TWI841072B - Furnace state estimation device, furnace state estimation method and molten steel manufacturing method - Google Patents

Abstract

The furnace state estimation device (1) of the present invention comprises: an input unit (11) for inputting performance information and refining process conditions; a model determination unit (14) for determining the model parameters in the refining process of the target furnace by using past model parameters obtained from a database storing model parameters of models related to the blowing reaction, performance information and refining process conditions; and a furnace state calculation unit (15) for calculating the model parameters in the refining process of the target furnace by using the determined model parameters. A model parameter calculating unit (13) calculates the state quantities in the furnace including the temperature and component concentration of the melt and the component concentration of the slag; and a model parameter calculating unit (13) uses performance information including the refining result of the target furnace to calculate the model parameters in the refining process of the target furnace based on an evaluation function including terms representing the mass balance error and heat balance error in the furnace from the start point to the end point of a specific period in the refining process.

Description

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

The present disclosure relates to a furnace state estimation device, a furnace state estimation method and a molten steel manufacturing method. In particular, the present disclosure relates to a furnace state estimation device, a furnace state estimation method and a molten steel manufacturing method for estimating the component concentration in the molten metal and the slag in the refining equipment of the steel industry.

In an iron smelter, the composition and temperature of the molten iron flowing out of the blast furnace are adjusted in the refining equipment such as the pre-treatment equipment, converter and secondary refining equipment. The converter is a process that removes impurities in the molten metal and raises the temperature by blowing oxygen into the furnace, and plays a very important role in steel quality management and rationalization of refining costs. Here, in the control of the composition and temperature of the molten metal in the converter, for example, the flow rate and speed of the top blowing oxygen, the height of the top blowing gun, the flow rate of the bottom blowing gas, etc. are used as operating variables. In addition, the amount and timing of the input of auxiliary raw materials such as lime and iron ore, the timing of sampling the molten metal, the timing of ending the blowing, etc. are used as operating variables. These operating quantities should be optimized according to the furnace conditions such as the melt temperature and the melt and slag composition. In order to estimate the furnace conditions with high accuracy and adapt the operating quantities, the following method is proposed: mass balance and heat balance calculations in the furnace are performed using measurement information about the refining equipment, including exhaust gas measurement values that can be continuously measured during the refining process. This method can usually estimate the melt temperature and the melt and slag composition 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 equipment environment, such as the wear of refractories in the furnace of the refining equipment, changes in the composition of the auxiliary raw materials fed in, and a decrease in measurement accuracy.

As a method of optimizing model parameters to maintain such model accuracy, Patent Document 1, for example, proposes a method of extracting performance with similar processing conditions from past performance information and calculating parameter values in an independent model formula for a converter.

Furthermore, for example, Patent Document 2 proposes a method for determining multiple parameters of a melt temperature estimation model in a secondary refining device by finding an approximate solution to a simultaneous equation set in a heat balance manner. [Prior Art Document] [Patent Document]

Patent document 1: Japanese Patent Publication No. 2005-036289 Patent document 2: Japanese Patent Publication No. 2004-360044

[Problems to be solved by the invention] Here, Patent Document 1 takes an independent physical reaction model or a one-time combined model as an object in the form of a model. Therefore, the technology of Patent Document 1 is difficult to apply to a model in which the reaction amount and temperature rise amount in the furnace interact in a complex manner, such as a state estimation model based on mass balance and heat balance calculation in the furnace.

Patent document 2 proposes a method of obtaining an approximate solution to a simultaneous equation set in a heat balance manner for a temperature estimation model in which a plurality of model equations and parameters interact. However, Patent document 2 does not describe the calculation of the mass balance in the furnace using the measurement information including the exhaust gas measurement value in the calculation of the reaction amount in the furnace. Therefore, the technology of Patent document 2 is difficult to apply to a model in which the mass balance in the furnace interacts with each other in addition to the heat balance.

Therefore, a method for determining model parameters that is effective even for a model in which the mass balance and heat budget in a furnace interact in a complex manner is sought.

The present disclosure made in view of the above circumstances aims to provide a furnace state estimation device, a furnace state estimation method and a molten steel manufacturing method capable of estimating the component concentration in the molten metal and the slag with high accuracy and continuously. [Means for solving the problem]

(1) A furnace state estimation device according to an embodiment of the present disclosure includes: an input unit for inputting performance information and refining treatment conditions, wherein the performance information includes measurement results of the temperature and component concentration of the melt and the component concentration of the slag in the refining equipment before or during the refining treatment, and measurement results of the refining equipment including the flow rate and component concentration of the exhaust gas discharged from the refining equipment; a model determination unit for determining the model parameters in the refining treatment of the target furnace by using the model parameters of the model related to the blowing reaction in the refining equipment, the performance information, and the past model parameters obtained from a database storing the refining treatment conditions; A furnace state calculation unit calculates the state quantity in the furnace including the temperature and component concentration of the molten metal and the component concentration of the slag using the determined model parameters; and a model parameter calculation unit calculates the model parameters in the refining process of the target furnace using the performance information including the refining process result of the target furnace, based on an evaluation function including terms representing the mass balance error and heat balance error in the furnace from the start point to the end point of the specific period in the refining process.

