Information processing device, information processing method, sintered ore manufacturing method, blast furnace operation method, coke manufacturing method, and program

Abstract

An information processing device (20) is provided with a control unit that: acquires observation data obtained for each observation time by observing, through use of a sensor (30), the surface layer of a deposited raw material (12) that is sequentially conveyed; uses the acquired observation data to calculate an index related to the particle diameter of each particle included in the deposited raw material (12) for each observation time; acquires measurement data obtained for each measurement time corresponding to each observation time by measuring the state of the deposited raw material (12); corrects the calculated index of the observation time corresponding to each measurement time by using the acquired measurement data; and outputs an index for each corrected observation time.

Description

Information processing device, information processing method, sintered ore manufacturing method, blast furnace operation method, coke manufacturing method, and program

[0001] The present disclosure relates to an information processing device, an information processing method, a sintered ore manufacturing method, a blast furnace operation method, a coke manufacturing method, and a program.

[0002] In manufacturing processes using raw materials such as minerals, the particle size, particle shape, and particle size distribution of the raw materials affect the operation of the manufacturing process. Therefore, it is necessary to measure the particle size, particle shape, or particle size distribution of the raw materials in advance. In particular, in blast furnaces, it is important to identify the particle size distribution of raw materials such as ore or coke, which affects the ventilation inside the furnace.

[0003] Conventionally, raw materials are sampled periodically and analyzed using sieves. However, because the analysis takes time, it is difficult to reflect the results in real time on blast furnace operation. Therefore, a technology that can measure the particle size distribution of raw materials in real time is required.

[0004] Known techniques for measuring the particle size distribution of raw materials in real time include a method of observing an image or shape of the top of the raw material on a conveyor using a camera or a laser rangefinder. For example, in the method disclosed in Patent Document 1, the distance from the laser rangefinder to the raw material on the conveyor is measured and unevenness data of the raw material is extracted. The particle size distribution of the surface layer is calculated by performing image processing on the unevenness data. The calculated particle size distribution is corrected to match the particle size distribution previously measured using a sieve.

[0005] International Publication No. 2019 / 193971

[0006] The particle size of the deposited raw material is not uniform throughout the material, but varies depending on the position. In other words, particle size segregation occurs in the deposited raw material. Therefore, when the particle size distribution of the surface layer is calculated, the calculated particle size distribution differs from the particle size distribution of the entire layer, including hidden layers. Therefore, it is necessary to obtain a parameter that represents the relationship between the particle size distribution of the surface layer and the particle size distribution of the entire layer in advance, and to estimate the particle size distribution of the entire layer using the parameter from the calculated particle size distribution.

[0007] The relationship between the particle size distribution of the surface layer and that of all layers including the lower layer changes successively depending on the state of the raw material, such as size or composition, etc. Therefore, it is difficult to perform high-precision correction by simply calculating the difference between the particle size distribution measured in advance using a sieve and the calculated particle size distribution, as in the conventional method.

[0008] An object of the present disclosure is to measure an index related to the particle size of a deposition raw material with high accuracy.

[0009] (1) An information processing device according to an embodiment of the present disclosure includes a control unit that: acquires observation data obtained at each observation time by observing the surface layer of sequentially transported deposition raw material with a sensor; calculates an index related to the particle diameter of each particle contained in the deposition raw material for each observation time using the acquired observation data; acquires measurement data obtained at each measurement time corresponding to each observation time by measuring the state of the deposition raw material; corrects the calculated index for the observation time corresponding to each measurement time using the acquired measurement data; and outputs the corrected index for each observation time.

[0010] (2) An information processing device according to an embodiment of the present disclosure is the information processing device according to (1), in which the control unit corrects the index according to a dimension of the deposition raw material determined from the measurement data.

[0011] (3) An information processing device according to an embodiment of the present disclosure is the information processing device according to (2), in which the dimensions of the deposition source material include a layer thickness of the deposition source material.

[0012] (4) An information processing device according to one embodiment of the present disclosure is the information processing device described in (3), wherein the control unit acquires distance data obtained by measuring the distance from the sensor to the surface of the deposition raw material as the observation data, and also uses the distance data as the measurement data.

[0013] (5) An information processing device according to one embodiment of the present disclosure is the information processing device described in (1), wherein the control unit acquires a camera image obtained by photographing the surface of the deposition raw material as the measurement data, and corrects the index according to the brightness of the camera image.

[0014] (6) An information processing device according to an embodiment of the present disclosure is the information processing device according to (5), in which the control unit also uses the camera image as the observation data.

[0015] (7) An information processing device according to an embodiment of the present disclosure is the information processing device described in (1), in which the control unit acquires, as the measurement data, data obtained by measuring the transport amount of the deposition raw material.

[0016] (8) An information processing device according to an embodiment of the present disclosure is the information processing device according to (1), wherein the control unit acquires, as the measurement data, data obtained by measuring a composition of the deposition source material.

[0017] (9) An information processing device according to an embodiment of the present disclosure is the information processing device according to (8), in which the composition of the deposition raw material includes a moisture content of the deposition raw material.

[0018] (10) An information processing method according to one embodiment of the present disclosure includes: an information processing device acquiring observation data obtained at each observation time by observing the surface layer of sequentially transported deposition raw material with a sensor; the information processing device calculating an index related to the particle diameter of each particle contained in the deposition raw material for each observation time using the acquired observation data; the information processing device acquiring measurement data obtained at each measurement time corresponding to each observation time by measuring the state of the deposition raw material; the information processing device correcting the calculated index for the observation time corresponding to each measurement time using the acquired measurement data; and the information processing device outputting the corrected index for each observation time.

