Blast furnace operation prediction method, blast furnace operation training method, blast furnace operation design method, program, and terminal device

Abstract

A blast furnace operation prediction method according to the present invention predicts the state of a blast furnace using an unsteady mathematical model which calculates the state of a blast furnace, said blast furnace operation prediction method comprising: a first step for calculating the liquid level height of a molten material in the blast furnace; and a second step for adjusting, on the basis of the liquid level height of the molten material calculated in the first step, a process constant which is included in the unsteady mathematical model and which is related to a gas flow in the radial direction in the blast furnace.

Description

Blast furnace operation prediction method, blast furnace operation training method, blast furnace operation design method, program, and terminal device

[0001] The present invention relates to a blast furnace operation prediction method, a blast furnace operation education method, a blast furnace operation design method, a program, and a terminal device.

[0002] The blast furnace process is the main steelmaking process, accounting for more than 80% of Japan's crude steel production. In the blast furnace process, raw materials are charged from the top of the blast furnace and oxygen-containing gas is blown into the bottom of the furnace to produce molten iron. The internal volume of the blast furnace is approximately 5,000 m ３ The blast furnace process is a large-scale process, with a scale of up to 1000 tonnes. The goal of the blast furnace process is to produce molten iron stably with high productivity and a low reducing agent ratio (the amount of reducing agent required to produce one ton of molten iron). In particular, once a blast furnace's condition deteriorates, it takes a long time to return to a healthy state, resulting in significant losses such as a deterioration in the reducing agent ratio and a decrease in production volume over several months. For this reason, the ultimate goal of blast furnace operation is to carry out appropriate operating procedures according to the condition of the blast furnace and stabilize the condition of the blast furnace.

[0003] Blast furnaces have become larger to improve production efficiency, but their size means that the time constant for operation (the time required for the furnace's state to actually change after an operation is performed) is large. Furthermore, there are many factors that affect the state of a blast furnace, such as the particle size and reaction characteristics of the raw materials, and the molten material present in the basin. Therefore, stable blast furnace operation relies heavily on the many years of experience of skilled operators. To achieve stable blast furnace operation without relying on the experience of skilled operators, it is effective to utilize simulation technology that predicts the state of a blast furnace, which has been developed with the recent development of computer technology, and to provide training aimed at improving operator operation techniques.

[0004] Against this background, Patent Document 1 proposes a method for predicting the molten iron temperature using a physical model that can calculate the state inside a blast furnace in an unsteady state. Furthermore, education aimed at improving operator operation techniques has been carried out by methods such as education using textbooks, oral transfer of techniques from experienced operators to younger operators, and a method that allows operators to experience simulated blast furnace operation (see Patent Document 2).

[0005] Japanese Patent No. 6531782 Japanese Patent Application Laid-Open No. 2003-328017 International Publication No. 2022 / 168556

[0006] Kawasaki Steel Technical Report, Vol.14(1982) No.2, pp.134-144

[0007] As described above, in order to achieve stable blast furnace operation without relying on the experience of skilled operators, education aimed at improving operator operation techniques is effective. However, against the backdrop of personnel rationalization, a decrease in the number of skilled operators due to retirement associated with aging, and a decrease in the number of young operators due to a decline in the productive labor force, it is becoming difficult to pass on skills through textbook-based education or oral transmission. For this reason, a method that allows simulated experience of blast furnace operation, such as that described in Patent Document 2, could be a promising means. However, the effectiveness of this method largely depends on the calculation accuracy and calculation range of the simulation technology that calculates the state of the blast furnace, which is the core of the method.

[0008] The accuracy and scope of simulation techniques for calculating the state of a blast furnace depend heavily on the physical phenomena considered in the simulation. However, conventional simulation techniques do not consider the influence of molten material present in the basin at the bottom of the blast furnace. This is because there is no way to accurately grasp the state of the basin during actual blast furnace operation, making it impossible to quantitatively evaluate its influence. As a result, conventional simulation techniques have had problems such as reduced calculation accuracy during operating periods when the influence of molten material present in the basin is significant, or being unable to reproduce such operating periods.

