Method for estimating molten steel temperature, method for controlling molten steel temperature, and method for manufacturing molten steel.

Abstract

The molten steel temperature estimation method estimates the molten steel temperature in a process that handles molten steel in the steelmaking process. The molten steel temperature estimation method includes the steps of: dividing the process from start to finish into multiple sections based on at least one molten steel temperature measurement point during the process; measuring the molten steel temperature at the start of each of the multiple sections; estimating the change in molten steel temperature in the current section to which the current time belongs, and the change in molten steel temperature in the sections after the current section, based on a molten steel temperature estimation model constructed for each of the multiple sections; and estimating the molten steel temperature at the end of the process by combining the estimated change in molten steel temperature in the current section and the change in molten steel temperature in the sections after the current section.

Description

Technical Field

[0001] The present disclosure relates to a molten steel temperature estimation method, a molten steel temperature control method, and a molten steel manufacturing method.

Background Art

[0002] In the steelmaking process, refining treatment and casting treatment are performed. The refining treatment controls the component concentration of molten steel within a desired range. The casting treatment solidifies the molten steel. To perform these treatments smoothly, it is important to control the molten steel temperature within an appropriate range.

[0003] In the refining treatment, primary refining and secondary refining are performed. The secondary refining has a molten steel temperature adjustment function as one of its main roles.

[0004] When adjusting the molten steel temperature in secondary refining, usually, an operator measures the molten steel temperature during the process as needed and then estimates the change in the molten steel temperature. When the difference between the estimated molten steel temperature and the target temperature is large, the operator performs an operation to heat or cool the molten steel.

[0005] As methods for estimating the change in molten steel temperature, an estimation method based on a physical model and an estimation method based on a statistical model are known. For example, Patent Document 1 discloses a method for estimating the change in molten steel temperature based on a physical model. Also, for example, Patent Documents 2 and 3 disclose methods for estimating the change in molten steel temperature based on a statistical model.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

[0007] Patent Document 1 discloses a method for estimating changes in molten steel temperature based on a physical model. In order to estimate changes in molten steel temperature based on a physical model, it is necessary to accurately understand the amount of heat contained in an object such as a ladle that is in direct contact with the molten steel and exchanges heat with it. For this reason, Patent Document 1 discloses a method for estimating the temperature distribution of a ladle based on a physical model and estimating the molten steel temperature based on the estimated temperature distribution of the ladle.

[0008] However, repeated use of a ladle leads to an accumulation of errors in estimating the temperature distribution of the ladle. Furthermore, it is difficult to accurately estimate the temperature distribution of a ladle based on a single temperature measurement. Therefore, it is difficult to accurately estimate changes in molten steel temperature based on the method disclosed in Patent Document 1.

[0009] Patent documents 2 and 3 disclose a method for estimating changes in molten steel temperature based on a statistical model constructed from past processing results. Therefore, the methods disclosed in patent documents 2 and 3 do not require estimating the temperature distribution of the ladle, as does the method disclosed in patent document 1.

[0010] The methods disclosed in Patent Documents 2 and 3 construct a model that estimates the change in molten steel temperature throughout the entire refining process by using the molten steel temperature at the start and end of the refining process as training data. However, the model constructed in this way has the following two problems.

[0011] The first problem is that while the refining process involves various steps, the methods disclosed in Patent Documents 2 and 3 only consider the molten steel temperature at the start and end of the process. Therefore, they may not quantitatively and correctly isolate the factors that affect the molten steel temperature. For example, they may overestimate the amount of heat exchange between the molten steel and the ladle, and underestimate the amount of heat exchange between the molten steel and the added auxiliary materials. In such cases, using the constructed statistical model to estimate the molten steel temperature for unknown data carries the risk of large errors in the molten steel temperature estimation.

[0012] The second problem is that the methods disclosed in Patent Documents 2 and 3 only consider the molten steel temperature at the start and end of the process, and therefore cannot accurately estimate the molten steel temperature during the refining process. For example, the heat exchange rate between the ladle and the molten steel decreases as the temperature difference between them decreases, so it is clear that the molten steel temperature changes nonlinearly with respect to time. Therefore, if the change in molten steel temperature is estimated assuming that it changes linearly with respect to time, it is not possible to accurately estimate the change in molten steel temperature during the refining process.

[0013] In normal operations, operators measure the molten steel temperature during the refining process and attempt to estimate the subsequent molten steel temperature based on the measured temperature. However, the statistical models used in Patent Documents 2 and 3 cannot estimate the subsequent molten steel temperature based on the temperature measured midway through the process. Therefore, it is difficult to compare the estimated molten steel temperature with the target temperature and perform operations such as heating or cooling the molten steel to minimize the difference between the estimated and target temperatures.