[0019] (11) A sintered ore manufacturing method according to an embodiment of the present disclosure includes adjusting the blending ratio of at least one of three types of raw materials, namely, an iron-containing raw material, an auxiliary raw material, and a carbon-containing raw material, which are sintered blended raw materials, using an index for each observation time output by the information processing method described in (10).

[0020] (12) A sintered ore manufacturing method according to one embodiment of the present disclosure includes adjusting at least one of three operating conditions of a granulator, namely, rotation speed, residence time, and added moisture, using an indicator for each observation time output by the information processing method described in (10).

[0021] (13) A sintered ore manufacturing method according to one embodiment of the present disclosure includes adjusting at least one of three operating conditions of a raw material charging device, namely, the drum feeder rotation speed, the chute angle, and the drum chute rotation speed, using an indicator for each observation time output by the information processing method described in (10).

[0022] (14) A blast furnace operation method according to one embodiment of the present disclosure includes adjusting the operating conditions of a blast furnace into which raw materials are charged using indicators for each observation time output by the information processing method described in (10).

[0023] (15) A coke production method according to one embodiment of the present disclosure includes adjusting the rotation speed of a coal pulverizer using an index for each observation time output by the information processing method described in (10).

[0024] (16) A program according to an embodiment of the present disclosure causes a computer to perform operations including: obtaining observation data for each observation time by observing the surface layer of sequentially transported deposition raw material with a sensor; calculating an index related to the particle diameter of each particle contained in the deposition raw material for each observation time using the obtained observation data; obtaining measurement data for each measurement time corresponding to each observation time by measuring the state of the deposition raw material; correcting the calculated index for the observation time corresponding to each measurement time using the obtained measurement data; and outputting the corrected index for each observation time.

[0025] According to the present disclosure, an index relating to the particle size of the deposition raw material can be measured with high accuracy.

[0026] 4B is a diagram illustrating a configuration of a measurement system according to an embodiment of the present disclosure. FIG. 4C is a block diagram illustrating a configuration of an information processing device according to an embodiment of the present disclosure. FIG. 4D is a flowchart illustrating an operation of an information processing device according to an embodiment of the present disclosure. FIG. 4E is a diagram illustrating an example of distance data of granulated particles as an image. FIG. 4F is a diagram illustrating the result of particle detection from the distance data of FIG. 4A. FIG. 4G is a diagram illustrating an example of a camera image of an ore. FIG. 4H is a diagram illustrating the result of particle detection from the camera image of FIG. 4C. FIG. 4H is a graph illustrating the relationship between layer thickness and the difference between sieve analysis values ​​and measurements of the weight percentage of a specific particle size range obtained for distance data of granulated particles. FIG. 4I is a graph illustrating the relationship between brightness and the difference between sieve analysis values ​​and measurements of the weight percentage of a specific particle size range obtained for distance data of granulated particles. FIG. 4J is a graph illustrating the relationship between conveyance amount and the difference between sieve analysis values ​​and measurements of the weight percentage of a specific particle size range obtained for distance data of an ore. FIG. 4J is a graph illustrating the relationship between brightness and the difference between sieve analysis values ​​and measurements of the weight percentage of a specific particle size range obtained for camera images of an ore. 1 is a graph showing the relationship between the difference between the sieve analysis value and the measured value of the weight proportion of a specific particle size fraction obtained for a camera image of ore, and the conveying amount. 2 is a graph showing the correlation between the sieve analysis value and the uncorrected measured value of the weight proportion of a specific particle size fraction obtained for distance data of granulated particles. 3 is a graph showing the correlation between the sieve analysis value and the measured value corrected according to layer thickness obtained for distance data of granulated particles. 4 is a graph showing the correlation between the sieve analysis value and the measured value corrected according to brightness obtained for distance data of granulated particles. 5 is a graph showing the correlation between the sieve analysis value and the measured value corrected according to conveying amount obtained for distance data of granulated particles. 6 is a graph showing the correlation between the sieve analysis value and the uncorrected measured value of the weight proportion of a specific particle size fraction obtained for a camera image of ore. 7 is a graph showing the correlation between the sieve analysis value and the measured value corrected according to layer thickness obtained for camera images of ore.Graph showing the correlation between sieve analysis values ​​of the weight proportion of a specific particle size fraction obtained for a camera image of an ore and measurements corrected for brightness. Graph showing the correlation between sieve analysis values ​​of the weight proportion of a specific particle size fraction obtained for a camera image of an ore and measurements corrected for the conveying amount. Graph showing the correlation between sieve analysis values ​​of the average diameter obtained for distance data of granulated particles and uncorrected measurements. Graph showing the correlation between sieve analysis values ​​of the average diameter obtained for distance data of granulated particles and measurements corrected for layer thickness. Graph showing the correlation between sieve analysis values ​​of the average diameter obtained for distance data of granulated particles and measurements corrected for brightness. Graph showing the correlation between sieve analysis values ​​of the average diameter obtained for distance data of granulated particles and measurements corrected for the conveying amount. Graph showing the correlation between sieve analysis values ​​of the average diameter obtained for camera images of an ore and uncorrected measurements. Graph showing the correlation between sieve analysis values ​​of the average diameter obtained for camera images of an ore and measurements corrected for layer thickness. Graphs showing the correlation between the sieve analysis value of the average diameter obtained from a camera image of the ore and the measurement value corrected according to brightness, and graphs showing the correlation between the sieve analysis value of the average diameter obtained from a camera image of the ore and the measurement value corrected according to the conveyance amount.