[0009] The present invention has been made to solve the above-mentioned problems, and its object is to provide a blast furnace operation prediction method and program that can accurately predict the state of a blast furnace by taking into account the influence of molten material present in the basin. Another object of the present invention is to provide a blast furnace operation training method and terminal device that can improve blast furnace operation techniques. Another object of the present invention is to provide a blast furnace operation design method and terminal device that can suppress operational fluctuations and equipment troubles.

[0010] The blast furnace operation prediction method of the present invention is a method for predicting the state of a blast furnace using a non-steady mathematical model that calculates the state of the blast furnace, and includes a first step of calculating the liquid level of the molten material in the blast furnace, and a second step of adjusting a process constant related to the gas flow in the radial direction inside the blast furnace, which is included in the non-steady mathematical model, based on the liquid level of the molten material calculated in the first step.

[0011] The process constants may include a heat extraction coefficient and a reduction rate constant.

[0012] The first step may include a step of calculating the liquid surface height of the melt using the calculation results of the unsteady mathematical model.

[0013] The first step may include a step of calculating the liquid level of the molten material using operation monitoring information of the blast furnace.

[0014] The blast furnace operation education method of the present invention is a method for educating trainees on blast furnace operation by presenting the trainee with the state of the blast furnace predicted using the blast furnace operation prediction method of the present invention, and includes a third step of calculating changes in the state of the blast furnace within the calculation period by changing the process constants included in the non-steady mathematical model within the calculation period based on a predetermined fluctuation pattern, and a fourth step of presenting to the trainee changes in the blast furnace operation parameters and / or monitoring information obtained from the calculation results of the third step.

[0015] It is preferable that the method includes a fifth step of recalculating, in response to input of an operation variable for an operation at any time within the calculation period, a change in the state of the blast furnace starting from the any time using the input operation variable for the operation and the unsteady mathematical model, and a sixth step of presenting to the trainee the changes in the operation parameters and / or monitoring information of the blast furnace obtained from the recalculation results of the fifth step and the changes in the operation parameters and / or monitoring information of the blast furnace obtained from the calculation results of the third step.

[0016] The fifth step may be performed multiple times.

[0017] The blast furnace operation design method of the present invention is a method for designing blast furnace operation based on the state of the blast furnace predicted using the blast furnace operation prediction method of the present invention, and includes a third step of calculating changes in the state of the blast furnace within a calculation period by changing process constants included in the non-steady mathematical model within the calculation period based on a preset fluctuation pattern, and a fourth step of presenting to the designer changes in the blast furnace operation parameters and / or monitoring information obtained from the calculation results of the third step.

[0018] The program according to the present invention causes an information processing device to execute the blast furnace operation prediction method according to the present invention.

[0019] The terminal device according to the present invention includes an output means for outputting information relating to the state of the blast furnace predicted using the blast furnace operation prediction method according to the present invention.

[0020] It is preferable to provide an input means for inputting information about the operational control items to be input to the unsteady mathematical model and the manipulated variables of the operational control items.

[0021] The blast furnace operation prediction method and program according to the present invention enable accurate prediction of the state of the blast furnace by taking into account the influence of molten material present in the basin. Furthermore, the blast furnace operation training method and terminal device according to the present invention enable improvement of blast furnace operation techniques. Furthermore, the blast furnace operation design method and terminal device according to the present invention enable suppression of operational fluctuations and equipment troubles.

