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 at each observation time by observing the surface layer of a stacked raw material (12) conveyed in sequence by means of a sensor (30), and that calculates an index relating to the particle diameter of each particle included in the stacked raw material (12) at each observation time using the acquired observation data. And a control unit that acquires measurement data obtained at each measurement time corresponding to each observation time by measuring the state of the deposited raw material (12), 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.

Description

Technical Field

[0001] This disclosure relates to information processing apparatus, information processing method, sinter manufacturing method, blast furnace operation method, coke manufacturing method, and procedure. Background Technology

[0002] In manufacturing processes using minerals and other raw materials, the particle size, shape, and size distribution of the raw materials affect the operation of the process. Therefore, it is necessary to determine the particle size, shape, or size distribution of the raw materials beforehand. This is especially important in blast furnaces, where determining the particle size distribution of raw materials such as ores or coke that affect the gas flow within the furnace is crucial.

[0003] Previously, raw material sampling and sieve-based analysis were performed periodically. However, because the analysis takes time, it is difficult to reflect the results in real time during blast furnace operation. Therefore, a technology for real-time determination of the particle size distribution of raw materials is required.

[0004] As a technique for real-time measurement of the particle size distribution of raw materials, methods are known to use cameras or laser rangefinders to observe images or shapes of the upper part of the raw material on a conveyor. For example, in the method disclosed in Patent Document 1, the unevenness data of the raw material is extracted by measuring the distance from the laser rangefinder to the raw material on the conveyor. The particle size distribution of the surface is calculated by performing image processing on the unevenness data. The calculated particle size distribution is corrected to be consistent with the particle size distribution previously measured using sieves.

[0005] Patent Document 1: International Publication No. 2019 / 193971

[0006] For stockpiled raw materials, the overall particle size does not become uniform; instead, it varies depending on location. That is, particle size segregation occurs within the stockpiled material. Therefore, when calculating the particle size distribution of the surface layer, since the calculated distribution differs from the particle size distributions of all layers, including the obscured layers, it is necessary to pre-determine parameters representing the relationship between the surface particle size distribution and the particle size distributions of all layers. These parameters are then used to infer the particle size distributions of all layers based on the calculated distribution.

[0007] The relationship between the particle size distribution of the surface layer and the particle size distribution of all layers, including the lower layers, varies sequentially depending on the size or composition of the raw material. Therefore, as with existing methods, simply calculating the difference between the particle size distribution determined by sieve testing and the calculated particle size distribution is insufficient for high-precision correction. Summary of the Invention

[0008] The purpose of this disclosure is to determine with high precision the parameters related to the particle size of the packed raw materials.

[0009] (1) One embodiment of the information processing apparatus disclosed herein includes a control unit.

[0010] The aforementioned control unit acquires observation data at each observation moment by using sensors to observe the surface of the stacked raw materials that are being transported sequentially.

[0011] The aforementioned control unit uses the acquired observation data to calculate an index related to the particle size of each particle included in the aforementioned stockpiled raw material at each observation time.

[0012] The aforementioned control unit obtains measurement data at each measurement time corresponding to each observation time by measuring the state of the aforementioned stockpiled raw materials.

[0013] The aforementioned control unit uses the acquired measurement data to correct the calculated indicators for the observation times corresponding to each measurement time.

[0014] The aforementioned control unit outputs corrected indicators for each observation time.

[0015] (2) An information processing apparatus according to one embodiment of this disclosure is based on the information processing apparatus described in (1), wherein,

[0016] The control unit corrects the above-mentioned indicators based on the dimensions of the above-mentioned stacked raw materials, and the dimensions of the above-mentioned stacked raw materials are determined based on the above-mentioned measurement data.

[0017] (3) One embodiment of the information processing apparatus disclosed herein is based on the information processing apparatus described in (2), wherein,

[0018] The dimensions of the aforementioned stacked raw materials include the layer thickness of the aforementioned stacked raw materials.

[0019] (4) An information processing apparatus according to one embodiment of this disclosure is based on the information processing apparatus described in (3), wherein,

[0020] The control unit acquires distance data obtained by measuring the distance from the sensor to the surface of the piled raw material as the observation data.

[0021] The aforementioned control unit also uses the aforementioned distance data as the aforementioned measurement data.

[0022] (5) An information processing apparatus according to one embodiment of this disclosure is based on the information processing apparatus described in (1), wherein,

[0023] The aforementioned control unit acquires camera images obtained by photographing the surface of the aforementioned stockpiled raw materials as the aforementioned measurement data.

