JP2020097768A - Converter blowing control device, converter blowing control method, and program - Google Patents

Abstract

To improve prediction accuracy of a phosphorus concentration in molten steel at a time before the start of blowing.SOLUTION: The converter blowing control device includes prediction value calculation means and correction term calculation means. The prediction value calculation means calculates a prediction value of a phosphorus concentration in molten steel by adding a correction term to a function. The function relates the molten steel data for the molten steel to be blown in a converter and the auxiliary raw material data for the auxiliary raw material to be fed into the converter to the phosphorus concentration in the molten steel during the blowing process in the converter. The correction term is learned based on predicted actual performance data including the prediction and actual values of the phosphorus concentration in the molten steel in the past blowing process in the converter. The correction term calculation means expresses a correction term in a state space model, and calculates the correction term by applying a Kalman Filter using a difference between the predicted value and the actual value included in the predicted actual performance data as an observation value to the state space model.SELECTED DRAWING: Figure 1

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 bring the molten steel component concentration (for example, carbon concentration) and the molten steel temperature at the time of blowing stop to a target value. In the static control, before starting the blowing, a mathematical model based on the mass balance and the heat balance is used to determine the blown oxygen amount and the input amounts of various auxiliary raw materials for achieving the above target. In the dynamic control, the same calculation is executed based on the molten steel component concentration and the molten steel temperature measured by using the sublance during blowing, and the blown oxygen amount and the input amounts of various auxiliary raw materials are corrected.

Here, in Patent Document 1, exhaust gas components and exhaust gas flow rates during converter blowing are periodically measured, and blowing is performed using dephosphorization rate constants estimated based on these measured values and operating conditions. A technique for sequentially estimating the phosphorus concentration in molten steel in the medium is described. By changing the operating condition in accordance with the result of such an estimation, the accuracy of controlling the phosphorus concentration in the molten steel at the time of blowing can be improved.

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

JP, 2013-23696, A JP-A-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 a solvent solution such as quick lime that is added in the early stage of blowing. The solvent is a source of CaO used for the dephosphorization reaction as shown 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 suppressed to the minimum by adding a necessary and sufficient amount of the medium-melting material.
3(CaO)+5(FeO)+2[P]=(3CaO·P ₂ O ₅ )+5[Fe]
*() indicates inside the slag, and [] indicates inside the hot metal.

However, since the technique described in the above-mentioned Patent Document 1 sequentially estimates the phosphorus concentration in the molten steel by using the measured values of the exhaust gas component and the exhaust gas flow rate after the start of the blowing, therefore It cannot be used to optimize the input amount. Further, the technique described in the above-mentioned Patent Document 2 is premised on that an error corresponding to the learning term similarly occurs in each charge. That is, when the error value varies in each charge, it is difficult to accurately control the lime input amount and the molten steel phosphorus concentration even by using 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 an aspect of the present invention, the hot metal data relating to the hot metal blown in the converter and the auxiliary raw material data relating to the auxiliary raw material to be fed into the converter are related to the phosphorus concentration in the molten steel during the blowing process in the converter. Calculate the predicted value of phosphorus concentration in molten steel by adding to the function a correction term that is learned based on the predicted value of the phosphorus concentration in molten steel at the time of the past blowing process in the converter and the actual performance data including the actual value. Prediction value calculation means and the correction term are expressed by a state space model, and the correction term is applied to the state space model by applying a Kalman filter whose difference between the predicted value and the actual value included in the predicted actual data is the observed value. There is provided a converter blowing control device including a correction term calculation means for calculating.
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 the essential process variation contained in the correction term and the noise can be distinguished. You can 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 hot metal data relating to the hot metal blown in the converter, and the auxiliary raw material data relating to the auxiliary raw material to be fed into the converter are set to the phosphorus concentration in the molten steel during the blowing process in the converter. Calculating the predicted value of phosphorus concentration in molten steel by adding a correction term that is learned based on the predicted value of phosphorus concentration in molten steel during the past blowing process in the converter and the actual performance data including the actual value to the function to be associated Prediction value calculation process and the correction term are expressed by a state space model, and the correction term is applied to the state space model by applying a Kalman filter whose observed value is the difference between the predicted value and the actual value included in the predicted actual data. A converter blowing control method including a correction term calculating step for calculating

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. It is a flow chart which shows roughly the process of the converter blowing control method concerning one embodiment of the present invention. 3 is an example of a time chart when the method shown in FIG. 2 is executed. 6 is a graph for explaining the effect of the state space model in the embodiment of the present invention. 6 is a graph for explaining the effect of the state space model in the embodiment of the present invention. 6 is a graph for explaining the effect of the state space model in the embodiment of the present invention. It is a graph for explaining the optimization of the amount of the solvent material input in the embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are designated by the same reference numerals, and duplicate description will be omitted.