[0022] FIG. 1 is a diagram showing the configuration of a cold model. FIG. 2 is a diagram showing the relationship between measured and calculated values ​​of the liquid level shape. FIG. 3 is a diagram showing the relationship between measured and calculated values ​​of the pressure drop amount on the outlet side of the tuyere. FIG. 4 is a diagram showing the calculation results of the gas flow in the cold model. FIG. 5 is a diagram showing the relationship between the lower furnace heat removal coefficient and the liquid level height. FIG. 6 is a diagram showing the relationship between the reduction rate constant and the liquid level height. FIG. 7 is a diagram showing the time changes in blast furnace operation parameters. FIG. 8 is a diagram showing the time changes in blast furnace operation monitoring information. FIG. 9 is a block diagram showing the configuration of a blast furnace operation simulator according to one embodiment of the present invention. FIG. 10 is a diagram showing an example of a user interface screen. FIG. 11 is a diagram showing an example of an operation screen. FIG. 12 is a diagram showing the time changes in the blast flow rate, coke ratio, and Si concentration in molten iron.

[0023] [Concept of the Present Invention] The inventors of the present invention conducted a test using a cold model to quantitatively clarify the influence of molten material present in the molten metal basin on the state of a blast furnace. The configuration of the cold model used in the test is shown in Figure 1. In this test, first, spherical polyvinyl chloride particles simulating coke were filled into the cold model 1, and then water simulating molten material present in the molten metal basin was introduced into the lower part of the cold model 1. Next, air was injected from the inlet sides of four horizontally installed tuyere 2, and the pressure at the outlet side of the tuyere 2 was measured using a pressure gauge 3, as well as the shape of the water surface (liquid surface shape) in the cold model 1. Next, a numerical model was constructed that could calculate the pressure and liquid surface shape at the outlet side of the tuyere 2 based on the flow rate distribution (gas flow) of the injected air in the cold model 1. The pressure at the outlet side of the tuyere 2 and the liquid surface shape in the cold model 1 were calculated using the constructed numerical model.

[0024] In detail, in order to reproduce the experimental system of the cold model 1, the numerical model assumed that the entire furnace body of the cold model 1 was the calculation target, that the furnace body had a tuyere 2 at the bottom, and that packed particles and liquid existed inside the furnace body. Then, in order to reproduce the phenomenon in which the liquid surface shape and the pressure on the outlet side of the tuyere 2 change depending on the amount of liquid and the flow rate of the air blown in from the tuyere 2, simultaneous equations relating to the flow of air blown in from the tuyere 2 and the liquid surface shape were solved. Other calculation conditions such as particle size, viscosity, and density used were the values ​​of the material used in the actual test.

[0025] Specifically, the calculation was performed assuming that the air flow follows the Ergin equation shown in the following formula (1), the continuity equation shown in the following formula (2), and the equation of state for an ideal gas shown in the following formula (3).

[0026]

[0027]

[0028]

[0029] where ρ ｇ is the density of air (kg / m ３ ), p is the air pressure (Pa), ε is the void ratio of the packed bed (-), d ｒ is the packed particle diameter (m), q is the air flow velocity between packed particles (m / s), and ν is the dynamic viscosity of air (m ２ / s), r is the radial distance of the furnace body (m), K is the gas constant (J / K / kg), and T is the gas temperature (°C).

[0030] The liquid surface shape was calculated using the following equation (4), assuming that the drag force due to the injected air was equal to the hydrostatic pressure.

[0031]

[0032] where ρ ｌ is the density of the liquid (kg / m ３ ), g is the gravitational acceleration (m / s ２ ), h indicates the height of the liquid (m).

[0033] Figures 2(a) and 2(b) show the relationship between the measured and calculated liquid surface profile for Tests 1 and 2, in which different amounts of water were introduced into the cold model 1. Figures 3(a) and 3(b) show the relationship between the measured and calculated pressure drop at the outlet of the tuyere 2 for Tests 1 and 2. As shown in Figures 2(a) and 3(b) and 3(a) and 3(b), the calculated values ​​for both the liquid surface profile and the pressure drop closely matched the measured values. This indicates that the constructed numerical model reproduces with sufficient accuracy the relationship between the molten material in the blast furnace basin and the radial gas flow within the blast furnace. Figures 4(a) and 4(b) show the calculated gas flow within the cold model 1 for Tests 1 and 2. As shown in Figures 4(a) and 4(b), as the water level increased, the gas-free region rose to the tuyere level, and the proportion of gas flowing near the wall of the cold model 1 increased. This means that in blast furnace operation, the gas flow becomes peripheral depending on the height of the molten material, and the peripheral gas flow causes an increase in the amount of heat removed from the furnace wall and a decrease in reduction efficiency.