[0024] The control unit corrects the above-mentioned indicators based on the brightness of the camera image.

[0025] (6) An information processing apparatus according to one embodiment of this disclosure is based on the information processing apparatus described in (5), wherein,

[0026] The control unit also uses the camera images as the observation data.

[0027] (7) An information processing apparatus according to one embodiment of this disclosure is based on the information processing apparatus described in (1), wherein,

[0028] The control unit obtains data by measuring the amount of the stockpiled raw materials being transported, which is used as the measurement data.

[0029] (8) An information processing apparatus according to one embodiment of this disclosure is based on the information processing apparatus described in (1), wherein,

[0030] The control unit obtains data by measuring the composition of the aforementioned stockpiled raw materials as the aforementioned measurement data.

[0031] (9) An information processing apparatus according to one embodiment of this disclosure is based on the information processing apparatus described in (8), wherein,

[0032] The composition of the aforementioned stockpiled raw materials includes the moisture content of the aforementioned stockpiled raw materials.

[0033] (10) An information processing method according to one embodiment of this disclosure includes:

[0034] The information processing device obtains observation data at each observation moment by using sensors to observe the surface of the stacked raw materials that are being transported in sequence;

[0035] The aforementioned information processing device uses the acquired observation data to calculate an index related to the particle size of each particle included in the aforementioned stockpiled raw material at each observation time.

[0036] The aforementioned information processing device acquires measurement data at each measurement time corresponding to each observation time by measuring the state of the aforementioned stockpiled raw materials.

[0037] The aforementioned information processing device uses the acquired measurement data to correct the calculated indicators corresponding to the observation times at each measurement time; and

[0038] The aforementioned information processing device outputs corrected indicators for each observation time.

[0039] (11) One embodiment of the present disclosure relates to a method for manufacturing sintered ore, comprising:

[0040] The proportion of at least one of the three raw materials—iron-containing raw materials, auxiliary raw materials, and carbon-containing raw materials—is adjusted using the index output at each observation time through the information processing method described in (10).

[0041] (12) One embodiment of the present disclosure relates to a method for manufacturing sintered ore, comprising:

[0042] The operating conditions of the granulator are adjusted using the indexes output at each observation time by the information processing method described in (10), which are the rotational speed, residence time and water addition.

[0043] (13) One embodiment of the present disclosure relates to a method for manufacturing sintered ore, comprising:

[0044] The index at each observation moment output by the information processing method described in (10) is used to adjust at least one of the three operating conditions of the raw material loading device: the speed of the cylindrical feeder, the chute angle, and the speed of the cylindrical chute.

[0045] (14) One embodiment of the blast furnace operation method disclosed herein includes:

[0046] The operating conditions of the blast furnace being loaded with raw materials are adjusted using the indicators output at each observation time through the information processing method described in (10).

[0047] (15) One embodiment of the coke manufacturing method disclosed herein includes:

[0048] The rotational speed of the coal pulverizer is adjusted using the index output at each observation time through the information processing method described in (10).

[0049] (16) In one embodiment of this disclosure, the program causes a computer to perform the following actions:

[0050] The observation data is obtained at each observation time by using sensors to observe the surface of the stacked raw materials that are being transported in sequence.

[0051] The acquired observation data was used to calculate the indices related to the particle size of each particle included in the aforementioned stockpile at each observation time.

[0052] The measurement data at each measurement time corresponding to each observation time is obtained by measuring the state of the above-mentioned piled raw materials.

[0053] The obtained measurement data was used to correct the calculated indicators for the observation times corresponding to each measurement time; and

[0054] Output corrected metrics for each observation time.

[0055] According to this disclosure, it is possible to measure indicators related to the particle size of packed raw materials with high precision. Attached Figure Description

[0056] Figure 1 This is a diagram illustrating the configuration of the measurement system according to the embodiments of this disclosure.

[0057] Figure 2 This is a block diagram illustrating the configuration of an information processing apparatus according to embodiments of the present disclosure.

[0058] Figure 3 This is a flowchart illustrating the operation of the information processing apparatus according to the embodiments of this disclosure.

[0059] Figure 4A This is an example of distance data for granulated particles shown as an image.

[0060] Figure 4B It means according to Figure 4A The image shows the results of particle detection based on distance data.

[0061] Figure 4C This is an example of a camera image representing a mineral.

[0062] Figure 4D It means according to Figure 4C The image shows the results of particle detection from the camera image.