In one embodiment of the present invention to be described below, in the hot metal blowing process (converter blowing process) in a converter, before the start of the blowing process, a model formula is used to melt phosphorus in molten steel during the blowing process. Calculate the predicted concentration. Here, in the present specification, the blowing process means a period from the start of the blowing process to the end (blowing stop) of the blowing process, and the predicted value of the phosphorus concentration in the molten steel is arbitrary during this period. It is calculated for the time point. Specifically, for example, the predicted value of the phosphorus concentration in the molten steel at the time of the intermediate sublance measurement described below may be calculated, or the predicted value of the phosphorus concentration in the molten steel at the time of blowing stop may be calculated.

Further, 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 in the past blowing process in the converter is referred to, but this actual value is also arbitrary in the blowing process. What was measured at the time of can be used. Therefore, for example, if the actual value of the phosphorus concentration in the molten steel at the time of the intermediate sublance measurement is available, the actual value of the phosphorus concentration in the molten steel at the time of blowing is not necessarily required. Therefore, one embodiment of the present invention described below can be used even in the case where a so-called direct tap is adopted in which steel is tapped without measuring the molten steel component concentration and the molten steel temperature at the time of blowing.

(System configuration)
FIG. 1 is a diagram showing a schematic configuration of refining equipment including a converter blowing control device according to an embodiment of the present invention. As shown in FIG. 1, the refining equipment 1 includes a converter equipment 10, a measurement control device 20, and a converter blowing control device 30. Of these, the converter equipment 10 includes a converter 11, an upper blowing lance 12, and a charging device 13. In the converter equipment 10, the decarburization process of the primary refining is performed by the oxygen gas supplied from the upper blowing lance 12 inserted from the furnace opening of the converter 11 to the hot metal 111. The hot metal 111 that has undergone decarburization is sent to the next step as molten steel 112. Further, in the decarburization treatment, phosphorus and silicon in the hot metal 111 also react with the oxygen gas 121 or the auxiliary raw material contained in the slag 113, and are taken into the slag 113 and stabilized. The charging device 13 charges the auxiliary raw material 131 including the quick lime forming the slag 113 into the converter 11. When the auxiliary raw material 131 is a powder, it is possible to blow it together with the oxygen gas 121 using the upper blowing lance 12.

The measurement control device 20 executes various measurements regarding refining processing in the converter equipment 10 and controls refining processing. Specifically, the measurement control device 20 includes a sublance 21, an oxygen supply device 22, and an auxiliary material charging control device 23. The sub-lance 21 is inserted together with the upper blowing lance 12 from the furnace opening of the converter 11, and the measuring device provided at the tip is immersed in the molten steel 112 at a predetermined timing during the decarburization treatment, so that the carbon concentration and the phosphorus concentration can be reduced. The component concentration of the molten steel 112 containing, the temperature of the molten steel 112 (hereinafter, also referred to as molten steel temperature), and the like are measured. The measurement using the sublance 21 during blowing is referred to as an intermediate sublance measurement in this specification. 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 upper blowing lance 12. The flow rate of the oxygen gas 121 supplied can be adjusted. The auxiliary material charging control device 23 controls the charging of the auxiliary material 131 by the charging device 13. Specifically, the auxiliary raw material input control device 23 controls the timing and the input amount of the auxiliary raw material 131. The above-described operations of the oxygen supply device 22 and the auxiliary raw material charging control device 23 are both executed according to a control signal 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 various communication devices that communicate with each element of the measurement control device 20 in a wired or wireless manner. The communication unit 31 receives the measurement result obtained by the measurement control device 20 and transmits a control signal to the measurement control device 20. To 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), a RAM (Random Access Memory), and a ROM (Read Only Memory). The program is stored in the ROM or the storage unit 33. In the converter blowing control device 30 described above, the operation unit 32 functions as a molten steel phosphorus concentration prediction unit 321, a correction term calculation unit 322, and a medium-fluid material 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. These data are stored as initial data, for example, and are updated at any time according to the result of the calculation in the calculation unit 32. The input/output unit 34 includes an output device such as a display or a printer and an input device such as a keyboard, a mouse, or a touch panel. The output device outputs, for example, a value such as the phosphorus concentration in molten steel during blowing, which is predicted by the phosphorus concentration in molten steel prediction unit 321. The input device acquires, for example, an instruction input regarding the control executed by the medium-melting material amount correction unit 323.

