Method for predicting sinter quality and method for producing sinter using the same

Abstract

To provide a method for predicting sintered ore quality capable of managing a yield by capturing a melting rate of sintered ore online and a method for producing sintered ore therewith.SOLUTION: In a method for predicting the quality of sintered ore produced by sintering a loading layer by forming the loading layer by loading a sintered raw material into an endless mobile pallet trolley of a Dwight-Lloyd type sintering machine, the sintering temperature history in the loading layer is calculated using the sintered ore manufacturing conditions and heat transfer model, the melting rate of the sintered ore is predicted using the calculated sintering temperature history and chemical components, and the yield is predicted from the melting rate of the predicted sintered ore.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the quality of sintered ore used as a raw material for a blast furnace, and a method for producing sintered ore using the prediction method. [Background technology]

[0002] Sintered ore, which accounts for the majority of the main raw material in a blast furnace, is continuously fired as a sintered body, typically approximately 5 m wide and 600 mm thick (hereafter referred to as the "sinter cake"). After being crushed and screened, the resulting sintered ore with a particle size of 5 mm or more (hereafter referred to as "product sintered ore") is sent to the blast furnace. Increasing the weight ratio of the product sintered ore to the sintered ore (hereafter referred to as the "sintered ore yield") is an important issue, as it reduces production costs and CO2 emissions. Therefore, to improve the yield, it is necessary to suppress the generation of fine particles with a particle size of -5 mm (less than 5 mm). Since fine particles are presumably generated during the crushing of the sintered cake and the transportation of the product sintered ore, the strength of the sintered ore is presumably the dominant factor in determining the sintered ore yield.

[0003] According to Non-Patent Document 1, increasing the liquid phase component (e.g., calcium ferrite) is effective in improving the strength of sintered ore. As can be seen from the fact that sintered ore ensures its strength through liquid phase bonding, it is clear that increasing the liquid phase ratio in sintered ore (hereinafter referred to as the melting ratio) is important in improving the strength of sintered ore.

[0004] However, the melting rate of sintered ore must be evaluated offline by sampling the sintered ore after production and using cross-sectional observation or XRD, making it difficult to use for online control. For these reasons, online prediction of the melting rate of sintered ore is necessary. The melting rate of sintered ore depends on the temperature in the sintering bed and the raw material composition. The temperature is influenced by the raw material conditions and operating conditions. As can be seen from the Fe2O3-CaO binary phase diagram, for example, the melting point varies greatly depending on the CaO concentration. Sintered ore is a multi-component system that also contains SiO2, Al2O3, MgO, etc., but the composition of these sintered ore components can be predicted by calculating the blending ratio of each iron ore raw material.

[0005] On the other hand, as a method for measuring the sintering temperature history of the sintering raw material charging layer during sinter production, for example, Patent Document 1 discloses a method for managing the operational state by calculating feature values ​​from temperature data in the height direction actually measured with a thermocouple. However, even if the temperature can be measured, the fundamental cause of the temperature increase or decrease is unknown, so the problem is that this does not lead to operational improvements through a theoretical approach.

[0006] A method for theoretically estimating the sintering temperature history has already been mathematically formulated using a heat transfer model in Non-Patent Document 2. For example, Patent Document 2 discloses a method that uses the heat transfer model of Non-Patent Document 3 to calculate the temperature history with relatively high accuracy even when using carbonaceous materials with different combustibility. This method makes it possible to extract factors for controlling the temperature as physical parameters. However, there is a lack of a method for estimating the melting rate from the temperature calculated by the heat transfer model, which has led to the issue of not being able to control the sinter yield online. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-44491 [Patent Document 2] Japanese Patent Application Laid-Open No. 2015-137368 [Non-patent literature]

