JP7173115B2 - Static Blow Control Method, Temperature Correction Term Estimation Device, and Converter Control Device - Google Patents

Description

The present invention is a static blowing method that predicts, at the start of blowing, the amount of oxygen to be supplied and the amount of cold material or heating material required to set the molten steel temperature and molten steel component concentration to target values in converter blowing. The present invention relates to a smelting control method, a temperature correction term estimation device, and a converter control device.

A converter operation is a steelmaking process in which oxygen is supplied to main raw materials such as molten pig iron and scraps for oxidation refining (blowing) to obtain molten steel. In converter operation, blowing control is performed by combining static control and dynamic control in order to keep the molten steel temperature at the end point of blowing (blowing end) and the concentration of components such as carbon concentration in molten steel within target values. Static control uses mathematical models based on mass balance and heat balance to determine the amount of oxygen to be supplied and the amount of cold or heating material required to achieve the above target values before the start of blow tempering. It is something to do. On the other hand, dynamic control measures the temperature and carbon concentration of molten steel with a sublance during blowing, and the amount of oxygen supplied and the amount of cold or heating material that has been determined by static control are The amount of oxygen up to the blow stop and the amount of cooling material or heating material to be charged are finally determined based on the mathematical model based on the reaction model.

In the control method of converter operation that combines static control and dynamic control, if the error in static control is too large, it becomes difficult to correct it with dynamic control, and the molten steel temperature at blow stop and the component concentration such as carbon concentration in molten steel may not be able to meet the target value. Therefore, it is necessary to minimize errors in static control.

The mathematical model of static control consists of two types of calculations: heat balance calculation and oxygen balance calculation. Among these, in the heat balance calculation, as shown in the following formula (1), the amount of cold material or heating material input (W _ore ) is calculated so that the total amount of heat input into the furnace and the total amount of heat output are equal. do.

上記（１）式において、Ｑ_ｉｎは入熱確定項である。ΔＱは入熱誤差項である。Ｋはオペレーター補正項である。Ｑ_ｏｕｔは出熱確定項である。ΔＨ_ｏｒｅは冷却能または昇熱能である。これらは既知項であり、当該既知項を用いて冷材または昇熱材の投入量（Ｗ_ｏｒｅ）を算出する。このように、熱収支計算は、入熱確定項、出熱確定項、冷却能または昇熱能、誤差項、およびオペレーターによる温度補正項により構成される。

In the above equation (1), Q _in is a deterministic term of input heat. ΔQ is the heat input error term. K is an operator correction term. Q _out is a deterministic term for heat output. ΔH _ore is cooling power or heating power. These known terms are used to calculate the input amount (W _ore) of the cooling material or the heating material. Thus, the heat balance calculation is composed of a deterministic heat input term, a deterministic heat output term, a cooling capacity or heating capacity, an error term, and a temperature correction term by the operator.

In addition, in the oxygen balance calculation, as shown in the following equation (2), the blowing oxygen amount (V _O2 ) is calculated so that the sum of the amount of oxygen entering the furnace and the sum of the amount of oxygen leaving the furnace are equal.

上記（２）式において、Ｖ_ｉｎは入酸素確定項である。Ｖ_ｏｕｔは出酸素確定項である。ΔＶは出酸素誤差項である。Ｌはオペレーター補正項である。これらは既知項であり、当該既知項を用いて、吹錬酸素量（Ｖ_Ｏ２）を算出する。

In the above equation (2), V _in is a determination term of incoming oxygen. V _out is an output oxygen determination term. ΔV is the oxygen output error term. L is the operator correction term. These are known terms, and the known terms are used to calculate the blown oxygen content (V _O2 ).