[0063] Figure 5A It is a graph showing the relationship between the difference between the sieve analysis value and the measured value, which represents the weight ratio of a specific particle size division, and the layer thickness, obtained from the distance data of granulated particles.

[0064] Figure 5B It is a graph showing the relationship between the difference and brightness of the sieve analysis value and the measured value, which represent the weight proportion of a specific particle size division, obtained from the distance data of granulated particles.

[0065] Figure 5C It is a graph showing the relationship between the difference between the sieve analysis value and the measured value, which represents the weight ratio of a specific particle size division, and the conveying rate, obtained from the distance data of granulated particles.

[0066] Figure 5D It is a graph showing the relationship between the difference between the sieve analysis value and the measured value, which represent the weight ratio of a specific particle size distribution, obtained from a camera image of the ore, and the layer thickness.

[0067] Figure 5E It is a graph showing the relationship between the difference and brightness of the sieve analysis value and the measured value, which represent the weight proportion of a specific particle size division, obtained from a camera image of the ore.

[0068] Figure 5F It is a graph showing the relationship between the difference between the sieve analysis value and the measured value, which represents the weight ratio of a specific particle size distribution, obtained from a camera image of the ore, and the conveying rate.

[0069] Figure 6A It is a graph showing the correlation between sieve analysis values ​​representing the weight proportion of a specific particle size division and uncorrected measurement values, obtained from distance data of granulated particles.

[0070] Figure 6B It is a graph showing the correlation between sieve analysis values ​​representing the weight proportion of a specific particle size division and measured values ​​corrected for layer thickness, obtained from distance data of granulated particles.

[0071] Figure 6C It is a graph showing the correlation between sieve analysis values ​​representing the weight proportion of a specific particle size division, obtained from distance data of granulated particles, and measured values ​​corrected for brightness.

[0072] Figure 6D It is a graph showing the correlation between sieve analysis values ​​representing the weight proportion of a specific particle size division, obtained from distance data of granulated particles, and measured values ​​corrected for the feed rate.

[0073] Figure 6E It is a graph showing the correlation between sieve analysis values ​​representing the weight proportion of a specific particle size division obtained from camera images of the ore and uncorrected measurement values.

[0074] Figure 6F It is a graph showing the correlation between sieve analysis values ​​representing the weight proportion of a specific particle size division obtained from camera images of the ore and measured values ​​corrected for layer thickness.

[0075] Figure 6G It is a graph showing the correlation between sieve analysis values ​​representing the weight proportions of a specific particle size division obtained from camera images of the ore and measured values ​​corrected for brightness.

[0076] Figure 6H It is a graph showing the correlation between sieve analysis values ​​representing the weight proportions of a specific particle size division obtained from camera images of the ore and measured values ​​corrected for the feed rate.

[0077] Figure 7A It is a graph showing the correlation between the average diameter sieve analysis value and the uncorrected measurement value, obtained from the distance data of granulated particles.

[0078] Figure 7B It is a graph showing the correlation between the average diameter obtained from the distance data of granulated particles and the measured value corrected according to the layer thickness.

[0079] Figure 7C It is a graph showing the correlation between the average diameter obtained from the distance data of granulated particles and the measured value corrected according to the brightness.

[0080] Figure 7D It is a graph showing the correlation between the average diameter obtained from the distance data of the granulated particles and the measured value corrected according to the conveying volume.

[0081] Figure 7E It is a graph showing the correlation between the average diameter of the sieve analysis value obtained from camera images of the ore and the uncorrected measured value.

[0082] Figure 7F It is a graph showing the correlation between the average diameter obtained from camera images of the ore and the measured value corrected for layer thickness.

[0083] Figure 7G It is a graph showing the correlation between the average diameter obtained from camera images of the ore and the measured value corrected for brightness.

[0084] Figure 7H It is a graph showing the correlation between the average diameter of the ore obtained from camera images and the measured value corrected for the conveying volume. Detailed Implementation

[0085] Hereinafter, one embodiment of the present disclosure will be described with reference to the accompanying drawings.

[0086] In each figure, the same or equivalent parts are labeled with the same reference numerals. In the description of this embodiment, the description of the same or equivalent parts is appropriately omitted or simplified.

[0087] Reference Figure 1 The structure of the measurement system 10 according to this embodiment will be described.

[0088] The measurement system 10 includes an information processing device 20 and a sensor 30. The measurement system 10 may also include other sensors.

[0089] 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 can also communicate with other sensors directly or via a network such as a LAN or the Internet.

