JP7067533B2 - Si concentration prediction method for hot metal, operation guidance method, blast furnace operation method, molten steel manufacturing method and Si concentration prediction device for hot metal - Google Patents

Description

The present invention relates to a method for predicting the Si concentration of hot metal, an operation guidance method, a method for operating a blast furnace, a method for producing molten steel, and an apparatus for predicting the Si concentration of hot metal.

In the blast furnace / converter method, the components in the hot metal (particularly Si concentration) are important variables for optimizing the operation action across processes. For example, when the Si concentration of hot metal is high, it becomes a heat source in a converter, which leads to a reduction in the hot metal distribution ratio and contributes to an increase in crude steel production. On the other hand, if the Si concentration of the hot metal is too high, sloping may occur in the converter and the productivity of the converter may decrease.

Further, even in the hot metal pretreatment step such as topped dephosphorization, the de-Si reaction competes with the de-P reaction, which affects the blowing time and affects the lead time of the hot metal from the blast furnace to the converter. In this way, the components in the hot metal have a large effect on the productivity of the blast furnace / converter method, so if the future can be predicted, it will be possible to take appropriate actions to ensure productivity. .. For example, in a steelworks where there are multiple blast furnaces, the hot metal allocation plan is to preferentially allocate the hot metal from the blast furnace, which is predicted to have a high Si concentration in the future, to the charge of steelmaking with a high target temperature. It is possible to improve the accuracy. Furthermore, positive manipulation of the Si concentration value may lead to optimization of the pig iron distribution ratio.

On the other hand, since the blast furnace process has a large thermal inertia, for example, fluctuations in the hot metal temperature for about 8 hours in the future and the Si concentration of the hot metal accompanying it are large in the accumulation of past operations such as the pulverized coal flow rate, which is the operating variable of the blast furnace. Be affected.

Henrik Saxen, "Nonlinear Prediction of the Hot Metal Silicon Content in the Blast Furnace", ISIJ International, Vol.47 (2007), No.12, pp.1732-1737

Here, as a technique for predicting the Si concentration of hot metal, a statistical approach based on a database is known, for example, as shown in Non-Patent Document 1. However, in general, the prediction accuracy of statistical methods is guaranteed only when the database contains operating conditions similar to those to be predicted, and the prediction accuracy is guaranteed for unknown operating conditions. There is a concern that it will decline.

The operating conditions of the blast furnace include many instrumental variables such as coke ratio, air flow rate, and air temperature, and the time constant is long. When making predictions based on past actual data as in the technique of Non-Patent Document 1, it is necessary to consider the time series data of the operation variables, but the number of combinations of the time series data of the operation variables is enormous. Therefore, time series data with high similarity does not always exist.

The present invention has been made in view of the above, and can be applied to operating conditions unprecedented in the past, and can predict the Si concentration of the hot metal with high accuracy. It is an object of the present invention to provide a method, an operation guidance method, a blast furnace operation method, a molten steel manufacturing method, and a Si concentration prediction device for hot metal.

In order to solve the above-mentioned problems and achieve the object, the method for predicting the Si concentration of hot metal according to the present invention uses a physical model capable of calculating the state in the blaster in a non-steady state, and presents the operating variables of the blaster. Based on the first step of calculating the predicted value of the future hot metal temperature when the operation amount of the above is maintained and the actual data of the blast furnace, the degree of influence of the hot metal temperature on the Si concentration of the hot metal in the blast furnace is obtained. By inputting the second step of constructing the regression equation and the predicted value of the hot metal temperature calculated in the first step into the regression equation constructed in the second step, the future Si concentration of the hot metal is predicted. It is characterized by including a third step of calculating a value.

Further, in the method for predicting the Si concentration of hot metal according to the present invention, in the above invention, when the first step holds the current manipulated amount of the operating variable of the blast furnace using the physical model, future hot metal The predicted value of the temperature and the predicted value of the future ironmaking speed are calculated, and the second step is the influence of the hot metal temperature and the iron forming on the Si concentration of the hot metal in the blast furnace based on the actual data of the blast furnace. A regression equation is constructed by obtaining the degree of influence of the speed, and the third step constructs the predicted value of the hot metal temperature and the predicted value of the hot metal forming speed calculated in the first step in the second step. By inputting into the formula, the predicted value of the Si concentration of the hot metal in the future is calculated.

