Information processing device, information processing method, and storage medium

Abstract

Provided are an information processing device capable of calculating manufacturing conditions, an information processing method, and a storage medium.　An information processing device according to the present invention comprises: an acquisition unit that acquires the composition ratio and the mass of a raw material containing, as a main component, waste discharged from a thermal power plant that uses coal as fuel; and a manufacturing condition calculation unit that, on the basis of the composition ratio and the mass acquired by the acquisition unit, uses a molecular dynamics method to calculate conditions for manufacturing an amorphous material from the raw material.

Description

Information processing device, information processing method, and storage medium

[0001] The present invention relates to an information processing device, an information processing method, and a storage medium for calculating manufacturing conditions.

[0002] Since the Great East Japan Earthquake, restrictions on the operation of nuclear reactors have been imposed, resulting in an increased proportion of thermal power generation in the energy supply. Thermal power generation uses coal as fuel, and examples of such power generation include coal-fired thermal power plants, fluidized-bed combustors, and integrated coal gasification combined cycle (IGCC). In IGCC systems, coal gasification gas is used as fuel to drive a gas turbine to generate electricity, and exhaust heat from the gas turbine is recovered to generate steam, which then drives a steam turbine to generate electricity.

[0003] However, the only established utilization method for waste generated during the operation of coal-fired thermal power plants, fluidized bed combustion furnaces, IGCCs, and other thermal power plants that generate electricity using coal as fuel has been to process the waste, such as by pulverizing it, and use it as aggregate for cement. For this reason, it has been proposed to utilize the waste in a fibrous form (for example, see Patent Document 1).

[0004] Patent No. 7401079

[0005] The manufacturing conditions may differ depending on the type of waste, and there is a demand for easier determination of the manufacturing conditions.

[0006] The present invention has been made in view of the above-mentioned problems, and has an object to provide an information processing device, an information processing method, and a storage medium that are capable of calculating manufacturing conditions.

[0007] In order to solve the above problems, the information processing device of the present invention includes an acquisition unit that acquires the composition ratio and mass of a raw material whose main component is waste discharged from a coal-fired thermal power plant, and a production condition calculation unit that calculates the conditions for producing an amorphous material from the raw material using a molecular dynamics method based on the composition ratio and mass acquired by the acquisition unit.

[0008] According to the present invention, it is possible to provide an information processing device, an information processing method, and a storage device that can calculate manufacturing conditions.

[0009] 1 is a diagram showing an example of a spinning process using an information processing device according to an embodiment. FIG. 2 is a schematic diagram showing an example of a connection between a feeder and a mixer according to an embodiment. FIG. 3 is a diagram showing an example of a spinning apparatus according to an embodiment. FIG. 4 is a diagram showing an example of a temperature condition according to an embodiment. FIG. 5 is a diagram showing an example of a configuration of an information processing device according to an embodiment. FIG. 6 is a diagram showing an example of a function of an information processing device according to an embodiment. FIG. 7 is a diagram showing an example of a molecular dynamics calculation program according to an embodiment. FIG. 8 is a diagram showing an example of a molecular dynamics calculation program according to an embodiment. FIG. 9 is a diagram showing an example of a molecular dynamics calculation program according to an embodiment. FIG. 10 is a diagram showing an example of a molecular dynamics calculation program according to an embodiment. FIG. 11 is a diagram showing an example of a molecular dynamics calculation program according to an embodiment. FIG. 12 is a diagram showing an example of a molecular dynamics calculation program according to an embodiment. FIG. 10 is a diagram showing a structural diagram of the REE precipitate state obtained by molecular dynamics. FIG. 11 is a diagram showing an example of the RDF result of Si—O in Modified Example 4 of the embodiment. FIG. 12 is a diagram showing an example of the RDF result of Al—O in Modified Example 4 of the embodiment. FIG. 13 is a diagram showing an example of the result of Ca—O in Modified Example 4 of the embodiment. FIG. 14 is a diagram showing an example of the result of Fe—O in Modified Example 4 of the embodiment. FIG. 15 is a diagram showing an example of the difference in viscosity between standard raw material G and the viscosity obtained using molecular dynamics in Modified Example 4 of the embodiment.

[0010] [Embodiment] In the following description, the raw material G is primarily composed of waste (hereinafter simply referred to as waste) discharged from a thermal power plant that uses coal as fuel. The raw material G may also contain additives and other components other than waste. Thermal power plants include coal-fired power plants, fluidized-bed combustion furnaces, and integrated coal gasification combined cycle (IGCC) plants. The invention according to this embodiment is characterized by the use of a so-called direct melt method, in which the raw material G is directly melted to produce an amorphous material. In this embodiment, as a specific example of producing an amorphous material from the raw material G using the direct melt method, the raw material G is spun into amorphous fibers. The amorphous material may also contain a substantially amorphous material (for example, a material with 96% amorphous components and 4% crystalline components).

[0011] First, referring to FIG. 1, a spinning process using a direct melt method using an information processing device 5 will be described. (1-1) A portion of the raw material G is taken out and its composition is inspected by the composition inspection device 4. (1-2) The composition inspection device 4 transmits the results of the composition inspection (composition and composition ratio (mass%); hereinafter, also referred to as composition data) to the information processing device 5. (1-3) The raw material G is fed into the feeder 1. (1-4) The mass of the raw material G fed into the feeder 1 is input into the information processing device 5. (1-5) The information processing device 5 calculates the conditions under which spinning is possible (specifically, the temperature conditions, the amount of raw material G fed, etc.) based on the mass and composition of the raw material G. (1-6) The information processing device 5 transmits the calculated amount of raw material G fed to the feeder 1. (1-7) The information processing device 5 transmits the calculated conditions under which spinning is possible to the spinning device 3. (1-8) The feeder 1 feeds the amount of raw material G sent from the information processing device 5 into the mixer 2. (1-9) The mixer 2 agitates the introduced raw material G. (1-10) The mixer 2 introduces the agitated raw material G into a furnace 3a (e.g., an electric furnace) of the spinning device 3. (1-11) The spinning device 3 melts the raw material G into amorphous fibers under the conditions transmitted from the information processing device 5 that enable spinning.

