JP7135850B2 - Converter blowing control device, converter blowing control method and program - Google Patents

Description

The present invention relates to a converter blowing control device, a converter blowing control method, and a program.

In converter blowing, blowing control combining static control and dynamic control is performed in order to hit the target values of molten steel component concentration (for example, carbon concentration) and molten steel temperature at the end of blowing. In static control, the amount of oxygen to be blown and the input amount of various auxiliary materials to achieve the above targets are determined using a mathematical model based on the material balance and heat balance before the start of blowing. In dynamic control, similar calculations are performed based on molten steel component concentration and molten steel temperature measured using a sublance during blowing, and the amount of oxygen to be blown and the input amount of various auxiliary materials are corrected.

Here, in Patent Document 1, the exhaust gas components and the exhaust gas flow rate during the converter blowing are periodically measured, and the dephosphorization rate constant estimated based on these measured values and the operating conditions is used for the blowing. A technique for sequentially estimating the phosphorus concentration in molten steel is described. By changing the operating conditions according to the result of such estimation, it is possible to improve the accuracy of control of the phosphorus concentration in the molten steel at the time of stopping blowing.

Further, Patent Document 2 describes a technique for appropriately controlling the phosphorus concentration in molten steel at the time of blowing stop using a function representing a lime input amount calculation formula or a lime input amount calculation procedure. The function includes terms representing the molten steel temperature, phosphorus concentration in molten steel, carbon concentration in molten steel, and other operational factors, and learning terms that are updated for each charge of blowing, and by sequentially updating the learning terms, It is possible to calculate an appropriate amount of lime input with high accuracy and appropriately control the phosphorus concentration in molten steel.

JP 2013-23696 A Japanese Patent Application Laid-Open No. 2000-309817

In recent years, in the primary refining process including converter blowing, there is an increasing need to suppress the amount of slag generated. One of the main sources of slag is medium solvent such as quicklime that is put in at the initial stage of blowing. The solvent medium is a source of CaO used for the dephosphorization reaction described below. Therefore, if the phosphorus concentration in the molten steel can be accurately estimated at the initial stage of blowing, the amount of slag generated can be minimized by adding a necessary and sufficient amount of solvent.
3(CaO) ₊₅ (FeO) ₊₂ [P]=(3CaO.P2O5)+5[Fe]
* ( ) indicates inside slag, [ ] indicates inside hot metal

However, the technique described in Patent Document 1 above sequentially estimates the phosphorus concentration in molten steel using the measured values of the exhaust gas components and the exhaust gas flow rate after the start of blowing. It cannot be used to optimize inputs. Further, the technique described in Patent Literature 2 is based on the premise that an error corresponding to the learning term occurs similarly in each charge. In other words, when the error value fluctuates in each charge, it is difficult to accurately control the amount of lime input and the phosphorus concentration in molten steel even with this technique.

Therefore, an object of the present invention is to provide a converter blowing control device, a converter blowing control method, and a program capable of improving the prediction accuracy of the phosphorus concentration in molten steel before the start of blowing. .

According to one aspect of the present invention, molten iron data on molten iron blown in a converter and auxiliary raw material data on auxiliary raw materials fed into the converter are associated with the phosphorus concentration in the molten steel during the blowing treatment in the converter. By adding to the function a first correction term learned based on first predicted actual data including predicted values and actual values of the phosphorus concentration in the molten steel during the past blowing process in the converter, the phosphorus concentration in the molten steel A multivariate state space model expressing the first correction term and the second correction term is constructed, and a Kalman filter is applied to the state space model to obtain the first The second correction term is a correction term added to a function that associates the molten iron data and the auxiliary raw material data with the carbon concentration in the molten steel during the blowing treatment in the converter. , is learned based on the second prediction actual data including the predicted value and the actual value of the carbon concentration in the molten steel during the past blowing process in the converter, and the Kalman filter learns the phosphorus in the molten steel contained in the first prediction actual data A converter blowing control device is provided that uses, as observed values, the difference between the predicted value and the actual value of the concentration and the difference between the predicted value and the actual value of the carbon concentration in molten steel included in the second predicted actual data.
According to the above configuration, the learning of the correction term for calculating the predicted value of the phosphorus concentration in the molten steel can be sequentially executed, and the influence of essential process fluctuations included in the correction term can be distinguished from noise. can be done. Also, by expressing the correction term with a multivariate state space model, the prediction accuracy of the correction term is improved. Therefore, it is possible to improve the prediction accuracy of the phosphorus concentration in the molten steel before the start of blowing.

