TW202321468A - Intra-furnace state inference device, intra-furnace state inference method, and molten steel manufacturing method - Google Patents

Abstract

An intra-furnace state inference device (1) comprises: an input unit (11) to which past record information and a refining process condition are inputted; a model determination unit (14) for, by using a past model parameter acquired from a database in which a model parameter of a model concerning blowing reaction, past record information, and a refining process condition are stored, determining a model parameter in a refining process for a target charge; and an intra-furnace state calculation unit (15) for, by using the determined model parameter, calculating an intra-furnace state amount that includes the temperature and component concentration of molten metal and component concentration of slug; and a model parameter calculation unit (13) for, by using the past record information including a result of the refining process of the target charge, calculating a model parameter in the refining process for the target charge on the basis of an evaluation function that includes terms indicating a material balance error and heat balance error in a furnace from a start point to an end point of a specific period in the refining process.

Description

Device for estimating state in furnace, method for estimating state in furnace, and method for manufacturing molten steel

The 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 component concentrations in molten metal and slag in refining facilities in the iron and steel industry.

In ironworks, the composition and temperature of molten iron flowing out of the blast furnace are adjusted in refining facilities such as pretreatment facilities, converters, and secondary refining facilities. The converter is a process that removes impurities in the melt and raises the temperature by blowing oxygen into the furnace. It plays a very important role in the quality control of steel and the rationalization of refining costs. Here, in the control of the melt composition and melt temperature in the converter, for example, the flow rate and speed of top-blown oxygen, the height of the top-blown lance, the flow rate of bottom-blown gas, etc. are used as operating variables. In addition, the input amount and input timing of auxiliary raw materials such as lime and iron ore, the timing of sampling the melt, the timing of finishing blowing, and the like are used as operation quantities. These operating quantities should be optimized according to the state of the furnace such as the temperature of the molten metal and the components of the molten metal and slag. In order to estimate the state of the furnace with high precision and adapt the operating volume, a method is proposed that uses measurement information about the refining facility, including exhaust gas measurement values that can be continuously measured during the refining process, to perform mass balance in the furnace and Calculation of thermal budget. This method can usually estimate the melt temperature, melt composition 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 refinery environment, such as loss of refractories in the furnace in the refinery, changes in the composition of auxiliary raw materials to be fed, and a decrease in measurement accuracy.

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

Furthermore, for example, Patent Document 2 proposes a method of determining a plurality of parameters of a melt temperature estimation model in a secondary refining facility by obtaining an approximate solution of simultaneous equations set in a heat budget balance. [Prior art literature] [Patent Document]

Patent Document 1: Japanese Patent Laid-Open No. 2005-036289 Patent Document 2: Japanese Patent Laid-Open No. 2004-360044

[Problem to be Solved by the Invention] Here, Patent Document 1 targets an independent physical reaction model or a primary binding model in a model form. Therefore, it is difficult to apply the technique of Patent Document 1 to a model in which the amount of reaction in the furnace and the amount of temperature rise interact in a complex manner, such as a state estimation model based on the mass balance and heat balance calculation in the furnace.

Patent Document 2 proposes a method of obtaining an approximate solution of simultaneous equations set so as to balance the heat budget with respect to a temperature estimation model equation in which a plurality of model equations interact with parameters. However, Patent Document 2 does not describe the calculation of the mass balance in the furnace using measurement information including the measured value of exhaust gas in the calculation of the amount of reaction in the furnace. Therefore, it is difficult to apply the technique of patent document 2 to the model which interacts with mass balance in a furnace in addition to heat balance.

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

An object of the present disclosure made in view of the above circumstances is to provide a furnace state estimation device, a furnace state estimation method, and a molten steel manufacturing method capable of accurately and continuously estimating component concentrations in molten metal and slag. [Means to solve the problem]

(1) A furnace state estimation device according to an embodiment of the present disclosure includes: The input unit is configured to input actual performance information including measurement results of molten temperature, component concentration, and slag component concentration in the refining facility before or during the refining process, and conditions of the refining process, and the conditions of the refining process. the results of refinery-related measurements of the flow rate and component concentrations of the off-gases from said refinery; The model determining unit determines the target furnace using the past model parameters obtained from a database storing model parameters related to the blowing reaction in the refining facility, the actual performance information, and the refining process conditions. the model parameters in the refining process of times; A furnace state calculation unit calculates a state quantity in the furnace including the temperature and component concentration of the molten metal and the component concentration of the molten slag by using the determined model parameters; and The model parameter calculation unit uses the actual performance information including the refining process result of the target heat, based on a mass balance error in the furnace representing a mass balance error in the refining process from a start point to an end point of a specific period in the refining process. and an evaluation function of a term of a heat balance error, and calculate the model parameters in the refining process of the target heat.