[0034] In this way, the above-mentioned numerical model makes it possible to quantitatively evaluate the effect of the height of the molten material in the blast furnace on blast furnace operation, i.e., the effect of the height of the molten material in the blast furnace on the radial gas flow in the blast furnace. Therefore, in the present invention, the relationship between the height of the molten material and the radial gas flow in the blast furnace is taken into consideration in an unsteady mathematical model, which is a physical model that can calculate the state inside the blast furnace in an unsteady state.

[0035] [Unsteady-state mathematical model] The unsteady-state mathematical model according to the present invention is a physical model for calculating the state of a blast furnace in an unsteady state, using the region from the furnace top to the hearth as a calculation domain. It is composed of a group of partial differential equations representing various reactions occurring in the blast furnace. Examples of input variables for the unsteady-state mathematical model include operational parameters such as blast conditions and raw material conditions, and the liquid level of the molten material. This unsteady-state mathematical model can predict the state of a blast furnace, such as the temperature and pressure distribution in the blast furnace and the distribution of the raw material fraction in the solid phase, by taking into account heat exchange and chemical reactions between the solid, liquid, and gas phases, pressure losses due to gas flow, etc. Examples of phenomena considered as chemical reactions include the reduction reaction of ore by reducing gas, the reduction reaction of ore by coke, the gasification reaction of coke by carbon dioxide and steam, the melting reaction of iron and slag, the combustion reaction of coke and materials injected into the tuyere (e.g., pulverized coal, hydrocarbon gas, etc.), and carburization reactions.

[0036] The movement of solids is assumed to occur as solids within the calculation domain are lost due to the above-mentioned reactions, and the lost solids are supplied continuously or intermittently from the furnace top (the upper part of the calculation domain). The boundary conditions for the calculation include the raw material flow conditions from the furnace top, the blast conditions near the tuyere, the heat removal conditions from the furnace body, and the molten material discharge conditions from the taphole. In some cases, the blast conditions from the shaft can also be used as boundary conditions. The unsteady mathematical model also contains process constants for tuning the calculated values ​​to the measured values. Examples of such process constants include constants for adjusting the reaction rate and heat exchange rate, and the void ratio of the packed bed.

[0037] [Method for predicting blast furnace operation] The blast furnace operation method according to the present invention is a method for predicting the state of a blast furnace using the above-described unsteady mathematical model, and includes: a first step of calculating a liquid level of molten material in the blast furnace; and a second step of adjusting a process constant, which is included in the unsteady mathematical model and is related to a gas flow in the radial direction of the blast furnace, based on the liquid level of molten material calculated in the first step. In the first step, the liquid level of molten material in the blast furnace is calculated using a known method. For example, the liquid level of molten material may be calculated using the calculation results of the unsteady mathematical model, or the liquid level of molten material may be calculated using blast furnace operation monitoring information (such as the iron-making rate, the iron-slag tapping rate, and the void fraction of the coke in the lower part of the furnace) (see, for example, Patent Document 3).

[0038] In the second step, the process constants related to the gas flow in the radial direction inside the blast furnace, which are included in the unsteady mathematical model, are adjusted based on the smelt level calculated in the first step. When using a one-dimensional unsteady mathematical model capable of predicting the state inside the blast furnace only in the vertical direction, it is advisable to consider the effect of the smelt level on blast furnace operation as follows. That is, the coefficient for adjusting the amount of heat removed from the furnace wall is changed according to the smelt level so as to simulate the increase in the amount of heat removed from the furnace wall due to the gas becoming a peripheral flow depending on the smelt level. Furthermore, the coefficient for adjusting the reduction rate is changed so as to simulate the decrease in reduction efficiency. At this time, the relationship between the smelt level and the various adjustment coefficients differs depending on the blast furnace; however, the analysis method and results for a specific blast furnace are shown below.

