Return ore ratio control method, sintered ore manufacturing method and return ore ratio control device

Abstract

To provide a return ore rate control method capable of predicting the return ore rate of a sintered ore and controlling it to a target value, a sintered ore manufacturing method, and a return ore rate control device.SOLUTION: A return ore rate control method for controlling a sintering process performed in a facility including a sintering machine and a cooler on the basis of a predicted value of a return ore rate predicted using a return ore rate prediction model using at least data on the cooler as an explanatory variable and the return ore rate as an objective variable, the method comprises: an obtaining step (S1) of obtaining a target value of the return ore rate and input data on the return ore rate prediction model including the data on the cooler, in consideration of a delay time relative to timing at which the return ore rate can be specified; a return ore rate prediction step (S2) of predicting the return ore rate using the input date and the return ore rate prediction model; and a manipulated value calculation step (S3) of calculating a manipulated value of a manipulated variable for a sintered process to reduce a deviation between the predicted value and the target value of the return ore rate.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present disclosure relates to a return ore rate control method, a sintered ore manufacturing method, and a return ore rate control device. [Background technology]

[0002] In the steel industry, the quality of iron ore has declined due to years of mining. As a result, the proportion of fine ore with a high fineness that has been dressed at the mine is increasing, and the sintering process, in which the fine ore is solidified to produce sintered ore before being charged into a blast furnace, is becoming increasingly important.

[0003] Figure 1 shows an overview of the sintering process. Sintered ore is produced by burning small-grained iron ore with the heat of combustion of the agglomerating agent. If powdered iron ore is directly charged into the sintering machine, the combustion reaction is suppressed due to poor ventilation. Therefore, in the granulation process, iron ore is mixed with water in a mixer along with other raw materials such as quicklime and coke, and processed into granules with larger grain sizes than the original raw materials. The granules are charged into the sintering machine, ignited in an ignition furnace, and then the combustion reaction progresses gradually in layers from top to bottom by air suction from below. After firing, the sintered ore is discharged from the sintering machine, crushed in a crusher, and then sent to a cooler. After being cooled by air in the cooler duct, it is sorted by a sieve, and the large grains are sent to the blast furnace as good products ("product" in Figure 1). Sintered ore with small grain sizes (for example, grain size of 4 mm or less) is fed back into the sintering machine as return ore.

[0004] Here, the return ore ratio is defined as the ratio of return ore to the sintered ore after firing. As a method for reducing the return ore ratio and improving the yield, it is effective to promote combustion to prevent sintered ore from passing through the sintering machine unfired. For example, it is effective to increase the mixing ratio of the agglomeration material, which is a heat source, to increase the mixing ratio of quicklime, which acts as a binder during granulation to improve ventilation, and to increase the ratio of fine coke in the upper layer to make it easier to ignite in the ignition furnace. In addition, it is effective to reduce the pallet speed to ensure firing time. On the other hand, there is a trade-off between the reduction of the return ore ratio and productivity, and increasing the mixing ratio of the agglomeration material, quicklime, and fine coke increases production costs, while reducing the pallet speed reduces production volume. Therefore, it is necessary to control the return ore ratio by setting an appropriate target value according to the operating conditions.

[0005] The return ore ratio is measured in a process after separation by sieving. Therefore, it takes time from the start of firing until the return ore ratio is measured, for example, about two hours. Therefore, even if the return ore ratio increases, it will not be detected until, for example, two hours later, and corrective action will be delayed by two hours. The same applies if the return ore ratio decreases, and corrective action will be delayed.

[0006] In order to detect and control the fluctuation of the return ore rate at an early stage, it is necessary to predict the future return ore rate and take action in advance. For example, Patent Document 1 discloses a method for predicting the return ore amount from the calcium ferrite content, slag content, pore size distribution index, and porosity, which are minerals constituting sintered ore. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 7-11349 Summary of the Invention [Problem to be solved by the invention]

[0008] Here, the method of Patent Document 1 requires sampling tests to identify physical properties, and is unable to continuously predict the return ore rate, making it impossible to detect fluctuations in the return ore rate at an early stage.