(2) As one embodiment of the present disclosure, in (1), The model determination unit performs the process by storing the model parameters of the past refining process similar to the refining process conditions of the target heat, among the past model parameters stored in the database. The model parameters in the refining process of the target heat are determined on average.

(3) As one embodiment of the present disclosure, in (1), The model determining unit stores a relationship between the past model parameters stored in the database and refining processing conditions including at least one of the number of times of processing in the refining process, the date and time of processing, and the number of times of use of refining facilities. Modeling is performed, and the parameters in the refining process of the target heat are determined based on the model.

(4) As an embodiment of the present disclosure, in any one of (1) to (3), The model parameters include the cumulative amount of carbon charged into the furnace for a specific period, the cumulative amount of carbon discharged outside the furnace for a specific period, the amount of oxygen charged into the furnace, and the amount of carbon discharged to the furnace. At least one of the amount of oxygen outside the furnace, the amount of oxygen used to oxidize various metal impurities in the melt, and the cumulative amount of the melt temperature change caused by the heat change in the furnace for a specific period is corrected. Coefficient or constant term.

(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 the carbon balance, a term representing the oxygen balance, and a term representing the heat balance.

(6) Regarding the furnace state estimation method according to one embodiment of the present disclosure, is performed by the furnace state estimation device, and the furnace state estimation method includes: An input step of inputting actual performance information and refining treatment conditions, the actual performance information including the temperature and component concentration of the molten metal before or during the refining treatment in the refining facility, and the measurement results of the component concentration of the molten slag, and the results of refinery-related measurements of the flow rate and component concentrations of the off-gases exiting said refinery; In the model determining step, the target furnace is determined using the past model parameters acquired from a database storing model parameters related to the blowing reaction in the refining facility, the actual performance information, and the refining treatment conditions. the model parameters in the refining process of times; a step of calculating the state in the furnace, using the determined model parameters, to calculate a state quantity in the furnace including the temperature and component concentration of the molten metal and the component concentration of the molten slag; and The model parameter calculation step is based on using the actual performance information including the refining process result of the target heat, based on including a mass balance error in the furnace indicating from a start point to an end point of a specific period in the refining process and an evaluation function of a term of a heat balance error, and calculate the model parameters in the refining process of the target heat.

(7) As an embodiment of the present disclosure, in (6), The model determining step is performed by storing the model parameters of the past refining process similar to the refining process conditions of the target heat, among the past model parameters stored in the database. The model parameters in the refining process of the target heat are determined on average.

(8) As an embodiment of the present disclosure, in (6), In the model determining step, the relationship between the past model parameters stored in the database and refining processing conditions including at least one of the number of times of processing in the refining process, the date and time of processing, and the number of times of use of refining equipment Modeling is performed, and the parameters in the refining process of the target heat are determined based on the model.

(9) Regarding the molten steel manufacturing method of one embodiment of the present disclosure, Based on the temperature and component concentration of the melt and the component concentration of the molten slag estimated by the method of estimating the state in the furnace as in any one of (6) to (8), the flow rate and speed of the top-blown oxygen, and the top-blown oxygen are determined. At least one of the height of the blowing lance, the flow rate of bottom blowing gas, the input amount and input timing of auxiliary raw materials such as lime and iron ore, the timing of sampling the melt, and the timing of ending the blowing are used to carry out the refining operation, to make molten steel. [Effect of the invention]

According to the present disclosure, it is possible to provide a furnace state estimation device, a furnace state estimation method, and a molten steel manufacturing method capable of accurately and continuously estimating the concentration of components in molten metal and slag.

Hereinafter, a furnace state estimation device, a furnace state estimation 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 configuration of a furnace state estimation device 1 according to an embodiment of the present disclosure. In the present embodiment, the furnace state estimation device 1 is used as a part of facilities for producing molten steel in the iron and steel industry. The facility for producing molten steel includes a refining facility 2 and a blowing control system including a furnace state estimation device 1 .

As shown in FIG. 1 , the refining equipment 2 includes a converter 100 , a spray gun 102 and a pipeline 104 . The spray gun 102 is arranged on the melt 101 in the converter 100 . High-pressure oxygen is sprayed from the tip of the spray gun 102 toward the melt 101 below. The impurities in the melt 101 are oxidized by the high-pressure oxygen and sent into the slag 103 (refining treatment). The pipe 104 is a flue device for flue gas conduction, and is arranged on the upper part of the converter 100 .

An exhaust gas detection unit 105 is disposed inside the duct 104 . The exhaust gas detector 105 detects the flow rate and component concentration (for example, the concentration of CO, CO ₂ , O ₂ , N ₂ , Ar, etc.) of the exhaust gas discharged along with the refining process. As exhaust gas measurement, the exhaust gas detector 105 measures the flow rate of the exhaust gas in the duct 104 based on, for example, the differential pressure across 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, at 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 .

