JP7087766B2 - Particle size distribution constant estimation device, particle size distribution constant estimation program, and particle size distribution constant estimation method - Google Patents

Description

The present invention relates to a particle size distribution constant estimation device, a particle size distribution constant estimation program, and a particle size distribution constant estimation method.

In the iron-making method using a blast furnace and a reduction furnace of the direct reduction iron-making method (hereinafter, simply referred to as a blast furnace, etc.), the quality of the raw materials for iron-making charged into the blast furnace, etc. is controlled to stabilize the operation of the blast furnace, etc. It is known. Raw materials for ironmaking include sinter, calcined pellets, non-firing lump ore, lump ore and the like. The quality of raw materials for iron making controlled for the operation of blast furnaces, etc. includes, for example, chemical components such as iron, powder ratio, strength, reduction resistance, and reduction powder resistance, and the measurement method is specified in JIS. There is. For example, the strength of the raw material for iron making is defined by the drop strength SI (JIS-M8711 (2011)) and the rotational strength TI (JIS-M8712 (2009)), and the reducibility of the raw material for iron making is the reduction index RI (Reduction Index,). It is specified by JIS-M8713 (2009)). The reduction powder resistance of the raw material for iron making includes a temperature raising step for raising the temperature of the raw material for iron making, a reduction step for reducing the raw material for iron making, a cooling step for cooling the raw material for iron making, and a rolling process for rolling the raw material for iron making. It is defined by the reduction degradation index RDI (Reduction Degradation Index, JIS-M8720 (2009)) measured in the RDI test including the dynamic process. Workers who manage the operation of blast furnaces, etc. adjust the manufacturing method of raw materials for iron making so that the quality items of raw materials for iron making measured by the measurement method specified by JIS satisfy the control conditions, and make them into blast furnaces, etc. By adjusting the mixing ratio of the raw materials for iron making to be charged, the operation of the blast furnace and the like can be stabilized.

The work process in the RDI test specified in JIS-M8720 (2009) is shown below.
(1) While nitrogen is flowed at a flow rate of about 5 L / min to replace the air in the reduction reaction tube, the reduction reaction tube containing the iron-making raw material is heated in an electric heating furnace to bring the iron-making raw material to 550 ° C ± 10 ° C. Heat until it reaches (heating step).
(2) Nitrogen is flowed at a flow rate of 15 L / min to maintain an isothermal temperature of 550 ° C. for at least 15 minutes for temperature equilibrium (holding step).
(3) Nitrogen is replaced with a reducing gas, and the reducing gas is passed through a reduction reaction tube at a flow rate of 15 L / min ± 0.5 L / min for 30 minutes to reduce the raw material for iron making (reduction step).
(4) The heating of the electric furnace is stopped, and nitrogen is flowed at a flow rate of about 5 L / min ± 0.5 L to cool the steelmaking raw material to a temperature of 100 ° C. or lower (cooling step).
(5) The raw material for iron making is taken out from the reduction reaction tube, charged into a drum, and the drum is rotated at a rotation speed of 30 rotations / minute ± 1 rotation / minute for a total of 900 rotations (rolling step).
(6) All the raw materials for iron making are taken out from the drum, and the raw materials for iron making are sifted using a sieve having a nominal opening of 2.8 mm (sieving step).
(7) The reduced powder index RID is calculated by the following formula (calculation step).

Here, m ₀ is the mass (g) after the reduction of the iron-making raw material and before rolling, and m ₁ is the mass (g) of the iron-making raw material remaining in the 2.8 mm sieve. In the RDI test specified in JIS, the raw material for iron making is reduced by passing a reducing gas through a reduction reaction tube having a temperature of 550 ° C., and the reduced raw material for iron making is rolled in a rotating drum to produce powder. By measuring the degree of pulverization, the reducing pulverizability under simulated conditions such as in a blast furnace is measured.

Further, there is known an AE method for evaluating cracks and the like by measuring the acoustic emission (AE) energy of elastic waves generated by cracks and crack fractures and analyzing the waveform of the measured AE energy.