[0027] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings.

[0028] In each drawing, the same or corresponding parts are denoted by the same reference numerals. In the description of this embodiment, the description of the same or corresponding parts will be omitted or simplified as appropriate.

[0029] The configuration of a measurement system 10 according to this embodiment will be described with reference to FIG.

[0030] The measurement system 10 includes an information processing device 20 and a sensor 30. The measurement system 10 may further include other sensors.

[0031] The information processing device 20 can communicate with the sensor 30 directly or via a network such as a LAN or the Internet. "LAN" is an abbreviation for local area network. The information processing device 20 may also be able to communicate with other sensors directly or via a network such as a LAN or the Internet.

[0032] As an example of a process to which this embodiment is applied, a sintering machine in the steel industry and a conveyor 11 that transports granulated particles used in the sintering machine are shown, but this embodiment can also be applied to other processes. As an example of the deposited raw material 12, granulated particles, which are one of the raw materials used in the steel industry, are shown, but the deposited raw material 12 may be ore, which will be described later, or, for example, coke, sintered ore, pellets, limestone, rock, coal, or fine ore.

[0033] The information processing device 20 may be a general-purpose computer such as a PC, a server computer such as a cloud server, a microcomputer installed in a mobile device such as a smartphone or tablet, or a dedicated computer. "PC" is an abbreviation for personal computer. The information processing device 20 is a device for measuring the particle diameter of the deposition raw material 12. The information processing device 20 calculates the particle diameter of the deposition raw material 12 from distance data indicating the distance to the deposition raw material 12 measured by the sensor 30. Alternatively, the information processing device 20 may calculate the particle diameter of the deposition raw material 12 from an image captured by the sensor 30. The information processing device 20 corrects the calculated particle diameter value or an index calculated from the particle diameter value according to the state of the deposition raw material 12. The state of the deposition raw material 12 refers to conditions that can be measured over time for the deposition raw material 12. Examples of such conditions include operating conditions and raw material conditions. The operating conditions are, for example, the raw material deposition state. The raw material deposition state is, for example, the angle of repose, layer thickness, or widthwise spread of the raw material deposited on the conveyor 11 after being discharged from the raw material storage hopper or transferred to the conveyor 11. The angle of repose of the raw material is the maximum angle of the slope at which the raw material remains stable when piled up. The layer thickness of the raw material is the height dimension of the raw material. The widthwise spread of the raw material is the widthwise dimension of the raw material. The raw material conditions are, for example, the composition of the raw material itself, such as the moisture content. By using the information processing device 20, it is possible to measure the particle size of all layers of the deposited raw material 12, including the lower layer, with high accuracy, even if the state of the deposited raw material 12 changes.

[0034] The sensor 30 is a two-dimensional laser distance meter. The laser distance meter irradiates a laser beam in a line along the width direction of the conveyor 11 and measures the distance to the granulated particles line by line. The granulated particles are transported and moving on the conveyor 11. Therefore, the laser distance meter measures the distance to the granulated particles line by line at a constant measurement period. Three-dimensional shape data of the granulated particles is obtained by integrating the measured values ​​of these lines. The above method is a method of obtaining the three-dimensional shape of the object to be measured using a so-called light cutting method. Conventional laser distance meter and data processing means may be used for this purpose. The laser distance meter preferably has a measurement area equal to the width of the conveyor 11 and is capable of measuring the entire granulated particles, i.e., the entire surface, transported by the conveyor 11. The shorter the measurement period, the more preferable. In this embodiment, the measurement period is set to 4 kHz. The sensor 30 may be other than a laser distance meter. For example, a distance measurement camera using a stereo method or a ToF method with two cameras, or a camera that simply captures visible light images, may be used. "ToF" is an abbreviation for Time of Flight.

[0035] The configuration of the information processing device 20 according to this embodiment will be described with reference to FIG.

[0036] The information processing device 20 includes a control unit 21 , a storage unit 22 , a communication unit 23 , an input unit 24 , and an output unit 25 .

[0037] The control unit 21 includes at least one processor, at least one programmable circuit, at least one dedicated circuit, or any combination thereof. The processor is a general-purpose processor such as a CPU or GPU, or a dedicated processor specialized for specific processing. "CPU" is an abbreviation for central processing unit. "GPU" is an abbreviation for graphics processing unit. An example of the programmable circuit is an FPGA. "FPGA" is an abbreviation for field-programmable gate array. An example of the dedicated circuit is an ASIC. "ASIC" is an abbreviation for application specific integrated circuit. The control unit 21 executes processing related to the operation of the information processing device 20 while controlling each unit of the information processing device 20.

[0038] The storage unit 22 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or any combination thereof. The semiconductor memory is, for example, a RAM, a ROM, or a flash memory. "RAM" is an abbreviation for random access memory. "ROM" is an abbreviation for read only memory. RAM is, for example, an SRAM or a DRAM. "SRAM" is an abbreviation for static random access memory. "DRAM" is an abbreviation for dynamic random access memory. ROM is, for example, an EEPROM. "EEPROM" is an abbreviation for electrically erasable programmable read only memory. Flash memory is, for example, an SSD. "SSD" is an abbreviation for solid-state drive. Magnetic memory is, for example, an HDD. "HDD" is an abbreviation for hard disk drive. The storage unit 22 functions, for example, as a main storage device, an auxiliary storage device, or a cache memory. The storage unit 22 stores data used in the operation of the information processing device 20 and data obtained by the operation of the information processing device 20 .

