ray CT device to obtain a plurality of cross-sectional image data at different cross-sectional positions;
a measuring step of creating a three-dimensional model of the interior of the sintered cake from the plurality of cross-sectional image data and measuring the volume and surface area of pores contained in at least a portion of the sintered cake;
an analysis step of determining the sphericity of the pores from the volume and surface area of the pores;
a prediction step of predicting a yield of at least a portion of the sintered cake using a correspondence relationship between the sphericity and the yield of the sintered ore;
The method for predicting the yield of sintered ore comprises:
[2] In the measuring step, the volume and surface area of the pores contained in two or more different regions of the interior are measured,
The method for predicting a yield of sintered ore according to [1], wherein the prediction step predicts the yield in each of the two or more different regions.
[3] A method for predicting the yield of sintered ore according to [1] or [2], comprising binarizing the multiple cross-sectional image data and creating the three-dimensional model from the multiple cross-sectional image data that have been binarized.
[4] A method for producing sintered ore, comprising: specifying a sphericity that results in a target sintered ore yield using the correspondence relationship between sphericity and sintered ore yield in the sintered ore yield prediction method described in [1]; and producing sintered ore under production conditions for sintered ore such that the pores in the sintered cake have the specified sphericity.
[5] an image acquisition unit that acquires a plurality of cross-sectional image data at different cross-sectional positions of a sintered cake that is cross-sectionally photographed by an X-ray CT device;
a measuring unit that creates a three-dimensional model of the inside of the sintered cake from the plurality of cross-sectional image data and measures a volume and a surface area of pores contained in at least a part of the sintered cake;
an analysis unit for determining the sphericity of the pores from the volume and surface area of the pores;
a prediction unit for predicting a yield of at least a portion of the sintered cake using a correspondence relationship between sphericity and a yield of the sintered ore;
The sintered ore yield prediction device has the following features.

The sintered ore yield prediction method and yield prediction device of the present invention can predict the yield from the sphericity of the pores contained in the sintered cake, so that it is possible to evaluate the yield of any region of the sintered cake, not just the entire region.

1 is a functional block diagram of a sintered ore yield prediction device according to an embodiment of the present invention. FIG. This is a cross-sectional image of a sintered cake before binarization (black: pores, white: sintered ore). This is a cross-sectional image of a sintered cake after binarization (black: pores, white: sintered ore). 1 is a graph showing the relationship between sphericity and yield of sintered ore. 1 is a graph showing the relationship between the sphericity of pores and the yield of sintered ore in the upper, middle and lower layers. 1 is a graph showing the relationship between porosity and sintered ore yield.

The following describes an embodiment of the present invention with reference to the drawings. Sinter cake is produced by granulating and firing sinter raw materials including iron-containing raw materials, auxiliary raw materials, and carbonaceous materials. For example, a Dwight Lloyd sintering machine is used as equipment for producing sinter cake. The sinter cake is then crushed and cooled, and is then sieved into product sintered ore with a particle size of 5 mm or more and return ore with a particle size of less than 5 mm to produce sintered ore. The return ore with a particle size of less than 5 mm may be mixed with the sinter raw materials and reused as a raw material for sintered ore.

In the sintered ore yield prediction method according to this embodiment, the sintered ore yield is predicted from the sphericity of the pores contained in the sintered cake before crushing. The sphericity of the pores contained in the sintered cake is calculated using the volume and surface area of the pores.

The volume and surface area of the pores contained in the sintered cake are measured by taking cross-sectional images of the sintered ore using X-ray CT to obtain multiple cross-sectional image data at different cross-sectional positions, and then using the multiple cross-sectional image data to create a three-dimensional model of the inside of the sintered ore.