[0039] First, the operational parameters of a specific blast furnace for a specific period (preferably one year or more) are input into a one-dimensional unsteady mathematical model to obtain calculated operational results for the blast furnace. The method described in Patent Document 3 and other publications is then used to calculate the transition of the molten material level height during the specific period. Next, several periods of eight hours or more during which the operational state does not change significantly are extracted from the specific period, and the average values ​​of the operational parameters and actual operational results for each period are calculated. The calculated values ​​are then used as the average values ​​of the operational parameters and actual operational results for each extracted period. Furthermore, the average values ​​for the calculated operational results obtained by the one-dimensional unsteady mathematical model for the same period are also calculated, and the calculated values ​​are used as the average values ​​of the calculated operational results for each period.

[0040] Then, the process constants related to the radial gas flow in the blast furnace are adjusted so that the average value of the actual operation results in each extraction period matches the average value of the calculated operation results. Adjustable process constants related to the radial gas flow in the blast furnace include the heat removal rate Q [kJ / m ２ Examples include the heat transfer coefficient Xh [-] in the formula (Q = Xh × h (Tg - Te)) which expresses the heat transfer coefficient [kJ / m ２ / s / K], Tg is the furnace gas temperature [K], Te is the ambient temperature [K], Pr is the gas reduction potential [mol], and Rr is the reduction resistance [s] determined by the gas flow rate, ambient temperature, reducibility of the raw material, etc.

[0041] Other adjustable process constants related to the radial gas flow in the blast furnace include a coefficient for adjusting the gasification rate of the coke, a coefficient for adjusting the apparent void fraction (distribution) in the packed bed, and a coefficient for adjusting the heat transfer rate between the solid phase and the gas phase. The coefficient for adjusting the gasification rate of the coke is, for example, the parameter k in Equation (15) described in Non-Patent Document 1. Ｃ ＊ An example of a coefficient for adjusting the heat transfer rate between the solid phase and the gas phase is the dimensionless coefficient that is outer multiplied by the parameter h in equation (5) described in Non-Patent Document 1.

[0042] As described above, adjustable process constants related to the radial gas flow in the blast furnace include (a) the heat extraction coefficient, (b) the reduction rate constant, (c) a coefficient for adjusting the coke gasification rate, (d) the apparent porosity, and (e) a coefficient for adjusting the heat transfer rate between the solid and gas phases. In a one-dimensional unsteady mathematical model, the process coefficients that can be adjusted (the process coefficients are adjusted as fixed values) are the process constants (a) to (e). In a two-dimensional unsteady mathematical model, the process coefficients that can be adjusted (the process coefficients are adjusted according to the radial direction) are the process constants (a) to (e) (in principle, adjustment is possible by simply adjusting the process constant (d)).

[0043] In this example, the actual and calculated values ​​of heat loss were matched using the lower furnace heat removal coefficient, and the actual and calculated values ​​of gas utilization were matched using the reduction rate constant. The process constants can be adjusted manually by trial and error, or mechanically using a search method or other techniques. Figures 5 and 6 show the relationship between the lower furnace heat removal coefficient and reduction rate constant, adjusted to match the actual and calculated values, and the liquid level. In this example, a linear relationship between the two, as shown by the solid line, was formulated, and the formulated relationship was incorporated into a one-dimensional unsteady mathematical model.