[0009] An object of the present disclosure is to provide a return ore rate control method, a sintered ore manufacturing method, and a return ore rate control device that make it possible to predict the return ore rate of sintered ore and control it to a target value. [Means for solving the problem]

[0010] (1) A method for controlling a return ore ratio according to an embodiment of the present disclosure, A method for controlling a sintering process performed in a facility including a sintering machine and a cooler, based on a predicted value of a return ore rate predicted using a return ore rate prediction model having data related to at least a cooler as an explanatory variable and a return ore rate as a response variable, an acquisition step of acquiring input data for the return ore rate prediction model including the target value of the return ore rate and data related to the cooler, taking into consideration a delay time based on a timing at which the return ore rate can be identified; A return ore rate prediction step of predicting the return ore rate using the input data and the return ore rate prediction model; and calculating an operation amount of an operation variable for the sintering process so that a deviation between the predicted value and a target value of the return ore rate is reduced.

[0011] (2) As an embodiment of the present disclosure, in (1), The data regarding the cooler includes at least one of a duct pressure and a blowing pressure of the cooler.

[0012] (3) As an embodiment of the present disclosure, in (1) or (2), The input data includes data relating to baking.

[0013] (4) As an embodiment of the present disclosure, in (3), The data on the firing is exhaust gas NO xconcentration, exhaust gas O2 concentration, exhaust gas temperature, and pallet speed.

[0014] (5) As an embodiment of the present disclosure, in any one of (1) to (4), The input data includes data regarding raw materials to be charged to the sinter machine.

[0015] (6) As an embodiment of the present disclosure, in (5), The data on the raw materials includes at least one of the raw material brand, the raw material moisture content, the water spray flow rate in the granulation mixer, the mixing ratio of the coagulant, the mixing ratio of the quicklime, and the mixing ratio of the iron ore.

[0016] (7) As an embodiment of the present disclosure, in any one of (1) to (6), The return ore rate prediction model is generated by machine learning using learning data in which data corresponding to the explanatory variables extracted from performance data in the sintering process and the return ore rate extracted from the performance data are associated with each other in consideration of the delay time.

[0017] (8) A method for producing sintered ore according to one embodiment of the present disclosure includes: Sintered ore is produced using the manipulated variable calculated by any one of the return ore rate control methods (1) to (7).

[0018] (9) A return ore ratio control device according to an embodiment of the present disclosure is A return ore ratio control device that controls a sintering process performed in a facility including a sintering machine and the cooler based on a predicted value of the return ore ratio predicted using a return ore ratio prediction model that uses at least data related to the cooler as an explanatory variable and a return ore ratio as a response variable, an acquisition unit that acquires input data for the return ore rate prediction model, the input data including the target value of the return ore rate and data related to the cooler, while taking into consideration a delay time based on a timing at which the return ore rate can be identified; a return ore rate prediction unit that predicts the return ore rate using the input data and the return ore rate prediction model; and an operation amount calculation unit that calculates an operation amount of an operation variable for the sintering process so as to reduce a deviation between the predicted value and a target value of the return ore rate. Effect of the Invention

[0019] According to the present disclosure, it is possible to provide a return ore rate control method, a sintered ore manufacturing method, and a return ore rate control device that make it possible to predict the return ore rate of sintered ore and control it to a target value. [Brief description of the drawings]