In the converter blowing control device 30, the hot metal/auxiliary raw material data 331 stored in the storage unit 33 is the hot metal data relating to the hot metal 111 blown in the converter 11, and the auxiliary hot metal supplied to the converter 11. And auxiliary material data regarding the material 131. The hot metal data includes, for example, the initial hot metal weight for each charge, the concentrations of hot metal components (carbon, silicon, phosphorus, manganese, etc.), hot metal temperature, hot metal ratio, and the like. In addition, the auxiliary material data includes the components of the auxiliary material 131, the input amount for each charge, and the like. As described above, in the present embodiment, since the predicted value of the phosphorus concentration in the molten steel during the blowing process is calculated before the start of the blowing process, the hot metal data and the auxiliary raw material data are newly predicted to be the phosphorus concentration in the molten steel. Contains the planned value in the charge for which the value is calculated The predicted performance data 332 includes a predicted value and a performance value of the phosphorus concentration in the molten steel during the past blowing process in the converter 11. The target data 333 includes target values such as the component concentration and temperature in the hot metal 111 (or the molten steel 112) at the time of the intermediate sublance measurement or at the time of blowing stop, for example. The parameter 334 includes a parameter of a model formula for calculating a predicted value of the phosphorus concentration in molten steel described later.

In the computing unit 32, the molten steel phosphorus concentration predicting unit 321 which is the predicted value calculating unit in the present embodiment, based on the molten pig iron/sub-source material data 331 and the predicted performance data 332 read from the storage unit 33, Calculate the predicted value of phosphorus concentration in molten steel. Specifically, the molten steel phosphorus concentration predicting unit 321 calculates a predicted value of the molten steel phosphorus concentration by adding a correction term to a function that associates the molten pig iron/auxiliary raw material data 331 with the molten steel phosphorus concentration during the blowing process. To do. As will be described later, the correction term calculation unit 322 expresses the correction term used for calculating the predicted value of the phosphorus concentration in the molten steel in the state space model, and the molten steel medium phosphorus contained in the predicted performance data for this state space model. The correction term is calculated by applying a Kalman filter using the difference between the predicted concentration value and the measured value as the observed value. In addition, in the calculation unit 32, the medium-melting-material-amount correction unit 323, which is the input amount correction unit in the present embodiment, uses the auxiliary raw material 131 based on the predicted value of the phosphorus concentration in molten steel calculated by the phosphorus concentration in molten steel prediction unit 321. The amount of CaO-containing auxiliary material contained in the above, specifically, the amount of the medium-melting material such as quick lime, is corrected to a more appropriate value from the planned value included in the hot metal/auxiliary material data 331.

(Outline of process)
FIG. 2 is a flowchart schematically showing steps of a converter blowing control method according to an embodiment of the present invention. The illustrated process is executed before the start of the blowing process in the converter 11. In the illustrated example, first, the molten steel phosphorus concentration predicting unit 321 of the converter blowing control device 30 reads the hot metal/auxiliary raw material data 331 and the predicted performance data 332 from the storage unit 33 (steps S11 and S12). Here, the correction term calculation unit 322 calculates a correction term for predicting the phosphorus concentration in the molten steel based on the read predicted performance data 332. (Step S13). Next, the molten steel phosphorus concentration predicting unit 321 calculates a predicted value of the molten steel phosphorus concentration using the calculated correction term (step S14). Next, the medium-melting material amount correction unit 323 determines the medium-melting material 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 phosphorus concentration in molten steel prediction unit 321. The input amount of is corrected (step S15). The control signal including the corrected charging amount is transmitted to the auxiliary material charging control device 23 via the communication unit 31. After that, in the initial stage of the blowing process, the auxiliary material injection control device 23 injects a medium-soluble material such as quick lime as the auxiliary material 131 according to the corrected input amount.