Further, a technique for estimating the reduced powder index RDI using the AE method is known (see, for example, Patent Document 1). In the technique described in Patent Document 1, the reduction powder index RDI is the sum of the AE energy in the RDI test and the reduction powder index change amount ΔRDI, which is the difference between the reduction powder index reduction powder index and the rotational intensity SI. Estimated based on the correlation between. That is, in the technique described in Patent Document 1, the reduced pulverization index RDI is estimated based on the finding that the total amount of AE energy in the RDI test correlates with the amount of change in the reduced pulverization index ΔRDI. The technique described in Patent Document 1 can easily and directly estimate the reduced powder index RDI from the total amount of AE energy generated during the RDI measurement.

Japanese Unexamined Patent Publication No. 2016-79500

The initial particle size of the iron-making raw material to be charged, the powder ratio generated by the reduction of the iron-making raw material (which can be associated with the reduction powdering index RDI estimated in the technique described in Patent Document 1), and the iron-making raw material. If the particle size distribution of the powder generated by the reduction of the iron is known, the particle size distribution in the furnace after the reduction powder of the raw material for iron making can be estimated. From this particle size distribution, the pressure loss indicating the air permeability of a blast furnace or the like can be estimated using the Ichida's formula and the Ergun's formula.

However, in a blast furnace or the like, a technique for simply and directly estimating the particle size distribution of powder generated by the reduction of raw materials for iron making is not known.

Therefore, an object of the present invention is to provide a method for estimating the particle size distribution constant, which can easily and directly estimate the particle size distribution of the powder generated by the reduction of the raw material for iron making.

The gist of the present invention for solving such a problem is the particle size distribution constant estimation device, the particle size distribution constant estimation program, and the particle size distribution constant estimation method described below.
(1) In the reduction process in the reduction furnace, an elastic wave signal indicating an elastic wave propagating from a raw material for iron making is acquired.
Calculate the AE energy corresponding to each elastic wave signal,
Calculate the fractal dimension of AE energy,
Estimate the constant based on the correspondence between the fractal dimension and the constant of the particle size distribution formula showing the particle size distribution of the powder generated by the reduction of the raw material for iron making.
Outputs a particle size distribution constant signal indicating a constant,
A method for estimating the particle size distribution constant, which is characterized by including the above.
(2) The constant is an index when the ratio is the base in the formula showing the relationship between the particle size distribution of the powder and the ratio of each particle size of the powder to the maximum particle size of the powder. Constant estimation method.
(3) The particle size distribution constant estimation method according to (2), wherein the constant is inversely proportional to the fractal dimension.
(4) In the reduction process in the reduction furnace, an elastic wave signal indicating an elastic wave propagating from a raw material for iron making is acquired.
Calculate the AE energy corresponding to each elastic wave signal,
Calculate the fractal dimension of AE energy,
Estimate the constant based on the correspondence between the fractal dimension and the constant of the particle size distribution formula showing the particle size distribution of the powder generated by the reduction of the raw material for iron making.
Outputs a particle size distribution constant signal indicating a constant,
A particle size distribution constant estimation program characterized by having a computer perform processing.
(5) An elastic wave acquisition unit that acquires an elastic wave signal indicating an elastic wave propagating from a raw material for iron making in the reduction process in the reduction furnace.
The AE energy calculation unit that calculates the AE energy corresponding to each elastic wave signal,
A fractal dimension calculation unit that calculates the fractal dimension of AE energy,
A particle size distribution constant estimation unit that estimates the constant based on the correspondence between the fractal dimension and the constant of the particle size distribution formula that shows the particle size distribution of the powder generated by the reduction of the raw material for iron making.
Particle size distribution constant output unit that outputs a particle size distribution constant signal indicating a constant, and a particle size distribution constant output unit
A particle size distribution constant estimation device characterized by having.

In one embodiment, it is possible to accurately control the reduced pulverization property of the raw material for iron making inside a blast furnace or the like.

It is a schematic diagram of the particle size distribution constant estimation system which concerns on embodiment. It is a figure which shows the arithmetic unit shown in FIG. FIG. 5 is a flowchart of a particle size distribution constant estimation process in which the particle size distribution constant estimation system shown in FIG. 1 manages the reduced powderability of a raw material for iron making charged into a blast furnace or the like. It is a figure which shows an example of the fractal dimension of AE energy. It is a figure which shows an example of the result of the experiment which determines α and β in the formula (3).