[0020] [Figure 1] FIG. 1 is a diagram showing an outline of the sintering process. [Diagram 2] FIG. 2 is a diagram showing the error of the predicted value of the return ore rate prediction model according to the number of explanatory variables. [Diagram 3] FIG. 3 is a diagram illustrating a configuration example of a return ore rate control device according to one embodiment. [Figure 4] FIG. 4 is a flowchart showing a return ore rate control method according to one embodiment. [Diagram 5] FIG. 5 shows the results of Example 1. [Figure 6] FIG. 6 shows the results of Example 2. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Hereinafter, a return ore ratio control method, a sintered ore manufacturing method, and a return ore ratio control device according to an embodiment of the present disclosure will be described with reference to the drawings. In summary, the return ore ratio control method according to this embodiment predicts the return ore ratio using a machine learning model in which the pressure inside the cooler duct is included as an explanatory variable. Then, by calculating and outputting an operation amount so as to reduce the fluctuation of the return ore ratio based on the predicted value, it is possible to control the return ore ratio to a target value or to implement an appropriate operational action.

[0022] The method for controlling the return ore ratio according to this embodiment uses a relationship in which the higher the return ore ratio in the duct of the cooler downstream of the sintering machine (i.e., the finer the particle size of the sintered ore), the worse the ventilation becomes, and the lower the suction pressure of the air when cooling the sintered ore in the cooler (the higher the negative pressure) or the higher the air blowing pressure. In other words, at least "data related to the cooler" is used as an explanatory variable of the return ore ratio prediction model. The data related to the cooler includes at least one of the duct pressure and the air blowing pressure of the cooler. The duct pressure and the air blowing pressure are measured, for example, by a sensor provided in the cooler. In this embodiment, the data related to the cooler is described as being the duct pressure. The sensor for measuring the duct pressure may be any sensor capable of measuring pressure, and for example, a strain gauge type, metal gauge type, semiconductor gauge type, or semiconductor diaphragm type pressure gauge may be used.

[0023] In the return ore ratio control method according to the present embodiment, the return ore ratio is predicted using a return ore ratio prediction model. The return ore ratio prediction model uses at least data related to the cooler as explanatory variables and the return ore ratio as a response variable. Then, based on the predicted value of the return ore ratio, the sintering process executed in the equipment including the sinter machine and the cooler can be controlled. For example, the equipment can perform an operational action automatically or via an operator's operation so as to reduce the fluctuation of the return ore ratio in the sintering process.

[0024] Here, the return ore ratio prediction model includes data on the cooler as an explanatory variable, but other operation factors may be used in combination. The explanatory variables of the return ore ratio prediction model (i.e., input data for the return ore ratio prediction model) may include "data on calcination." The data on calcination may include exhaust gas NO x At least one of the following may be included: exhaust gas NO concentration, exhaust gas O2 concentration, exhaust gas temperature, and pallet speed. x The NO concentration, exhaust gas O2 concentration, and exhaust gas temperature are the NO concentration in the exhaust gas generated during firing. x The pallet speed is the speed at which the pallet transports the raw material (pseudo-particles) charged from the surge hopper. For example, if there is a lack of heat, the partial pressure of CO will drop and the NO xAs the concentration increases, NO x The higher the concentration, the higher the return rate. x It is possible to use concentration in combination as an explanatory variable.

[0025] The input data for the return ore ratio prediction model may include "data on raw materials" to be charged into the sintering machine. The data on raw materials may include at least one of the raw material brand, raw material moisture, water spray flow rate in the granulation mixer, coagulant blend ratio, quicklime blend ratio, and iron ore blend ratio. The raw material brand and raw material moisture are the brand of iron ore in the raw material and the amount of moisture in the raw material, respectively.

[0026] The return ore rate prediction model is not limited to a specific one as long as it is configured to obtain a response variable (return ore rate) from an explanatory variable. The return ore rate prediction model may be, for example, a physical model or a machine learning model. In the present embodiment, the return ore rate prediction model is generated by machine learning. As a machine learning method, linear regression, neural network, decision tree, GBDT (Gradient Boosting Decision Tree), random forest, etc. can be used, and are not particularly limited. The return ore rate prediction model is generated, for example, by using performance data (past measured values, set values, etc.) in the sintering process before the return ore rate is predicted.

