Temperature estimation method, furnace operation method, furnace design method, furnace operation process development method, method for setting object filling state, and processing device

Abstract

To provide a temperature estimation method and a processing apparatus which can estimate the temperature of a fluid passing through voids in a packed bed of plural objects and / or the objects.SOLUTION: A temperature estimation method according to the present invention includes the steps of: creating the data of a fluid space as a space through which a fluid flows from the shape data of a packed bed; creating calculation grid points at the inside of the fluid space; regarding the created respective calculation grid points, calculating a signed distance showing a distance between the calculation grid points and the surfaces of the objects different in signs depending on whether the positions of the calculation grid points are inside the objects or not; determining whether the created respective grid points are located inside the objects or not based on the calculated signed distance; calculating the speed of the fluid in the fluid space by solving a fluid equation in the calculation grid points determined not to be inside the objects; and estimating the temperatures of the fluid and the objects by solving heat advection-diffusion equations in the respective calculation grid points using the calculated speed of the fluid.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a temperature estimation method for estimating the temperature of a fluid and / or an object passing through a void in a packed bed filled with a plurality of objects, a furnace operation method, a furnace design method, a furnace operation process development method, a method for setting the object packing state, and a processing device. [Background technology]

[0002] To improve the operational efficiency of electric furnaces, there are preheating electric furnaces that use high-temperature exhaust gases generated in the electric furnace to preheat scrap, which is an iron source. These preheating electric furnaces include a preheating section that preheats the scrap and a melting section that melts the scrap using the heat of an arc generated by graphite electrodes or the like. The preheating section and the melting section are connected to each other, and in the preheating section, the scrap is preheated by the high-temperature exhaust gases generated in the melting section. The scrap is then loaded into the melting section by its own weight or mechanical manipulation, and melted. To improve the operational efficiency of these preheating electric furnaces, it is necessary to steadily transfer the heat from the exhaust gases to the scrap in the preheating section.

[0003] Therefore, to determine operating conditions that maximize preheating efficiency for each scrap, it is necessary to investigate how the heat of the exhaust gas is transferred to each scrap. One possible method for this investigation is to use heat transfer calculations, a type of simulation that can investigate in detail the time-dependent changes in the temperature of the scrap and exhaust gas. Specifically, methods using heat transfer equations, such as those described in Patent Documents 1 and 2, can be considered as a method for estimating the temperature of a scrap packed bed in a preheating section, where multiple scraps are packed. Patent Document 1 describes a method for estimating the thickness of the slag coating on the side of an electric furnace by performing unsteady heat transfer calculations using a model that employs the finite element method. Patent Document 2 also describes a method for determining scrap melting and dropping by predicting the furnace wall temperature based on the sensor temperature using an unsteady heat conduction equation. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-85549 [Patent Document 2] Japanese Patent Application Publication No. 2017-226864 Summary of the Invention [Problem to be solved by the invention]

[0005] However, scrap comes in a variety of shapes because it is generated from various products, such as automobiles, home appliances, and construction materials, or during the manufacturing of those products. Therefore, it is extremely difficult to assume that the scrap packed bed in the preheating section is a collection of "perfectly spherical objects," as is typically done in simulations using information processing devices such as computers. Furthermore, the shape of the scrap packed bed varies depending on the type of scrap charged and the charging method. For example, a scrap packed bed containing a large amount of thin plate scrap will have a different shape than a scrap packed bed containing a large amount of H-section steel, even if they have the same weight.

[0006] The methods described in Patent Documents 1 and 2 cannot accurately predict the temperature of flue gas passing through a scrap packed bed having a complex shape. Specifically, the method described in Patent Document 1 assumes that the slag has a substantially cylindrical thickness, and does not incorporate complex shapes into the calculation. Furthermore, Patent Document 1 does not describe a method for simulating the flue gas flow, making it impossible to calculate the flow of flue gas entering the scrap packed bed. Similarly, Patent Document 2 only describes a theoretical formula for the heat conduction equation of an electric furnace wall, but does not describe a calculation method for heat transfer to a scrap packed bed having a complex shape. Furthermore, Patent Document 2 does not describe a method for calculating the flue gas flow, making it impossible to estimate the temperature of flue gas passing through the scrap packed bed.