[0008] [Non-Patent Document 1] Iron and Steel Vol. 73 (1987) No. 8 P964 [Non-patent document 2] Bengyan et al., Analysis of Sintering Operation, Iron and Steel, Vol. 56 (1970), No. 3, pp. 371-381 [Non-patent document 3] Koichiro Ohno and four others, Effect of coke combustion rate equation on numerical simulation of temperature distribution estimation in sintering process bed, Iron and Steel, Vol. 101, (2015), No. 1, pp. 19-24 Summary of the Invention [Problem to be solved by the invention]

[0009] As mentioned above, it is thought that the yield depends on the melting rate, but to estimate the melting rate of sintered ore, it is necessary to sample the sintered ore after production offline and evaluate it using cross-sectional observation, XRD, etc. As a result, the melting rate, which affects the sintered ore yield, cannot be controlled online, making it difficult to predict the sintered ore yield, which has been an issue.

[0010] The present invention has been made in view of the above circumstances, and its object is to propose a method for predicting sintered ore quality that can grasp the sintered ore melting rate online and manage yield, and a method for manufacturing sintered ore using the same. [Means for solving the problem]

[0011] The present invention is a method for predicting the quality of sintered ore produced by charging sinter raw materials onto an endless moving pallet cart of a Dwight Lloyd type sintering machine to form a charging bed and sintering the charging bed, the method comprising: calculating a sintering temperature history in the charging bed using sintered ore production conditions and a heat transfer model; predicting the melting rate of the sintered ore using the calculated sintering temperature history and chemical components; and predicting the yield from the predicted melting rate of the sintered ore.

[0012] In the method for predicting sinter quality according to the present invention configured as described above, (1) The manufacturing conditions of the sintered ore include the component concentration, particle size, and blending amount of the raw materials blended into the sintered raw material, the speed of the pallet cart, the layer thickness of the charging bed, and the negative pressure. (2) The sintered ore production conditions further include one or more of a gaseous fuel injection amount, an oxygen injection amount, and a circulating exhaust gas amount. (3) The prediction of the sintered ore melting rate is carried out based on the calculated sintering temperature history using a correspondence relationship between the sintered ore components and the sintered ore temperature history obtained in advance; This is considered to be a more preferable solution.

[0013] The present invention also provides a method for producing sintered ore using the above-mentioned method for predicting sintered ore quality, characterized in that production conditions for sintered ore that result in a predetermined sintered ore yield are determined from the yield predicted using the method for predicting sintered ore quality, and the sintered ore is produced under the determined production conditions. [Effects of the Invention]

[0014] By implementing the method for predicting sinter ore quality according to the present invention, the sinter ore melting rate can be predicted in a short time, allowing the sinter ore melting rate to be grasped online and yield to be managed. Furthermore, by setting the sinter ore production conditions so that the predicted sinter ore melting rate becomes the target sinter ore melting rate, the sinter ore melting rate and yield can be controlled, and high-quality sinter ore can be produced. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a schematic diagram showing an example of a sintered ore manufacturing facility in which a method for predicting a sintered melting rate according to an embodiment of the present invention can be implemented. [Figure 2] 1 is a graph showing an example of a sintering temperature history obtained from the relationship between the sintering temperature of the sintering bed and the sintering time. [Figure 3] 1 is a graph showing an example of a method for calculating a melting rate from a sintering temperature and components. [Figure 4] Graphs (a) and (b) are graphs showing the correlation between the yield and the comparative example and the correlation between the yield and the example, respectively. [Figure 5] 1 is a graph showing an example of a method for calculating a sintering temperature from operating conditions. [Figure 6] 1 is a graph showing the results of yields according to the conventional method and the present method. DETAILED DESCRIPTION OF THE INVENTION

[0016] The following describes in detail the embodiments of the present invention. Note that the following embodiments are intended to exemplify devices and methods for embodying the technical concept of the present invention, and are not intended to limit the configuration to that described below. In other words, the technical concept of the present invention can be modified in various ways within the technical scope defined in the claims.