[0014] The object of this disclosure is to provide a method for estimating the temperature of molten steel, a method for controlling the temperature of molten steel, and a method for manufacturing molten steel, which can accurately estimate the amount of change in molten steel temperature. [Means for solving the problem]

[0015] [1] A method for estimating the temperature of molten steel in a process of handling molten steel in the steelmaking process, The steps include dividing the process from start to finish into multiple sections based on at least one point in time during the process at which the molten steel temperature is measured, The steps include measuring the molten steel temperature at the start of each of the aforementioned multiple sections, Based on the molten steel temperature estimation models constructed for each of the aforementioned multiple intervals, the steps include estimating the change in molten steel temperature in the current interval to which the present time belongs, and the change in molten steel temperature in the intervals after the current interval, The steps include: estimating the molten steel temperature at the end of the process by combining the estimated change in molten steel temperature in the current section with the change in molten steel temperature in the section after the current section; A method for estimating molten steel temperature, including the method described above.

[0016] [2] The molten steel temperature estimation method described in [1] above, wherein the parameters of the molten steel temperature estimation model are determined based on past operational performance.

[0017] [3] In the molten steel temperature estimation model, The inputs are: operational information of the process preceding the said process; operational information up to the previous section in the said process; operational schedule information for the section in which the molten steel temperature estimation model is constructed; and equipment information for the said process. The molten steel temperature estimation method according to [1] or [2] above, wherein the output is the change in molten steel temperature from the start to the end of the section in which the molten steel temperature estimation model is constructed.

[0018] [4] The operational information of the preceding process includes at least one of the molten steel information, the amount of operational work, and the processing time information in the preceding process. The operational information up to the previous section in the aforementioned process includes at least one of the molten steel information, operational volume information, and processing time information up to the previous section. The operational schedule information for the section in which the molten steel temperature estimation model is constructed includes at least one of operational volume information and processing time information for the section in which the molten steel temperature estimation model is constructed. The equipment information of the above-mentioned treatment includes information on the usage history of the ladle used in the treatment and the measured temperature information of the ladle, and is the molten steel temperature estimation method according to any one of [1] to [3] above.

[0019] [5] The above-mentioned treatment is a secondary refining treatment, The pre-process of the above-mentioned treatment is a primary refining treatment, The operation information of the pre-process of the above-mentioned treatment includes at least one of the weight of the molten steel and the molten steel temperature at the end of the pre-process, The operation information up to the previous section in the above-mentioned treatment includes at least the information on the molten steel temperature measured from after the end of the immediately preceding section to before the start of the current section, The planned operation information of the section for constructing the above-mentioned molten steel temperature estimation model includes at least one of the amount of auxiliary raw material input, the amount of oxygen blown, and the treatment time, The equipment information of the above-mentioned treatment includes at least one of the measured temperature information obtained by measuring the ladle used in the treatment in an empty state, the time when the ladle is in an empty state from discharging the previously received molten steel to receiving the current molten steel, and the time from the end of the immediately preceding secondary refining treatment to the start of the current secondary refining treatment, and is the molten steel temperature estimation method according to any one of [1] to [4] above

[0020] [6] The above-mentioned molten steel temperature estimation model is a neural network model in the sections immediately after the start and immediately before the end of the secondary refining treatment, and is a linear regression model in other sections, and is the temperature estimation method according to any one of [1] to [5] above Molten steel Temperature estimation method.

[0021] [7] Calculate the molten steel temperature adjustment operation amount for adjusting the molten steel temperature so that the difference between the molten steel temperature at the end of the above-mentioned treatment estimated by the molten steel temperature estimation method according to any one of [1] to [6] above and the target temperature of the molten steel temperature at the end of the above-mentioned treatment is within a predetermined range. Molten steel temperature control method.

[0022] [8] A method for manufacturing molten steel, which adjusts the molten steel temperature using the molten steel temperature control method described in [7] above to manufacture molten steel.

Advantages of the Invention

[0023] According to the molten steel temperature estimation method, molten steel temperature control method, and molten steel manufacturing method described herein, the amount of change in molten steel temperature can be estimated with high accuracy. [Brief explanation of the drawing]

[0024] [Figure 1] This figure shows an example of a molten steel temperature control device that implements a molten steel temperature estimation method and a molten steel temperature control method according to one embodiment of the present disclosure. [Figure 2] This is a block diagram showing an example of the configuration of a molten steel temperature control device according to one embodiment of the present disclosure. [Figure 3] This figure shows an example of how the refining process is divided into two sections. [Figure 4] This figure shows an example of how the refining process is divided into three sections. [Figure 5] This flowchart shows an example of a molten steel temperature estimation method and a molten steel temperature control method according to one embodiment of the present disclosure. [Figure 6] This figure compares the estimation error of the molten steel temperature change between a comparative example and this embodiment. [Modes for carrying out the invention]

[0025] The embodiments of this disclosure will be described below with reference to the drawings.