In order to solve the above-mentioned problems and achieve the object, the operation guidance method according to the present invention presents the predicted value of the Si concentration of the hot metal calculated by the above-mentioned method of predicting the Si concentration of the hot metal in the blast furnace. It is characterized by including steps to support the operation.

In order to solve the above-mentioned problems and achieve the object, the operation method of the blast furnace according to the present invention operates the blast furnace based on the predicted value of the Si concentration of the hot metal calculated by the above-mentioned method of predicting the Si concentration of the hot metal. It is characterized by including a step of adjusting the manipulated variable amount and controlling the blast furnace.

In order to solve the above-mentioned problems and achieve the object, the method for producing molten steel according to the present invention appropriately obtains hot metal based on the predicted value of the Si concentration of the hot metal calculated by the above-mentioned method for predicting the Si concentration of the hot metal. It is characterized by including a step of manufacturing molten steel by allocating it to a charge of steelmaking.

In order to solve the above-mentioned problems and achieve the object, the Si concentration predictor of the hot metal according to the present invention uses a physical model capable of calculating the state in the blaster state in the unsteady state, and presents the operating variables of the blaster. Based on the first means of calculating the predicted value of the future hot metal temperature when the operation amount of the above is maintained and the actual data of the blast furnace, the degree of influence of the hot metal temperature on the Si concentration of the hot metal in the blast furnace is obtained. By inputting the second means for constructing the regression equation and the predicted value of the hot metal temperature calculated by the first means into the regression equation constructed by the second means, the Si concentration of the hot metal in the future can be predicted. It is characterized by comprising a third means for calculating a value.

According to the hot metal Si concentration prediction method, the operation guidance method, the blast furnace operation method, the molten steel manufacturing method, and the hot metal Si concentration prediction device according to the present invention, it can be applied to operating conditions unprecedented in the past. It is possible to predict the Si concentration of the hot metal with high accuracy. This makes it possible to improve the productivity of molten steel produced by the blast furnace / converter method.

FIG. 1 is a block diagram showing a schematic configuration of a hot metal Si concentration predictor according to an embodiment of the present invention. FIG. 2 is a diagram showing input variables and output variables of a physical model used in the method for predicting the Si concentration of hot metal according to the embodiment of the present invention. FIG. 3 shows the operating variables (blowing flow rate, coke ratio, pulverized coal flow rate, blowing humidity, blowing air) when calculating the prediction transition of the control variable by the physical model in the Si concentration prediction method of the hot metal according to the embodiment of the present invention. It is a figure which shows the change of the operation amount of (temperature). FIG. 4 shows the prediction transition of the control variables (gas utilization rate, sol loss carbon amount, reducing material ratio, hot metal forming speed, hot metal temperature) calculated by the physical model in the Si concentration prediction method of hot metal according to the embodiment of the present invention. It is a figure which shows. FIG. 5 is a diagram showing the relationship between the predicted value of the change amount of the hot metal temperature calculated by the physical model and the actual value of the change amount of the hot metal temperature in the Si concentration prediction method of the hot metal according to the embodiment of the present invention. .. FIG. 6 is a diagram showing the correlation between the Si concentration of the hot metal and the hot metal temperature and the hot metal forming speed. FIG. 7 is a diagram showing the relationship between the predicted value of the change in the Si concentration of the hot metal predicted by the method for predicting the Si concentration of the hot metal according to the embodiment of the present invention and the actual value of the change in the Si concentration of the hot metal. ..

The Si concentration prediction method for hot metal, the operation guidance method, the operation method for a blast furnace, the manufacturing method for molten steel, and the Si concentration prediction device for hot metal according to the embodiment of the present invention will be described with reference to the drawings.

[Configuration of hot metal Si concentration prediction device]
First, the configuration of the hot metal Si concentration prediction device (hereinafter referred to as “control device”) according to the embodiment of the present invention will be described with reference to FIG. The control device 100 includes an information processing device 101, an input device 102, and an output device 103.

