JP2021031684A - Converter blowing control device, statistic model construction device, converter blowing control method, statistic model construction method and program - Google Patents

Abstract

To improve the controllability of a phosphorous concentration in converter blowing by performing an operation of adjusting an Si concentration in molten iron at a suitable timing.SOLUTION: A converter control device comprises a desiliconization speed constant calculation part where, using a statistic model using molten iron data regarding molten iron to be subjected to blowing treatment by a converter as explanatory variables, the estimation value of the primary desiliconization constant of the molten iron is calculated and an Si concentration estimation part where, based on an Si concentration before the blowing treatment of the molten iron and the primary desiliconization speed constant, the Si concentration of the molten iron at a prescribed time during blowing treatment is estimated.SELECTED DRAWING: Figure 1

Description

The present invention relates to a converter smelting control device, a statistical model construction device, a converter smelting control method, a statistical model construction method and a program.

In the preliminary hot metal treatment using a converter, the dephosphorization treatment is the main purpose. The dephosphorization reaction is represented by the following formula, and it is necessary to increase the CaO concentration and FeO concentration in the slag in order to promote the reaction. In the formula (1), () means a substance in the slag, and [] means a substance in the hot metal.
3 (CaO) + 5 (FeO) + 2 [P] = (3CaO · P ₂ O ₅ ) + 5 [Fe]

In the general case, (FeO) in the above formula, that is, the FeO concentration in the slag increases after the desiliconization reaction is completed. Therefore, the above dephosphorization reaction proceeds after the completion of the desiliconization reaction.

Here, since the desiliconization reaction occurs at the same time as the decarburization reaction, the desiliconization reaction rate is affected by the carbon concentration of the hot metal. That is, if the carbon concentration of the hot metal is high, the amount of oxygen distributed to the decarburization reaction increases, so that the desiliconization reaction rate decreases. Therefore, the desiliconization reaction rate varies from charge to charge.

In addition, in recent years, the Si concentration of hot metal ejected from the blast furnace tends to be higher than before. When the Si concentration of the hot metal is high, the difference in the desiliconization reaction end time for each charge caused by the variation in the desiliconization reaction rate as described above becomes large, and it becomes difficult to predict the start time of the desiliconization reaction. As a result, the phosphorus concentration Controllability is reduced.

On the other hand, Patent Document 1 describes a mathematical model expressing a fire point reaction, which is an oxidation reaction between the hot metal and the blown oxygen supplied from the top blown lance to the hot metal, and a region where the fire point reaction proceeds. The Si concentration of hot metal is sequentially estimated using a composite reaction model that combines a mathematical model that expresses the slag metal interface reaction, which is the reaction at the interface between hot metal and slag that progresses in a region different from the hot spot region. , A technique for adjusting the Si concentration of hot metal by adding an auxiliary raw material containing iron oxide or increasing the amount of oxygen blown from the top-blown lance according to the estimation result is described.

JP-A-2018-95943

According to the technique described in Patent Document 1 above, it is expected that by adjusting the Si concentration of the hot metal, the variation in the desiliconization end time for each charge is reduced, and as a result, the controllability of the phosphorus concentration is improved. To. However, for example, when an auxiliary raw material is added, preparatory work such as cutting out iron ore is required, so it takes time to actually add the auxiliary material after it is determined that the Si concentration needs to be adjusted. In addition, since it takes time for the Si concentration in the hot metal to change after the addition of auxiliary materials and the adjustment of the amount of oxygen blown in, the Si concentration is actually adjusted by control according to the sequential estimation results. It was found that the timing of the desiliconization may be delayed and the effect of reducing the variation in the desiliconization end time may not be sufficient.

Therefore, the present invention improves the controllability of the phosphorus concentration in the converter blowing by making it possible to carry out the operation for adjusting the Si concentration in the hot metal at an appropriate timing. It is an object of the present invention to provide a model construction device, a converter blowing control method, a statistical model construction method and a program.

According to a certain viewpoint of the present invention, the primary desiliconization rate constant of hot metal (desiliconization of desiliconization reaction assuming a primary reaction) is used by using a statistical model using hot metal data related to hot metal blown in a converter as an explanatory variable. The desiliconization rate constant calculation unit that calculates the estimated value of the rate constant) and the Si concentration of the hot metal at a predetermined time during the blowing process are estimated based on the Si concentration before the hot metal blowing process and the primary desiliconization rate constant. A converter blowing control device including a Si concentration estimation unit is provided.

According to another aspect of the present invention, a data collecting unit that collects hot metal data regarding hot metal that is blown in a converter and data on operating factors during the blowing process, and blowing based on the hot metal data and operating factors. Based on the Si concentration sequential estimation unit that sequentially estimates the Si concentration in the hot metal in the smelting process and the result of the sequential estimation, a statistical model is constructed with the hot metal data as the explanatory variable and the primary desiliconization rate constant of the hot metal as the objective variable. A statistical model construction device including a statistical model construction unit is provided.

According to yet another viewpoint of the present invention, a step of calculating an estimated value of the primary desiliconization rate constant of hot metal using a statistical model using hot metal data related to hot metal blown in a converter as an explanatory variable, and hot metal. Provided is a converter blowing control method including a step of estimating the Si concentration of hot metal at a predetermined time during the blowing process based on the Si concentration before the blowing process and the primary desiliconization rate constant.

According to still another aspect of the present invention, a step of collecting hot metal data on hot metal to be blown in a converter and data on operating factors during the blowing process, and blowing based on the hot metal data and operating factors. Statistics including a step of sequentially estimating the Si concentration in the hot metal in the treatment and a step of constructing a statistical model using the hot metal data as an explanatory variable and the primary desiliconization rate constant of the hot metal as the objective variable based on the result of the sequential estimation. A model building method is provided.