[0026] Figure 1 shows an example of a molten steel temperature control device that implements a molten steel temperature estimation method and a molten steel temperature control method according to one embodiment of the present disclosure.

[0027] Figure 1 shows an example of a secondary refining process in the steelmaking process. In Figure 1, the secondary refining apparatus 1 performs the secondary refining process on the molten steel in the ladle 2.

[0028] The molten steel temperature control device 10 estimates the molten steel temperature for molten steel undergoing secondary refining. Based on the estimated molten steel temperature, the molten steel temperature control device 10 determines a molten steel temperature manipulation amount to adjust the molten steel temperature and controls the molten steel temperature.

[0029] In this embodiment, the molten steel temperature estimation method and molten steel temperature control method are described using the application of the molten steel temperature control method to a secondary refining process as an example. However, this is just one example, and the molten steel temperature estimation method and molten steel temperature control method according to this embodiment can be applied to any process in the steelmaking process that processes molten steel. For example, the molten steel temperature estimation method and molten steel temperature control method according to this embodiment can also be applied to a primary refining process. In the primary refining process, the molten steel temperature rises by approximately 300°C due to desilicate and decarburization reactions, whereas the change in molten steel temperature in the secondary refining process is 20 to 40°C. Therefore, the required accuracy of molten steel temperature estimation is higher in the secondary refining process than in the primary refining process. For this reason, the molten steel temperature estimation method and molten steel temperature control method according to this embodiment can be more effective when applied to a secondary refining process.

[0030] Figure 2 is a block diagram showing an example of the configuration of a molten steel temperature control device 10 according to one embodiment of the present disclosure. The molten steel temperature control device 10 may be a general-purpose computer such as a workstation or personal computer, or it may be a dedicated computer configured to function as a molten steel temperature control device 10.

[0031] The molten steel temperature control device 10 comprises a control unit 11, an input unit 12, an output unit 13, a storage unit 14, and a communication unit 15.

[0032] The control unit 11 includes at least one processor, at least one dedicated circuit, or a combination thereof. The processor is a general-purpose processor such as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit), or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0033] The control unit 11 reads programs, data, etc., stored in the memory unit 14 and executes various functions.

[0034] The input unit 12 includes one or more input interfaces that detect user input and acquire input information based on user operations. The input unit 12 includes, for example, physical keys, capacitive keys, a touchscreen integrated with the display of the output unit 13, or a microphone that accepts voice input.

[0035] The output unit 13 includes one or more output interfaces for outputting information and notifying the user. The output unit 13 includes, for example, a display for outputting information as an image, a speaker for outputting information as sound, etc. The display included in the output unit 13 may be, for example, an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, etc.

[0036] The storage unit 14 is, for example, a flash memory, a hard disk, or an optical memory. Part of the storage unit 14 may be located outside the molten steel temperature control device 10. In this case, part of the storage unit 14 may be a hard disk, memory card, or the like, connected to the molten steel temperature control device 10 via any interface.

[0037] The memory unit 14 stores programs for the control unit 11 to execute various functions, data used by those programs, and so on.

[0038] The communication unit 15 includes at least one of a communication module that supports wired communication and a communication module that supports wireless communication. The molten steel temperature control device 10 can communicate with other devices via the communication unit 15.

[0039] Next, the molten steel temperature estimation method and molten steel temperature control method executed by the molten steel temperature control device 10 will be explained using the case of secondary refining as an example.

[0040] In secondary refining, the timing of measuring the molten steel temperature during the refining process may be determined depending on the type of steel being refined. Hereafter, the timing of measuring the molten steel temperature during secondary refining will be referred to as the "molten steel temperature measurement point." Secondary refining has at least one molten steel temperature measurement point.

[0041] The molten steel temperature estimation method according to this embodiment divides the period from the start to the end of the secondary refining process into multiple intervals based on at least one molten steel temperature measurement point.

[0042] Figure 3 shows an example of how the secondary refining process is divided into two sections, from start to finish.

[0043] Figure 3 shows an example of a process in which molten steel sampling is performed during the secondary refining process, and the component concentrations of the sampled molten steel are measured.

[0044] In the example shown in Figure 3, at time t0, the operator starts the secondary refining process. At time t0, when the secondary refining process begins, the operator measures the molten steel temperature. At time t0, the molten steel temperature is T0.

[0045] At time ta, the operator takes a sample of molten steel and measures the component concentrations of the sampled molten steel.

[0046] At time t1, immediately following time ta, the operator measures the molten steel temperature. Therefore, time t1 is the time when the molten steel temperature is measured. At time t1, the molten steel temperature is T1.

[0047] At time t2, the operator completes the secondary refining process. At time t2, when the secondary refining process is completed, the operator measures the molten steel temperature. At time t2, the molten steel temperature is T2.

[0048] In the example shown in Figure 3, the process from the start to the end of the secondary refining is divided into two sections based on the molten steel temperature measurement time t1. In the example shown in Figure 3, section 1 is from time t0 to time t1, and section 2 is from time t1 to time t2.

