Operation calculation device and operation calculation method

Abstract

To provide an operational calculation device and an operational calculation method for controlling operational conditions by gathering a plurality of steps in a steel making process so as to derive optimum operational conditions.SOLUTION: An operational calculation device for controlling operational conditions up to a converter blowing step so as to optimize the operational conditions of each step, has: molten iron pre-treatment control means of controlling the operational conditions in a molten iron pre-treatment step; converter step control means of estimating a componential value of a prescribed component in the converter blowing step using the operational conditions controlled by the molten iron pre-treatment control means; and cold iron source control means of controlling a cold iron source input amount in the converter blowing step.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates in particular to an operation calculation device and an operation calculation method suitable for centrally managing the operating conditions of multiple processes. [Background technology]

[0002] Traditionally, in steelmaking processes consisting of multiple stages such as molten iron pretreatment and converter blowing, operational instructions are given for each stage by optimizing the composition and cost of the molten iron. However, due to fluctuations in the composition of the molten iron and the grade of scrap, there is a certain probability that some components will fall outside the upper and lower limits of the target composition range after tapping. In such cases, additional processes are added in secondary refining (hereinafter referred to as "additional processes"), significantly increasing operational costs. For example, because the amount of sulfur (S) input into the molten iron fluctuates due to fluctuations in the composition of the molten iron and the grade of scrap, it is not possible to optimize the S concentration in a single process, resulting in unplanned secondary refining or unsuitable components that fall outside the target composition range.

[0003] As described above, optimizing operating conditions for a single process may result in the need for additional processes; therefore, it is desirable to optimize the operating conditions of multiple processes simultaneously. To achieve this, it is necessary to identify factors that cause deviations from the target composition from multiple factors and to modify the operating conditions as appropriate for learning. Patent Document 1 discloses a method for calculating parameters in a smelting process, in which the amount of oxygen, heat-generating material, and coolant required in the first and second periods, respectively, up to the first and second time points, when the amount of components contained in the molten steel and the molten steel temperature in the smelting process are measured, are predicted using machine learning results to predict unknown heat and unknown oxygen, and the amount of oxygen, heat-generating material, and coolant required in the second period is calculated using the measured amount of components and measured molten steel temperature at the first time point, as well as the measured amount of unknown heat and measured amount of unknown oxygen. Patent Document 2 discloses a method for predicting the components of a molten iron refining process based on a first-order reaction equation. This method involves estimating the component concentrations and reaction rate coefficients at equilibrium, and then estimating the component concentrations each time. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Patent No. 6516906 [Patent Document 2] Japanese Patent Publication No. 2020-15959 [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] As mentioned above, there is a certain probability that some components may exceed the upper limit based on product specifications after tapping. Possible causes include deviations from predicted values ​​in the components of molten iron fed into the converter, residual slag in the molten iron ladle, cold iron source, and auxiliary raw materials. Predicting the components of cold iron sources such as scrap is particularly difficult, and the method described in Patent Document 1 does not take into account the components of scrap, making it impossible to sufficiently optimize operating conditions. The method described in Patent Document 2 is not suitable for component systems in which the refining reaction is difficult to proceed, and its scope of application is limited. Therefore, a new technology is needed that can predict component fluctuations based on mass balance and past performance, regardless of whether it is advantageous or disadvantageous for the refining reaction.

[0006] In view of the aforementioned problems, the present invention aims to provide an operational calculation device and an operational calculation method that more accurately predict the composition of a cold iron source and derive optimal operating conditions. [Means for solving the problem]

[0007] The operational calculation device according to the present invention relates to the converter blowing process. of Manage operating conditions hand An operational calculation device for optimization, comprising molten iron pretreatment. Obtaining the concentration of a predetermined component in the molten iron afterwards. means and the aforementioned acquisition by means acquisition done The concentration of the predetermined component and the concentration of the predetermined component in the cold iron source introduced in the converter blowing process. Using The aforementioned In the converter blowing process The aforementioned The specified components Material balanceTo estimate This allows us to estimate the concentration of the predetermined component in the molten steel after the converter blowing process. a converter process management means, Based on the difference between the estimated concentration of the predetermined component estimated by the converter process control means and the actual concentration of the predetermined component in the molten steel after the converter blowing process, the concentration of the predetermined component in the cold iron source is corrected, and the information of the cold iron source is used. and a cold iron source management means for managing the amount of cold iron source input in the converter blowing process, characterized by having the same.