Stirring gas is blown into the melt 101 in the converter 100 through vent holes 106 formed in the bottom of the converter 100 . The stirring gas is an inert gas such as Ar. The blown stirring gas stirs the melt 101 to promote 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 concentrations of the melt 101 were analyzed immediately before blowing and after blowing. Moreover, the temperature and component concentration of the melt 101 are measured one or more times during the blowing process, and the supply amount (oxygen supply amount) and speed (oxygen supply rate) of high pressure oxygen and the stirring gas are determined based on the measured temperature and component concentration. The flow rate (stirring gas flow rate), etc.

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 rate, and stirring gas flow rate so that the temperature and component concentration of the melt 101 fall within a desired range, and collects actual performance value data of the oxygen supply amount, oxygen supply rate, and stirring gas flow rate. The display device 20 may include, for example, a liquid crystal display (Liquid Crystal Display, LCD) or a cathode ray tube (Cathode Ray Tube, CRT) display. The display device 20 can display calculation results output from the furnace state estimating device 1 and the like.

The furnace state estimation device 1 is a device for estimating the temperature and component concentration of the melt 101 processed by the refining facility 2 and the component concentration of the molten slag 103 . 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 facility 2 , that is, performance information (performance data), and the like. The input unit 11 can 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 actual performance information, parameter setting values, and the like from the outside, and writes the information into the database 12 and transmits the information to the furnace state calculation unit 15 . Performance information is input to the input unit 11 from the control terminal 10 . The actual 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 input amount of raw materials (main raw materials, auxiliary raw materials), melting The temperature and component concentration of the liquid 101 and the component concentration of the molten slag 103 and the like. These pieces of information correspond to item 1 to item M in the performance information of FIG. 2 described below. Furthermore, in the input unit 11 , for example, an operator of the refining facility 2 can manually input data (manual input). By manual input, it is possible to input parameter setting values of a model formula (hereinafter also referred to simply as "model"). In the present embodiment, the input unit 11 also receives conditions and operation amount information of refining processing described below. Furthermore, the input unit 11 may acquire performance information and the like before the refinement process starts, during the process, or after the process ends.

The database 12 stores model information related to blowing reactions in the refining facility 2 , actual performance information of refining processes, and calculation results of the furnace state estimating device 1 . The database 12 includes storage devices such as a memory and a hard disk drive, for example. The memory device can further store computer programs. The database 12 stores model formulas and model formula parameters (hereinafter referred to as "model parameters") as information of the model related to the blowing reaction. The model parameters are calculated by the model parameter calculation unit 13 . In addition, various information input to the input unit 11 , calculation and analysis results in actual blowing results calculated by the furnace state calculation unit 15 can be stored in the database 12 .

FIG. 2 is a diagram showing a configuration example of the database 12 . In this embodiment, the database 12 associates and memorizes the conditions in N times (N heat) refining processes, the actual performance information, and the model parameters which are calculation results, and the identification number of the heat. N is, for example, an integer of 2 or more. In the example of FIG. 2, the leftmost column shows the identification number of a heat. For example, in the N-time refining process, the database 12 memorizes the actual performance information and model parameters in the past N-1 refining processes. In the N-time refining process, when the model determination unit 14 determines the model parameters as follows, the information of the past N-1 refining processes stored in the database 12 is used as a candidate. Then, when the N-th refinement process ends, the actual performance information and calculation results in the N-th refinement process are added to the database 12 (see the thick framed portion in FIG. 2 ). Thereafter, in the N+1th refinement process, when the model determination unit 14 determines 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 determination unit 14 , and the furnace state calculation unit 15 include, for example, arithmetic processing devices such as a central processing unit (Central Processing Unit, CPU). The model parameter calculation unit 13, the model determination unit 14, and the furnace state calculation unit 15 can be realized by, for example, reading and executing a computer program by an arithmetic processing device. Moreover, the model parameter calculation part 13, the model determination part 14, and the furnace state calculation part 15 may have a dedicated calculation device or a calculation circuit.

Based on the mass balance and heat balance in the furnace, the model parameter calculation unit 13 calculates the model parameters of the model related to the blowing reaction so that the balance error is minimized, and stores them in the database 12 . The model parameter calculation unit 13 calculates the mass balance and heat balance by using the actual performance information as a result of the refining process after the primary refining process is completed.

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 was calculated based on the input amount of the main raw material and the auxiliary raw material to the converter 100 , the oxygen supply from the lance 102 , and the amount of air entrainment from outside the converter 100 . The discharge amount of each component is calculated based on the exhaust gas flow rate and the exhaust gas component concentration.

The heat balance calculation calculates the input heat and the exhaust heat in the furnace of the converter 100 . The heat input is calculated from the sensible heat of the main raw material charged into the converter 100 , the reaction heat due to the reaction in the furnace, the fusion heat of the auxiliary raw material charged into the converter 100 , and the like. The exhaust heat is calculated from heat radiation from the furnace body surface, radiant heat from the furnace mouth, exhaust heat caused by stirring gas, molten slag 103 discharged outside the furnace, sensible heat of exhaust gas, and the like.