[0012] 2 is a schematic diagram showing an example of the connection between the input device 1 and the mixer 2. The raw material G is input from the upper part 1a of the input device 1. The lower part 1b of the input device 1 is formed in a funnel shape, and the lower end of this funnel is connected to the input port 2a of the mixer 2. The raw material G input into the input device 1 is input into the mixer 2 from the lower end of this funnel. Note that the lower part 1b of the input device 1 and the input port 2a of the mixer 2 do not necessarily have to be connected as long as the raw material G can be input from the input device 1 to the mixer 2.

[0013] FIG. 3 is a diagram showing an example of the configuration of the spinning device 3. The spinning device 3 includes at least an electric melting furnace 3a (furnace 3a) and one or more winding mechanisms 35. The furnace 3a includes at least one or more electrodes 31 for heating the input raw material G, a melting tank 32 for storing the molten raw material G, and one or more draw-out pipes 34 for drawing out the molten raw material G as fibers F. The control device 33 controls the current flowing through the electrodes 31 based on information from a thermometer (not shown) that measures the temperature of the furnace 3a and temperature control instructions transmitted from the information processing device 5. The winder 35 winds up the fibers F. As described above, the raw material G is input from the input device 2 into the furnace 3a and melted under the manufacturing conditions calculated by the information processing device 5. The molten raw material G flows out of the draw-out pipe 34 provided in the furnace 3a by gravity. The flow-out raw material G is cooled and solidified by contact with the outside air, becoming fibers F. The fiber F is wound by a winding machine 35 .

[0014] The temperature conditions are expressed as the relationship between temperature (°C) and time (minutes), and include the temperature gradient at which the furnace temperature is increased or decreased, and the temperature and time at which the temperature is maintained, as shown in Figure 4. The temperature may be expressed in either Celsius or Fahrenheit. The time may be expressed in seconds, minutes, or hours.

[0015] Furthermore, the steps (1-1) to (1-11) do not necessarily have to be performed in the same order. The order of the steps may be reversed or performed simultaneously as long as the effects of this embodiment are not impaired. For example, steps (1-6) and (1-7) can be performed simultaneously. In the above description, the composition data is transmitted from the composition testing device 4 to the information processing device 5. However, the composition data obtained by the composition testing device 4 may be stored in another storage device (e.g., a USB memory, an external solid state drive (SSD), or a hard disk drive (HDD)), and the stored composition data may be read by the information processing device 5. The user may also manually input the composition data obtained by the composition testing device 4. In the above description, the composition of the raw material G is tested by the composition testing device 4. However, the composition may be stored for each type of raw material G, and the stored composition may be used. For example, an ID may be assigned to each type of raw material G, and the ID and composition may be associated and stored. When the ID is input, the composition data associated with the ID may be transmitted to the information processing device 5.

[0016] 5 shows the main hardware configuration of the information processing device 5, and the information processing device 5 has a configuration in which a communication IF 500A, a storage device 500B, and a CPU 500C are connected via a bus, etc. Although not shown in FIG. 5, the information processing device 5 may also include an input device (for example, a mouse, a keyboard, a touch panel, etc.) and a display device (a CRT (Cathode Ray Tube), a liquid crystal display, an organic EL display, etc.).

[0017] The communication IF 500A is an interface for communicating with other devices (for example, the composition inspection device 4 and the spinning device 3).

[0018] The storage device 500B is, for example, a hard disk drive (HDD) or a semiconductor storage device (solid state drive (SSD)). The storage device 500B stores various data in addition to programs.

[0019] Some or all of the programs and various data stored in the storage device 500B may be stored in an external storage device such as a USB (Universal Serial Bus) memory or an external HDD, or in a storage device of another information processing device connected via a network. In this case, the information processing device 5 refers to or acquires the programs and various data stored in the external storage device or the storage device of the other information processing device.

[0020] The CPU 500C controls the information processing device 5 according to this embodiment, and includes a ROM, a RAM, and the like (not shown).

[0021] (Functions of Information Processing Device 5) Fig. 6 is a functional block diagram showing an example of the functions of the information processing device 5. As shown in Fig. 6, the information processing device 5 has functions such as a receiving unit 501, a transmitting unit 502, a storage device control unit 503, and a manufacturing condition calculation unit 504. Note that the functions shown in Fig. 6 are realized by the CPU 500C executing programs stored in the storage device 500B.

[0022] The receiving unit 501 receives, for example, data (composition data and mass) from the composition testing device 4 .

[0023] The transmitting unit 502 transmits, for example, the temperature conditions calculated by the production condition calculating unit 504 to the spinning device 3 .

[0024] The storage device control unit 503 controls the storage device 500B and writes and reads data to and from the storage device 500B.

[0025] The manufacturing condition calculation unit 504 calculates the manufacturing conditions, more specifically, the temperature conditions and input amount of raw material G required to produce fibers from raw material G, based on the data (composition data and mass) from the composition inspection device 4. The manufacturing condition calculation unit 504 uses molecular dynamics (MD) to calculate the temperature conditions and input amount of raw material G required to produce fibers from raw material G. Here, a substance is an aggregate of particles (atoms, molecules, or ions), and its properties (e.g., density, hardness, internal energy, etc.) are determined by the arrangement and motion of the particles. Molecular dynamics is a technique for determining the time changes in the position and velocity of each particle by numerically integrating the equation of motion of each particle over time using a finite difference method. In other words, molecular dynamics calculates the coordinates (x, y, z) of all atoms or molecules in a system in four dimensions (x, y, z, t) based on the potential between atoms or molecules.