According to yet another aspect of the present invention, the desiliconization rate constant for calculating the estimated value of the primary desiliconization rate constant of the hot metal using a statistical model using the hot metal data for the hot metal blown in the converter as an explanatory variable. A converter blowing control including a calculation unit and a Si concentration estimation unit that estimates the Si concentration of the hot metal at a predetermined time during the blowing process based on the Si concentration before the hot metal blowing process and the primary desiliconization rate constant. A program for operating a computer as a device is provided.

According to yet another aspect of the present invention, a data collection unit that collects hot metal data regarding hot metal that is blown in a converter and data on operating factors during the blowing process, and based on the hot metal data and operating factors. Based on the Si concentration sequential estimation unit that sequentially estimates the Si concentration in the hot metal in the blowing process and the result of the sequential estimation, a statistical model is constructed with the hot metal data as the explanatory variable and the primary desiliconization rate constant of the hot metal as the objective variable. A program for operating a computer as a statistical model building device including a statistical model building unit is provided.

With the above configuration, for example, it is possible to determine whether or not an operation for adjusting the Si concentration in the hot metal is necessary before the start of blowing, so that the Si concentration in the hot metal can be adjusted more effectively. As a result of reducing the difference in the end time of the desiliconization reaction for each charge, the controllability of the phosphorus concentration in the converter blowing can be improved.

It is a figure which shows the schematic structure of the refining equipment including the converter blowing control device which concerns on one Embodiment of this invention. It is a flowchart which shows roughly the process of the converter blowing control method which concerns on one Embodiment of this invention. It is a conceptual diagram of a composite reaction model. It is a graph which shows the accuracy verification result of the estimation of the primary desiliconization rate constant k _{Si by a statistical model.} It is a graph for demonstrating the optimization of the amount of a melt | melt material input in one Embodiment of this invention. It is a figure which shows the schematic structure of the refining equipment including the statistical model construction apparatus which concerns on other embodiment of this invention. It is a flowchart which shows the process of the statistical model construction method which concerns on other Embodiment of this invention. It is a graph which shows the Example and the comparative example of this invention.

Preferred embodiments of the present invention will be described in detail below 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, so that duplicate description will be omitted.

(Outline of equipment)
FIG. 1 is a diagram showing a schematic configuration of a refining facility including a converter blowing control device according to an 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. Hereinafter, each part will be further described.

The converter equipment 10 includes a converter 11, a top blowing lance 12, a flue 13, a tuyere 14, and a charging device 15. In the converter equipment 10, the top blowing lance 12 inserted from the furnace port of the converter 11 supplies oxygen gas 121 to the hot metal 111 in the converter 11. In the decarburization treatment of the primary refining, the carbon in the hot metal 111 reacts with the oxygen gas 121 _{to become CO gas or CO 2} gas, and these gases are discharged via the flue 13. The hot metal 111 that has undergone the decarburization treatment is sent to the next process 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 to be stabilized. On the other hand, a bottom-blown gas 141 such as nitrogen gas or argon gas is blown from the tuyere 14 to stir the hot metal 111 and promote the above reaction. The charging device 15 charges the auxiliary raw material 151 containing quicklime or limestone constituting the slag 113 and an oxygen-containing auxiliary material such as iron ore for supplying oxygen to the hot metal 111 into the converter 11. When the auxiliary material 151 is a powder, it can be blown together with the oxygen gas 121 by using the top blowing lance 12.

The measurement control device 20 executes 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 exhaust gas analyzer 22, and an exhaust gas flow meter 23 as a measurement system. The sublance 21 is inserted from the furnace port of the converter 11 together with the top blown lance 12, and the measuring device provided at the tip is immersed in the molten steel 112 at a predetermined timing during the decarburization treatment, whereby the molten steel 112 containing a carbon concentration is contained. (The carbon concentration in the molten steel is referred to as the molten steel carbon concentration), the temperature of the molten steel 112 (also referred to as the molten steel temperature), and the like are measured. Such a measurement using the sublance 21 is also referred to as a sublance measurement in the following description. The exhaust gas analyzer 22 analyzes the components of the gas discharged via the flue 13. Specifically, the exhaust gas analyzer 22 _{measures the concentrations of CO, CO 2} and O ₂ contained in the exhaust gas (the concentration of each component is referred to as the exhaust gas component concentration). On the other hand, the exhaust gas flow meter 23 measures the flow rate of the gas discharged through the flue 13 (referred to as the exhaust gas flow rate). The result of the above sublance measurement and the measurement result of the exhaust gas analyzer 22 and the exhaust gas flow meter 23 are transmitted to the converter blowing control device 30.

On the other hand, the measurement control device 20 includes a lance drive device 24, an oxygen supply device 25, a bottom blowing gas supply device 26, and an input control device 27 as a control system. The lance drive device 24 drives the top blow lance 12 in the vertical direction. Thereby, the height of the top blowing lance 12, that is, the distance from the hot metal 111 at the position where the oxygen gas 121 is supplied in the converter 11 can be adjusted. The oxygen supply device 25 supplies the oxygen gas 121 to the top blowing lance 12. The amount of oxygen blown, that is, the flow rate of the supplied oxygen gas 121 is adjustable. The bottom-blown gas supply device 26 supplies the bottom-blown gas 141 to the tuyere 14. The flow rate of the bottom blown gas 141 to be supplied can also be adjusted. The charging control device 27 controls the charging of the auxiliary raw material 151 by the charging device 15. Specifically, the charging control device 27 controls the charging timing and the charging amount of the auxiliary raw material 151. The operations of the lance drive device 24, the oxygen supply device 25, the bottom blowing gas supply device 26, and the charging control device 27 may be controllable according to a control signal from the converter blowing control device 30.