The model determination unit 14 acquires past model parameters stored in the database 12 . The model determination unit 14 determines model parameters to be used in the furnace state calculation unit 15 using past model parameters, and sends the model parameters to the furnace state calculation unit 15 .

The furnace state calculation unit 15 calculates (estimates) the temperature and component concentration of the molten metal 101 and the composition of the slag 103 based on the model parameters determined by the model determination unit 14 , the actual performance information collected by the input unit 11 and parameter setting values, etc. A state quantity inside the converter 100 of the concentration. The estimated state quantity in the converter 100 is sent to the output unit 16 .

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

The furnace state estimating device 1 having such a structure estimates the temperature and component concentration in the molten metal 101 and the component concentration in the molten slag 103 with high accuracy by executing the processing of the furnace state estimating method described below. The state quantity within 100. Hereinafter, the operation of the furnace state estimation device 1 when executing the furnace state estimation method will be described with reference to the flowchart shown in FIG. 3 .

[How to estimate the state in the furnace] FIG. 3 is a flowchart showing processing of a method for estimating a state in a furnace according to an embodiment of the present disclosure. The flowchart shown in FIG. 3 starts at an arbitrary timing before the refining process starts. That is, at an arbitrary timing before the start of the refining process, the furnace state estimation process proceeds to the process of step S1.

In the process of step S1, the input unit 11 acquires the conditions of the refining process. In this embodiment, the conditions of the refining treatment include the refining form, the predetermined amount of auxiliary raw materials to be added, the target values of the component concentrations and temperatures of the molten metal 101 and the slag 103, the number of times of treatment, the date and time of the treatment, including furnaces, spray guns, and measuring equipment. The number of times the equipment is used, etc. The input unit 11 transmits the obtained refining process conditions to the database 12 and the furnace state calculation unit 15 . Thereby, the process of step S1 is completed, and the furnace state estimation process proceeds to the process of step S2. Step S1 corresponds to a part of the "input step". The data input in step S1 are used in the processing of the model determination unit 14 .

In the process of step S2 , the model determination unit 14 determines model parameters to be used by the furnace state calculation unit 15 based on the conditions of the refining process using the past model parameters stored in the database 12 . Step S2 corresponds to the "model determination step". Specifically, the model parameters determined in the process of step S2 can be obtained by calculation or selection based on model parameters corresponding to past refining processes already stored in the database 12 . As described above, for example, in the Nth refining process, the model determination unit 14 determines model parameters using the information of the past N−1 refining processes stored in the database 12 . When it takes time to obtain the refining process results and other past N-1 model parameters or refining process condition data are incomplete, the model parameters can be determined by extracting the required model parameters or refining process condition data from the actual performance information . The Nth refining process in this example, that is, the currently executing refining process may be referred to as the refining process of the target heat.

The model determining unit 14 determines, for example, by extracting model parameters having refining processing conditions similar to those of the target heat among model parameters stored in the database 12 , and averaging the extracted model parameters. The model determination unit 14 may extract only model parameters in the latest predetermined number of heats, that is, exclude old model parameters from extraction targets, and perform averaging. The degree of similarity (Ds) between the conditions in the refining process of the target heat and the past performance can be evaluated by calculating the Euclidean distance as shown in the following formula (1), for example.

[number 1]

Among them, k is the condition number of the refining process. CA _k represents a condition in the past results. CP _k represents the conditions in the refining process of the target heat. G _k is a parameter for weighting each refining treatment condition. Refining treatment conditions include, for example, the date and time of refining treatment, the weight of charged molten iron, the weight of charged scrap, molten iron temperature, the concentration of components represented by C, Si, Mn, and P in molten iron, refining furnace and top blowing. The number of times the spray gun is used, etc. In addition, for example, the melt temperature after the treatment and the elapsed time from the treatment in the refining treatment carried out before, the weight and composition of the remaining slag, the input weight of each auxiliary raw material type input before the refining treatment, and each 1. Input weight of waste types, etc. These conditions correspond to item 1 to item L in the refining treatment conditions of FIG. 2 . In addition, when evaluating the similarity, it is possible to target only the actual results that match the form of the refining furnace in use, the form of the top-blowing lance, and the form of the bottom-blowing nozzle. Here, the similarity is not limited to the Euclidean distance shown in formula (1), but can also be performed by evaluating the distance between city block distances, Mingshi distance, Mahalanobis distance, and k-dimensional vectors represented by cosine similarity evaluate. Here, a high similarity is synonymous with a short distance between the calculated k-dimensional vectors. Extraction of the past results of refining processing can extract the results whose calculated similarity is higher than the set threshold value, or can extract the past results of an upper arbitrary number with a higher similarity. In addition, as a method of extracting similar actual results, a method may be used in which, with respect to each item of condition k of the refining process, the difference between the calculation target processing condition and the past actual performance condition is calculated, and k differences are each smaller than a set threshold value. performance.