[0026] Using the molecular dynamics method described above, the manufacturing condition calculation unit 504 calculates the manufacturing conditions, namely, the temperature conditions and the input amount of raw material G, based on the following steps: (2-1) Set the manufacturing conditions (temperature conditions, input amount of raw material G) for raw material G. (2-2) Initially arrange the atoms or molecules (hereinafter also referred to as molecules, etc.) that make up raw material G. (2-3) Set a potential function between the initially arranged atoms or molecules. (2-4) Simulate the state of the atoms or molecules based on the settings. (2-5) Modify the manufacturing conditions until the state of the atoms or molecules satisfies the preset conditions (conditions under which raw material G becomes fiber), and find manufacturing conditions that satisfy the conditions.

[0027] The molecular dynamics method allows calculation of various statistical ensembles such as isothermal, constant pressure, isothermal and constant pressure, constant energy, constant volume, constant chemical potential, and grand canonical, and also allows the addition of various constraints such as fixing bond lengths and positions. For example, the following items can be calculated: (3-1) Structure (3-2) Density (3-3) Coefficient of volume expansion (3-4) Viscosity (3-5) Specific heat (3-6) Thermal conductivity (3-7) Electrical resistivity (3-8) Fe-Redox (oxidation-reduction of iron (Fe)) (3-9) Erosion rate of heat-resistant bricks (3-10) Wear rate of heater electrodes (pure molybdenum) (3-11) Other parameters that can be calculated

[0028] Although the data (3-1) to (3-11) above can be obtained through experiments, it takes a long time to obtain the temperature conditions when the components of raw material G are changed and to determine the temperature conditions under which spinning is possible according to the component ratio of raw material G. Therefore, by using molecular dynamics to calculate the temperature conditions under which spinning is possible according to the component ratio of raw material G, it becomes possible to efficiently produce fibers from raw material G. Furthermore, since items (3-1) to (3-11) are calculated using molecular dynamics, the design and operation of the spinning apparatus 3 can be more efficient. For example, by calculating the erosion rate of the heat-resistant bricks (3-9), the frequency of heat-resistant brick replacement can be predicted. Furthermore, by calculating the wear rate of the heater electrodes (e.g., pure molybdenum) (3-10), the frequency of heater electrode replacement can be predicted. In molecular dynamics, the way the potential function is set is important. It is preferable to correct (feedback) the potential function used in the molecular dynamics based on measurement data (actual measurement data) obtained using an actual furnace. It is more preferable to correct (feedback) the potential function multiple times while changing the component ratio of the raw material G and the temperature conditions.

[0029] In this embodiment, waste discharged from a coal-fired thermal power plant is used as the fiber raw material G. Therefore, for example, a simulation may be performed using the following steps: (4-1) Set the numerical values ​​of the composition ratios (based on data from the composition analysis device 4) of the coal ash components, such as SiO2, Al2O3, Fe2O3, and CaO. (4-2) Set the initial structure based on an assumption of the unit cell of the space group. (4-3) Generate random atomic structure coordinates using the set composition ratios and unit cell as conditions. Specifically, the simulation begins with the atomic coordinates of the coal ash in a random state. Then, the random atomic coordinates of the coal ash, in other words, the initial structure, are heated to a temperature of several thousand degrees (°C) to dynamically simulate the situation in which molecular dissociation occurs, and the atomic coordinates are generated. Then, the result is gradually cooled to approximate the atomic arrangement of a real liquid, and physical parameters of the desired conditions at the atomic coordinates are derived using molecular dynamics. In other words, an atomic arrangement state close to a gas is generated from a mathematically created random state, and then, in the cooling process, atomic coordinates that simulate a realistic liquid are calculated, and then calculations are performed to transition this into a solid.

[0030] Examples (parts) of such programs are shown in Figures 7 to 14. As shown in Figures 7 to 14, in this embodiment, parameters such as the number of atoms per side (^3 is the number of atoms contained in the cube), the mass ratio of the unit cell, the volume of the unit cell, the mass of the unit cell, the number of atoms in the unit cell, and the volume of the unit cell (taking mass percent into consideration) are set. Based on the above parameter settings, the following are calculated: the ratio of the number of atoms calculated from the number of required unit cells, the ratio of the number of atoms per system (taking mass percent into consideration), the number of atoms allocated by the ratio of the number of atoms per system, the number of required cells per system (rounded up), the number of atoms, the total volume (Å^3), the length of one side of the cube (Å) (rounded up), the total number of atoms, and the lattice vector. In the above explanation, the system refers to the "material (system)" initially set on the computer.

[0031] (Information Processing) Fig. 15 is a flowchart showing an example of information processing of the information processing system 10. Hereinafter, the information processing of the information processing system 10 will be described with reference to Fig. 15. In the following description, the same components as those described with reference to Figs. 1 to 6 will be assigned the same reference numerals, and duplicated description will be omitted.

[0032] (Step S101) The receiving unit 501 of the information processing device 5 receives the composition data and mass of the raw material G.

[0033] (Step S102) The manufacturing condition calculation unit 504 of the information processing device 5 calculates, by molecular dynamics, manufacturing conditions for turning the raw material G into fibers. Note that the method for calculating the temperature conditions by molecular dynamics has already been described, so description thereof will be omitted.

