KR20240090437A - Furnace state estimation device, in-furnace state estimation method, and molten steel manufacturing method - Google Patents

Abstract

The in-furnace state estimation device 1 includes an input unit 11 where performance information and conditions for refining processing are input, model parameters of a model related to blow refining reaction, performance information, and past conditions obtained from a database where refining processing conditions are stored. a model determination unit 14 that determines model parameters in the refining process of the target charge using the model parameters, and a furnace containing the temperature and component concentration of the molten metal and the component concentration of the slag using the determined model parameters. Using the in-furnace state calculation unit 15 to calculate the state amount in the furnace and the performance information including the result of the refining process of the target charge, the material balance error and heat balance in the furnace from the start to the end point of a specific period in the refining process It is provided with a model parameter calculation unit 13 that calculates model parameters in the refining process of the target charge based on an evaluation function including a term representing an error.

Description

Furnace state estimation device, in-furnace state estimation method, and molten steel manufacturing method

This disclosure relates to an in-furnace state estimation device, an in-furnace state estimation method, and a molten steel manufacturing method. The present disclosure particularly relates to an in-furnace state estimating device for estimating component concentrations in molten metal and slag in a refining facility in the steel industry, an in-furnace state estimating method, and a molten steel manufacturing method.

In a steel mill, the components and temperature of molten iron tapped from a blast furnace are adjusted in refining facilities such as preliminary treatment facilities, converters, and secondary refining facilities. The converter is a process that removes impurities and raises the temperature of the molten metal by blowing oxygen into the furnace, and plays a very important role in controlling the quality of steel and rationalizing refining costs. Here, in controlling the molten metal components and molten metal temperature in the converter, for example, the flow rate and speed of top blowing oxygen, the height of the top blow lance, the flow rate of bottom blow gas, etc. are used as operating variables. In addition, the input amount and timing of auxiliary materials such as lime and iron ore, the timing of sampling the molten metal, the timing of ending blow tempering, etc. are used as operating variables. These operating quantities must be optimized according to the conditions within the furnace, such as molten metal temperature and molten metal components and slag components. In order to estimate the condition within the furnace with high precision and optimize the operating amount, a method of calculating the material balance and heat balance within the furnace is proposed using measurement information about the refining facility, including measurement values of exhaust gas that can be continuously measured during the refining process. It is done. This method is generally known to be able to estimate molten metal temperature, molten metal components, and slag components with high accuracy and in real time. However, there is a problem that the accuracy of the state estimation model often deteriorates due to changes in the refining facility environment, such as wear and tear of the refractory material in the refining facility, changes in the composition of input auxiliary materials, and lowering of measurement accuracy.

As a method of optimizing model parameters in order to maintain the accuracy of such a model, for example, Patent Document 1 extracts performance with similar processing conditions from past performance information as the value of the parameter in the independent model equation of the converter. We propose a method to calculate it.

Also, for example, Patent Document 2 proposes a method of determining a plurality of parameters of a molten metal temperature estimation model in a secondary refining facility by obtaining approximate solutions of simultaneous equations set so that the heat balance is balanced.

Japanese Patent Publication No. 2005-036289 Japanese Patent Publication No. 2004-360044

Here, Patent Document 1 targets an independent physical reaction model or a first-order coupling model as a model equation. Therefore, the technology of Patent Document 1 is difficult to apply to models in which the reaction amount and temperature rise amount within the furnace interact in a complex manner, such as a state estimation model based on calculation of the material balance and heat balance within the furnace.

Patent Document 2 proposes a method of obtaining an approximate solution of simultaneous equations set so that the heat balance is balanced for a temperature estimation model equation in which a plurality of model equations and parameters interact with each other. However, Patent Document 2 does not describe the in-furnace material balance calculation using measurement information including exhaust gas measurement values in calculating the amount of reaction in the furnace. Therefore, it is difficult to apply the technology of Patent Document 2 to a model in which the material balance in the furnace interacts with the heat balance.

Therefore, there is a need for a model parameter determination method that is effective even for models in which the material balance and heat balance within the furnace interact in a complex manner.

The purpose of the present disclosure, made in view of these circumstances, is to provide an in-furnace state estimation device, an in-furnace state estimation method, and a molten steel manufacturing method that can accurately and continuously estimate the component concentrations in molten metal and slag.

(1) An in-furnace state estimation device according to an embodiment of the present disclosure,

Measurement results of the temperature and component concentration of molten metal and component concentration of slag before or during the start of refining processing in the refining facility, and measurement results for the refining facility, including the flow rate and component concentration of exhaust gas discharged from the refining facility. An input unit where performance information including and conditions for the refining process are input,

Using the model parameters of the model related to the blow refining reaction in the refining equipment, the performance information, and the past model parameters acquired from a database storing the conditions of the refining process, a model determination unit that determines the model parameters,

a furnace state calculation unit that calculates state quantities in the furnace including the temperature and component concentration of the molten metal and the component concentration of slag using the determined model parameters;

An evaluation function including terms representing a material balance error and a heat balance error in the furnace from the start to the end point of a specific period in the refining process, using the performance information including the results of the refining process for the target charge. and a model parameter calculation unit that calculates the model parameter in the refining process of the target charge based on the above.

(2) As an embodiment of the present disclosure, in (1),

The model determination unit averages the model parameters of past refining processes that are similar to the conditions of the refining process of the target charge among the past model parameters stored in the database, thereby performing the refining process of the target charge. Determine the model parameters.

