Sintered ore yield prediction method, sintered ore manufacturing equipment control method, sintered ore manufacturing method, yield prediction model generation method, and sintered ore yield prediction device

Abstract

To provide a yield prediction method of sintered ore capable of accurately predicting a yield of the sintered ore in a sintered ore production facility that produces the sintered ore using an ignition furnace.SOLUTION: A method for predicting a yield of sintered ore for predicting the yield of sintered ore in a sintered ore production facility in which sintered ore is produced using an ignition furnace that burns a charcoal material of a charging layer after forming the charging layer by charging a sintered raw material containing charcoal material on an endless mobile pallet, wherein from the outside of an ignition furnace cover of the ignition furnace through a measurement window of the ignition furnace cover, input data including a surface temperature of the charging layer inside the ignition furnace cover measured by a radiation thermometer having a wavelength selected from a range of 0.5 μm or more and 4.0 μm or less as a measurement wavelength is input to a yield prediction model, and the yield of the sintered ore is output to predict the yield of the sintered ore.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the yield of sintered ore, a method for controlling sintered ore manufacturing equipment, a method for manufacturing sintered ore, a method for generating a yield prediction model, and a device for predicting the yield of sintered ore. [Background technology]

[0002] Dwight Lloyd sintering machines are used in the iron ore sintering process. In these machines, powdered iron ore is mixed with water, limestone, and a carbonaceous material (solid fuel) such as coke breeze or anthracite, and the sinter raw material is granulated in a granulator. In the Dwight Lloyd sintering machine, the sinter raw material is loaded onto a pallet via a transport conveyor, a feed hopper, and a chute. In the Dwight Lloyd sintering machine, for example, 400 to 800 mm thick sinter raw material is loaded onto the pallet, and the loaded sinter raw material becomes the loading layer in the sintering machine.

[0003] The sintering machine is equipped with an ignition furnace equipped with combustion burners (ignition furnace burners) above the pallets, and a wind box (wind box) for air intake is installed below the pallets. When the carbonaceous material on the surface of the charging layer is ignited by the combustion burners in the ignition furnace, the carbonaceous material in the sintering raw material burns along the airflow from above to below created by the air intake by the wind box. The sintering reaction caused by this combustion gradually moves from the upper layer to the lower layer of the charging layer as the pallets move forward.

[0004] In the ignition furnace, multiple ignition furnace burners are arranged in the width direction of the pallet. The ignition furnace burners inject flames at approximately the same position in the width direction of the sintering bed. The flames injected from the ignition furnace burners ignite the carbonaceous material on the surface of the sintering bed. Then, limestone and some of the iron ore contained in the sintering bed melt into a molten liquid, which bonds the raw materials together to produce a sintered cake.

[0005] In the sintering process, it is necessary to appropriately control the maximum temperature and the holding time at high temperatures during the sintering reaction in the sintering bed. This results in the formation of calcium ferrite, which has high strength and relatively high reducibility, in the sintered ore. For example, if the ignition temperature of the sintering bed is too low, the molten liquid is not sufficiently generated in the sintering bed, weakening the bonds between the raw materials and reducing the strength of the sintered ore. In particular, the upper layer of the sintering bed is easily cooled after ignition due to the influence of airflow from the wind box. This cooling shortens the holding time at high temperatures and reduces the yield of sintered ore. On the other hand, if the ignition temperature of the sintering bed is too high, the calcium ferrite in the sintered ore may decompose into amorphous silicate and secondary hematite.

[0006] Amorphous silicates reduce the strength and reducibility of sintered ore, and secondary hematite makes it more susceptible to reduction and disintegration. Such low-strength sintered ore is re-sintered as return ore because it is finely divided when the sinter cake is crushed. In other words, if the ignition temperature of the sintering bed is too high, the strength of the sintered ore decreases, which reduces the yield of sintered ore and reduces the production efficiency of sintered ore.

[0007] If the yield of sintered ore can be predicted in advance, it can be prevented by taking measures in advance. As a technique for predicting the yield of sintered ore, Patent Document 1 discloses a method for predicting the yield of sintered ore by determining a relational expression that expresses the correlation between the calorific value of the sintering raw material fed into a sintering machine and the sintered ore yield, and applying the measured value of the calorific value to the relational expression. In Patent Document 1, the measured value of the calorific value is specified as a function of the calorific value of limestone decomposition, the sensible heat of sintering, and the sensible heat of exhaust gas, and the sensible heat of exhaust gas is calculated using the exhaust gas temperature.

[0008] Patent Document 2 discloses a method for predicting the return ore generation ratio, which indicates the yield of sintered ore, by accumulating operational condition data required for the sintering process, including the return ore generation ratio for each past case, and predicting the return ore generation ratio based on the operational condition data for each case. Patent Document 2 uses the M gas flow rate, pallet speed, sintering completion point, lime ratio, coke ratio, raw material components, and average particle size as the operational condition data required for the sintering process. Here, the sintering completion point is an index representing the position at which sintering is completed when the charging layer on the pallet is ignited in an ignition furnace and sintering progresses downward from the surface as the pallet moves forward.

[0009] Patent Document 3 discloses a technology for preventing a decrease in the cold strength of sintered ore and improving the sinter yield. According to Patent Document 3, the decrease in the cold strength of sintered ore can be prevented by adjusting the fuel flow rate of the ignition furnace burner so that the surface temperature near the pallet side plate measured at the outlet of the ignition furnace is higher than the temperature at the center of the pallet. [Prior art documents] [Patent documents]

[0010] [Patent Document 1] Japanese Patent Application Publication No. 8-13047 [Patent Document 2] Japanese Patent Application Laid-Open No. 2010-7992 [Patent Document 3] Japanese Patent Application Publication No. 2-153027 Summary of the Invention [Problem to be solved by the invention]

[0011] However, the above-mentioned conventional techniques have the following problems. In Patent Document 1, the sinter yield is predicted using the calorific value. The calorific value is calculated using physical quantities of the sintering raw materials, such as the specific heat of limestone decomposition and the specific heat of sintering. This means that the effects of fluctuations in operating conditions during the sintering process are not taken into consideration. Furthermore, although the exhaust gas temperature is used to calculate the exhaust gas sensible heat, this exhaust gas temperature is not an index that directly reflects the temperature history of the sintering bed during the sintering reaction. In other words, there is a problem in that it is not possible to distinguish between cases where the temperature of the sintering bed transported by the pallet is quickly cooled from a high temperature state and cases where a relatively low temperature state is maintained for a long time. Therefore, the technique disclosed in Patent Document 1 has the problem of not being able to predict the sinter yield with high accuracy.

[0012] In Patent Document 2, the return ore generation ratio is predicted using operational condition data required for the sintering process. However, although the operational condition data includes information on the composition of the sintering raw materials as well as information on the operational conditions of the ignition furnace, such as the M gas flow rate, pallet speed, and sintering completion point, these are not indicators that directly reflect the temperature history of the sintering bed during the sintering reaction, and therefore there is a problem in that the prediction accuracy of the sinter ore yield is low. For this reason, the technology disclosed in Patent Document 2 has the problem of not being able to predict the sinter ore yield with high accuracy.

[0013] Patent Document 3 describes a technology for improving sinter yield by adjusting the fuel flow rate of the ignition furnace burner so that the surface temperature near the pallet side plate measured at the outlet of the ignition furnace is higher than the temperature at the center of the pallet. However, it does not describe a technology for predicting sinter yield. Furthermore, in the ignition furnace, sintering begins at least on the surface of the sintered ore immediately after the ignition furnace burner ignites the surface of the sintered ore. Therefore, since the temperature of the sintered ore surface has already decreased downstream of the ignition furnace, measuring the temperature of the sintered ore bed using a radiation thermometer does not provide information on the maximum temperature on the sintered ore bed surface or the time it is held at a high temperature.