FIG. 3 is an example of a time chart when the processing shown in FIG. 2 is executed. In the illustrated example, the predicted value of the phosphorus concentration in the molten steel at the time of the intermediate sublance measurement at the nth charge (n=1, 2,...) Is calculated, and the phosphorus concentration in the molten steel is actually measured at the intermediate sublance measurement. The value is retrieved. When the intermediate sublance measurement (step S21) is executed in the n-th charge, the actual measurement value of the phosphorus concentration in the molten steel is acquired, and the series of steps shown in FIG. 2 can be executed for the (n+1)th charge. become. Specifically, the correction term calculating unit 322 calculates the correction term (step S22), and the phosphorus concentration predicting unit 321 in the molten steel uses the calculated correction term to determine the in-molten steel during the intermediate sublance measurement at the (n+1)th charge. A predicted value of phosphorus concentration is calculated (step S23).

Next, the medium-melting-material-amount correction unit 323 corrects the amount of the medium-melting material to be charged (step S24), thereby setting the appropriate amount of the medium-melting material in the (n+1)th charge. The series of steps may be executed so that the above step S24 is completed before the addition of the medium material 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 nth charge, but these processes are completed after the blowing process of the nth charge is completed. May be started from. Alternatively, the processes up to step S24 may be finished before the nth charge blowing process is finished.

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

(Model formula)
In the present embodiment, the temporal change of the phosphorus concentration in molten steel (hereinafter, also referred to as [P] (%)) is represented by the primary reaction equation of the following equation (1). In addition, [P] _ini (%) represents an initial value of [P] (phosphorus concentration in hot metal), and k (sec ⁻¹ ) represents a dephosphorization rate constant.

From the equation (1), [P] after t seconds from the start of the blowing process is represented by the following equation (2). However, the dephosphorization rate constant k is assumed to be represented by a multiple regression equation with the operating factor X _j included in the hot metal/sub-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 hot metal/auxiliary raw material data 331 with the phosphorus concentration [P] in the molten steel during the blowing process. In the present embodiment, the accuracy of the predicted value of the phosphorus concentration in molten steel [P] is improved by adding the correction term (learning term) β _p to this function as shown in the following equation (4).

Considering the continuity of charging as shown in FIG. 3 above, the correction term β _p in the equation (4) can be regarded as a kind of time series data. Therefore, in the present embodiment, the correction term β _p is expressed by a state space model, which is one of the time-series data modeling methods. State-space models can be continuous or discrete, periodic or not, univariate or multivariate, stationary or non-stationary, It is a framework of a wide range of statistical models that can be applied to various time series data, and has the feature that the increase and decrease of time series data can be decomposed into factors such as trends, seasonal fluctuations, and regression fluctuations. The correction term β _p used in this embodiment is considered to be non-stationary, but the state space model can be applied as described above. On the other hand, other general time-series data modeling methods, such as autoregressive models, are applied to the correction term β _p , which is considered to be non-stationary, because the data to be analyzed is assumed to be stationary. It is not easy to do (it becomes necessary to make constant by variable conversion and difference processing). In addition, it is difficult for the autoregressive model to decompose the increase and decrease in time series data.

The state space model uses two equations, a state equation and an observation equation. The state equation expresses the quantity (state quantity) that is not measured, 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. When the state equation and the observation equation are both linear and the observation error can be assumed to be a normal distribution, an algorithm called a Kalman filter that corrects the state quantity using time series data of the observation value has been established. In the present embodiment, the correction term β _p is used as the state quantity, and the difference between the predicted value and the actual value of the phosphorus concentration [P] in the molten steel during the past blowing process is the observed value (value obtained by adding the measurement error to the state quantity). ), the Kalman filter is applied to accurately predict the correction term β _p .

As described above, the state space model has a feature that the increase/decrease in time series data can be decomposed into elements. More specifically, in the formulation of the state space model, the concept of state quantity is used, and the observation value is obtained by adding noise to the state quantity.Therefore, it is necessary to distinguish between the state quantity and noise included in the prediction error. Will be possible. Therefore, in the present embodiment, by accurately predicting the correction term β _p represented by the state space model using the Kalman filter, the prediction error corresponding to the correction term β _p is determined to be an element due to an essential process variation. It can be decomposed into noise elements. For elements that are caused by essential process fluctuations, the relationship between the prediction error and the operation factors can be clarified by associating the fluctuations of the elements with the specific fluctuations of the operation factors.