(2) As an embodiment of the present disclosure, in (1), the model determination unit determines the model parameters in the refining process of the target furnace by averaging the model parameters of the past refining process that are similar to the refining process conditions of the target furnace among the past model parameters stored in the database.

(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 refining process conditions including at least one of the number of times the refining process is performed, the processing date and time, and the number of times the refining equipment is used, and determines the parameters in the refining process of the target furnace according to the model.

(4) As an embodiment of the present disclosure, in any one of (1) to (3), the model parameter includes a coefficient or constant term for correcting at least one of the cumulative amount of carbon input into the furnace during a specific period, the cumulative amount of carbon discharged out of the furnace during a specific period, the amount of oxygen input into the furnace, the amount of oxygen discharged out of the furnace, the amount of oxygen used to oxidize various metal impurities in the melt, and the cumulative amount of melt temperature change caused by heat change in the furnace during a specific period.

(5) As an embodiment of the present disclosure, in any one of (1) to (4), the evaluation function includes a weighted sum of a term representing carbon balance, a term representing oxygen balance, and a term representing heat balance.

(6) A method for estimating the state of a furnace in an embodiment of the present disclosure is performed by a device for estimating the state of a furnace in an embodiment of the present disclosure, and the method for estimating the state of a furnace in an embodiment of the present disclosure comprises: an input step for inputting performance information and refining treatment conditions, wherein the performance information comprises the measurement results of the temperature and component concentration of the molten metal and the component concentration of the slag in the refining equipment before or during the refining treatment, and the measurement results of the refining equipment including the flow rate and component concentration of the exhaust gas discharged from the refining equipment; A model determination step, using the model parameters of the model related to the blowing reaction in the refining equipment, the performance information and the past model parameters obtained from the database of the refining treatment conditions to determine the model parameters in the refining treatment of the target furnace; A furnace state calculation step, using the determined model parameters to calculate the state quantity in the furnace including the temperature and component concentration of the melt and the component concentration of the slag; and The model parameter calculation step uses the performance information including the refining result of the target furnace to calculate the model parameters in the refining process of the target furnace based on an evaluation function including terms representing the mass balance error and heat balance error in the furnace from the start point to the end point of the specific period in the refining process.

(7) As an embodiment of the present disclosure, in (6), the model determination step determines the model parameters in the refining process of the target furnace by averaging the model parameters of the past refining process similar to the refining process conditions of the target furnace among the past model parameters stored in the database.

(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 refining process conditions including at least one of the number of times the refining process is performed, the processing date and time, and the number of times the refining equipment is used, and determines the parameters in the refining process of the target furnace according to the model.

(9) Regarding a method for producing molten steel in an embodiment of the present disclosure, Based on the temperature and composition concentration of the molten steel and the composition concentration of the slag estimated by the furnace state estimation method as described in any one of (6) to (8), at least one of the flow rate and speed of top blowing oxygen, the height of the top blowing nozzle, the flow rate of bottom blowing gas, the amount and timing of adding auxiliary raw materials such as lime and iron ore, the timing of sampling the molten steel, and the timing of ending the blowing is determined to perform a refining operation to produce molten steel. [Effect of the Invention]

According to the present disclosure, a furnace state estimating device, a furnace state estimating method, and a molten steel manufacturing method capable of estimating the component concentrations in the molten metal and the slag with high accuracy and continuously can be provided.

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

[Structure of furnace state estimation device] FIG. 1 is a schematic diagram showing the structure of a furnace state estimation device 1 of an embodiment of the present disclosure. In this embodiment, the furnace state estimation device 1 is used as a part of a facility for producing molten steel in the steel industry. The facility for producing molten steel includes a refining facility 2 and a blowing control system including the furnace state estimation device 1.

As shown in FIG1 , the refining equipment 2 includes a converter 100, a spray gun 102, and a pipe 104. The spray gun 102 is disposed on the molten metal 101 in the converter 100. High-pressure oxygen is sprayed from the front end of the spray gun 102 toward the molten metal 101 below. The impurities in the molten metal 101 are oxidized by the high-pressure oxygen and sent into the slag 103 (refining treatment). The pipe 104 is a flue device for exhaust gas fume conduction, which is arranged at the upper part of the converter 100.

An exhaust gas detection unit 105 is disposed inside the duct 104. The exhaust gas detection unit 105 detects the flow rate and component concentration (e.g., the concentration of CO, CO ₂ , O ₂ , N ₂ , Ar, etc.) of the exhaust gas discharged in the course of the refining process. As exhaust gas measurement, the exhaust gas detection unit 105 measures the flow rate of the exhaust gas in the duct 104 based on, for example, the differential pressure before and after the venturi provided in the duct 104. Furthermore, as exhaust gas measurement, the exhaust gas detection unit 105 measures the concentration [%] of each component in the exhaust gas. The flow rate and component concentration of the exhaust gas are measured, for example, in a cycle of several 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 melt 101 in the converter 100 through a vent 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 melt 101 and promotes the reaction between the high-pressure oxygen and the melt 101. The flow meter 107 measures the flow rate of the stirring gas blown into the converter 100. The temperature and component concentration of the melt 101 are analyzed just before and after the blowing. In addition, the temperature and component concentration of the melt 101 are measured once or multiple times during the blowing process, and the supply amount (oxygen supply amount) and speed (oxygen supply speed) of high-pressure oxygen and the flow rate (stirring gas flow rate) of the stirring gas are determined based on the measured temperature and component concentration.