[0027] Figure 2 shows an example of an evaluation of a return ore ratio prediction model generated by the linear regression technique. The number (type) of explanatory variables in machine learning was added, and the error between the predicted value of the return ore ratio by each machine learning model and the actual return ore ratio was measured. For example, the prediction error of a return ore ratio prediction model in which the only explanatory variable is duct pressure is 1.4%. In contrast, when the explanatory variables are, for example, duct pressure, exhaust gas NO2, etc., the prediction error is 1.4%. xThe prediction error of the return ore ratio prediction model, which is the concentration, exhaust gas O2 concentration, and exhaust gas temperature, is 1.0%. Thus, the error tends to decrease as the number of explanatory variables is increased. Here, Figure 2 shows an example of the order in which explanatory variables are added. For example, the mixing ratio of the coagulant may be added after the duct pressure as an explanatory variable. Regardless of the addition order in Figure 2, the error tends to decrease as the number of explanatory variables is increased.

[0028] As mentioned above, various operational factors (operational variables for the sintering process) can be used as input data for the return ore rate prediction model, but the time it takes for the operational variables to affect the return ore rate varies. For example, the blending ratio of quicklime, which is data related to raw materials, is located upstream of the sintering process, so it can affect the return ore rate after three hours. For example, the NOx content of exhaust gas, which is data related to calcination, x The concentration is located in the middle of the sintering process, so it can affect the return ore rate after two hours. For example, the pressure in the duct, which is data related to the cooler, is located downstream of the sintering process, so it can affect the return ore rate after one hour. The time until it affects the return ore rate can also vary depending on the pallet speed. Therefore, for accurate prediction, it is preferable that the input data of the return ore rate prediction model is acquired taking into consideration the delay time based on the timing at which the return ore rate can be identified. Here, the timing at which the return ore rate can be identified is, for example, the time when the process of sorting by a sieve after cooling in the cooler and determining whether the particle size is equal to or less than the standard is executed. The operation variable that affected the sintered ore at this judgment is acquired taking into consideration the above-mentioned delay time. Similarly, it is preferable that the return ore rate prediction model is generated by machine learning using learning data in which data corresponding to the explanatory variables extracted from the actual data in the sintering process and the return ore rate extracted from the actual data are associated with each other taking into consideration the above-mentioned delay time.

[0029] A process of predicting the return ore ratio is executed using the input data and the return ore ratio prediction model as described above. In addition, the manipulated variables of the sintering process are calculated so that the deviation between the predicted value of the return ore ratio and the target value is reduced. In calculating the manipulated variables, a machine learning model using the manipulated variables as explanatory variables may be constructed, and the manipulated variables that make the predicted value of the return ore ratio coincide with the target value may be calculated by inverse analysis.

[0030] As an action for reducing the return ore ratio, it is effective to promote combustion to prevent the sintered ore from passing through the sintering machine in an unburned state. For example, it is possible to increase the mixing ratio of the agglomerating agent, which is a heat source. In addition, it is also effective to increase the mixing ratio of quicklime, which acts as a binder during granulation to improve ventilation, to increase the ratio of fine coke in the upper layer to make it easier to ignite in the ignition furnace, and to secure the firing time by reducing the pallet speed. As an action for improving productivity when the return ore ratio is low, it is effective to reduce the mixing ratio of the agglomerating agent, reduce the mixing ratio of quicklime, reduce the ratio of fine coke in the upper layer, and increase the pallet speed. Here, as described above, there is a trade-off between the reduction of the return ore ratio and the productivity, and increasing the mixing ratio of the agglomerating agent, quicklime, and fine coke increases the production cost, and reducing the pallet speed reduces the production volume. Therefore, it is preferable that the target value of the return ore ratio is determined from the viewpoint of production cost and production volume, and it is preferable that the return ore ratio is maintained at the target value. Here, the corrective action may be to determine a range of the amount of manipulation of the manipulated variable per operation and perform the operation within the range so as to prevent excessive manipulation (excessive amount of manipulation).