(3) As an embodiment of the present disclosure, in (1),

The model determination unit models the relationship between the past model parameters stored in the database and the conditions of the refining process, including at least one of the number of processes in the refining process, the date and time of the process, and the number of uses of the refining equipment, , determine the parameters in the refining process of the target charge from the model.

(4) As an embodiment of the present disclosure, in any one of (1) to (3),

The model parameters include the accumulated amount of carbon introduced into the furnace in a specific period, the accumulated amount of carbon discharged outside the furnace in a specific period, the amount of oxygen introduced into the furnace, the amount of oxygen discharged outside the furnace, and various types of molten metal in the molten metal. It includes a coefficient or constant term that corrects at least one of the amount of oxygen used to oxidize metal impurities and the accumulated amount of the molten metal temperature change over a specific period due to the change in the amount of heat in the furnace.

(5) As an embodiment of the present disclosure, in any one of (1) to (4),

The evaluation function is composed of a weighted sum of a term representing the carbon balance, a term representing the oxygen balance, and a term representing the heat balance.

(6) The in-furnace state estimation method related to one embodiment of the present disclosure is:

An in-furnace state estimation method executed by an in-furnace state estimation device, comprising:

Measurement results of the temperature and component concentration of molten metal and component concentration of slag before or during the start of refining processing in the refining facility, and measurement results for the refining facility, including the flow rate and component concentration of exhaust gas discharged from the refining facility. An input step in which performance information including and conditions for the refining process are input,

Using the model parameters of the model related to the blow refining reaction in the refining equipment, the performance information, and the past model parameters acquired from a database storing the conditions of the refining process, A model decision step for determining the model parameters,

a furnace state calculation step for calculating state quantities in the furnace including temperature and component concentration of the molten metal and component concentration of slag using the determined model parameters;

An evaluation function including terms representing a material balance error and a heat balance error in the furnace from the start to the end point of a specific period in the refining process, using the performance information including the results of the refining process for the target charge. and a model parameter calculation step of calculating the model parameters in the refining process of the target charge based on the above.

(7) As an embodiment of the present disclosure, in (6),

The model determination step determines the refining process of the target charge by averaging the model parameters of the past refining process that are similar to the conditions of the refining process of the target charge among the past model parameters stored in the database. Determine the model parameters in

(8) As an embodiment of the present disclosure, in (6),

The model determination step models the relationship between the past model parameters stored in the database and the conditions of the refining process, including at least one of the number of processes in the refining process, the date and time of the process, and the number of uses of the refining equipment. And, the parameters in the refining process of the target charge are determined from the model.

(9) The molten steel manufacturing method related to one embodiment of the present disclosure is:

Based on the temperature and component concentration of the molten metal and the component concentration of the slag estimated by the in-furnace state estimation method of any one of (6) to (8), the flow rate and speed of top blowing oxygen, the height of the top blow lance, and the bottom blow gas A refining operation is performed by determining at least one of the flow rate, the input amount and timing of auxiliary materials such as lime and iron ore, the timing of sampling the molten metal, and the timing of ending blow refining, and molten steel is manufactured.

According to the present disclosure, it is possible to provide an in-furnace state estimation device, an in-furnace state estimation method, and a molten steel manufacturing method that can accurately and continuously estimate the component concentrations in molten metal and slag.

1 is a schematic diagram showing the configuration of an in-furnace state estimation device according to an embodiment of the present disclosure.
Fig. 2 is a diagram showing an example of the configuration of a database.
FIG. 3 is a flow chart showing processing of the in-furnace state estimation method according to an embodiment of the present disclosure.

Hereinafter, an in-furnace state estimation device, an in-furnace state estimation method, and a molten steel manufacturing method related to an embodiment of the present disclosure will be described with reference to the drawings.

[Configuration of in-furnace state estimation device]

1 is a schematic diagram showing the configuration of an in-furnace state estimation device 1 according to an embodiment of the present disclosure. In this embodiment, the in-furnace state estimation device 1 is used as a part of equipment for manufacturing molten steel in the iron and steel industry. Equipment for manufacturing molten steel is provided with a refining equipment (2) and a blow smelting control system including an in-furnace state estimation device (1).

As shown in FIG. 1, the refining facility 2 is equipped with a converter 100, a lance 102, and a duct 104. The lance 102 is disposed on the molten metal 101 in the converter 100. High-pressure oxygen is ejected from the tip of the lance 102 toward the molten metal 101 below. Impurities in the molten metal 101 are oxidized by this high-pressure oxygen and introduced into the slag 103 (refining treatment). The duct 104 is a flue facility for conducting exhaust gas and is installed at the upper part of the converter 100.

Inside the duct 104, an exhaust gas detection unit 105 is disposed. The exhaust gas detection unit 105 detects the flow rate and component concentration (for example, concentrations of CO, CO ₂ , O ₂ , N ₂ , Ar, etc.) of exhaust gas discharged following the refining process. As an exhaust gas measurement unit, for example, the exhaust gas detection unit 105 measures the flow rate of exhaust gas in the duct 104 based on the differential pressure before and after the Venturi tube formed in the duct 104. Additionally, the exhaust gas detection unit 105 measures the concentration [%] of each component in the exhaust gas as exhaust gas measurement. The flow rate and component concentration of exhaust gas are measured, for example, every few seconds. A signal indicating the detection result of the exhaust gas detection unit 105 is sent to the control terminal 10.