The blowing control system includes a control terminal 10, a display device 20, and a furnace state estimation device 1 as main components. The control terminal 10 may include an information processing device such as a personal computer or a workstation. The control terminal 10 controls the oxygen supply amount, oxygen supply speed, and stirring gas flow rate in such a way that the temperature and component concentration of the melt 101 are within the required range, and collects performance value data of the oxygen supply amount, oxygen supply speed, and stirring gas flow rate. The display device 20 may include, for example, a liquid crystal display (LCD) or a cathode ray tube (CRT) display. The display device 20 may display the calculation results output from the furnace state estimation device 1, etc.

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

The input unit 11 is an input interface for inputting various measurement results related to the refining equipment 2, i.e., performance information (performance data). The input unit 11 may be, for example, at least one of a keyboard, a mouse, a pointing device, a data receiving device, and a graphical user interface (GUI). In the present embodiment, the input unit 11 receives performance information, parameter setting values, etc. from the outside, writes the information to the database 12, and sends it to the furnace state calculation unit 15. The performance information is input to 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 oxygen supply amount and oxygen supply speed, information on the flow rate of the stirring gas, information on the amount of raw materials (main raw materials, auxiliary raw materials) input, the temperature and component concentration of the melt 101, and the component concentration of the slag 103. These information correspond to items 1 to M in the performance information of FIG. 2 described below. In addition, the input unit 11 can manually input data (manual input) by, for example, an operator of the refining equipment 2. By manual input, parameter setting values of the model formula (hereinafter also referred to as "model") can be input. In this embodiment, the input unit 11 also receives the conditions and operation amount information of the refining process described below. Furthermore, the input unit 11 can also obtain performance information before, during, or after the refinement process begins.

The database 12 stores information on the model related to the blowing reaction in the refining equipment 2, information on the performance of the refining process, and calculation results of the in-furnace state estimation device 1. The database 12 includes, for example, a storage device such as a memory and a hard disk drive. The storage device can further store a computer program. The database 12 stores model formulas and parameters of the model formulas (hereinafter referred to as "model parameters") as information on the model related to the blowing reaction. The model parameters are calculated by the model parameter calculation unit 13. In addition, the database 12 can store various information input to the input unit 11, and calculation and analysis results in the blowing performance calculated by the in-furnace state calculation unit 15.

FIG2 is a diagram showing a structural example of the database 12. In the present embodiment, the database 12 stores conditions, performance information, and model parameters as calculation results in N times (N furnaces) of refining processing by associating them with the identification number of the furnace. N is, for example, an integer greater than 2. In the example of FIG2 , the leftmost column represents the identification number of the furnace. For example, in the Nth refining processing, the database 12 stores the performance information and model parameters in the past N-1 refining processings. In the Nth refining processing, when the model determination unit 14 determines the model parameters as follows, the information of the past N-1 refining processings stored in the database 12 is used as a backup. Furthermore, when the Nth refinement process is completed, the performance information and calculation results in the Nth refinement process are added to the database 12 (refer to the bold frame portion in FIG. 2 ). Thereafter, in the N+1th refinement process, when the model determination unit 14 determines the model parameters, the information of the past N refinement processes stored in the database 12 is used as a candidate.

The model parameter calculation unit 13, the model decision unit 14, and the furnace state calculation unit 15 include, for example, a central processing unit (CPU) or other computing device. The model parameter calculation unit 13, the model decision unit 14, and the furnace state calculation unit 15 can be realized by, for example, the computing device reading and executing a computer program. Furthermore, the model parameter calculation unit 13, the model decision unit 14, and the furnace state calculation unit 15 can have a dedicated computing device or computing circuit.

The model parameter calculation unit 13 calculates the model parameters of the model related to the blowing reaction in a manner that minimizes the balance error based on the mass balance and heat balance in the furnace, and stores them in the database 12. After a refining process is completed, the model parameter calculation unit 13 calculates the mass balance and heat balance using the performance information as the result of the refining process.

The mass 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 based on the input amount of the main raw material and auxiliary raw material into the converter 100, the oxygen supplied from the spray gun 102, and the amount of air entrained from outside the converter 100. The discharge amount of each component is calculated based on the exhaust gas flow rate and exhaust gas component concentration.

The heat balance calculation calculates the input heat and exhaust heat in the converter 100. The input heat is calculated based on the sensible heat of the main raw material charged into the converter 100, the reaction heat caused by the reaction in the furnace, the melting heat of the auxiliary raw material charged into the converter 100, etc. The exhaust heat is calculated based on the heat dissipated from the furnace surface, the radiation heat from the furnace mouth, the exhaust heat caused by the stirring gas, the slag 103 discharged to the outside of the furnace, the sensible heat of the exhaust gas, etc.

The model determination unit 14 obtains the past model parameters stored in the database 12. The model determination unit 14 uses the past model parameters to determine the model parameters to be used in the furnace state calculation unit 15, and sends them to the furnace state calculation unit 15.