[0031] Here, the calculated manipulated variables may be output so that they can be reflected by a process computer that manages the sintering process. Here, the output of the manipulated variables includes an output as operation guidance for an operator who operates the sintering machine. In other words, the manipulated variables may be output so that appropriate manipulated variables of the manipulated variables are reflected in the sintering process through the judgment of the operator. The information output as operation guidance includes at least the optimal manipulated variables of the manipulated variables, and may be displayed on a display that the operator can see.

[0032] FIG. 3 is a diagram showing an example of the configuration of the return ore rate control device 10 according to an embodiment. As shown in FIG. 3, the return ore rate control device 10 includes a storage unit 11, an acquisition unit 12, a return ore rate prediction unit 13, an operation amount calculation unit 14, and an output unit 15. The return ore rate control device 10 acquires actual values ​​and target values ​​in a sintering process performed in a facility including a sintering machine and a cooler from an operation data server 60. The actual values ​​may include various measured values ​​indicating an operation state and a current operation variable. The target value is a target value of the return ore rate. The operation data server 60 can communicate with the return ore rate control device 10 via a network and may be realized by, for example, a computer that manages the production of sintered ore. The network is, for example, the Internet. The return ore rate control device 10 predicts the return ore rate using the above process, i.e., the return ore rate prediction model, and executes a process of obtaining the operation amount of an operation variable such as the duct pressure so that the future return ore rate is maintained near the target value. In the present embodiment, the return ore rate control device 10 has a function of outputting the manipulated variable to the equipment by the output unit 15 or a function of presenting it as a guidance manipulated variable. When the output unit 15 presents the guidance manipulated variable, the return ore rate control device 10 functions as an operation guidance device. The display unit 30 displays the guidance manipulated variable output from the return ore rate control device 10 (operation guidance device). The return ore rate control device 10 may be configured as a computer (for example, a process computer that manages the operation of the sintering machine or a sintering operation guidance server) other than the operation data server 60. The display unit 30 may be a display device such as a liquid crystal display (Liquid Crystal Display) or an organic EL panel (Organic Electro-Luminescence Panel). The display unit 30 may be realized by a display of a terminal device such as a smartphone or a tablet. The terminal device can communicate with the return ore rate control device 10 via a network. A sintering operation guidance system may be configured by a sintering operation guidance server having the function of the return ore rate control device 10 and a terminal device having the function of the display unit 30. The sintering operation guidance server and the terminal device may be located in the same place (for example, in the same factory) or may be located physically apart.The sintering operation guidance system may further include an operation data server 60.

[0033] Here, the return ore rate prediction model may be generated by the return ore rate control device 10 and stored in the memory unit 11, or may be generated by another computer and stored in the memory unit 11. When the return ore rate control device 10 generates the return ore rate prediction model, it may further include a model generation unit that generates the return ore rate prediction model by the above-mentioned method and stores it in the memory unit 11.

[0034] The components of the return ore rate control device 10 are described below. The memory unit 11 stores a return ore rate prediction model. The memory unit 11 also stores programs and data related to return ore rate control. The memory unit 11 may store acquired actual values ​​and target values. The memory unit 11 may store various information obtained in the process for return ore rate control. The memory unit 11 may include any memory device such as a semiconductor memory device, an optical memory device, and a magnetic memory device. The semiconductor memory device may include, for example, a semiconductor memory. The memory unit 11 may include multiple types of memory devices.

[0035] The acquisition unit 12 acquires input data for a return ore rate prediction model including a target value of the return ore rate and data related to the cooler, taking into consideration a delay time based on the timing at which the return ore rate can be identified.

[0036] The return ore rate prediction unit 13 predicts the return ore rate by using the input data and the return ore rate prediction model.

[0037] The manipulated variable calculation unit 14 calculates manipulated variables for the sintering process so as to reduce the deviation between the predicted value and the target value of the return ore rate.

[0038] The output unit 15 outputs the calculated manipulated variable to the equipment, or displays it on the display unit 30 as a guidance manipulated variable.