[0007] The present invention has been made to solve the above-mentioned problems, and its object is to provide a temperature estimation method and processing device that can accurately estimate the temperature of a fluid and / or objects passing through voids in a packed bed filled with multiple objects. Another object of the present invention is to provide a furnace operation method, a furnace design method, a furnace operation process development method, and a method for setting the object filling state that can reduce operating costs and operate the furnace stably. [Means for solving the problem]

[0008] [1] The temperature estimation method according to the present invention is a temperature estimation method for estimating the temperature of a fluid and / or an object passing through a void in a packed bed filled with a plurality of objects, and includes the following steps: a first step of creating data on a fluid space, which is a space through which the fluid flows, from shape data of the packed bed; a second step of creating computational grid points within the fluid space; a third step of calculating, for each computational grid point created in the second step, a signed distance indicating the distance between the computational grid point and the surface of the object, the sign of which differs depending on whether the computational grid point is located within the object; a fourth step of determining, based on the signed distance calculated in the third step, whether each computational grid point created in the second step is located within the object; a fifth step of calculating the velocity of a fluid in the fluid space by solving a fluid equation at computational grid points determined not to be within the object in the fourth step; and a sixth step of estimating the temperature of the fluid and the object by solving a thermal advection-diffusion equation at each computational grid point using the fluid velocity calculated in the fifth step.

[0009] [2] The temperature estimation method according to the present invention is the temperature estimation method according to [1], wherein the fifth step includes a step of calculating the velocity of the fluid using the lattice Boltzmann method, which uses the lattice Boltzmann equation as the fluid equation.

[0010] [3] The temperature estimation method according to the present invention is the temperature estimation method according to [1] or [2], wherein the sixth step includes a step of calculating the temperature by setting a heat transfer coefficient at a computational grid point located within the object and at a computational grid point adjacent to a computational grid point not located within the object.

[0011] [4] The method for operating a furnace according to the present invention is a method for operating a furnace equipped with a heat source for burning an object, and includes an estimation step of estimating the temperature of the object charged into the furnace and / or the fluid in the furnace using any one of the temperature estimation methods [1] to [3], and a control step of controlling the amount of the object charged into the furnace and / or the heat source based on the temperature of the object and / or the fluid in the furnace estimated in the estimation step.

[0012] [5] The furnace design method of the present invention is a method for designing a furnace equipped with a heat source for burning an object, and sets the shape of the furnace and / or the specifications of the heat source based on the temperature of the object loaded into the furnace and / or the temperature of the fluid in the furnace, which are estimated in advance using any of the temperature estimation methods [1] to [3].

[0013] [6] The method for developing an operating process for a furnace according to the present invention is a method for developing an operating process for a furnace equipped with a heat source for burning an object, which involves developing an operating process for the furnace including controlling the amount of the object charged into the furnace and / or the heat source based on the temperature of the object charged into the furnace and / or the fluid in the furnace, which is estimated in advance using any one of the temperature estimation methods [1] to [3].

[0014] [7] The method for setting the filling state of an object according to the present invention is a method for setting the filling state of an object to be loaded into a furnace for burning objects, which determines the amount of the object to be loaded into the furnace and / or its placement at the time of loading based on the temperature of the object loaded into the furnace and / or the temperature of the fluid in the furnace, which are estimated in advance using any of the temperature estimation methods [1] to [3].

[0015] [8] A processing device according to the present invention is a processing device that estimates the temperature of a fluid and / or an object passing through a void in a packed bed filled with a plurality of objects, and includes: a spatial data creation unit that creates data on a fluid space, which is a space through which the fluid flows, from shape data of the packed bed; a computational grid point creation unit that creates computational grid points within the fluid space; a signed distance function calculation unit that calculates, for each computational grid point created by the computational grid point creation unit, a signed distance that indicates the distance between the computational grid point and the surface of the object, the sign of which differs depending on whether the position of the computational grid point is within the object; a space determination unit that determines whether each computational grid point created by the computational grid point creation unit is located within the object based on the signed distance calculated by the signed distance function calculation unit; a flow calculation unit that calculates the velocity of a fluid in the fluid space by solving a fluid equation at computational grid points that are determined not to be within the object by the space determination unit; and a temperature estimation unit that estimates the temperature of the fluid and the object by solving a thermal advection-diffusion equation at each computational grid point using the fluid velocity calculated by the flow calculation unit. [Effects of the Invention]