The furnace state calculation unit 15 calculates (estimates) state quantities in the converter 100 including the temperature and component concentration of the melt 101 and the component concentration of the slag 103 based on the model parameters determined by the model determination unit 14, the performance information and parameter setting values collected by the input unit 11, etc. The estimated state quantities in the converter 100 are sent to the output unit 16.

The output unit 16 sends the state quantity in the converter 100 calculated by the furnace state estimation device 1 to the control terminal 10. In the refining process, various operation quantities are determined and the operation conditions are changed based on the calculation results output from the furnace state estimation device 1. In addition, the output unit 16 also has a function of sending the information calculated by the furnace state estimation device 1 to the display device 20, and can display the calculation results output from the furnace state estimation device 1.

The furnace state estimating device 1 having such a structure estimates the state quantity in the converter 100 including the temperature and component concentration in the melt 101 and the component concentration in the slag 103 with high accuracy by executing the furnace state estimating method described below. The operation of the furnace state estimating device 1 when executing the furnace state estimating method will be described below with reference to the flowchart shown in FIG.

[Method for estimating the state in the furnace] FIG. 3 is a flowchart showing the processing of the method for estimating the state in the furnace according to an embodiment of the present disclosure. The flowchart shown in FIG. 3 starts at any time before the start of the refining process. That is, at any time before the start of the refining process, the processing for estimating the state in the furnace enters the processing of step S1.

In the processing of step S1, the input unit 11 obtains the conditions of the refining treatment. In the present embodiment, the conditions of the refining treatment include the refining morphology, the predetermined amount of auxiliary raw materials to be fed, the target values of the component concentration and temperature of the melt 101 and the slag 103, the number of treatments, the date and time of treatment, the number of times the equipment including the furnace, the spray gun and the measuring machine are used, etc. The input unit 11 sends the obtained conditions of the refining treatment to the database 12 and the furnace state calculation unit 15. Thereby, the processing of step S1 is completed, and the furnace state estimation processing enters the processing of step S2. Step S1 corresponds to a part of the "input step". The data input in step S1 is used for the processing of the model determination unit 14.

In the processing of step S2, the model determination unit 14 uses the past model parameters stored in the database 12 to determine the model parameters to be used in the furnace state calculation unit 15 based on the conditions of the refining process. Step S2 corresponds to the "model determination step". In detail, the model parameters determined in the processing of step S2 can be obtained by calculation or selection based on the model parameters corresponding to the past refining processes that have been stored in the database 12. As described above, for example, in the Nth refining process, the model determination unit 14 uses the information of the past N-1 refining processes stored in the database 12 to determine the model parameters. In the case where it takes time to obtain the refinement results and the data of the past N-1 model parameters or refinement conditions are incomplete, the model parameters can be determined by extracting the performance information of the required model parameters or refinement conditions with complete data. In this case, the Nth refinement process, that is, the refinement process currently being executed, is sometimes called the refinement process of the target furnace.

The model determination unit 14 extracts model parameters whose refining process conditions are similar to the conditions of the target furnace from the model parameters stored in the database 12, and averages the extracted model parameters to determine the model parameters. The model determination unit 14 may extract only the model parameters of the most recent specified number of furnaces, that is, exclude old model parameters from the extraction targets and average them. The similarity (Ds) between the conditions in the refining process of the target furnace and the past performance can be evaluated by calculating the Euclidean distance as shown in the following formula (1), for example.

[Number 1]

Here, k is the number of refining conditions. CA _k represents the conditions in the past performance. CP _k represents the conditions in the refining of the target furnace. G _k is a parameter used to weight each refining condition. As refining conditions, for example, the refining date and time, the weight of the charged molten iron, the weight of the charged scrap, the molten iron temperature, the concentration of components represented by C, Si, Mn, and P in the molten iron, the number of times the refining furnace and the top blowing gun are used, etc. can be listed. Furthermore, for example, the melt temperature after the refining treatment performed previously and the time elapsed after the treatment, the weight and composition of the remaining slag, the input weight of each auxiliary raw material type input before the start of the refining treatment, the input weight of each waste material type, etc. can be listed. These conditions correspond to items 1 to L in the refining treatment conditions of Figure 2. Moreover, when evaluating the similarity, only the consistent performance of the shape of the refining furnace in use, the shape of the top blowing nozzle, the shape of the bottom blowing nozzle, etc. can be used as the object. Here, the similarity is not limited to the Euclidean distance shown in formula (1), and can also be evaluated by evaluating the distance between urban blocks, Minkowski distance, Mahalanobis distance, and distances between k-dimensional vectors represented by cosine similarity. Here, high similarity is synonymous with short distances between the calculated k-dimensional vectors. The extraction of past refined processing results can extract results whose calculated similarity is higher than a set threshold value, or can extract any upper arbitrary number of past results with high similarity. Moreover, as a method for extracting similar results, the following method can be used, that is, for each item of the refined processing condition k, the difference between the calculation object processing condition and the past performance condition is calculated, and k results whose differences are less than the set threshold value are extracted.

Furthermore, the model determination unit 14 can model the relationship between the model parameters stored in the database 12 and the refining process conditions including the number of times of refining process, the date and time of the refining process, the number of times the refining equipment including the furnace, the spray gun and the measuring machine is used, etc. Furthermore, the model determination unit 14 can calculate the optimal parameters according to the input values of the refining process conditions of the target furnace by model calculation. The model determination unit 14 sends the determined model parameters to the furnace state calculation unit 15. Thus, the processing of step S2 is completed, and the furnace state estimation processing enters the processing of step S3.