According to another aspect of the present invention, the molten iron data on the molten iron to be blown in the converter and the auxiliary raw material data on the auxiliary raw materials put into the converter are converted into the phosphorus concentration in the molten steel during the blowing treatment in the converter. By adding a first correction term learned based on first predicted actual data including a predicted value and an actual value of the phosphorus concentration in the molten steel during the past blowing process in the converter to the function to be associated, the phosphorus in the molten steel is A multivariate state space model expressing a predicted value calculation step of calculating a predicted value of concentration and a first correction term and a second correction term is constructed, and a Kalman filter is applied to the state space model to obtain a first A correction term calculation step of calculating the first correction term, and the second correction term is a correction term added to a function that associates the molten iron data and the auxiliary raw material data with the carbon concentration in the molten steel during the blowing treatment in the converter. Yes, it is learned based on second prediction actual data including the predicted value and the actual value of the carbon concentration in the molten steel during the past blowing treatment in the converter, and the Kalman filter is learned in the molten steel included in the first prediction actual data A converter blowing control method is provided in which the difference between the predicted value and the actual value of the phosphorus concentration and the difference between the predicted value and the actual value of the carbon concentration in the molten steel included in the second predicted actual data are used as the observed values. .

According to still another aspect of the present invention, the molten iron data on the molten iron to be blown in the converter and the auxiliary raw material data on the auxiliary raw materials fed into the converter are combined into in the molten steel by adding a first correction term learned based on first predicted actual data including predicted and actual values of the phosphorus concentration in the molten steel during the past blowing process in the converter to the function associated with By constructing a multivariate state space model expressing a predicted value calculation means for calculating a predicted value of the phosphorus concentration and a first correction term and a second correction term, and applying a Kalman filter to the state space model correction term calculation means for calculating the first correction term, and the second correction term is a correction term added to a function that associates the molten iron data and the auxiliary raw material data with the carbon concentration in the molten steel during the blowing treatment in the converter. , and is learned based on the second predicted actual data including the predicted value and the actual value of the carbon concentration in the molten steel during the past blowing treatment in the converter, and the Kalman filter is the molten steel included in the first predicted actual data A computer is used as a converter blowing control device in which the difference between the predicted value and the actual value of the middle phosphorus concentration and the difference between the predicted value and the actual value of the carbon concentration in the molten steel included in the second predicted actual data are the observed values. A program is provided to make it work.

BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows the schematic structure of the refining equipment containing the converter blowing control apparatus which concerns on one Embodiment of this invention. 4 is a flowchart schematically showing steps of a converter blowing control method according to an embodiment of the present invention; FIG. 3 is an example of a time chart when executing the method shown in FIG. 2; FIG. 5 is a graph for explaining the effect of the state space model in one embodiment of the present invention; 5 is a graph for explaining the effect of the state space model in one embodiment of the present invention; 5 is a graph for explaining the effect of the state space model in one embodiment of the present invention; 5 is a graph for explaining the effect of the state space model in one embodiment of the present invention; FIG. 4 is a graph for explaining the optimization of the medium solvent input amount in one embodiment of the present invention. FIG.

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the present specification and drawings, constituent elements having substantially the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description.

In one embodiment of the present invention described below, in the blowing treatment of molten iron in a converter (converter blowing), the phosphorus in the molten steel during the blowing treatment is determined using a model formula before the start of the blowing treatment. Calculate the predicted concentration. Here, in this specification, the time of blowing treatment means the period from the start of the blowing treatment to the end of the blowing treatment (blowing stop), and the predicted value of the phosphorus concentration in the molten steel can be any value during this period. Calculated for time points. Specifically, for example, a predicted value of the phosphorus concentration in the molten steel at the time of intermediate sublance measurement, which will be described later, may be calculated, or a predicted value of the phosphorus concentration in the molten steel at the time of stopping blowing may be calculated.

In addition, in the process of calculating the predicted value of the phosphorus concentration in the molten steel, the actual value of the phosphorus concentration in the molten steel during the past blowing treatment in the converter is referred to. can be used. Therefore, for example, if the actual value of the phosphorus concentration in molten steel at the time of intermediate sublance measurement is available, the actual value of the phosphorus concentration in molten steel at the time of stopping the blow is not necessarily required. Therefore, one embodiment of the present invention described below can be used even when so-called direct tapping is employed, in which steel is tapped without measuring the concentration of molten steel components and the temperature of molten steel at the time of stopping blowing.

(System configuration)
FIG. 1 is a diagram showing a schematic configuration of a refining facility including a converter blowing control device according to one embodiment of the present invention. As shown in FIG. 1 , the refining facility 1 includes a converter facility 10 , a measurement control device 20 and a converter blowing control device 30 . Among them, the converter facility 10 includes a converter 11 , a top-blowing lance 12 , and a charging device 13 . In the converter equipment 10 , primary refining decarburization is performed by oxygen gas supplied to the hot metal 111 by the top-blowing lance 12 inserted from the throat of the converter 11 . Molten iron 111 that has undergone decarburization is sent to the next process as molten steel 112 . In the decarburization process, phosphorus and silicon in the hot metal 111 also react with the oxygen gas 121 or auxiliary materials contained in the slag 113 and are taken into the slag 113 to be stabilized. The feeding device 13 feeds the auxiliary raw material 131 containing quicklime and the like that constitutes the slag 113 into the converter 11 . If the auxiliary material 131 is powder, it can be blown together with the oxygen gas 121 using the top-blowing lance 12 .