[0014] The present invention has been made to solve the above problems, and its object is to provide a sintered ore yield prediction method, a sintered ore yield prediction device, and a yield prediction model generation method that can accurately predict the yield of sintered ore in a sintered ore manufacturing facility that produces sintered ore using an ignition furnace that combusts carbonaceous material in a sintering bed. Another object of the present invention is to provide a sintered ore manufacturing facility control method and a sintered ore manufacturing method that can improve the yield of sintered ore. [Means for solving the problem]

[0015] The means for solving the above problems are as follows. [1] A method for predicting the yield of sintered ore in a sintering ore manufacturing facility that charges sintering raw materials including carbonaceous material onto an endless moving pallet to form a charging layer, and then produces sintered ore using an ignition furnace that combusts the carbonaceous material in the charging layer, wherein input data including the surface temperature of the charging layer inside the ignition furnace cover, measured from the outside of the ignition furnace cover through a measurement window on the ignition furnace cover with a radiation thermometer that measures a wavelength selected from the range of 0.5 μm to 4.0 μm, is input into a yield prediction model, and the yield of sintered ore is output to predict the yield of sintered ore. [2] The method for predicting a yield of sintered ore according to [1], wherein the transmittance of the measurement window at the measurement wavelength is 50% or more. [3] The method for predicting the yield of sintered ore according to [1] or [2], wherein the pressure above the sintering bed inside the ignition furnace cover is made negative. [4] A method for predicting the yield of sintered ore according to any one of [1] to [3], wherein the surface of the charging bed, which is ignited by a flame sprayed from an ignition furnace burner provided in the ignition furnace, is defined as an ignition part, and the surface temperature includes the surface temperature of the ignition part. [5] A method for controlling sintered ore manufacturing equipment, comprising: specifying a surface temperature at which the sintered ore yield predicted by the sintered ore yield prediction method according to any one of [1] to [4] becomes equal to or greater than a target yield value; and setting the operating conditions of the ignition furnace so that the surface temperature of the charging bed becomes the specified surface temperature. [6] A method for producing sintered ore, which produces sintered ore using the method for controlling sintered ore production equipment described in [5]. [7] A method for generating a yield prediction model that predicts the yield of sintered ore in a sintering facility that charges sintering raw materials including carbonaceous material onto an endless moving pallet to form a charging bed, and then produces sintered ore using an ignition furnace that combusts the carbonaceous material in the charging bed, the method comprising: acquiring a plurality of data sets each consisting of a pair of actual values ​​of input data including the surface temperature of the charging bed inside the ignition furnace cover measured from the outside of the ignition furnace cover through a measurement window of the ignition furnace cover with a radiation thermometer having a measurement wavelength selected from a range of 0.5 μm to 4.0 μm, and an actual value of the yield of sintered ore; and generating a yield prediction model that uses the input data as input and outputs the yield of the sintered ore through machine learning using the acquired plurality of data sets as training data. [8] A sintered ore yield prediction device that predicts the yield of sintered ore in a sintered ore production facility that produces sintered ore using an ignition furnace that burns the carbonaceous material in the sintered ore bed after charging sintered raw materials including carbonaceous material onto an endless moving pallet to form a charging layer, the device comprising: a data acquisition unit that acquires input data including the surface temperature of the charging layer inside the ignition furnace cover, measured from the outside of the ignition furnace cover through a measurement window of the ignition furnace cover with a radiation thermometer that measures a wavelength selected from a range of 0.5 μm to 4.0 μm; and a sintered ore yield prediction unit that inputs the input data into a yield prediction model, outputs the sintered ore yield, and predicts the sintered ore yield. [9] The sintered ore yield prediction device according to [8], wherein the transmittance of the measurement window at the measurement wavelength is 50% or more. [Effects of the Invention]

[0016] The method for generating a sinter yield prediction model and the method for predicting sinter yield according to the present invention enable accurate prediction of the yield of sinter in a sinter production facility. Furthermore, the method for controlling sinter production facility and the method for producing sinter according to the present invention enable sinter to be produced with a high yield. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a schematic diagram showing an example of a sintered ore manufacturing facility for manufacturing sintered ore. [Figure 2] FIG. 2 is a schematic side cross-sectional view of the sintering raw material charging device and the ignition furnace. [Figure 3] FIG. 3 is a schematic cross-sectional view of the ignition furnace. [Figure 4] FIG. 4 is a schematic diagram showing one of a plurality of burner nozzles that constitute the ignition furnace burner. [Figure 5] FIG. 5 is a schematic diagram showing an example of an ignition furnace having a measurement window provided on the side cover. [Figure 6] FIG. 6 is a graph showing the transmittance of light including the infrared wavelength band. [Figure 7] FIG. 7 is a schematic diagram showing the temperature measurement positions using a radiation thermometer. [Figure 8] FIG. 8 is a graph showing the intensity distribution of infrared rays emitted from a flame. [Figure 9] FIG. 9 is a graph showing the transmittance of light passing through carbon dioxide gas. [Figure 10] FIG. 10 is a graph showing the transmittance when lead selenide (PbSe) and lead sulfide (PbS) are selected as the detection element of the radiation thermometer and quartz glass is used as the measurement window 86. [Figure 11] FIG. 11 is a graph showing the light transmittance of water vapor. [Figure 12] FIG. 12 is a diagram schematically illustrating an example of the configuration of a sintered ore yield prediction device 110. As shown in FIG. [Figure 13] FIG. 13 is a graph showing the relationship between the flow rate of the fuel gas supplied to the ignition furnace burner and the surface temperature of the sintering bed S. [Figure 14] FIG. 14 is a schematic diagram showing an example of a yield prediction model generated using a neural network. [Figure 15] FIG. 15 is a graph showing the change in surface temperature in the sintering bed S over time from ignition. [Figure 16]FIG. 16 is a graph showing the results of measuring the temperature distribution in the width direction of the sintering bed. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The following embodiments show preferred examples of the present invention, and the present invention is not limited to these examples.

[0019] FIG. 1 is a schematic diagram showing an example of a sinter ore production facility 140 for producing sinter ore. The sinter ore production facility 140 includes a blending tank 10, a drum mixer 16, a sintering machine 30, a crusher 50, a cooler 52, a screening device 54, a process computer 100, and a sinter ore yield prediction device 110. The blending tank 10 is composed of multiple surge hoppers 12, which are storage tanks. Each of the multiple surge hoppers 12 stores raw materials for sinter ore. In this embodiment, four surge hoppers 12 are provided, and each stores, for example, an iron-containing raw material, a CaO-containing raw material, an MgO-containing raw material, and a carbonaceous material.

[0020] The iron-containing raw materials used to produce sintered ore include various iron ores such as hematite and magnetite. The CaO-containing raw materials include limestone and quicklime. The MgO-containing raw materials include dolomite and refined nickel slag. The carbonaceous material is a solid fuel such as coke breeze and anthracite. The iron-containing raw materials may include dust generated in steelworks. The raw materials for sintered ore may also include return ore, which is fine sintered ore that does not meet the specified size.

[0021] A predetermined amount of each raw material stored in the blending tank 10 is cut out and transported to a drum mixer 16 by a blended raw material transport conveyor 14. An appropriate amount of water is added to each raw material transported to the drum mixer 16, and the raw materials are granulated into pseudo-particles with an average particle size of, for example, 3.0 to 6.0 mm.

[0022] The drum mixer 16 is an example of a granulating device that mixes the raw materials and granulates them to form the sintering raw material. Multiple granulating devices may be used, and a pelletizer may be used instead of the drum mixer 16. Also, multiple types of granulating devices may be used, for example, both a drum mixer and a pelletizer may be used. The sintering raw material 18 granulated by the drum mixer 16 is transported to the sintering machine 30 by the sintering raw material transport conveyor 20.

[0023] The sintering machine 30 is, for example, a downward suction-type Dwight Lloyd sintering machine. The sintering machine 30 has a sintering raw material charging device 32, an endless moving pallet 34, an ignition furnace 36, a gaseous fuel supply device 38, and a wind box 40. The sintering raw material charging device 32 charges the sintering raw materials 18 transported by the sintering raw material transport conveyor 20 into the pallet 34 to form a sintering bed S. The endless moving pallet 34 transports the sintering raw materials in one direction (hereinafter sometimes referred to as the traveling direction of the pallet 34). The ignition furnace 36 is installed above the pallet 34 and ignites (lights) the surface of the sintering bed S. The gaseous fuel supply device 38 supplies gaseous fuel from above the sintering bed S. The wind box 40 is installed below the pallet 34 and generates an airflow from above to below by sucking air inside the sintering bed S.

[0024] 2 is a schematic cross-sectional side view of the sintering raw material charging device 32 and the ignition furnace 36. The sintering raw material charging device 32 has a feed hopper 60, a drum feeder 62, a charging gate 64, and a chute 66. The sintering raw material 18 in the feed hopper 60 is cut out by the drum feeder 62, slides down on the chute 66, and charged onto the pallet 34. A charging layer S of sintering raw material is formed by the sintering raw material 18 charged onto the pallet 34.

[0025] The charging gate 64 is a plate-like member provided along the width direction of the chute 66. Generally, a plurality of charging gates 64 are provided along the width direction of the chute 66, and the opening of the charging gate 64 is adjusted by an opening adjustment device (not shown). By adjusting the opening of the charging gate 64, the charging speed and charging state of the sinter raw material 18 charged onto the pallet 34 are controlled. The thickness of the charging layer S formed by charging onto the pallet 34 is adjusted by a baffle 70 when it is transported to the ignition furnace 36.

[0026] The ignition furnace 36 is a device that ignites the surface of the charging bed S, the thickness of which is adjusted by the baffle 70, to cause a sintering reaction from the upper layer to the lower layer of the charging bed S. The ignition furnace 36 has an ignition furnace burner 72 and an ignition furnace cover 74.

[0027] The ignition furnace burner 72 injects a flame toward the surface of the charging bed S charged in the ignition furnace 36. This ignites the carbonaceous material on the surface of the charging bed S. Because the wind box 40 sucks the air inside the charging bed S downward, when the carbonaceous material on the surface of the charging bed S is ignited, the carbonaceous material inside the charging bed S burns sequentially downward. This combustion of the carbonaceous material forms a molten zone in the sintering raw material, and this molten zone moves downward in the charging bed S due to the combustion of the carbonaceous material. Since the charging bed S is continuously transported by the pallet 34, the flame of the ignition furnace burner 72 continuously ignites the surface of the charging bed S transported to the position of the ignition furnace burner. Then, as the pallet 34 moves, the sintering reaction progresses from the upper layer to the lower layer of the charging bed S, and the sintering raw material 18 is sintered to produce a sintered cake.