The information processing device 101 is composed of a general-purpose device such as a personal computer or a workstation, and includes a RAM 111, a ROM 112, and a CPU 113. The RAM 111 temporarily stores a processing program and processing data related to the processing executed by the CPU 113, and functions as a working area of the CPU 113.

The ROM 112 stores a control program 112a that executes the method for predicting the Si concentration of hot metal according to the embodiment of the present invention, and a processing program and processing data that control the operation of the entire information processing apparatus 101.

The CPU 113 controls the operation of the entire information processing apparatus 101 according to the control program 112a and the processing program stored in the ROM 112. The CPU 113 functions as a first means for performing the first step, a second means for performing the second step, and a third means for performing the third step in the method for predicting the Si concentration of the hot metal described later.

The input device 102 is composed of devices such as a keyboard, a mouse pointer, and a numeric keypad, and is operated when various information is input to the information processing device 101. The output device 103 is composed of a display device, a printing device, and the like, and outputs various processing information of the information processing device 101.

[Physical model configuration]
Next, a physical model used in the method for predicting the Si concentration of hot metal according to the embodiment of the present invention will be described. The physical model used in the present invention is the same as the method described in Reference 1 (Michiharu Hanedano et al .: “Study of burning operation by unsteady model of blast furnace”, Iron and Steel, vol.68, p.2369). It consists of a group of partial differential equations that take into account multiple physical phenomena such as reduction of iron ore, heat exchange between iron ore and coke, and melting of iron ore, and is a variable that indicates the state in the blast furnace in the unsteady state. It is a physical model that can calculate the output variable) (hereinafter referred to as "unsteady model").

As shown in FIG. 2, the main variables (input variables, blast furnace instrumental variables (also referred to as operating factors)) that change with time in the boundary conditions given to this unsteady model are as follows.
(1) Coke ratio at the top of the furnace (CR) [kg / t]: Ratio of the amount of iron ore to the amount of coke input from the top of the furnace (2) Blower flow rate (BV) [Nm ³ / min]: Blasted to the blast furnace Flow of air (3) Flow of enriched oxygen (BVO) [Nm ³ / min]: Flow of enriched oxygen blown into the blast furnace (4) Blast temperature (BT) [° C]: Temperature of air blown to the blast furnace (5) Flow rate of blast furnace (amount of blast furnace blown, PCI) [kg / min]: Weight of blast furnace used for 1 ton of hot metal production (6) Blast furnace moisture (BM) [g / Nm ³ ]: Humidity of the air blown to the blast furnace

The main output variables formed by the unsteady model are as follows.
(1) Gas utilization rate in the furnace (ηCO): CO ₂ / (CO + CO ₂ )
(2) Raw material and gas temperature (3) Ore reduction rate (4) Solution loss carbon amount (sol loss carbon amount) [kg / t]
(5) Oxygen intensity unit (6) Hot metal temperature (7) Hot metal formation speed (hot metal formation speed) [t / min]
(8) Amount of heat loss in the furnace body: Amount of heat taken by the cooling water when the furnace body is cooled by the cooling water (9) Reduction material ratio (RR) [kg / t]: Flow rate of pulverized coal per ton of hot metal and coke ratio The sum of).

In the present invention, the time step (time interval) when calculating the output variable is set to 30 minutes. However, the time step is variable depending on the purpose and is not limited to the value of the present embodiment. In the present invention, the output variables including the hot metal temperature and the hot metal forming rate, which change from moment to moment, are calculated using the above-mentioned unsteady model.

[Method for predicting the Si concentration of hot metal]
Next, a method for predicting the Si concentration of the hot metal according to the present embodiment will be described. The method for predicting the Si concentration of hot metal according to the present embodiment predicts the Si concentration of hot metal by using the above-mentioned non-stationary model capable of calculating the state in the blast furnace in the unsteady state. That is, in the present embodiment, the future prediction of the thermal state in the blast furnace is performed by the unsteady model considering the heat transfer and the reaction phenomenon in the blast furnace, and the Si concentration of the future hot metal is predicted through the information. Do it statistically.