The measurement control device 20 performs various measurements related to the refining process in the converter facility 10 and controls the refining process. Specifically, the measurement control device 20 includes a sublance 21 , an oxygen supply device 22 , and a secondary raw material input control device 23 . The sublance 21 is inserted from the throat of the converter 11 together with the top blowing lance 12, and the carbon concentration and the phosphorus concentration are measured by immersing the measuring device provided at the tip in the molten steel 112 at a predetermined timing during the decarburization process. The component concentration of the molten steel 112 containing the molten steel 112 and the temperature of the molten steel 112 (hereinafter also referred to as molten steel temperature) are measured. A measurement using the sublance 21 during blowing is referred to herein as an intermediate sublance measurement. The result of the sublance measurement is transmitted to the converter blowing control device 30 . The oxygen supply device 22 supplies the oxygen gas 121 to the top blowing lance 12 . The flow rate of the supplied oxygen gas 121 is adjustable. The auxiliary raw material charging control device 23 controls charging of the auxiliary raw material 131 by the charging device 13 . Specifically, the secondary material input control device 23 controls the timing and amount of input of the secondary material 131 . Both of the operations of the oxygen supply device 22 and the auxiliary material input control device 23 are performed according to control signals received from the converter blowing control device 30 .

The converter blowing control device 30 includes a communication unit 31 , a calculation unit 32 , a storage unit 33 and an input/output unit 34 . The communication unit 31 is a communication device that communicates with each element of the measurement control device 20 by wire or wirelessly. do. The calculation unit 32 is a calculation device that executes various calculations according to a program, and includes, for example, a CPU (Central Processing Unit), RAM (Random Access Memory), and ROM (Read Only Memory). The program is stored in the ROM or storage unit 33 . In the above-described converter blowing control device 30, the calculation unit 32 functions as a molten steel phosphorus concentration prediction unit 321, a correction term calculation unit 322, and a medium solvent amount correction unit 323 by operating according to a program. The storage unit 33 is a storage capable of storing various data, and stores hot metal/auxiliary raw material data 331, predicted performance data 332, target data 333, and parameters 334. FIG. These data are stored, for example, as initial data, and are updated as needed according to the results of calculations in the calculation unit 32 . The input/output unit 34 includes an output device such as a display or printer, and an input device such as a keyboard, mouse, or touch panel. The output device outputs, for example, values such as the molten steel phosphorus concentration during blowing predicted by the molten steel phosphorus concentration prediction unit 321 . The input device acquires, for example, an instruction input related to the control executed by the solvent material amount correction unit 323 .

In the converter blowing control device 30 described above, the molten iron/auxiliary raw material data 331 stored in the storage unit 33 includes the molten iron data related to the molten iron 111 to be blown in the converter 11 and the auxiliary material data to be fed into the converter 11. sub-ingredient data for ingredients 131; The hot metal data includes, for example, the initial hot metal weight per charge, concentrations of hot metal constituents (such as carbon, silicon, phosphorus, and manganese), hot metal temperature, hot metal rate, and the like. Further, the auxiliary raw material data includes the components of the auxiliary raw material 131 and the input amount for each charge. As described above, in this embodiment, the predicted value of the phosphorus concentration in the molten steel during the blowing treatment is calculated before the start of the blowing treatment. Contains the expected value at which the charge is calculated. The predicted actual data 332 includes the predicted value and the actual value (first predicted actual data) of the phosphorus concentration in the molten steel during the past blowing treatment in the converter 11, and the predicted value and the actual value (second predicted actual data), and a predicted value and actual value of molten steel temperature (third predicted actual data). The target data 333 includes target values such as concentration and temperature of constituents in the molten iron 111 (or molten steel 112) at the time of intermediate sublance measurement, blowing stop, or the like. The parameters 334 include parameters of a model formula for calculating a predicted value of phosphorus concentration in molten steel, which will be described later.

In the calculation unit 32, the molten steel phosphorus concentration prediction unit 321, which is the prediction value calculation means in the present embodiment, calculates the molten iron/auxiliary raw material data 331 and the prediction performance data 332 read from the storage unit 33. Calculate the predicted value of the phosphorus concentration in the molten steel. Specifically, the molten steel phosphorus concentration prediction unit 321 adds a later-described correction term β _p (first correction term) to the function that associates the molten iron/auxiliary raw material data 331 with the molten steel phosphorus concentration during the blowing process. Calculate the predicted value of the phosphorus concentration in the molten steel. As will be described later, the correction term calculation unit 322 constructs a multivariate state space model that expresses the correction term β _p used for calculating the predicted value of the phosphorus concentration in molten steel, and applies a Kalman filter to this state space model. Calculate the correction term β _p by applying In addition, in the calculation unit 32, the medium solvent amount correction unit 323, which is the input amount correction means in the present embodiment, calculates the secondary raw material 131 based on the predicted value of the phosphorus concentration in molten steel calculated by the molten steel phosphorus concentration prediction unit 321. The input amount of the CaO-containing auxiliary raw material contained in the hot metal/auxiliary raw material data 331 is corrected to a more appropriate value.