[0039] The communication unit 23 includes at least one communication module. The communication module is, for example, a module compatible with a wired LAN communication standard such as Ethernet (registered trademark), a wireless LAN communication standard such as IEEE 802.11, or a mobile communication standard such as LTE, 4G, or 5G. "IEEE" is an abbreviation for Institute of Electrical and Electronics Engineers. "LTE" is an abbreviation for Long Term Evolution. "4G" is an abbreviation for 4th generation. "5G" is an abbreviation for 5th generation. The communication unit 23 communicates with the sensor 30. The communication unit 23 may also communicate with other sensors. The communication unit 23 receives data used in the operation of the information processing device 20 and transmits data obtained by the operation of the information processing device 20.

[0040] The input unit 24 includes at least one input device. The input device is, for example, a physical key, a capacitive key, a pointing device, a touch screen integrated with a display, a camera, or a microphone. The input unit 24 accepts an operation to input data used for the operation of the information processing device 20. The input unit 24 may be connected to the information processing device 20 as an external input device instead of being provided in the information processing device 20. As a connection interface, an interface compatible with standards such as USB, HDMI (registered trademark), or Bluetooth (registered trademark) can be used. "USB" is an abbreviation for Universal Serial Bus. "HDMI (registered trademark)" is an abbreviation for High-Definition Multimedia Interface.

[0041] The output unit 25 includes at least one output device. The output device is, for example, a display, a printer, or a speaker. The display is, for example, an LCD or an organic EL display. "LCD" is an abbreviation for liquid crystal display. "EL" is an abbreviation for electroluminescent. The output unit 25 outputs data obtained by the operation of the information processing device 20. The output unit 25 may be connected to the information processing device 20 as an external output device instead of being provided in the information processing device 20. As a connection interface, an interface compatible with standards such as USB, HDMI (registered trademark), or Bluetooth (registered trademark) can be used.

[0042] The functions of the information processing device 20 are realized by executing a program according to this embodiment on a processor serving as the control unit 21. That is, the functions of the information processing device 20 are realized by software. The program causes a computer to execute the operations of the information processing device 20, thereby causing the computer to function as the information processing device 20. That is, the computer functions as the information processing device 20 by executing the operations of the information processing device 20 in accordance with the program.

[0043] The program can be stored on a non-transitory computer-readable medium. Examples of the non-transitory computer-readable medium include flash memory, magnetic recording devices, optical disks, magneto-optical recording media, and ROMs. The program can be distributed by selling, transferring, or lending portable media such as SD cards, DVDs, or CD-ROMs that store the program. "SD" is an abbreviation for Secure Digital. "DVD" is an abbreviation for digital versatile disc. "CD-ROM" is an abbreviation for compact disc read only memory. The program can also be distributed by storing it in the storage of a server and transferring it from the server to another computer. The program can also be provided as a program product.

[0044] A computer temporarily stores a program stored on a portable medium or transferred from a server in its main storage device. The computer then reads the program stored in the main storage device with a processor and executes processing in accordance with the read program. The computer may also read the program directly from the portable medium and execute processing in accordance with the program. The computer may also execute processing in accordance with the received program each time a program is transferred from the server to the computer. Processing may also be executed using a so-called ASP-type service that realizes functions simply by issuing execution instructions and obtaining results, without transferring the program from the server to the computer. "ASP" is an abbreviation for application service provider. Programs include information used for processing by a computer that is equivalent to a program. For example, data that is not a direct instruction to a computer but has properties that define computer processing falls under the category of "equivalent to a program."

[0045] Some or all of the functions of the information processing device 20 may be realized by a programmable circuit or a dedicated circuit as the control unit 21. In other words, some or all of the functions of the information processing device 20 may be realized by hardware.

[0046] The operation of the information processing device 20 according to this embodiment will be described with reference to Fig. 3. The operation described below corresponds to the information processing method according to this embodiment. That is, the information processing method according to this embodiment includes steps S1 to S5 shown in Fig. 3.

[0047] In S1, the control unit 21 acquires observation data Xi obtained at each observation time by observing the surface layer of the sequentially transported deposition raw material 12 with the sensor 30. Specifically, the control unit 21 receives the observation data Xi from the sensor 30 via the communication unit 23.

[0048] In this embodiment, the control unit 21 acquires, as the observation data Xi, distance data obtained by measuring the distance from the sensor 30 to the surface of the deposited raw material 12. FIG. 4A is an image of a bird's-eye view of three-dimensional granulated particle profile data acquired by a two-dimensional laser rangefinder. In this image, the whiter the grayscale, the higher the height and the closer the distance to the rangefinder. The horizontal dimension is 500 pixels in the laser width direction. 1 pixel is 0.3 mm. The vertical dimension is 200 pixels in the conveying direction of the conveyor 11. 1 pixel is 0.3 mm. The resolution in the height direction is 5 μm. The granulated particles on the conveyor 11 are accumulated and transported. The data typically acquired is only a single three-dimensional shape data. To calculate the particle diameter of the granulated particles, it is necessary to detect and identify individual particles. From this profile data, including the unevenness of the granulated particles, signal processing is performed to separate each individual granulated particle. The particle size can be calculated by counting the number of particles for each particle size of the granulated particles separated by the particle separation process and creating a histogram. A known method, such as the method described in Reference 1 below, can be used to detect and separate particles and calculate their particle size. Figure 4B shows the results of particle separation. As shown in Figure 4B, individual particles can be detected well. [Reference 1] Matthew J. Thurley, "Automated Online Measurement of Particle Size Distribution using 3D Range Data," IFAC Proceedings Volumes, 2009, 42, 134-139

[0049] The control unit 21 may acquire, as the observation data Xi, a camera image obtained by photographing the surface of the deposited raw material 12. Figures 4C and 4D show the results of applying a general particle separation method such as that described in Reference 1 to the deposited raw material 12 using a camera instead of a laser rangefinder as the sensor 30 and targeting ore instead of granulated particles as the deposited raw material 12. Figure 4C shows a photographed image of the ore. The image has a pixel count of 1280 x 1024, with one pixel being approximately 1 mm. Figure 4D shows the particle separation results, with individual particles displayed in grayscale. As shown in Figure 4D, it is possible to accurately detect individual particles from a simple camera image without using distance data obtained using a laser rangefinder.