[0028] Referring again to FIG. 1, a wind box 40 is provided below the pallet 34 to suck air from inside the sintering bed S downward and generate an airflow moving from top to bottom in the thickness direction of the sintering bed S. The wind box 40 is connected to a main duct 42, and an exhaust fan 46 sucks the gas from the main duct 42, causing the wind box 40 to suck air from inside the sintering bed S downward and generating an airflow moving from top to bottom in the thickness direction of the sintering bed S. The exhaust fan 46 is, for example, a blower or a pump. A dust collector 44 is provided between the main duct 42 and the exhaust fan 46 to remove dust and other contaminants from the gas exhausted from the main duct 42. The exhaust fan 46 is connected to a chimney 48, and the gas exhausted from the main duct 42 is discharged from the chimney 48 after harmful substances have been removed.

[0029] By providing a wind box 40 below the pallets 34 passing through the ignition furnace 36, the sintering reaction of the sintering raw materials progresses from the upper layer to the lower layer of the sintering bed S, allowing for efficient production of sintered cakes. In addition, a gaseous fuel supply device 38 is provided downstream of the ignition furnace 36 in the traveling direction of the pallets 34. By supplying gaseous fuel from the gaseous fuel supply device 38 into a hood installed above the sintering bed S, it is possible to maintain a high temperature in the upper layer of the sintering bed S, which is difficult to maintain. However, the gaseous fuel supply device 38 does not have to be provided in the sinter ore manufacturing facility 140.

[0030] When a sinter cake is produced downstream of the ignition furnace 36, the sinter cake is crushed by a crusher 50 to become sintered ore. The sintered ore crushed by the crusher 50 is cooled by a cooler 52. The sintered ore cooled by the cooler 52 is sieved by a screening device 54 having multiple sieves into finished sintered ore 56 (hereinafter, the finished sintered ore 56 may be referred to as sintered ore 56) having a predetermined particle size or larger, and return ore 58 having a particle size smaller than the predetermined particle size. The finished sintered ore 56 obtained in this manner is called sintered ore, and the yield of sintered ore is the sintered ore yield. The sintered ore 56 is charged into the blast furnace as a blast furnace raw material, while the return ore 58 is transported to a blending tank 10 and used as a raw material for the sintered ore 56.

[0031] The process computer 100 is, for example, a general-purpose computer such as a workstation or a personal computer. The process computer 100 controls the operation of each device constituting the sinter ore production equipment 140 to produce sinter 56 of the desired product quality. The process computer 100 also collects and stores the operational parameters of the sinter ore production equipment 140, raw material information for the sinter raw materials, sensor information measured by sensors installed in each device of the sinter ore production equipment 140, and actual values ​​of the sinter ore yield. The sinter ore yield is preferably measured at intervals of 5 to 180 minutes. Measuring the sinter ore yield at intervals of less than 5 minutes is not preferable because changes in operational conditions are small and the actual yield value is unlikely to change. Measuring the sinter ore yield at intervals longer than 180 minutes is also not preferable because it is difficult to obtain accurate actual values ​​of the yield in response to changes in operational conditions. The sinter ore yield is more preferably measured at intervals of 30 to 90 minutes.

[0032] The operation parameters of the sinter ore manufacturing facility 140 include, for example, the blending ratio of coke fines, the amount of moisture added to the sintering raw material, the conveying speed of the pallet 34, the thickness of the charging bed S, the flow rate of fuel gas supplied to the ignition furnace 36, the air flow rate, and the air-fuel ratio (air ratio). The sensor information includes, for example, the surface temperature of the charging bed S in the ignition furnace 36 (described later), the amount of exhaust gas sucked into the wind box 40, the oxygen concentration of the atmospheric gas above the charging bed S in the ignition furnace 36, and the oxygen concentration of the exhaust gas sucked into the wind box 40. The raw material information of the sintering raw material includes the contents of quicklime and silica stone contained in the sintering raw material, the brand of iron ore, and the average particle size of the sintering raw material.

[0033] The sintered ore yield prediction device 110 inputs input data including the surface temperature of the sintering bed S in the ignition furnace 36 into a yield prediction model, outputs the sintered ore yield, and predicts the sintered ore yield. The sintered ore yield prediction device 110 also identifies the surface temperature at which the predicted sintered ore yield will be equal to or greater than a target yield value, and identifies the operating parameters of the ignition furnace 36 that can achieve the identified surface temperature. The sintered ore yield prediction device 110 outputs the identified operating parameters of the ignition furnace 36 to the process computer 100, and sets the identified operating parameters as the operating conditions of the ignition furnace 36.

[0034] Next, the ignition furnace 36 will be described. Fig. 3 is a cross-sectional schematic diagram of the ignition furnace 36. Fig. 3(a) is a cross-sectional view taken along a plane perpendicular to the traveling direction of the pallet 34, and Fig. 3(b) is a cross-sectional view taken along a plane parallel to the traveling direction of the pallet 34.

[0035] The length of the ignition furnace 36 (the distance in the conveying direction of the pallet) is, for example, 2 to 4 m. The thickness of the charging bed S is, for example, 400 to 800 mm. The width of the charging bed S is, for example, 3000 to 6000 mm. Pallet side plates 88 are provided on the side surfaces of the charging bed S. The pallet side plates 88 are provided to support the charging bed S from the sides and maintain the layer thickness distribution of the charging bed S.

[0036] The ignition furnace cover 74 of the ignition furnace 36 has a top cover 80 covered with castable refractory and an iron shell, and a side cover 82, and is provided to cover the charging bed S. The top cover 80 covers the upper part of the charging bed S, and the side cover 82 covers the side part of the charging bed S. Although a certain gap is generated between the side cover 82 and the pallet side plate 88, the size of this gap is configured to be kept relatively small. This limits air leakage from the side, and maintains a certain degree of airtightness inside the ignition furnace 36.

[0037] A baffle 70 is provided at the entrance of the ignition furnace 36, and is configured to maintain a narrow gap between the baffle 70 and the upper surface of the charging bed S. Similarly, the exit of the ignition furnace 36 is configured to maintain a narrow gap between the baffle 70 and the upper surface of the charging bed S. In this way, the gaps at both the charging entrance and the unloading exit are maintained narrow by the baffle 70, thereby maintaining a certain degree of airtightness inside the ignition furnace 36.

[0038] The ignition furnace burner 72 has a plurality of burner nozzles in the width direction of the charging bed S. The ignition furnace burner 72 generates combustible gas by mixing fuel gas and combustion air, and sprays a flame from the tip of the burner nozzle. The number of burner nozzles provided in the width direction of the charging bed S may be two or more and eight or less.

[0039] The ignition furnace burner 72 is attached to the top cover 80, and the burner nozzle of the ignition furnace burner 72 is disposed inside the ignition furnace 36. The ignition furnace burner 72 is connected to supply pipes for fuel gas and combustion air used for burner heating. The fuel gas and combustion air are supplied from supply devices provided outside the ignition furnace 36. The fuel gas supply pipe to the ignition furnace burner 72 is provided with a fuel gas flow rate adjustment valve 90 for adjusting the fuel gas flow rate and a fuel gas flow meter 92 for measuring the fuel gas flow rate. This controls the flow rate of the fuel gas combusted in the ignition furnace burner 72. In addition, the combustion air supply pipe to the ignition furnace burner 72 is provided with an air flow rate adjustment valve 94 for adjusting the combustion air flow rate and an air flow meter 96 for measuring the combustion air flow rate. This controls the air ratio during combustion in the ignition furnace burner 72.

[0040] FIG. 4 is a schematic diagram showing one of the multiple burner nozzles 76 constituting the ignition furnace burner 72. The ignition furnace burner 72 has multiple burner nozzles 76 that emit flames from the upper cover 80 toward the surface of the charging bed S along the width direction of the pallet 34. In the burner nozzle 76 shown in FIG. 4, combustion air is injected toward the gas flow into which the fuel gas is injected. As a result, the fuel gas and combustion air are mixed inside the ignition furnace 36, generating combustible gas and causing a combustion reaction. For this reason, in the burner nozzle 76, the combustion air is injected at a certain angle relative to the gas flow into which the fuel gas is injected so that the fuel gas and combustion air are mixed.

[0041] The fuel gas for the ignition furnace burner 72 may be any of coal gas, city gas, natural gas, methane gas, ethane gas, propane gas, and shale gas. A mixed gas of two or more fuel gases selected from these may also be used as the fuel gas. The coal gas may be any of coke oven gas, blast furnace gas, converter gas, and electric furnace gas. These gases are by-product gases generated in the manufacturing process at steelworks, and by using these gases, these by-product gases can be reused as fuel gas.