[0016] The temperature estimation method and processing device according to the present invention enable accurate estimation of the temperature of a fluid and / or an object passing through a void in a packed bed filled with a plurality of objects. Furthermore, the furnace operation method, furnace design method, furnace operation process development method, and object packing state setting method according to the present invention enable stable furnace operation with reduced operating costs. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a schematic diagram showing the configuration of an electric furnace according to one embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing the configuration of a processing device according to an embodiment of the present invention. [Figure 3] FIG. 3 is a flowchart showing the flow of the temperature estimation process according to one embodiment of the present invention. [Figure 4]FIG. 4 is a diagram showing an example of a scrap packed bed. [Figure 5] FIG. 5 is a diagram showing an example of a fluid space. [Figure 6] FIG. 6 is a diagram illustrating an example of computational grid points. [Figure 7] FIG. 7 is a diagram for explaining the object signal and the fluid space signal. [Figure 8] FIG. 8 is a diagram for explaining the boundary conditions. [Figure 9] FIG. 9 is a diagram showing an example of calculation of the fluid flow in the scrap packed bed. [Figure 10] FIG. 10 is a diagram showing an example of estimated temperature distribution of the scrap and the fluid. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, a temperature estimation method and a furnace operation method according to an embodiment of the present invention will be described with reference to the drawings.

[0019] [Configuration of electric furnace] First, with reference to FIG. 1, the configuration of an electric furnace to which a temperature estimation method and a furnace operation method according to one embodiment of the present invention are applied will be described.

[0020] FIG. 1 is a schematic diagram showing the configuration of an electric furnace according to one embodiment of the present invention. As shown in FIG. 1, the electric furnace 1 according to one embodiment of the present invention is a preheating-type electric furnace that preheats scrap S, an iron source, using high-temperature exhaust gas G generated in the electric furnace 1. In this embodiment, the electric furnace 1 includes a preheating section 2 that preheats a packed bed of scrap S filled with multiple pieces of scrap S using the exhaust gas G, and a melting section 4 that melts the scrap S using the heat of an arc generated by graphite electrodes 3 to produce molten iron SA. The preheating section 2 and the melting section 4 are connected to each other. In the preheating section 2, the scrap S in the packed bed of scrap preheated by the high-temperature exhaust gas G generated in the melting section 4 is charged into the melting section 4 by its own weight or mechanical operation and melted.

[0021] [Configuration of processing device] Next, with reference to FIG. 2, the configuration of a processing device that executes a temperature estimation method according to one embodiment of the present invention will be described.

[0022] In this embodiment, the scrap S described above will be used as an example of an object. It is extremely difficult to assume the shape of the scrap packed bed as a collection of "perfectly spherical objects." This is because scrap S has a wide variety of shapes. Therefore, the void distribution in the scrap packed bed varies depending on the scrap S. As a result, the way in which the scrap packed bed is heated by the exhaust gas G varies depending on the scrap S. The processing device and temperature estimation process described below can accurately reflect the void distribution in the scrap packed bed without significantly increasing the load on an information processing device such as a computer that performs the simulation. Therefore, the present invention is more effective when the shape of the objects in the packed bed is "other than a perfect sphere." Of course, the present invention can also be effective when the shape of the objects in the packed bed is "perfectly spherical." In this invention, a "non-perfectly spherical object" refers to an object whose surface has different distances at some or all positions within the object. On the other hand, a "perfectly spherical object" refers to an object whose surface has a curved surface and whose surface is the same at all points within the object.