The particle size distribution constant estimation device, the particle size distribution constant estimation program, and the particle size distribution constant estimation method will be described below with reference to the drawings. However, the technical scope of the present invention is not limited to those embodiments.

(Outline of particle size distribution constant estimation method according to the embodiment)
The inventors of the present invention have found that there is a correspondence between the fractal dimension of AE energy generated in the reduction process in the reduction furnace and the constant of the particle size distribution formula showing the particle size distribution of the powder after reduction pulverization. The fractal dimension of the AE energy generated in the reduction process in the reduction furnace is indicated by the slope when the amplitude of the AE energy and the number of elastic waves corresponding to the AE energy having each amplitude are shown in both logarithms. When the absolute value of the fractal dimension of the AE energy is large, it indicates that the amplitude spread of the AE energy generated by the cracks generated in the ironmaking raw material is narrow, so that the cracks generated in the ironmaking raw material in the reduction process in the reduction furnace are uniform. Indicates that there is. On the other hand, when the absolute value of the fractal dimension of the AE energy is small, it is shown that the amplitude of the AE energy generated by the cracks generated in the ironmaking raw material is wide, so that the cracks generated in the ironmaking raw material in the reduction process in the reduction furnace are present. Indicates non-uniformity. As described above, the fractal dimension of the AE energy generated in the reduction process in the reduction furnace becomes a value according to the mode of pulverization due to the cracking of the raw material for iron making in the reduction process in the reduction furnace.

Based on this finding, the particle size distribution constant estimation method according to the embodiment is based on the fractal dimension of the AE energy, the fractal dimension of the AE energy, and the constant of the particle size distribution formula showing the particle size distribution of the powder generated by the reduction of the raw material for iron making. The constant of the particle size distribution formula is estimated based on the correspondence between. The method for estimating the particle size distribution constant according to the embodiment is to estimate the particle size distribution of the powder generated by the reduction of the raw material for iron making simply and directly by estimating the constant of the particle size distribution formula from the fractal dimension of the AE energy. Can be done.

(Particle size distribution constant estimation system according to the embodiment)
FIG. 1 is a schematic diagram of a particle size distribution constant estimation system according to an embodiment.

The particle size distribution constant estimation system 1 includes a reduction furnace unit 10, a gas supply unit 20, an exhaust gas treatment unit 30, an AE detection unit 40, and an arithmetic unit 50. The particle size distribution constant estimation system 1 estimates the constant of the formula showing the particle size distribution of the powder generated by the reduction of the raw material for iron making from the fractal dimension of the AE energy in the reduction step of the RDI test.

The reduction furnace section 10 has a reaction tube 11 for accommodating the raw material S for iron making, and a heating furnace 12 containing and heating the reaction tube 11. The reaction tube 11 includes a reaction tube inner tube 13, a reaction tube outer tube 14, a pair of gas rectifying perforated plates 15, a gas inlet 16, a gas discharge port 17, and a thermoelectric pair 18 for measuring sample temperature. , With a reaction tube lid 19. The outer tube 14 of the reaction tube forms the outer edge of the reaction tube 11, and the inner tube 13 of the reaction tube forms the inner edge of the reaction tube 11. The pair of gas rectifying perforated plates 15 are separated from each other in the vertical direction of the reaction tube inner tube 13. The iron-making raw material S is charged between the pair of gas rectifying perforated plates 15. The gas inflow port 16 is an introduction port for introducing the gas supplied from the gas supply unit 20 into the inside of the reaction tube 11, and the gas discharge port 17 is an discharge port for discharging gas from the inside of the reaction tube 11. The sample temperature measuring thermocouple 18 is a thermocouple in which a current corresponding to the temperature inside the reaction tube 11 flows, and the current flowing through the sample temperature measuring thermocouple 18 is supplied to a control device (not shown). The control device to which the current is supplied from the thermocouple 18 for measuring the sample temperature controls the heating furnace 12 so that the temperature inside the reaction tube inner tube 13 becomes a desired temperature. The reaction tube lid 19 is a lid that is detachably arranged in the opening of the reaction tube 11 and seals the reaction tube 11.