[0050] In S2, the control unit 21 uses the observation data Xi acquired in S1 to calculate, for each observation time, an index Zi relating to the particle diameter of each particle contained in the deposition material 12. The index Zi may be the particle diameter value itself, or may be an index calculated from the particle diameter value, such as an average diameter.

[0051] In S3, the control unit 21 acquires measurement data Yi obtained at each measurement time corresponding to each observation time by measuring the state of the deposition raw material 12. Each measurement time may be a time before the corresponding observation time or a time after the corresponding observation time. Specifically, the control unit 21 receives the measurement data Yi from the sensor 30 or another sensor via the communication unit 23.

[0052] In this embodiment, the control unit 21 also uses the distance data acquired in S1 as the measurement data Yi. Alternatively, the control unit 21 may acquire, as the measurement data Yi, a camera image obtained by photographing the surface layer of the deposition raw material 12. If the control unit 21 acquires a camera image instead of distance data in S1, the control unit 21 may also use the camera image as the measurement data Yi. Alternatively, the control unit 21 may acquire, as the measurement data Yi, data obtained by measuring the transport amount of the deposition raw material 12. Alternatively, the control unit 21 may acquire, as the measurement data Yi, data obtained by measuring the composition of the deposition raw material 12. The composition of the deposition raw material 12 includes, for example, the moisture content of the deposition raw material 12. The moisture content of the deposition raw material 12 can be measured using a moisture meter.

[0053] In S4, the control unit 21 corrects the observation time index Zi corresponding to each measurement time calculated in S2 using the measurement data Yi acquired in S3.

[0054] In this embodiment, the control unit 21 corrects the index Zi according to the dimensions of the deposition raw material 12 obtained from the measurement data Yi. The dimensions of the deposition raw material 12 include, for example, the layer thickness of the deposition raw material 12. Alternatively, if a camera image is acquired in S3, the control unit 21 may correct the index Zi according to the brightness of the camera image.

[0055] In S5, the control unit 21 outputs the index Zi for each observation time corrected in S4. Specifically, the control unit 21 transmits the index Zi for each observation time to a device that controls the process or a terminal of a user that manages the process via the communication unit 23. Alternatively, the control unit 21 displays, prints, or outputs the index Zi for each observation time as audio via the output unit 25.

[0056] Generally, when the raw material is transported by the conveyor 11, it is piled up and transported. Therefore, the sensor 30 can only measure the surface layer of the raw material. However, the particle size distribution of the entire layer, including the hidden lower layer, is not completely identical to the particle size distribution of the surface layer, and segregation occurs. For example, because small particles tend to be hidden, a situation may occur in which there are more large particles in the surface layer and more small particles in the lower layer. To eliminate this segregation effect, it is necessary to estimate the particle size distribution of the entire layer from the particle size distribution of the surface layer. For example, it is possible to sample the entire raw material in the width direction of the conveyor 11, obtain the true particle size distribution of the entire layer, and calculate in advance the relationship between the measured particle size distribution of the surface layer and the parameter indicating this relationship. By using the measured particle size distribution of the surface layer and a parameter indicating this relationship, it is possible to obtain the particle size distribution of the entire layer in real time. However, this parameter changes as the operating conditions or raw material conditions change. During operation, the operating conditions and raw material conditions are constantly changing. According to this embodiment, high-precision correction is possible by incorporating the operating conditions or raw material conditions.

[0057] An example will be described in which correction is performed using one of the operational conditions, the layer thickness or an index correlated with the layer thickness, which is an index representing the deposition state. The inventors discovered that the relationship between the particle size of the surface layer and the particle size of all layers, including the lower layer, changes depending on the layer thickness or the index correlated with the layer thickness, which is one of the indexes representing the deposition state, and demonstrated that highly accurate particle size measurement is possible by performing correction using this.

[0058] The laser rangefinder measures the distance from the raw material surface to the sensor 30 in the width direction of the conveyor 11. By measuring the conveyor profile without raw material and calculating the difference, the raw material's width-wise height information can be obtained. In this embodiment, the layer thickness is determined by averaging the width-wise height information. The definition of layer thickness is not limited to this, and there are multiple indicators that correlate with layer thickness. For example, since the positional relationship between the conveyor 11 and the laser rangefinder does not change, the distance data from the laser rangefinder to the raw material surface can be used as an indicator. The brightness of a camera image can also be used. For example, a camera can be prepared and used as another sensor to capture an image of the raw material surface, and the average brightness of the image can be used as an indicator. This is because brightness is correlated with the distance to the raw material surface, and the average brightness essentially corresponds to the distance to the raw material surface. The raw material transport volume can also be used as an indicator that correlates with layer thickness. The transport volume per hour is determined by the speed of the conveyor 11 and the weight of the raw material on the conveyor 11. The unit of transport volume is ton / h. When the speed of the conveyor 11 is constant, the transport amount is determined by the weight of the raw material measured by the load cell. This weight of the raw material is determined by the amount of raw material dispensed from the upstream hopper. Therefore, a larger weight means that more raw material is loaded on the conveyor 11, and the layer thickness is essentially greater. However, the device that measures the weight of the conveyor 11 is a device that measures the weight of the raw material in a certain section, and does not capture the ever-changing variations in the pile-up and raw material layer thickness in the conveying direction of the conveyor 11. Therefore, although it is possible to use this device as another sensor to use the raw material transport amount as an index related to layer thickness, it is preferable to use a separate sensor 30, such as a laser distance meter or camera, that can continuously measure, as this can capture more subtle layer thickness variations and therefore improve correction accuracy.