[0049] Figure 4 shows an example of how the secondary refining process is divided into three sections, from start to finish.

[0050] Figure 4 shows an example of a decarburization treatment performed during the secondary refining process. Decarburization of molten steel proceeds by reacting carbon in the molten steel with oxygen. When molten steel is continuously cast, it is necessary to sufficiently reduce the oxygen concentration in the molten steel. Therefore, after performing a decarburization treatment to reduce the carbon concentration in the molten steel to the target value, the operator adds a deoxidizer to further reduce the oxygen concentration in the molten steel.

[0051] In the example shown in Figure 4, at time t0, the operator starts the secondary refining process. At time t0, when the secondary refining process begins, the operator measures the molten steel temperature. At time t0, the molten steel temperature is T0.

[0052] At time tb, the operator adds a deoxidizing agent to the molten steel. However, at time t1, prior to time tb, the operator measures the oxygen concentration in the molten steel to determine the amount of deoxidizing agent to add. At the same time, time t1, the operator also measures the temperature of the molten steel. Therefore, time t1 is the time when the molten steel temperature is measured. At the time of measurement t1, the temperature of the molten steel is T1.

[0053] At time tb, the operator adds a deoxidizer and then stirs the molten steel. Subsequently, at time t2, the operator measures the oxygen concentration in the molten steel to confirm that it has decreased sufficiently. At the same time, at time t2, the operator also measures the temperature of the molten steel. Therefore, time t2 is the time when the temperature of the molten steel is measured. At the time of measurement t2, the temperature of the molten steel is T2.

[0054] At time t3, the operator completes the secondary refining process. At time t3, when the secondary refining process is completed, the operator measures the molten steel temperature. At time t3, the molten steel temperature is T3.

[0055] In the example shown in Figure 4, the period from the start to the end of the secondary refining process is divided into three sections based on the molten steel temperature measurement times t1 and t2. In the example shown in Figure 4, section 1 is from time t0 to time t1. Section 2 is from time t1 to time t2. Section 3 is from time t2 to time t3.

[0056] The molten steel temperature estimation method according to this embodiment measures the molten steel temperature at the start of each of the multiple sections. Then, based on the molten steel temperature estimation model constructed for each of the multiple sections, the method estimates the change in molten steel temperature in the current section to which the current time belongs, and the change in molten steel temperature in the sections after the current section. Details of the molten steel temperature estimation model will be described later.

[0057] For example, in the example shown in Figure 4, if the current time is between t0 and t1, the interval to which the current time belongs is interval 1. The molten steel temperature estimation method according to this embodiment estimates the change in molten steel temperature from the start to the end of interval 1, which is the current interval, based on the molten steel temperature estimation model constructed for interval 1. Furthermore, for interval 2, which is the interval after interval 1, which is the current interval, the change in molten steel temperature from the start to the end of interval 2, based on the molten steel temperature estimation model constructed for interval 2. Furthermore, for interval 3, which is the interval after interval 1, which is the current interval, the change in molten steel temperature from the start to the end of interval 3, based on the molten steel temperature estimation model constructed for interval 3.

[0058] In this way, by dividing the process from the start to the end of the secondary refining treatment into multiple sections based on the molten steel temperature measurement point, and estimating the amount of change in molten steel temperature for each section, the molten steel temperature estimation method according to this embodiment can accurately estimate the amount of change in molten steel temperature for each section. For example, in the example shown in Figure 4, when a deoxidizer is added at time tb, the temperature of the molten steel rises, but such a temperature rise is observed only in section 2. Therefore, it is sufficient that only the molten steel temperature estimation model for section 2 takes into account the addition of the deoxidizer, and by constructing a molten steel temperature estimation model for each section in this way, the molten steel temperature estimation method according to this embodiment can accurately estimate the amount of change in molten steel temperature for each section.

[0059] As described above, the molten steel temperature estimation model is constructed for each interval. The molten steel temperature estimation model may be constructed using any model form that can accurately estimate the change in molten steel temperature from the start to the end of the interval. For example, the model form of the molten steel temperature estimation model may be a linear regression model, a support vector regression model, a neural network model, etc.