In this way, in the sintered ore yield prediction method according to this embodiment, a cross-sectional image of the sintered cake is taken using X-ray CT, so a sintered cake of a size that can be cross-sectionally photographed using X-ray CT is used. Since the size of the sintered cake that can be cross-sectionally photographed varies depending on the penetrating power of X-rays, it is preferable to photograph a cross-section of a sintered cake with a diameter of about 100 to 150 mm if the X-ray tube voltage of the imaging device is about 320 kV. The following embodiment will be described using an example in which a cross-section of a sintered cake fired using a sintering pot testing device is photographed using X-ray CT, and the sintered ore yield is predicted using the sintered ore yield prediction device 1.

Figure 1 is a functional block diagram of the sintered ore yield prediction device 1 according to this embodiment. The sintered ore yield prediction device 1 is a general-purpose computer such as a workstation or a personal computer having a processing unit 2, a storage unit 7, and an output unit 8. The processing unit 2 is, for example, a CPU, and realizes each function of the sintered ore yield prediction device 1 using programs and data stored in the storage unit 7. The storage unit 7 is, for example, an updatable flash memory, a hard disk built-in or connected via a data communication terminal, an information recording medium such as a memory card, and a read/write device thereof. The storage unit 7 stores in advance programs for realizing each function of the sintered ore yield prediction device 1, data used during execution of the programs, etc. The output unit 8 is, for example, an LCD display, etc.

The processing unit 2 has an image acquisition unit 3, a measurement unit 4, an analysis unit 5, and a prediction unit 6. The X-ray CT device 9 performs an imaging process in which cross-sections of the inside of the sintered cake are captured at predetermined intervals to generate multiple cross-sectional image data at different cross-sectional positions. The predetermined interval is, for example, 0.2 mm. The X-ray CT device outputs multiple cross-sectional image data to the image acquisition unit 3.

The image acquisition unit 3 acquires multiple cross-sectional image data from the X-ray CT device 9 at different cross-sectional positions of the sintered ore, and outputs the multiple cross-sectional image data to the measurement unit 4. The measurement unit 4 creates a three-dimensional model of the inside of the sintered cake using the multiple acquired cross-sectional image data. The measurement unit 4 measures the volume and surface area of pores contained in at least a part of the sintered cake using the created three-dimensional model. The creation of the three-dimensional model and the measurement of the volume and surface area of the pores by the measurement unit 4 are performed using a program that creates a three-dimensional model from the cross-sectional image data stored in the storage unit 7 and a program that measures the volume and surface area of the internal space (pores) from the three-dimensional model. The above processing by the measurement unit 4 constitutes the measurement process.

When the sintered ore yield prediction device 1 predicts the yield of a portion of the sintered cake, the measurement unit 4 measures the volume and surface area of the pores contained in the portion of the sintered cake, and the average values are taken as the volume and surface area of the pores. On the other hand, when the sintered ore yield prediction device 1 predicts the yield of the entire sintered cake, the measurement unit 4 measures the volume and surface area of the pores contained in the sintered cake, and the average values are taken as the volume and surface area of the pores.

The measurement unit 4 may also perform binarization processing on the multiple cross-sectional image data acquired, and create a three-dimensional model of the inside of the sintered cake from the multiple binarized cross-sectional image data. Here, binarization processing refers to image processing that converts an image into only two colors, black and white.

Figure 2 is a cross-sectional image of a sintered cake before binarization (black: pores, white: sintered ore). Figure 3 is a cross-sectional image of a sintered cake after binarization (black: pores, white: sintered ore). As shown in Figure 2, the cross-sectional image data obtained by the X-ray CT scanner contains a lot of noise, so the calculation accuracy of the volume and surface area of the pores measured from the three-dimensional model created from the cross-sectional image data is low. In contrast, by performing binarization, the noise in the cross-sectional image data is removed as shown in Figure 3, so that the use of the cross-sectional image data can improve the calculation accuracy of the volume and surface area of the pores measured from the three-dimensional model inside the sintered ore.