[0008] The operation calculation method according to the present invention is an operation calculation method executed by an operation calculation device that manages and optimizes operation conditions in a converter blowing process, including a hot metal pretreatment of step, and using the hand obtained by the Obtaining the concentration of a predetermined component in the molten iron afterwards. step to estimate the acquisition predetermined components in the converter blowing process, The concentration of the predetermined component and the concentration of the predetermined component in the cold iron source introduced in the converter blowing process. characterized by having a converter process management step and a cold iron source management step for managing the amount of cold iron source input in the converter blowing process. The aforementioned in the converter blowing process The aforementioned of a predetermined component Material balance To estimate This allows us to estimate the concentration of the predetermined component in the molten steel after the converter blowing process. a converter process management step, Based on the difference between the estimated concentration of the predetermined component estimated by the converter process control step and the actual concentration of the predetermined component in the molten steel after the converter blowing process, the concentration of the predetermined component in the cold iron source is corrected, and the information of the cold iron source is used. and a cold iron source management step for managing the amount of cold iron source input in the converter blowing process, characterized by having the same.

Effect of the Invention

[0009] According to the present invention, the components of the cold iron source can be predicted more accurately to derive optimal operation conditions. As a result, the operation conditions of multiple processes in the steelmaking process can be managed collectively to derive optimal operation conditions.

Brief Description of the Drawings

[0010] [Figure 1] It is a diagram for explaining the process of performing desulfurization treatment in an embodiment of the present invention. [Figure 2] It is a block diagram showing a functional configuration example for estimating the S concentration in hot metal after hot metal pretreatment of an operation calculation device according to an embodiment of the present invention. [Figure 3] It is a block diagram showing a functional configuration example for estimating the S concentration in molten steel after operation in the converter process of an operation calculation device according to an embodiment of the present invention. [Figure 4] It is a block diagram showing a hardware configuration example of an operation calculation device according to an embodiment of the present invention. [Figure 5]This flowchart shows an example of a processing procedure for correcting the sulfur concentration in cold iron sources for each brand. [Modes for carrying out the invention]

[0011] Embodiments of the present invention will be described below with reference to the drawings. In this embodiment, S, whose input amount tends to fluctuate in the molten iron pretreatment process and the converter process, will be used as an example. However, the invention is not limited to S as a component, and can be similarly applied to components that are difficult to remove in the converter, such as Cu, or to components that can be removed to a certain extent in the converter, such as Mn.

[0012] Figure 1 is a diagram illustrating the process of performing desulfurization in this embodiment. First, desulfurization is performed in the molten iron pretreatment process, and sulfur is removed from the molten iron to a predetermined concentration or lower according to the product specifications. In this embodiment, a method (KR method) is used in which a refractory impeller (rotating blade) is immersed in the molten iron in the ladle and rotated to mechanically mix the molten iron and the desulfurizing agent and promote the desulfurization reaction.

[0013] In this process, the criteria for the amount of desulfurizing agent to be added are coarse, and the accuracy of the predicted S concentration in the molten iron after treatment tends to be low. Therefore, in this embodiment, the S concentration in the molten iron after treatment is estimated using the KR model or the like, which will be described later. If the analytical value (actual value) of the S concentration in the molten iron can be derived before the operation of the next process, the converter process, that actual value is used for the operation of the converter process. However, if the actual value cannot be derived, the S concentration in the molten iron after treatment is estimated using the KR model or the like, which will be described later, and that estimated value is used for the operation of the converter process.

[0014] Furthermore, in the converter process, a portion of the slag generated in the molten iron pretreatment process is carried over as residual slag. In this embodiment, the residual slag is quantitatively evaluated by methods such as photography with a slag residual amount determination camera, visual inspection, or other methods. The component values ​​(S content) in the residual slag are used to estimate the mass balance in the converter process, but the average value of past slag component data may be used, or a value estimated from the difference in S concentration in the molten iron before and after the molten iron pretreatment may be used.