The fixed heat input and the fixed heat output in the heat balance calculation are the sensible heat of the molten pig iron charged and the amount of heat generated by the decarburization reaction, the sensible heat of the molten steel at blowing stop, the melting of scrap, and the absorption of slag slag. and are theoretical value terms determined based on physical property values and thermodynamic data. Here, the amount of reaction heat is treated as the amount of heat generated with respect to the amount of oxygen after calculating the amount of oxygen required to remove the concentration of hot metal components (carbon, phosphorus, etc.) to the target value. The cooling or heating term is given by the product of the cooling capacity or heating capacity per unit mass of the cold or heating material and the amount of cold or heating material input. is the solution for the heat balance calculation.

The error term is a statistical model term that summarizes the factors that affect blowing, which cannot be expressed as theoretical terms such as the number of furnaces and the number of furnaces, as multiple regression terms. The temperature correction term by the operator is a term for the operator to qualitatively evaluate the factors that cannot be expressed as a deterministic term or an error term, and to correct the static calculation model.

In order to improve the temperature hit rate during static control, it is necessary to perform the heat balance calculation with appropriate values given to the error term and the temperature correction term. As such a technique, in Patent Document 1, the temperature distribution in the thickness direction of the lining refractory of the converter is obtained immediately before the hot metal is charged, and the heat absorption amount by the lining refractory during processing is measured. and incorporating it into the static control factor is disclosed. In Patent Document 2, the surface temperature of the refractory lining of the converter is measured with a radiation thermometer, the cooling curve is obtained from the measured temperature and time information, and the temperature drop in the subsequent blowing is predicted. A method for incorporating heat balance calculations in static control is disclosed. In Patent Document 3, operation information in converter steelmaking is input, a neural network is constructed with an unknown heat amount and an unknown oxygen amount in a static blowing control method as an output, and the unknown heat amount and unknown amount are constructed using the neural network. A static blow control method is disclosed that estimates the amount of oxygen.

JP-A-63-171821 JP 2012-87345 A JP-A-6-200312

However, the method disclosed in Patent Document 1 has the problem that it takes time to measure the temperature distribution. In addition, constants such as the specific heat of the furnace body are used when calculating the temperature distribution and heat absorption amount into the static control element, but the state of the furnace body refractory, etc. changes with the passage of time, such as progressing wear and tear. , there was a problem that if these were not updated regularly, a discrepancy would occur between the calculation and the actual situation. In the method disclosed in US Pat. No. 6,200,000, the average accuracy of static calculations may still result in large errors.

In the method disclosed in Patent Document 3, the input is a neural network constructed as operation information in converter steelmaking, and the value reflecting the state of the converter, which will be described later, is not considered. It could not be estimated with high precision. SUMMARY OF THE INVENTION The present invention has been made in view of the problems of the prior art, and an object of the present invention is to provide a static control method, a temperature correction term estimating device, and a transfer system capable of obtaining an appropriate temperature correction term in heat balance calculation. It is to provide a furnace control system.