A stirring gas is blown into the molten metal 101 in the converter 100 through a ventilation hole 106 formed at the bottom of the converter 100. The stirring gas is an inert gas such as Ar. The blown stirring gas stirs the molten metal 101 and promotes the reaction between high-pressure oxygen and the molten metal 101. The flow meter 107 measures the flow rate of the stirring gas blown into the converter 100. Immediately before the start of blow tempering and after blow tempering, the temperature and component concentration of the molten metal 101 are analyzed. In addition, the temperature and component concentration of the molten metal 101 are measured once or multiple times during blow tempering, and based on the measured temperature and component concentration, the supply amount (delivery amount) and speed (delivery rate) of high-pressure oxygen and the stirring gas The flow rate (stirring gas flow rate), etc. are determined.

The blow temper control system is equipped with a control terminal 10, a display device 20, and an in-furnace state estimation device 1 as main components. The control terminal 10 may be configured by an information processing device such as a personal computer or workstation. The control terminal 10 controls the delivery amount, delivery speed, and stirring gas flow rate so that the temperature and component concentration of the molten metal 101 are within the desired range, and also controls the data of actual values of the delivery amount, delivery speed, and stirring gas flow rate. Collect. The display device 20 may be configured by, for example, a liquid crystal display (Liquid Crystal Display) or a CRT (Cathode Ray Tube) display. The display device 20 may display calculation results output from the in-furnace state estimation device 1, etc.

The in-furnace state estimation device 1 is a device that estimates the temperature and component concentration of the molten metal 101 processed in the refining equipment 2 and the component concentration of the slag 103. The in-furnace state estimation device 1 is configured by an information processing device such as a personal computer or workstation. The in-furnace state estimation device 1 includes an input unit 11, a database 12, a model parameter calculation unit 13, a model decision unit 14, an in-furnace state calculation unit 15, and an output unit 16. there is.

The input unit 11 is an input interface into which performance information (performance data), which is various measurement results related to the refining facility 2, is input. The input unit 11 may be, for example, at least one of a keyboard, mouse, pointing device, data reception device, and graphical user interface (GUI). In this embodiment, the input unit 11 receives performance information, parameter setting values, etc. from the outside, records the information in the database 12, and transmits it to the furnace state calculation unit 15. Performance information is input into the input unit 11 from the control terminal 10. The performance information includes information on the flow rate and component concentration of the exhaust gas measured by the exhaust gas detection unit 105, information on the delivery amount and delivery speed, information on the stirring gas flow rate, information on the input amount of raw materials (main raw materials, secondary raw materials), and molten metal. It includes the temperature and ingredient concentration of (101) and the ingredient concentration of slag (103). These information correspond to items 1 to item M in the performance information in FIG. 2 described later. Additionally, the input unit 11 may enable manual data input (manual input) by, for example, an operator of the refining facility 2. By manual input, the parameter settings of the model equation (hereinafter also simply referred to as “model”) can be input. In this embodiment, the input unit 11 also receives information on the conditions and operating amount of the refining process, which will be described later. Additionally, the input unit 11 may acquire performance information, etc. before the start of the refining process, during the process, or after the end of the process.

The database 12 stores model information regarding the blow refining reaction in the refining facility 2, performance information of the refining process, and calculation results of the in-furnace state estimation device 1. The database 12 is comprised of, for example, memory and storage devices such as a hard disk drive. The storage device may additionally store a computer program. The database 12 stores a model formula and the parameters of the model formula (hereinafter "model parameters") as information on the model regarding the blow temper reaction. The model parameters are calculated by the model parameter calculation unit 13. Moreover, the database 12 may store various information input into the input unit 11 and the calculation and analysis results in the blow temper performance calculated by the in-furnace state calculation unit 15.

FIG. 2 is a diagram showing a configuration example of the database 12. In this embodiment, the database 12 stores model parameters that are conditions, performance information, and calculation results for N times (N charges) of refining processing in association with the identification number of the charge. N is, for example, an integer greater than or equal to 2. In the example of Figure 2, the leftmost column indicates the identification number of the charge. For example, in the N-th refining process, the database 12 stores performance information and model parameters in the past N-1 refining processes. In the N-th refining process, when the model determination unit 14 determines model parameters as described later, information on the past N-1 refining processes stored in the database 12 is used as a candidate. . In addition, when the N-th refining process is completed, the performance information and calculation results for the N-th refining process are added to the database 12 (see the bold frame portion in FIG. 2). Thereafter, in the N + 1st refining process, when the model determination unit 14 determines model parameters, the information of the past N refining processes stored in the database 12 is used as a candidate.

The model parameter calculation unit 13, the model determination unit 14, and the in-furnace state calculation unit 15 are comprised of an arithmetic processing unit such as a CPU, for example. The model parameter calculation unit 13, the model determination unit 14, and the in-furnace state calculation unit 15 may be realized, for example, by an arithmetic processing unit reading and executing a computer program. In addition, the model parameter calculation unit 13, the model determination unit 14, and the in-furnace state calculation unit 15 may have a dedicated calculation device or calculation circuit.

The model parameter calculation unit 13 calculates the model parameters of the model regarding the blow temper reaction so that the resin error is minimized based on the material balance and heat balance in the furnace, and stores them in the database 12. After one refining process is completed, the model parameter calculation unit 13 calculates the material balance and heat balance using performance information resulting from the refining process.