[0015] Next, in the converter process, a cold iron source is charged into the converter from a scrap chute, and then molten iron that has undergone pre-treatment is charged into the converter, and desulfurization is performed by adding auxiliary materials such as desulfurizing agents. In this embodiment, the sulfur mass balance in the converter is estimated based on the in-furnace sulfur management model described later, and the sulfur concentration in the molten steel after blowdown is also estimated. Furthermore, since it is difficult to estimate the amount of sulfur from the cold iron source when estimating the sulfur mass balance, the amount of sulfur in the cold iron source is corrected for each brand according to the difference between the estimated value and the actual value of the sulfur concentration. The details of this correction will be described later, but the sulfur concentration in the molten iron after blowdown in the converter is estimated, and the sulfur concentration in the molten iron is measured, and the difference between the estimated value and the actual value is calculated. Then, the difference value is calculated for each brand, and if that value is less than or equal to a predetermined standard error, the amount of sulfur contained in that brand is corrected. Furthermore, cold iron sources include scrap iron generated during the steel manufacturing process, molded iron produced by pouring molten iron into molds and letting it solidify, other iron by-products such as crude iron and miscellaneous iron, which are molten iron that has solidified in various shapes, and converter coarse dust.

[0016] Figure 4 is a block diagram showing an example of the hardware configuration of the operation calculation device 100 according to this embodiment. The operation calculation unit 100 includes a CPU 401, a ROM 402, a RAM 403, a storage device 404, an input / output interface 405, and a communication interface 406. The CPU 401 reads control programs stored in the ROM 402 and executes various processes. The RAM 403 is used as the main memory and temporary storage area for the CPU 401, such as a work area. The storage device 404 stores various data and programs. The display device 405 displays various information. The input / output interface 406 is an interface for receiving various operations from the user via a keyboard or mouse and for displaying calculation results on a display device (not shown). The communication interface 407 is an interface for acquiring information from external devices via a network.

[0017] Figure 2 is a block diagram showing an example of the functional configuration of the operational calculation device 100 according to this embodiment for estimating the S concentration in molten iron after molten iron pretreatment. The performance data storage unit 200 stores performance data related to past operational performance, and the operation calculation device 100 manages the operating conditions for molten iron pretreatment and estimates the S concentration after molten iron pretreatment. For the operating conditions, conditions optimized using the KR model are used to estimate the S concentration after molten iron pretreatment.

[0018] Here, we will explain the KR model. The KR model is a machine learning model that introduces a gray-box model that predicts two variables according to a first-order reaction equation. The model construction unit 220 performs optimization calculations using 13 types of parameters (expected molten iron temperature, molten iron volume, pre-treatment C concentration, pre-treatment Si concentration, pre-treatment Mn concentration, pre-treatment P concentration, pre-treatment S concentration, molten metal surface height, set impeller immersion depth, number of impeller uses, CaO usage, secondary refining slag usage, and Al ash usage) obtained from the actual data storage unit 200 by the actual data acquisition unit 210. Then, the post-treatment S concentration is estimated according to the optimized operating conditions. For the estimation of the S concentration, the model disclosed in Patent Document 2 is adopted. That is, the post-treatment S concentration [%S] (mass%) is calculated using the following equation (1).

[0019]

number

[0020] In equation (1), [%S]0 represents the S concentration before treatment [mass%], V represents the amount of molten iron [tons], and t represents the set stirring time [minutes]. Also, f(x1) is a function relating to the equilibrium theory contribution, and g(x2) is a function relating to the kinetic contribution.

[0021] Furthermore, the KR model assumes that the desulfurization reaction follows ideal conditions following a first-order reaction, resulting in variability between the estimated post-treatment sulfur concentration and the actual post-treatment sulfur concentration. To stabilize in-furnace sulfur control, it is necessary to control the system so that the actual post-treatment sulfur concentration remains below the target sulfur concentration, taking variability into account. Therefore, in this embodiment, the model construction unit 220 defines the boundary of the region where the estimated post-treatment sulfur concentration is equal to or higher than the actual post-treatment sulfur concentration with a certain probability as a quantile, and introduces quantile calculation to perform optimal calculation of operating conditions from the operating results below the quantile. Here, the certain probability is a value predetermined within a range of 50% or more, and preferably, by setting it to a high value while maintaining predetermined intervals such as 70%, 80%, 90%, 95%, 97%, and 99%, the rate of achieving the target post-treatment sulfur concentration can be increased.

[0022] Furthermore, in this embodiment, in order to avoid calculating operating conditions that deviate significantly from past operating results in the optimization calculation, the constraint setting unit 230 adds a likelihood function calculated based on the probability density function of operating results estimated from past performance data to the evaluation function, and the optimal solution search unit 240 uses the model constructed by the model construction unit 220 to search for operating conditions within a reliable range using the likelihood function. Then, the optimal solution output unit 250 outputs the optimal solution (processed S concentration) searched by the optimal solution search unit 240.