Means for solving the above problems are as follows.
(1) Static calculation of the amount of supplied oxygen and the amount of cold material or heating material input for setting the molten steel composition and molten steel temperature at the end of blowing to the target values using heat balance calculation and material balance calculation In the blowing control method, the heat balance calculation includes a temperature correction term, and a learned learning model that outputs the temperature correction term from values reflecting the operating conditions of blowing and the state of the converter, A static blowing control method for inputting operating conditions for blowing to be calculated and a value reflecting the state of the converter and outputting the temperature correction term.
(2) The static blowing control according to (1), wherein the value reflecting the state of the converter is one or more selected from values indicating the amount of base metal deposited on the furnace throat and the state of the bottom blowing tuyere. Method.
(3) The static blow blow control method according to (2), wherein the value reflecting the state of the converter further includes one or more selected from the temperature of the refractory in the furnace and the remaining thickness of the refractory in the furnace.
(4) The static blow blow control method according to (2) or (3), wherein the deposit amount of the furnace throat base metal is represented by an index indicating the deposit amount in a plurality of stages.
(5) Any one of (2) to (4), wherein the adhesion amount of the furnace throat base metal is calculated from image data generated by imaging the furnace throat of the converter before the main raw material is charged. 1. Static blow control method according to one.
(6) The static blow blow control method according to (3), wherein the temperature of the in-furnace refractories is the surface temperature of the in-furnace refractories below the outlet of the converter before the main raw material is charged.
(7) The static blow control method according to (6), wherein the surface temperature is measured with a radiation thermometer.
(8) Machine learning of the learning model using the operating conditions of blowing, the value reflecting the state of the converter, and the actual temperature correction term calculated after the end of blowing, (1) to (7) Static blow control method according to any one of.
(9) Temperature for outputting a temperature correction term in static blowing control for calculating the supplied oxygen amount and the input amount of cold material or heating material for setting the molten steel composition and molten steel temperature at the end of blowing to the target values A temperature correction term estimating device having a temperature correction term estimating unit that inputs values reflecting the operating conditions of blowing and the state of the converter into a learned learning model and outputs the temperature correction term. estimation device.
(10) It further has an updating unit that updates the learned learning model, and after blowing is completed, the updating unit updates the operating conditions of the blowing, the value reflecting the state of the converter, and the actual temperature The temperature correction term estimating device according to (9), wherein the learning model is machine-learned using the correction term.
(11) According to (9) or (10), the value reflecting the state of the converter is one or more selected from values indicating the amount of deposit of the furnace throat base metal and the state of the bottom blowing tuyere. Temperature correction term estimator.
(12) The temperature correction term estimation device according to (11), wherein the value reflecting the state of the converter further includes one or more selected from the temperature of the refractories in the furnace and the residual thickness of the refractories in the furnace.
(13) The temperature correction term estimating device according to any one of (9) to (12), and performing heat balance calculation and material balance calculation including the temperature correction term output by the temperature correction term estimating device. and a process computer for calculating the supplied oxygen amount and the input amount of the cooling material or the heating material.
(14) Further comprising: a camera for capturing an image of the throat of the converter to generate image data; , (13).
(15) The converter control device according to (13) or (14), further comprising a radiation thermometer for measuring the surface temperature of the in-furnace refractories below the tapping port of the converter.

In the static blowing control method according to the present invention, in addition to the operating conditions of blowing, a learned learning model that has been machine-learned using values that reflect the state of the converter is used to calculate the heat balance. Since the temperature correction term is estimated, an appropriate temperature correction term can be set regardless of the operator's years of experience or ability. By performing static blowing control using the temperature correction term set in this way, the amount of supplied oxygen that makes the molten steel temperature and molten steel component concentration closer to the target values, and the amount of cold material or heating material input can be calculated.

1 is a block diagram of a converter control device 10 capable of implementing a static blowing control method according to the present embodiment; FIG. 5 is a graph showing the percentage of temperature correction terms of Comparative Example 1 and Invention Examples 1 to 5 falling within the allowable error range.

The static blowing control method is used to calculate the supplied oxygen amount and the input amount of cold material or heating material for setting the molten steel composition and molten steel temperature at the end of blowing to the target values. In the static blowing control method, the amount of supplied oxygen and the input amount of cold material or heating material (hereinafter referred to as "cold material, etc.") are calculated using heat balance calculation and material balance calculation.

The heat balance calculation is composed of, for example, a fixed heat input term, a fixed heat output term, a cooling or heating term, an error term, an error term, and a temperature correction term by an operator. Of these, the temperature correction term is a term that summarizes factors that cannot quantitatively measure the influence on the heat balance or that it is not realistic to quantitatively grasp the influence on the heat balance. This is the term set by the operator. In this way, since the temperature correction term includes factors that are not realistic to grasp quantitatively, it largely depends on the years of experience and ability of the operator, and there were cases where variations occurred depending on the operator in charge of blowing.

On the other hand, in the static blowing control method according to the present invention, a learning model that has been machine-learned using past operating conditions of blowing, a value reflecting the state of the converter, and an actual temperature correction term is used to estimate the temperature correction term, an appropriate temperature correction term can be estimated regardless of the operator's years of experience or ability. Hereinafter, the static blowing control method according to the present invention will be described through its embodiments. In the following embodiments, an example will be described in which the adhesion amount of furnace throat base metal is used as a value reflecting the state of the converter, but the value reflecting the state of the converter is not limited to this. At least one value selected from the amount of gold deposited, the temperature of the refractories in the furnace, the remaining thickness of the refractories in the furnace, and the state of the bottom blowing tuyere may be used.