The material balance calculation calculates the input amount of each component into the converter 100 and the discharge amount of each component from the converter 100. The input amount of each component is calculated from the input amount of main and secondary raw materials to the converter 100, the oxygen supplied from the lance 102, and the amount of air mixed from outside the converter 100. The emission amount of each component is calculated from the exhaust gas flow rate and the exhaust gas component concentration.

The heat balance calculation calculates the amount of heat input and amount of heat discharged within the furnace of the converter 100. The amount of heat input is calculated from the sensible heat of the main raw material inserted into the converter 100, the reaction heat due to the reaction occurring within the furnace, the heat of dissolution of the secondary raw material introduced into the converter 100, etc. The amount of discharged heat is calculated from heat radiation from the furnace surface, radiant heat from the furnace mouth, heat generation by the stirring gas, slag 103 discharged to the outside of the furnace, sensible heat of the exhaust gas, etc.

The model determination unit 14 acquires past model parameters stored in the database 12. The model determination unit 14 uses past model parameters to determine model parameters to be used by the in-furnace state calculation unit 15 and transmits them to the in-furnace state calculation unit 15 .

The in-furnace state calculation unit 15 calculates the temperature and component concentration of the molten metal 101 and the slag 103 based on the model parameters determined in the model determination unit 14, performance information and parameter set values collected by the input unit 11, etc. Calculate (estimate) the state quantity in the converter 100 containing the component concentration. The estimated state quantity in the converter 100 is transmitted to the output unit 16.

The output unit 16 transmits the state quantity in the converter 100 calculated by the in-furnace state estimation device 1 to the control terminal 10 . In the refining process, various operating variables are determined and operating conditions are changed based on the calculation results output from the in-furnace state estimation device 1. In addition, the output unit 16 also has a function of transmitting information calculated by the in-furnace state estimation device 1 to the display device 20, and displays the calculation results output from the in-furnace state estimation device 1. possible.

The in-furnace state estimation device 1 having such a configuration performs processing of the in-furnace state estimation method described below, thereby determining the temperature and component concentration in the molten metal 101 and the component concentration in the slag 103. The state quantity within (100) is estimated with high precision. Hereinafter, with reference to the flow chart shown in FIG. 3, the operation of the in-furnace state estimation device 1 when executing the in-furnace state estimation method is explained.

[How to estimate furnace condition]

FIG. 3 is a flow chart showing processing of the in-furnace state estimation method according to one embodiment of the present disclosure. The flow chart shown in FIG. 3 starts at an arbitrary timing before the refining process starts. In short, at an arbitrary timing before the refining process is started, the in-furnace state estimation process proceeds to the process of step S1.

In the process of step S1, the input unit 11 acquires the conditions for the refining process. In this embodiment, the conditions of the refining treatment include the type of refining, the expected amount of auxiliary material input, target values of component concentration and temperature of the molten metal 101 and slag 103, number of treatments, date and time of treatment, furnace, lance, and measuring equipment. Includes the number of times the equipment is used. The input unit 11 transmits the acquired refining processing conditions to the database 12 and the in-furnace state calculation unit 15. Thereby, the process of step S1 is completed, and the in-furnace state estimation process progresses to the process of step S2. Step S1 corresponds to part of the “input step”. The data input in step S1 is used in the processing of the model determination unit 14.

In the processing of step S2, the model determination unit 14 uses past model parameters stored in the database 12 to determine model parameters to be used in the furnace state calculation unit 15 based on the conditions of the refining process. decide Step S2 corresponds to the “model determination step”. To describe in detail, the model parameters determined in the process of step S2 are obtained by calculation or selection based on model parameters corresponding to past refining processes already stored in the database 12. As described above, for example, in the N-th refining process, the model determination unit 14 determines model parameters using information on the past N-1 refining processes stored in the database 12. In cases where the model parameters or refining process condition data for the past N-1 batches are not available, such as because it takes time to obtain the refining process results, the necessary model parameters or refining process condition data are available. Model parameters may be determined by extracting information. In this example, the Nth refining process, that is, the currently executing refining process, may be referred to as the refining process for the target charge.

The model decision unit 14 extracts, for example, those whose refining processing conditions are similar to the conditions of the target charge from among the model parameters stored in the database 12, and makes the decision by averaging the extracted model parameters. The model determination unit 14 may extract only the model parameters in the most recent predetermined number of occupancies, that is, exclude old model parameters from the extraction target and perform averaging. The similarity (Ds) between the conditions in the refining process of the target charge and past performance can be evaluated, for example, by calculating the Euclidean distance as shown in equation (1) below.

[Equation 1]

However, k is the condition number of the refining process. CA _k represents conditions in past performance. CP _k represents the conditions in the refining process of the target charge. G _k is a parameter for weighting each refining process condition. Conditions for refining treatment include, for example, refining treatment date and time, charged molten iron weight, charged scrap weight, molten iron temperature, concentration of components including C, Si, Mn, and P in molten iron, number of uses of the refining furnace and top blowing lance, etc. I can hear it. In addition, for example, the temperature of the molten metal after the treatment in the refining treatment performed immediately before and the time elapsed since the treatment, the weight and composition of the slag to be postponed, the input weight of each secondary material item input before the start of the refining treatment, and the amount of time for each scrap item. Input weight, etc. can be mentioned. These conditions correspond to items 1 to L in the conditions for the refining process in FIG. 2 . In addition, when evaluating similarity, only results matching the shape of the refining furnace being used, the shape of the top blowing lance, the shape of the bottom blowing nozzle, etc. may be considered. Here, the similarity is not limited to the Euclidean distance shown in equation (1), and can also be evaluated by methods that evaluate the distance between k-dimensional vectors, including city block distance, Minkowski distance, Mahalanobis distance, and cosine similarity. Here, high similarity means the same thing as short distance between calculated k-dimensional vectors. In the extraction of the performance of past refining processing, the performance with a calculated similarity higher than a set threshold may be extracted, or the past performance of a high arbitrary number with a high similarity may be extracted. In addition, as a method of extracting similar performance, for each item of the condition k of the refining process, the difference between the conditions of the process to be calculated and the conditions of the past performance is calculated, and the performance where the k differences are smaller than the threshold value set for each is extracted. It can be any method.