Furthermore, the model determination unit 14 can establish the relationship between the model parameters stored in the database 12 and the refining treatment conditions including the number of times of treatment in the refining treatment, the date and time of the treatment, the number of times of use of refining equipment including furnaces, spray guns, and measuring equipment, etc. Do the modeling. In addition, the model determination unit 14 may calculate the optimum parameters from the input value of the refining process condition of the target heat by model calculation. The model determination unit 14 sends the determined model parameters to the furnace state calculation unit 15 . Thereby, the process of step S2 is completed, and the furnace state estimation process proceeds to the process of step S3.

The processing of step S3 and step S4 is started at the timing of the start of one refining process, and is repeatedly performed at an arbitrary cycle during the refining process. In the process of step S3 , the input unit 11 acquires the operation amount information of the refining process and the measurement information in the converter 100 . The operation amount information is, for example, the height of the spray gun 102 , the speed of oxygen supply, the flow rate of the stirring gas, the input amount of auxiliary raw materials and other operation amount information. The measurement information is, for example, measured values such as 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 an analysis result (analysis value). Operation amount information and measurement information are collected at any period. When there is a large time delay between operation amount information and measurement information, data is created taking this delay into account. Furthermore, when the measurement information contains a lot of noise, the measurement value can be replaced with a value obtained by performing smoothing processing such as moving average calculation. Step S3 corresponds to a part of the "input step". The data input in step S3 are used for processing by the furnace state calculation unit 15 .

In the process of step S4 , the furnace state calculation unit 15 calculates the state quantity in the converter 100 using a model having the information acquired by the input unit 11 and the model parameters determined by the model determination unit 14 . The state quantities include, for example, the carbon concentration in the melt 101 , the Fe _t O concentration in the slag 103 , and the like. 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 charged into the converter 100 and the amount of carbon discharged out of the converter 100 can be represented by Equation (2) and Equation (3) shown below, respectively. Assuming that the amount of carbon remaining in the converter 100 obtained by subtracting the amount of discharged carbon from the amount of input carbon 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 entering and leaving the melt 101 is small compared to the total charge amount. Moreover, unless otherwise specified, "%" and various flow rates represent "mass%" and the original unit of flow rate.

[number 2]

[number 3]

Here, C _in [%], which is the amount of carbon input, is the concentration conversion value in the melt 101 of the sum of the amount of carbon in the main raw material and the amount of carbon in the auxiliary raw material. ρ _pig [%] is the carbon concentration charged into the molten iron. ρ _i ^Cscr [%] is the carbon concentration charged into the waste (type i). ρ _j ^Caux [%] is the carbon concentration input into the auxiliary raw material (species j). W _pig [t] is the weight of molten iron loaded. W _i ^scr [t] is the weight of loaded waste (species i). W _j ^aux [t] is the cumulative weight of input auxiliary raw materials (species j). W _charge [t] is the weight of the melt charged into the converter 100 . The carbon concentration (ρ _i ^Cscr , ρ _j ^Caux ) in the type i of the waste material and the type j of the auxiliary raw material input is stored in the database 12, and the furnace state calculation unit 15 acquires information on the type used for each target heat. C _out [%], which is the amount of discharged carbon, is a concentration-converted value of the amount of carbon contained in the exhaust gas in the melt 101 . V _CO ^OG [Nm ³ /t] and V _CO2 ^OG [Nm ³ /t] are the cumulative flow rates up to the time of calculation of CO and CO ₂ in the exhaust gas, respectively.

The Fe _t O concentration in the molten slag 103 can be calculated assuming that the amount obtained by subtracting the amount of oxygen discharged from the amount of input oxygen corresponds to the amount of oxygen remaining in the converter 100 . For example, the amount of oxygen charged into the converter 100 and the amount of oxygen discharged to the outside of the converter 100 can be represented by the following formulas (4) and (5), respectively.

[number 4]

[number 5]