[0090] As an example of a process using this embodiment, a sintering machine and a conveyor 11 for conveying granulated particles used in the sintering machine are shown in the steel industry, but this embodiment can also be applied to other processes. As an example of a stockpiled raw material 12, granulated particles, which are one of the raw materials used in the steel industry, are shown, but the stockpiled raw material 12 can be, for example, coke, sintered ore, granules, limestone, rock, coal, or fine ore, in addition to the ore described later.

[0091] The information processing device 20 is a microcomputer or special-purpose computer mounted on a general-purpose computer such as a PC, a server computer such as a cloud server, or a mobile device such as a smartphone or tablet. "PC" is an abbreviation for personal computer. The information processing device 20 is a device for measuring the particle size of the stockpiled raw material 12. The information processing device 20 calculates the particle size of the stockpiled raw material 12 based on distance data representing the distance to the stockpiled raw material 12 measured by the sensor 30. Alternatively, the information processing device 20 can also calculate the particle size of the stockpiled raw material 12 based on images captured by the sensor 30. The information processing device 20 corrects the calculated particle size value, or an index calculated based on the particle size value, according to the state of the stockpiled raw material 12. The state of the stockpiled raw material 12 refers to the conditions under which the stockpiled raw material 12 can be measured in a time series. Such conditions include, for example, operating conditions or raw material conditions. Operating conditions include, for example, the state of the raw material stockpiling. The state of the raw material stockpiling includes, for example, the angle of repose, layer thickness, or width expansion of the raw material stockpiled 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 refers to the maximum angle at which the slope spontaneously maintains stability when the raw material is stacked. The layer thickness of the raw material refers to the dimension in the height direction. The width expansion of the raw material refers to the dimension in the width direction. The raw material conditions include, for example, the composition of the raw material itself, such as the presence of moisture. If the information processing device 20 is used, even if the state of the stacked raw material 12 changes, the particle size of all layers of the stacked raw material 12, including the lower layer, can be measured with high precision.

[0092] Sensor 30 is a two-dimensional laser rangefinder. The laser rangefinder illuminates a laser beam linearly along the width of the conveyor 11, measuring the distance to the granulated particles along each line. The granulated particles are transported and moved by the conveyor 11. Therefore, the laser rangefinder measures the distance to the granulated particles linearly at a certain measurement cycle. By accumulating the measurement values ​​of these lines, the three-dimensional shape data of the granulated particles is obtained. The above method is a method of obtaining the three-dimensional shape of the measured object through a so-called optical cut-off method. The laser rangefinder and data processing unit for this purpose can use conventionally used components. Preferably, the laser rangefinder has a measurement area the same width as the conveyor 11, capable of measuring the entire surface of the granulated particles transported by the conveyor 11. A shorter measurement cycle is preferred. In this embodiment, the measurement cycle is set to 4 kHz. Sensors other than laser rangefinders can also be used as sensor 30, such as a distance measuring camera or a camera that simply captures visible light images, utilizing a stereo method or a ToF method using two cameras. "ToF" is an abbreviation for Time of Flight.

[0093] Reference Figure 2 The configuration of the information processing apparatus 20 according to this embodiment will be described.

[0094] 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.

[0095] 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 customized for specific processing. "CPU" is an abbreviation for Central Processing Unit. "GPU" is an abbreviation for Graphics Processing Unit. The programmable circuit is, for example, an FPGA. "FPGA" is an abbreviation for Field-Programmable Gate Array. The dedicated circuit is, for example, an ASIC. "ASIC" is an abbreviation for Application-Specific Integrated Circuit. The control unit 21 controls the various parts of the information processing device 20 and executes processing related to the operation of the information processing device 20.

[0096] Storage unit 22 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or any combination thereof. Semiconductor memory is, for example, RAM, ROM, or flash memory. "RAM" is an abbreviation for random access memory. "ROM" is an abbreviation for read-only memory. RAM is, for example, SRAM or DRAM. "SRAM" is an abbreviation for static random access memory. "DRAM" is an abbreviation for dynamic random access memory. ROM is, for example, EEPROM. "EEPROM" is an abbreviation for electrically erasable programmable read-only memory. Flash memory is, for example, SSD. "SSD" is an abbreviation for solid-state drive. Magnetic memory is, for example, HDD. "HDD" is an abbreviation for hard disk drive. Storage unit 22 functions as, for example, a primary storage device, an auxiliary storage device, or a cache memory. Storage unit 22 stores data used in the operation of information processing device 20 and data obtained through the operation of information processing device 20.