In addition, the model determination unit 14 includes model parameters stored in the database 12, the number of times in the refining process, the date and time of the process, the number of uses of the refining equipment including the furnace, lance, and measuring equipment, etc. The relationship with the conditions of the refining process may be modeled. Then, the model determination unit 14 may calculate optimal parameters through model calculation from the input values of the refining processing conditions of the target charge. The model determination unit 14 transmits the determined model parameters to the in-furnace state calculation unit 15. Thereby, the process of step S2 is completed, and the in-furnace state estimation process progresses to the process of step S3.

The processes of step S3 and step S4 are started at the timing when one refining process starts, and are repeated at arbitrary cycles during the refining process. In the process of step S3, the input unit 11 acquires the operation amount information of the refining process and the measurement information in the converter 100. The manipulated quantity information is, for example, information on manipulated quantities such as the height of the lance 102, the discharge speed, the stirring gas flow rate, and the input amount of auxiliary materials. The measurement information is, for example, measured values such as the flow rate and component concentration of exhaust gas. Here, the measured value is not limited to the measured value itself, and may also include the result (analysis value) after analysis. Manipulated quantity information and measurement information are collected at random intervals. If there is a large time delay between the manipulated quantity information and the measurement information, data is created taking the delay into account. Additionally, if the measurement information contains a lot of noise, the measured value may be replaced with a value that has undergone smoothing processing such as moving average calculation. Step S3 corresponds to part of the “input step”. The data input in step S3 is used in the processing of the in-furnace state calculation unit 15.

In the process of step S4, the in-furnace state calculation unit 15 calculates the state quantity in the converter 100 using the information acquired by the input unit 11 and a model having model parameters determined by the model decision unit 14. Examples of the state quantity include the carbon concentration in the molten metal 101 and the Fe _tO concentration in the slag 103. Step S4 corresponds to “furnace state calculation step”.

The carbon concentration in the molten metal 101 is determined, for example, by calculating the amount of carbon remaining in the converter 100. The amount of carbon introduced into the converter 100 and the amount of carbon discharged outside the converter 100 can be expressed as equations (2) and (3) shown below, respectively. The carbon concentration in the molten metal 101 can be calculated by assuming that the amount of carbon remaining in the converter 100, obtained by subtracting the amount of carbon discharged from the amount of carbon input, corresponds to the amount of carbon in the molten metal 101. Here, it was assumed that the amount of carbon entering and exiting the molten metal 101 was small compared to the total amount charged. In addition, unless otherwise specified, “%” and various flow rates indicate “mass%” and flow rate unit.

[Equation 2]

[Equation 3]

Here, C _in [%], which is the amount of carbon input, is the concentration conversion value in the molten metal 101 by summing the amount of carbon in the main raw material and the amount of carbon in the input auxiliary raw materials. ρ _pig [%] is the carbon concentration in charged molten iron. ρ _i ^Cscr [%] is the carbon concentration in the charged scrap (item i). ρ _j ^Caux [%] is the carbon concentration in the input additive (item j). W _pig [t] is the weight of charged molten iron. W _i ^scr [t] is the weight of charged scrap (item i). W _j ^aux [t] is the integrated weight of the input auxiliary material (item j). W _charge [t] is the weight of molten metal charged into the converter (100). The carbon concentration (ρ _i ^Cscr , ρ _j ^Caux ) in item i of the charged scrap and item j of the input auxiliary material is stored in the database 12, and the in-furnace state calculation unit 15 calculates the items used in the target charge. Obtain information about C _out [%], which is the amount of carbon discharged, is a concentration conversion value in the molten metal 101 of the amount of carbon contained in the exhaust gas. V _CO ^OG [N㎥/t] and V _CO2 ^OG [N㎥/t] are the accumulated flow rates of CO and CO ₂ in the exhaust gas up to the calculation time, respectively.

The Fe _tO concentration in the slag 103 can be calculated by assuming that the amount obtained by subtracting the discharged oxygen amount from the input oxygen amount corresponds to the amount of oxygen remaining in the converter 100. For example, the amount of oxygen introduced into the converter 100 and the amount of oxygen discharged outside the converter 100 can be expressed as equations (4) and (5) shown below, respectively.