The heating furnace 12 has a housing 120 and a thermocouple 121 for controlling the furnace temperature. The housing 120 has a size capable of containing the reaction tube 11. Each of the thermocouples 121 for controlling the furnace temperature generates heat when the current controlled by the control device to which the current is supplied from the thermocouple 18 for measuring the sample temperature is energized from a power source (not shown). The heat generated by each of the thermocouples 121 for controlling the furnace temperature heats the reaction tube 11 that houses the iron-making raw material S.

The gas supply unit 20 has a plurality of gas cylinders 21, a gas flow meter 22 connected to each of the plurality of gas cylinders 21, and a gas mixing container 23, and is supplied from each of the plurality of gas cylinders 21. The gas is mixed in the gas mixing container 23 to produce a reduced gas. Each of the plurality of gas cylinders 21 accommodates N ₂ gas, CO gas, CO ₂ gas, and H ₂ gas. Each of the gas flow meters 22 measures the flow rate of the gas supplied from each of the plurality of gas cylinders 21. In the gas mixing container 23, for example, N ₂ gas is supplied in the temperature raising step, the holding step and the cooling step, and the volume fraction of CO gas is 30% and the volume fraction of N ₂ gas is 70% in the reducing step. A certain reducing gas is supplied.

The exhaust gas treatment unit 30 includes an exhaust gas pipe 31 and an exhaust gas treatment facility 32. One end of the exhaust gas pipe 31 is connected to the gas discharge port 17, the other end is connected to the exhaust gas treatment equipment 32, and the reduced gas or the like is discharged from the inside of the reaction pipe 11 to the exhaust gas treatment device 32. The exhaust gas treatment facility 32 is a facility capable of performing reaction treatment according to the type and amount of exhaust gas containing toxic CO gas, explosive H2 gas, and the like.

The AE detection unit 40 has an AE waveguide member 41 and an AE sensor 42. The AE waveguide member 41 is a rod-shaped AE waveguide rod, which extends into the sample S charged between the pair of gas rectifying perforated plates 15 and generates elastic waves from the sample S. Propagate to the AE sensor 42. The AE sensor 42 includes a piezoelectric element such as lead zirconate titanate (PZT), detects elastic waves propagating in the AE waveguide member 41, and outputs a signal corresponding to the detected elastic waves.

FIG. 2 is a diagram showing an arithmetic unit 50.

The arithmetic unit 50 includes a communication unit 51, a storage unit 52, an input unit 53, an output unit 54, and a processing unit 60. The communication unit 51, the storage unit 52, the input unit 53, the output unit 54, and the processing unit 60 are connected to each other via the bus 200. The arithmetic unit 50 estimates the constant based on the correspondence between the fractal dimension of the AE energy in the reduction step of the RDI test and the constant of the formula indicating the particle size distribution of the powder generated by the reduction of the raw material for iron making. In one example, the arithmetic unit 50 is a monitoring control device that monitors and controls the transfer of charged materials to a blast furnace or the like. Further, although the arithmetic unit 50 is shown as a single device, it may be configured as a plurality of devices. For example, the arithmetic unit 50 is an AE measuring device that measures the frequency and AE energy of the elastic wave detected by the AE sensor 42, and an analysis that assumes the particle size of the object from the frequency and AE energy of the elastic wave measured by the AE measuring device. It may be configured with a personal computer for use.

The communication unit 51 has a wired communication interface circuit such as Ethernet (registered trademark). The communication unit 51 communicates with the AE sensor 42 and a higher-level control device (not shown) via the LAN 43.

The storage unit 52 includes, for example, at least one of a semiconductor storage device, a magnetic tape device, a magnetic disk device, or an optical disk device. The storage unit 52 stores an operating system program, a driver program, an application program, data, and the like used for processing in the processing unit 60. For example, as an application program, the storage unit 52 causes the processing unit 60 to execute a particle size distribution constant estimation process for estimating the constant of the particle size distribution formula indicating the particle size distribution of the powder generated by the reduction of the raw material for iron making. Store the estimation program, etc. The particle size distribution constant estimation program may be installed in the storage unit 52 from a computer-readable portable recording medium such as a CD-ROM or a DVD-ROM using a known setup program or the like. Further, the storage unit 52 stores various data used in the particle size distribution constant estimation process.

The input unit 53 may be any device as long as data can be input, and is, for example, a touch panel, a keyboard, or the like. The operator can input characters, numbers, symbols, etc. using the input unit 53. When the input unit 53 is operated by an operator, the input unit 53 generates a signal corresponding to the operation. Then, the generated signal is supplied to the processing unit 60 as an instruction of the operator.