FIG. 1 is a functional block diagram illustrating the configuration of a converter control device 10 capable of implementing the static blowing control method according to the present embodiment. The converter controller 10 has a process computer 12 and a temperature correction term estimator 14 .

The process computer 12 is a device that performs operation control of blowing in the converter facility 100 and data processing/accumulation. The heat balance calculation and the oxygen balance calculation in the static blowing control method are performed by the process computer 12 to calculate the amount of supplied oxygen and the input amount of cold or heating material.

The temperature correction term estimating device 14 is a device that outputs a temperature correction term in the heat balance calculation from the operating conditions of blowing and the amount of deposit of the furnace mouth base metal. The temperature correction term estimation device 14 has a control section 20 , a storage section 30 and an input section 40 . The controller 20 has a temperature correction term estimator 22 and an updater 24 . The control unit 20 is, for example, a CPU or the like, and uses programs and data stored in the storage unit 30 to execute predetermined processing in the temperature correction term estimation unit 22 and the update unit 24 .

The storage unit 30 is, for example, an updateable flash memory, a built-in or connected hard disk, a memory card, or other information recording medium, and a read/write device therefor. The storage unit 30 stores in advance programs for realizing predetermined processing in the temperature correction term estimation unit 22 and the update unit 24, data used during execution of the programs, and the like. Further, the storage unit 30 stores a learned learning model 32 which inputs the operating conditions of blowing and the amount of deposit of furnace throat base metal and outputs a temperature correction term, and a database 34 . In the present embodiment, a machine learning tool called “DataRobot” manufactured by DataRobot is used to build the learning model 32 .

In the database 34, a predetermined number of pieces of data on operating conditions for blow tempering performed in the past, the amount of metal deposited on the furnace throat, and actual temperature correction terms are stored in association with, for example, a serial number assigned to each tapping heat. ing. Here, the actual temperature correction term stored in the database 34 is a temperature correction term that is back-calculated from the actual values of the supply amount of oxygen and the input amount of cold material determined after blow tempering and the heat balance calculation. The actual temperature correction term is calculated by the process computer 12 . The learning model 32 is made into a learned learning model by performing machine learning using these data stored in the database 34 as teacher data, and is stored in the storage unit 30 . In this embodiment, the operating conditions for blowing used to construct the learning model are shown in Table 1 below.

A value estimated by the operator 50 visually observing the furnace throat of the converter before charging the main raw material for blowing is used as the amount of deposit of the furnace throat base metal. In this embodiment, for example, the amount of deposit of the furnace throat metal is determined in three stages (the amount of deposit that requires immediate metal removal: 3, and the amount of metal removal is performed if there is a free space in the converter). Amount: 2, adhesion amount that does not require metal removal: Use the index (1 to 3) shown in 1). In addition, the number of indices indicating the amount of adhesion of the furnace throat base metal is not limited to three, and may be two or more.

The furnace throat ingot is formed by droplets of molten steel generated during blowing adhering to the refractory at the throat, solidifying and growing. If the amount of the furnace throat base metal deposited is small, the heat dissipation to the furnace throat increases. Also, if the amount of the furnace throat base metal deposited is large, the base metal that falls during the blow melts, making it difficult for the temperature of the molten steel to rise. As described above, the deposit amount of the furnace throat base metal affects the heat balance of blowing, so it is a factor to be considered in the heat balance calculation. However, since it is difficult to quantify the effect on the heat balance, the amount of deposit of the furnace throat base metal has not been reflected in the heat balance calculation.