The measurement unit 4 outputs the average value of the volume and the average value of the surface area of the pores in the measurement range as the volume per pore and the surface area per pore to the analysis unit 5. When the analysis unit 5 acquires the volume per pore and the surface area per pore, it reads the following formula (1) from the storage unit 7 and calculates the sphericity φ of the pores in the measurement range using the volume per pore, the surface area per pore, and the following formula (1). The volumes and surface areas of all the pores in the measurement range may be output from the measurement unit 4 to the analysis unit 5. In that case, the analysis unit 5 calculates the sphericity φ of all the pores, then calculates the average value of the sphericity φ, and sets the average value as the sphericity φ of the pores in the measurement range.

In the above formula (1), φ is the sphericity (-), V is the volume per pore (m ³ ), and S is the surface area per pore (m ² ).

The analysis unit 5 outputs the calculated sphericity of the pores to the prediction unit 6. The above processing by the analysis unit 5 constitutes the analysis process.

When the prediction unit 6 acquires the sphericity of the pores, it reads out the correspondence between the sphericity and the yield of sintered ore stored in the storage unit 7, and acquires a predicted value of the yield of sintered ore using the correspondence and the acquired sphericity. The above processing by the prediction unit 6 constitutes the prediction process.

Fig. 4 is a graph showing the correspondence between sphericity and sintered ore yield. In the storage unit 7, a regression equation between sphericity and sintered ore yield shown in Fig. 4 is calculated and stored in advance as the correspondence between sphericity and sintered ore yield. The regression equation between sphericity and sintered ore yield is calculated using sintered ore with known yield and sphericity of pores. The coefficient of determination ( ^R2 ) of the regression equation shown in Fig. 4 is 0.8952, which shows that there is a high correlation between the sphericity of pores and the yield of sintered ore.

When the prediction unit 6 acquires the predicted value of the sintered ore yield, it outputs the predicted value of the yield to the output unit 8. If the output unit 8 is an LCD, it causes the predicted value of the sintered ore yield to be displayed on the output unit 8. The operator can grasp the predicted value of the sintered ore yield by visually checking the predicted value of the yield displayed on the output unit 8. In this way, the sintered ore yield prediction device 1 according to this embodiment predicts the sintered ore yield using multiple cross-sectional image data of different cross-sectional positions photographed by the X-ray CT device 9. Note that, in the above example, the yield prediction device 1 has an output unit 8, but the output unit 8 may be a device separate from the yield prediction device 1.

Next, a process for identifying the manufacturing conditions of sintered ore from the predicted value of the yield predicted by the prediction unit 6 and a manufacturing method of sintered ore under the identified manufacturing conditions will be described. The prediction unit 6 identifies the sphericity of the pores corresponding to the target yield using the correspondence between the sphericity and the yield of sintered ore. The storage unit 7 stores a plurality of data set tables for each manufacturing condition of sintered ore, each of which is a set of the manufacturing conditions of sintered ore and the sphericity of the sintered ore manufactured under the corresponding manufacturing conditions, and the prediction unit 6 reads out the manufacturing conditions of sintered ore under which a sintered cake having pores of the identified sphericity is manufactured from the table. The data set table may be created by conducting an experiment in advance, or may be created using the yield, sphericity, and manufacturing conditions of sintered ore acquired by implementing the sintered ore yield prediction method according to this embodiment. The prediction unit 6 displays the read manufacturing conditions of sintered ore on the output unit 8. The operator can grasp the manufacturing conditions of sintered ore by visually checking the manufacturing conditions of sintered ore displayed on the output unit 8. By producing sintered ore under these production conditions, it is possible to produce sintered ore while satisfying the target sintered ore yield.

As described above, the sintered ore yield prediction device 1 and sintered ore yield prediction method according to this embodiment predict the yield from the sphericity of the pores contained in the sintered cake, and are therefore non-destructive devices and methods that can predict not only the yield of the sintered cake, but also the yield of any region of the sintered cake.