The output unit 54 may be any device as long as it can display an image, an image, or the like, and is, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The output unit 54 displays a video corresponding to the video data supplied from the processing unit 60, an image corresponding to the image data, and the like. Further, the output unit 54 may be an output device that prints a video, an image, characters, or the like on a display medium such as paper.

The processing unit 60 includes one or more processors and peripheral circuits thereof. The processing unit 60 comprehensively controls the overall operation of the arithmetic unit 50, and is, for example, a CPU. The processing unit 60 executes processing based on a program (driver program, operating system program, application program, etc.) stored in the storage unit 52. Further, the processing unit 60 can execute a plurality of programs (application programs and the like) in parallel.

The processing unit 60 includes an elastic wave acquisition unit 61, an AE energy calculation unit 62, a fractal dimension calculation unit 63, a particle size distribution constant estimation unit 64, and a particle size distribution constant output unit 65. Each of these units is a functional module realized by a program executed by the processor included in the processing unit 60. Alternatively, each of these parts may be mounted on the arithmetic unit 50 as firmware.

(Particle size distribution constant estimation process by the particle size distribution constant estimation system according to the embodiment)
FIG. 3 is a flowchart of the particle size distribution constant estimation process executed by the particle size distribution constant estimation system 1. The particle size distribution constant estimation process shown in FIG. 3 is mainly executed by the processing unit 60 in cooperation with each element of the arithmetic unit 50 based on the program stored in the storage unit 52 in advance.

First, the elastic wave acquisition unit 61 acquires elastic wave signals indicating the waveforms of the plurality of elastic waves detected by the AE sensor 42 during the reduction step (S101). The elastic wave acquisition unit 61 may detect that the discharge valve of the gas cylinder 21 accommodating the CO gas is opened and start acquisition of the elastic wave signal, via an input unit 53 by an operator (not shown). Acquisition of the elastic wave signal may be started in response to the input of the reduction step start instruction. Further, the elastic wave acquisition unit 61 may end the acquisition of the elastic wave signal by detecting that the discharge valve of the gas cylinder 21 accommodating the CO gas is closed, and the input unit 53 by an operator (not shown) may end the acquisition. The acquisition of the elastic wave signal may be completed in response to the input of the reduction step end instruction via.

Next, the AE energy calculation unit 62 calculates the AE energy of each of the plurality of elastic waves corresponding to the elastic wave signals acquired by the elastic wave acquisition unit 61 during the reduction step (S102). The AE energy calculation unit 62 calculates the area of the portion surrounded by the envelope of each waveform of the plurality of elastic waves as the AE energy of each of the plurality of elastic waves.

Next, the fractal dimension calculation unit 63 calculates the fractal dimension of the AE energy calculated in the process of S102 (S103). As shown in the equation (1), the fractal dimension of the AE energy is when the amplitude A of the AE energy and the number of elastic waves f (A) corresponding to the AE energy having each amplitude are shown in both logarithms. Is indicated by the slope F of.

In equation (1), c is a constant.

FIG. 4 is a diagram showing an example of the fractal dimension of AE energy. In FIG. 4, the horizontal axis shows the amplitude of the AE energy in a logarithm, and the vertical axis shows the number of elastic waves corresponding to the AE energy having each amplitude in a logarithm.

The linear equation shown by the approximate straight line 401 is shown by "y = -1.57 x + 7.60". In the approximate straight line 401, x corresponds to the logarithm log ₁₀ A of the amplitude of the AE energy, and y corresponds to the logarithm log ₁₀ f (A) of the number of elastic waves corresponding to the AE energy having each amplitude. In FIG. 4, the fractal dimension F is 1.57.

Next, the particle size distribution constant estimation unit 64 is based on the correspondence between the fractal dimension of the AE energy and the constant of the formula indicating the particle size distribution of the powder generated by the reduction of the ironmaking raw material, and the powder generated by the reduction of the ironmaking raw material. Estimate the constant of the equation showing the particle size distribution of (S104).

The formula showing the particle size distribution of the powder generated by the reduction of the raw material for iron making is, for example, the formula showing the Gaudin-Schuhmann distribution represented by the formula (2).