Here, O ₂ ⁱⁿ [Nm ³ /t] as the amount of oxygen input is the cumulative amount of top-blown oxygen V _O2 ^blow [Nm ³ /t] from the spray gun 102, the cumulative amount of oxygen input into the auxiliary raw material, and the amount of oxygen from the rotary furnace 100. The sum of the cumulative amount of oxygen in the air drawn into the furnace. ρ _i ^Oscr [%] is a conversion value of the oxygen content charged into the waste material (type i). ρ _j ^Oaux [(Nm ³ /t)/t] is the conversion value of the oxygen content in the auxiliary raw material (type j) input. The oxygen content (ρ _i ^Oaux , ρ _j ^Oaux ) in the type i of the waste material and the type j of the input auxiliary raw material is stored in the database 12, and the furnace state calculation unit 15 acquires information on the type used for each target heat. Regarding ρ _i ^Oscr [%] and W _j ^aux [t], analytical values or calculated values regarding the composition and weight of the molten slag 103 left from the previous heat may be included. Moreover, in the calculation of the input oxygen amount, for example, as shown in this example, when the _N2 concentration and Ar concentration are not obtained as exhaust gas measurement, the oxygen amount in the air involved can be calculated according to the formula (4) Calculated in the manner of the fourth term. Here, in the fourth term of Equation (4), it is assumed that V _rem ^OG [Nm ³ /t], which is the amount of unanalyzed exhaust gas in the exhaust gas except for O ₂ , CO, and CO ₂ , is subtracted as the bottom blowing gas flow The amount obtained from V _bot [Nm ³ /t] is equivalent to N ₂ and Ar involved in the air.

O ₂ ^out [Nm ³ /t], which is the amount of exhausted oxygen, is calculated from the amount of oxygen contained in the exhaust gas. V _O2 ^OG [Nm ³ /t] is the cumulative flow rate up to the calculation time of O ₂ in the exhaust gas. V _CO ^OG [Nm ³ /t] and V _CO2 ^OG [Nm ³ /t] are the same as the formula (3). The amount of oxygen remaining in the converter 100 is the amount obtained by subtracting the amount of discharged oxygen from the amount of input oxygen. 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. Here, the oxidation amount of metal impurities is calculated using the oxidation reaction model of impurity metals stored in the model in the database 12 . For example, V _O2 ^Si [Nm ³ /t], which is the amount of oxygen used for Si oxidation in the melt 101 , is represented by the following formula (6).

[number 6]

Here, ρ _pig ^Si [%] is the concentration of Si charged into the molten iron. ρ _i ^Siscr [%] is the Si concentration charged into the scrap (type i). ρ _j ^Siaux [%] is the Si concentration in the auxiliary raw material (species j). K _Si is the oxidation reaction rate constant of Si. Furthermore, similarly to the formula (6), the amount of oxygen used for oxidation of various metal impurities in the molten metal 101 such as Mn and P can be calculated. Here, the total amount of oxygen used for oxidation of various metal impurities in the melt 101 such as Si, Mn, and P is V _O2 ^met [Nm ³ /t]. The amount of Fe _t O in the slag 103 can be calculated by assuming that the amount of oxygen discharged is subtracted from the amount of input oxygen, and V _O2 ^met is subtracted from the obtained amount.

When one refining process (refining process of the target furnace) is completed, the processes in steps S3 and S4 are terminated (Yes in step S5 ), and the furnace state estimation process proceeds to the process in step S6 . When the primary refining process has not been completed (No (No) in step S5), the furnace state estimation process returns to the processes in steps S3 and S4.

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

In the process of step S7 , the model parameter calculation unit 13 calculates model parameters of a model related to the blowing reaction to minimize the balance error based on the mass balance and heat balance in the furnace, and stores the model parameters 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 heat. The model parameter calculation unit 13 corrects the model parameters used by the furnace state calculation unit 15 using the results of the refining process of the target furnace (actual performance information). Furthermore, the model parameter calculation unit 13 stores the corrected and more accurate model parameters in the database 12 . In other words, the model parameters associated with the target furnace and stored in the database 12 are not model parameters of the model used by the furnace state calculation unit 15 for calculation (estimation). The model parameters associated with the target heat and stored in the database 12 are model parameters calculated (corrected) by the model parameter calculation unit 13 based on the actual performance information of the refining process of the target heat.

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

The model parameter calculation unit 13 may, for example, add the above-mentioned coefficients as variables of the evaluation function such as Equation (7), and obtain model parameters that minimize the evaluation function. Here, in the present embodiment, the model parameter calculation unit 13 minimizes the evaluation function, but it is also possible to use an evaluation function that is maximized with appropriate model parameters. That is, the model parameter calculation unit 13 can obtain model parameters that minimize or maximize the evaluation function.

[number 7]

C _in is the cumulative amount of carbon charged into the converter 100 for a specific period. C _out is the cumulative amount of the amount of carbon discharged out of the converter 100 due to exhaust gas or the like for a specific period. O ₂ ⁱⁿ is the amount of oxygen charged into the converter 100 . O ₂ ^out is the amount of oxygen discharged to the outside of the converter 100 due to discharge of exhaust gas and slag 103 . 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 internal temperature of the converter 100 including the reaction heat generated by the reaction in the converter 100, the exhaust heat caused by the exhaust gas and slag 103 discharged to the outside of the furnace, the heat radiation from the furnace body, and the radiation heat radiation from the furnace mouth. The cumulative amount of the melt temperature change caused by the heat change in a specific period. T _ini is a temperature measurement value of the melt 101 at the start of a specific period in the refining process. Also, [C], V _O2 ^FetO , and T _fin are calculated from the measured value of the amount of carbon in the molten metal 101 at the end of a specific period in the refining process and the measured value of the amount of Fe _t O in the slag 103, respectively. Oxygen amount used for Fe _t O formation, measured value of melt 101 temperature. σ _C ² , σ _O ² , and σ _T ² are constants that can be set arbitrarily. Also, A to I and ΔC correspond to the first to Kth parameters in FIG. 2 . In this embodiment, the model parameters include coefficients or constant terms for correcting at least one of the integrated amount and the oxygen amount used in the formula (7).