[Equation 4]

[Equation 5]

Here, the input oxygen amount, O ₂ ⁱⁿ [N㎥/t], is the top blow oxygen accumulated amount from the lance 102, V _O2 ^blow [N㎥/t], the oxygen accumulated amount in the input auxiliary materials, and the oxygen accumulated from outside the converter 100 into the furnace. It is the sum of the accumulated amount of oxygen in the mixed air. ρ _i ^Oscr [%] is the converted value of the oxygen content in the charged scrap (item i). ρ _j ^Oaux [(N㎥/t)/t] is the converted value of the oxygen content in the input secondary raw material (item j). The oxygen content (ρ _i ^Oaux , ρ _j ^Oaux ) in the item i of the charged scrap and the item j of the input auxiliary material is stored in the database 12, and the in-furnace state calculation unit 15 calculates the items used in the target charge. Obtain information about Regarding ρ _i ^Oscr [%] and W _j ^aux [t], analysis or calculation values for the components and weight of the slag 103 put off from the rechargeable battery may be included. In addition, in calculating the amount of input oxygen, for example, as in this example, when the N ₂ concentration and the Ar concentration cannot be obtained by measuring the exhaust gas, the amount of oxygen in the mixed air is You can calculate it together. Here, in the fourth term of equation (4), V _rem ^OG [N㎥/t], which is the amount of unanalyzed exhaust gas other than O ₂ , CO, and CO ₂ in the exhaust gas, is changed to V _bot [N㎥/t], which is the flow rate of the downflow gas. It is assumed that the amount obtained by subtracting [t] is equivalent to N ₂ and Ar in the mixed air.

The amount of oxygen discharged, O ₂ ^out [N㎥/t], is calculated from the amount of oxygen contained in the exhaust gas. V _O2 ^OG [N㎥/t] is the accumulated flow rate of _O2 in exhaust gas up to the calculation time. V _CO ^OG [N㎥/t] and V _CO2 ^OG [N㎥/t] are the same as equation (3). The amount obtained by subtracting the discharged oxygen amount from the input oxygen amount is the amount of oxygen remaining in the converter 100. The oxygen remaining in the converter 100 is used for oxidation of metal impurities such as Si, Mn, and P in the molten metal 101 and for oxidation of iron. Among them, the amount of oxidation of the metal impurity is calculated using the oxidation reaction model of the impurity metal among the models stored in the database 12. For example, _VO2 ^Si [Nm3/t], which is the amount of oxygen used for oxidizing Si in the molten metal 101, is expressed as equation (6) shown below.

[Equation 6]

Here, ρ _pig ^Si [%] is the Si concentration in the charged molten iron. ρ _i ^Siscr [%] is the Si concentration in the charging scrap (item i). ρ _j ^Siaux [%] is the Si concentration in the input auxiliary material (item j). K _Si is the oxidation reaction rate constant of Si. In addition, similarly to equation (6), the amount of oxygen used for oxidation of various metal impurities in the molten metal 101, such as Mn and P, can be calculated. Here, the total amount of oxygen used for oxidation of various metal impurities in the molten metal 101, such as Si, Mn, and P, is said to be V _O2 ^met [N㎥/t]. The amount of Fe _tO in the slag 103 can be calculated by assuming that it corresponds to the amount obtained by subtracting the discharged oxygen amount from the input oxygen amount and further subtracting V _O2 ^met .

At the timing when one refining process (refining process of the target charge) ends, the processes of steps S3 and S4 are completed (Yes in step S5), and the in-furnace state estimation process proceeds to the process of step S6. When one refining process has not been completed (No in step S5), the in-furnace state estimation process returns to the processes in steps S3 and S4.

In the processing of step S6, the input unit 11 acquires the result of the refining process as performance information. In this embodiment, the results of the refining treatment include the temperature and component concentration of the molten metal 101, the component concentration of the slag 103, and the flow rate and component concentration of the exhaust gas. The input unit 11 stores the obtained results of the refining process in the database 12. Thereby, the process of step S6 is completed, and the in-furnace state estimation process progresses to the process of step S7. Step S6 corresponds to part of the “input step”. The data input in step S6 is used in the processing of the model parameter calculation unit 13.

In the process of step S7, the model parameter calculation part 13 calculates the model parameters of the model regarding a blow temper reaction based on the material balance and heat balance in the furnace so that a resin error may be minimized, and stores them in the database 12. Step S7 corresponds to the “model parameter calculation step”. As described above, the in-furnace state calculation unit 15 estimates the state quantity in the converter 100 in the refining process of the target charge using the model parameters determined by the model determination unit 14. The model parameter calculation unit 13 corrects the model parameters used by the in-furnace state calculation unit 15 using the results (performance information) of the refining process of the target charge. Then, the model parameter calculation unit 13 stores more accurate model parameters after correction in the database 12. In other words, the model parameters associated with the target charge and stored in the database 12 are not the model parameters of the model used by the in-furnace state calculation unit 15 for calculation (estimate). The model parameters associated with the target charge and stored in the database 12 are model parameters calculated (corrected) by the model parameter calculation unit 13 based on performance information of the refining process of the target charge.

The model parameter calculation unit 13 may calculate coefficients for correction as model parameters. The coefficient for correction may include, for example, a correction coefficient A for the measured value of the flow rate of the exhaust gas, and a correction coefficient B for the measured value of the component concentration of the exhaust gas. Coefficients for correction include, for example, a correction coefficient ΔC for the measured value of the component concentration of the molten metal 101, a correction coefficient D for the measured value of the temperature of the molten metal 101, a constant E related to the yield of the reaction in the furnace of the charged scrap, and the input A constant F related to the yield of the in-furnace reaction of the auxiliary raw materials may be included. The coefficient for correction includes the coefficient H for the heat gain and endothermic amount accompanying various reactions in the furnace, such as the oxidation reaction of the components in the molten metal 101, the reduction reaction of the components in the slag 103, and the dissolution of auxiliary materials. You can do it. In addition, the coefficient for correction may include a coefficient I for heat loss such as the sensible heat of the gas and slag 103 and the amount of heat radiation from the furnace hole and furnace body.