[0044] The results of actual blast furnace operation were analyzed using the unsteady mathematical model created by the above method. The liquid level during actual operation was estimated using the method described in Patent Document 3. Furthermore, actual operational parameters (blast conditions, raw material conditions) and liquid level were input into the created unsteady mathematical model, and operation monitoring information such as molten iron temperature and solution loss reaction amount was output. Figures 7(a) to 7(d) show the time changes in the blast furnace operational parameters, and Figures 8(a) to 8(c) show the time changes in the operation monitoring information. In Figures 8(a) to 8(c), the solid lines show actual values, the dashed-dotted lines show values ​​calculated using a mathematical model that does not take the liquid level level into account, and the dashed lines show values ​​calculated using a mathematical model that takes the liquid level level into account.

[0045] As shown in Figures 7(a) to 7(d) and 8(a) to 8(c), during Period A, the amount of solution loss reaction increased, and the molten iron temperature subsequently decreased, even though the operational parameters did not change significantly. This is because the amount of slag tapped during Period A was low relative to the amount of slag produced, causing molten material to accumulate and the molten iron level to remain high. In fact, calculations that do not take the molten iron level into account are unable to reproduce the behavior during Period A. In contrast, calculations that do take the molten iron level into account are able to reproduce the actual operational behavior during Period A, such as the increase in the amount of solution loss reaction and the decrease in molten iron temperature. This confirms that the present invention allows for accurate prediction of the blast furnace state by taking into account the influence of molten iron in the basin.

[0046] [Educational method for blast furnace operation] Fig. 9 is a block diagram showing the configuration of a blast furnace operation simulator according to one embodiment of the present invention. As shown in Fig. 9, the blast furnace operation simulator according to one embodiment of the present invention includes an information processing device 10, an input device 11, and an output device 12. The information processing device 10 is configured by an information processing device such as a workstation or a personal computer. An operation simulation program 10a is stored in the information processing device 10. The information processing device 10 functions as a blast furnace operation simulator that executes the above-described blast furnace operation prediction method according to the present invention by having an arithmetic processing device such as a CPU in the information processing device 10 execute the operation simulation program 10a.

[0047] The input device 11 is composed of operation input devices such as a mouse pointer and a keyboard. The input device 11 inputs operation input information to the information processing device 10. The output device 12 is composed of output devices such as a printing device, a display device, and an audio output device. The output device 12 outputs various information in accordance with control signals from the information processing device 10. The input device 11 and the output device 12 function as terminal devices according to the present invention. The input device 11 and the output device 12 may be composed of a single device. Furthermore, the information processing device 10, the input device 11, and the output device 12 may be connected via a telecommunications line such as the Internet.

[0048] In the blast furnace operation simulator shown in FIG. 9 , an operator operates an input device 11 to input blast furnace operation parameters, etc., and an information processing device 10 predicts changes in the state of the blast furnace based on the input information and outputs the predicted information to an output device 12. This configuration allows trainees to virtually experience the operation of a blast furnace, making the blast furnace operation simulator useful as a training tool for operators on operation. Specifically, when using this blast furnace operation simulator, the trainee first selects the target blast furnace and case study example. By being able to select the target blast furnace, the calculation domain, boundary conditions, and initial values ​​for the state inside the furnace, which are determined by the furnace shape, can be changed to suit the trainee's actual experience.

[0049] As case studies, possible operational fluctuations or equipment troubles that may occur can be set, such as an increase in the fineness ratio of charged raw materials, an inability to tap hot metal due to a malfunction of the drilling machine, or the shutdown of the pulverized coal injection equipment. At this time, the information processing device 10 changes the calculation boundary conditions and internal variables according to a pre-set fluctuation pattern in response to the operational fluctuations or equipment troubles. This allows trainees to simulate operational fluctuations and equipment troubles. In particular, as described above, the unsteady mathematical model according to the present invention takes into account the influence of the molten liquid level, allowing trainees to learn operational procedures in response to changes in the molten liquid level, which could not be reproduced using conventional calculation models.