Here, ΔR _m is the reaction amount of various reactions m in the furnace, such as an oxidation reaction of components in the molten metal 101 , a reduction reaction of components in the slag 103 , and melting of auxiliary raw materials. ΔL _n is the amount of heat loss related to the heat loss path n in the melt 101 , such as the sensible heat of the gas and the slag 103 , and the amount of heat radiation from the furnace mouth and the furnace body.

The evaluation function J shown in Formula (7) becomes the weighted sum of the following three terms. In the evaluation function J, the first term and the second term are terms representing a mass balance error, and the third term is a term representing a heat budget error. The first term is the square value of the difference between the amount of carbon remaining in the converter 100 obtained by subtracting the amount of discharged carbon from the amount of input carbon and the measured value of the amount of carbon in the melt 101 . When this item becomes 0, it means that the carbon balance is maintained in the converter 100 . The second term is the amount obtained by subtracting the amount of oxygen discharged and the amount of oxygen used for oxidation of impurity metals from the amount of input oxygen, and the amount used for iron oxidation in the molten metal 101 calculated from the measured value of Fe _t O in the slag 103. The square value of the difference in oxygen content. When this item becomes 0, it means that the oxygen balance is maintained in the converter 100 . The third term is the difference between the measured value of the temperature change of the molten metal 101 from the beginning to the end of the specific period in the refining process, and the calculated value of the temperature change of the molten metal 101 calculated from the reaction heat and exhaust heat in the converter 100. The squared value of the difference. A term close to zero indicates that the heat budget is maintained within the converter 100 . The "specific period" in the description of the above formula (7) can be set to a different period for each of the three items.

The weighting factors (σ _C ² , σ _O ² , and σ _T ² ) of the denominators of each item of the evaluation function J are set, for example, by the user. Various algorithms are proposed for the nonlinear programming problem of minimizing the evaluation function J based on constraints, and calculations for obtaining model parameters can be performed by known methods.

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

Based on the temperature and component concentration of the molten metal 101 and the component concentration of the slag 103 estimated by the process of the furnace state estimation method, the operation amount is determined, and the refining operation is performed to produce good molten steel. Determine the optimum flow rate and speed of top-blown oxygen, the height of the top-blown lance, the flow rate of bottom-blown gas, the amount and timing of input of auxiliary raw materials such as lime and iron ore, the timing of sampling the melt, and the end of blowing At least one of the timings is used as the operation amount. In this way, based on the state in the furnace calculated by the method of estimating the state in the furnace, a good molten steel manufacturing method can be realized.

As described above, the furnace state estimation device 1, the furnace state estimation method, and the molten steel manufacturing method according to the present embodiment are based on the terms including the mass balance error and the heat balance error in the furnace through the above-mentioned structures and processes. The evaluation function optimizes the model parameters and memorizes them in the database 12 . In addition, in the estimation of the state in the furnace during the refining process, the past optimized model parameters stored in the database 12 can be used, so that the temperature and component concentration of the molten metal 101 and the component concentration of the slag 103 can be increased. Inferred accuracy.

Although the embodiment of the present disclosure has been described based on the respective drawings and examples, it should be noted that various modifications and corrections can be easily made by a person in the industry based on the present disclosure. Therefore, it should be noted that such changes and modifications are included within the scope of the present disclosure. For example, the functions included in each structural unit or each step may be rearranged in a logically consistent manner, and a plurality of structural units or steps may be combined into one or divided. The embodiments of the present disclosure can also be realized as a program executed by a processor included in a device or as a storage medium recording 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-mentioned embodiments, as long as the mass balance error and heat balance error in the furnace can be minimized The transformed form will have the same effect. Furthermore, the model is not limited to the examples illustrated in the above-mentioned embodiment such as formula (2) to formula (6), and a melt temperature estimation model, a scrap melting model, an auxiliary raw material melting, a yield rate model, and a decarburization efficiency may also be used. model, dephosphorization model, Fe _t O formation reduction model, etc. Furthermore, in this embodiment, the furnace state estimating device 1 and the furnace state estimation method for the converter 100 are shown, but in the secondary refining facility or the pretreatment facility, based on the mass balance and heat balance in the furnace, Calculation of model parameters for calculation is also valid.