[0023] As described above, the KR model according to this embodiment introduces quantile calculation and likelihood functions in addition to the gray-box model described above to derive a highly reliable estimate of the S concentration.

[0024] Figure 3 is a block diagram showing an example of the functional configuration of the operation calculation device 100 according to this embodiment for estimating the sulfur concentration in molten steel after operation in the converter process. In this embodiment, the operating conditions for desulfurization treatment in the converter are managed based on historical data related to past operating performance in the converter, and the sulfur concentration in the molten steel after blowdown is estimated. In this embodiment, the sulfur mass balance in the converter is estimated according to the in-furnace sulfur management model, and the sulfur concentration in the molten steel after blowdown is estimated.

[0025] Here, the in-furnace S management model will be described. In the in-furnace S management model, in addition to improving the S mass balance estimation formula in the converter, an S estimation model for cold iron sources is introduced to estimate the S content of the charged cold iron sources and optimize the charging amount for each brand. First, in the blow-down component estimation unit 350, the S concentration X in the molten steel after blow-down is estimated using the S mass balance estimation formula expressed by the following formula (2). LDslag , chute , , B , , B ,

[0027] , pig , , sub , KRslag , pig , B ,

[0028] , , pig , <00001​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​First, the amount of slag carried over from the molten iron pretreatment is measured by taking a picture with a camera for determining the amount of residual slag. Then, the molten iron pretreatment estimated component output unit 310 considers the average value of past slag component data as the S concentration in the residual slag, and calculates the amount of S Z from the residual slag. KRslag This is calculated. Alternatively, the value may be calculated using the difference in sulfur concentration in the molten iron before and after pretreatment. In this case as well, if the analysis is not completed in time and an actual measured value cannot be obtained, the sulfur concentration in the residual slag may be estimated using the difference between the sulfur concentration in the molten iron before pretreatment and the estimated sulfur concentration in the molten iron after pretreatment calculated using the KR model.

[0029] On the other hand, cold iron source S amount Z chute This is calculated by the cold iron source input amount calculation unit 340 based on the brand-specific S concentration of the cold iron source managed by the cold iron source information acquisition unit 320. The S concentration of the cold iron source cannot be measured directly and its composition fluctuates greatly. Cold iron sources purchased from other companies, in particular, have many uncertainties, such as the possibility of contamination with cold iron sources generated in various processes, resulting in especially large fluctuations in composition. Therefore, in this embodiment, we focus on a method for estimating the S concentration of this cold iron source and use a cold iron source S estimation model to perform estimation calculations using machine learning.

[0030] The cold iron source information acquisition unit 320 manages information on dozens to hundreds of types of cold iron sources, including scrap generated in other processes, scrap purchased from other companies, die-cast iron, and other by-products. In the converter process, it selects 5 to 7 different combinations of types from this information for each charge and feeds them into the scrap chute. Materials generated from a single process are managed as the same type. The cold iron source information acquisition unit 320 also estimates the sulfur (S) concentration in the cold iron source for each type using a cold iron source S estimation model, and corrects any discrepancies between the estimated and actual values ​​as needed.

[0031] Figure 5 is a flowchart showing an example of a processing procedure for correcting the sulfur concentration in the cold iron source for each brand. Each process shown in Figure 5 is performed for each charge in the converter process, and is carried out by the CPU 401 reading and executing a control program stored in the ROM 402. First, in S601, the component error calculation unit 370 acquires the actual value of the S concentration after blowing in the converter process (analytical value actual 300). After blowing in the converter process, a component analysis of the molten steel is performed, and the actual value of the S concentration is input as analytical value actual 300.

[0032] Next, in S602, the component error calculation unit 370 calculates the concentration difference between the estimated value of the S concentration after blowing, output from the estimated component output unit 360, and the actual value input in S601. Here, the estimated value of the S concentration after blowing is the value calculated by the blowing-stop component estimation unit 350 using the S mass balance estimation formula of equation (2) described above.