The converter blowing control device 30 is implemented by a device including a communication unit 31, a calculation unit 32, a storage unit 33, and an input / output unit 34, for example, a computer. The communication unit 31 is various communication devices that communicate with each element of the measurement control device 20 by wire or wirelessly, 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 arithmetic unit 32 is an arithmetic unit that executes various arithmetic operations according to a program. The arithmetic unit 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 of the arithmetic unit or the storage unit 33. The calculation unit 32 functions as a desiliconization rate constant calculation unit 321, a Si concentration estimation unit 322, and a blowing process control unit 323 by operating according to the program. The storage unit 33 is a storage capable of storing various types of data. The storage unit 33 contains, for example, hot metal data 331 for hot metal 111 to be blown in the converter 11, a statistical model 332 for calculating an estimated value of the _{primary desiliconization rate constant k Si, which will be described later, and a target Si concentration 333.} Stored. 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 Si concentration of the hot metal 111 at a predetermined time during the blowing process estimated by the Si concentration estimation unit 322. The input device acquires, for example, an instruction input regarding the control executed by the blowing process control unit 323. The arithmetic unit may be a PLC (Programmable Logic Controller) or may be realized by dedicated hardware such as an ASIC (Application Specific Integrated Circuit).

In the converter blowing control device 30, the desiliconization rate constant calculation unit 321 is blown in the converter 11 based on the statistical model 332 using the hot metal data 331 read from the storage unit 33 as an explanatory variable. The estimated value of the primary desiliconization rate constant k _{Si of the hot metal 111 is calculated.} As will be described later, _{the value of the primary desiliconization rate constant k Si} differs for each charge of the hot metal 111. Si concentration estimating unit 322, based on the estimated value of the blowing process before the Si concentration _{([Si] ini),} and the primary de-珪速constants _{k Si} calculated in de珪速degree constant calculating unit 321 of the hot metal 111, predetermined time during the blowing process, in particular to estimate the Si concentration of the molten iron 111 in desiliconization complete reference time _{t end ([Si] end)} . _{The [Si] end} estimated by the Si concentration estimation unit 322 is output to the user via the input / output unit 34, and an instruction input by the user referring to the output [Si] _{end via the input / output unit 34.} The blowing process control unit 323 may control the amount of auxiliary raw material input or the amount of blown oxygen during the blowing process according to the input. Alternatively, the blowing process control unit 323 compares the [Si] _end estimated by the Si concentration estimation unit 322 with the target Si concentration 333, and based on the result, the amount of auxiliary raw material input or the amount of blown oxygen during the blowing process. May be controlled automatically.

(Outline of process)
FIG. 2 is a flowchart schematically showing a process of a converter blowing control method according to an embodiment of the present invention. The illustrated process is performed, for example, before the start of the blowing process in the converter 11. In the illustrated example, first, the desiliconization rate constant calculation unit 321 of the converter blowing control device 30 is preliminarily measured and stored in the storage unit 33 with hot metal data 331, and in a process as described later. based on the constructed statistical model 332 to calculate the estimated value of the primary de-珪速constants _{k Si} hot metal 111 (step S11). Next, the Si concentration estimating unit 322, the estimated value of the blowing process before the Si concentration _{([Si] ini),} and the primary de-珪速constants _{k Si} calculated in de珪速degree constant calculating unit 321 of the hot metal 111 based on estimates the Si concentration of the molten iron 111 in desiliconization complete reference time _{t end ([Si] end)} ( step S12).

Next, the blowing process control unit 323 compares the [Si] _end estimated by the Si concentration estimation unit 322 with the target Si concentration 333, and based on the result, the amount of auxiliary raw material input or the blown oxygen during the blowing process. The amount is determined (step S13). More specifically, the blowing process control unit 323 increases or decreases the amount of auxiliary raw material input or the amount of blown oxygen from the reference value according to the difference between the _{estimated [Si] end and the target Si concentration 333.} A control signal including the determined auxiliary raw material input amount or blown oxygen amount is transmitted to the oxygen supply device 25 and the input control device 27 via the communication unit 31. After that, at an appropriate time during the blowing process, the charging control device 27 inputs the auxiliary raw material 151 according to the determined auxiliary raw material input amount, or the oxygen supply device 25 adds oxygen according to the determined blowing oxygen amount. The gas 121 is blown in (step S14).

By the above-mentioned treatment, for example, it is determined whether or not an operation for adjusting the Si concentration in the hot metal 111 at the time before the start of blowing, specifically, the amount of oxygen blown and the amount of auxiliary raw material input need to be corrected. possible for the operation to adjust the Si concentration of, for example, than the desiliconization complete reference time t _end run well before, it is possible to make the more reliably Si concentration in desiliconization complete reference time t _end to a predetermined value .. As a result, the difference in the end time of the desiliconization reaction for each charge becomes small, and the start time of the dephosphorization reaction can be predicted with high accuracy. As a result, the controllability of the phosphorus concentration in the converter blowing can be improved.

(The method for calculating the primary de珪速constants _{k Si)}
In actual operation, predetermined time, for example because it is difficult to measure the Si concentration [Si] _{end The} molten iron 111 in desiliconization complete reference time t _{end The,} primary de珪速constants in this embodiment in the blowing process k _Si is used. The primary desiliconization rate constant k _Si is a reaction rate constant when the desiliconization reaction during the blowing process is expressed by a primary reaction as shown in the equation (1). Equation (1), blowing start (Si concentration _{[Si] end The} molten iron 111) (time t = 0, blowing pretreatment of Si concentration _{[Si] ini} molten iron 111) desiliconization completed from the reference time _{t end} Eq. (2) is obtained by integrating in the interval up to.