1: Furnace state estimation device 2: Refining equipment 10: Control terminal 11: Input part 12: Database 13: Model parameter calculation department 14:Model decision department 15: Furnace state calculation department 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

FIG. 1 is a schematic diagram showing the configuration of a furnace state estimation device as one embodiment of the present disclosure. FIG. 2 is a diagram showing a structural example of a database. FIG. 3 is a flowchart showing processing of a method for estimating a state in a furnace as an embodiment of the present disclosure.

1: Furnace state estimation device

2: Refining equipment

10: Control terminal

11: Input part

12: Database

13: Model parameter calculation department

14:Model decision department

15: Furnace state calculation department

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 (9)

A furnace state estimation device, comprising: The input unit is input with actual performance information including measurement results of the temperature and component concentration of the molten metal and the component concentration of molten slag before or during the refining process in the refining facility, and refining processing conditions; measurements on the refinery including flow rates and constituent concentrations of off-gases exiting said refinery; The model determination unit determines an object by using the past model parameters obtained from a database storing model parameters related to the blowing reaction in the refining facility, the actual performance information, and the conditions of the refining process. said model parameters in said refining process of a heat; A furnace state calculation unit calculates a state quantity in the furnace including the temperature and component concentration of the molten metal and the component concentration of the molten slag by using the determined model parameters; and The model parameter calculation unit uses the actual performance information including the result of the refining process of the target heat, based on a mass balance indicating a mass balance in the furnace from a start point to an end point of a specific period in the refining process. An evaluation function of a term of an error and a heat balance error calculates the model parameters in the refining process of the target heat. The furnace state estimation device according to claim 1, wherein The model determination unit memorizes the model parameters of the past refining process similar to the conditions of the refining process of the target heat, among the past model parameters stored in the database. The average is performed to determine the model parameters in the refining process of the target heat. The furnace state estimation device according to claim 1, wherein The model determination unit stores the past model parameters stored in the database, and the refining process conditions including at least one of the number of times of processing in the refining process, the date and time of processing, and the number of times of use of refining facilities. The relationship is modeled, and the parameters in the refining process of the target heat are determined based on the model. The furnace state estimation device according to any one of claim 1 to claim 3, wherein The model parameters include the cumulative amount of carbon charged into the furnace for a specific period, the cumulative amount of carbon discharged outside the furnace for a specific period, the amount of oxygen charged into the furnace, and the amount of carbon discharged to the furnace. At least one of the amount of oxygen outside the furnace, the amount of oxygen used to oxidize various metal impurities in the melt, and the cumulative amount of the melt temperature change caused by the heat change in the furnace for a specific period is corrected. Coefficient or constant term. The furnace state estimation device according to any one of claim 1 to claim 4, wherein The evaluation function includes a weighted sum of a term representing the carbon balance, a term representing the oxygen balance, and a term representing the heat balance. A furnace state estimation method, executed by a furnace state estimation device, the furnace state estimation method comprising: An input step of inputting actual performance information and refining treatment conditions, the actual performance information including the temperature and component concentration of the molten metal before or during the refining treatment in the refining facility, and the measurement results of the component concentration of the molten slag, and the results of refinery-related measurements of the flow rate and component concentrations of the off-gases exiting said refinery; In the model determining step, an object is determined using the past model parameters acquired from a database storing model parameters related to the blowing reaction in the refining facility, the actual performance information, and the conditions of the refining process. said model parameters in said refining process of a heat; a step of calculating the state in the furnace, using the determined model parameters, to calculate a state quantity in the furnace including the temperature and component concentration of the molten metal and the component concentration of the molten slag; and The model parameter calculation step is based on the actual performance information including the refining process results of the target heat, based on the mass balance in the furnace representing the mass balance in the refining process from the beginning to the end of a specific period. An evaluation function of a term of an error and a heat balance error calculates the model parameters in the refining process of the target heat. The furnace state estimation method as described in Claim 6, wherein In the model determining step, among the past model parameters in the database, the model parameters of the past refining process similar to the conditions of the refining process of the target heat are memorized. The average is performed to determine the model parameters in the refining process of the target heat. The furnace state estimation method as described in Claim 6, wherein In the model determining step, a combination of the past model parameters stored in the database, and refining process conditions including at least one of the number of times of processing in the refining process, the date and time of processing, and the number of times of use of refining facilities The relationship is modeled, and the parameters in the refining process of the target heat are determined based on the model. A molten steel manufacturing method, which is determined based on the temperature and component concentration of the molten metal and the component concentration of the molten slag estimated by the method for estimating the state in a furnace according to any one of claim 6 to claim 8 The flow rate and speed of top-blown oxygen, the height of top-blown lance, the flow rate of bottom-blown gas, the amount and timing of input of auxiliary raw materials such as lime and iron ore, the timing of sampling the melt, and the timing of ending blowing At least one of them is subjected to a refining operation to produce molten steel.

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