In the formula (2), d _max indicates the maximum particle size (diameter) of the powder generated by the reduction of the iron-making raw material, and d _p indicates the respective particle sizes of the powder generated by the reduction of the iron-making raw material. m is an exponential (constant) representing the spread of the particle size distribution. U (d _p ) indicates the particle size distribution of the powder generated by the reduction of the raw material for iron making, and is represented by the cumulative ratio from zero particle size to d _p . The index m in the formula (2) is an example of a constant of the formula indicating the particle size distribution of the powder generated by the reduction of the raw material for iron making estimated in S104. That is, the constant is a ratio (d _p / d _max ) in the formula showing the relationship between the particle size distribution U (d _p ) of the powder and the ratio (d _p / d _max ) of each particle size of the powder to the maximum particle size of the powder. ) May be the index m at the bottom.

The particle size distribution constant estimation unit 64 estimates the exponent m of the equation showing the Gaudin-Schuhmann distribution based on the correspondence information between the fractal dimension F of the AE energy and the exponent m of the equation showing the Gaudin-Schuhmann distribution. The correspondence information may be stored in the storage unit 52 as a linear expression represented by the equation (3), or may be stored in the storage unit 52 as a table showing the correspondence relationship between the fractal dimension F of the AE energy and the exponent m. good.

In the formula (3), α and β are constants determined in advance by an experiment using a plurality of raw materials for iron making.

FIG. 5 is a diagram showing an example of the results of an experiment for determining α and β in the formula (3). In FIG. 5, the horizontal axis shows the fractal dimension F of the AE energy generated in the reduction process in the reduction furnace, and the vertical axis shows the exponent m of the equation showing the Gaudin-Schuhmann distribution.

FIG. 5 was prepared using Samples 1-9 shown in Table 1. Each of the samples 1 to 9 is a sinter, and in Table 1, the item "component" indicates the content of the compound contained in the samples 1 to 9, and the item "RDI" is the reduced pulverization calculated by the RDI test. Shows the index. Further, in Table 1, the item "GS distribution index" indicates the index m of the formula (2) indicating the Gaudin-Schuhmann distribution, and the item "Fractal dimension of AE" indicates the AE energy corresponding to the elastic wave generated in the RDI test. Shows the absolute value of the fractal dimension. In the RDI test, cracks occur in the iron-making raw material not only in the reduction process but also in the cooling process. Therefore, the item "AE fractal dimension" corresponds to the elastic wave generated in the reduction process and the cooling process of the RDI test. Shows the absolute value of the fractal dimension of energy.

In the example shown in FIG. 5, α is 0.487 and β is 1.365.

Then, the particle size distribution constant output unit 65 outputs a particle size distribution constant signal indicating the constant of the formula indicating the particle size distribution of the powder generated by the reduction of the raw material for iron making calculated in the process of S104 (S105).

(Action and effect of the particle size distribution constant estimation system according to the embodiment)
The particle size distribution constant estimation system 1 estimates the particle size distribution of powder generated by the reduction of raw materials for iron making simply and directly by estimating the constant of the particle size distribution formula showing the particle size distribution from the fractal dimension of AE energy. be able to.

(Modified example of the particle size distribution constant estimation system according to the embodiment)
In the particle size distribution constant estimation system 1, an equation showing the Gaudin-Schuhmann distribution is used as an equation showing the particle size distribution of the powder, but in the particle size distribution constant estimation system according to the embodiment, another equation showing the particle size distribution of the powder is used. You may use it. Equation (4) is an equation showing the particle size distribution of the powder and the equation showing the rosin-Rammler distribution that can be used in the particle size distribution constant estimation system according to the embodiment.

In the formula (4), de indicates the particle size of the powder having R of 0.368, _d indicates the particle size of each of the powders generated by the reduction of the raw material for iron making, and n indicates the spread of the particle size distribution. It is a parameter, and R is also referred to as distribution on a sieve, and indicates the mass fraction of powder having a particle size of d or more.

In the particle size distribution constant estimation system according to the embodiment, the parameter n shown in the equation (4) may be a constant of the equation indicating the particle size distribution of the powder generated by the reduction of the raw material for iron making.