[0033] Next, in S603, the component error calculation unit 370 calculates the brand-specific S concentration correction value. Here, the amount of cold iron source input for the k charge and brand j is v j (k) The total amount of molten iron charged is V(k), and Δ(k) is defined as the concentration difference between the estimated S concentration after the k charge and the actual S concentration after the k charge. In this case, the S concentration correction value β is specific to each brand. j (k) is calculated by the following equation (3). β j (k) = Δ(k-1) * V(k-1) / v j (k-1) ···(3)

[0034] As mentioned above, when adding cold iron sources, multiple brands of cold iron sources are usually added, but equation (3) assumes that the error in S concentration occurs uniformly for all brands. However, in reality, there are various brands, some of which are easy to determine the S concentration of, and others which are difficult to determine. Therefore, when calculating the S concentration correction value for each brand, a different correction coefficient (j) (0 < correction coefficient (j) ≤ 1) is used for the S concentration correction value β for each brand. j You can also perform weighting by multiplying by (k).

[0035] Next, in S604, the component error calculation unit 370 determines whether the absolute value of the brand-specific S concentration correction value calculated in S603 is less than the brand-specific standard error. The cold iron source S estimation model assumes that all errors between the estimated and actual S concentrations after blowing are due to the influence of the cold iron source components. However, in actual operation, there are remaining factors that cannot be represented by the cold iron source S estimation model, such as the influence of residual metal in the furnace, melting during mold seizing, and analytical errors, and it is necessary to eliminate these influences. Furthermore, even for brands with small variations in S concentration to begin with, if the input amount is large, the correction amount becomes large, and if the correction value is applied directly, it will deviate significantly from the actual S concentration. Therefore, the brand-specific standard error is used, and if the component variation range of each brand exceeds the standard error, no correction is made to the brand-specific S concentration. In other words, if the absolute value of the brand-specific S concentration correction value calculated in S603 is less than the brand-specific standard error, proceed to S605; if the absolute value of the brand-specific S concentration correction value calculated in S603 is greater than or equal to the brand-specific standard error, proceed to S606.

[0036] The standard error for each brand is calculated in advance based on the fluctuations in the S concentration of the cold iron source for each brand, which are managed by the cold iron source information acquisition unit 320.

[0037] In S605, the cold iron source information acquisition unit 320 corrects the S concentration of the cold iron source for the corresponding brand. For the k charge, the S concentration of the cold iron source for brand j is set to z j (k) The retention rate by brand is α j If (k) is used, the S concentration is corrected according to equation (4) below. z j (k=z j (k-1)+α j (k) × β j (k) ···(4)

[0038] As can be seen from equation (4), the previous operating conditions are used to correct the S concentration. Therefore, if the brand is not used, equation (4) is not used and no component correction is performed.

[0039] Next, in S606, the component error calculation unit 370 determines whether there are still stocks to be calculated. If, as a result of this determination, there are still stocks to be calculated, the process returns to S603 and performs the same process for the next stock. On the other hand, if there are no stocks to be calculated, the process ends there.

[0040] As described above, in this embodiment, the amount of cold iron source S Z, which has large component fluctuations, chute To improve accuracy, the sulfur (S) concentration in the cold iron source is optimized through correction for each brand. As mentioned above, the correction value is calculated assuming that the S concentration error is uniform across all brands, but by repeating this process, the S concentration for each brand will converge to the actual S concentration. For example, even if only one of the six brands used has a significantly different S concentration from the actual one, by changing the combination of brands each time a charge is made, the S concentrations of the remaining five brands will also approach the actual S concentration through different combinations of brands, and the S concentration for each brand will converge to the actual S concentration.

[0041] Next, the loading amount for each type will be explained. Before starting converter operation, the cold iron source input amount calculation unit 340 optimizes the loading amount for each type using a loading optimization model. The cold iron source information acquisition unit 320 holds information not only on the components of each type of cold iron source, but also on the price, inventory quantity, and other constraints for each type. When optimizing the loading amount for each type, first, the upper limit of the loading amount for each type is determined by the constraints set by the constraint setting unit 330 (such as input amount restrictions for each type of trump element management, input amount restrictions for each type of blowing, input amount restrictions based on adsorbed moisture value set based on past troubles such as bumping, upper limit on the use of the same type for inventory management, and upper limit on the total amount of cold iron source that is difficult to dissolve to prevent undissolved material), and then it is determined whether or not there is inventory. Then, appropriate selections are made from multiple types with inventory, and the sum of the S concentrations for each type of cold iron source, taking the constraints into account, is determined to be the acceptable amount of cold iron source S Z. chute We will seek the optimal cost point within the range that does not exceed the upper limit. The acceptable amount of cold iron source S Z is also specified. chuteThe upper limit is calculated by the constraint setting unit 330 based on the mass balance of the converter process and the upper limit of the S concentration in molten steel according to the product specifications.