[0034] (Step S103 ) The transmitting unit 502 of the information processing device 5 transmits the temperature conditions calculated by the production condition calculating unit 504 to the spinning device 3 .

[0035] As described above, the information processing device 5 according to this embodiment is an information processing device that calculates the manufacturing conditions for manufacturing fibers made of an amorphous inorganic composition from raw material G, which includes waste discharged from a coal-fired thermal power plant. The information processing device 5 includes a receiving unit 501 that receives the composition ratio and mass of the raw material G, and a manufacturing condition calculation unit 504 that calculates the manufacturing conditions by molecular dynamics based on the composition ratio and mass received by the receiving unit 501. Therefore, it is possible to calculate the temperature conditions and the input amount of raw material G that are spinnable according to the component ratio of the raw material G, without having to acquire temperature conditions when the components of the raw material G are changed by repeating numerous experiments or manufacturing processes.

[0036] Furthermore, the manufacturing condition calculation unit 504 of the information processing device 5 sets conditions for spinning the raw material G, initially arranges the atoms or molecules that make up the raw material G, sets a potential function between the initially arranged atoms or molecules, simulates the state of the atoms or molecules based on the settings, modifies the manufacturing conditions until the state of the atoms or molecules satisfies the preset conditions, and calculates spinning conditions for the raw material G that satisfy the conditions. This makes it possible to calculate spinning conditions with higher accuracy.

[0037] Furthermore, in this embodiment, the actual measurement values ​​are used to correct at least one of the initial positions of the atoms or molecules constituting the raw material G and the potential function between the initially positioned atoms or molecules, thereby enabling the calculation of manufacturing conditions with higher accuracy.

[0038] [First Modification of the Embodiment] In the above embodiment, as shown in FIG. 16, a plurality of spinning apparatuses 3 and composition inspection apparatuses 4 may be connected to the information processing device 5 via a network 6 or the like. In this case, as shown in FIG. 17, an ID may be assigned to each of the spinning apparatuses 3 and composition inspection apparatuses 4, and sets of the spinning apparatuses 3 and composition inspection apparatuses 4 may be managed using the assigned IDs. In the example shown in FIG. 17, the spinning apparatus 3 with ID "A001" and the composition inspection apparatus 4 with ID "B001" are a set, and therefore the information processing device 5 transmits the manufacturing conditions obtained based on the inspection results of the composition inspection apparatus 4 with ID "B001" to the spinning apparatus 3 with ID "A001". The same applies to other sets. In addition, some or all of the functions of the information processing device 5 may be provided in the user terminal 3.

[0039] [Variation 2 of the Embodiment] In the above-described embodiment and Variation 1, an imaging device 6 may be provided, and image data of the spinning device 3 captured by the imaging device 6 (e.g., image data of the molten state of the raw material G in the furnace 3a) may be transmitted to the information processing device 5. FIG. 18 is a diagram showing an example of a spinning process using the information processing device 5 according to Variation 2 of the embodiment. An example of a spinning process using the information processing device 5 according to Variation 2 of the embodiment will be described below with reference to FIG. 18. The storage device 500B of the information processing device 5 according to Variation 2 of the embodiment stores a trained model that has learned image data of the molten state of the raw material G in the furnace 3a when the raw material G is spun normally. The manufacturing condition calculation unit 504 of the information processing device 5 inputs image data of the prevention device 3 captured by the imaging device 6 into the trained model to determine whether the raw material G can be spun normally. If it is determined that the raw material G can be spun normally, spinning is continued as is. If it is determined that the raw material G cannot be spun normally, the spinning conditions (e.g., the temperature conditions of the furnace 3a) may be corrected. The trained model does not necessarily need to be stored in the storage device 500B of the information processing device 5, but may be stored in an external storage device such as a USB (Universal Serial Bus) memory or an external HDD, or in the storage device of another information processing device connected via a network.

[0040] [Variation 3 of the embodiment] Furthermore, in the above embodiment and variations 1 and 2, the conditions for winding the fiber in the winding mechanism 35 may be added to the conditions for spinning that are calculated by the production condition calculation unit 504. In this case, for example, in variation 2 of the embodiment, the storage device 500B of the information processing device 5 stores a trained model that has learned image data such as the winding state of the fiber in the winding mechanism 35 when the raw material G is normally spun. The production condition calculation unit 504 of the information processing device 5 inputs image data of the prevention device 3 captured by the imaging device 6 into the trained model to determine whether the raw material G can be normally spun, and if it is determined that the raw material G can be normally spun, spinning is continued as is, but if it is determined that the raw material G cannot be normally spun, the spinning conditions (for example, the temperature conditions of the furnace 3a or the winding conditions of the winding mechanism 35) may be corrected.

[0041] [Fourth Modification of the Embodiment] Coal ash may contain rare earth elements (hereinafter also referred to as REE). REE has a high melting point and aggregates in the furnace 3a under the temperature conditions exemplified in the above embodiment, which can cause clogging of the drawing pipe 34. For this reason, it is preferable to extract and remove as many REE aggregates (hereinafter also referred to as agglomerates) as possible prior to spinning. FIG. 19 shows an example of an image of aggregates clogging the drawing pipe 34. In FIG. 19, fibers are drawn from the drawing pipes 34 arranged vertically and horizontally. However, it can be seen that some of the drawing pipes 34 are clogged with aggregates, preventing normal drawing of fibers. Thus, although aggregates can be used as a source of REE, if these aggregates reach the fiber drawing hole or fiber drawing pipe, they clog the hole and pipe, making it impossible to draw fibers. In other words, REE contained in coal ash is an undesirable factor that inhibits the production of fibers from coal ash.