[0097] The communication unit 23 includes at least one communication module. This communication module may be a module corresponding to wired LAN communication standards such as Ethernet (registered trademark), wireless LAN communication standards such as IEEE 802.11, or mobile communication standards such as LTE, 4G, or 5G. "IEEE" is an abbreviation for the 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 also transmits data obtained through the operation of the information processing device 20.

[0098] The input unit 24 includes at least one input device. The input device may be, for example, a physical key, a capacitive key, a pointing device, a touchscreen integrated with the display, a camera, or a microphone. The input unit 24 accepts input of data used in the operation of the information processing device 20. The input unit 24 may also be connected to the information processing device 20 as an external input device, instead of being equipped on the information processing device 20. As a connection interface, interfaces compatible with standards such as USB, HDMI, or Bluetooth can be used. "USB" is an abbreviation for Universal Serial Bus. "HDMI" is an abbreviation for High-Definition Multimedia Interface.

[0099] Output unit 25 includes at least one output device. The output device may be, for example, a monitor, printer, or speaker. The monitor may be, for example, an LCD or an organic EL display. "LCD" is an abbreviation for liquid crystal display. "EL" is an abbreviation for electroluminescent. Output unit 25 outputs data obtained through the operation of information processing device 20. Output unit 25 may also be connected to information processing device 20 as an external output device, instead of being equipped within information processing device 20. As a connection interface, an interface compatible with standards such as USB, HDMI, or Bluetooth can be used.

[0100] The functions of the information processing device 20 are implemented by the processor, which serves as the control unit 21, executing the program described in this embodiment. That is, the functions of the information processing device 20 are implemented through software. The program causes the computer to perform the actions of the information processing device 20, thereby enabling the computer to function as the information processing device 20. In other words, the computer functions as the information processing device 20 by executing the actions of the information processing device 20 according to the program.

[0101] Programs can be stored on non-transitory computer-readable media. Examples of non-transitory computer-readable media include flash memory, magnetic recording devices, optical discs, optical-magnetic recording media, or ROM. Program distribution can occur, for example, through the sale, transfer, or rental of portable media such as SD cards, DVDs, or CD-ROMs containing the program. "SD" is an abbreviation for Secure Digital. "DVD" is an abbreviation for digital versatile disc. "CD-ROM" is an abbreviation for compact discread only memory. Programs can also be stored in the memory of a server and transferred from the server to other computers, thereby enabling program distribution. Programs can also be provided as program products.

[0102] Computers may temporarily store programs stored on portable media or transferred from servers in main memory. The computer then reads the programs stored in main memory using its processor and executes the processing according to the read programs. The computer can also directly read programs from portable media and execute the processing according to the programs. Whenever a program is transferred from a server to the computer, the computer can also sequentially execute the processing according to the received programs. Alternatively, the transfer of programs from the server to the computer can be omitted, and the processing can be performed by a so-called ASP-type server that only executes instructions and retrieves results. "ASP" is an abbreviation for Application Service Provider. Programs include program-based information used for processing by the computer. For example, data that, while not direct instructions to the computer, has the nature of specifying the computer's processing is equivalent to "program-based data."

[0103] Some or all of the functions of the information processing device 20 may also be implemented by a programmable circuit or a dedicated circuit, which serves as the control unit 21. That is, some or all of the functions of the information processing device 20 may also be implemented by hardware.

[0104] Reference Figure 3 The operation of the information processing apparatus 20 according to this embodiment will be described. 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... Figure 3 The steps S1 to S5 are shown.

[0105] In S1, the control unit 21 acquires observation data Xi at each observation time by observing the surface of the sequentially conveyed stacked raw material 12 using the sensor 30. Specifically, the control unit 21 receives the observation data Xi from the sensor 30 via the communication unit 23.

[0106] In this embodiment, the control unit 21 acquires distance data obtained by measuring the distance from the sensor 30 to the surface of the stacked raw material 12 as observation data Xi. Figure 4A This is an image of the three-dimensional granulation particle contour data obtained from a two-dimensional laser rangefinder, viewed from above. The image shows that the whiter the grayscale, the higher the height and the closer the distance to the rangefinder. The lateral dimension is 500 pixels in the laser width direction. One pixel is 0.3 mm. The longitudinal dimension is 200 pixels in the conveying direction of conveyor 11. One pixel is 0.3 mm. The resolution in the height direction is 5 μm. Granulation particles are stacked and conveyed on conveyor 11. Typically, only a single three-dimensional shape data is acquired. To calculate the particle size, each particle needs to be detected and determined. Based on the contour data, including the convexity and concavity of the granulation particle, particle separation processing is performed on a particle-by-particle basis through signal processing. The particle size can be calculated by counting the number of particles of each particle size separated by the particle separation processing and histogramting the result. Known methods, such as the method described in Reference 1 below, can be used as methods for detecting and separating the particles and calculating the particle size. Figure 4B This is the result of particle separation. For example... Figure 4B As shown, it can detect individual particles well.