[0060] The model format of the molten steel temperature estimation model may differ depending on the dominant factors of temperature accuracy for each section. For example, the molten steel temperature changes nonlinearly over time due to changes in the heat exchange rate between ladle 2 and the molten steel. In the initial section of the secondary refining process (immediately after the start), when the temperature of ladle 2 is relatively low, the heat exchange rate from the molten steel to the refractory on the inner wall of ladle 2 is high, and the heat exchange rate is greatly influenced by the amount of heat stored in the refractory on the ladle 2 just before receiving the steel. In the section after the initial stage, when the temperature of ladle 2 rises due to heat transfer from the molten steel, the heat exchange rate between the molten steel and ladle 2 decreases, and the exothermic reactions caused by the operation of adding auxiliary materials and blowing in oxygen dominate the temperature accuracy. On the other hand, in the section immediately before the end of the secondary refining process, when the amount of temperature change of the molten steel between the molten steel and ladle 2 is small, the temperature change of the molten steel itself is small, and uncertain factors such as the variation in the amount of heat conducted between the molten steel and the ladle due to the wear and change in properties of the refractory on the inner wall of ladle 2 dominate the temperature accuracy. Based on this property, in the initial (immediately after start) and immediately before the end of the secondary refining process, a nonlinear model such as a multi-layer neural network (deep learning), support vector regression, decision tree, or ensemble learning may be used as a model that can reflect the nonlinearity of temperature changes and predict complex temperature changes by incorporating information on the usage history of ladle 2. In the section from the beginning of the secondary refining process onward, where the exothermic reaction due to the mass balance of the addition of auxiliary materials and oxygen blowing dominates the temperature accuracy, a linear model may be used, prioritizing the robustness of the model (stability against outliers and noise in the training data).

[0061] The inputs to the molten steel temperature estimation model are operational information of the process preceding the secondary refining process, operational information up to the previous section in the secondary refining process, operational schedule information for the section in which the molten steel temperature estimation model is constructed, and equipment information for the secondary refining process.

[0062] Here, the pre-process of the secondary refining process refers to the process performed before the secondary refining process, which may be, for example, the primary refining process.

[0063] The operational information for the pre-process of the secondary refining treatment may include at least one of the following: molten steel information, operational volume information, and processing time information. The molten steel information may include, for example, the weight of the molten steel, the temperature of the molten steel at the end of the pre-process, and the component concentration of the molten steel at the end of the pre-process. The weight of the molten steel correlates with the heat capacity of the molten steel contained in ladle 2, the temperature of the molten steel at the end of the pre-process of the secondary refining treatment becomes the initial value of the molten steel temperature in the section immediately after the start of the secondary refining treatment, and the component concentration information of the molten steel affects the exothermic reaction caused by the addition of auxiliary materials and oxygen blowing in during the secondary refining treatment, and therefore greatly influences the temperature change during the secondary refining treatment. Operational volume information may include, for example, the weight of auxiliary materials added in the previous process. The amount of slag in the secondary refining charge affects the molten steel temperature in the form of heat dissipation from the top surface of the ladle 2, and the amount of slag may increase when auxiliary materials are added in the previous process for the purpose of modifying the slag. Processing time information may include, for example, information such as the time from the end of the previous process to the start of the secondary refining process. This is because it greatly affects the amount of heat exchange between the molten steel and the ladle before the start of the secondary refining process, and therefore greatly affects the change in molten steel temperature during the secondary refining process.

[0064] Furthermore, the operational information up to the previous section in the secondary refining process refers, for example, to the operational information in sections 1 and 2 if the molten steel temperature estimation model is constructed for section 3.

[0065] The operational information up to the previous section in the secondary refining process may include at least one of the following: molten steel information, equipment information, operational volume information, and processing time information for the section up to the previous section. The molten steel information may include, for example, information on the molten steel temperature measured from the end of the previous section to the start of the current section. By using the information on the molten steel temperature before the start of the current section as input information (explanatory variable) to the molten steel temperature estimation model as an initial condition, the prediction accuracy can be improved. Operational data may include information such as the amount of auxiliary materials added and the amount of oxygen injected. This is because temperature changes associated with operations in the previous section may be observed in the next section with a delay. The processing time information may include information on the processing time for each section in the previous section. This is because the processing time for each section in the previous section greatly affects the amount of heat exchanged between the molten steel and the ladle in the previous section, and therefore greatly affects the amount of heat exchanged between the molten steel and the ladle in the next section.

[0066] Furthermore, the operational schedule information for the section in which the molten steel temperature estimation model is constructed refers to, for example, the operational schedule information for section 3 if the molten steel temperature estimation model is constructed for section 3.

[0067] The operational schedule information for the section in which the molten steel temperature estimation model is constructed may include at least one of the operational volume information and processing time information for the section in which the molten steel temperature estimation model is constructed. The operational volume information may include, for example, information such as the planned amount of auxiliary materials to be added and the planned amount of oxygen to be blown in. This is because the addition of auxiliary materials directly affects the heat balance of the molten steel, and oxygen blowing promotes exothermic reactions such as oxidation and decarburization of the molten steel, and therefore these are factors that greatly affect the accuracy of the molten steel temperature estimation model for that section. The processing time information may include, for example, information on the planned processing time for the section in which the molten steel temperature estimation model is constructed.