1 Particle size distribution constant estimation system 10 Reduction furnace unit 20 Gas supply unit 30 Exhaust gas treatment unit 40 AE detection unit 50 Computing device 61 Elastic wave acquisition unit 62 AE Energy calculation unit 63 Fractal dimension calculation unit 64 Particle size distribution constant estimation unit 65 Particle size distribution constant Output section

Claims (5)

In the reduction process in the reduction furnace, an elastic wave signal indicating the elastic wave propagating from the raw material for iron making is acquired.
The AE energy corresponding to each of the elastic wave signals is calculated, and the result is calculated.
The fractal dimension F of the AE energy is calculated using the equation (1) .
The constant m is estimated based on the equations (2) and (3) showing the correspondence between the fractal dimension F and the constant m of the particle size distribution equation showing the particle size distribution of the powder generated by the reduction of the raw material for iron making.
Including outputting a particle size distribution constant signal indicating the constant m .

In the formula (1), A is the amplitude of the AE energy, and f (A) is the number of elastic waves corresponding to the AE energy having each amplitude.
In the formula (2), d _max indicates the maximum particle size of the powder generated by the reduction of the raw material for iron making, d _p indicates the respective particle sizes of the powder generated by the reduction of the raw material for iron making, and U (d _p ) indicates the particle size of each of the powders generated by the reduction of the raw material for iron making . The particle size distribution of the powder generated by the reduction of raw materials is shown.
A method for estimating a particle size distribution constant, characterized in that α and β are constants in the equation (3) . The constant is an index when the ratio is at the bottom in the formula showing the relationship between the particle size distribution of the powder and the ratio of each particle size of the powder to the maximum particle size of the powder, according to claim 1. Particle size distribution constant estimation method. The method for estimating a particle size distribution constant according to claim 2, wherein the constant is proportional to the fractal dimension. In the reduction process in the reduction furnace, an elastic wave signal indicating the elastic wave propagating from the raw material for iron making is acquired.
The AE energy corresponding to each of the elastic wave signals is calculated, and the result is calculated.
The fractal dimension F of the AE energy is calculated using the equation (1) .
The constant m is estimated based on the equations (2) and (3) showing the correspondence between the fractal dimension F and the constant m of the particle size distribution equation showing the particle size distribution of the powder generated by the reduction of the raw material for iron making.
Outputs a particle size distribution constant signal indicating the constant m .
A distribution constant estimation program that causes a computer to perform processing.

In the formula (1), A is the amplitude of the AE energy, and f (A) is the number of elastic waves corresponding to the AE energy having each amplitude.
In the formula (2), d _max indicates the maximum particle size of the powder generated by the reduction of the raw material for iron making, d _p indicates the respective particle sizes of the powder generated by the reduction of the raw material for iron making, and U (d _p ) indicates the particle size of each of the powders generated by the reduction of the raw material for iron making . The particle size distribution of the powder generated by the reduction of raw materials is shown.
A particle size distribution constant estimation program characterized in that α and β are constants in the equation (3) . In the reduction process in the reduction furnace, an elastic wave acquisition unit that acquires an elastic wave signal indicating an elastic wave propagating from a raw material for iron making, and an elastic wave acquisition unit.
An AE energy calculation unit that calculates AE energy corresponding to each of the elastic wave signals,
A fractal dimension calculation unit that calculates the fractal dimension F of the AE energy using the equation (1) ,
The particle size for estimating the constant m based on the equations (2) and (3) showing the correspondence between the fractal dimension F and the constant m of the particle size distribution formula showing the particle size distribution of the powder generated by the reduction of the raw material for iron making. Distribution constant estimation part and
A particle size distribution constant estimation device having a particle size distribution constant output unit that outputs a particle size distribution constant signal indicating the constant m .

In the formula (1), A is the amplitude of the AE energy, and f (A) is the number of elastic waves corresponding to the AE energy having each amplitude.
In the formula (2), d _max indicates the maximum particle size of the powder generated by the reduction of the raw material for iron making, d _p indicates the respective particle sizes of the powder generated by the reduction of the raw material for iron making, and U (d _p ) indicates the particle size of each of the powders generated by the reduction of the raw material for iron making . The particle size distribution of the powder generated by the reduction of raw materials is shown.
A particle size distribution constant estimation device, characterized in that α and β are constants in the equation (3) .

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