For example, the model parameter calculation unit 13 may insert coefficients as described above as variables of an evaluation function such as equation (7) and obtain model parameters that minimize the evaluation function. Here, in this embodiment, the model parameter calculation unit 13 minimizes the evaluation function, but in the case of suitable model parameters, a maximizing evaluation function may be used. In short, the model parameter calculation unit 13 may obtain model parameters that minimize or maximize the evaluation function.

[Equation 7]

C _in is the accumulated amount of carbon input into the converter 100 over a specific period. C _out is the accumulated amount of carbon discharged outside the converter 100 by exhaust gas, etc. during a specific period. O ₂ ⁱⁿ is the amount of oxygen introduced into the converter 100. O ₂ ^out is the amount of oxygen discharged outside the converter 100 due to discharge of exhaust gas and slag 103, etc. V _O2 ^met is the amount of oxygen used to oxidize various metal impurities in the molten metal (101) such as Si, Mn, and P. ΔT is the change in the amount of heat in the converter 100, including reaction heat generated by the reaction in the converter 100, heat generated by exhaust gas and slag 103 discharged to the outside of the furnace, heat radiation from the furnace body, and radiant heat radiation from the furnace opening. It is the accumulated amount of molten metal temperature change over a specific period. T _ini is a measured value of the temperature of the molten metal 101 at a specific period in the refining process. In addition, [C], _VO2 ^FetO , and T _fin are Fe _t calculated from the measured carbon content in the molten metal 101 and the measured Fe _tO content in the slag 103 at the end of a specific period in the refining treatment, respectively. This is the measured value of the amount of oxygen used to generate O and the temperature of the molten metal (101). σ _C ² , σ _O ² , and σ _T ² are integers that can be arbitrarily set. Additionally, A to I and ΔC correspond to the first to K parameters in FIG. 2 . In this embodiment, the model parameter includes a coefficient or constant term that corrects at least one of the integrated amount and the oxygen amount used in equation (7).

However, ΔR _m is the reaction amount for various reactions m in the furnace, such as the oxidation reaction of the components in the molten metal 101, the reduction reaction of the components in the slag 103, and the dissolution of auxiliary materials. ΔL _n is the amount of heat loss with respect to the heat loss path n in the molten metal 101, such as the sensible heat of the gas and slag 103, and the amount of heat radiation from the furnace hole and furnace body.

The evaluation function J shown in equation (7) is a weighted sum of the following three terms. In the evaluation function J, the first and second terms are terms representing the material balance error, and the third term is a term representing the heat balance error. The first term is the square value of the difference between the amount of carbon remaining in the converter 100 obtained by subtracting the amount of carbon discharged from the amount of carbon input and the measured value of the amount of carbon in the molten metal 101. This term being 0 indicates that the carbon balance is maintained within the converter 100. The second term is the square value of the difference between the amount of oxygen discharged and the amount of oxygen used for oxidizing impurity metals from the amount of oxygen input and the amount of oxygen used for iron oxidation in the molten metal 101 calculated from the measured value of Fe _tO in the slag 103. . This term being 0 indicates that the oxygen balance is maintained within the converter 100. Clause 3 is the difference between the measured temperature change value of the molten metal 101 from the start to the end point of a specific period in the refining process and the calculated temperature change value of the molten metal 101 calculated from the reaction heat and heat generation in the converter 100, etc. It is the win value. The fact that this term approaches 0 indicates that the heat balance is maintained within the converter 100. The “specific period” in the explanation of equation (7) above may be a different period in each of the three items.

The weighting factors (σ _C ² , σ _O ² , σ _T ² ) in the denominator of each term of the evaluation function J are set by the user, for example. Various algorithms have been proposed for the nonlinear programming problem of minimizing the evaluation function J under constraints, and calculations to obtain model parameters may be performed using known methods.

The model parameters calculated by the model parameter calculation unit 13 are stored in the database 12 and are used for in-furnace state estimation processing in the next and subsequent refining processes. Thereby, the process of step S7 is completed, and the in-furnace state estimation process completes the process in the refining process.

Based on the temperature and component concentration of the molten metal 101 and the component concentration of the slag 103 estimated by the process of the in-furnace state estimation method, the operating amount is determined, the refining operation is performed, and good molten steel is produced. As the operating amount, at least one of the optimal flow rate and speed of top blowing oxygen, the height of the top blow lance, the flow rate of bottom blow gas, the input amount and timing of auxiliary materials such as lime and iron ore, the timing of sampling the molten metal, and the timing of ending blow tempering is determined. do. In this way, based on the in-furnace state calculated by the above-mentioned in-furnace state estimation method, a good molten steel manufacturing method can be realized.

As described above, the furnace state estimation device 1, the furnace state estimation method, and the molten steel manufacturing method according to the present embodiment have an evaluation function including terms representing the material balance error and heat balance error in the furnace by the above configuration and process. The model parameters are optimized based on and then stored in the database (12). In addition, in estimating the state of the furnace in the refining process, since the past optimized model parameters accumulated in the database 12 can be used, the temperature and component concentration of the molten metal 101, the component concentration of the slag 103, etc. The estimation precision can be improved.