The following two are known as mechanisms for determining the Si concentration of hot metal in a blast furnace. In the reaction formula shown below, [x] indicates a component in hot metal, and (x) indicates a component in slag.
(1) A mechanism in which SiO gas is generated in the combustion part of the tuyere of the blast furnace (SiO ₂ + C = SiO + CO) and absorbed by the hot metal dripping from the lower part of the furnace of the blast furnace (SiO + [C] = [Si] + CO).
(2) A mechanism in which Si is incorporated into the hot metal via a SiO ₂ reduction reaction ((SiO ₂ ) + 2C = [Si] + 2CO) at the slag-metal interface of the bottom of the blast furnace.

Since the thermodynamic equilibrium [Si] exceeds 10% and the actual Si concentration is at most about 2%, it is considered that the [Si] value has not reached the equilibrium and the kinetic factor is large. Has been done. Since the thermal state in the blast furnace has a great influence on both the above mechanisms (1) and (2), the present invention predicts the thermal state in the blast furnace by using the above non-stationary model. Use that as a foothold. Further, in the present invention, the hot metal temperature is used as a representative value of the thermal state in the blast furnace.

(First step)
In this step, using the above-mentioned unsteady model, the predicted value of the future hot metal temperature and the predicted value of the future hot metal formation rate when the current manipulated variable of the blast furnace operating variable is held are calculated. That is, in this step, it is assumed that the current manipulated variable of the manipulated variable is kept constant in the future, and the hot metal temperature and the hot metal forming speed are predicted by repeatedly executing the calculation by the unsteady model.

FIG. 3 shows an example of the instrumental variables of the blast furnace, and FIG. 4 shows an example of the control variables including the hot metal temperature. In FIGS. 3 and 4, the origin of time is the present time, that is, the timing of making a prediction. The figure also includes actual values (see broken line) in future sections that cannot be obtained at this time.

As shown in FIGS. 3 and 4, the predicted values of the hot metal temperature and the hot metal forming speed (see the solid line) are the actual values (see the broken line) even though the manipulated variables of the manipulated variables in the future section are not reflected in the prediction calculation. ) And correspondingly. This means that due to the large heat capacity of the process, for example, the transition of the hot metal temperature up to 10 hours in the future is greatly influenced by the accumulation of past operations of the manipulated variables.

FIG. 5 shows an example of the prediction accuracy of the predicted value of the hot metal temperature calculated in this step. The horizontal axis of the figure is the actual value of the change in the hot metal temperature 8 hours ahead, and the vertical axis is the predicted value of the change in the hot metal temperature 8 hours ahead. As shown in the figure, it can be seen that the amount of change in the hot metal temperature 8 hours ahead can be predicted with high accuracy.

(Second step)
In this step, the regression equation is calculated by obtaining the degree of influence of the hot metal temperature and the degree of influence of the iron forming speed on the Si concentration of the hot metal in the blast furnace based on the actual data of the blast furnace accumulated in the actual database (not shown). To construct.

Here, as shown in FIG. 6A, the Si concentration in the hot metal has a high correlation with the hot metal temperature. Since [Si] is transferred into the metal by the reduction reaction (endothermic) of the oxide, the higher the temperature, the higher the concentration. Further, as shown in FIG. 6 (b), a negative correlation is observed between the Si concentration in the hot metal and the iron forming rate. This is because, as described above, the reduction reaction of (SiO ₂ ) is extremely slow, so that the iron forming speed is low and the Si concentration increases as the hot metal residence time in the furnace increases.

In this step, a regression equation for predicting the Si concentration of hot metal is constructed by using the information of the predicted value of the hot metal temperature and the predicted value of the hot metal forming rate in the future (for example, 8 hours ahead) by the unsteady model. The format of the regression equation is as shown in the following equation (1), and the coefficients α, β, and γ in the equation are determined by the least squares method based on the actual data of the blast furnace accumulated in the actual database (not shown).