(Overview of process)
FIG. 2 is a flow chart schematically showing steps of a converter blowing control method according to an embodiment of the present invention. The illustrated steps are performed before the start of blowing treatment in the converter 11 . In the illustrated example, first, the molten steel phosphorus concentration prediction unit 321 of the converter blowing control device 30 reads the molten iron/auxiliary raw material data 331 and the prediction performance data 332 from the storage unit 33 (steps S11 and S12). Here, the correction term calculator 322 calculates a correction term β _p for predicting the phosphorus concentration in molten steel based on the read prediction result data 332 (step S13). Next, the molten steel phosphorus concentration prediction unit 321 calculates a predicted value of the molten steel phosphorus concentration using the calculated correction term β _p (step S14). Next, the medium solvent amount correction unit 323 performs the medium solvent based on the target value of the phosphorus concentration in molten steel included in the target data 333 and the predicted value of the phosphorus concentration in molten steel calculated by the molten steel phosphorus concentration prediction unit 321. is corrected (step S15). A control signal containing the corrected input amount is transmitted to the secondary material input control device 23 via the communication unit 31 . After that, in the initial stage of the blow tempering process, the auxiliary material charging control device 23 introduces a medium solvent such as quicklime as the auxiliary material 131 in accordance with the corrected charging amount.

FIG. 3 is an example of a time chart when executing the processing shown in FIG. In the illustrated example, the predicted value of the phosphorus concentration in the molten steel at the time of the intermediate sublance measurement in the n-th charge (n = 1, 2, ...) is calculated, and the phosphorus concentration in the molten steel is actually measured in the intermediate sublance measurement. value is retrieved. For the molten steel carbon concentration and the molten steel temperature, predicted values are similarly calculated and measured values are acquired. When the intermediate sublance measurement (step S21) is performed in the n-th charge, the measured values such as the phosphorus concentration in the molten steel are acquired, and the series of steps shown in FIG. 2 are performed for the n+1-th charge. be possible. Specifically, the correction term calculation unit 322 calculates the correction term β _p (step S22), and using the calculated correction term β _p , the molten steel phosphorus concentration prediction unit 321 predicts the intermediate sublance measurement in the n+1th charge. A predicted value of the phosphorus concentration in the molten steel at the point in time is calculated (step S23).

Next, the medium solvent amount correction unit 323 corrects the amount of medium solvent to be introduced (step S24), thereby setting the proper amount of medium medium to be introduced in the (n+1)th charge. The series of steps may be executed so that the above step S24 ends before the solvent is added in the n+1-th charge. Therefore, in the illustrated example, the processes after step S22 are started immediately after the intermediate sublance measurement (step S21) in the n-th charge, but these processes are performed after the blowing process of the n-th charge is completed. may be started from Alternatively, the processes up to step S24 may be completed before the n-th charge blowing process is completed.

Hereinafter, the model formula and the correction term used to calculate the predicted value of the phosphorus concentration in molten steel in this embodiment, and the optimization of the amount of the solvent medium added based on the predicted value will be described more specifically.

(model formula)
In the present embodiment, change over time of the phosphorus concentration in molten steel (hereinafter also referred to as [P] (%)) is represented by the following first-order reaction formula (1). [P] _ini (%) represents the initial value of [P] (phosphorus concentration in hot metal), and k (sec ⁻¹ ) represents the dephosphorization rate constant.

From formula (1), [P] after t seconds from the start of the blow tempering process is represented by the following formula (2). However, the dephosphorization rate constant k is represented by a multiple regression equation with the operating factor _Xj included in the hot metal/auxiliary raw material data 331 as an explanatory variable, as shown in Equation (3). Note that α _j represents a regression coefficient.

The above formula (2) is an example of a function that associates the molten iron/auxiliary raw material data 331 with the phosphorus concentration [P] in the molten steel during the blow tempering process. In this embodiment, as shown in the following equation (4), by adding a correction term (learning term) β _p to this function, the accuracy of the predicted value of the phosphorus concentration in molten steel [P] is improved. The correction term β _p is learned based on the predicted value and actual value (first predicted actual data) of the phosphorus concentration in molten steel [P].

Here, as a formula widely known in the field of refining of molten steel, there is Healy's phosphorus distribution formula shown as formula (5) below. In formula (5), Lp is the distribution ratio, (%P) is the phosphorus concentration in the slag, [P] is the phosphorus concentration in the molten steel, T is the temperature, (%T.Fe), (%CaO) in the slag T. Fe, CaO concentrations, a, b, c, d are constants.