Although the embodiments of the present disclosure have been described based on various drawings and examples, please note that it is easy for those skilled in the art to make various changes or modifications based on the present disclosure. Accordingly, please note that these variations or modifications are included within the scope of the present disclosure. For example, functions included in each component or step can be rearranged without logical contradiction, and multiple components or steps can be combined into one or divided. Embodiments related to the present disclosure can also be realized with a program executed by a processor included in the device or a storage medium on which the program is recorded. It is intended to be understood that these are also included in the scope of the present disclosure.

For example, the type of model parameter to be determined, the number of model parameters, and the form of the evaluation function to be minimized are not limited to those in the above embodiment, and the same effect is achieved as long as it is in a form that can minimize the material balance error and heat balance error in the furnace. do. In addition, the model is not limited to those exemplified as Equations (2) to Equations (6) in the above embodiment, but includes a molten metal temperature estimation model, scrap dissolution model, secondary material dissolution/yield model, decarburization efficiency model, dephosphorization model, Fe A production reduction model of _tO , etc. may be used. In addition, in this embodiment, the furnace state estimation device 1 and the furnace state estimation method for the converter 100 are shown, but also in the secondary refining facility or preliminary treatment facility, based on the material balance and heat balance in the furnace. It is valid for calculation of model parameters being calculated.

1: In-furnace state estimation device
2: Refining equipment
10: control terminal
11: input unit
12: database
13: Model parameter calculation unit
14: Model decision unit
15: Furnace state calculation unit
16: output unit
20: display device
100: Converter
101: molten metal
102: Lance
103: slag
104: duct
105: exhaust gas detection unit
106: Ventilation hole
107: flow meter

Claims (9)

Measurement results of the temperature and component concentration of molten metal and component concentration of slag before or during the start of refining processing in the refining facility, and measurement results for the refining facility, including the flow rate and component concentration of exhaust gas discharged from the refining facility. An input unit where performance information including and conditions for the refining process are input,
Using the model parameters of the model related to the blow refining reaction in the refining equipment, the performance information, and the past model parameters acquired from a database storing the conditions of the refining process, a model determination unit that determines the model parameters,
a furnace state calculation unit that calculates state quantities in the furnace including the temperature and component concentration of the molten metal and the component concentration of slag using the determined model parameters;
An evaluation function including terms representing a material balance error and a heat balance error in the furnace from the start to the end point of a specific period in the refining process, using the performance information including the results of the refining process for the target charge. Based on this, an in-furnace state estimation device comprising a model parameter calculation unit that calculates the model parameter in the refining process of the target charge. According to claim 1,
The model determination unit averages the model parameters of past refining processes that are similar to the conditions of the refining process of the target charge among the past model parameters stored in the database, thereby performing the refining process of the target charge. An in-furnace state estimation device for determining the model parameters. According to claim 1,
The model determination unit models the relationship between the past model parameters stored in the database and the conditions of the refining process, including at least one of the number of processes in the refining process, the date and time of the process, and the number of uses of the refining equipment, , An in-furnace state estimation device that determines the parameters in the refining process of the target charge from the model. The method according to any one of claims 1 to 3,
The model parameters include the accumulated amount of carbon introduced into the furnace in a specific period, the accumulated amount of carbon discharged outside the furnace in a specific period, the amount of oxygen introduced into the furnace, the amount of oxygen discharged outside the furnace, and various types of molten metal in the molten metal. A furnace state estimation device comprising a coefficient or constant term for correcting at least one of the amount of oxygen used to oxidize metal impurities and the accumulated amount of the molten metal temperature change in a specific period due to the change in heat content in the furnace. The method according to any one of claims 1 to 4,
The evaluation function is comprised of a weighted sum of a term representing the carbon balance, a term representing the oxygen balance, and a term representing the heat balance. An in-furnace state estimation method executed by an in-furnace state estimation device, comprising:
Measurement results of the temperature and component concentration of molten metal and component concentration of slag before or during the start of refining processing in the refining facility, and measurement results for the refining facility, including the flow rate and component concentration of exhaust gas discharged from the refining facility. An input step in which performance information including and conditions for the refining process are input,
Using the model parameters of the model related to the blow refining reaction in the refining equipment, the performance information, and the past model parameters acquired from a database storing the conditions of the refining process, A model decision step for determining the model parameters,
a furnace state calculation step for calculating state quantities in the furnace including temperature and component concentration of the molten metal and component concentration of slag using the determined model parameters;
An evaluation function including terms representing a material balance error and a heat balance error in the furnace from the start to the end point of a specific period in the refining process, using the performance information including the results of the refining process for the target charge. Based on this, a model parameter calculation step of calculating the model parameters in the refining process of the target charge. According to claim 6,
The model determination step determines the refining process of the target charge by averaging the model parameters of the past refining process that are similar to the conditions of the refining process of the target charge among the past model parameters stored in the database. An in-furnace state estimation method for determining the model parameters. According to claim 6,
The model determination step models the relationship between the past model parameters stored in the database and the conditions of the refining process, including at least one of the number of processes in the refining process, the date and time of the process, and the number of uses of the refining equipment. and determining the parameters in the refining process of the target charge from the model. Based on the temperature and component concentration of the molten metal and the component concentration of the slag estimated by the method for estimating the state of boiling water according to any one of claims 6 to 8, the flow rate and speed of top blowing oxygen, the height of the top blowing lance, and bottom blowing A molten steel production method in which a refining operation is performed by determining at least one of the flow rate of gas, the input amount and timing of secondary materials such as lime and iron ore, the timing of sampling the molten metal, and the timing of ending blow refining, and manufacturing molten steel.

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