[0050] Next, the information processing device 10 executes the unsteady mathematical model according to the boundary conditions of the selected blast furnace and case study. At this time, the boundary conditions and internal variables either maintain constant values ​​or change according to a pattern preset for each case study. The input and output items of the unsteady mathematical model are sequentially displayed in a graph within a user interface screen, such as that shown in FIG. 10 , and updated at predetermined time intervals. The trainee may select the input or output items to be displayed in the graph within the user interface screen. Furthermore, the trainee may input operational control items and their control quantities at any time within the calculation period into an operation screen, such as that shown in FIG. 11 , to reflect the input conditions in the unsteady mathematical model.

[0051] Examples of operational control items include the coke ratio, blast volume, blast temperature, and the distribution of the ore coke ratio in the charging materials, which are operated by operators in actual blast furnace operations. The unsteady mathematical model uses the furnace state at the time the trainee inputs the operation as the initial condition, and the value reset based on the input operation item as the boundary condition, and performs recalculation from the time the operation amount is input. In this case, by displaying the results before and after the recalculation on the same graph, the trainee can learn the impact of operational control on blast furnace operation.

[0052] An example of how the blast furnace operation simulator is used is shown below. ３ Assuming a blast furnace with a pulverized coal injection equipment problem, this example shows a case where pulverized coal injection stops one hour after the start of the calculation. In this case, in accordance with the equipment specifications of an actual blast furnace, the oxygen injection amount is set to 0 when pulverized coal injection stops. Example calculation results for the base and cases 1 to 4 are shown in Table 1.

[0053]

[0054] As shown in Table 1, under the base conditions where no action was taken, the hot metal temperature fell below 1400°C after a certain time, well below the typical operational control value. In contrast, in Cases 1 through 4, where the coke rate was increased shortly after pulverized coal injection was stopped, the hot metal temperature decline was contained within a certain range. Furthermore, in Case 1, after the hot metal temperature recovered to approximately 1500°C, the maximum silicon concentration in the hot metal [Si] significantly exceeded the control value of 0.6 due to the high pre-tuyere temperature. In Cases 2 through 4, where the pre-tuyere temperature was lowered as a counteraction, the maximum silicon concentration in the hot metal [Si] was successfully reduced. However, if the pre-tuyere temperature reduction action was taken too early, the hot metal temperature would rise slowly. Therefore, Case 4, in which the pre-tuyere temperature reduction was waited for the hot metal temperature to return to approximately 1500°C, was confirmed to be the superior operational procedure among the five calculation examples presented here.

[0055] [Method for Designing Blast Furnace Operation] The blast furnace operation prediction method according to the present invention is intended to be used for the purpose of operation design, rather than for training trainees on operational fluctuations, and can also be used for blast furnace operation design that prevents operational fluctuations and equipment troubles. In this case, an operation designer inputs the operation control items and their operation amounts on behalf of the trainee and performs recalculation multiple times. Then, assuming a case such as start-up operation of a blast furnace after a long period of shutdown, the operation designer repeatedly adjusts the operation amounts of the operation control items so that the recalculated blast furnace operational parameters and / or monitoring information approach the target results, thereby performing blast furnace operation design.

[0056] A method for designing blast furnace operation using the blast furnace operation prediction method according to the present invention will be described using blow-in as an example. ３ The operational specifications were designed for a blast furnace from blow-in to 100 hours later. Figures 12(a)-(c) show the time-dependent changes in blast flow rate, coke ratio, and silicon concentration in molten pig iron. In Figures 12(a)-(c), the solid lines and plots represent actual values, the dashed lines represent planned values ​​or values ​​calculated using a mathematical model, and the dashed-dotted lines represent target values. The silicon concentration in molten pig iron is removed in the next process, the steelmaking process. However, if the weight percentage of silicon is too high, it takes time to remove the silicon, delaying the processing of the molten pig iron and affecting the production rate of the steelworks. Generally, the silicon concentration in molten pig iron tends to be higher at the start of blow-in. Therefore, a target silicon concentration (upper limit) was set and operational design was carried out taking the above reasons into consideration. As shown in Figures 12(a) to 12(c), although there were some deviations in the operational parameters, most of the operation proceeded according to the design values, and with regard to the Si concentration in the molten iron, which is the operational result, the calculated value by the mathematical model and the actual value roughly agreed. It was confirmed that by utilizing this mathematical model in operational design, it is possible to achieve a blow-in operation with a Si concentration below the target.