The present inventors focused on the carbon concentration in molten steel (hereinafter also referred to as [C] (%)), which has no direct causal relationship from the phosphorus distribution formula of formula (5). Since the molten steel carbon concentration [C] is considered to be correlated with (%T.Fe) and (%CaO) and the temperature T, from the above equation (5), the molten steel phosphorus concentration [P] It can be said that there is a relationship with Therefore, in the present embodiment, in addition to the correction term β _p (first correction term) in the model formula used to calculate the predicted value of the phosphorus concentration in molten steel, _{Further phosphorus} To improve the accuracy of the predicted value of the concentration [P].

Based on the above studies, the molten steel carbon concentration [C] and the molten steel temperature T are expressed by multiple regression equations such as Equations (6) and (7). Equations (6) and (7) are functions that associate the molten iron/auxiliary raw material data 331 with the molten steel carbon concentration [C] and the molten steel temperature T during blow tempering, respectively. Correction terms (learning terms) β _C and β _T are added in order to improve the accuracy of the predicted value, as in the example of . The correction term β _C is learned based on the predicted value and actual value (second predicted actual data) of carbon concentration [C] in molten steel, and the correction term β _T is learned based on the predicted value and actual value of molten steel temperature T (third is learned based on predicted performance data).

Considering the continuity of charge as shown in FIG. 3 above, the correction terms β _p , β _C , and β _T in equations (4), (6), and (7) are a kind of time-series data can be regarded as Therefore, in the present embodiment, the correction terms β _p , β _C , and β _T are represented by a state space model, which is one of modeling techniques for time series data. State-space models, whether continuous or discrete, periodic or not, univariate or multivariate, stationary or nonstationary, It is a framework of a wide range of statistical models that can be applied to various time-series data, and has the characteristic of being able to decompose changes in time-series data into factors such as trends, seasonal fluctuations, and regression fluctuations. Although the correction terms β _p , β _C , and β _T handled in this embodiment are considered to be multivariate and non-stationary, it is possible to apply the state space model as described above. On the other hand, the autoregressive model, which is another general time-series data modeling method, assumes that the data to be analyzed is stationary, so the correction terms β _p , β It is not easy to apply to _C and β _T (need to be stationary by variable transformation or difference processing). In addition, it is difficult to decompose changes in time-series data using an autoregressive model.

The state-space model uses two equations, a state equation and an observation equation. The equation of state expresses the unmeasured quantity (state quantity), and the idea is that the observed value is obtained by the observation equation in which the observation error is added to the state quantity. If both the state equation and the observation equation are linear and the observation error can be assumed to have a normal distribution, an algorithm called the Kalman filter has been established that corrects the state quantity using time-series data of observed values. In the present embodiment, the correction terms β _p , β _C , and β _T are used as state quantities, and the predicted values of the molten steel phosphorus concentration [P], the molten steel temperature, and the molten steel carbon concentration [C] during the past blowing process and The correction term β _p is accurately predicted by applying the Kalman filter using the difference from the actual value as the observed value (value obtained by adding the measurement error to the state quantity).

Although only the correction term β _p is used to predict the final phosphorus concentration in molten steel [P], as described above, the molten steel temperature T and the carbon concentration in molten steel [C] are both ], the correction term for the phosphorus concentration in molten steel [P] is calculated using a multivariate and single state space model including the correction terms β _C and β _T for the molten steel temperature T and the carbon concentration in the molten steel [C]. By expressing β _p , the prediction accuracy of the correction term β _p is improved, and the prediction accuracy of the phosphorus concentration in molten steel [P] is also improved.

As described above, the state space model has the feature of being able to decompose changes in time-series data into elements. More specifically, the concept of state quantity is used in the formulation of the state space model, and noise is added to the state quantity as the observed value. becomes possible. Therefore, in this embodiment, by accurately predicting the correction term β _p represented by the state space model using a Kalman filter, the prediction error corresponding to the correction term β _p is treated as an element caused by essential process variation. can be decomposed into noise elements and For elements caused by essential process variations, it is also possible to clarify the relationship between prediction errors and operational factors by associating variations in the elements with variations in specific operational factors.

(Overview of Kalman filter)
The Kalman filter is a technique for sequentially estimating the state quantities inside the model from observed values when the dynamics of the target process follows a linear state space model. In this embodiment, it is assumed that the state-space model of the correction term β _p in equation (4) is linear, so a Kalman filter can be applied. The Kalman filter targets a linear Gaussian state-space model as expressed in Equation (8) below. Here, _xt is a state quantity vector, _yt is an observed value vector, _Ft is a time-varying n×m matrix, _{Gt is a time-varying n×1 matrix, Ht is} a time-varying n×m matrix, R ⁿ represents an n-dimensional vector space.

In equation (8) above, _vt is called system noise and _wt is observation noise. In this embodiment, v _t and x _t are assumed to follow a multidimensional normal distribution such as the following equation (9). Note that N(0, Q _t ) represents a multidimensional normal distribution with an average of 0 and a variance-covariance matrix Q _t , and N(0, R _t ) represents a multidimensional normal distribution with an average of 0 and a variance-covariance matrix R _t . Hereinafter, _Qt is also referred to as a variance-covariance matrix of system noise, and _Rt is also referred to as a variance-covariance matrix of observation noise.