In the sintered ore yield prediction device 1 and sintered ore yield prediction method according to this embodiment, an example has been shown in which the prediction unit 6 has the function of identifying the sintered ore production conditions, but the prediction unit 6 does not have to have this function. However, as described above, by having the prediction unit 6 have the function of identifying the sintered ore production conditions, it becomes possible to produce sintered ore while satisfying the target sintered ore yield, so it is preferable that the prediction unit 6 has the function of identifying the sintered ore production conditions.

The measurement unit 4 may also measure the volume and surface area of pores contained in two or more different regions inside the sintered cake. In this case, the analysis unit 5 calculates the sphericity of each of the pores contained in the two or more different regions, and the prediction unit 6 predicts the yield of the two or more different regions from the sphericity of each pore. This makes it possible to predict the yield of sintered ore in any two or more regions inside the sintered cake.

The iron ore and auxiliary materials were mixed so that the _Al2O3 content in the sintered _ore was 1.6 to 2.2 mass%, and coke powder was added as a coagulant in an amount of 3.7 to 4.7 mass% by weight. The sintered raw material was charged into a pot test apparatus and fired to prepare a sintered cake. After firing, the sintered cake was removed from the pot test apparatus non-destructively to prepare the sintered cake.

The prepared sintered cake was subjected to X-ray CT imaging. The imaging conditions were 310 kV and 240 μA. It is preferable to use a device with as high output as possible, but if the imaging area is limited to a specific area of the sintered cake, it is not necessary to use a high-output X-ray CT device. In this example, due to the limitations of the imaging equipment, cross-sectional imaging was performed on three areas of the prepared sintered cake: the upper layer, middle layer, and lower layer. After imaging, the yield of sintered ore in the three areas was measured.

Here, the yield of sintered ore is a value calculated by the following formula (2).
(Mass of sintered ore having a particle size of 5 mm or more × 100) / (Mass of sintered cake) ... (2)
In this example, the sintered cake was crushed by dropping it from a height of 2 m four times, and the mass ratio (mass %) of the sintered ore remaining on a sieve with 5 mm openings to the total mass of the crushed sintered cake was defined as the yield of sintered ore.

The cross-sectional image data generated by the cross-sectional photography was binarized, and a three-dimensional model was created using the binarized cross-sectional image data. The three-dimensional model was divided into three layers: upper, middle, and lower layers, and each layer was further divided into three regions, and the average volume and average surface area of the pores contained in each region were calculated. The sphericity of the pores in each region was calculated using the average volume and average surface area of each region and the above formula (1). Figure 5 is a graph showing the relationship between the sphericity of the pores and the yield of sintered ore in the upper, middle, and lower layers.

As shown in Figure 5, the coefficient of determination ( ^R2 ) of the regression equation between the sphericity of pores and the yield of sintered ore was 0.8365, indicating that there is a high correlation between the sphericity of pores and the yield of sintered ore, and that the higher the sphericity of the pores in the sintered ore, the higher the yield of sintered ore. Furthermore, it was confirmed that the yield of sintered ore in any region can be predicted by obtaining the sphericity of pores in any region by taking a cross-sectional image of the inside of the sintered cake with an X-ray CT device and predicting the yield of sintered ore from the sphericity of the pores in the sintered cake obtained by the cross-sectional image.

Fig. 6 is a graph showing the relationship between porosity and yield of sintered ore. As a comparative example, the relationship between the porosity and yield of a sintered cake was examined, and as shown in Fig. 6, the coefficient of determination ( ^R2 ) of the regression equation of the porosity and yield of the sintered ore cake was 0.0784. From this result, it was confirmed that there is no correlation between the porosity and yield of the sintered cake, and the yield of sintered ore cannot be predicted from the porosity.