[0042] In Variant 4, we describe a method for producing fibers by extracting and removing as much REE as possible from coal ash. The removed REE can still be reused. Figure 20 shows the freezing points of REE oxides. The freezing points shown in Figure 20 are the freezing points of each oxide alone. When different REE oxides are combined, the freezing points decrease (freezing point depression). For example, the freezing points of Sc2O3 and Lu2O3 are 2485°C and 2490°C, respectively. However, the freezing point of a mixture of these oxides, such as (Sc0.45Y0.555)2O3, drops to 2053°C. The types and amounts of REE contained in coal ash vary, and the composition of coal ash varies depending on the type and operating conditions of the power plant. Therefore, the freezing point of different REE oxides combined is unknown, and the REE mixture ratio also varies. Therefore, it is preferable to calculate the REE freezing point each time using molecular dynamics simulations based on the raw material composition.

[0043] The spinning process in Example 4 is broadly composed of the following steps to extract and remove as much REE as possible from the coal ash: (4-1) Melt the coal ash to precipitate and remove as many aggregates as possible; (4-2) Produce fiber F from the molten coal ash after removing the aggregates. As a method for removing as many aggregates as possible, a drain 36 may be provided at the bottom of the furnace 3a as shown in FIG. 21 to remove aggregates that have sunk to the bottom of the furnace 3a (the aggregates have a higher density than the molten coal ash and therefore sink to the bottom of the furnace 3a). In this case, it is preferable that the bottom of the furnace 3a is inclined, with the drain 36 as the lowest point. Alternatively, the drain 36 may not be provided, and the supernatant of the molten coal ash (which contains almost no aggregates) may be sent to the side where the extraction pipe 34 is provided. In this case, it is necessary to periodically remove the aggregates that have settled to the bottom of the furnace 3a.

[0044] Next, the spinning process using the direct melt method in this modified example 4 will be described. (5-1) A portion of raw material G is taken out and its composition is inspected by the composition inspection device 4. (5-2) The composition inspection device 4 transmits the results of the composition inspection (composition and composition ratio (mass%); hereinafter, also referred to as composition data) to the information processing device 5. (5-3) Raw material G is fed into the feeder 1. (5-4) The mass of raw material G fed into the feeder 1 is input to the information processing device 5. (5-5) Based on the mass and composition of raw material G, the information processing device 5 calculates the temperature at which REE oxides separate and precipitate (hereinafter, also referred to as the precipitation temperature) and the conditions under which spinning is possible (specifically, the temperature range, the amount of raw material G fed, etc.). (5-6) The information processing device 5 transmits the calculated amount of raw material G fed to the feeder 1. (5-7) The information processing device 5 transmits the conditions calculated in (5-5) to the spinning device 3. (5-8) The feeder 1 feeds the feed amount of raw material G sent from the information processing device 5 into the mixer 2. (5-9) The mixer 2 agitates the fed raw material G. (5-10) The mixer 2 feeds the agitated raw material G into the furnace 3a of the spinning device 3. (5-11) The spinning device 3 controls the temperature inside the furnace 3a to the precipitation temperature sent from the information processing device 5, and causes the aggregates to precipitate as much as possible at the bottom of the furnace 3a. (5-12) The aggregates that have precipitated as much as possible at the bottom of the furnace 3a are taken out through the drain 36. (5-13) The spinning device 3 melts the raw material G to form amorphous fibers under the conditions that enable spinning sent from the information processing device 5.

[0045] In Example 4, the manufacturing condition calculation unit 504 calculates the precipitation temperature, the temperature conditions for manufacturing the fiber, and the input amount of raw material G based on the data (composition data and mass) from the composition inspection device 4. In Example 4, in addition to (3-1) to (3-11) described in the embodiment, the following items are calculated: (3-12) Precipitation and aggregation of REE

[0046] In order to calculate the precipitation temperature, it is more preferable to apply gravity to molecules and calculate various statistical ensembles. Furthermore, when accelerating the rate of REE precipitation and aggregation using a magnetic field (B), it is more preferable to apply the Lorentz force to charged molecules and calculate various statistical ensembles. In this case, the manufacturing condition calculation unit 504 also calculates the acceleration of REE precipitation and aggregation due to the magnetic field (B) using molecular dynamics. The molecular dynamics method accurately simulates experimental values ​​and has been shown to be suitable for calculating conditions for manufacturing fibers from raw material G. On the other hand, the Urbain model, which is used to predict the viscosity of silica glass and the like, differs from experimental values ​​in both absolute values ​​and temperature trends, and is therefore not useful for calculating the manufacturing conditions. As an example of the above (3-11), the conditions for extracting REE from a melt of raw material G were calculated using molecular dynamics. Low-grade coal ash contains 300 ppm or more of REE (mainly Nd, Tb, Sc, Y, Er, V, Nb, and Dy), standard coal ash contains 500 ppm or more, and high-grade coal ash contains 1,000 ppm or more of REE. Furthermore, raw material G also contains a corresponding amount of REE (mainly Nd, Tb, Sc, Y, Er, V, Nb, and Dy). Figure 22 shows the structure of the REE precipitated state calculated by molecular dynamics at a temperature lower than that required for spinning in furnace 3a. The REE (here, Dy) indicated by the white circles is biased toward the bottom. While some of the Dy can be seen in the upper part of the figure, this is due to the periodic boundary of the system used in the calculation, and in reality, Dy is biased toward the bottom due to gravity. In the above (3-11), the Lorentz force term is added to the equation of motion, and then the vector field of the magnetic field (B) is defined, and the Lorentz force is applied to the charged species to perform molecular dynamics calculations. For example, in the case of Dy, the charged species is Dy ３＋ By applying a magnetic field in one direction, the REE is more efficiently biased downward.