In the Kalman filter algorithm, in the state space model as described above, the initial value x _{0 | 0} of the estimated value of the state quantity vector and the initial value V _{0 | 0} of the error variance covariance matrix of the estimated value of the state quantity vector are given. Above, iteratively repeats the prediction and filtering procedure as described below.

First, in the prediction procedure, as shown in the following equation (10), the estimated value x _t−1|t−1 of the state quantity vector at time (t−1) and the error variance of the estimated value of the state quantity vector The covariance matrix V _t−1|t−1 is used to compute the respective predicted values x _t|t−1 and V _t|t−1 at time t.

Next, in the filtering procedure, as shown in the following equation (11), the corrected value V _t|t of the error variance covariance matrix of the estimated value of the state quantity vector at time t and the Kalman gain K _t are calculated. .

Furthermore, using the Kalman gain K _t calculated by the above formula (11) and the observed value vector y _t at the time t, the predicted value of the state vector at the time t calculated by the above formula (10) A modified value x _t|t of x _t|t−1 can be calculated as shown in equation (12) below.

The above equations (8) to (12) are examples of equations used in state estimation using the Kalman filter. The Kalman filter has already been widely used as a method of state estimation, and the specific mathematical expressions used are not limited to the above examples, and various ones are known. These other examples can of course also be applied in this embodiment.

(When the state quantity is randomly walked)
As described above, in the present embodiment, the correction terms β _p , β _C , and β _T in the equations (4), (6), and (7) for calculating the predicted value of the phosphorus concentration in molten steel are The correction terms β _p , β _C , and β _T are calculated by expressing with a model and applying a Kalman filter to this state space model.

Here, in equation (8), a setting such as the following equation (13) is considered.

This setting means that the state variables and observations are multivariate, and the state transitions of the state variables are based on random walks. In this setting, equation (8) becomes the state-space model expressed by equations (14) and (15). Note that x _t =(x _1,t ,x _2,t ,x _3,t ) is a state quantity representing the true value of the correction terms β _p , β _C ,β _T , and y _t =(y _{1, t} 1 , y _2,t , y _3,t ) are observed values corresponding to the correction terms β _p , β _C , β _T , respectively.

The state space model represented by the above equations (14) and (15) converts the state quantity x _t (true values of the correction terms β _p , β _C , β _T ) into a random walk (state quantity x _t is a motion that is randomly determined probabilistically), and expresses a situation in which the current state quantity x _t is very similar to the previous state quantity x _t−1 . Note that Q and R in the equations (14) and (15) are the actual prediction error value data of the phosphorus concentration in molten steel [P] obtained in advance, the actual prediction error value data of the molten steel temperature T, and the It is assumed that the value is calculated by the maximum likelihood method or the like using the prediction error actual value data of the carbon concentration [C]. In multivariate state-space models such as Eqs. (14) and (15), v _i,t and w _i,t are variance-covariance matrices (e.g., r ₁₁ is x ₁ (the true value of the correction term β _p : state quantity), and r ₁₂ represents the covariance of x ₁ (true value of correction term β _p : state quantity) and x ₂ (true value of correction term β _C : state quantity)) It is said to follow a normal distribution with Q and R as variances, and the correlation between variables is incorporated through this relationship. By applying the Kalman filter to the state space model represented by equations (14) and (15), the observed value y _t−1 in the previous charge is converted to the state quantity x _t in the current charge (correction terms β _p , β _C , the true value of β _T ) and the observed value y _t can be calculated.

(Effect of state space model)
4-7 are graphs to illustrate the effect of using state-space models in one embodiment of the present invention. In each graph, the predicted value and actual measured value of the phosphorus concentration [P] in the molten steel are both the values at the time of the intermediate sublance measurement, and are therefore indicated as SL[P]cal and SL[P]act. Note that both SL[P]cal and SL[P]act values are normalized. FIG. 4 shows, as case ₀ , the predicted value SL[ _P ] _cal and the actual measurement The relationship with the value SL[P]act is shown. In FIG. 5, as case 1, the above equation (4) including the correction term β _p of the phosphorus concentration in molten steel [P] and equations (8) to (15) are used, and equations (14) and ( In 15), the predicted value SL[P]cal and the actual value SL[P]act when [P] is predicted by introducing only the correction term _βp (ignoring the correction terms _βC and _βT ) relationship is shown. FIG. 6 shows, as case 2, using the above equations (4), (6), and (8) to (15) including the correction term β _C in addition to the correction term β _p , and the equation (14 ) and equation (15), the relationship between the predicted value SL[P]cal and the actual value SL[P]act when the correction terms β _p and β _C (ignoring the correction term β _T ) [P] are predicted It is shown. FIG. 7 shows, as case 3, the above equations (4), (6), (7), and (8) to (15) including all of the correction terms β _p , β _C , and β _T . The relationship between the predicted value SL[P]cal and the measured value SL[P]act is shown when [P] is predicted using .