On the other hand, in the static blowing control method according to the present embodiment, in addition to the operating conditions of blowing, the amount of adhesion of the furnace throat base metal is indexed, and learned learning is performed by machine learning with teacher data including the index. Estimate the temperature correction term using the model. As a result, the estimated temperature correction term reflects the influence of the adhesion amount of the furnace throat base metal, so the accuracy of the estimated temperature correction term is increased.

The input unit 40 receives input from the operator 50 . The input unit 40 receives an input from the operator 50 and outputs the input value to the control unit 20 . The input unit 40 is, for example, an input device such as a keyboard or mouse.

Next, a method for calculating the temperature correction term will be described. The timing to perform the static calculation for calculating the amount of supplied oxygen and the amount of cold material input is immediately before the blow temper. At this timing, the operator 50 inputs an index indicating the adhesion amount of furnace throat bare metal from the input unit 40 and causes the process computer 12 to transmit the operating conditions of the blow to the temperature correction term estimating unit 22 . The input unit 40 outputs the exponent to the temperature correction term estimation unit 22 .

The temperature correction term estimating unit 22 reads out the learned learning model 32 from the storage unit 30 upon receiving the operating conditions of the blow temper and the index indicating the adhesion amount of the furnace throat base metal. The temperature correction term estimating unit 22 inputs the operating conditions and the indices of the furnace throat base metal adhesion amount to the learning model 32 and causes the learning model 32 to output the temperature correction term. In this manner, the temperature correction term estimator 22 estimates the temperature correction term used for heat balance calculation.

The temperature correction term estimator 22 outputs the estimated temperature correction term to the process computer 12 . The process computer 12 performs heat balance calculation and material balance calculation including the temperature correction term, and calculates the supplied oxygen amount and the input amount of the cooling material or heating material.

The temperature correction term estimated by the temperature correction term estimating unit 22 is a highly accurate temperature correction term that takes into consideration the influence of the amount of deposits of furnace mouth base metal that affects the heat balance of the converter operation. Therefore, by performing static blowing control using the temperature correction term, the molten steel temperature and molten steel component concentration at the end of blowing can be brought closer to the target values.

Next, updating of the learning model 32 will be described. In the static blow temper control method according to the present embodiment, the learned learning model 32 may be updated after the blow temper is finished. The learning model 32 that has been learned is updated by the update unit 24 after the end of the blow temper using the operating conditions of the blow temper, the index indicating the adhesion amount of the furnace throat base metal, and the actual temperature correction term.

After the blow temper is completed, the process computer 12 calculates the actual temperature correction term using the fixed supply oxygen amount, the fixed input amount of cold materials, etc., and the error term. The process computer 12 outputs the operating conditions and the actual temperature correction term to the updating unit 24 . The index indicating the amount of deposit of the furnace throat base metal is output from the temperature correction term estimator 22 to the updater 24 . Note that the operating conditions may be output from the temperature correction term estimation unit 22 to the update unit 24 .

When the update unit 24 acquires the operating conditions, the index of the amount of metal deposited on the furnace throat, and the actual temperature correction term, the updating unit 24 records these data in the database 34 in association with the serial number assigned to each tapping heat. When recording these data, the update unit 24 may specify the data with the smallest serial number among the serial numbers stored in the database 34 and erase the data. After recording the data in the database 34, the updating unit 24 inputs the data stored in the database 34 to the learning model 32 as teacher data for machine learning. In this way, the learned learning model 32 is updated by the updating unit 24 after blowing is finished.

Next, the result of confirming the accuracy of the temperature correction term estimated by the temperature correction term estimator will be described. Inventive example 1 is a case in which the operating conditions, the amount of deposited metal on the furnace throat, and the actual temperature correction term shown in Table 1 are used as teacher data to be input to the learning model 32 . Inventive example 2 is a case in which the operating conditions, the in-furnace refractory temperature, and the actual temperature correction term shown in Table 1 are used as teaching data. Inventive example 3 is a case where the operation conditions, the remaining thickness of the refractory in the furnace, and the actual temperature correction term are used as teaching data. Inventive Example 4 is a case in which the operating conditions, the state of the bottom blowing tuyere, and the actual temperature correction terms shown in Table 1 are used as training data. Inventive Example 5 is a case in which the operating conditions, the amount of base metal deposited on the furnace throat, the refractory temperature in the furnace, and the actual temperature correction term shown in Table 1 are used as teaching data. On the other hand, Comparative Example 1 is a case in which the operating conditions and the actual temperature correction term are used as teaching data.