(Outline of Kalman filter)
The Kalman filter is a method for sequentially estimating the state quantity inside the model from the observed value when the dynamics of the target process follows a linear state space model. In the present embodiment, it is assumed that the state space model of the correction term β _p in Expression (4) is linear, and thus the Kalman filter can be applied. The Kalman filter targets a linear Gaussian state space model represented by the following equation (5). Note that x _t is a state quantity vector, y _t is an observation value vector, F _t is a time-varying n×m matrix, G _t is a time-varying n×1 matrix, H _t is a time-varying n×m matrix, and R _t ⁿ represents an n-dimensional vector space.

In the above equation (5), v _t is called system noise and w _t is called observation noise. In the present embodiment, it is assumed that v _t and x _t follow a multidimensional normal distribution such as the following Expression (6). Note that N(0,Q _t ) represents the mean 0, the multidimensional normal distribution of the variance-covariance matrix Q _t , and N(0,R _t ) represents the mean 0, the multidimensional normal distribution of the variance-covariance matrix R _t . Hereinafter, the variance-covariance matrix of system noise Q _t, also called variance-covariance matrix of the observation noise R _t.

In the Kalman filter algorithm, the initial value x _0|0 of the estimated value of the state vector and the initial value V _0|0 of the error covariance matrix of the estimated value of the state vector are given in the above state space model. Above, the procedure of prediction and filtering as described below is sequentially repeated.

First, in the procedure of prediction, as shown in the following Expression (7), the error variance of the estimated value x _{t−1 |t−1} of the state quantity vector and the estimated value of the state quantity vector at time (t−1) Using the covariance matrix Vt _-1|t-1 , the respective predicted values _xt|t-1 and Vt _|t-1 at time _t are calculated.

Next, in the filtering procedure, the correction value V _{t |t} and the Kalman gain K _t of the error covariance matrix of the estimated value of the state quantity vector at time _t are calculated as shown in the following equation (8). ..

Furthermore, using the Kalman gain K _t calculated by the above equation (8) and the observed value vector y _t at the time t, the predicted value of the state quantity vector at the time t calculated by the above equation (7) x _{t | t-1} of the correction value _{x t |} a _t, can be calculated as shown in the following equation (9).

The above equations (5) to (9) are examples of equations used in state estimation using the Kalman filter. The Kalman filter is already widely used as a state estimation method, and various specific mathematical expressions are known, not limited to the above example. Of course, these other examples can also be applied in the present embodiment.

(When randomly walking the state quantity)
As described above, in the present embodiment, the correction term β _p in the equation (4) for calculating the predicted value of the phosphorus concentration in molten steel is represented by the state space model, and the Kalman filter is applied to this state space model. Then, the correction term β _p is calculated.

Here, in equation (5), consider the setting as in equation (10) below.

この設定は、状態量と観測値が単変量で、状態量の状態遷移がランダムウォークに基づくことを意味している。この設定の場合、式（５）は式（１１）および式（１２）で表される状態空間モデルとなる。なお、ｘ_ｔは補正項β_ｐの真の値を表す状態量であり、ｙ_ｔは観測値である。

This setting means that the state quantity and the observed value are univariate, and the state transition of the state quantity is based on a random walk. In this setting, the equation (5) becomes the state space model represented by the equations (11) and (12). Note that x _t is a state quantity that represents the true value of the correction term β _p , and y _t is an observed value.

In the state space model expressed by the above equations (11) and (12), the state quantity x _t (the true value of the correction term β _p ) is randomly walked (the state quantity x _t at the next time point is stochastically The state quantity x _{t of} this time is very similar to the previous state quantity x _t−1 . Note that v _t and w _t are calculated in advance by the maximum likelihood method or the like using the difference between the predicted value and the actual value of the phosphorus concentration in molten steel [P] during the past blowing process. By applying the Kalman filter to the state space model represented by the equations (11) and (12), the state quantity x _t (correction term β _p true) of the observed value y _t−1 at the previous charge is calculated. Value) and the observed value y _t can be calculated.