In the above formula (2), _{the value of the Si concentration [Si] ini} before the blowing treatment of the hot metal 111 can be obtained by measuring the component concentration of the hot metal 111, and the desiliconization completion reference time _tend is used as an operational variable. It is set appropriately according to the equipment conditions and the type of steel to be manufactured. Therefore, if it is determined primary de珪速constants _{k Si} is able to estimate the Si concentration _{[Si] end The} molten iron 111 using equation (2) in the desiliconization complete reference time _{t end The.}

Therefore, in the present embodiment, a composite reaction model for sequentially estimating the Si concentration of hot metal described in JP-A-2018-95943 is used. Specifically, for example, the Si concentration in the hot metal 111 in the past blowing treatment is sequentially estimated using a composite reaction model, and the first Si concentration below a predetermined value (for example, 0.01%) is set to [Si. ] and _{end the,} the time at that time and desiliconization complete reference time _{t end the.} Then, _[Si] ini in equation (2), both are _{[Si] end The} and _{t end} becomes a known value, it is possible to calculate the primary de珪速constants _{k Si.} Furthermore, by preparing a sufficient number of pairs of hot metal data 331 in the past blowing process and the calculated primary desiliconization rate constant k _Si , the hot metal data 331 can be obtained with the primary desiliconization rate constant k _{Si as the objective variable.} A statistical model 332 can be constructed as an explanatory variable. Using a statistical model 332, will now be estimated value of the primary de-珪速constants k _Si for hot metal 111 to be blowing is calculated is as already mentioned.

(Composite reaction model for sequentially estimating the Si concentration of hot metal)
Here, a composite reaction model for sequentially estimating the Si concentration of hot metal, which is described in JP-A-2018-95943 and the like, will be further described. FIG. 3 is a conceptual diagram of a composite reaction model. The combined reaction model includes (a) a hot spot reaction which is an oxidation reaction between blown oxygen (oxygen gas 121) and hot metal 111 in the converter 11, and (b) hot metal 111 excluding the region where the hot spot reaction proceeds. The slag metal interface reaction, which is the reaction in the interface region with the slag 113, is modeled. In this composite reaction model, the slag metal interfacial reaction is described in, for example, S. Ohguchi et. Al, “Simultaneous dephosphorization and desulfurization of molten pig iron”, Ironmaking and Steelmaking, 1984, Vol. 11, No. 4, p. 41. Represented by a competitive reaction model.

(Fire point reaction model)
First, a mathematical model that expresses the fire point reaction will be described. In the fire point region, oxygen is supplied to the hot metal from a top-blown lance or the like, so that an oxidation reaction (fire point reaction) of Si, C, and Fe contained in the hot metal occurs. The mass balances of Si, C, and Fe in the fire point reaction are represented by the following formulas (3) to (5).

Here, in the above formulas (3) to (5), [Si] _f , [C] _f , and [Fe] _f are the Si concentration (%), C concentration (%), and C concentration (%) of the hot metal in the hot spot region. Fe concentration (%), [Si], [C], and [Fe] are the Si concentration (%), C concentration (%), and Fe concentration (%), k _{Si of the hot metal in the region other than the hot spot region.} Desiliconization reaction rate constant (% / sec), ΔC is the decarburization rate (% / sec) obtained from the exhaust gas data, ΔFe is the iron oxidation rate (% / sec) obtained from the exhaust gas data, and V _f is the fire point region. The volume of hot metal (m ³ ) and Q in the above are the amount of reflux of hot metal in the converter (m ³ / sec).

The decarburization rate ΔC obtained from the exhaust gas data is calculated from the following equation (6) using the measurement data of the exhaust gas analyzer 22 and the exhaust gas flow meter 23. In the formula (6), _Qoffgas is the exhaust gas flow rate (Nm ³ / hr) _{measured by the exhaust gas flow meter 23, and b g} (CO) and b _g (CO ₂ ) are in the exhaust gas measured by the exhaust gas analyzer 22. The CO concentration (%), the CO ₂ concentration (%), and W _f are the weights (ton) of the hot metal in the hot spot region.

_{When cheaper limestone (calcium carbonate: CaCO 3} is the main component) is used instead of quicklime (calcium oxide: CaO as the main component) as the CaO source that promotes the dephosphorization reaction, the hot metal is removed. and CO ₂ generated by the coal reaction, slag formation reactions calcium carbonate (CaCO ₃ → CaO + CO ₂₎ the following equation (7) be calculated more accurately decarburization rate ΔC by distinguishing and CO ₂ generated by the , May be used instead of equation (6). In the formula (7), [Delta] W _CaCO3 is slag formation speed of limestone (ton / sec), for example, the following equation is assumed time constant _{T CaCO3} first-order lag with respect to the input amount _{W CaCO3} of limestone (8) 60 It is calculated as (sec). It is a coefficient of [Delta] W _CaCO3 -0.12 is a constant determined stoichiometrically from the reaction formula _{(CaCO 3 → CaO + CO 2} ).

The iron oxidation rate ΔFe obtained from the exhaust gas data is consumed in the decarburization reaction and the desiliconization reaction from _{the oxygen amount FO 2} (Nm ³ / hr) supplied to the hot metal in the converter in the following formula (9). It is calculated using the amount of oxygen obtained by subtracting the amount of oxygen obtained. However, when the value on the right side of the equation (9) is smaller than 0 as shown in the equation (10), ΔFe = 0 is set.

On the other hand, the change in the concentration of Si and Fe oxidized in the fire point region corresponds to the change in the concentration _{of SiO 2 and FeO in the slag due to the fire point reaction.} This is because Si and Fe reduced by the oxidation reaction in the fire point region _{are changed to SiO 2} and FeO and incorporated into the slag. Therefore, the concentration change (% / sec) of SiO2 and FeO in the slag due to the fire point reaction is represented by the following formulas (11) and (12) from the above formulas (3) and (5). In the formulas (11) and (12), <SiO ₂ > _f and <FeO> _{f are the SiO 2} concentration (%) and FeO concentration (%) in the slag generated in the fire point region, _{and WS} is the weight of the slag. (Ton).