[0068] The equipment information for the secondary refining process may include, for example, at least one of the following: measured temperature information of the inner wall surface of ladle 2 obtained when ladle 2 is empty (ladle surface temperature), the number of times ladle 2 has been used (the number of times ladle 2 has received molten steel so far), the time when ladle 2 is empty from the time the previously received molten steel is discharged until the current molten steel is received (ladle time), and the time from the end of the previous secondary refining process to the start of the current secondary refining process (RH tank empty time). These are factors that affect the amount of heat stored in the secondary refining apparatus 1 and ladle 2 used in the secondary refining process, and by using them as inputs (explanatory variables) to the model, they contribute to improving the temperature accuracy in the early stages (immediately after the start) and immediately before the end of the secondary refining process.

[0069] Furthermore, the output of the molten steel temperature estimation model is the change in molten steel temperature from the start to the end of the interval in which the molten steel temperature estimation model is constructed. For example, if the molten steel temperature estimation model is constructed for interval 3, the output is the change in molten steel temperature from the start to the end of interval 3.

[0070] The parameters of the molten steel temperature estimation model may be determined based on data obtained from past operational performance. When constructing the molten steel temperature estimation model, all or part of the data obtained from past operational performance may be used. If only some data is used, the choice of which data to use may be determined by applying a known method. A known method may be, for example, cross-validation.

[0071] Furthermore, the parameters of the molten steel temperature estimation model may be updated each time a secondary refining process is performed, using newly acquired operational data.

[0072] The constructed molten steel temperature estimation models for each section may be stored in the memory unit 14.

[0073] The molten steel temperature estimation method and molten steel temperature control method according to this embodiment will be described with reference to the flowchart shown in Figure 5. The process shown in Figure 5 may be executed in accordance with the operator's instructions after the secondary refining process has started, or it may be executed at a predetermined timing.

[0074] Step S101: The control unit 11 of the molten steel temperature control device 10 acquires the operation information of the previous process. The control unit 11 may, for example, acquire the operation information of the previous process transmitted by another information processing device via the communication unit 15.

[0075] The operational information of the preceding process may include at least one of the following from the preceding process of the secondary refining: molten steel information, operational volume information, and processing time information.

[0076] Step S102: The control unit 11 obtains the target temperature of the molten steel at the end of the secondary refining process. The control unit 11 may obtain the target temperature entered by the operator through an input operation to the input unit 12, or it may obtain the target temperature transmitted by another information processing device via the communication unit 15.

[0077] Step S103: The control unit 11 acquires operational information up to the previous section in the secondary refining process as operational information during the secondary refining process. The control unit 11 may, for example, acquire operational information up to the previous section transmitted by another information processing device via the communication unit 15.

[0078] The operational information up to the previous section in the secondary refining process may include at least one of the following: molten steel information, equipment information, operational volume information, and processing time information up to the previous section.

[0079] Step S104: The control unit 11 determines which section the current time belongs to. The control unit 11 may determine the section to which the current time belongs based on, for example, the operational information acquired in step S103. Alternatively, the control unit 11 may determine the section to which the current time belongs based on, for example, the elapsed time from the start of the secondary refining process to the current time. Alternatively, the control unit 11 may determine the section to which the current time belongs based on, for example, the content of events performed from the start of the secondary refining process to the current time. The event information may include information such as molten steel temperature measurement, molten steel oxygen concentration measurement, and molten steel sampling performed up to the previous section. Hereafter, the section to which the current time belongs may be referred to as the "current section".

[0080] Step S105: The control unit 11 acquires the operation schedule information for the current section. The control unit 11 may, for example, acquire the operation schedule information for the current section transmitted by another information processing device via the communication unit 15. Alternatively, the control unit 11 may, for example, acquire the operation schedule information for the current section stored in the storage unit 14 from the storage unit 14.

[0081] The operational schedule information for the current section may include at least one of the operational volume information and processing time information for the current section.

[0082] Step S106: The control unit 11 inputs the operation information of the previous process obtained in step S101, the operation information up to the previous section in the secondary refining process obtained in step S103, the operation schedule information for the current section obtained in step S105, and the equipment information for the secondary refining process into the molten steel temperature estimation model constructed for the current section, and estimates the amount of change in molten steel temperature in the current section.

[0083] Step S107: The control unit 11 determines whether the current section is the final section in the secondary refining process. If it is not the final section (No in step S107), the control unit 11 proceeds to step S108. If it is the final section (Yes in step S107), the control unit 11 proceeds to step S109.

[0084] Step S108: The control unit 11 moves from the current section to the next section and repeats the processes of steps S105 and S106. For example, if the current section is section 2, the control unit 11 moves from section 2 to section 3 and estimates the change in molten steel temperature in section 3, which is the section after the current section, section 2. In this case, the control unit 11 inputs the operation information of the previous process, the operation information up to section 2, and the planned operation information for section 3 into the molten steel temperature estimation model constructed for section 3, and estimates the change in molten steel temperature in section 3.