[0057] Although the present invention has been described above as an embodiment, the present invention is not limited to the descriptions and drawings that form part of the disclosure of the present invention. In other words, other embodiments, examples, and operational techniques that can be made by those skilled in the art based on the present invention are all included in the scope of the present invention.

[0058] According to the present invention, it is possible to provide a blast furnace operation prediction method that can accurately predict the state of a blast furnace by taking into account the influence of molten material present in the basin. Also, according to the present invention, it is possible to provide a blast furnace operation training method and terminal device that can improve blast furnace operation techniques. Also, according to the present invention, it is possible to provide a blast furnace operation design method and terminal device that can suppress the occurrence of operational fluctuations and equipment troubles.

[0059] REFERENCE SIGNS LIST 1 cold model 2 tuyere 3 pressure gauge 10 information processing device 10a operation simulation program 11 input device 12 output device

Claims

1. A method for predicting the operation of a blast furnace, which predicts the state of a blast furnace using a non-steady-state mathematical model for calculating the state of the blast furnace, comprising: a first step of calculating a liquid level of molten material in the blast furnace; and a second step of adjusting a process constant related to gas flow in the radial direction inside the blast furnace, which is included in the non-steady-state mathematical model, based on the liquid level of the molten material calculated in the first step.

2. The method for predicting blast furnace operation according to claim 1, wherein the process constants include a heat extraction coefficient and a reduction rate constant.

3. A method for predicting blast furnace operation according to claim 1 or 2, wherein the first step includes a step of calculating the liquid surface height of the molten material using the calculation results of the unsteady mathematical model.

4. A method for predicting the operation of a blast furnace according to claim 1 or 2, wherein the first step includes a step of calculating the liquid surface height of the molten material using operation monitoring information of the blast furnace.

5. A method for educating trainees on the operation of a blast furnace by presenting the trainee with the state of the blast furnace predicted using the blast furnace operation prediction method according to any one of claims 1 to 4, the method comprising: a third step of calculating changes in the state of the blast furnace during the calculation period by varying the process constants included in the non-steady mathematical model during the calculation period based on a preset fluctuation pattern; and a fourth step of presenting to the trainee changes in the operational parameters and / or monitoring information of the blast furnace obtained from the calculation results of the third step.

6. A method for training in blast furnace operation as described in claim 5, comprising: a fifth step of, in response to input of operation variables for operation at any time within the calculation period, recalculating changes in the state of the blast furnace starting from the any time using the input operation variables for operation and the unsteady mathematical model; and a sixth step of presenting to the trainee changes in the operation parameters and / or monitoring information of the blast furnace obtained from the recalculation results of the fifth step and changes in the operation parameters and / or monitoring information of the blast furnace obtained from the calculation results of the third step.

7. The method for training in blast furnace operation according to claim 6, wherein the fifth step is performed a plurality of times.

8. A method for designing blast furnace operation, which designs the operation of a blast furnace based on the state of the blast furnace predicted using the method for predicting blast furnace operation according to any one of claims 1 to 4, comprising: a third step of calculating changes in the state of the blast furnace during the calculation period by changing process constants included in the unsteady mathematical model during the calculation period based on a preset fluctuation pattern; and a fourth step of presenting to the designer changes in the operational parameters and / or monitoring information of the blast furnace obtained from the calculation results of the third step.

9. A program that causes an information processing device to execute the blast furnace operation prediction method according to any one of claims 1 to 4.

10. A terminal device comprising an output means for outputting information relating to the state of a blast furnace predicted using the blast furnace operation prediction method according to any one of claims 1 to 4.

11. The terminal device according to claim 10, further comprising an input means for inputting information about operation control items to be input to the unsteady mathematical model and the operation amounts of the operation control items.

Images