[0042] The combustion conditions of the ignition furnace burner 72 are, for example, a fuel gas flow rate of 1200 Nm per 1 m of the width of the sintering bed S. 3 / (m hr), and combustion air is supplied so that the air to fuel gas ratio is about 0.9. This allows the surface temperature of the sintering bed S to be raised to about 1200°C.

[0043] Referring again to Figure 3, the ignition furnace cover 74 is provided with a measurement window 86 that is arranged at a position where the upper surface of the charging bed S can be directly viewed. The position where the upper surface of the charging bed S can be directly viewed is a position where the upper surface of the charging bed S inside the ignition furnace cover 74 can be visually captured from the outside of the ignition furnace cover 74 through the measurement window 86.

[0044] FIG. 5 is a schematic diagram showing an example of an ignition furnace 36 in which a measurement window 86 is provided in the side cover 82. FIG. 5(a) is a schematic side view of the ignition furnace 36. As shown in FIG. 5(a), the measurement window 86 is disposed at a position higher than the upper surface of the charging bed S in the ignition furnace 36. One or more measurement windows 86 may be provided in the ignition furnace cover 74. As shown in FIG. 5, by providing multiple measurement windows 86 in the ignition furnace cover 74, the surface temperature of the charging bed S can be measured at different positions in the traveling direction of the pallet 34.

[0045] 5(b) is a diagram schematically illustrating the field of view observed when the upper surface of the charging bed S is viewed directly through the measurement window 86. As shown in FIG. 5(b), the measurement window 86 is preferably provided at a position where the area where the flame 98 injected from the burner nozzle 76 collides with the upper surface of the charging bed S (hereinafter, this area will be referred to as an ignition part 99) can be viewed directly. By measuring the temperature of the ignition part 99, the maximum temperature on the surface of the charging bed S can be determined.

[0046] The installation position of the measurement window 86 is not limited to the side cover 82, as long as it is a position where the top surface of the charging bed S can be directly viewed. The measurement window 86 may be provided in the top cover 80 of the ignition furnace 36, at a position where the top surface of the charging bed S can be visually grasped. However, if the measurement window 86 is provided in the top cover 80, the field of view of the top surface of the charging bed S may be limited by the supply pipes for the ignition furnace burner 72 and fuel gas, etc. Furthermore, if a part of the top cover 80 is opened, the strength of the top cover 80 decreases, making it easier for firebricks, etc. to fall off. For this reason, it is preferable that the measurement window 86 be provided in the side cover 82.

[0047] Heat-resistant glass made of quartz glass or borosilicate glass is preferably used for the measurement window 86. Furthermore, the measurement window 86 is preferably made of a material that transmits infrared rays, such as barium fluoride, calcium fluoride, zinc sulfide, zinc selenide, or germanium.

[0048] Figure 6 is a graph showing the transmittance of light including infrared wavelength bands. The horizontal axis of Figure 6 represents wavelength (μm), and the vertical axis represents transmittance (%). As shown in Figure 6, although the transmittance of light varies depending on the material selected, by selecting a wavelength of light that provides high transmittance, it is possible to receive light (radiated light) generated inside the ignition furnace cover 74 outside the ignition furnace cover 74 through the measurement window 86.

[0049] The ignition furnace 36 has a radiation thermometer 84 that is provided outside the ignition furnace cover 74 and measures the surface temperature of the sintered bed S through a measurement window 86. The radiation thermometer 84 uses a wavelength selected from the range of 0.5 μm or more and 4.0 μm or less as a measurement wavelength.

[0050] The installation position of the radiation thermometer 84 will be described with reference to FIG. 3 again. The radiation thermometer 84 is installed at a position where it can receive radiant light from the surface of the charging bed S inside the ignition furnace 36 through the measurement window 86. Since the measurement window 86 is installed at a position higher than the surface of the charging bed S, the radiation thermometer 84 is installed so that its light-receiving portion faces downward. Meanwhile, the direction in which the light-receiving portion of the radiation thermometer 84 faces can be arbitrarily changed left and right toward the measurement window 86. Therefore, by adjusting the left and right position of the light-receiving portion of the radiation thermometer 84, it is possible to measure the surface temperature of the charging bed S at any position relative to the traveling direction of the pallet 34, such as the direction of the entrance or exit of the ignition furnace 36, or the direction of an ignition portion 99 where a flame 98 from the ignition furnace burner 72 collides with the charging bed S.

[0051] The radiation thermometer 84 may be a spot radiation thermometer capable of measuring local temperatures, a scanning radiation thermometer capable of scanning the field of view in one axial direction using a rotating mirror or the like, or a thermograph capable of measuring the in-plane temperature distribution. When a scanning radiation thermometer is used, it may be scanned in the direction of travel of the charging bed S or in the width direction.

[0052] FIG. 7 is a schematic diagram showing the temperature measurement position by the radiation thermometer 84. FIG. 7(a) is a diagram showing an example of measuring the temperature at a specific position on the surface of the charging bed S using a spot radiation thermometer. In the example shown in FIG. 7(a), the surface temperature of the charging bed S is measured at the center in the width direction (black circle in the figure) within the range of an ignition part 99 where a flame 98 from the ignition furnace burner 72 collides with the charging bed S. This makes it possible to grasp the maximum temperature on the top surface of the charging bed S.

[0053] Fig. 7(b) is a diagram showing an example of measuring the surface temperature by scanning the measurement point in the width direction of the sintering bed S using a scanning radiation thermometer. In the example shown in Fig. 7(b), the average temperature in the width direction (black line in the figure) is measured within the range of an ignition part 99 where a flame 98 from the ignition furnace burner 72 collides with the sintering bed S. This makes it possible to grasp the surface temperature distribution in the width direction of the sintering bed S.

[0054] Fig. 7(c) is a diagram showing an example in which the measurement range includes the entire width direction of the charging bed S and the surface temperature of a certain range in the longitudinal direction is measured using a thermograph. In the example shown in Fig. 7(c), the temperature is measured in a range including an ignition part 99 where a flame 98 from the ignition furnace burner 72 collides with the charging bed S (the range surrounded by the black line in the figure). This makes it possible to measure the surface temperature distribution of the charging bed S in the width direction and the longitudinal direction.

[0055] The measurement wavelength of the radiation thermometer is selected from the range of 0.5 μm to 4.0 μm. By using a measurement wavelength within this range, temperature measurement errors are reduced, allowing the surface temperature of the sintering bed S to be measured with high accuracy. On the other hand, if the measurement wavelength of the radiation thermometer is less than 0.5 μm, the change in radiant energy is small in the temperature range of 600 to 1400 °C, resulting in a large temperature measurement error. Furthermore, if the measurement wavelength of the radiation thermometer is greater than 4.0 μm, the temperature measurement error increases due to the influence of the flame 98 from the ignition furnace burner 72. Furthermore, if the measurement wavelength of the radiation thermometer is greater than 4.0 μm, the temperature measurement error increases due to the influence of atmospheric gases such as carbon dioxide generated in the ignition furnace.

[0056] Figure 8 is a graph showing the intensity distribution of infrared rays emitted from a flame. The horizontal axis of Figure 8 is wavelength (μm), and the vertical axis is relative radiation intensity (%). The relative radiation intensity is a relative value of radiation intensity normalized using the maximum value of the measured radiation intensity. The flame shown in Figure 8 is a flame produced by burning coke oven gas.

[0057] Because coke oven gas contains a relatively high amount of hydrogen, a flame fueled by coke oven gas appears transparent under visible light. However, as shown in FIG. 8, the wavelength of the infrared light emitted from the flame overlaps with the wavelength measured by the radiation thermometer, and the flame can interfere with temperature measurement. For this reason, in the ignition furnace 36 according to this embodiment, the measurement wavelength of the radiation thermometer is set to 4.0 μm or less. This prevents the flame from affecting the surface temperature of the charging bed S even when measured through the flame. As a result, the temperature measurement error by the radiation thermometer 84 can be reduced. Note that if the combustion of the ignition furnace burner 72 is in an oxygen-deficient state, the flame color may turn yellow. To avoid this, the combustion of the ignition furnace burner 72 is preferably performed under oxygen-rich conditions, in which the air ratio of the fuel gas to the theoretical air amount is 1 or greater.

[0058] FIG. 9 is a graph showing the transmittance of light passing through carbon dioxide gas. The horizontal axis of FIG. 9 represents wavelength (μm), and the vertical axis represents transmittance (%). As shown in FIG. 9, it can be seen that carbon dioxide (carbonate gas) generated by burning fuel gas in the ignition furnace burner also disturbs temperature measurement. For this reason, in the ignition furnace 36 according to this embodiment, the measurement wavelength of the radiation thermometer is set to 4.0 μm or less. This makes it difficult for light transmission to be obstructed by carbon dioxide generated by the combustion of fuel gas, thereby reducing errors in temperature measurement by the radiation thermometer 84.