The processing of step S3 and step S4 starts at the start of a refining process and is repeated at any cycle in the refining process. In the processing of step S3, the input unit 11 obtains the operating quantity information of the refining process and the measurement information in the converter 100. The operating quantity information is, for example, information on operating quantities such as the height of the spray gun 102, the oxygen supply speed, the stirring gas flow rate, and the input amount of auxiliary raw materials. The measurement information is, for example, the measured values of the flow rate and component concentration of the exhaust gas. Here, the measured value is not limited to the measured value itself, but may also include the result of the analysis (analysis value). The operating quantity information and the measurement information are collected at any cycle. When there is a large time delay between the operating quantity information and the measurement information, the data is created taking this delay into consideration. Furthermore, when the measurement information contains a large amount of noise, the measurement value may be replaced by a value obtained by performing a smoothing process such as a moving average calculation. Step S3 corresponds to a part of the "input step". The data input in step S3 is used for processing in the furnace state calculation unit 15.

In the processing of step S4, the furnace state calculation unit 15 calculates the state quantity in the converter 100 using the model having the information obtained by the input unit 11 and the model parameters determined by the model determination unit 14. The state quantity may be, for example, the carbon concentration in the molten metal 101, the Fe _t O concentration in the slag 103, etc. Step S4 corresponds to the "furnace state calculation step".

The carbon concentration in the melt 101 is obtained, for example, by calculating the amount of carbon remaining in the converter 100. The amount of carbon fed into the converter 100 and the amount of carbon discharged from the converter 100 can be expressed as the following equations (2) and (3), respectively. Assuming that the amount of carbon remaining in the converter 100 obtained by subtracting the amount of carbon discharged from the amount of carbon fed is equivalent to the amount of carbon in the melt 101, the carbon concentration in the melt 101 can be calculated. Here, it is assumed that the amount of carbon fed into and out of the melt 101 is small compared to the total amount fed. In addition, unless otherwise specified, "%" and various flow rates indicate "mass % (mass %)" and the original unit of flow rate.

[Number 2]

[Number 3]

Here, C _in [%] as the input carbon amount is a value converted to the concentration in the melt 101 of the sum of the carbon amount in the main raw material and the carbon amount in the input auxiliary raw material. ρ _pig [%] is the carbon concentration in the charged molten iron. _{ρ i} ^Cscr [%] is the carbon concentration in the charged scrap (variety i). ρ _j ^Caux [%] is the carbon concentration in the charged auxiliary raw material (variety j). W _pig [t] is the weight of the charged molten iron. _{W i} ^scr [t] is the weight of the charged scrap (variety i). W _j ^aux [t] is the cumulative weight of the input auxiliary raw materials (variety j). _{W charge} [t] is the weight of the melt charged to the converter 100. The carbon concentrations ( _ρi ^Cscr , _ρj ^Caux ) in the type i of the charged waste material and the type j of the input auxiliary raw material are stored in the database 12, and the furnace state calculation unit 15 obtains information on the types used in each target furnace. _Cout [%], which is the amount of discharged carbon, is a value converted from the concentration of the amount of carbon contained in the exhaust gas to the melt 101. _VCO ^OG [ ^Nm3 /t] and _VCO2 ^OG [ ^Nm3 /t] are the cumulative flow rates of CO and _CO2 in the exhaust gas, respectively, up to the calculation time.

The _FetO concentration in the slag 103 can be calculated by assuming that the amount obtained by subtracting the amount of discharged oxygen from the amount of input oxygen is equivalent to the amount of oxygen remaining in the converter 100. For example, the amount of oxygen input into the converter 100 and the amount of oxygen discharged out of the converter 100 can be expressed as the following equations (4) and (5), respectively.

[Number 4]

[Number 5]

Here, _O2in [ ^Nm3 /t] as the input oxygen amount is the ^sum of the top blow oxygen cumulative amount _VO2 ^blow [ ^Nm3 /t] from the lance 102, the oxygen cumulative amount in the input auxiliary raw material, and the oxygen cumulative amount in the air swept into the furnace from the rotary furnace 100. _ρiOscr [%] ^{is the converted value of the oxygen content in the charged waste material (variety i). ρjOaux [} ( ^Nm3 /t)/t] is the converted value of the oxygen content in the input auxiliary raw material (variety j). The oxygen contents ( _ρiOaux , _ρjOaux ) in the variety i of ^the _charged waste material and the variety j of the input auxiliary raw material are stored in the database 12, and the furnace state calculation unit 15 obtains information on ^the variety used in each target furnace. Regarding ρ _i ^Oscr [%] and W _j ^aux [t], the analyzed value or calculated value regarding the composition and weight of the slag 103 left over from the forehearth may be included. In addition, in the calculation of the amount of oxygen input, for example, as shown in the present embodiment, when the N ₂ concentration and the Ar concentration are not obtained as exhaust gas measurements, the amount of oxygen in the air drawn in may be calculated as in the fourth term of formula (4). Here, in the fourth term of formula (4), it is assumed that the amount obtained by subtracting V bot [Nm ³ /t] as _the bottom blowing gas flow rate from V _rem ^OG [Nm ³ /t] as the amount of unanalyzed exhaust gas excluding O ₂ , _CO , and CO 2 in the exhaust gas is equivalent to N ₂ and Ar drawn in the air.