[0059] Figures 5A to 5F show that when a laser rangefinder is used for granulated particles and when a camera is used for ore, the relationship between the particle size of the surface layer and the particle size of the entire layer, including the lower layer, changes depending on the layer thickness or the brightness or transport amount, which are indicators correlated with the layer thickness.

[0060] Figures 5A to 5C show the results of similar verification of distance data for granulated particles obtained with a laser distance meter. Figure 5A shows the relationship between the difference between the sieve analysis value and the measured value of the weight percentage of the particle size fraction of 4.75 mm to 8 mm, i.e., the error, and layer thickness. A correlation of R = -0.40 was observed between the error and layer thickness, indicating that layer thickness influences the relationship between surface layer particle size and total layer particle size. Being able to explain the error in terms of layer thickness means that using layer thickness can reduce error and improve accuracy. Figure 5B shows the relationship between error and brightness. A correlation of R = -0.37 was observed between the error and brightness, indicating that brightness influences the relationship between surface layer particle size and total layer particle size. Figure 5C shows the relationship between error and transport volume. A correlation of R = -0.38 was observed between the error and transport volume, indicating that transport volume influences the relationship between surface layer particle size and total layer particle size. These results indicate that, like layer thickness, using either the brightness or transport volume indicator can reduce error and improve accuracy.

[0061] Figures 5D to 5F show the results of similar verification of camera images of ore. Figure 5D shows the relationship between the difference between the sieve analysis value and the measured value of the weight percentage of the particle size fraction of 15 mm to 20 mm, i.e., error, and layer thickness. A correlation of R = 0.60 was observed between error and layer thickness, indicating that layer thickness influences the relationship between surface layer particle size and total layer particle size. Figure 5E shows the relationship between error and brightness. A correlation of R = 0.58 was observed between error and brightness, indicating that brightness influences the relationship between surface layer particle size and total layer particle size. Figure 5F shows the relationship between error and transport volume. A correlation of R = 0.47 was observed between error and transport volume, indicating that transport volume influences the relationship between surface layer particle size and total layer particle size. These results indicate that, in both laser distance measurement and camera image capture, using either layer thickness or brightness and transport volume as indicators can reduce error and improve accuracy.

[0062] The results of correcting the measured weight proportion of a certain particle size fraction when using a laser distance meter for granulated particles and when using a camera for ore are shown in Figures 6A to 6H.

[0063] Figures 6A to 6D show the results obtained for distance data of granulated particles obtained with a laser distance meter. Figures 6E to 6H show the results obtained for camera images of the ore. Multiple regression was used as the correction method. The actual sieve analysis value was set as the positive value, and regression coefficients were determined based on the measured particle size distribution and layer thickness information, and estimation was performed. Any estimation method can be used as long as it performs calculations based on measured values ​​and layer thickness information. Figure 6A shows the result of linear regression correction of uncorrected measured values ​​with sieve analysis values. Figure 6B shows the result of linear regression correction of measured values ​​corrected by layer thickness with sieve analysis values. Figure 6C shows the result of linear regression correction of measured values ​​corrected by brightness with sieve analysis values. Figure 6D shows the result of linear regression correction of measured values ​​corrected by conveyance amount with sieve analysis values. In Figure 6A, the correlation coefficient R = 0.50, while in Figure 6B, it was confirmed that accuracy was significantly improved, with R = 0.64. Figures 6C and 6D also showed improved accuracy, with R = 0.61 and R = 0.60, respectively. Figure 6E shows the results of linear regression correction of uncorrected measurements with sieve analysis values. Figure 6F shows the results of linear regression correction of measurements corrected by layer thickness with sieve analysis values. Figure 6G shows the results of linear regression correction of measurements corrected by brightness with sieve analysis values. Figure 6H shows the results of linear regression correction of measurements corrected by conveyance amount with sieve analysis values. In Figure 6E, the correlation coefficient R = 0.40, while in Figure 6F, R = 0.64, confirming a significant improvement in accuracy. Figures 6G and 6H also showed improved accuracy, with R = 0.63 and R = 0.53, respectively. These results demonstrate that high accuracy can be achieved in particle size measurements using both laser distance measurement and camera image capture.

[0064] In this example, the individual measured particle diameters were tabulated as particle diameter classes and then corrected, but other methods may be used. For example, all the individual measured particle diameter values ​​may be averaged to determine the measured average diameter, and correction may be performed by determining the relationship in advance from the measured layer thickness, the measured average diameter, and the sieve analysis average diameter. Correction can also be used for all indicators determined from the individual measured particle diameters. Instead of the weight proportions for each particle diameter class, a similar method may be applied to the weights of the particle diameter classes themselves.

[0065] The results of correcting the average diameter when a laser distance meter was used for the granulated particles and when a camera was used for the ore are shown in Figures 7A to 7H.