[0042] The operation calculation device 100 according to this embodiment can construct a composite model by combining the KR model and the in-furnace S management model described above, and can control the molten iron pretreatment process and the converter process in a unified manner to construct optimal operating conditions.

[0043] As described above, according to this embodiment, the difference between the estimated and actual values ​​of the sulfur concentration at the time of blowdown is calculated, and the components of the cold iron source, which have large component fluctuations, are corrected as needed and reflected in subsequent charges. Therefore, when the loading amount of the cold iron source is optimized, the operating conditions can be optimized with greater accuracy. As a result, a composite model can be constructed to manage the operating conditions in the molten iron pretreatment and converter processes collectively and optimize the operating conditions.

[0044] (Other embodiments) Furthermore, the operational calculation device of the embodiment described above is specifically composed of a computer system or device. Therefore, it goes without saying that the above functions can also be achieved by supplying a storage medium containing program code for software that realizes the aforementioned functions to the system or device, and having the computer (or CPU or MPU) of that system or device read and execute the program code stored in the storage medium.

[0045] In this case, the program code read from the storage medium itself will realize the functions of the embodiment described above, and the program code itself and the storage medium storing the program code will constitute the present invention. Examples of storage media that can be used to supply the program code include flexible disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, etc.

[0046] The embodiments of the present invention described above are merely examples of how the invention can be implemented, and the technical scope of the invention should not be interpreted as being limited by them. In other words, the present invention can be implemented in various ways without departing from its technical concept or its main features. [Explanation of symbols]

[0047] 310 Molten Iron Pre-treatment Estimated Component Output Unit 320 Cold Iron Source Information Acquisition Department 330 Constraint Setting Unit 340 Cold iron source input calculation section 350 Blowing component estimator 360 Estimated component output unit 370 Component error calculation section

Claims

1. An operational calculation device for managing and optimizing the operating conditions of a converter blowing process, An acquisition means for obtaining the concentration of a predetermined component in molten iron after pretreatment of molten iron, A converter process management means estimates the concentration of the predetermined component in the molten steel after the converter blowing process by estimating the mass balance of the predetermined component in the converter blowing process using the concentration of the predetermined component obtained by the acquisition means and the concentration of the predetermined component in the cold iron source introduced in the converter blowing process, A cold iron source management means corrects the concentration of the predetermined component in the cold iron source based on the difference between the estimated concentration of the predetermined component estimated by the converter process management means and the actual concentration of the predetermined component in the molten steel after the converter blowing process, and manages the amount of cold iron source input in the converter blowing process as information about the cold iron source. An operational calculation device characterized by having the following features.

2. The cold iron source management means determines the amount of cold iron source for each brand to be introduced in the converter blowing process based on cost and at least one of a predetermined upper limit for the amount of cold iron source used for each brand and an upper limit for the total amount of cold iron source determined by operational constraints, within a range in which the sum of the concentrations of the predetermined components in the cold iron source introduced in the converter blowing process does not exceed an upper limit based on the mass balance of the predetermined components and the upper limit for the concentration of the predetermined components in molten steel according to the product specifications. This is the operation calculation device according to claim 1.

3. The acquisition means further acquires the amount of residual slag carried over along with the molten iron that has undergone molten iron pretreatment, The operation calculation device according to claim 1 or 2, characterized in that the converter process management means estimates the mass balance of the predetermined component using the amount of the predetermined component based on the amount of residual slag obtained.

4. The operation calculation device according to claim 3, characterized in that the acquisition means acquires the concentration of the predetermined component in the residual slag based on the difference in the concentration of the predetermined component before and after the molten iron pretreatment.

5. An operational calculation method executed by an operational calculation device that manages and optimizes the operating conditions of a converter blowing process, A step to obtain the concentration of a predetermined component in the molten iron after pretreatment of the molten iron, A converter process control step that estimates the concentration of the predetermined component in the molten steel after the converter blowing process by estimating the mass balance of the predetermined component in the converter blowing process using the concentration of the predetermined component obtained in the acquisition step and the concentration of the predetermined component in the cold iron source introduced in the converter blowing process, A cold iron source management step that corrects the concentration of the predetermined component in the cold iron source based on the difference between the estimated concentration of the predetermined component estimated by the converter process management step and the actual concentration of the predetermined component in the molten steel after the converter blowing process, and manages the amount of cold iron source input in the converter blowing process as information on the cold iron source, A method for calculating operations, characterized by having the following features.

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