[0017] <Regarding the sintered ore manufacturing equipment to which the present invention is applied> FIG. 1 is a schematic diagram showing an example of a sinter ore production facility 1 in which the sinter quality prediction method according to this embodiment can be implemented. The sinter ore production facility 1 includes a drum mixer 2, which is a granulating device, a sintering machine 3, a crusher 4, a cooler 5, and a sieving device 6. Sinter raw materials, which include iron-containing raw materials, auxiliary raw materials, and coagulating agents such as carbonaceous material and coke powder, are granulated in the drum mixer 2 after granulation water is added. The granulated sinter raw materials are transported to the sintering machine 3.

[0018] The sintering machine 3 is, for example, a Dwight Lloyd type sintering machine. The sintering machine 3 has a sintering material supply device 11, an endless movable pallet cart 12, an ignition furnace 13, and a wind box 14. The granulated sintering material is charged from the sintering material supply device 11 onto the pallet cart 12, and a charging layer of sintering material is formed. The coagulant contained in the surface layer of the sintering material is ignited in the ignition furnace 13, and air in the sintering material is sucked downward through the wind box 14, thereby moving the combustion and melting zone in the sintering material bed downward. This movement of the combustion and melting zone sinters the sintering material bed into a sintered cake.

[0019] When the air in the sintering bed is sucked downward through the wind box 14, gaseous fuel and / or oxygen-enriched air may be supplied from above the sintering bed. The gaseous fuel is any combustible gas selected from blast furnace gas, coke oven gas, converter gas, city gas, natural gas, methane gas, ethane gas, propane gas, and mixtures thereof.

[0020] The sinter cake is crushed by a crusher 4 and cooled in a cooler 5. The crushed sinter cake is sieved into sinter ore with a particle size of 5 mm or more and return ore with a particle size of less than 5 mm in a sieving device 6. The return ore is reused as a sinter raw material. In this way, sinter ore is produced.

[0021] <Method for predicting sinter quality according to the present invention> In the method for predicting sinter ore quality according to this embodiment, the sinter ore melting rate of sintered ore produced in sinter ore production equipment 1 is predicted. The sinter ore melting rate is predicted by first (1) calculating the sintering temperature history using the sinter ore production conditions and a heat transfer model, then (2) calculating the sinter ore components from the raw material blending ratio, and finally (3) predicting the sinter ore melting rate using the aforementioned components and the calculated sintering temperature history. In a preferred embodiment, the sinter ore melting rate is predicted based on the sintering temperature history calculated using a correspondence relationship between the sintering temperature history and the sinter ore melting rate, which is previously determined. The yield is then predicted from the predicted sinter ore melting rate.

[0022] <(1) Calculation of sintering temperature history> The sintering temperature history is calculated using a known method described in Non-Patent Document 2. Specifically, the sintering temperature history is calculated using the component concentrations, particle sizes, and blending amounts of the iron-containing raw materials, auxiliary raw materials, and coagulants blended into the sintering raw materials, the pallet cart speed, the thickness of the charging layer, and the sintering ore production conditions, including negative pressure, and a heat transfer model. In this embodiment, the heat transfer model is, for example, a solid phase heat balance equation (Equation (1) below) and a gas phase heat balance equation (Equation (2) below) derived from the energy conservation equation.

[0023]

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[0024]

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[0025] In the above equations (1) and (2), ρ s is the density of the solid [kg / m3 ] and C p·s is the specific heat of the solid [J / (kg×K)], and T s is the temperature of the solid [K], t is the time [sec], d is the average particle size [m], and ε a is the void fraction [-], and h is the convective heat transfer coefficient [J / (m 2 × s × K)], and T g is the gas temperature [K], and k s is the solid thermal conductivity [J / (m 2 × s × K), and Z is the coordinate [m]. Q is the reaction heat [J / s] of each substance contained in the sintering raw material, and the reaction heat is calculated by multiplying the reaction heat of each substance by the reaction rate. In the above formula (2), ρ g is the density of the gas [kg / m 3 ] and C p·g is the specific heat of the gas [J / (kg×K)], u is the gas flow velocity [m / s], T is the temperature [K], and k g is the gas thermal conductivity [J / (m 2 ×s×K).