Change in Si concentration of future hot metal = α × Change in future hot metal temperature (* calculated by unsteady model) + β × Change in future hot metal formation rate (* calculated by unsteady model) + γ ・ ・ ・ (1)

As a method for statistically obtaining the influence of the hot metal temperature on the Si concentration of the hot metal and the influence of the hot metal forming speed based on the actual data, a statistical method other than the least squares method may be used. Further, the specific time corresponding to the "future" in the above equation (1) is determined according to the time constant for each process, but is often, for example, about 8 hours.

(Third step)
In this step, by inputting the predicted value of the hot metal temperature and the predicted value of the hot metal forming speed calculated in the first step into the regression equation of the above formula (1) constructed in the second step, the Si concentration of the future hot metal Calculate the predicted value of.

FIG. 7 shows an example of the Si concentration of the future hot metal predicted in this step. In the figure, the horizontal axis is the actual value of the change in the Si concentration of the hot metal 8 hours ahead, and the vertical axis is the predicted value of the change in the Si concentration of the hot metal 8 hours ahead. As shown in the figure, it can be seen that the amount of change in the Si concentration of the hot metal 8 hours ahead can be predicted with high accuracy.

[Operation guidance method]
It is also possible to apply the method for predicting the Si concentration of hot metal according to the present embodiment to the operation guidance method. It is also possible to apply the method for predicting the Si concentration of hot metal according to the present embodiment to the method for operating a blast furnace. In this case, in addition to the first step, the second step, and the third step described above, the predicted value of the Si concentration of the hot metal calculated in the third step is obtained by, for example, the steelmaking step or the hot metal pretreatment step via the output device 103. By presenting it to the operator of the blast furnace, steps are taken to support the operation of the blast furnace.

[Blast furnace operation method]
It is also possible to apply the method for predicting the Si concentration of hot metal according to the present embodiment to the method for operating a blast furnace. In this case, in addition to the first step, the second step and the third step described above, the manipulated variable of the blast furnace operating variable is adjusted based on the predicted value of the Si concentration of the hot metal calculated in the third step. Take steps to control the blast furnace. In this step, for example, when the future Si concentration is high (for example, more than 0.5 wt%), an operation of reducing the operation amount such as the pulverized coal ratio and the coke ratio is performed.

[Manufacturing method of molten steel]
It is also possible to apply the method for predicting the Si concentration of hot metal according to the present embodiment to the method for producing molten steel. In this case, in addition to the first step, the second step and the third step described above, the hot metal is assigned to an appropriate steelmaking charge based on the predicted value of the Si concentration of the hot metal calculated in the third step. Perform the steps to manufacture molten steel. In this step, for example, hot metal from a blast furnace predicted to have a high future Si concentration (for example, more than 0.5 wt%) is assigned to the charge of steelmaking having a high target temperature, and the future Si concentration is low (for example). The hot metal from the blast furnace, which is predicted to be less than 0.5 wt%), is allocated to the charge of steelmaking with a low target temperature.

According to the hot metal Si concentration prediction method, the operation guidance method, the blast furnace operation method, the molten steel manufacturing method, and the hot metal Si concentration prediction device according to the above-described embodiment, the operating conditions unprecedented in the past can be met. It can also be applied, and the Si concentration of the hot metal can be predicted with high accuracy. This makes it possible to improve the productivity of molten steel produced by the blast furnace / converter method.

That is, according to the hot metal Si concentration prediction method, the operation guidance method, the blast furnace operation method, the molten steel manufacturing method, and the hot metal Si concentration prediction device according to the present embodiment, the hot metal is kept close to the target value. Since the iron forming speed can be controlled while controlling the temperature or controlling the hot metal temperature to be constant, a highly efficient and stable blast furnace process can be realized.

As described above, the Si concentration prediction method for hot metal, the operation guidance method, the operation method for the blast furnace, the manufacturing method for molten steel, and the Si concentration prediction device for hot metal according to the present invention will be specifically described with reference to embodiments and examples for carrying out the invention. However, the gist of the present invention is not limited to these descriptions, and must be broadly interpreted based on the description of the scope of claims. Needless to say, various changes, modifications, etc. based on these descriptions are also included in the gist of the present invention.