During actual operation, the operating conditions are changed so that the desired sphericity is obtained based on the relationship between the measured sphericity and the yield. The increase in heat during the sintering reaction promotes the movement of the melt during the sintering reaction, so that the pores approach the sphere, increasing the sphericity. At this time, the sintering reaction is progressing significantly. In addition, it is considered that the viscosity of the melt during the sintering reaction increases by increasing the Al ₂ O ₃ concentration in the raw material, so that the movement distance of the melt decreases and the sphericity decreases. At this time, the progress of the sintering reaction is suppressed. As described above, the sphericity is affected by the change in heat and the raw material components, so it is considered that the sphericity represents the degree of progress of the sintering reaction. Therefore, it is considered that the sphericity increases and the sintered ore product yield improves by preparing a sintering raw material with a composition that increases the amount of fine coke and reduces the amount of Al ₂ O ₃ in the ore. The sphericity may be changed by changing other operating factors.

When the amount of Al ₂ O ₃ was changed while keeping the basicity of the sintering raw material constant and firing the sintered cake, when the amount of Al ₂ O ₃ was changed from 2.2 mass% to 1.6 mass%, the sphericity of the entire sintered cake improved from 0.54 to 0.69, and the yield improved from 71.5 to 74.1 mass%.

Similarly, when the raw material mix was kept constant but the coke content was changed from 3.7% by mass to 4.7% by mass, the sphericity of the entire sintered cake improved from 0.44 to 0.72, and the yield improved from 66.2% by mass to 80.5% by mass.

By adjusting the Al ₂ O ₃ concentration and the amount of coke, aiming for a sphericity of the entire cake of more than 0.65, it was possible to operate at a certain yield or higher.

1: Yield prediction device 2: Processing unit 3: Image acquisition unit 4: Measurement unit 5: Analysis unit 6: Prediction unit 7: Storage unit 8: Output unit 9: X-ray CT device

Claims (5)

An imaging process of granulating and firing the sintered raw materials including the iron-containing raw materials, the auxiliary raw materials, and the carbonaceous material, and imaging the inside of the sintered cake using an X-ray CT device to obtain a plurality of cross-sectional image data at different cross-sectional positions;
a measuring step of creating a three-dimensional model of the interior of the sintered cake from the plurality of cross-sectional image data and measuring the volume and surface area of pores contained in at least a portion of the sintered cake;
an analysis step of calculating the sphericity of the pores from the volume and surface area of the pores using the following formula (1) ;
a prediction step of predicting a yield of at least a portion of the sintered cake using the correspondence relationship between the sphericity and the yield of the sintered ore;
The method for predicting the yield of sintered ore comprises:

However, in the above formula (1),
φ: sphericity,
π: Pi,
V: volume per pore (m ³ );
S: surface area per pore (m ² );
It is. In the measuring step, a volume and a surface area of the pores included in two or more different regions of the interior are measured,
The method for predicting a yield of sintered ore according to claim 1 , wherein the prediction step predicts the yield in each of the two or more different regions. The method for predicting the yield of sintered ore according to claim 1 or 2, wherein the multiple cross-sectional image data are binarized, and the three-dimensional model is created from the multiple cross-sectional image data that have been binarized. A method for producing sintered ore, which uses the correspondence between sphericity and sintered ore yield in the sintered ore yield prediction method described in claim 1 to specify the sphericity that will result in a target sintered ore yield, and produces sintered ore under production conditions that result in the pores of the sintered cake having the specified sphericity. an image acquisition unit that acquires a plurality of pieces of cross-sectional image data at different cross-sectional positions of the sintered cake, the cross-sections of which are photographed by an X-ray CT device;
a measuring unit that creates a three-dimensional model of the inside of the sintered cake from the plurality of cross-sectional image data and measures a volume and a surface area of pores contained in at least a part of the sintered cake;
an analysis unit that calculates the sphericity of the pores from the volume and surface area of the pores using the following formula (1) ;
a prediction unit that predicts a yield of at least a portion of the sintered cake using a correspondence relationship between the sphericity and a yield of the sintered ore;
The sintered ore yield prediction device has the following features.

However, in the above formula (1),
φ: sphericity,
π: Pi,
V: volume per pore (m ³ );
S: surface area per pore (m ² );
It is.

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