[0023] Fig. 2 is a block diagram showing the configuration of a processing device according to one embodiment of the present invention. As shown in Fig. 2, processing device 10 according to one embodiment of the present invention is configured as an information processing device such as a computer. An input device 21 and an output device 22 are connected to processing device 10. Input device 21 is configured as a known input device such as a keyboard or a mouse pointer, and inputs various information such as setting values ​​to processing device 10. Output device 22 is configured as a known output device such as a display device, a printer, or an audio output device, and outputs various information in accordance with control signals from processing device 10.

[0024] The processing device 10 includes an object-packed layer creation unit 11, a space data creation unit 12, a computational grid point creation unit 13, a signed distance function calculation unit 14, a space determination unit 15, a flow calculation unit 16, and a temperature estimation unit 17. The functions of each of these units are realized by an arithmetic processing unit such as a CPU in the information processing device executing a computer program stored in the storage unit. The functions of each of these units will be described later. The storage unit may be a storage medium fixed to the computer or the like, or a storage medium removable from the computer or the like. Examples of storage media fixed to the computer or the like include an EPROM (Erasable Programmable ROM) and a hard disk drive (HDD, Hard Disk Drive).

[0025] Examples of recording media that can be removed from a computer or the like include USB (Universal Serial Bus) memory, flexible disks, magneto-optical disks, CD-ROMs (Compact Disc - Read Only Memory), CD-RWs (Compact Disc - Rewritable), DVDs (Digital Versatile Discs), BDs (Blu-ray (registered trademark) Discs), DATs (Digital Audio Tapes), 8mm tapes, and memory cards. Solid-state drives (SSDs) can be used as both removable and fixed storage media for computers or the like. Computer programs may be stored on a computer connected to a telecommunications line such as the Internet and provided by downloading via the telecommunications line. Computer programs may also be provided or distributed via a telecommunications line such as the Internet.

[0026] The processing device 10 having such a configuration executes the temperature estimation process described below to estimate the temperatures of the flue gas G and the scrap S passing through the gaps in the scrap packed bed formed in the preheating section 2. Hereinafter, with reference to FIG. 3, the operation of the processing device 10 when executing the temperature estimation process will be described.

[0027] [Temperature estimation process] 3 is a flowchart showing the flow of a temperature estimation process according to one embodiment of the present invention. The flowchart shown in FIG. 3 starts when an instruction to execute the temperature estimation process is input to the processing device 10, and the temperature estimation process proceeds to step S1.

[0028] In step S1, the object packed bed creation unit 11 simulates the process of scrap S being introduced into and piled up in the preheating unit 2 using objects of multiple scraps S of various shapes created using a shape creation tool such as 3D CAD software. An example of a simulation method is a method of simulating the movement of objects (e.g., falling motion, contact motion) using an equation of motion. The object packed bed creation unit 11 then uses the simulation results to create shape data of a scrap packed bed in which multiple scraps S are piled up, as shown in FIG. 4. The shape data of the scrap packed bed includes shape data of each scrap S (e.g., external shape, dimensions) and position data within the scrap packed bed. Alternatively, the shape data of the scrap packed bed may be obtained by actually measuring the shape of the scrap packed bed in the preheating unit 2 using a measurement method such as a laser scanner. This completes step S1, and the temperature estimation process proceeds to step S2.

[0029] In the process of step S2, the spatial data creation unit 12 uses the shape data of the scrap packed bed created in the process of step S1 to create data on the fluid space, which is the space through which a fluid such as exhaust gas G passing through the scrap packed bed flows. Specifically, the spatial data creation unit 12 calculates data on the space encompassing the scrap packed bed as fluid space data, as shown in FIG. 5. Note that if the region to be calculated is only a part of the scrap packed bed, fluid space data may be created for only the region to be calculated. This completes the process of step S2, and the temperature estimation process proceeds to the process of step S3.