Further, the heat balance equation in the fire point region can be expressed as the following equation (13) in consideration of each reaction heat generated in the fire point reaction. In the formula (13), cp _f is the specific heat (J / (kg · ° C)) of the hot metal in the hot spot region. T _f is the temperature of the hot metal in the hot spot region (° C.), H _{reac, f} is the heat of reaction (J / sec) in the hot spot region, for example, [Si] _f , [C] _f , and [Fe] _f . Calculated using. RQ is the amount of heat flowing into the fire point region (J / sec) due to reflux in the converter, and is calculated using, for example, the hot metal temperature T (° C.) in a region other than the fire point region. RQ _f is the amount of heat outflow (J / sec) from the hot spot region due to reflux in the converter, and is _{calculated using, for example, the hot metal temperature T f} (° C.) in the hot spot region.

It is generated by the behavior of the hot metal component in the slag region and the slag reaction according to the above equations (3) to (5), equation (6) or equation (7), equations (9) to (13). _{The behavior of oxides (SiO 2} and FeO) flowing into the slag can be expressed.

(Slag metal interface reaction model)
Next, the slag metal interfacial reaction will be described. The reaction relating to the hot metal component at the slag metal interface is expressed by mass transfer driven by the difference between the equilibrium concentration of the hot metal component at the slag metal interface and the concentration of the hot metal component outside the fire point region. Specifically, the mass balance of the elements X _i contained in the hot metal in the slag metal interface is expressed by the following equation (14). In this embodiment, {X _i } = {C, Si, Mn, P, Fe}. In the formula (14), [X _i ] is the element X _i concentration (%) of the hot metal in the region other than the fire point region, V is the volume of the hot metal in the region other than the fire point region (m ³ ), and A is the slag metal interface. Area (m ² ), K _Xi is the mass transfer coefficient of element X _i (m / sec), [X _i ] ^* is the equilibrium concentration (%) of _{element X i} contained in the hot metal at the slag metal interface _{, [X i} ] _f the hot metal in the fire spot area element _{X i} concentration _{(%), W Xi, sUB} is a charged term to account for increases in the element _{X i} is a hot metal component due to introduction of auxiliary material (% / sec) is there.

Here, the equilibrium concentration [X _i ] ^* _{of the element X i} contained in the hot metal at the interface of the slag metal is expressed using ^{the interfacial oxygen concentration ao *} described in the above-mentioned document of S. Ohguchi et al. The interfacial oxygen concentration ao ^* is calculated from the oxygen balance equation at the slag metal interface.

On the other hand, the reaction related to the slag component at the slag metal interface is also expressed by mass transfer driven by the difference between the equilibrium concentration of the slag component at the slag metal interface and the concentration of the component in the bulk of the slag, similar to the reaction related to the hot metal component. Will be done. _{Specifically, the mass balance of the oxide XO j} contained in the slag at the slag metal interface is represented by the following formula (15). In this embodiment, {XO _j } = {CaO, SiO ₂ , FeO, MnO, P ₂ O ₅ , Al ₂ O ₃ }. In the formula (15), <XO _{j > is the concentration (%) of the oxide XO j} contained in the slag, VS is the volume of the slag (m ³ ), and KXO _j is the substance transfer coefficient (m / sec) of the oxide XO _j. ), <XO _j > ^* is the equilibrium concentration (%) of _{oxide XO j} contained in slag, _{and W XOj, SUB is the equilibrium concentration (%) of oxide XO j} contained in slag due to the addition of auxiliary materials such as quicklime, limestone, and scale. It is an input term (% / sec) for considering the increase.

Note that <SiO ₂ > and <FeO> are represented by the following equations (16) and (17) in which the production rates _{of SiO 2} and FeO in the fire point reaction are added to the right side of the equation (15). This makes it possible to express the change in the concentration of the slag component due to the inflow of the oxide produced by the fire point reaction into the slag.

Further, the heat balance of the hot metal portion and the heat balance of the slag portion constituting the slag metal interfacial reaction are represented by the following equations (18) and (19) in consideration of the heat of reaction such as the slag metal interfacial reaction. To. In the formula (18), W is the weight of the hot metal in the region other than the hot spot region (ton), cp is the specific heat of the hot metal in the region other than the hot spot region (J / (kg · ° C)), and T is other than the hot spot region. It is the temperature (° C.) of the hot metal in the region of. H _reac is the heat of reaction (J / sec) distributed to the hot metal out of the heat of reaction generated in the slag metal interfacial reaction, and is calculated based on the mass transfer of each component accompanying the slag metal interfacial reaction. Q _SUB is the amount of heat removed (J / sec) generated by the dissolution of the auxiliary raw material in the hot metal. In the formula (19), cp _S slag specific heat (J / (kg · ℃) ), T S is the temperature of the slag (° C.). H _{reac and S} are reaction heats (J / sec) distributed to the slag among the reaction heats generated in the slag metal interfacial reaction, and are calculated based on the mass transfer of each component accompanying the slag metal interfacial reaction. RQ _S is the amount of heat removed from the slag surface (J / sec).

From the above equations (14) to (19), the behavior of the hot metal component and the slag component at the slag metal interface including the behavior of the hot metal component due to the fire point reaction can be expressed.

The Si concentration of the hot metal during blowing can be sequentially estimated by the combined reaction model in which the fire point reaction model and the slag metal interfacial reaction model as described above are combined. Specifically, the Si concentration of the hot metal is determined by sequentially solving the simultaneous equations of equations (3) to (5), equation (6) or equation (7), and equations (9) to (19). The concentration of the hot metal component and the concentration of the slag component can be estimated sequentially.