O ₂ ^out [Nm ³ /t], which is the amount of oxygen discharged, is calculated from the amount of oxygen contained in the exhaust gas. V _O2 ^OG [Nm ³ /t] is the cumulative flow rate of O ₂ in the exhaust gas up to the time of calculation. V _CO ^OG [Nm ³ /t] and V _CO2 ^OG [Nm ³ /t] are the same as in formula (3). The amount obtained by subtracting the amount of oxygen discharged from the amount of oxygen input 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 melt 101 and oxidation of iron. The oxidation amount of metal impurities is calculated using the oxidation reaction model of impurity metals in the model stored in the database 12. For example, V _{O 2} ^Si [Nm ³ /t], which is the amount of oxygen used for oxidation of Si in the melt 101 , is represented by the following formula (6).

[Number 6]

Here, ρ _pig ^Si [%] is the Si concentration in the charged molten iron. ρ _i ^Siscr [%] is the Si concentration in the charged scrap (variety i). _{ρ j} ^Siaux [%] is the Si concentration in the charged auxiliary raw material (variety j). K _Si is the oxidation reaction rate constant of Si. In addition, the amount of oxygen used for oxidation of various metal impurities such as Mn and P in the melt 101 can be calculated in the same manner as in equation (6). Here, the total amount of oxygen used for oxidation of various metal impurities such as Si, Mn, and P in the melt 101 is V _O2 ^met [Nm ³ /t]. The Fe _{t O amount in the slag 103 can be calculated by assuming that the amount of Fe t} O in the slag 103 is equivalent to the amount obtained by subtracting the amount of discharged oxygen from the amount of input oxygen and then subtracting V _O2 ^met from the obtained amount.

When one refining process (the refining process of the target furnace) is completed, the processes of step S3 and step S4 are completed (step S5 is Yes), and the furnace state estimation process enters the process of step S6. If one refining process is not completed (step S5 is No), the furnace state estimation process returns to the processes of step S3 and step S4.

In the processing of step S6, the input unit 11 obtains the result of the refining treatment as performance information. In the present embodiment, the refining treatment result includes the temperature and component concentration of the melt 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 refining treatment result in the database 12. Thereby, the processing of step S6 is completed, and the furnace state estimation processing enters the processing of step S7. Step S6 corresponds to a part of the "input step". The data input in step S6 is used for processing in the model parameter calculation unit 13.

In the processing of step S7, the model parameter calculation unit 13 calculates the model parameters of the model related to the blowing reaction in a manner that minimizes the balance error based on the mass balance and heat balance in the furnace, and stores them in the database 12. Step S7 corresponds to the "model parameter calculation step". As described above, the furnace state calculation unit 15 uses the model parameters determined by the model determination unit 14 to estimate the state quantity in the converter 100 during the refining process of the target furnace. The model parameter calculation unit 13 uses the results (performance information) of the refining process of the target furnace to correct the model parameters used by the furnace state calculation unit 15. In addition, the model parameter calculation unit 13 stores the corrected more accurate model parameters in the database 12. In other words, the model parameters stored in the database 12 in association with the target furnace are not the model parameters of the model used for calculation (estimation) by the furnace state calculation unit 15. The model parameters stored in the database 12 in association with the target furnace are the model parameters calculated (corrected) by the model parameter calculation unit 13 based on the performance information of the refining process of the target furnace.

The model parameter calculation unit 13 can calculate the correction coefficient as the model parameter. The correction coefficient may include, for example, a correction coefficient A for the measured value of the exhaust gas flow rate and a correction coefficient B for the measured value of the component concentration of the exhaust gas. The correction coefficient may include, for example, a correction coefficient ΔC for the measured value of the component concentration of the melt 101, a correction coefficient D for the measured value of the temperature of the melt 101, a constant E related to the furnace reaction yield of the charged waste material, and a constant F related to the furnace reaction yield of the charged auxiliary raw material. The correction coefficient may also include a coefficient H related to the heat rise and heat absorption accompanying various reactions in the furnace, such as the oxidation reaction of the components in the melt 101, the reduction reaction of the components in the slag 103, and the melting of the auxiliary raw material. Furthermore, the correction coefficient may include a coefficient I related to heat loss, such as the sensible heat of the gas and slag 103, and the heat dissipated from the furnace mouth and the furnace body.

The model parameter calculation unit 13 can, for example, add the coefficients described above as variables of the evaluation function such as equation (7) to obtain the model parameters that minimize the evaluation function. Here, in this embodiment, the model parameter calculation unit 13 minimizes the evaluation function, but an evaluation function that is maximized under appropriate model parameters can also be used. That is, the model parameter calculation unit 13 can obtain the model parameters that minimize or maximize the evaluation function.