[0030] In step S3, the computational grid point creation unit 13 creates multiple computational grid points (mesh points) required for numerical calculations within the fluid space created in steps S1 and S2. Figure 6 shows an example of computational grid points in a cross section of a scrap-packed bed. In Figure 6, the intersections of the grid lines represent computational grid points N, the black areas represent scrap, and the white areas represent fluid space. The spacing between adjacent computational grid points should be set equal to or smaller than the spacing between computational grid points that can be created within the scrap S area. Creating a larger number of computational grid points by shortening the spacing between computational grid points significantly increases the calculation time, but improves calculation accuracy. Alternatively, multiple computational grid points may be locally created in the required area. A structured grid, in which computational grid points are regularly arranged, is more convenient for numerical calculations, and a non-structured grid, which can more easily fit the shape of the object, may also be used. This completes step S3, and the temperature estimation process proceeds to step S4.

[0031] In the processing of step S4, the signed distance function calculation unit 14 calculates the signed distance from the nearest scrap S (object) surface position for each computational grid point created in the processing of step S3. The signed distance indicates the distance from the nearest scrap S surface position (the position of a perpendicular line dropped from the computational grid point to the scrap surface) to the target computational grid point, and is assigned a negative sign if the computational grid point is located within the scrap S, and a positive sign if it is not located within the fluid space. However, as long as it can be determined whether the computational grid point is located within the scrap S or within the fluid space, it does not matter whether the value is positive or negative. This completes the processing of step S4, and the temperature estimation processing proceeds to the processing of step S5.

[0032] In step S5, the space determination unit 15 assigns an object signal or a fluid space signal to the data of each computational grid point based on the signed distance calculated in step S4. Specifically, as shown in FIG. 7, if the signed distance (distance from the surface SF of the scrap S) calculated for the computational grid point NA to be processed is a positive value, the space determination unit 15 determines that the computational grid point NA is located within the scrap S and assigns an object signal to the data of the computational grid point NA. On the other hand, if the signed distance calculated for the computational grid point NB to be processed is a negative value, the space determination unit 15 determines that the computational grid point NB is located within the fluid space and assigns a fluid space signal to the data of the computational grid point NB. Note that the signal may be a number such as 0 or 1, or a symbol such as True or False. This completes step S5, and the temperature estimation process proceeds to step S6.

[0033] In step S6, the flow calculation unit 16 discretizes fluid equations, such as the Navier-Stokes equations, using the finite volume method, the finite difference method, the finite element method, or the like. The flow calculation unit 16 then calculates the fluid velocity at each computational grid point using the discretized fluid equations based on the multiple computational grid points created in step S3 and the signals assigned to each computational grid point in step S5. The calculation of the fluid velocity may be performed as a steady-state calculation assuming a steady state in which physical variables, such as velocity, do not change over time, or as a transient calculation calculating the process by which physical variables change over time. However, because the fluid space within the scrap packed bed varies greatly, with locally narrow areas and suddenly wide spaces, the law of conservation of mass is easily violated, resulting in unstable calculations and divergence. Furthermore, because of the locally narrow areas, the spacing between computational grid points must be shortened and a large number of computational grid points must be installed, which increases the calculation time and may not be completed within a realistic time frame.

[0034] Therefore, the lattice Boltzmann method, which uses the lattice Boltzmann equation as the fluid equation, is desirable as a method for calculating fluid velocity. The Boltzmann equation is a commonly used higher-order equation of fluid equations derived using kinetic theory of gas molecules. By imposing continuity constraints, continuum fluid equations such as the Navier-Stokes equation can be derived from the Boltzmann equation. The Boltzmann equation generally requires a large amount of calculation because it assumes collisions between gas molecules. However, the lattice Boltzmann equation can reduce the amount of calculation by modeling the collisions of gas molecules. Since the calculation considers the fluid as virtual particles and takes into account the collisions of virtual fluid particles, mass conservation is less likely to be violated. Applying this lattice Boltzmann method to the fluid space within a scrap packed bed, where locally narrow spaces and suddenly wide spaces exist, allows for stable calculation of fluid velocity.

[0035] The lattice Boltzmann method for unsteady calculations is an explicit method because it calculates fluid movement through particle collisions. With an explicit method, for example, when updating the next time, only physical variables such as the current velocity are required. This allows for easy parallelization of each block of computational grid points, making it easy to achieve speed improvements through parallel computing. In particular, using a computational accelerator such as a GPU with multiple computational cores for parallel computation of the lattice Boltzmann method can sometimes improve computational speed by several hundred times. Even for flow calculations with a large number of computational grid points, such as the computational grid points for the fluid space in a scrap packed bed, the lattice Boltzmann method can complete the calculation within a reasonable time.