As already described, the Si concentration of the hot metal during the blowing process can be sequentially calculated by using the composite reaction model as described above (the calculation may be carried out after the fact), and the result. A statistical model as shown in equation (20), with the _{primary desiliconization rate constant k Si} in the past blowing process calculated using the above as the objective variable and the operating factor Y _{k included in the hot metal data as the explanatory variable.} Multiple regression model) can be constructed.

_{Here, for example, when calculating the estimated value of the primary desiliconization rate constant k Si} before the start of the blowing process in the converter _{, the operating factor Y k,} which is the explanatory variable of the statistical model of the equation (20), is at the start of the blowing. Limited to what can be obtained by. Table 1 shows examples of such operating factors.

FIG. 4 is a graph showing the accuracy verification result of the estimation of the primary desiliconization rate constant k _{Si by the statistical model. The primary desiliconization rate constant k Si} ( _{shown as k Si} cal in the graph) estimated by the statistical model of Eq. (20) from the hot metal data obtained up to the start of blasting is the same for the blasting treatments carried out in the past. _{Regarding the blowing process, the correlation with k Si} ( _{shown as k Si} act in the graph) calculated from the result of sequential estimation of Si concentration by the composite reaction model was high, and the estimation accuracy of the statistical model was good.

(Converter blowing control using the primary de珪速constants k _Si)
An example of converter blowing control using the estimated value of the _{primary desiliconization rate constant k Si} calculated as described above will be described below. First, before the start of blowing, the estimated value of the _{primary desiliconization rate constant k Si} is calculated from the new hot metal data using the statistical model constructed at that time. On the other hand, for example, the desiliconization completion reference time _tend is set by the following procedure.

The desiliconization completion reference time _tend is set, for example, as a timing at which the Si concentration of the hot metal _{should reach a predetermined target Si concentration [Si] aim} (for example, 0.01%). Specifically, for example, the operating conditions such as the Si concentration, the hot metal temperature, and the basicity of the hot metal, and the equipment conditions such as the gas flow rate of the top blow and the bottom blow included in the new hot metal data obtained before the blowing process. and depending on the steel species to be produced, it is possible to set the desiliconization complete reference time t _{end the.} Table 2 shows examples of desiliconization complete reference time _{t end} to be set in accordance with the Si concentration _{[Si] ini} of hot metal pre-blowing process.

Next, using a primary de珪速level estimation value of the constant _{k Si,} blowing pretreatment of the Si concentration of the molten iron _{[Si] ini,} and desiliconization complete reference time _{t end,} which is set above formulas (2) estimating the Si concentration _{[Si] end the} molten iron in desiliconization complete reference time _{t end the.} Based on the result of comparing the estimated [Si] _end with the target Si concentration [Si] _aim , the planned input amount and / or the planned injection of the auxiliary material (CaO or oxygen-containing auxiliary material) during the blowing process. Correct the amount of oxygen. An example of such control is shown below.

(In the case of [Si] _end > [Si] _aim )
_{The required input amount W FeOre} (ton) of iron ore to be input as an oxygen-containing auxiliary raw material is determined according to the following formula (21). In the formula (21), input amount _{W Feore} iron ore, defined as the input amount required to oxidize the Si that is expected to be present in the hot metal in the desiliconizing complete reference time _{t end (Si + O 2 →} SiO 2) Will be done. In the formula (21), α is a conversion coefficient (Nm ³ / (% · ton)), and the value of α determined stoichiometrically (theoretically) is 8.0. W _M is the insertion amount of hot metal _{(ton), O FeOre} is oxygen content of iron ore ^(Nm 3 / ton).

However, if there is no thermal margin for the input amount of the auxiliary raw material, that is, if the following formula (22) does not hold, the iron ore input amount _W'FeOre (ton), that is, the formula (22) that can be input is equal. _{The amount of blown oxygen ΔO 2} corresponding to the difference between the input amount of iron ore and the required input amount W _FeOre, which is the formula, is calculated by the formula (23). _{Here, the cooling coefficient γ FeOre} in the formula (22) is based on the cooling effect of the scrap, assuming that the hot metal temperature drops by 20 ° C. when the scrap is charged with 1% of the hot metal weight. Shows the amount of temperature drop in hot metal temperature due to stone injection. If γ _FeOre is 1.0, the cooling effect of iron ore is equivalent to that of scrap. In the formula _{(22), f (T M} , T aim, ...) is a function representing the heat margin (° C.).

(In the case of [Si] _end ≤ [Si] _aim )
Blow control of the converter consists of static control and dynamic control. Static control is the amount of operation such as the amount of oxygen blown and the amount of auxiliary raw materials (medium-melting material and cold material) input required to hit the target values of the molten steel component concentration and molten steel temperature at the time of blowing stop before the start of blowing. To determine. These manipulated quantities are obtained using a mathematical model based on the material (oxygen) balance and the heat balance. Dynamic control aims to improve the hit rate by correcting the manipulated variable (the amount of oxygen blown and the amount of cold material) obtained by static control using the measured values of molten steel temperature and carbon concentration during blowing by sublance measurement. It is a thing. The manipulated variable in the dynamic control is also obtained by using a mathematical model composed of the oxygen balance equation and the heat balance equation as in the static control.