[Number 7]

C _in is the cumulative amount of carbon input into the converter 100 during a specific period. C _out is the cumulative amount of carbon discharged to the outside of the converter 100 due to exhaust gas, etc. during a specific period. O ₂ ⁱⁿ is the amount of oxygen input into the converter 100. O ₂ ^out is the amount of oxygen discharged to the outside of the converter 100 due to the discharge of exhaust gas and slag 103, etc. V _O2 ^met is the amount of oxygen used for oxidation of various metal impurities in the melt 101 such as Si, Mn, and P. ΔT is the cumulative amount of the amount of change in melt temperature caused by heat change in the converter 100 including reaction heat generated by the reaction in the converter 100, exhaust heat caused by exhaust gas and slag 103 discharged to the outside of the furnace, heat dissipated from the furnace body, and radiation heat dissipated from the furnace mouth during a specific period. T _ini is a measured value of the temperature of the melt 101 at the start of a specific period in the refining process. [C], V _O2 ^FetO , and T _fin are respectively a measured value of the carbon amount in the melt 101 at the end of a specific period in the refining process, an amount of oxygen used for Fe _t O generation calculated from a measured value of Fe _t O in the slag 103, and a measured value of the temperature of the melt 101. σ _C ² , σ _O ² , and σ _T ² are constants that can be set arbitrarily. A to I and ΔC correspond to the first parameter to the Kth parameter in FIG. 2 . In the present embodiment, the model parameters include a coefficient or a constant term for correcting at least one of the cumulative amount and the oxygen amount used in equation (7).

Here, _ΔRm is the reaction amount of various reactions m in the furnace, such as oxidation reaction of components in the melt 101, reduction reaction of components in the slag 103, melting of auxiliary raw materials, etc. _ΔLn is the heat loss amount of heat loss path n in the melt 101, such as sensible heat of gas and slag 103, heat dissipation from the furnace mouth and furnace body.

The evaluation function J shown in formula (7) is a weighted sum of the following three terms. In the evaluation function J, the first and second terms are terms representing mass balance errors, and the third term is a term representing heat balance errors. 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 carbon amount in the melt 101. If this term is 0, it means that the carbon balance is maintained in the converter 100. The second term is the square value of the difference between the amount obtained by subtracting the amount of oxygen discharged and the amount of oxygen used for oxidation of impurities from the amount of oxygen input and the amount of oxygen used for oxidation of iron in the melt 101 calculated based on the _FetO measured value in the slag 103. If this term is 0, it means that the oxygen balance is maintained in the converter 100. The third term is the square value of the difference between the measured value of the temperature change of the melt 101 from the start point to the end point of the specific period in the refining process and the calculated value of the temperature change of the melt 101 calculated based on the reaction heat and exhaust heat in the converter 100. This term is close to 0, which means that the heat balance is maintained in the converter 100. The "specific period" in the description of the above formula (7) can be set to a different period in each of the three terms.

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 planning problem of minimizing the evaluation function J under constraints, and the calculation of the model parameters can be performed by a known method.

The model parameters calculated by the model parameter calculation unit 13 are stored in the database 12 and used for the furnace state estimation process in the next refining process. Thus, the process of step S7 is completed, and the 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 method for estimating the state in the furnace, the operation amount is determined, and the refining operation is performed to produce good molten steel. At least one of the optimal flow rate and speed of top blowing oxygen, the height of the top blowing lance, the flow rate of bottom blowing gas, the amount and timing of adding auxiliary raw materials such as lime and iron ore, the timing of sampling the molten metal, and the timing of ending the blowing is determined as the operation amount. In this way, based on the state in the furnace calculated by the method for estimating the state in the furnace, a good method for producing molten steel can be realized.

As described above, the furnace state estimation device 1, the furnace state estimation method, and the molten steel manufacturing method of the present embodiment optimize the model parameters based on the evaluation function including the terms representing the mass balance error and the heat balance error in the furnace by the above-mentioned structure and process, and store them in the database 12. In addition, in the estimation of the furnace state in the refining process, the past optimized model parameters stored in the database 12 can be used, so the estimation accuracy of the temperature and component concentration of the melt 101 and the component concentration of the slag 103 can be improved.

In the embodiments of the present disclosure, the description is based on various figures and embodiments, but it should be noted that the industry can easily make various modifications or corrections based on the present disclosure. Therefore, it should be noted that these modifications or corrections are included in the scope of the present disclosure. For example, the functions included in each structural part or each step can be reconfigured in a logically non-contradictory manner, and multiple structural parts or steps can be combined into one, or divided. The embodiments of the present disclosure can also be implemented as a program executed by a processor included in the device or a storage medium that records the program. It should be understood that these are also included in the scope of the present disclosure.

For example, the type of model parameters to be determined, the number of model parameters, and the form of the evaluation function to be minimized are not limited to the examples listed in the above embodiment. As long as the form can minimize the mass balance error and heat balance error in the furnace, the same effect can be exerted. In addition, the model is not limited to the examples shown in the above embodiment such as equations (2) to (6), and a melt temperature estimation model, a waste material melting model, an auxiliary raw material melting, a yield model, a decarburization efficiency model, a dephosphorization model, a _FetO generation reduction model, etc. can also be used. In addition, in the present embodiment, the furnace state estimation device 1 and the furnace state estimation method for the converter 100 are shown, but the model parameter calculation based on the mass balance and heat balance in the furnace is also effective in the secondary refining equipment or the pretreatment equipment.