[0026] density of solid ρ s and the density of the gas ρ g A tentative value is used for the density of the solid ρ s Using the values ​​listed below as provisional values ​​of the density of the gas ρ g The values ​​listed below are used as provisional values ​​of the specific heat of solids, C p·s and the specific heat of the gas, C p·g The values ​​listed below are used for each.

[0027] <Solid physical properties (using hematite)>

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[0028] <Gas properties (using air)>

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[0029] Each physical property will be explained below. Temperature of the solid, T s is the calculated temperature of the solid, and the gas temperature T g is the calculated temperature of the current gas. Time t is a set value determined by the time interval over which the temperature change is calculated using the model. The average particle size d uses the actual measured value of the average particle size of the granulated sintering raw material. Porosity ε a A tentative value is used. Porosity ε a A provisional value of "0.5" is used. The convective heat transfer coefficient h can be calculated by calculating the air volume from the negative pressure, porosity, and average particle size, and then using this air volume and the Lantz-Marshall equation. The solid thermal conductivity k s The values ​​described above are used for the reaction heat Q. The reaction heat Q can be calculated using the concentration, particle size and amount of the components mixed in the sintering raw material, the reaction heat of each component and the reaction rate. The gas flow velocity u can be calculated using the negative pressure, porosity and average particle size. The negative pressure is calculated using the actual measured value of the pressure gauge installed in the wind box. The thermal conductivity k of the gas g The values ​​described above are used for .

[0030] The speed of the pallet cart is preset as a sinter production condition and is used to determine the ignition time (time exposed to high temperature) and the time from the start to the end of the calculation. The layer thickness is also preset as a sinter production condition and is used to determine the time from the start to the end of the calculation.

[0031] In addition, when using a sintering machine having a gaseous fuel injection device or an oxygen gas injection device, the reaction heat Q in equation (1) may be corrected using one or more of the operating conditions, namely, the amount of gaseous fuel injected, the amount of oxygen gas injected, and the amount of circulating exhaust gas.

[0032] The above values ​​and T s and T g When you enter the current temperature, the temperature change after the set time interval (Δt: for example, 1 second) is calculated. s and T gThe initial temperature is 1300°C, which is the temperature of the ignition furnace. For example, if the time interval is 1 second and the calculation time is 30 minutes, 1800 temperature data points will be acquired, and a graph showing the sintering temperature history, as shown in Figure 2, can be created using these temperature data points.

[0033] <(2) Prediction of sinter melting rate> As explained below, the calculated sintering temperature history is used to predict the sinter ore melting rate. First, the sintering temperature history of the charging layer is calculated using equations (1) and (2) and operational data. Because the sintering process uses a lower layer suction method, the temperature of the lower layer inevitably tends to be higher. In addition, since the temperature of the upper layer generally rises as the temperature of the lower layer also rises, the highest temperature (Tmax) in the vertical direction of the sinter machine was extracted in this study.

[0034] While various methods, such as XRD and cross-sectional observation, can be used to calculate the melting ratio, we adopted a theoretically based calculation using a phase diagram. Factstage (registered trademark) was used as a calculation tool for multi-component systems. However, because calculations take time, calculations were performed under multiple conditions in advance to construct a multiple regression, and a simple online calculation was performed using the following formula. Figure 3 shows the relationship between the melting ratio in equation (5) and the melting ratio calculated using Factstage (registered trademark). A very good correlation was confirmed, confirming that this method can predict the melting ratio without any problems.

[0035]

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[0036] Figures 4(a) and (b) show the relationship between the yield and maximum temperature (comparison example) and the melting rate (example) of an actual sintering machine. Compared to organizing the temperature alone, a good correlation with the yield was obtained by using the melting rate. From this relationship, the current melting rate can be calculated, and it is possible to predict the yield online. In addition, by setting a target value for the yield, it is possible to calculate the target melting rate. Next, the operating conditions were changed to control the melting rate.