[0085] Step S109: The control unit 11 combines the estimated change in molten steel temperature in the current section with the change in molten steel temperature in the sections following the current section to estimate the molten steel temperature at the end of the secondary refining process.

[0086] Step S110: The control unit 11 determines whether the difference between the molten steel temperature at the end of the secondary refining process, estimated in step 109, and the target temperature of the molten steel obtained in step S102, is within a predetermined range. If the difference is within the predetermined range (Yes in step S110), the control unit 11 terminates the process. If the difference is not within the predetermined range (No in step S110), the control unit 11 proceeds to step S111.

[0087] Step S111: The control unit 11 calculates the amount of molten steel temperature adjustment operation required to bring the difference between the estimated molten steel temperature at the end of the secondary refining process and the target molten steel temperature within a predetermined range, and returns to step S105. Here, the amount of molten steel temperature adjustment operation is the amount to which the operation to adjust the molten steel temperature is performed in order to adjust the molten steel temperature.

[0088] For example, if the estimated molten steel temperature at the end of the secondary refining process is higher than the target temperature, the molten steel temperature can be lowered by adding cooling auxiliary materials to the molten steel. In this case, the control unit 11 calculates how much cooling auxiliary material should be added as the molten steel temperature adjustment operation amount.

[0089] Furthermore, for example, if the estimated molten steel temperature at the end of the secondary refining process is lower than the target temperature, the molten steel temperature can be increased by blowing oxygen into the molten steel. In this case, the control unit 11 calculates how much oxygen should be blown in as the molten steel temperature adjustment operation amount.

[0090] The control unit 11 may, for example, calculate the amount of molten steel temperature adjustment operation based on the relationship between the amount of molten steel temperature adjustment operation and the amount of change in molten steel temperature, which is stored in advance in the storage unit 14.

[0091] As described above, the molten steel temperature estimation method according to this embodiment estimates the amount of change in molten steel temperature in each section using a molten steel temperature estimation model constructed for each section, so the amount of change in molten steel temperature in each section can be estimated with high accuracy, and the molten steel temperature at the end of the secondary refining process can be estimated with high accuracy. Furthermore, the molten steel temperature control method according to this embodiment allows the molten steel temperature to be adjusted based on the amount of molten steel temperature adjustment operation calculated in step S111, so the molten steel temperature can be controlled with high accuracy.

[0092] (Examples) Figure 6 is a diagram comparing the estimation error of the molten steel temperature change between the comparative example and this embodiment.

[0093] Figure 6 shows the results of estimating the molten steel temperature during vacuum degassing with decarburization. In the example shown in Figure 6, the vacuum degassing process is divided into three sections. Section 1 is the section from the start of the process to the point of measurement of the molten steel temperature immediately before the addition of the deoxidizer. Section 2 is the section from the point of measurement of the molten steel temperature immediately before the addition of the deoxidizer to the point of measurement of the molten steel temperature immediately after the addition of the deoxidizer. Section 3 is the section from the point of measurement of the molten steel temperature immediately after the addition of the deoxidizer to the end of the process.

[0094] The comparative example shows the results of estimating the change in molten steel temperature using a model that estimates the change in molten steel temperature throughout the entire vacuum degassing process. The model used in the comparative example is a linear model. The model used in the comparative example was constructed based on 590 sets of actual data. The change in molten steel temperature at the end of section 1 and the end of section 2 was estimated assuming that the molten steel temperature changes linearly over time.

[0095] This embodiment presents the results of estimating the change in molten steel temperature using molten steel temperature estimation models constructed for each of the three intervals (1 to 3). The molten steel temperature estimation model for interval 1 used a neural network model. The molten steel temperature estimation model for interval 2 used a linear model. The molten steel temperature estimation model for interval 3 also used a neural network model. The models for intervals 1 to 3 used in this embodiment were constructed based on 590 sets of actual data.

[0096] Figure 6 shows the estimation error between the estimated molten steel temperature and the actually measured molten steel temperature during the vacuum degassing process. The estimation error is calculated using the root mean square error based on the estimated molten steel temperature and the molten steel temperatures measured 103 times.

[0097] Referring to Figure 6, it can be seen that the molten steel temperature estimation method according to this embodiment is able to estimate the change in molten steel temperature with greater accuracy than the comparative example. In particular, at intermediate points such as the end of section 1 and the end of section 2, the estimation error of the comparative example is considerably larger, indicating that the molten steel temperature estimation method according to this embodiment is considerably more accurate than the comparative example.

[0098] As described above, the molten steel temperature estimation method according to this embodiment includes the steps of: dividing the time from the start to the end of the process into multiple sections; measuring the molten steel temperature at the start of each of the multiple sections; estimating the amount of change in molten steel temperature in the current section to which the current time belongs, and the amount of change in molten steel temperature in the sections after the current section, based on a molten steel temperature estimation model constructed for each of the multiple sections; and estimating the molten steel temperature at the end of the process by combining the estimated amount of change in molten steel temperature in the current section and the amount of change in molten steel temperature in the sections after the current section. In this way, by dividing the time from the start to the end of the process into multiple sections and estimating the amount of change in molten steel temperature for the current section and the sections after the current section, the molten steel temperature estimation method according to this embodiment can accurately estimate the amount of change in molten steel temperature for each section and the amount of change in molten steel temperature up to the end of the process.