[0107] [Reference 1] Matthew J. Thurley, “Automated Online Measurement ofParticle Size Distribution using 3D Range Data, IFAC Proceedings Volumes, 2009, 42, 134-139

[0108] The control unit 21 can also acquire camera images obtained by photographing the surface of the stockpiled raw material 12 as observation data Xi. Figure 4C and Figure 4D The results show that a camera is used instead of a laser rangefinder as sensor 30, and ore is used instead of granulated particles as packing material 12, and a general particle separation method as described in Reference 1 is applied. Figure 4C This is a photograph of the ore. The image has a resolution of 1280 x 1024 pixels. One pixel is approximately 1 mm in size. Figure 4D This is the result of particle separation, with each particle represented by grayscale. For example... Figure 4D As shown, even without using distance data obtained from a laser rangefinder, individual particles can be detected well based solely on images captured by a camera.

[0109] In S2, the control unit 21 uses the observation data Xi acquired in S1 to calculate an index Zi related to the particle size of each particle included in the stockpiled raw material 12 at each observation time. The index Zi can be the particle size value itself, or it can be an index calculated based on the particle size value, such as the average diameter.

[0110] In S3, the control unit 21 acquires measurement data Yi at each measurement time corresponding to each observation time by measuring the state of the stockpiled raw material 12. Each measurement time can be a time before or after the corresponding observation time. Specifically, the control unit 21 receives the measurement data Yi from the sensor 30 or other sensors via the communication unit 23.

[0111] In this embodiment, the control unit 21 uses the distance data acquired in S1 as measurement data Yi. Alternatively, the control unit 21 may acquire a camera image obtained by photographing the surface of the stockpiled raw material 12 as measurement data Yi. When a camera image is acquired in S1 instead of distance data, the control unit 21 may use the camera image as measurement data Yi. Alternatively, the control unit 21 may acquire data obtained by measuring the conveying amount of the stockpiled raw material 12 as measurement data Yi. Alternatively, the control unit 21 may acquire data obtained by measuring the composition of the stockpiled raw material 12 as measurement data Yi. The composition of the stockpiled raw material 12 includes, for example, the moisture content of the stockpiled raw material 12. The moisture content of the stockpiled raw material 12 can be measured using a moisture meter.

[0112] In S4, the control unit 21 uses the measurement data Yi obtained in S3 to correct the index Zi of the observation time corresponding to each measurement time calculated in S2.

[0113] In this embodiment, the control unit 21 corrects the index Zi based on the size of the stacked material 12, which is determined based on the measurement data Yi. The size of the stacked material 12 includes, for example, the layer thickness of the stacked material 12. Alternatively, when a camera image is acquired in S3, the control unit 21 may also correct the index Zi based on the brightness of the camera image.

[0114] In S5, the control unit 21 outputs the index Zi for each observation moment, which has been corrected in S4. Specifically, the control unit 21 sends the index Zi for each observation moment to the equipment controlling the process or the terminal of the user managing the process via the communication unit 23. Alternatively, the control unit 21 displays, prints, or outputs the index Zi for each observation moment via the output unit 25.

[0115] Generally, when conveying using conveyor 11, the raw material is accumulated and transported. Therefore, only the surface layer of the raw material can be measured by sensor 30. However, the particle size distribution of all layers, including the obscured lower layer, is not exactly the same as the particle size distribution of the surface layer, resulting in segregation. For example, small particles are easily obscured, sometimes resulting in a situation where there are more large particles on the surface layer and more small particles on the lower layer. To eliminate the effect of this segregation, it is necessary to infer the particle size distribution of all layers based on the particle size distribution of the surface layer. For example, considering sampling all raw materials in the width direction of conveyor 11, the true particle size distribution of all layers is obtained, and the relationship with the measured particle size distribution of the surface layer is pre-calculated. By using a parameter representing this relationship and the measured particle size distribution of the surface layer, the particle size distribution of all layers can be obtained in real time. However, this parameter changes due to changes in operating conditions or raw material conditions. During operation, operating conditions and raw material conditions are constantly changing. According to this embodiment, high-precision correction can be performed by introducing operating conditions or raw material conditions.