[0066] Figures 7A to 7D show the results obtained for distance data of granulated particles obtained with a laser distance meter. Figures 7E to 7H show the results obtained for camera images of the ore. Multiple regression was used as the correction method. The actual sieve analysis value was set as the positive value, and regression coefficients were determined based on the measured particle size distribution and layer thickness information, and estimation was performed. Any estimation method can be used as long as it performs calculations based on measured values ​​and layer thickness information. Figure 7A shows the result of linear regression correction of uncorrected measured values ​​with sieve analysis values. Figure 7B shows the result of linear regression correction of measured values ​​corrected by layer thickness with sieve analysis values. Figure 7C shows the result of linear regression correction of measured values ​​corrected by brightness with sieve analysis values. Figure 7D shows the result of linear regression correction of measured values ​​corrected by conveyance amount with sieve analysis values. In Figure 7A, the correlation coefficient R = 0.47, while in Figure 7B, it was confirmed that accuracy was significantly improved, with R = 0.58. Figures 7C and 7D also showed improved accuracy, with R = 0.51 and R = 0.58, respectively. Figure 7E shows the results of linear regression correction of uncorrected measurements with sieve analysis values. Figure 7F shows the results of linear regression correction of measurements corrected by layer thickness with sieve analysis values. Figure 7G shows the results of linear regression correction of measurements corrected by brightness with sieve analysis values. Figure 7H shows the results of linear regression correction of measurements corrected by conveyance amount with sieve analysis values. In Figure 7E, the correlation coefficient R = 0.37, while in Figure 7F, R = 0.71, confirming a significant improvement in accuracy. Figures 7G and 7H also showed improved accuracy, with R = 0.69 and R = 0.66, respectively. These results demonstrate that high accuracy can be achieved in particle size measurements using both laser distance measurement and camera image capture.

[0067] Instead of the correction based on the operating conditions, correction based on the raw material conditions, or correction based on both the raw material conditions and the operating conditions, may be performed. Among the indices representing the raw material deposition state, another index such as the width direction spread may be used instead of the layer thickness.

[0068] A method for utilizing this embodiment in operations will be described.

[0069] In sintering plants, it is generally desirable that the granulated particles after granulation be large in size, always uniform over time, and have a uniform particle size. However, due to variations in the raw material properties before granulation, for example, the influence of moisture carried over, which is difficult to identify, the granulated particle size after granulation may vary over time, even though a constant operating amount is maintained, and the particle size may become smaller than desired. As a result, when the granulated particles are charged into a sintering machine, this may cause unstable or reduced aeration, resulting in the production of low-quality sintered ore.

[0070] The information processing method according to this embodiment makes it possible to constantly identify the particle size of granulated particles. By changing operational factors that affect particle size, it is possible to control the particle size to a desired value and reduce particle size variation. Specifically, in a sinter ore production method, the index Zi for each observation time output in S5 can be used to adjust the blending ratio of at least one of the three raw materials that make up the sinter blend: the iron-containing raw material, the auxiliary raw material, and the carbon-containing raw material. According to such a sinter ore production method, controlling the particle size after granulation stabilizes ventilation when the granulated particles are charged into a sinter machine, making it possible to produce high-quality sintered ore. Alternatively, in a sinter ore production method, the index Zi for each observation time output in S5 can be used to adjust at least one of the three operating conditions of the granulator: the rotation speed, residence time, and added moisture. According to such a sinter ore production method, it is possible to control the particle size to a desired value and similarly produce high-quality sintered ore.

[0071] The method of charging the granulated particles into the sinter machine after granulation is also important. After being stored in a surge hopper, the granulated particles are charged onto the sinter machine pallet via a drum feeder and a chute or drum chute. It is desirable that the particle size distribution in the height direction of the granulated particles deposited on the pallet does not change over time. However, if the method of charging into the sinter machine is not changed when there is a change in the variation in particle size after granulation, the particle size distribution in the height direction of the granulated particles deposited on the pallet will change. As a result, the ventilation of the sinter machine is likely to become unstable or deteriorate. The unstable or deteriorated ventilation can deteriorate the quality of the sintered ore produced.

[0072] In the sinter ore production method, it is possible to use the index Zi for each observation time output in S5 to adjust at least one of three operating conditions of the raw material charging device: the drum feeder rotation speed, the chute angle, and the drum chute rotation speed. According to this sinter ore production method, ventilation is stabilized by adjusting the temporal change in the particle size variation in the height direction of the raw material piled on the pallet of the sinter machine to a constant value. As a result, high-quality sinter ore can be produced.

[0073] Another method for utilizing this embodiment in operations will now be described.

[0074] In a blast furnace, iron raw materials mainly composed of iron oxide and coke are charged alternately from the top of the furnace, the coke is burned by hot air blown in from tuyere holes at the bottom of the furnace, and the iron oxide in the sintered ore or lump ore is reduced by the resulting reducing gas containing CO, to produce pig iron. Therefore, in order to ensure the permeability of the blast furnace, it is important to specify the particle size, particle shape, or particle size distribution of the raw materials charged into the blast furnace and charge the raw materials in such a way as to ensure the formation of voids between the coke.