[0099] Furthermore, the molten steel temperature control method according to this embodiment calculates the amount of molten steel temperature adjustment operation so that the difference between the molten steel temperature at the end of the process estimated by the molten steel temperature estimation method according to this embodiment and the target temperature of the molten steel at the end of the process falls within a predetermined range. This allows for precise control of the molten steel temperature at the end of the process.

[0100] This disclosure is not limited to the embodiments described above. For example, multiple blocks described in the block diagram may be combined, or a single block may be divided. Instead of executing multiple steps described in the flowchart in chronological order as described, they may be executed in parallel or in a different order, depending on the processing capacity of the device performing each step, or as necessary. Other modifications are possible without departing from the spirit of this disclosure.

[0101] For example, in the embodiments described above, we explained a case in which the molten steel temperature estimation model is constructed as a statistical model based on past operating results, but the invention is not limited to this. The molten steel temperature estimation model may also be constructed as a physical model. [Explanation of symbols]

[0102] 1. Secondary refining apparatus 2 ladle 10. Molten steel temperature control device 11 Control Unit 12 Input section 13 Output section 14 Storage section 15 Communications Department

Claims

1. A method for estimating the molten steel temperature in the molten steel processing in the steelmaking process, The steps include dividing the process from start to finish into multiple sections based on at least one point in time during the process at which the molten steel temperature is measured, The steps include measuring the molten steel temperature at the start of each of the aforementioned multiple sections, Based on multiple molten steel temperature estimation models constructed for each of the aforementioned multiple intervals, the steps include estimating the change in molten steel temperature in the current interval to which the present time belongs, and the change in molten steel temperature in the intervals after the current interval, The steps include: estimating the molten steel temperature at the end of the process by combining the estimated change in molten steel temperature in the current section, the change in molten steel temperature in the section after the current section, and the molten steel temperature at the start of the current section; Includes, The time at which the molten steel temperature is measured is a predetermined time. In the aforementioned molten steel temperature estimation model, The inputs are: operation information of a preceding process executed before the said process; operation information up to the previous section in the said process; planned operation information for the section in which the molten steel temperature estimation model was constructed; and equipment information for the said process. A method for estimating molten steel temperature, wherein the output is the change in molten steel temperature from the start to the end of the section in which the molten steel temperature estimation model was constructed.

2. The molten steel temperature estimation method according to claim 1, wherein the parameters of the molten steel temperature estimation model are determined based on past operational performance.

3. The operational information of the preceding process performed before the aforementioned process includes at least one of the molten steel information, operational amount information, and processing time information in the preceding process. The operational information up to the previous section in the aforementioned process includes at least one of the molten steel information, operational volume information, and processing time information up to the previous section. The operational schedule information for the section in which the molten steel temperature estimation model is constructed includes at least one of the operational volume information and processing time information for the section in which the molten steel temperature estimation model is constructed. The method for estimating molten steel temperature according to claim 1, wherein the equipment information for the processing includes information regarding the usage history of the ladle used for the processing and information regarding the measured temperature of the ladle.

4. The aforementioned process is a secondary refining process, The pre-process performed before the aforementioned process is a primary refining process. The operational information of the preceding process performed before the aforementioned process includes at least one of the weight of the molten steel and the temperature of the molten steel at the end of the preceding process. The operational schedule information for the section where the molten steel temperature estimation model was constructed includes at least one piece of information: the amount of auxiliary materials input, the amount of oxygen blown in, and the processing time. The molten steel temperature estimation method according to claim 3, wherein the equipment information for the processing includes at least one of the measured temperature information obtained by measuring the ladle used for the processing when it is empty, the time when the ladle is empty from the time when the molten steel received in the previous processing is discharged until the molten steel received in the current processing is received, and the time from the end of the immediately preceding secondary refining process to the start of the said secondary refining process.

5. The molten steel temperature estimation method according to claim 4, wherein the molten steel temperature estimation model is a neural network model in the period immediately after the start and immediately before the end of the secondary refining process, and a linear regression model in the other periods.

6. A molten steel temperature control method that calculates a molten steel temperature adjustment amount for adjusting the molten steel temperature so that the difference between the molten steel temperature at the end of the process, estimated by the molten steel temperature estimation method according to any one of claims 1 to 5, and the target temperature of the molten steel at the end of the process is within a predetermined range.

7. A method for producing molten steel, comprising adjusting the molten steel temperature using the molten steel temperature control method described in claim 6.