[0116] An example is described where a calibration was performed using one of the indicators representing the packing state, namely layer thickness or a layer thickness-related indicator, in the operating conditions. The inventors discovered that the relationship between the particle size of the surface layer and the particle size of all layers, including the underlying layers, varies with changes in a layer thickness and a layer thickness-related indicator representing the packing state, confirming that particle size determination can be performed with high precision by using this calibration.

[0117] A laser rangefinder measures the distance from the surface of the raw material in the width direction of conveyor 11 to sensor 30. By measuring the profile of the conveyor in a state without raw material and obtaining the difference, the height information of the raw material in the width direction can be obtained. In this embodiment, the height information in the width direction is averaged to obtain the layer thickness. The definition of layer thickness is not limited to this, and there are several indicators related to layer thickness. For example, since the positional relationship between conveyor 11 and laser rangefinder does not change, the distance data from laser rangefinder to the surface of raw material can be directly 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 surface of raw material, and the average brightness of the image can be used as an indicator. This is because brightness is correlated with the distance to the surface of raw material, so the average brightness is actually equivalent to the distance to the surface of raw material. The raw material conveying rate can also be used as an indicator related to layer thickness. The conveying rate per hour is determined by the speed of conveyor 11 and the weight of the raw material on conveyor 11. The unit of conveying rate is ton / h. When the speed of conveyor 11 is constant, the conveying rate is determined by the weight of raw material measured by a load cell. The weight of the raw material is determined by the amount of raw material cut from the previous hopper. Therefore, a higher weight means more raw material is loaded onto conveyor 11, resulting in a higher actual layer thickness. However, the device for measuring the weight of conveyor 11 measures the weight of raw material within a constant range and cannot detect the constantly changing accumulation variations in the conveying direction of conveyor 11 and the variations in raw material layer thickness. Therefore, while this device can be used as another sensor, using the raw material conveying volume as an indicator related to layer thickness, it is preferable to use another sensor 30, such as a laser rangefinder or camera, which can continuously measure and capture more subtle variations in layer thickness, thus improving the accuracy of the calibration.

[0118] Figures 5A-5F For cases involving granulated particles using a laser rangefinder and for cases involving ore using a camera, the relationship between the particle size of the surface layer and the particle size of all layers, including the underlying layer, varies depending on the layer thickness, or on variations in brightness or delivery rate as indicators related to layer thickness.

[0119] Figures 5A-5C This also verifies the results of the distance data of the granulated particles obtained using a laser rangefinder. Figure 5A The difference between the sieve analysis values ​​and the measured values ​​for the weight proportions of particle sizes greater than 4.75 mm and less than 8 mm is shown, i.e., the relationship between error and layer thickness. A correlation of R = -0.40 can be observed between error and layer thickness, indicating that layer thickness affects the relationship between surface particle size and particle size across all layers. The ability to express error using layer thickness means that by using layer thickness, error can be reduced and accuracy improved. Figure 5BThe relationship between error and brightness is shown. A correlation of R = -0.37 can be observed between error and brightness, indicating that brightness affects the relationship between surface grain size and grain size across all layers. Figure 5C The relationship between error and feed rate is shown. A correlation of R = -0.38 can be observed between error and feed rate, indicating that feed rate affects the relationship between surface particle size and particle size across all layers. These findings suggest that, similar to layer thickness, error can be reduced and accuracy improved by using either brightness or feed rate.

[0120] Figures 5D-5F The results also verified the camera images of the ore. Figure 5D The difference between the sieve analysis value and the measured value, i.e., the error, and the layer thickness is shown for the weight proportion of particles with a size range of 15 mm to 20 mm. A correlation of R = 0.60 can be observed between the error and the layer thickness, indicating that the layer thickness affects the relationship between the surface particle size and the particle size of all layers. Figure 5E The relationship between error and brightness is shown. A correlation of R = 0.58 can be observed between error and brightness, indicating that brightness affects the relationship between surface grain size and grain size across all layers. Figure 5F The relationship between error and feed rate is shown. A correlation of R = 0.47 can be observed between error and feed rate, indicating that feed rate affects the relationship between surface grain size and the grain size of all layers. These findings suggest that in either laser distance measurement or camera image acquisition methods, error can be reduced and accuracy improved by using any of the following indicators: layer thickness, brightness, or feed rate.