[0059] Furthermore, the detection element of radiation thermometer 84 is preferably a photoelectric type. Thermoelectric detection elements such as pyroelectric elements and thermopiles have a measurement wavelength of 8 to 13 μm, which is undesirable because the measurement wavelength is affected by flames and atmospheric gases. For example, it is preferable to use one of lead selenide (PbSe), lead sulfide (PbS), indium gallium arsenide (InGaAs), and silicon (Si) as the photoelectric detection element. Lead selenide has a measurement wavelength of 4 μm, lead sulfide has a measurement wavelength of 2 μm, indium gallium arsenide has a measurement wavelength of 1.55 μm, and silicon has a measurement wavelength of 0.9 μm. Regardless of which element is used, radiation thermometer 84 will have a measurement wavelength in the range of 0.5 μm to 4.0 μm.

[0060] Furthermore, the transmittance of the measurement window 86 at the measurement wavelength of the radiation thermometer 84 is preferably 50% or more. FIG. 10 is a graph showing the transmittance when lead selenide (PbSe) and lead sulfide (PbS) are selected as the detection element of the radiation thermometer and quartz glass is used as the measurement window 86. Since the measurement wavelength of lead sulfide is 2.0 μm, the transmittance of quartz glass at the measurement wavelength is nearly 100%. Therefore, the radiation with a wavelength of 2.0 μm emitted from the surface of the charging bed S in the ignition furnace 36 passes through the measurement window 86 without being attenuated by the flame or atmospheric gas in the ignition furnace. This shows that the surface temperature of the charging bed S can be measured with high accuracy using the radiation thermometer 84 installed outside the measurement window 86.

[0061] On the other hand, when lead selenide is used as the detection element of the radiation thermometer 84, the measurement wavelength is 4.0 μm, and the transmittance of quartz glass for the measurement wavelength is about 70%. Therefore, although the radiation light with a wavelength of 4.0 μm emitted from the surface of the charging bed S in the ignition furnace 36 reaches the measurement window 86 without being attenuated by the flame or atmospheric gas in the ignition furnace, about 30% of the radiation light is blocked when passing through the measurement window 86. Therefore, the radiation thermometer 84 installed outside the measurement window 86 receives the radiation light with an intensity of 70% that passes through the measurement window 86 to measure the surface temperature of the charging bed S.

[0062] Therefore, in such a case, it is preferable to correct the temperature measured by the radiation thermometer 84 using the transmittance of the measurement window 86. This allows the surface temperature of the charging bed S to be measured with high accuracy even when a measurement window 86 with low transmittance at the measurement wavelength is used. On the other hand, as the transmittance becomes lower, the amount of transmittance correction becomes larger, resulting in greater variability in the measurement values. For this reason, it is preferable that the transmittance at the measurement wavelength through the measurement window 86 be 50% or more.

[0063] The correction method when the transmittance of the measurement wavelength through the measurement window 86 is less than 100% is as follows: The radiation thermometer 84 receives radiation from the object to be measured and converts it into temperature, and the self-luminous intensity E of the loading layer to be measured is expressed by the following equation (1).

[0064]

number

[0065] In addition, when the radiation light is blocked by the measurement window 86, the received light intensity Er (W / m 3 ) is expressed by the following equation (2) using the spontaneous emission intensity E and transmittance ξ (%) in the above equation (1).

[0066] Er=ξE (2)

[0067] When the above formulas (1) and (2) are used for correction, the transmittance ξ of the window material of the measurement window is determined in advance by measuring the electromagnetic waves emitted from the blackbody furnace with a radiation thermometer through the window material used for measurement offline. Then, by using the transmittance ξ and the above formulas (1) and (2), the surface temperature T of the charging bed S can be calculated with high accuracy even if the transmittance of the measurement window 86 is not 100% for the selected measurement wavelength.

[0068] In the ignition furnace 36 according to this embodiment, it is preferable to make the pressure above the charging bed S inside the ignition furnace cover 74 negative by suctioning air using a wind box 40 provided below the pallet 34. Here, the negative pressure means that the pressure inside the ignition furnace 36 is lower than that outside.

[0069] Referring again to FIG. 1 , the exhaust fan 46 is a device that sucks gas from the main duct 42, and is, for example, a blower or a pump. The exhaust fan 46 sucks gas from the main duct 42, thereby creating a negative pressure in the wind box 40 connected to the main duct 42. When the negative pressure is created in the wind box 40, the wind box 40 sucks air from inside the sintering bed S, and the pressure above the sintering bed S inside the ignition furnace cover 74 becomes negative. The pressure above the sintering bed S may be represented by the pressure measured inside the ignition furnace cover 74. In this case, it is preferable to measure the pressure downstream of the ignition furnace 36, and it is more preferable to measure the pressure near the center of the ignition furnace 36 in the width direction.

[0070] In this way, the wind box 40 creates a negative pressure above the charging bed S inside the ignition furnace cover 74. The pressure above the charging bed S inside the ignition furnace cover 74 is preferably a negative pressure that is 2 to 10 kPa lower than the pressure outside the ignition furnace 36. By creating a negative pressure above the charging bed S, water vapor generated by the combustion of the fuel gas is drawn into the charging bed S from above. In addition, dust generated by ignition of the charging bed S is prevented from floating above the charging bed S. This allows the radiation emitted from the surface of the charging bed S to pass through the measurement window 86 without being affected by water vapor and dust generated inside the ignition furnace 36. As a result, the surface temperature of the charging bed S can be measured with even higher accuracy using the radiation thermometer 84.

[0071] Specifically, if the fuel gas used in the ignition furnace burner 72 contains hydrogen (H2) or methane (CH4), water vapor will be generated by the combustion of this fuel gas. Figure 11 is a graph showing the light transmittance of water vapor. The horizontal axis of Figure 11 represents wavelength (μm), and the vertical axis represents transmittance (%).

[0072] As shown in Fig. 11, water vapor has low transmittance of a part of the radiation light within the range of 0.5 µm or more and 4.0 µm or less (3 to 4 µm range), which is the measurement wavelength of the radiation thermometer 84. Therefore, the radiation light emitted from the surface of the charging bed S is attenuated by the water vapor in the ignition furnace 36, and the light intensity received by the radiation thermometer 84 installed outside the measurement window 86 decreases. Therefore, it can be seen that the attenuation of the radiation light due to the water vapor is suppressed by drawing the water vapor in the ignition furnace 36 into the charging bed S, and the surface temperature of the charging bed S can be measured with high accuracy by the radiation thermometer 84 installed outside the measurement window 86.

[0073] Next, the sintered ore yield prediction device 110 will be described. As described above, the sintered ore yield prediction device 110 is a device that inputs input data including the surface temperature of the sintering bed S in the ignition furnace 36 into a yield prediction model, outputs the sintered ore yield, and predicts the sintered ore yield. Furthermore, the sintered ore yield prediction device 110 is also a device that identifies the surface temperature at which the predicted sintered ore yield will be equal to or greater than the target yield value, and identifies the operating parameters of the ignition furnace 36 that can achieve the identified surface temperature.

[0074] FIG. 12 is a diagram schematically illustrating an example of the configuration of a sintered ore yield prediction device 110. The sintered ore yield prediction device 110 is, for example, a general-purpose computer such as a workstation or a personal computer. The sintered ore yield prediction device 110 has a control unit 112, an input unit 114, an output unit 116, and a storage unit 118. The control unit 112 is, for example, a CPU, and executes various programs stored in the storage unit 118, causing the control unit 112 to function as a data acquisition unit 120, a yield prediction unit 122, an ignition furnace operation parameter identification unit 124, and a yield prediction model generation unit 126.

[0075] The input unit 114 is, for example, a keyboard, a touch panel integrated with a display, or the like. The output unit 116 is, for example, an LCD or CRT display, or the like. The storage unit 118 is, for example, an updatable flash memory, a built-in hard disk or a hard disk connected via a data communication terminal, a memory card, or other information recording medium and a read / write device for the medium. The storage unit 118 stores programs and data for realizing the production of sintered ore 56 by the sintered ore production equipment 140. The storage unit 118 also stores a database 128 and a yield prediction model 130. The database 128 stores 30 or more, preferably 100 or more, and more preferably 500 or more data sets, each set consisting of a record of the surface temperature of the charging bed S and a record of the yield of sintered ore previously produced by the sintered ore production equipment 140. If necessary, the database 128 may store a data set in which actual values ​​of the operating parameters of the ignition furnace 36 described below, actual values ​​of the operating parameters of the sintering process, and actual values ​​of the attribute information of the sintering bed S are acquired from the process computer 100 and associated with the actual values ​​of the surface temperature of the sintered ore sintering bed S and the actual values ​​of the yield.

[0076] Next, the sinter yield prediction process performed by the sinter yield prediction device 110 will be described. The data acquisition unit 120 acquires the surface temperature of the sintering bed S from the process computer 100 as input data. The data acquisition unit 120 acquires the same type of surface temperature of the sintering bed S as the input data for the yield prediction model 130, so that the data can be input to the yield prediction model 130 generated by the yield prediction model generation unit 126 (described later). Therefore, the data acquisition unit 120 may acquire the surface temperature of the sintering bed S at a specific position as the surface temperature of the sintering bed S, or may acquire the average value of the surface temperature of the sintering bed S. For example, the surface temperature of the sintering bed S at a position (ignition point) where a flame emitted from the ignition furnace burner 43 collides with the sintering bed S in the ignition furnace 36, or at a representative position set downstream of the ignition point, or the average temperature in the width direction of the sintering bed S may be used. The data acquisition unit 120 outputs the acquired input data to the yield prediction unit 122.