In case 0 (FIG. 4), the standard deviation of the predicted value SL[P]cal is 0.858, and the error average with respect to the actual value SL[P]act is 0.101. In contrast, Case 1 (FIG. 5) has a standard deviation of 0.753 and an error mean of −0.0171. In case 1, the standard deviation (variation of predicted values) was improved, and the tendency for the predicted value SL[P]cal in case 0 to be higher than the actual value SL[P]act as a whole was improved. there is In Case 2 (Fig. 6), the standard deviation is 0.639 and the error mean is -0.0172, and the center of the distribution of the predicted value SL[P]cal remains close to the actual value SL[P]act. However, the standard deviation is much improved. Furthermore, in case 3 (Fig. 7), the standard deviation is 0.426 and the error mean is 0.0236. A state in which the center of the distribution is close to the measured value SL[P]act can be maintained.

(Optimization of medium solvent input amount)
By using the predicted value of the phosphorus concentration [P] in the molten steel calculated by introducing the correction terms β _p , β _C , and β _T as described above, the CaO-containing auxiliary raw material introduced at the beginning of the blowing process Specifically, it is possible to optimize the input amount of a medium solvent such as quicklime. As already mentioned, in converter blowing, blowing control is performed by combining static control and dynamic control. _{[ _ _} P] is used for correction.

FIG. 8 is a graph for explaining the optimization of the amount of medium solvent input in this embodiment. As indicated by the solid line in FIG. 8 , the amount of solvent added per weight of molten iron (kg/ton) in static control is determined in advance according to the target phosphorus concentration (%) in molten steel. Specifically, when the target value of the phosphorus concentration in the molten steel in the current charge is P ₁ (%), the pre-determined amount of solvent added is W _CaO (kg/ton). On the other hand, when the predicted value of the phosphorus concentration in molten steel calculated by introducing the correction term β _p is P ₂ (%) (P ₂ < P ₁ ), if the solvent input amount W _CaO is maintained , ΔP=P ₁ −P ₂ (%). In this case, it is considered that the relationship between the target phosphorus concentration in the molten steel and the amount of solvent medium charged per weight of molten iron indicated by the solid line in FIG. 8 shifts to the broken line graph. Therefore, for example, as shown in the following equation (16), the amount of solvent added is corrected from W _CaO to W′ _CaO using the solvent amount correction function f(ΔP). As a result, a necessary and sufficient amount of solvent can be added for dephosphorization, and the amount of slag generated due to the solvent can be minimized while achieving the target phosphorus concentration in molten steel. can.

In one embodiment of the present invention as described above, a state space model assuming that the state quantity is a random walk is introduced, a Kalman filter is applied, and the correction for calculating the predicted value of the phosphorus concentration in molten steel [P] Learning the terms β _p , β _C , β _T can be performed sequentially. Also, by applying the Kalman filter to the state-space model that introduces regression effects, it is possible to distinguish between the effects of intrinsic process variations and noise contained in the correction terms β _p , β _C , β _T . Therefore, it is possible to improve the prediction accuracy of the phosphorus concentration [P] in the molten steel before the start of blowing. If the prediction accuracy of the phosphorus concentration in molten steel [P] is improved, it is possible to optimize the amount of CaO-containing auxiliary materials that are introduced in the initial stage of the blowing process, and achieve the target value of the phosphorus concentration in molten steel. , the amount of slag generated due to the medium solvent can be minimized.

Although the preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, the present invention is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention belongs can conceive of various modifications or modifications within the scope of the technical idea described in the claims. It is understood that these also naturally belong to the technical scope of the present invention.

DESCRIPTION OF SYMBOLS 1... Refining equipment, 10... Converter equipment, 11... Converter, 12... Top blowing lance, 13... Input apparatus, 20... Measurement control apparatus, 21... Sublance, 22... Oxygen supply apparatus, 23... Sub-raw material input control apparatus , 30... Converter blowing control device, 31... Communication unit, 32... Calculation unit, 321... Phosphorus concentration prediction unit in molten steel, 322... Correction term calculation unit, 323... Solvent amount correction unit, 33... Storage unit, 331 Molten iron/auxiliary raw material data 332 Predicted performance data 333 Target data 334 Parameter 34 Input/output unit 111 Molten iron 112 Molten steel 113 Slag 121 Oxygen gas 131 Auxiliary raw material.