As the in-furnace refractory temperature, the value obtained by measuring the surface temperature of the refractory below the tapping port with a thermoviewer immediately before charging the main raw material was used. A thermoviewer is an example of a radiation thermometer. In addition, the refractory temperature does not have to be measured every time immediately before charging the main raw material. may be used. When using the measured value of the refractory temperature in a plurality of blows, it is preferable to use the measured value of the refractory temperature with the closest serial number, which is measured at the closest timing among the data stored in the storage unit 30.

As the residual thickness of the refractories in the furnace, the difference value obtained by measuring the laser profile data of the entire circumference of the furnace immediately before charging the main raw material and subtracting the measured profile data from the reference profile data immediately after the furnace repair was used. . The profile measurement of the in-furnace refractory does not have to be measured every time immediately before the blow, and for the same converter, the value of the remaining thickness of one in-furnace refractory is used as a temperature correction term for a plurality of blows. may be used for the calculation of When the value of the remaining thickness of the refractory in the furnace is used for a plurality of blows, the number of furnace times measured at the closest timing among the data stored in the storage unit 30 (after the latest furnace repair, the converter It is preferable to use the value of the remaining thickness of the in-furnace refractory that is close to the number of times of blowing in . Since the temperature of the refractories and the residual thickness of the refractories in the furnace affect the heat radiation from the converter, it is considered that they affect the heat balance of the converter. Therefore, by using these as teaching data, the calculation accuracy of the temperature correction term that can hit the end point temperature is improved. As a value indicating the state of the hearth tuyeres, a nozzle constant calculated by the following equation (3) was used.

上記（３）式におけるＱは、炉底から吹き込む不活性ガスの総和（ＮＬ／ｍｉｎ）である。Ｐは羽口前圧力（ＭＰａ）であり、転炉の底部に設けられた各羽口に分岐する前の配管内ガス圧力である。

Q in the above equation (3) is the total sum (NL/min) of the inert gas blown from the bottom of the furnace. P is the pre-tuyere pressure (MPa), which is the gas pressure in the pipe before branching to each tuyere provided at the bottom of the converter.

Table 2 below shows the number of data used as teacher data and the number of data used for estimation in Comparative Example 1 and Invention Examples 1 to 5. As shown in Table 2, 80% of the total data was used as teacher data for the learning model, and 20% of the data was used for verification.

Whether or not the end point temperature of the molten steel in the blowing was on target was determined by whether or not the absolute value of "actual temperature - target temperature" was less than 10°C. When 10°C is converted into heat quantity using the operating conditions, it becomes 46.8 x 104 kcal. Therefore, in the present embodiment, when the absolute value of the difference between the temperature correction term estimated by the temperature correction term estimation unit 22 and the actual temperature correction term is 46.8×10 ⁴ kcal or less, the temperature correction term is It was assumed that it was within the allowable error. On the other hand, when the absolute value of the difference between the temperature correction term estimated by the temperature correction term estimation unit 22 and the actual temperature correction term is greater than 46.8×10 ⁴ kcal, the temperature correction term is not within the allowable error range. and For Comparative Example 1 and Invention Examples 1 to 5, the percentage of temperature correction terms within the allowable error range among all temperature correction terms estimated using prediction data was calculated. The results are shown in FIG.