[0077] When the yield prediction unit 122 acquires input data from the data acquisition unit 120, it reads out the yield prediction model 130 stored in the storage unit 118. The yield prediction unit 122 inputs the input data into the yield prediction model and outputs the yield of sintered ore, thereby predicting the yield of sintered ore. The yield prediction unit 122 may display the predicted yield of sintered ore on the output unit 116. This allows the operator to confirm the predicted value of the yield of sintered ore by visually checking the output unit 116.

[0078] As described above, in the sintered ore yield prediction device 110 according to this embodiment, the surface temperature of the charging bed S in the ignition furnace 36 is input into the yield prediction model 130, and the yield of sintered ore is output, thereby predicting the yield of sintered ore. The surface temperature of the charging bed S is measured from the outside of the ignition furnace cover 74 through the measurement window 86 by the radiation thermometer 84, which measures a wavelength selected from the range of 0.5 μm to 4.0 μm. This surface temperature is measured with high accuracy, with the influence of the flame 98 and carbon dioxide suppressed. Therefore, by predicting the yield of sintered ore using input data including this surface temperature, the yield of sintered ore can also be predicted with high accuracy.

[0079] Next, the process of setting the operation parameters of the ignition furnace 36 by the sinter ore yield prediction device 110 will be described. The yield prediction unit 122 outputs the predicted sinter ore yield to the ignition furnace operation parameter specification unit 124. The ignition furnace operation parameter specification unit 124 determines whether the predicted value of the sinter ore yield obtained from the yield prediction unit 122 is equal to or greater than the target yield value. The target yield value may be input by the operator via the input unit 114, or may be input by the operator in advance and stored in the storage unit 118.

[0080] If the acquired predicted value of the sintered ore yield is equal to or greater than the target yield, the ignition furnace operation parameter specifying unit 124 specifies that the surface temperature acquired by the data acquiring unit 120 is the surface temperature that can make the yield of the sintered ore bed S equal to or greater than the target yield. In this case, the ignition furnace operation parameter specifying unit 124 does not change the fuel gas flow rate and the air ratio, which are the operation parameters of the ignition furnace, so that the current surface temperature is maintained.

[0081] On the other hand, if the acquired predicted value of the sintered ore yield is less than the target yield value, the ignition furnace operation parameter identification unit 124 changes the surface temperature acquired by the data acquisition unit 120 and outputs the changed surface temperature to the yield prediction unit 122. The yield prediction unit 122 inputs the changed surface temperature back into the yield prediction model 130, outputs the sintered ore yield, and predicts the sintered ore yield. The yield prediction unit 122 outputs the predicted value of the sintered ore yield to the ignition furnace operation parameter identification unit 124. The ignition furnace operation parameter identification unit 124 determines whether the acquired predicted value of the sintered ore yield is equal to or greater than the target yield value. The yield prediction unit 122 and the ignition furnace operation parameter identification unit 124 repeat this process until the predicted value of the sintered ore yield becomes equal to or greater than the target yield value.

[0082] If the sintered ore yield predicted at the changed surface temperature is equal to or greater than the target yield value, the ignition furnace operation parameter identification unit 124 identifies the changed surface temperature as the surface temperature that can make the sintered ore yield equal to or greater than the target yield value.

[0083] FIG. 13 is a graph showing the relationship between the flow rate of the fuel gas (C gas) supplied to the ignition furnace burner 72 and the surface temperature of the sintering bed S. In the example shown in FIG. 13, coke oven gas (C gas) was used as the fuel gas for the ignition furnace burner 72. The flow rate of the fuel gas on the horizontal axis is the flow rate of the fuel gas supplied per unit width of the sintering bed S (Nm 3 / (h m)). The radiation thermometer 84 used to measure the surface temperature of the charging bed S is a thermograph capable of measuring the surface temperature distribution of the charging bed S. The measuring element is InGaAs, the measuring wavelength is 1.55 μm, and the measuring window 86 is quartz glass. Air was sucked in from the wind box 40, and the pressure inside the ignition furnace 36 was controlled to be 5 kPa lower than the outside pressure.

[0084] 13(a) is a graph showing an example of measuring the spot temperature at the widthwise center of the sintering bed S as the surface temperature of the sintering bed S. The spot temperature at the widthwise center is a temperature measured using surface temperature data at a specific position 100 mm away from the ignition part 99 in the ignition furnace 36 in the conveying direction of the pallet 34.

[0085] 13(b) is a graph showing an example in which the average temperature in the width direction of the sintering bed S is used as the surface temperature of the sintering bed S. The average temperature in the width direction of the sintering bed S was calculated from the surface temperature distribution data at a position 100 mm away from the ignition part 99 in the ignition furnace 36 in the conveying direction of the pallet 34.

[0086] As shown in Figures 13(a) and 13(b), increasing the fuel gas flow rate increases the surface temperature of the sintering bed S. The storage unit 118 stores the correspondence between the fuel gas flow rate and the surface temperature of the sintering bed S, as shown in Figures 13(a) and 13(b). The ignition furnace operation parameter identification unit 124 reads the correspondence from the storage unit 108 and identifies the fuel gas flow rate, which is an operation parameter of the ignition furnace 36, by using the correspondence and the surface temperature that can achieve a target yield value or higher. The ignition furnace operation parameter identification unit 124 outputs the identified operation parameters of the ignition furnace 36 to the process computer 100. The process computer 100 sets the operation parameters acquired from the ignition furnace operation parameter identification unit 124 as the operation conditions of the ignition furnace 36 and adjusts the fuel gas flow control valve 90 to satisfy the conditions. In this way, the sintered ore yield prediction device 110 according to this embodiment can identify the operational parameters of the ignition furnace that can make the sintered ore yield equal to or higher than the target yield, and set the operational conditions of the ignition furnace 36 to the identified operational parameters. By changing the operational conditions of the ignition furnace 36 in this way, it becomes possible to produce sintered ore 56 at a yield equal to or higher than the target yield in the sintered ore production facility 140.

[0087] Next, a method for generating the yield prediction model 130 used to predict the yield of sintered ore will be described. The data acquisition unit 120 acquires the surface temperature of the sintering bed S and the actual value of the yield of sintered ore from the process computer 100, and stores a set of these data sets in the database 128 of the storage unit 118. The number of data sets stored in the database 128 is at least 30 or more, preferably 100 or more, and more preferably 500 or more.

[0088] The yield prediction model generation unit 126 reads out a pre-stored machine learning model from the storage unit 118, and performs machine learning on the machine learning model using multiple data sets stored in the database 128 as training data to generate a trained machine learning model. This trained machine learning model becomes the yield prediction model 130. Note that the machine learning model used in the sinter ore yield prediction method and sinter ore yield prediction device 110 according to this embodiment may be, for example, a neural network, decision tree learning, random forest, or support vector regression. Furthermore, an ensemble model combining multiple machine learning models may also be used as the machine learning model. Furthermore, a classification model such as a k-nearest neighbor method or logistic regression may also be used as the machine learning model.

[0089] FIG. 14 is a schematic diagram showing an example of a yield prediction model 130 generated using a neural network. The yield prediction model 130 shown in FIG. 14 is a yield prediction model 130 that uses the surface temperature of the sintering bed S and the operational parameters of multiple ignition furnaces (described later) as input data and the sinter yield as output data. L1, L2, and L3 represent the input layer, middle layer, and output layer, respectively. The yield prediction model 130 shown in FIG. 13 has two middle layers, three nodes, and uses a sigmoid function as the activation function. However, if the operational parameters of the ignition furnace are not used as input data for the yield prediction model 130, a neural network is configured that uses only the surface temperature of the sintering bed S as input data.

[0090] By using such a machine learning model, it is possible to freely select, as input data, for example, operational parameters of an ignition furnace that are correlated with the sintered ore yield without considering the problem of multicollinearity. By including operational parameters of an ignition furnace that are correlated with the sintered ore yield in the input data, it is possible to improve the prediction accuracy of the sintered ore yield by the yield prediction model 130.

[0091] Furthermore, the yield prediction model generation unit 126 may improve the accuracy of sinter ore yield prediction by dividing the data set stored in the database 128 into training data and test data and performing machine learning. For example, the yield prediction model generation unit 126 may perform machine learning of weight coefficients of a neural network using the training data, and generate the yield prediction model 130 while appropriately changing the structure of the neural network (the number of intermediate layers and the number of nodes) so as to increase the accuracy rate of the sinter ore yield for the test data. The error propagation method may be used to update the weight coefficients.

[0092] The yield prediction model 130 may be updated to a new model by re-learning, for example, every six months or every year. This is because the more data stored in the database 128, the more accurately a yield prediction model can be generated that can predict yields. By generating and updating the yield prediction model using the latest data, a yield prediction model that reflects changes over time in the sinter ore manufacturing equipment 140 can be generated.