[0037] To control the melting rate, it is necessary to control the temperature inside the layer (Tmax in this case). Ideally, it would be desirable to calculate a firing model under multiple conditions and propose the conditions for achieving the target Tmax, but the issue is that online control takes time. Therefore, we used a simple prediction method by extracting operational factors that are highly correlated with Tmax and constructing a multiple regression equation (6) below.

[0038]

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[0039] Equation (6) is just an example, but it is preferable to include factors that are highly correlated with the calculated temperature as explanatory variables, and that particularly indicate the characteristics of the sintering machine to be installed. Figure 5 shows the relationship between the temperature calculated using equation (4) and the temperature calculated by the model. The temperature could be predicted with relatively high accuracy, and we investigated melting rate control using equation (6).

[0040] <(3) Yield forecast> Figure 6 shows the yield results for the conventional method and this method. In this method, we proposed a coke action using equation (6). Specifically, we input the current operating values ​​into the explanatory variables other than coke in equation (4), and input the temperature at which the melting rate becomes the target value into Tmax, and then back-calculated coke. As shown in Figure 6, we succeeded in reducing the variation in yield and further improving it. This time we focused on coke, but it is also possible to control another explanatory variable, or add an explanatory variable. For example, LNG, Various actions are possible, such as controlling the ignition time with PS, increasing the layer thickness to raise the temperature of the lower layer, etc. Furthermore, explanatory variables can be added, for example, if ventilation is closely related to temperature, the air volume can be included, or if the temperature distribution in the vertical direction is effective, the temperature of each layer and the segregation of coke in the vertical direction can be included.

[0041] <Regarding the method for producing sintered ore according to the present invention> By implementing the above-described method for predicting sinter ore quality, the sinter ore melting rate can be predicted in a short time, allowing the sinter ore yield to be grasped and managed online. Furthermore, by setting the sinter ore production conditions so that the predicted sinter ore melting rate becomes the target yield, i.e., the sinter ore melting rate, the sinter ore melting rate can be controlled, thereby realizing improvements in yield and improvement in yield variation. [Industrial Applicability]

[0042] According to the method for predicting sinter ore quality of the present invention, the yield of sinter ore can be grasped and managed online, and is industrially useful together with a method for producing sinter ore using the method for predicting the sinter ore melting rate online. [Explanation of symbols]

[0043] 1. Sintered ore manufacturing equipment 2 Drum Mixer 3. Sintering machine 4 Crusher 5. Cooler 6 Sieving device 11 Sintering raw material supply device 12 Pallet cart 13 Ignition furnace 14 Wind Box

Claims

1. A method for predicting the quality of sintered ore produced by charging sintering raw materials into an endless moving pallet cart of a Dwight Lloyd type sintering machine to form a charging bed and sintering the charging bed, comprising: Calculating the sintering temperature history in the sintering bed using the sintered ore production conditions and a heat transfer model; The highest temperature (Tmax) in the height direction of the sintering machine calculated from the sintering temperature history is determined, Predicting the melting rate of the sintered ore using the highest temperature (Tmax) in the height direction of the sintering machine and chemical components; A method for predicting sintered ore quality, comprising predicting a yield from the predicted melting rate of the sintered ore.

2. 2. The method for predicting sinter quality according to claim 1, wherein the sinter production conditions include the component concentration, particle size, and blending amount of the raw materials blended into the sinter raw material, the speed of the pallet cart, the layer thickness of the charging layer, and negative pressure.

3. 3. The method for predicting sinter quality according to claim 2, wherein the sinter production conditions further include at least one of an amount of gaseous fuel injected, an amount of oxygen injected, and an amount of circulating exhaust gas.

4. A method for producing sintered ore using the method for predicting sintered ore quality according to any one of claims 1 to 3, determining sinter ore production conditions that result in a predetermined sinter ore yield from the yield predicted using the sinter ore quality prediction method; A method for producing sintered ore, characterized in that the sintered ore is produced under the determined production conditions.

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