[0036] The flow calculation unit 16 sets the boundary conditions and physical properties of the fluid and calculates the velocity of the fluid within the fluid space using the above-mentioned calculation method. Specifically, the flow calculation unit 16 sets inflow boundary conditions of velocity or pressure at the fluid inflow point, sets wall boundary conditions that prevent the flow from penetrating the wall region, and sets outflow boundary conditions of pressure, velocity, or the contents of the fluid flowing out directly at the fluid outflow point. For example, as shown in Figure 8, the flow calculation unit 16 sets the lower boundary of the fluid space as the inflow boundary, the periphery of the fluid space as the wall boundary, and the upper boundary of the fluid space as the outflow boundary, and sets boundary conditions for each boundary. Note that the symbol LA in the figure indicates a scrap packed bed. As for fluid properties, the flow calculation unit 16 assigns fluid viscosity, density, and other fluid properties necessary for fluid calculation to the computational grid points to which the fluid space signal is assigned. After setting various conditions, the flow calculation unit 16 assigns initial conditions in the case of an unsteady state and performs numerical fluid calculations to calculate the fluid velocity within the fluid space (the computational grid points to which the fluid space signal is assigned). An example of fluid velocity calculation is shown in Figure 9. 9, it can be seen that it is possible to calculate the flow of fluid between the scraps S. This completes the process of step S6, and the temperature estimation process proceeds to the process of step S7.

[0037] In the processing of step S7, the temperature estimation unit 17 sets temperature boundary conditions for each boundary shown in FIG. 8. Then, the temperature estimation unit 17 estimates the temperatures of the scrap S (object) and the fluid using the fluid velocity in the fluid space calculated in the processing of step S6 and the discretized heat advection-diffusion equation. An example of the estimated temperature distribution of the scrap S and the fluid is shown in FIG. 10. From the example shown in FIG. 10, it can be confirmed that the scrap at its initial temperature absorbs heat from the exhaust gas, causing the exhaust gas temperature to drop. It can also be confirmed that the temperature of the scrap S is increased by the exhaust gas. This makes it possible to predict the temperature of the scrap packed bed and the exhaust gas flowing within the scrap packed bed, which are difficult to calculate.

[0038] Alternatively, the temperature of only one of the scrap and the exhaust gas may be calculated. The temperature estimation unit 17 may estimate the temperatures of the scrap S and the fluid by applying the lattice Boltzmann method described above. The computational grid points may be the same as those used by the flow calculation unit 16. Alternatively, to stabilize the calculation, a staggered grid may be used, in which a polyhedron with each computational grid point as a vertex is considered and the center of the polyhedron is used as the temperature calculation point. When estimating the temperature, the temperature estimation unit 17 assigns thermophysical properties, such as thermal conductivity, density, and specific heat, corresponding to the scrap S or the fluid space to each computational grid point in accordance with the object signal or fluid space signal at each computational grid point. When using a staggered grid, the thermophysical properties of the scrap S are assigned to the computational grid points to which the object signal is assigned, and the thermophysical properties of the fluid are assigned to the computational grid points to which the fluid space signal is assigned.

[0039] Furthermore, for computational grid points adjacent to the object signal and the fluid space signal, the temperature estimation unit 17 assigns a heat transfer coefficient because heat transfer, which is heat transfer between a fluid and a solid, occurs. The heat transfer coefficient may be assigned using Newton's law of cooling as a heat transfer coefficient term. For computational grid points to which a heat transfer coefficient is assigned, the temperature estimation unit 17 sets a heat transfer coefficient term representing heat transfer due to heat transfer as the source term of the thermal diffusion-advection equation. The fluid temperature may be calculated steadily by fixing the velocity at a certain time calculated by the flow calculation unit 16, or it may be calculated unsteadily by calculating the temperature at a certain time based on the fluid space velocity calculated at a certain time. Furthermore, the flow calculation unit 16 may change physical properties such as fluid viscosity based on the temperature calculated by the temperature estimation unit 17 and recalculate the fluid velocity. In the case of unsteady temperature calculation, the temperature that changes at each time can be calculated by setting the initial condition temperature at each computational grid point, assigning a fluid boundary temperature condition to the inflow boundary, and applying the temperature calculation method set for each time. This completes the process of step S7, and the series of temperature estimation processes ends.