[0093] The data acquiring unit 120 may acquire the surface temperature of the sintering bed S at a specific position as the surface temperature of the sintering bed S, or may acquire multiple surface temperatures of the sintering bed S measured at multiple positions. When the data acquiring unit 120 acquires actual data measured at multiple positions as the surface temperature, the number of nodes may be adjusted so that the actual data measured at those positions are input as the surface temperature of the sintering bed S to be input to the input layer L1 of the neural network. Furthermore, when the data acquiring unit 120 acquires a temperature chart created by scanning the sintering bed S in the width direction or length direction using a radiation thermometer 84 as the surface temperature, or when it acquires two-dimensional image data of the temperature distribution on the surface of the sintering bed using a thermograph capable of measuring the in-plane temperature distribution, the sinter yield prediction model may be generated using a convolutional neural network that uses these as inputs.

[0094] The data acquiring unit 120 may also acquire a heat retention index of the sintering bed S as the surface temperature of the sintering bed S. The heat retention index is a value obtained by integrating the temperature at or above a preset lower limit temperature over a retention time. This heat retention index is also an index representing the surface temperature of the sintering bed S.

[0095] FIG. 15 is a graph showing the change in surface temperature of the sintering bed S over time from ignition. The heat retention index is the area Sk of the sintering bed S where the surface temperature is maintained at 1000°C or higher in the graph showing the change in surface temperature over time from ignition shown in FIG. 15, where the lower limit temperature is 1000°C. The change in surface temperature of the sintering bed S over time is measured by thermography downstream of the ignition unit 99 in the ignition furnace 36 in the conveying direction of the pallets 34, and temperature data in the longitudinal direction at the center of the sintering bed S in the width direction is extracted from the surface temperature distribution data. The change in surface temperature over time is calculated from this temperature data and the conveying speed of the pallets 34, and the holding time for the temperature to be maintained at 1000°C or higher can be calculated from the change in surface temperature over time.

[0096] Fig. 16 is a graph showing the relationship between the flow rate of the fuel gas (C gas) supplied to the ignition furnace burner 72 and the heat retention index of the sintering bed S. As shown in Fig. 16, when the flow rate of the fuel gas is increased, the heat retention index of the sintering bed S also increases. Therefore, if the operation parameter specifying unit 124 of the ignition furnace specifies the heat retention index that can achieve the target yield or higher using the same method as above, the flow rate of the fuel gas, which is an operation parameter of the ignition furnace 36 that can achieve the target yield or higher, can be specified using the correspondence shown in Fig. 16.

[0097] The data acquiring unit 120 may also acquire, as input data, operation parameters of the ignition furnace 36, operation parameters of the sintering process, and attribute information of the charging bed S, along with the surface temperature of the charging bed S. This information is collected and stored by the process computer 100.

[0098] The operational parameters of the ignition furnace 36 are set values ​​for at least one of the fuel gas flow rate, air amount, and air ratio of the ignition furnace burner 72, which are among the sinter ore production conditions. These affect the flame temperature and injection speed injected from the ignition furnace burner 72, and therefore affect the ignition behavior and sintering reaction in the sintering bed S. Therefore, by including the operational parameters of the ignition furnace 36 in the input data, the yield prediction model 130 becomes a model that can predict the sinter ore yield with higher accuracy.

[0099] The operational parameters of the sintering process are at least one of the transport speed of the pallet 34 among the sinter ore production conditions, the amount of exhaust gas sucked into the wind box 40 among the sensor information, the oxygen concentration of the atmospheric gas above the charging layer S in the ignition furnace 36, and the oxygen concentration of the exhaust gas sucked into the wind box 40. If multiple wind boxes 40 are provided, an oxygen concentration meter may be installed in each wind box 40, and the oxygen concentration exhausted from each wind box 40 may be used.

[0100] When the conveying speed of the pallets 34 changes, the contact time between the flame and the surface of the sintering bed S changes, which changes the surface temperature of the sintering bed S. Therefore, the conveying speed of the pallets 34 affects the sintering reaction. In addition, the amount of exhaust gas sucked into the wind box 40 can be said to be the amount of gas that has passed through the interior of the sintering bed S. Since the temperature inside the sintering bed S changes depending on the amount of gas passing through the interior of the sintering bed S, the amount of exhaust gas also affects the sintering reaction. Furthermore, the oxygen concentration of the atmospheric gas above the sintering bed S in the ignition furnace 36 affects the progress of the sintering reaction as the gas passes through the sintering bed S. The oxygen concentration of the exhaust gas sucked into the wind box 40 affects the progress of the sintering reaction in the sintering bed S. Therefore, by including these operational parameters of the sintering process in the input data, the yield prediction model 130 becomes a model that can predict the sinter yield with even higher accuracy.

[0101] The attribute information of the sintering bed S is at least one of the following sinter ore production conditions: the thickness of the sintering bed S, the content of quicklime and silica contained in the sintering raw materials, the blending ratio of coke fines, the brand of iron ore, the moisture content of the sintering raw materials, and the average particle size of the sintering raw materials. A change in the thickness of the sintering bed S affects the distance between the ignition furnace burner 72 and the surface of the sintering bed S, which changes the ignition characteristics of the flame 98. Therefore, the thickness of the sintering bed S affects the sintering reaction. Changes in the content of quicklime, silica, and coke fines contained in the sintering raw materials or the brand of iron ore change the rate and heat generation of the melting reaction in the sintering bed S, which also affect the sintering reaction in the sintering bed S. Furthermore, changes in the moisture content and average particle size of the sintering raw materials change the permeability of the gas passing through the sintering bed S, which affects the amount of oxygen passing through the sintering bed S, which also affects the sintering reaction in the sintering bed S. Therefore, by including the attribute information of the sintering layer S in the input data, the yield prediction model 130 becomes a model that can predict the yield of sintered ore with even higher accuracy.

[0102] Furthermore, when these are included in the input data, in the process of identifying the surface temperature that results in a sintered ore yield equal to or greater than the target yield value, instead of changing the surface temperature, it is also possible to change the operational parameters of the sintering process or adjustable items among the attribute information of the sintering bed S. Then, the operational parameters and the like used when the predicted sintered ore yield is equal to or greater than the target yield value may be set as the production conditions of the sintered ore production equipment 140.

[0103] 1, the process computer 100 and the sintered ore yield prediction device 110 are separate devices, but this is not limiting. The sintered ore yield prediction device 110 may have the functions of the process computer 100, in which case the sintered ore manufacturing facility 140 does not need to have the process computer 100.

[0104] Furthermore, although an example has been described in which the sinter ore yield prediction device 110 has the ignition furnace operation parameter identification unit 124 and the yield prediction model generation unit 126, this is not limiting. If the yield of sinter ore is predicted, the sinter ore yield prediction device 110 does not need to have the ignition furnace operation parameter identification unit 124. Furthermore, if the yield prediction model 130 is generated externally, acquired through the data acquisition unit 120, and stored in the storage unit 118, the yield prediction model generation unit 126 may not be included. Furthermore, the yield prediction model 130 may be stored in another device capable of communicating with the sinter ore yield prediction device 110, rather than in the storage unit 118 of the sinter ore yield prediction device 110. [Example]

[0105] Next, an example will be described in which sintered ore was produced by predicting the sintered ore yield using the sintered ore yield prediction device 110 of the sintered ore production facility 140. In this example, the surface temperature of the charging bed S in the ignition furnace 36 was measured from the outside of the ignition furnace cover 74 provided in the ignition furnace 36 shown in FIG. 3 through a measurement window 86 using a radiation thermometer 84 equipped with a lead selenide (PbSe) element with a measurement wavelength of 4.0 μm. The measurement window 86 was made of calcium fluoride, quartz glass, or germanium, with respective radiation transmittances of 92%, 70%, and 65%. The radiation thermometer 84 was a scanning radiation thermometer thermograph, and measured the in-plane temperature distribution of the charging bed S downstream of the ignition unit 99 in the ignition furnace in the pallet transport direction.

[0106] In the examples of the invention, the surface temperature of the sintering bed S was determined by using the spot temperature at the center of the sintering bed S in the width direction, the average temperature of the sintering bed S in the width direction, and a heat retention index calculated from the surface temperature distribution of the sintering bed S. The heat retention index was determined by calculating the integral value of the surface temperature during the time when the surface temperature of the sintering bed S was maintained at 1000°C or higher, with the surface temperature of the sintering bed S set as the lower limit at 1000°C.

[0107] In the examples of the invention, the yield of sintered ore was predicted using not only yield prediction models (Examples 1 to 5) that used the above-mentioned surface temperature as input data, but also yield prediction models (Examples 6 to 9) that included operational parameters such as the fuel gas flow rate supplied to the ignition furnace burner 72, the conveying speed of the pallet 34, and / or the content of quicklime and powdered coke in the sintering raw materials as input data in addition to the surface temperature.

[0108] The yield prediction model 130 acquired a plurality of data sets, each set consisting of the actual value of the input data and the actual value of the yield of sintered ore, from the process computer 100 and stored them in the database 128.