[0075] By using the information processing method according to this embodiment, the particle size, particle shape, or particle size distribution of the raw materials can be accurately measured on the conveyor 11 before charging into the blast furnace, and the results can be reflected in the operation method of the blast furnace, i.e., the amount or distribution of the raw materials charged. This allows for stable operation with good permeability of the blast furnace. That is, in the blast furnace operation method, it is possible to adjust the operating conditions of the blast furnace to which the raw materials are charged using the index Zi for each observation time output in S5. Specifically, by adjusting the blast flow rate, which represents the amount of hot air sent from the tuyere of the blast furnace, or the coke ratio, which represents the amount of coke charged per ton of molten pig iron, according to the particle size, it is possible to stabilize the permeability of the blast furnace.

[0076] A further method for utilizing this embodiment in operations will now be described.

[0077] Coke used in blast furnaces is required to have high strength and consistent strength and particle size, i.e., uniform quality, to ensure gas permeability within the furnace. To produce high-strength, uniform-quality coke, it is necessary to increase the bulk density of the coal charged into the coke oven so that strong contact occurs between coal particles when the coal is heated and carbonized in the coke oven. To achieve this, it is important to optimize the particle size of the coal charged into the coke oven. When the coal is pulverized in a pulverizer, pulverization conditions must be selected to achieve the target particle size, thereby reducing particle size variation. Therefore, in the coke production method, it is possible to adjust the rotation speed of the coal pulverizer using the index Zi for each observation time output in S5. Specifically, the particle size distribution of the coal raw material at either or both the front and rear of the pulverizer can be measured using the information processing method according to this embodiment, and then the current value of the pulverizer motor or the rotation speed of the hammer can be controlled to achieve the target coal particle size after pulverization. As a result, high-strength, high-quality coke can be produced.

[0078] The present disclosure is not limited to the above-described embodiments. For example, two or more blocks shown in the block diagrams may be integrated, or one block may be divided. Two or more steps shown in the flowcharts may be executed in parallel or in a different order, instead of being executed in chronological order as described, depending on the processing capabilities of the device executing each step, or as needed. Other modifications are possible within the scope of the present disclosure.

[0079] REFERENCE SIGNS LIST 10 Measurement system 11 Conveyor 12 Deposited raw material 20 Information processing device 21 Control unit 22 Storage unit 23 Communication unit 24 Input unit 25 Output unit 30 Sensor

Claims

1. An information processing device having a control unit that: observes the surface layer of sequentially transported deposition raw material with a sensor to obtain observation data for each observation time; calculates an index related to the particle diameter of each particle contained in the deposition raw material for each observation time using the obtained observation data; measures the state of the deposition raw material to obtain measurement data for each measurement time corresponding to each observation time; corrects the calculated index for the observation time corresponding to each measurement time using the obtained measurement data; and outputs the corrected index for each observation time.

2. The information processing device according to claim 1, wherein the control unit corrects the index in accordance with a dimension of the deposition material determined from the measurement data.

3. The information processing device according to claim 2, wherein the dimensions of the deposition material include a layer thickness of the deposition material.

4. The information processing device according to claim 3, wherein the control unit acquires distance data obtained by measuring the distance from the sensor to the surface of the deposition raw material as the observation data, and uses the distance data as the measurement data.

5. The information processing device according to claim 1, wherein the control unit acquires a camera image obtained by photographing a surface layer of the deposited raw material as the measurement data, and corrects the index according to the brightness of the camera image.

6. The information processing device according to claim 5, wherein the control unit also uses the camera image as the observation data.

7. The information processing device according to claim 1, wherein the control unit acquires, as the measurement data, data obtained by measuring a transport amount of the deposition raw material.

8. The information processing device according to claim 1, wherein the control unit acquires, as the measurement data, data obtained by measuring a composition of the deposition source material.

9. The information processing device of claim 8, wherein the composition of the deposition source material includes a moisture content of the deposition source material.

10. An information processing method comprising: an information processing device acquiring observation data obtained at each observation time by observing the surface layer of sequentially transported deposition raw material with a sensor; the information processing device calculating an index related to the particle diameter of each particle contained in the deposition raw material for each observation time using the acquired observation data; the information processing device acquiring measurement data obtained at each measurement time corresponding to each observation time by measuring the state of the deposition raw material; the information processing device correcting the calculated index for the observation time corresponding to each measurement time using the acquired measurement data; and the information processing device outputting the corrected index for each observation time.

11. A method for producing sintered ore, comprising adjusting the blending ratio of at least one of three types of raw materials, namely, an iron-containing raw material, an auxiliary raw material, and a carbon-containing raw material, which are sintered raw materials, using an index for each observation time output by the information processing method described in claim 10.

12. A method for producing sintered ore, comprising adjusting at least one of three operating conditions of a granulator, namely, the rotation speed, residence time, and added moisture, using an index for each observation time output by the information processing method described in claim 10.

13. A method for producing sintered ore, comprising adjusting at least one of three operating conditions of a raw material charging device, namely, the drum feeder rotation speed, the chute angle, and the drum chute rotation speed, using an index for each observation time output by the information processing method described in claim 10.

14. A method for operating a blast furnace, comprising adjusting the operating conditions of the blast furnace into which raw materials are charged, using the index for each observation time output by the information processing method according to claim 10.

15. A method for producing coke, comprising adjusting the rotation speed of a coal pulverizer using an index for each observation time output by the information processing method according to claim 10.

16. A program that causes a computer to perform operations including: obtaining observation data for each observation time by observing the surface layer of the sequentially transported deposited raw material with a sensor; calculating an index relating to the particle diameter of each particle contained in the deposited raw material for each observation time using the obtained observation data; obtaining measurement data for each measurement time corresponding to each observation time by measuring the state of the deposited raw material; correcting the calculated index for the observation time corresponding to each measurement time using the obtained measurement data; and outputting the corrected index for each observation time.

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