Claims (8)

A function that associates molten iron data related to molten iron blown in a converter and auxiliary raw material data related to auxiliary raw materials fed into the converter with the phosphorus concentration in the molten steel during the blowing treatment in the converter, Calculate the predicted value of the phosphorus concentration in the molten steel by adding the first correction term learned based on the first predicted actual data including the predicted value and the actual value of the phosphorus concentration in the molten steel during the past blowing process a predicted value calculation means for
Correction term calculation for calculating the first correction term by constructing a multivariate state space model expressing the first correction term and the second correction term and applying a Kalman filter to the state space model comprising means and
The second correction term is a correction term added to a function that associates the molten iron data and the auxiliary raw material data with the carbon concentration in the molten steel during the blowing treatment in the converter, and is a correction term added to the past blowing treatment in the converter. Learned based on the second predicted actual data including the predicted value and actual value of the carbon concentration in the molten steel at the time of
The Kalman filter compares the difference between the predicted value and the actual value of the phosphorus concentration in the molten steel contained in the first predicted actual data, and the predicted value and the actual value of the molten steel carbon concentration contained in the second predicted actual data. A converter blowing control device in which the difference between is used as an observed value. 2. The converter blowing control apparatus according to claim 1, wherein said correction term calculation means expresses said first correction term and said second correction term with said single state space model. The first predicted actual data are the predicted value of the phosphorus concentration in the molten steel at the time of the intermediate sublance measurement during the past blowing treatment in the converter, and the actual value of the molten steel phosphorus concentration obtained in the intermediate sublance measurement. including
The second predicted actual data is the predicted value of carbon concentration in molten steel at the time of intermediate sublance measurement during past blowing treatment in the converter, and the actual value of molten steel carbon obtained in the intermediate sublance measurement. 3. A converter blow control system as claimed in claim 1 or claim 2 comprising. The state-space model is a multivariate state-space model representing a third correction term in addition to the first correction term and the second correction term,
The third correction term is a correction term added to a function that associates the molten iron data and the auxiliary raw material data with the molten steel temperature during blowing treatment in the converter, and is a correction term during past blowing treatment in the converter. Learned based on third predicted actual data including predicted and actual values of molten steel temperature,
The Kalman filter compares the difference between the predicted value and the actual value of the phosphorus concentration in the molten steel contained in the first predicted actual data, and the predicted value and the actual value of the molten steel carbon concentration contained in the second predicted actual data. The converter according to any one of claims 1 to 3, wherein the difference between the predicted value and the actual value of the molten steel temperature included in the third predicted actual data is used as the observed value in addition to the difference of Blowing control device. The converter blowing control device according to any one of claims 1 to 4, wherein the correction term calculation means randomly walks the state quantity in the state space model. 6. The method according to any one of claims 1 to 5, further comprising input amount correcting means for correcting an input amount of the CaO-containing auxiliary raw material contained in the auxiliary raw material based on the predicted value of the phosphorus concentration in the molten steel. Converter blowing control device. A function that associates molten iron data related to molten iron blown in a converter and auxiliary raw material data related to auxiliary raw materials fed into the converter with the phosphorus concentration in the molten steel during the blowing treatment in the converter, Calculate the predicted value of the phosphorus concentration in the molten steel by adding the first correction term learned based on the first predicted actual data including the predicted value and the actual value of the phosphorus concentration in the molten steel during the past blowing process a predicted value calculation step for
Correction term calculation for calculating the first correction term by constructing a multivariate state space model expressing the first correction term and the second correction term and applying a Kalman filter to the state space model and
The second correction term is a correction term added to a function that associates the molten iron data and the auxiliary raw material data with the carbon concentration in the molten steel during the blowing treatment in the converter, and is a correction term added to the past blowing treatment in the converter. Learned based on the second predicted actual data including the predicted value and actual value of the carbon concentration in the molten steel at the time of
The Kalman filter compares the difference between the predicted value and the actual value of the phosphorus concentration in the molten steel contained in the first predicted actual data, and the predicted value and the actual value of the molten steel carbon concentration contained in the second predicted actual data. A converter blowing control method in which the difference of is used as an observed value. A function that associates molten iron data related to molten iron blown in a converter and auxiliary raw material data related to auxiliary raw materials fed into the converter with the phosphorus concentration in the molten steel during the blowing treatment in the converter, Calculate the predicted value of the phosphorus concentration in the molten steel by adding the first correction term learned based on the first predicted actual data including the predicted value and the actual value of the phosphorus concentration in the molten steel during the past blowing process a predicted value calculation means for
Correction term calculation for calculating the first correction term by constructing a multivariate state space model expressing the first correction term and the second correction term and applying a Kalman filter to the state space model comprising means and
The second correction term is a correction term added to a function that associates the molten iron data and the auxiliary raw material data with the carbon concentration in the molten steel during the blowing treatment in the converter, and is a correction term added to the past blowing treatment in the converter. Learned based on the second predicted actual data including the predicted value and actual value of the carbon concentration in the molten steel at the time of
The Kalman filter compares the difference between the predicted value and the actual value of the phosphorus concentration in the molten steel contained in the first predicted actual data, and the predicted value and the actual value of the molten steel carbon concentration contained in the second predicted actual data. A program for functioning a computer as a converter blowing control device with the difference of .

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