[0039] When the manipulated variable is output from the output unit 15 to the equipment for producing sintered ore, the equipment may automatically update the manipulated variable. That is, the return ore rate control method according to the present embodiment may be executed as part of a production method for producing sintered ore. Furthermore, the operator may change the operation conditions of the sintering machine based on the guidance manipulated variable shown on the display unit 30. Such operation guidance for the sintering machine may be executed as part of a production method for producing sintered ore.

[0040] The return ore rate control device 10 may be realized by, for example, a computer as described above. The computer includes, for example, a memory, a hard disk drive (storage device), and a CPU (processing device). The program may be stored in the hard disk drive, and when executed by the CPU, it is read from the hard disk drive to the memory. Data during processing is stored in the memory, and if necessary, stored in the HDD. The memory unit 11 may be realized by, for example, a storage device. The acquisition unit 12, the return ore rate prediction unit 13, the operation amount calculation unit 14, and the output unit 15 may be realized by, for example, a CPU that reads and executes the program.

[0041] Fig. 4 is a flowchart showing a method for controlling the return ore ratio according to one embodiment. The return ore ratio control device 10 may calculate the manipulated variables and output them as guidance manipulated variables according to the flowchart shown in Fig. 4. The return ore ratio control method shown in Fig. 4 is also an operation guidance method, and may be executed as a part of a method for producing sintered ore.

[0042] The acquisition unit 12 acquires actual values ​​and target values ​​(step S1, acquisition step). In this embodiment, the actual values ​​include data related to the cooler, specifically, the pressure inside the duct. The return ore rate prediction unit 13 predicts the return ore rate using the input data and a return ore rate prediction model (step S2, return ore rate prediction step). The manipulated variable calculation unit 14 calculates manipulated variables of the manipulated variables for the sintering process so as to reduce the deviation between the predicted value and the target value of the return ore rate (step S3, manipulated variable calculation step). The output unit 15 outputs the calculated manipulated variables (step S4).

[0043] Example 1 Hereinafter, a specific example (Example) in which the optimal manipulated variable operation amount is determined by the above control method will be described. In Example 1, in a sintering line, the return ore rate two hours ahead was predicted by a return ore rate prediction model, which is a linear model with the pressure in the duct of the cooler as an explanatory variable. Assuming that an operation with a target return ore rate was being performed, an action to increase quicklime was taken when the predicted value of the return ore rate two hours ahead was higher than 15%. Also, an action to decrease quicklime was taken when the predicted value of the return ore rate two hours ahead was less than 15% lower than the target value. The operation amount of quicklime per time was fixed at 0.1%, and an action was taken every hour. As a result, as shown in FIG. 5, the difference between the target and actual return ore rates (MAE: Mean Absolute Error) was 1.5% after 100 hours of operation. As a comparative example, in a conventional operation, the difference between the target and actual return ore rates (MAE: Mean Absolute Error) was 3.1% after 100 hours of operation. In the conventional operation, an action was taken for the return ore rate, which is the current measured value, without using a model to obtain a predicted value.

[0044] Example 2 In addition, in Example 2, in the sintering line, the return ore rate two hours ahead was predicted by a return ore rate prediction model, which is a linear model with the pressure inside the cooler duct and the blending ratio of quicklime as explanatory variables. Compared to Example 1, a return ore rate prediction model with additional explanatory variables was used. The other conditions were the same as those of Example 1. As a result, as shown in FIG. 6, the difference between the target and actual return ore rates (MAE: Mean Absolute Error) after 100 hours of operation was 1.3%. It was shown that the above control method makes it possible to predict the return ore rate of sintered ore with high accuracy and control it to the target value.