(When introducing the regression effect to calculate the state quantity)
Furthermore, considering that the correction term β _p is influenced by the operation factor, the regression effect due to the operation factor may be introduced to calculate the state quantity x _t (true value of the correction term β _p ) and the observed value y _t. .. In this case, the above equations (11) and (12) of the state space model can be rewritten as the following equations (13) and (14). Here, X ₁ , X ₂ , and X ₃ are all variables corresponding to operating factors. Specifically, for example, X ₁ is the hot metal temperature (° C.), X ₂ is the CaO component concentration (%) in the hot recycled slag, and X ₃ is the hot recycled slag amount (ton). It is known from the operational knowledge that all of these variables have a great influence on the dephosphorization reaction. In other examples, more or fewer variables may be used, and different variables from the examples above may be used.

(Effect of state space model)
4 to 6 are graphs for explaining the effect of using the state space model in the embodiment of the present invention. In each graph, the predicted value and the actual measured value of the phosphorus concentration [P] in molten steel are the values at the time of the intermediate sublance measurement, and are therefore described as SL[P]cal and SL[P]act. The values of SL[P]cal and SL[P]act are both normalized. In FIG. 4, as Case 0, the predicted value SL[P]cal and the actually measured value SL[P]act when [P] is predicted using the above equation (2) not including the correction term β _p Relationships are shown. In FIG. 5, as the case 1, the correction term β _p is introduced by using the above equation (4) including the correction term β _p and the equations (5) to (12) to predict [P]. The relationship between the predicted value SL[P]cal and the measured value SL[P]act in the case is shown. In FIG. 6, as Case 2, the above equations (13) and (14) are used as the equations of the state space model, and the predicted value SL[P]cal and the actually measured value SL[ when the regression effect due to the operation factor is introduced are introduced. The relationship with P]act is shown.

In case 0 (FIG. 4), the standard deviation of the predicted value SL[P]cal is 0.752, and the average error of the measured value SL[P]act is 0.362. On the other hand, in case 1 (FIG. 5), the standard deviation is 0.750 and the error average is −0.076. That is, in Case 1, the tendency that the predicted value SL[P]cal in Case 0 as a whole becomes higher than the actually measured value SL[P]act is improved while maintaining the standard deviation (variation of predicted values). Furthermore, in case 2 (FIG. 6), the standard deviation is 0.744 and the error average is -0.013. That is, in Case 2, the center of the distribution of the predicted value SL[P]cal can be brought closer to the measured value SL[P]act than in Case 1 while maintaining the standard deviation.

(Adjusting the amount of solvent input)
By using the predicted value of the phosphorus concentration [P] in the molten steel, which is calculated by introducing the correction term β _p as described above, the CaO-containing auxiliary raw material to be introduced in the initial stage of the blowing process, specifically quicklime It is possible to optimize the input amount of the solvent material such as. As described above, in the converter blowing, blowing control combining static control and dynamic control is performed. In the present embodiment, in static control, an input amount of a solvent material such as quick lime determined in advance using a mathematical model based on a mass balance or a heat balance is used as a predicted value of [P] including a correction term β _p. Use to fix.

FIG. 7 is a graph for explaining the optimization of the amount of the solvent input in the present embodiment. As indicated by the solid line in FIG. 7, the amount of the medium-melting material input (kg/ton) per weight of the hot metal in the static control is determined in advance according to the target phosphorus concentration (%) in the molten steel. Specifically, when the target value of the phosphorus concentration in the molten steel in the current charge is P ₁ (%), the amount of the medium-solvent input determined in advance is W _CaO (kg/ton). On the other hand, when the predicted value of the phosphorus concentration in the molten steel calculated by introducing the correction term β _p is P ₂ (%) (P ₂ <P ₁ ), the medium-fluid material input amount W _CaO is maintained. , ΔP=P ₁ −P ₂ (%) causes excessive dephosphorization. In this case, it is considered that the relationship between the target phosphorus concentration in the molten steel and the amount of the medium-melting material input per the weight of the hot metal shown by the solid line in FIG. 7 is shifted to the broken line graph. Therefore, for example, as shown in the following formula (15), the amount of solvent admixture is corrected from W _CaO to W′ _CaO using the solvent adsorbent amount correction function f(ΔP). As a result, it is possible to add a sufficient amount of the medium material for dephosphorization, and to minimize the amount of slag generated due to the medium material while achieving the target value of the phosphorus concentration in the molten steel. it can.