FIG. 2 is a graph showing the proportion of the temperature correction terms of Comparative Example 1 and Invention Examples 1 to 5 falling within the allowable error range. As shown in FIG. 2, the ratio of the temperature correction terms of Inventive Examples 1 to 5 within the allowable error range was higher than that of Comparative Example 1. Based on these results, we used teaching data obtained by adding one or more values selected from the operating conditions to the amount of metal deposited on the furnace throat, the temperature of the refractory in the furnace, the remaining thickness of the refractory in the furnace, and the state of the bottom blowing tuyere. By using a learned model that has been machine-learned by machine learning, it is possible to calculate a temperature correction term that is within the allowable error at a high rate. confirmed to be possible.

Thus, in the static blowing control method according to the present embodiment, in addition to the operating conditions of blowing, using a learned learning model that has been machine-learned using values that reflect the state of the converter, heat Since the temperature correction term included in the balance calculation is estimated, an appropriate temperature correction term can be set regardless of the operator's years of experience and ability. By performing static blowing control using the temperature correction term set in this way, the amount of supplied oxygen that makes the molten steel temperature and molten steel component concentration closer to the target values, and the amount of cold material or heating material input can be calculated.

In this embodiment, an example is shown in which the operator 50 visually checks the throat of the converter and inputs the adhesion amount of the furnace throat base metal with an index of 1 to 3, but the present invention is not limited to this. For example, image data generated by imaging the furnace throat with a camera may be used to measure the adhesion amount of the furnace throat bare metal. In this case, the converter control device 10 further has a camera and a base metal adhesion amount calculation device. The camera captures an image of the throat of the converter to generate image data. The ingot amount calculation device compares the image data acquired from the camera with the image data captured after the converter was repaired and in a state in which no ingot adhered to the furnace throat. The image area of the base metal is specified, and the adhesion amount of the furnace throat base metal is measured based on the size of the specified image area of the base metal. The base metal deposition amount calculation device outputs the measured deposition amount of the furnace throat base metal to the temperature correction term estimator 22 .

When the temperature of the in-furnace refractories is used as the value reflecting the state of the converter, the temperature may be measured with a thermoviewer. In this case, the converter control device 10 further has the thermo viewer. The thermoviewer measures the surface temperature of the in-furnace refractories below the tapping port of the converter and outputs the measured value to the temperature correction term estimator 22 .

In this embodiment, an example in which a trained learning model is stored in the storage unit 30 is shown, but the present invention is not limited to this. A learning model is stored in the storage unit 30, and each time the temperature correction term estimation unit 22 estimates a temperature correction term, the learning model is machine-learned using the data of the database 34 stored in the storage unit 30 as teacher data. You may let Furthermore, although an example in which the temperature correction term estimation device 14 has the updating unit 24 has been shown, the updating unit 24 may not be provided when the learned learning model 32 is not updated.

Although the example in which the database 34 is stored in the storage unit 30 has been shown in the present embodiment, the present invention is not limited to this. Database 34 may be stored in process computer 12 . In this case, the learning model 32 is updated by transmitting the data stored in the database 34 from the process computer 12 to the updating unit 24 after the blow training is completed, and the updating unit 24 inputs the data to the learning model 32 and performs machine learning. It is implemented by letting

In the present embodiment, an example in which the operating conditions for blowing are transmitted from the process computer 12 to the temperature correction term estimating unit 22 is shown, but the present invention is not limited to this. For example, the blowing operating conditions may be input from the input unit 40 to the temperature correction term estimating unit 22 by the operator 50 . Furthermore, although an example of outputting the temperature correction term output by the temperature correction term estimation unit 22 to the process computer 12 has been shown, the present invention is not limited to this. For example, the temperature correction term output by the temperature correction term estimation unit 22 may be displayed on a display unit such as a display. In this case, the temperature correction term estimation device 14 further has a display unit such as a display.

Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the description of the scope of claims that forms with such modifications or improvements can also be included in the technical scope of the present invention.

In addition, the execution order of each process of the operations, procedures, and steps in the apparatus shown in the claims, the specification, and the drawings is not specified as "before", "before", etc. Also, as long as the output of the previous process is not used in the subsequent process, it can be implemented in any order.