[0047] In this embodiment, waste discharged from a coal-fired thermal power plant is used as the fiber raw material G. Therefore, for example, a simulation may be performed using the following steps: (4-1) The types and numerical ratios of all atoms constituting the coal ash are digitized using a composition analysis device 4, such as an X-ray fluorescence analyzer, an inductively coupled plasma mass spectrometer (ICP-MS), a cyclic voltammogram (CV), or a prompt gamma ray analyzer (PGA), and these numerical values ​​are set. Note that the composition analysis device 4 is not limited to the above devices, as long as it can obtain the types and numerical ratios of atoms in the raw material G, including REEs. (4-2) An initial structure is hypothesized and set based on the unit cell of the space group. (4-3) Atomic structure coordinates are randomly generated based on the set composition ratio and unit cell. Specifically, the simulation is started from a random state for the coordinates of the atoms constituting the coal ash. Then, the atomic coordinates of these random coal ash constituent atoms, in other words, the initial structure, are heated to several thousand degrees (°C) to mechanically simulate a situation in which the intermolecular bonding forces are weakened, and the atomic coordinates are created. Then, the system is gradually cooled to approximate the atomic arrangement of a real liquid, and the physical parameters of the desired conditions at the atomic coordinates are derived using molecular dynamics.

[0048] Note that Modification 4 can also be used as a REE extraction method, since it extracts and removes REE.

[0049] An example of molecular dynamics simulation in Modification 4 of the embodiment will be described below. Note that in the following description, duplicated explanations of the description already given in the embodiment and Modifications 1-3 thereof will be omitted. An example of calculation of the structure of (3-1) in paragraph 0027 by molecular dynamics simulation will be shown below. As a quantitative index of the structure, not only lattice constants and symmetry information from conventional crystal structure analysis but also a radial distribution function (DRF), which is a statistical distribution function based on interatomic distances, was used. The RDF is a function g that indicates the relative frequency with respect to the interatomic distance γ between any atomic species A and B. ＡＢ(γ) is expressed by the following equation (1): The radial distribution function is a standard method for evaluating the short-range order and medium-range order of a system. In the above formula (1), ρ B is the average density of atomic species B, δ is the Dirac delta function, and < > is the statistical average.

[0050] In this fourth modification, the LAMMPS (Large-scale Atomic / Molecular Massively Parallel Simulator) was used as the molecular dynamics simulator to calculate the RDF. Furthermore, a Tersoff-type potential was applied to the Si-O, Al-O, Ca-O, and Fe-O interatomic potentials, and a Born-Mayer-type potential was applied to the Dy-O potential. The system size was approximately 100,000 atoms, and the simulation steps were approximately 1 million. Furthermore, to initialize the system, the system was equilibrated using the NPT ensemble, and a statistical average was taken from all samples based on the time-series data of atomic coordinates obtained using the NVE or NVT ensemble. The RDF was calculated for each pair of Si-O, Al-O, Ca-O, and Fe-O, and the average bond distance and local structural order of each atom pair were analyzed from the peak position and height. Representative RDF results are shown in Figures 23 to 26. Figure 23 shows an example of the RDF results for Si-O. Figure 24 shows an example of the RDF results for Al-O. Figure 25 shows an example of the RDF results for Ca-O. Figure 26 shows an example of the RDF results for Fe-O. For example, the first peak appears around 1.63 Å for Si-O, which is thought to reflect a tetrahedral structure similar to silica glass. On the other hand, the distributions for Fe-O and Ca-O are broader, presumably a factor affecting the medium-range order. This structural information not only indicates the density (3-2) in paragraph 0027, but also reflects the volume expansion coefficient (3-3) in paragraph 0027 by comparing the RDF changes at each temperature. The results of this molecular dynamics calculation can be used as information useful for estimating the density change rate and expansion coefficient. The numerical values ​​of these items are used to determine and automatically control the fiber manufacturing temperature conditions in this invention.

[0051] A typical example of the viscosity (3-4) in paragraph 0027 calculated using molecular dynamics simulation is shown below. The Green-Kubo relation was used to derive the viscosity. This is expressed as the time integral of the autocorrelation function of the pressure tensor based on the fluctuation-dissipation theorem in statistical mechanics. In other words, the viscosity coefficient is defined by the following equation (2): In the above equation (2), V is the simulation cell volume, k Ｂ Boltzmann's constant, T is the absolute temperature, P αβ is the off-diagonal component of the pressure tensor. In a modified example of this embodiment, the viscosity was calculated by numerically integrating the time correlation function of the pressure tensor. The molecular dynamics simulator, interatomic potential, system size, simulation steps, and initialization are as described in paragraph 0046. Figure 27 shows the difference between the viscosity of standard raw material G (above (3-4)) and the viscosity obtained using the molecular dynamics method. The molecular dynamics method accurately simulates experimental values ​​and has been shown to be suitable for calculating viscosity conditions for producing fibers from raw material G. On the other hand, the Urbain model, which is commonly used to predict the viscosity of silica glass and the like, differs from the experimental values ​​in both absolute values ​​and temperature trends, making the molecular dynamics method more suitable for predicting the production conditions for the coal ash fiber of the present invention.