FIG. 5 is a graph for explaining the optimization of the amount of the filler material input in the case of _{[Si] end} ≤ [Si] _aim. As shown by the solid line in FIG. 5, the amount of the filler material charged per hot metal weight (kg / ton) in the static control is determined in advance according to the target phosphorus concentration [P] (%). Specifically, when the target value of the phosphorus concentration [P] is P ₁ (%), the amount of the admixture input determined in advance is W _CaO (kg / ton). In contrast, _[Si] state is _end ≦ _{[Si] aim,} since dephosphorization efficiency by medium welding material is in a state above the initially expected, medium welding material when maintaining the input amount _{W CaO ΔP} = _{P 1} - Excessive dephosphorization will occur by P _{2 (%).} In this case, it is considered that the relationship between the phosphorus concentration [P] shown by the solid line in FIG. 5 and the amount of the filler material input per hot metal weight is shifted to the broken line graph. Therefore, for example, as shown in the following equation (24), it is corrected to W _'CaO a medium welding material input amount from _{W CaO} with medium welding material amount correction function f ([Delta] P). This makes it possible to prevent excessive dephosphorization.

(Other embodiments)
FIG. 6 is a diagram showing a schematic configuration of a refining facility including a statistical model building apparatus according to another embodiment of the present invention. As shown in FIG. 6, the refining facility 1A includes a converter facility 10, a measurement control device 20, and a statistical model building device 30A. Since the converter equipment 10 and the measurement control device 20 are the same as the examples described with reference to FIG. 1, duplicated description will be omitted.

The statistical model construction device 30A is implemented by a device including a communication unit 31, a calculation unit 32, and a storage unit 33, for example, a computer, similarly to the converter blowing control device 30 in the example described with reference to FIG. Will be done. As a difference from the example of FIG. 1, in the statistical model building apparatus 30A, the data 334 of the operating factor during the blowing process is stored in the storage unit 33. Further, the calculation unit 32 functions as a data collection unit 324, a Si concentration sequential estimation unit 325, and a statistical model construction unit 326 by operating according to the program. Since the other configurations of the statistical model construction device 30A are the same as those of the converter blowing control device 30 in the example of FIG. 1, duplicate description will be omitted.

In the statistical model building apparatus 30A described above, the data collecting unit 324 collects hot metal data 331 regarding hot metal 111 to be blown in the converter 11 and data 334 of operating factors during the blowing process. Here, the operating factors during the blowing process are, for example, the component concentration of the molten steel 112 acquired by the measurement using the sublance 21 of the measurement control device 20, the temperature of the molten steel 112, and the exhaust gas component measured by the exhaust gas analyzer 22. Concentration, exhaust gas flow rate measured by the exhaust gas flow rate system, height of the top blown lance 12 controlled by the lance drive device 24, amount of blown oxygen supplied by the oxygen supply device 25, bottom blown supplied by the bottom blown gas supply device 26. It includes at least a part of the gas flow rate and the charging timing and charging amount of the auxiliary raw material 151 by the charging control device 27. The data collecting unit 324 collects, for example, hot metal data 331 and operating factor data 334 measured during the past blowing process and accumulated in the storage unit 33.

The Si concentration sequential estimation unit 325 sequentially estimates the Si concentration in the hot metal in the blowing process based on the hot metal data 331 and the operating factor data 334 collected by the data collection unit 324. The Si concentration in the hot metal can be estimated sequentially using, for example, the composite reaction model described above. The Si concentration sequential estimation unit 325 sequentially estimates the Si concentration in the hot metal for each of the past blowing treatments in which the data was collected, for example. Based on the result of the sequential estimation by the Si concentration sequential estimation unit 325, the statistical model construction unit 326 constructs a statistical model 332 using _{the hot metal data 331 as an explanatory variable and the hot metal primary desiliconization rate constant k Si as the objective variable.} Construction statistical model 332, for example by converter blowing controller 30 in the example of FIG. 1, is available from the new hot metal data 331 to calculate the estimated value of the primary de-珪速constants k _Si.

The converter blowing control device 30 and the statistical model construction device 30A described above may be the same device or different devices. When these are the same devices, the hot metal data 331, the statistical model 332, the target Si concentration 333, and the operating factor data 334 are stored in the storage unit 33, and the arithmetic unit 32 operates according to the program to calculate the desiliconization rate constant. It functions as a unit 321, a Si concentration estimation unit 322, a blowing process control unit 323, a data collection unit 324, a Si concentration sequential estimation unit 325, and a statistical model construction unit 326. In such an apparatus, the estimated value of the _{primary desiliconization rate constant k Si} is calculated from the new hot metal data 331 by using the statistical model 332 constructed based on the result of the sequential estimation of the Si concentration in the past blowing process. At the same time, the statistical model 332 can be updated by sequentially estimating the Si concentration for a new blowing process.

FIG. 7 is a flowchart schematically showing a process of a statistical model construction method according to another embodiment of the present invention. In the illustrated example, first, the data collection unit 324 of the statistical model construction apparatus 30A collects the hot metal data 331 regarding the hot metal 111 to be blown in the converter 11 and the data 334 of the operating factors during the hot water processing. (Step S21). Next, the Si concentration sequential estimation unit 325 sequentially estimates the Si concentration in the hot metal in the blowing process based on the hot metal data 331 and the operating factor data 334 collected in step S21 (step S22). Next, the statistical model construction unit 326 constructs a statistical model 332 using the hot metal data 331 as an explanatory variable and the hot metal primary desiliconization rate constant k _{Si as the objective variable based on the result of the sequential estimation in step S22 (step).} S23). Can be constructed above by processes such as, for example, a statistical model 332 for calculating the estimated value of the new hot metal data 331 primary de珪速constants k _Si.