1: Furnace state estimation device 2: Refining equipment 10: Control terminal 11: Input unit 12: Database 13: Model parameter calculation unit 14: Model determination unit 15: Furnace state calculation unit 16: Output unit 20: Display device 100: Converter 101: Melt 102: Spray gun 103: Slag 104: Pipeline 105: Exhaust gas detection unit 106: Vent hole 107: Flow meter

FIG1 is a schematic diagram showing the structure of a furnace state estimation device as an embodiment of the present disclosure. FIG2 is a diagram showing an example of the structure of a database. FIG3 is a flow chart showing the processing of a furnace state estimation method as an embodiment of the present disclosure.

1: Furnace status estimation device

2: Refining equipment

10: Control terminal

11: Input section

12: Database

13: Model parameter calculation department

14: Model determination department

15: Furnace status calculation unit

16: Output section

20: Display device

100: Converter

101: Melt

102: Spray gun

103: Slag

104: Pipeline

105: Exhaust gas detection department

106: Ventilation hole

107: Flow meter

Claims (10)

A furnace state estimation device comprises: an input unit to which performance information and refining treatment conditions are input, wherein the performance information includes measurement results of the temperature and component concentration of the melt and the component concentration of the slag in the refining equipment before the start of the refining treatment or during the treatment, and measurement results related to the refining equipment including the flow rate and component concentration of exhaust gas discharged from the refining equipment; and a model determination unit, which uses model parameters of a model related to a blowing reaction in the refining equipment, the performance information, and past model parameters obtained from a database storing the performance information and the refining treatment conditions. The model parameters in the refining process of the target furnace are determined; the furnace state calculation unit uses the determined model parameters to calculate the state quantity in the furnace including the temperature and component concentration of the molten metal and the component concentration of the slag; and the model parameter calculation unit uses the performance information including the result of the refining process of the target furnace to calculate the model parameters in the refining process of the target furnace based on an evaluation function including terms representing the mass balance error and heat balance error in the furnace from the start point to the end point of the specific period in the refining process. The furnace state estimation device as described in claim 1, wherein the model determination unit determines the model parameters in the refining process of the target furnace by averaging the model parameters of the past refining process that are similar to the conditions of the refining process of the target furnace among the past model parameters stored in the database. The furnace state estimation device as described in claim 1, wherein the model determination unit models the relationship between the past model parameters stored in the database and the refining process conditions including at least one of the number of times the refining process is performed, the processing date and time, and the number of times the refining equipment is used, and determines the parameters in the refining process of the target furnace according to the model. A furnace state estimation device as described in any one of claim 1 to claim 3, wherein the model parameter includes a coefficient or constant term for correcting at least one of the cumulative amount of carbon input into the furnace during a specific period, the cumulative amount of carbon discharged out of the furnace during a specific period, the amount of oxygen input into the furnace, the amount of oxygen discharged out of the furnace, the amount of oxygen used to oxidize various metal impurities in the molten metal, and the cumulative amount of the temperature change of the molten metal caused by the heat change in the furnace during a specific period. A furnace state estimation device as described in any one of claim 1 to claim 3, wherein the evaluation function includes a weighted sum of a term representing carbon balance, a term representing oxygen balance, and a term representing heat balance. A furnace state estimation device as described in claim 4, wherein the evaluation function includes a weighted sum of a term representing carbon balance, a term representing oxygen balance, and a term representing heat balance. A method for estimating a state in a furnace is performed by a device for estimating a state in a furnace. The method comprises: an input step of inputting performance information and refining treatment conditions, wherein the performance information includes measurement results of the temperature and component concentration of the melt and the component concentration of the slag in the refining equipment before or during the refining treatment, and measurement results of the refining equipment including the flow rate and component concentration of exhaust gas discharged from the refining equipment; a model determination step of obtaining a model parameter of a model related to the blowing reaction in the refining equipment, the performance information and the database of the refining treatment conditions; The model parameters in the refining process of the target furnace are determined by taking the model parameters of the past; a furnace state calculation step is to calculate the state quantity in the furnace including the temperature and component concentration of the molten metal and the component concentration of the slag using the determined model parameters; and a model parameter calculation step is to use the performance information including the result of the refining process of the target furnace to calculate the model parameters in the refining process of the target furnace based on an evaluation function including terms representing the mass balance error and heat balance error in the furnace from the start point to the end point of the specific period in the refining process. A method for estimating the state of a furnace as described in claim 7, wherein the model determination step determines the model parameters in the refining process of the target furnace by averaging the model parameters of the past refining process that are similar to the conditions of the refining process of the target furnace among the past model parameters stored in the database. The method for estimating the state of a furnace as described in claim 7, wherein the model determination step models the relationship between the past model parameters stored in the database and the refining process conditions including at least one of the number of times the refining process is performed, the processing date and time, and the number of times the refining equipment is used, and determines the parameters in the refining process of the target furnace according to the model. A method for producing molten steel, based on the temperature and component concentration of the molten steel and the component concentration of the slag estimated by the furnace state estimation method as described in any one of claim 7 to claim 9, determines at least one of the flow rate and speed of top blowing oxygen, the height of the top blowing lance, the flow rate of bottom blowing gas, the amount and timing of adding auxiliary raw materials including at least one selected from the group consisting of lime and iron ore, the timing of sampling the molten steel, and the timing of ending blowing, and performs refining operation to produce molten steel.