[0052] In carrying out the present invention, in addition to the structural information and dynamic properties, it is also possible to calculate the specific heat (3-5) above by molecular dynamics simulation, if necessary. v or specific heat at constant pressure C P is defined by the following equation (3) as the differential quantity of the internal energy (U) or enthalpy (H) obtained by molecular dynamics simulation with respect to the temperature change. Specifically, equilibrium states at multiple temperatures are simulated under constant volume (NVT ensemble) or constant pressure (NPT ensemble), and the specific heat can be numerically evaluated from the difference in average energy at each temperature. Furthermore, in the present invention, the thermal conductivity of the coal ash-derived multi-component melt described in paragraph 0027 (3-6) can also be evaluated using molecular dynamics. Thermal conductivity can be derived, for example, by numerically integrating the time autocorrelation function of heat flux based on the Green-Kubo equation. In some cases, non-equilibrium molecular dynamics (NEMD) can be applied to create an artificial temperature gradient within the material system, and then the thermal conductivity can be calculated from the heat flow and temperature difference in the steady state. The thermal conductivity obtained in this way is directly related to the thermal diffusion characteristics of the material, cooling profile design, uniform temperature gradient design within the furnace, and even the uniformity of product characteristics, contributing to the sophistication of the manufacturing control process of the present invention. In particular, in systems where complex structural changes and temperature distributions are intertwined, such as multi-component melts derived from coal ash, thermal conductivity information obtained by molecular dynamics may exhibit better evaluation accuracy than conventional theoretical predictions, as in the case of viscosity described in paragraph 0046. Furthermore, in the present invention, the electrical resistivity (or electrical conductivity) described in paragraph 0027 (3-7) can also be estimated using molecular dynamics. Specifically, by analyzing the ionic diffusion behavior and charge transfer characteristics in multi-component melts derived from coal ash, electrical conductivity can be derived based on the Green-Kubo equation, etc. Furthermore, if necessary, the contribution of electronic and ionic conduction can be quantitatively evaluated through non-equilibrium molecular dynamics (NEMD) or statistical analysis of ion diffusion coefficients. When coal ash is directly melted in an electric furnace and the components are adjusted before being produced into fibers, the electrical resistivity of the melt relative to the voltage and current applied to the electrodes is an extremely important control factor. In the present invention, by making it possible to evaluate this resistivity using molecular dynamics, it is possible to contribute to the control of electric heating in actual equipment, the optimization of energy efficiency, and green manufacturing design using renewable energy.

[0053] In addition, in the present invention, considering that the multi-component melt derived from coal ash is a melt system containing iron (Fe), it is possible to estimate the redox state (Redox state) of (3-8) above through molecular dynamics calculations. 2+ and Fe 3+ The redox state can be statistically evaluated based on information such as the local coordination structure, oxygen environment, and interatomic distances related to the ratio of Fe to Fe. This redox state can be estimated, if necessary, using a reactive force field (ReaxFF) or electron density-based structural evaluation methods (e.g., Bader analysis, local charge analysis), allowing for dynamic evaluation of the redox balance of the entire system. This Fe Redox state is closely related to the viscosity, infrared absorption, electrical conductivity, and even furnace atmosphere control (air / reducing atmosphere) of the melt, and can be applied to the determination and automatic control of fiber production conditions in the present invention.

[0054] The molecular dynamics system of the present invention is capable of modeling structural and chemical changes at the contact interface between molten coal ash and refractory brick materials. In particular, the molecular dynamics method can be used to evaluate the dynamic processes of inter-element reactions, interfacial diffusion, and atomic substitution between silica, alumina, and zirconia contained in the refractory brick material and calcium, iron, and other elements in the molten slag. As a result, by predicting the erosion rate (time dependence of the progression of surface structural destruction) of the refractory brick surface as described in paragraph 0029 (3-9) and optimizing the zirconia ratio, etc., it becomes possible to design an extension of furnace life and evaluate long-term stable operation. This allows the information processing system of the present invention to realize integrated management that can virtually evaluate the long-term soundness of the refractory bricks in addition to the physical properties, electrical properties, and structural changes of the multi-component molten coal ash-derived material. Furthermore, the present invention has great business significance in that it enables furnace design optimization, including refractory brick configuration, to achieve both electric furnace heating characteristics and insulation properties and to link with highly efficient renewable energy use.

[0055] Furthermore, in this invention, structural and chemical changes at the contact interface between molten coal ash slag and a heater electrode (e.g., pure molybdenum) are analyzed using molecular dynamics. Specifically, dynamic processes such as dissolution, diffusion, and oxidation reactions of molybdenum atoms at the interface are reproduced, and the wear rate of the heater electrode (e.g., pure molybdenum) described in paragraph 0027 (3-10) can be numerically derived based on the atomic number reduction and structural changes in the electrode material per unit time. This enables prediction of electrode wear life and setting of optimal replacement timing, contributing to reduced operating costs, minimized shutdown risk, and optimized preventive maintenance schedules. Especially in renewable energy-driven operation, designing electrode materials for extended life is closely related to CO2 reduction and overall manufacturing efficiency, and is therefore a technological element that combines environmental and economic value.

[0056] Furthermore, the information processing device of the present invention may include a configuration for analyzing the thermal behavior and interactions of rare earth elements (e.g., Dy, Sc, Tb, etc.) contained as trace components in the raw material, based on molecular dynamics analysis, in consideration of the possibility that such elements may cause pore blockage during the fiberization process. This allows the manufacturing condition calculation unit to derive manufacturing conditions, including a process for selectively removing or concentrating the rare earth elements under predetermined temperature conditions, with the aim of stabilizing the fiber production process. The simulation for calculating the manufacturing conditions may be configured as an atomic diffusion model that takes into account the temperature distribution in the furnace and external magnetic field conditions, and a method for predicting the uneven distribution of rare earth elements based on statistical evaluation of the diffusion coefficient, interaction potential, and thermal fluctuation may be employed.