In the embodiment of the present invention as described above, a correction for calculating the predicted value of the phosphorus concentration [P] in the molten steel by introducing the state space model assuming that the state quantity randomly walks and applying the Kalman filter. The learning of the term β _p can be performed sequentially. Further, by applying the Kalman filter to the state space model in which the regression effect is introduced, it is possible to distinguish the influence of the essential process variation contained in the correction term β _p from the noise. 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, the amount of CaO-containing auxiliary raw material that is introduced in the initial stage of the blowing process can be optimized, and while achieving the target value of the phosphorus concentration in molten steel. The amount of slag generated due to the solvent material can be suppressed to the minimum.

The preferred embodiments of the present invention have been described above in detail with reference to the accompanying drawings, but the present invention is not limited to these examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention pertains can come up with various changes or modifications within the scope of the technical idea described in the claims. Of course, it is understood that these also 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 device, 20... Measurement control device, 21... Sub lance, 22... Oxygen supply device, 23... Sub raw material input control device , 30... Converter blowing control device, 31... Communication unit, 32... Computing unit, 321... Molten steel phosphorus concentration predicting unit, 322... Correction term calculating unit, 323... Medium-fluid material amount correction unit, 33... Storage unit, 331 ... hot metal/auxiliary raw material data, 332... predicted performance data, 333... target data, 334... parameter, 34... input/output section, 111... hot metal, 112... molten steel, 113... slag, 121... oxygen gas, 131... auxiliary raw material.

Claims (7)

In the converter, the hot metal data relating to the hot metal blown in the converter, and the auxiliary raw material data relating to the auxiliary raw material to be fed into the converter are related to the phosphorus concentration in the molten steel during the blowing treatment in the converter. Prediction value calculating means for calculating the prediction value of the phosphorus concentration in the molten steel by adding a correction term learned based on the prediction result data including the prediction value and the actual value of the phosphorus concentration in the molten steel during the past blowing process ,
The correction term is expressed by a state space model, and the correction term is applied to the state space model by applying a Kalman filter whose observed value is the difference between the predicted value and the actual value included in the predicted actual data. A converter blowing control device comprising: a correction term calculating means for calculating. The predicted performance data includes a predicted value of the phosphorus concentration in molten steel at the time of the intermediate sublance measurement during the past blowing process in the converter, and the actual value acquired in the intermediate sublance measurement. The described converter blowing control device. The converter blowing control device according to claim 1 or 2, wherein the correction term calculation means randomly walks the state quantity in the state space model. The converter blowing control device according to claim 1 or 2, wherein the correction term calculating means calculates the state quantity in the state space model by introducing a regression effect due to an operation factor of the blowing process. The injection amount correcting means for correcting the injection 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, The input amount correcting means according to claim 1. Converter blowing control device. In the converter, the hot metal data relating to the hot metal blown in the converter, and the auxiliary raw material data relating to the auxiliary raw material to be fed into the converter are related to the phosphorus concentration in the molten steel during the blowing treatment in the converter. A predicted value calculation step of calculating the predicted value of the phosphorus concentration in the molten steel by adding a correction term learned based on the predicted result data including the predicted value and the actual value of the phosphorus concentration in the molten steel during the past blowing process, and ,
The correction term is expressed by a state space model, and the correction term is applied to the state space model by applying a Kalman filter whose observed value is the difference between the predicted value and the actual value included in the predicted actual data. A converter blowing control method, which comprises a step of calculating a correction term. In the converter, the hot metal data relating to the hot metal blown in the converter, and the auxiliary raw material data relating to the auxiliary raw material to be fed into the converter are related to the phosphorus concentration in the molten steel during the blowing treatment in the converter. Prediction value calculating means for calculating the prediction value of the phosphorus concentration in the molten steel by adding a correction term learned based on the prediction result data including the prediction value and the actual value of the phosphorus concentration in the molten steel during the past blowing process ,
The correction term is expressed by a state space model, and the correction term is applied to the state space model by applying a Kalman filter whose observed value is the difference between the predicted value and the actual value included in the predicted actual data. A program for causing a computer to function as a converter blowing control device including a correction term calculating means for calculating.

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