FIG. 8 is a graph showing an example and a comparative example of the present invention, in which charge A (Ch.A) and charge B (Ch.B) having the same _{Si concentration [Si] ini before the hot metal blowing treatment are shown.} The Si concentration [Si] (%) of the hot metal, the FeO concentration <FeO> in the slag, and the phosphorus concentration [P] of the hot metal are shown for each of the above. In charge A, it was decided to increase the input amount of iron ore (auxiliary raw material) at the stage before the start of smelting based on the estimated value of the primary desiliconization rate constant k _{Si, and iron ore of 13.7 kg / ton at a time of 250 sec.} I put in a stone. On the other hand, in charge B, the amount of iron ore input was controlled based on the result of successive estimation of the Si concentration of the hot metal, and at the time of 371 sec, when it became clear that the Si concentration was higher than planned, 4. 4 kg / ton of iron ore was added.

As a result, in Charge A, the FeO concentration in the slag increases from the time when the iron ore is charged, and the decrease in the Si concentration of the hot metal is promoted, so that the desiliconization end time is accelerated. As a result, the start of the dephosphorization reaction was accelerated, and the target phosphorus concentration (0.020% to 0.025%) was achieved at the end of the blowing (720 sec). On the other hand, even in Charge B, the FeO concentration in the slag increased from the time when the iron ore was added, but the iron ore was added when the Si concentration in the hot metal had already decreased and the desiliconization was about to end, so the demolding was completed. The timing was hardly advanced, and as a result of the delay in the start of the dephosphorization reaction, the target phosphorus concentration could not be achieved at the end of the blowing (poor dephosphorization). From this result, in the embodiment of the present invention, because that could modify the input amount of iron ore at an earlier stage of blowing on the basis of the estimated value of the primary de-珪速constants k _Si (auxiliary materials) suitably phosphorus concentration It can be said that it was possible to control.

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

1 ... Smelting equipment, 10 ... converter equipment, 11 ... converter, 12 ... top blown lance, 13 ... flue, 14 ... tuyere, 15 ... input device, 20 ... measurement control device, 21 ... sublance, 22 ... exhaust gas Analyzer, 23 ... Exhaust flow meter, 24 ... Lance drive device, 25 ... Oxygen supply device, 26 ... Bottom blowing gas supply device, 27 ... Input control device, 30 ... Converter blowing control device, 31 ... Communication unit, 32 ... Calculation unit, 33 ... Storage unit, 34 ... Input / output unit, 321 ... Desiliconization rate constant calculation unit, 322 ... Si concentration estimation unit, 323 ... Blow processing control unit, 331 ... Hot metal data, 332 ... Statistical model, 333 … Target Si concentration.

Claims (9)

A desiliconization rate constant calculation unit that calculates an estimated value of the primary desiliconization rate constant of the hot metal using a statistical model that uses hot metal data related to hot metal blown in a converter as an explanatory variable.
A converter blowing control including a Si concentration estimation unit that estimates the Si concentration of the hot metal at a predetermined time during the hot metal based on the Si concentration before the hot metal blowing treatment and the primary desiliconization rate constant. apparatus. The first aspect of the present invention, further comprising a blowing process control unit that controls the amount of auxiliary raw material input or the amount of blown oxygen during the blowing process based on the result of comparing the estimated Si concentration with the target Si concentration. Converter blowing control device. The converter blowing control device according to claim 1 or 2, wherein the statistical model is constructed based on the result of sequentially estimating the Si concentration in the hot metal in the past blowing treatment. The Si concentration in the hot metal in the past blowing treatment is the hot point reaction, which is the oxidation reaction between the blown oxygen and the hot metal in the converter, and the hot metal and slag excluding the region where the hot point reaction proceeds. The converter blowing control device according to claim 3, wherein the converter blown control device is sequentially estimated using a composite reaction model in which each slag metal interfacial reaction, which is a reaction in the interfacial region, is modeled. A data collection unit that collects hot metal data related to hot metal that is blown in a converter and data on operating factors during the blowing process.
A Si concentration sequential estimation unit that sequentially estimates the Si concentration in the hot metal in the blowing process based on the hot metal data and the operating factors.
A statistical model construction device including a statistical model construction unit that constructs a statistical model using the hot metal data as an explanatory variable and the primary desiliconization rate constant of the hot metal as an objective variable based on the result of the sequential estimation. A step of calculating an estimated value of the primary desiliconization rate constant of the hot metal using a statistical model using hot metal data related to hot metal blown in a converter as an explanatory variable, and
A converter blowing control method including a step of estimating the Si concentration of the hot metal at a predetermined time during the hot metal based on the Si concentration before the hot metal blowing treatment and the primary desiliconization rate constant. A process of collecting hot metal data on hot metal that is blown in a converter and data on operating factors during the blowing process, and
A step of sequentially estimating the Si concentration in the hot metal in the hot metal in the blowing process based on the hot metal data and the operating factors, and a step of sequentially estimating the Si concentration in the hot metal.
A statistical model construction method including a step of constructing a statistical model using the hot metal data as an explanatory variable and the primary desiliconization rate constant of the hot metal as an objective variable based on the result of the sequential estimation. A desiliconization rate constant calculation unit that calculates an estimated value of the primary desiliconization rate constant of the hot metal using a statistical model that uses hot metal data related to hot metal blown in a converter as an explanatory variable.
A converter blowing control including a Si concentration estimation unit that estimates the Si concentration of the hot metal at a predetermined time during the hot metal based on the Si concentration before the hot metal blowing process and the primary desiliconization rate constant. A program that allows a computer to function as a device. A data collection unit that collects hot metal data related to hot metal that is blown in a converter and data on operating factors during the blowing process.
A Si concentration sequential estimation unit that sequentially estimates the Si concentration in the hot metal in the blowing process based on the hot metal data and the operating factors.
Based on the result of the sequential estimation, the computer functions as a statistical model construction device including a statistical model construction unit for constructing a statistical model using the hot metal data as an explanatory variable and the primary desiliconization rate constant of the hot metal as an objective variable. Program for.

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