[0040] As is clear from the above description, in the temperature estimation process according to one embodiment of the present invention, the spatial data creation unit 12 creates data on the fluid space, which is the space through which the fluid flows, based on the shape data of the scrap packed bed. The computational grid point creation unit 13 creates computational grid points within the fluid space. The signed distance function calculation unit 14 calculates, for each computational grid point, a signed distance indicating the distance between the computational grid point and the scrap surface, with the sign varying depending on whether the computational grid point is located within the scrap. The space determination unit 15 then determines whether each computational grid point is located within the scrap based on the calculated signed distance. The flow calculation unit 16 calculates the fluid velocity within the fluid space by solving the fluid equation at computational grid points determined not to be within the scrap. The temperature estimation unit 17 estimates the temperatures of the fluid and the scrap by solving the heat advection-diffusion equation at each computational grid point using the calculated fluid velocity. This allows for accurate estimation of the temperatures of the fluid and the scrap passing through voids in the scrap packed bed.

[0041] (Example 1: Furnace operation method) The furnace operation method according to the present invention utilizes the temperature estimation method according to the embodiment. Specifically, after the processing of step S7, a control step is included in which the amount of scrap S to be charged into the preheating section 2 and the heat source, such as the arc heat generated by the graphite electrodes 3 or a burner, are controlled based on the estimated temperatures of the scrap S and the fluid. This control step may be performed immediately after the temperature designation process. Alternatively, the temperature estimation process may be performed separately before the control step, and the temperatures of the objects charged into the furnace and / or the fluid in the furnace may be calculated in advance. The furnace may also include a preheating section 2, as in the electric furnace 1 described in this embodiment. In this case, the "furnace" also includes the preheating section 2. By utilizing the temperature estimation method according to the present embodiment, it is possible to perform operations that reduce energy costs. Furthermore, by devising a scrap charging method based on the calculated temperatures of the scrap S and the fluid, it is possible to reduce the bias in heat transfer to the scrap S and achieve stable operation of the electric furnace 1.

[0042] (Example 2: Furnace design method) The temperature estimation method according to the embodiment can be used in a design method for a furnace to be filled with objects. In this case, the temperature of the objects charged into the furnace and / or the fluid therein is estimated in advance using the temperature estimation method. Then, the shape of the furnace and / or the specifications of the heat source are set based on the estimated temperatures of the objects charged into the furnace and / or the fluid therein. The furnace may also be equipped with a preheating section 2, as in the electric furnace 1 described in this embodiment. In this case, the preheating section 2 is also included in the term "furnace."

[0043] (Application example 3: Development method for furnace operation process) The temperature estimation method according to the embodiment can be used in a method for developing an operation process for a furnace to be charged with objects. In this case, the temperature estimation method is used to estimate the temperature of the objects charged into the furnace and / or the fluid therein in advance. Then, based on the estimated temperatures of the objects charged into the furnace and / or the fluid therein, an operation process for the furnace is developed, including control of the amount of objects charged into the furnace and / or the heat source.

[0044] (Example 4: How to set the filling state of objects in a furnace) The temperature estimation method according to the embodiment can be used to determine the filling state of objects to be charged into a furnace. In this case, the temperature of the objects charged into the furnace and / or the fluid in the furnace is estimated in advance using the temperature estimation method. Then, the amount of objects to be charged into the furnace and / or the placement of the objects when they are charged are determined based on the estimated temperatures of the objects charged into the furnace and / or the fluid in the furnace.