[0045] As described above, the return ore rate control method, sintered ore manufacturing method, and return ore rate control device 10 according to the present embodiment make it possible to predict the return ore rate of sintered ore and control it to a target value. Furthermore, the return ore rate control method, sintered ore manufacturing method, and return ore rate control device 10 according to the present embodiment can continuously predict the return ore rate and detect fluctuations at an early stage. As a result, the return ore rate can be kept close to the target value, and the yield in the production of sintered ore can be improved.

[0046] Although the embodiments of the present disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art can easily make various modifications or corrections based on the present disclosure. Therefore, it should be noted that these modifications or corrections are included in the scope of the present disclosure. For example, the functions included in each component or each step can be rearranged so as not to be logically inconsistent, and multiple components or steps can be combined into one or divided. The embodiments of the present disclosure can also be realized as a program executed by a processor included in the device or a storage medium on which a program is recorded. It should be understood that these are also included in the scope of the present disclosure.

[0047] The configuration of the return ore rate control device 10 shown in Fig. 3 is one example. The return ore rate control device 10 does not need to include all of the components shown in Fig. 3. Furthermore, the return ore rate control device 10 may include components other than those shown in Fig. 3. For example, the return ore rate control device 10 may be configured to further include a display unit 30. [Explanation of symbols]

[0048] 10 Return ore rate control device 11 Storage section 12 Acquisition Department 13. Return Ore Rate Prediction Section 14 Operation amount calculation section 15 Output section 30 Display section 60 Operational Data Server

Claims

1. A method for controlling a sintering process performed in a facility including a sintering machine and a cooler, based on a predicted value of a return ore rate predicted using a return ore rate prediction model having data related to at least a cooler as an explanatory variable and a return ore rate as a response variable, an acquisition step of acquiring input data for the return ore rate prediction model including the target value of the return ore rate and data related to the cooler, taking into consideration a delay time based on a timing at which the return ore rate can be identified; A return ore rate prediction step of predicting the return ore rate using the input data and the return ore rate prediction model; and calculating an operation amount of an operation variable for the sintering process so as to reduce a deviation between the predicted value of the return ore rate and a target value, The data relating to the cooler includes at least one of a duct pressure and a blowing pressure of the cooler.

2. The method for controlling a return ore rate according to claim 1 , wherein the input data includes data related to calcination.

3. The data on the firing are exhaust gas NO ｘ Concentration, exhaust gas O ２ The method for controlling the return ore rate according to claim 2, comprising at least one of concentration, exhaust gas temperature and pallet speed.

4. The method for controlling a return ore rate according to claim 1 , wherein the input data includes data regarding raw materials charged to the sintering machine.

5. The method for controlling a return ore rate according to claim 4, wherein the data on the raw materials includes at least one of a raw material brand, raw material moisture, a water spray flow rate in a granulation mixer, a blending ratio of a coagulant, a blending ratio of quicklime, and a blending ratio of iron ore.

6. 2. The return ore rate control method according to claim 1, wherein the return ore rate prediction model is generated by machine learning using learning data in which data corresponding to the explanatory variables extracted from performance data in the sintering process and the return ore rate extracted from the performance data are associated with each other in consideration of the delay time.

7. A method for producing sintered ore, comprising the steps of: producing sintered ore by using an operation amount of the operation variable calculated by the method for controlling the return ore rate according to any one of claims 1 to 6.

8. A return ore ratio control device that controls a sintering process performed in a facility including a sintering machine and the cooler based on a predicted value of the return ore ratio predicted using a return ore ratio prediction model that uses at least data related to the cooler as an explanatory variable and a return ore ratio as a response variable, an acquisition unit that acquires input data for the return ore rate prediction model, the input data including the target value of the return ore rate and data related to the cooler, while taking into consideration a delay time based on a timing at which the return ore rate can be identified; a return ore rate prediction unit that predicts the return ore rate using the input data and the return ore rate prediction model; an operation amount calculation unit that calculates an operation amount of an operation variable for the sintering process so as to reduce a deviation between the predicted value of the return ore rate and a target value, The data relating to the cooler includes at least one of a duct pressure and a blowing pressure of the cooler.

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