[0057] An example of the uneven distribution of the rare earth elements (e.g., Dy) calculated using molecular dynamics simulation is shown below. The LAMMPS molecular dynamics simulator was used. The interatomic potentials used were a Tersoff potential between Si-O, Al-O, Ca-O, and Fe-O, and a Born-Mayer potential between Dy-O. The system size was approximately 5,000 atoms, and the simulation steps were approximately 30,000. The initial atomic coordinates were thermally equilibrated using an NVT ensemble, and the temperature was gradually increased from room temperature to the furnace temperature every 0.0001 picoseconds. Figure 22 shows an example of the atomic coordinates of all the atoms obtained, with Dy visualized in white. As shown in Figure 22, Dy was confirmed to be unevenly distributed downward. Based on the analysis of the raw material composition, the information processing device of the present invention can avoid process instability due to the uneven distribution of rare earth elements, while simultaneously achieving stable fiber production and improving the recoverability of high-value-added resources.

[0058] In the above embodiment and its modifications 1 to 4, an electric melting furnace 3a (furnace 3a) is used, but it is not necessarily an electric melting furnace. For example, a furnace using gas or liquid fuel as a heat source may be used. Also, a hybrid furnace using multiple heat sources, such as electricity and gas, may be used. Furthermore, in the above embodiment and its modifications 1 to 4, the raw material G flowing out of the withdrawal pipe 34 is cooled by contact with the outside air, but it may also be cooled by cooling water. Furthermore, in the above embodiment and its modifications 1 to 4, the types and numerical ratios of all atoms constituting the coal ash may be converted into data using a composition analysis device 4 such as an X-ray fluorescence analyzer, an inductively coupled plasma mass spectrometer (ICP-MS), a cyclic voltametry device (CV), or a prompt gamma ray analyzer (PGA).

[0059] G: Raw material 1: Input machine 2: Mixer 3: Spinning device 3a: Furnace 4: Composition inspection device 5: Information processing device 6: Imaging device 500A: Communication IF 500B: Storage device 500C: CPU 501: Receiving unit 502: Transmitting unit 503: Storage device control unit 504: Manufacturing condition calculation unit

Claims

1. An information processing device comprising: an acquisition unit that acquires the composition ratio and mass of a raw material whose main component is waste discharged from a thermal power plant that uses coal as fuel; and a manufacturing condition calculation unit that calculates the conditions for manufacturing an amorphous material from the raw material using a molecular dynamics method based on the composition ratio and mass acquired by the acquisition unit.

2. The information processing device according to claim 1, characterized in that the manufacturing condition calculation unit sets manufacturing conditions for the material, initially positions atoms or molecules that constitute the raw material, sets a potential function between the initially positioned atoms or molecules, simulates the state of the atoms or molecules based on the settings, modifies the manufacturing conditions until the state of the atoms or molecules satisfies the predetermined conditions, and calculates manufacturing conditions for the material that satisfy the conditions.

3. The information processing device according to claim 2, characterized in that the actual measurement values ​​are used to correct at least one of the initial arrangement of atoms or molecules constituting the raw material, or the potential function between the initially arranged atoms or molecules.

4. The information processing device according to claim 1, characterized in that the manufacturing conditions are temperature conditions for manufacturing the material.

5. The information processing device according to claim 1, characterized in that the manufacturing condition calculation unit calculates the temperature conditions for agglomerating and precipitating the rare earths contained in the raw material using a molecular dynamics method based on the composition ratio and mass acquired by the acquisition unit.

6. An information processing device comprising: an acquisition unit that acquires the composition ratio and mass of a raw material whose main component is waste discharged from a coal-fired thermal power plant; and a condition calculation unit that calculates, based on the composition ratio and mass acquired by the acquisition unit, conditions for melting the raw material and agglomerating and precipitating rare earths contained in the raw material using a molecular dynamics method.

7. An information processing method comprising: a step in which an acquisition unit acquires the composition ratio and mass of a raw material whose main component is waste discharged from a thermal power plant that uses coal as fuel; and a step in which a production condition calculation unit calculates the conditions for producing an amorphous material from the raw material using a molecular dynamics method based on the composition ratio and mass acquired by the acquisition unit.

8. The information processing method according to claim 7, characterized in that the manufacturing condition calculation unit calculates the temperature conditions for agglomerating and precipitating the rare earths contained in the raw material using a molecular dynamics method based on the composition ratio and mass acquired by the acquisition unit.

9. An information processing method comprising: a step in which an acquisition unit acquires the composition ratio and mass of a raw material whose main component is waste discharged from a coal-fired thermal power plant; and a step in which a condition calculation unit calculates, based on the composition ratio and mass acquired by the acquisition unit, conditions for melting the raw material and agglomerating and precipitating the rare earths contained in the raw material using a molecular dynamics method.

10. A storage medium having stored thereon a program that causes a computer to function as: an acquisition unit that acquires the composition ratio and mass of a raw material whose main component is waste discharged from a coal-fired thermal power plant; and a manufacturing condition calculation unit that calculates the conditions for manufacturing an amorphous material from the raw material using a molecular dynamics method based on the composition ratio and mass acquired by the acquisition unit.

11. The storage medium according to claim 10, characterized in that the manufacturing condition calculation unit calculates the temperature conditions for agglomerating and precipitating the rare earths contained in the raw material using a molecular dynamics method based on the composition ratio and mass acquired by the acquisition unit.

12. A storage medium having stored thereon a program that causes a computer to function as: an acquisition unit that acquires the composition ratio and mass of a raw material whose main component is waste discharged from a coal-fired thermal power plant; and a condition calculation unit that calculates, based on the composition ratio and mass acquired by the acquisition unit, the conditions for melting the raw material and agglomerating and precipitating the rare earths contained in the raw material using a molecular dynamics method.

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