[0109] When the number of data sets stored in database 128 reached 3,000, 1,500 pieces of training data were extracted from database 128, and the remaining 1,500 were used as test data. The 1,500 pieces of training data were used to train a machine learning model. In the machine learning, the type of input data used in the yield prediction model was changed, and a yield prediction model corresponding to each input data was generated.

[0110] The generated yield prediction model was evaluated for its sinter yield prediction accuracy using 1,500 test data. The machine learning model used was a neural network with three hidden layers and five nodes in each hidden layer. The activation function used was a sigmoid function.

[0111] In Comparative Example 1, a yield prediction model was generated using 1,500 pieces of training data under the same conditions as in Invention Example 2, except that the measurement wavelength of the radiation temperature system was set to 4.5 μm, and the sintered ore yield was evaluated using 1,500 pieces of test data. In Comparative Example 2, a yield prediction model was generated using 1,500 pieces of training data under the same conditions as in Invention Example 2, except that the measurement window was removed and the surface temperature was measured, and the sintered ore yield was evaluated using 1,500 pieces of test data. The results of evaluating the sintered ore yield prediction error 3σ as the prediction accuracy of the sintered ore yield in the Invention Examples and Comparative Examples are shown in Table 1 below.

[0112] [Table 1]

[0113] As shown in Table 1, the prediction error 3σ of the sintered ore yield in Examples 1 to 9 was smaller than the prediction error 3σ of the sintered ore yield in Comparative Examples 1 and 2. These results confirmed that the yield prediction model of Examples 1 to 9 could be used to predict the sintered ore yield with high accuracy. By measuring the surface temperature of the sintered ore bed S from outside the measurement window 86 using a radiation thermometer 84 with a measurement wavelength of 4.0 μm, the effects of flames and carbon dioxide were suppressed, allowing the surface temperature of the sintered ore bed S to be measured with high accuracy. Therefore, it is believed that using this surface temperature as input data enabled the sintered ore yield to be predicted with high accuracy. Furthermore, in Examples 6 to 9, the input data also included operational parameters correlated with the sintered ore yield, so the prediction error 3σ of the sintered ore yield was even smaller than in Examples 1 to 5. These results confirmed that the yield of sintered ore can be predicted with even higher accuracy by using a yield prediction model that includes these operational parameters as input data.

[0114] On the other hand, in Comparative Example 1, although the conditions were similar to those of Invention Example 2, the value of the yield prediction error 3σ was larger than that of Invention Example 2. This result is thought to be because the measurement wavelength of the radiation thermometer 84 was a long wavelength of 4.5 μm, and carbon dioxide (carbonic acid gas) generated by the combustion of the fuel gas acted as a disturbance to the temperature measurement, increasing the measurement error of the surface temperature of the sintered ore bed obtained by the radiation thermometer 84, which resulted in a lower prediction accuracy of the sintered ore yield.

[0115] Furthermore, in Comparative Example 2, although the conditions were similar to those of Inventive Example 2, the value of the prediction error 3σ of the yield was larger than that of Inventive Example 2. In Comparative Example 2, the measurement window 86 was opened and the surface temperature of the sintering bed S was measured directly by the radiation thermometer 84, and therefore, a local temperature drop occurred in part of the surface of the sintering bed S due to the inflow of outside air when the measurement window 86 was opened. This local temperature drop increased the variance in the surface temperature measured by the radiation thermometer 84, which is thought to have reduced the accuracy of the sintered ore yield prediction.

[0116] Using the yield prediction model generated in Example 9, the operational parameters of the ignition furnace that would achieve a target yield or higher were identified, and sintering was carried out. Here, the target yield was, for example, 80.0%. The average temperature in the width direction that would achieve the target yield or higher was identified, and the C gas flow rate that would achieve the identified average temperature in the width direction was identified using the correspondence relationship in Figure 12(b), and the C gas flow rate was set as the operational condition of the ignition furnace 36.

[0117] The sintered ore production equipment 140 was operated for three days under the operating conditions of the ignition furnace 36 to produce sintered ore, resulting in a sintered ore yield of 81.6%. In contrast, the sintered ore production equipment 140 was operated for three days under conditions where the operating conditions of the ignition furnace 36 were not reset, resulting in a sintered ore yield of 77.3%. These results confirm that sintered ore can be produced with a high yield by using the sintered ore production equipment control method and sintered ore production method according to this embodiment. [Explanation of symbols]

[0118] 10 Blending tank 12 Surge Hopper 14. Mixed material conveyor 16 Drum Mixer 18 Sintering raw materials 20 Sintering material transport conveyor 30 Sintering machine 32 Sintering raw material charging device 34 palettes 36 Ignition Furnace 38 Gaseous fuel supply device 40 Wind Box 42 Main Duct 44 Dust collector 46 Exhaust fan 48 Chimney 60 Feeding Hopper 62 Drum Feeder 64 Charging gate 66 shots 70 Baffle 72 Ignition furnace burner 74 Ignition furnace cover 76 Burner nozzle 80 Top cover 82 Side cover 84 Radiation thermometer 86 Measuring window 88 Pallet side panel 90 Fuel gas flow control valve 92 Fuel gas flow meter 94 Air flow control valve 96 Air flow meter 98 Flame 99 Ignition part 100 Process Computer 110 Sintered ore yield prediction device 112 Control section 114 Input section 116 Output section 118 Storage Unit 120 Data Acquisition Unit 122 Yield Prediction Department 124 Ignition furnace operating parameter specification section 126 Yield prediction model generation unit 128 databases 130 Yield Prediction Model

Claims

1. A method for predicting the yield of sintered ore in a sintering ore production facility that charges sintering raw materials including carbonaceous material onto an endless moving pallet to form a charging layer, and then produces sintered ore using an ignition furnace that combusts the carbonaceous material in the charging layer, comprising: inputting input data including the surface temperature of the charging layer inside the ignition furnace cover, which is measured from the outside of the ignition furnace cover through a measurement window of the ignition furnace cover with a radiation thermometer having a measurement wavelength selected from a range of 0.5 μm to 4.0 μm, into a yield prediction model, and outputting the yield of sintered ore to predict the yield of sintered ore; A method for predicting a yield of sintered ore, wherein the transmittance of the measurement window at the measurement wavelength is 50% or more.

2. 2. The method for predicting a yield of sintered ore according to claim 1, wherein a pressure above the sintering bed inside the ignition furnace cover is made negative.

3. When the surface of the charging bed ignited by a flame sprayed from an ignition furnace burner provided in the ignition furnace is defined as an ignition part, The method for predicting a yield of sintered ore according to claim 1 , wherein the surface temperature includes a surface temperature of the ignition part.

4. When the surface of the charging bed ignited by a flame sprayed from an ignition furnace burner provided in the ignition furnace is defined as an ignition part, The method for predicting a yield of sintered ore according to claim 2 , wherein the surface temperature includes a surface temperature of the ignition part.

5. A surface temperature at which the yield of sintered ore predicted by the method for predicting the yield of sintered ore according to any one of claims 1 to 4 becomes equal to or greater than a target yield value is specified; A method for controlling a sintered ore manufacturing facility, comprising: setting operating conditions of the ignition furnace so that the surface temperature of the sintering bed becomes a specified surface temperature.

6. A method for producing sintered ore, comprising producing sintered ore using the method for controlling sintered ore production equipment according to claim 5.

7. A method for generating a yield prediction model for predicting the yield of sintered ore in a sintering ore production facility that produces sintered ore using an ignition furnace that burns the carbonaceous material in the sintering bed after charging sintering raw materials including carbonaceous material into an endless moving pallet to form a sintering bed, comprising: an actual value of input data including a surface temperature of the charging bed inside the ignition furnace cover measured from the outside of the ignition furnace cover of the ignition furnace through a measurement window of the ignition furnace cover with a radiation thermometer having a measurement wavelength selected from a range of 0.5 μm to 4.0 μm; a plurality of data sets each consisting of a set of the input data and an actual value of the sintered ore yield, and by machine learning using the plurality of data sets obtained as training data, a yield prediction model is generated in which the input data is used as an input and the yield of the sintered ore is used as an output; A method for generating a yield prediction model, wherein the transmittance of the measurement window at the measurement wavelength is 50% or more.

8. A sintered ore yield prediction device for predicting the yield of sintered ore in a sintered ore production facility that produces sintered ore by using an ignition furnace that burns the carbonaceous material in the sintered ore charging bed after charging sintered raw materials including carbonaceous material into an endless moving pallet to form a charging bed, a data acquiring unit that acquires input data including a surface temperature of the charging bed inside the ignition furnace cover measured from the outside of the ignition furnace cover through a measurement window of the ignition furnace cover by a radiation thermometer having a measurement wavelength selected from a range of 0.5 μm to 4.0 μm; a sintered ore yield prediction unit that inputs the input data into a yield prediction model, outputs the sintered ore yield, and predicts the sintered ore yield; and The sintered ore yield prediction device, wherein the transmittance of the measurement window at the measurement wavelength is 50% or more.

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