REFERENCE SIGNS LIST 10 converter control device 12 process computer 14 temperature correction term estimating device 20 control unit 22 temperature correction term estimating unit 24 updating unit 30 storage unit 32 learning model 34 database 40 input unit 50 operator 100 converter equipment

Claims (13)

Static blowing control that calculates the amount of oxygen supplied and the amount of cold material or heating material to set the molten steel composition and molten steel temperature at the end of blowing to the target values using heat balance calculation and material balance calculation. a method,
the heat balance calculation includes a temperature correction term;
A learning model that outputs the temperature correction term from the operating conditions of blowing and the value reflecting the state of the converter is added with the operating conditions of blowing for performing the calculation and the value reflecting the state of the converter. input and output the temperature correction term ;
The static blow control method , wherein the value reflecting the state of the converter is one or more selected from the amount of base metal deposited on the furnace throat and the nozzle constant calculated by the following equation (3) .

Q in the above equation (3) is the sum of the inert gas blown from the bottom of the furnace (NL / min), and P is the pressure before the tuyere (MPa), which is branched to each tuyere provided at the bottom of the converter. This is the gas pressure in the pipe before The static blow blow control method according to claim 1 , wherein the value reflecting the state of the converter further includes one or more selected from the temperature of the refractories in the furnace and the residual thickness of the refractories in the furnace. The static blow blow control method according to claim 1 or 2 , wherein the adhesion amount of the furnace throat base metal is represented by an index indicating the adhesion amount in a plurality of stages. 4. The method according to any one of claims 1 to 3 , wherein the adhesion amount of the furnace throat base metal is calculated from image data generated by imaging the furnace throat of the converter before charging the main raw material. Static blow control method as described. 3. The static blow blow control method according to claim 2 , wherein the temperature of the in-furnace refractory is the surface temperature of the in-furnace refractory below the tapping port of the converter before the main raw material is charged. 6. The static blow control method of claim 5 , wherein the surface temperature is measured with a radiation thermometer. Any one of claims 1 to 6 , wherein the learning model is machine-learned using operating conditions of blowing, a value reflecting the state of the converter, and an actual temperature correction term calculated after the end of blowing. The static blowing control method according to item 1. Temperature correction term estimation for outputting temperature correction terms in static blowing control for calculating the supply oxygen amount and the input amount of cold material or heating material for setting the molten steel composition and molten steel temperature at the end of blowing to the target values a device,
A temperature correction term estimating unit that inputs values reflecting the operating conditions of blowing and the state of the converter into a learned learning model and outputs a temperature correction term ,
The temperature correction term estimating device , wherein the value reflecting the state of the converter is one or more selected from the adhesion amount of furnace throat base metal and the nozzle constant calculated by the following equation (3) .

Q in the above equation (3) is the sum of the inert gas blown from the bottom of the furnace (NL / min), and P is the pressure before the tuyere (MPa), which is branched to each tuyere provided at the bottom of the converter. This is the gas pressure in the pipe before further comprising an updating unit that updates the learned learning model;
The updating unit performs machine learning on the learning model using operating conditions of the blow temper, a value reflecting the state of the converter, and an actual temperature correction term after the blow temper is finished. temperature correction term estimator. 10. The temperature correction term estimating device according to claim 8 or 9 , wherein the value reflecting the state of the converter further includes one or more selected from the temperature of the refractory in the furnace and the remaining thickness of the refractory in the furnace. . a temperature correction term estimating device according to any one of claims 8 to 10;
a process computer that performs heat balance calculation and material balance calculation including the temperature correction term output by the temperature correction term estimating device, and calculates the supplied oxygen amount and the input amount of the cold material or heating material; ,
A converter controller. a camera that captures an image of the throat of the converter and generates image data;
12. The converter control apparatus according to claim 11 , further comprising a base metal deposit amount calculation device that calculates the base metal deposit amount of the furnace throat using the image data. The converter control device according to claim 11 or 12 , further comprising a radiation thermometer for measuring the surface temperature of the refractories in the furnace below the tapping port of the converter.

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