[0045] 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. [Explanation of symbols]

[0046] 1 electric furnace 2 Preheating section 3. Graphite electrodes 4 Melting part 10 Processing equipment 11. Object packed layer creation section 12 Spatial Data Creation Department 13 Computational grid point creation section 14 Signed distance function calculation unit 15 Spatial judgment section 16 Flow calculation section 17 Temperature estimation section 21 Input Devices 22 Output Devices G. Exhaust gas N, NA, NB calculation grid points S Scrap SA Molten Iron

Claims

1. A temperature estimation method for estimating a temperature of a fluid and / or an object passing through a void in a packed bed filled with a plurality of objects, comprising: a first step of creating data on a fluid space, which is a space through which the fluid flows, from shape data of the packed bed; a second step of creating computational grid points in the fluid space; a third step of calculating, for each computational grid point created in the second step, a signed distance indicating a distance between the computational grid point and a surface of the object, the signed distance varying depending on whether the position of the computational grid point is within the object; a fourth step of determining whether or not each computational grid point created in the second step is located within the object based on the signed distance calculated in the third step; a fifth step of calculating a velocity of the fluid in the fluid space by solving a fluid equation at the computational grid points determined not to be within the object in the fourth step; a sixth step of estimating the temperatures of the fluid and the object by solving a thermal advection-diffusion equation at each computational grid point using the fluid velocity calculated in the fifth step; A temperature estimation method comprising:

2. The temperature estimation method according to claim 1 , wherein the fifth step includes a step of calculating a velocity of the fluid using a lattice Boltzmann method that uses a lattice Boltzmann equation as the fluid equation.

3. 2. The temperature estimation method according to claim 1, wherein the sixth step includes a step of calculating the temperature by setting a heat transfer coefficient to a computational grid point located within the object and a computational grid point adjacent to a computational grid point not located within the object.

4. 1. A method of operating a furnace having a heat source for combusting an object, comprising: a control step of controlling the amount of the object to be charged into the furnace and / or the heat source based on the temperature of the object charged into the furnace and / or the temperature of the fluid in the furnace, which are calculated in advance using the temperature estimation method according to any one of claims 1 to 3; A method of operating a furnace, including:

5. 1. A method for designing a furnace having a heat source for burning an object, comprising: A furnace design method, which sets the shape of the furnace and / or the specifications of the heat source based on the temperature of the object charged in the furnace and / or the temperature of the fluid in the furnace, which are calculated in advance using the temperature estimation method according to any one of claims 1 to 3.

6. 1. A method for developing an operating process for a furnace having a heat source for burning an object, comprising: A method for developing an operation process of a furnace, the method comprising: developing an operation process of the furnace, the operation process including controlling the amount of the object charged into the furnace and / or the heat source, based on the temperature of the object charged into the furnace and / or the temperature of the fluid in the furnace, which are calculated in advance using the temperature estimation method according to any one of claims 1 to 3.

7. 1. A method for setting a loading state of objects to be charged into a furnace for burning objects, comprising: A method for setting the filling state of objects, which determines the amount of objects to be charged into a furnace and / or their placement at the time of charging, based on the temperature of the objects charged into the furnace and / or the temperature of the fluid in the furnace, which are calculated in advance using the temperature estimation method described in any one of claims 1 to 3.

8. A processing device for estimating a temperature of a fluid and / or an object passing through a gap in a packed bed filled with a plurality of objects, comprising: a space data creation unit that creates data on a fluid space, which is a space through which the fluid flows, from the shape data of the packed bed; a computational grid point creation unit that creates computational grid points within the fluid space; a signed distance function calculation unit that calculates, for each computational grid point created by the computational grid points, a signed distance indicating a distance between the computational grid point and a surface of the object, the signed distance having a different sign depending on whether the position of the computational grid point is inside the object or not; a space determination unit that determines whether or not each computational grid point created by the computational grid point creation unit is located within the object based on the signed distance calculated by the signed distance function calculation unit; a flow calculation unit that calculates a velocity of a fluid in the fluid space by solving a fluid equation at a computational grid point that is determined by the space determination unit not to be within the object; a temperature estimation unit that estimates temperatures of the fluid and the object by solving a thermal advection-diffusion equation at each computational grid point using the fluid velocity calculated by the flow calculation unit; and A processing device comprising:

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