For example, in the above-described embodiment, the Si concentration of the hot metal is predicted by using the information of the hot metal forming speed in addition to the hot metal temperature, but it is also possible to predict the Si concentration only from the hot metal temperature. In this case, in the first step, the unsteady model is used to predict the future hot metal temperature when the current manipulated variable of the blast furnace is held, and in the second step, the actual data of the blast furnace is used. Based on this, a regression equation was constructed by obtaining the degree of influence of the hot metal temperature on the Si concentration of the hot metal in the blast furnace. By inputting to the constructed regression equation, the predicted value of the Si concentration of the hot metal in the future is calculated. It should be noted that, as in the above-described embodiment, the case of predicting the Si concentration of the hot metal by using the information of the hot metal forming speed in addition to the hot metal temperature is better than the case of predicting by using only the hot metal temperature information. Needless to say, the prediction accuracy is high.

100 Control device 101 Information processing device 102 Input device 103 Output device 111 RAM
112 ROM
112a control program 113 CPU

Claims (5)

Using a physical model that can calculate the state in the blast furnace in the unsteady state, the first step to calculate the predicted value of the future hot metal temperature when the current manipulated variable of the operating variable of the blast furnace is held.
The second step of constructing a regression equation by obtaining the degree of influence of the hot metal temperature on the Si concentration of the hot metal in the blast furnace based on the actual data of the blast furnace.
By inputting the predicted value of the hot metal temperature calculated in the first step into the regression equation constructed in the second step, the third step of calculating the predicted value of the Si concentration of the hot metal in the future, and
Including
In the first step, using the physical model, the predicted value of the future hot metal temperature and the predicted value of the future hot metal forming speed when the current manipulated variable of the operating variable of the blast furnace is held are calculated.
In the second step, a regression equation is constructed by obtaining the degree of influence of the hot metal temperature and the degree of influence of the iron forming speed on the Si concentration of the hot metal in the blast furnace based on the actual data of the blast furnace.
In the third step, the predicted value of the hot metal temperature and the predicted value of the hot metal forming speed calculated in the first step are input to the regression equation constructed in the second step, thereby predicting the Si concentration of the hot metal in the future. A method for predicting the Si concentration of hot metal, which comprises calculating a value . An operation guidance method comprising a step of supporting the operation of a blast furnace by presenting a predicted value of the Si concentration of the hot metal calculated by the Si concentration prediction method of the hot metal according to claim 1 . Based on the predicted value of the Si concentration of the hot metal calculated by the method for predicting the Si concentration of the hot metal according to claim 1, the operation amount of the instrumental variable of the blast furnace is adjusted, and the step of controlling the blast furnace is included. How to operate the blast furnace. Based on the predicted value of the Si concentration of the hot metal calculated by the method for predicting the Si concentration of the hot metal according to claim 1, the hot metal is assigned to the charge of steelmaking at the target temperature according to the predicted value to manufacture the molten steel. A method for producing molten steel, which comprises steps. Using a physical model that can calculate the state in the blast furnace in the unsteady state, the first means to calculate the predicted value of the future hot metal temperature when the current manipulated variable of the operating variable of the blast furnace is held.
A second means for constructing a regression equation by obtaining the degree of influence of the hot metal temperature on the Si concentration of the hot metal in the blast furnace based on the actual data of the blast furnace.
A third means for calculating the predicted value of the Si concentration of the hot metal in the future by inputting the predicted value of the hot metal temperature calculated by the first means into the regression equation constructed by the second means.
Equipped with
The first means uses the physical model to calculate a predicted value of future hot metal temperature and a predicted value of future hot metal forming speed when the current manipulated variable of the operating variable of the blast furnace is held.
The second means constructs a regression equation by obtaining the degree of influence of the hot metal temperature and the degree of influence of the hot metal making speed on the Si concentration of the hot metal in the blast furnace based on the actual data of the blast furnace.
The third means predicts the future Si concentration of hot metal by inputting the predicted value of the hot metal temperature and the predicted value of the hot metal forming speed calculated by the first means into the regression equation constructed by the second means. A Si concentration predictor for hot metal, which is characterized by calculating a value .

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