[0121] Figures 6A-6H For both cases involving granulated particles using a laser rangefinder and cases involving ore using a camera, the results of correcting the measured weight ratio for a certain particle size division are shown.

[0122] Figures 6A-6D The result is obtained from the distance data of the granulated particles obtained using a laser rangefinder. Figures 6E-6H The results were obtained from camera images of the ore. Multiple regression was used as the correction method. The measured sieve analysis value was positive, and the regression coefficients were determined based on the measured particle size distribution and layer thickness information. As for the inference method, any method that is based on measured values ​​and layer thickness information can be applied. Figure 6A The result is obtained by using sieve analysis values ​​to perform linear regression correction on the uncorrected measured values. Figure 6B The result is obtained by linear regression correction of the measured values, which have been corrected based on the layer thickness, using sieve analysis values. Figure 6C The result is obtained by linear regression correction of the measured values ​​that have been corrected based on brightness using sieve analysis values. Figure 6DThe result is obtained by linear regression correction of the measured values, which have been corrected based on the conveying volume, using sieve analysis values. This confirms that... Figure 6A The correlation coefficient R = 0.50, but... Figure 6B The accuracy was significantly improved to R = 0.64. Figure 6C and Figure 6D The accuracy was also improved to R=0.61 and R=0.60 respectively. Figure 6E The result is obtained by using sieve analysis values ​​to perform linear regression correction on the uncorrected measured values. Figure 6F The result is obtained by linear regression correction of the measured values, which have been corrected based on the layer thickness, using sieve analysis values. Figure 6G The result is obtained by linear regression correction of the measured values ​​that have been corrected based on brightness using sieve analysis values. Figure 6H The result is obtained by linear regression correction of the measured values, which have been corrected based on the conveying volume, using sieve analysis values. This confirms that... Figure 6E The correlation coefficient R = 0.40, but... Figure 6F The accuracy was significantly improved to R = 0.64. Figure 6G and Figure 6H The accuracy was also improved to R=0.63 and R=0.53, respectively. These results indicate that high-precision particle size measurement can be achieved in either laser distance measurement or camera image capture.

[0123] In this embodiment, the measured particle sizes are summed to form a particle size distribution and then corrected. However, other methods can also be used. For example, the measured average diameter can be calculated by averaging all measured particle size values. Correction can then be performed based on a pre-determined relationship between the measured layer thickness, the measured average diameter, and the sieve analysis average diameter. This allows correction to be applied to all indicators calculated based on each measured particle size. Alternatively, the same method can be applied to the weight of the particle size distribution itself, instead of the weight ratio of each particle size distribution.

[0124] Figures 7A-7H The results of correcting the average diameter are shown for both cases involving granulated particles using a laser rangefinder and cases involving ore using a camera.

[0125] Figures 7A-7D The result is obtained from the distance data of the granulated particles obtained using a laser rangefinder. Figures 7E-7H The results were obtained from camera images of the ore. Multiple regression was used as the correction method. The measured sieve analysis value was positive, and the regression coefficients were determined based on the measured particle size distribution and layer thickness information. As for the inference method, any method that is based on measured values ​​and layer thickness information can be applied. Figure 7A The result is obtained by using sieve analysis values ​​to perform linear regression correction on the uncorrected measured values.Figure 7B The result is obtained by linear regression correction of the measured values, which have been corrected based on the layer thickness, using sieve analysis values. Figure 7C The result is obtained by linear regression correction of the measured values ​​that have been corrected based on brightness using sieve analysis values. Figure 7D The result is obtained by linear regression correction of the measured values, which have been corrected based on the conveying volume, using sieve analysis values. This confirms that... Figure 7A The correlation coefficient R = 0.47, but... Figure 7B The accuracy was significantly improved to R=0.58. Figure 7C and Figure 7D The accuracy was also improved to R=0.51 and R=0.58 respectively. Figure 7E The result is obtained by using sieve analysis values ​​to perform linear regression correction on the uncorrected measured values. Figure 7F The result is obtained by linear regression correction of the measured values, which have been corrected based on the layer thickness, using sieve analysis values. Figure 7G The result is obtained by linear regression correction of the measured values ​​that have been corrected based on brightness using sieve analysis values. Figure 7H The result is obtained by linear regression correction of the measured values, which have been corrected based on the conveying volume, using sieve analysis values. This confirms that... Figure 7E The correlation coefficient R = 0.37, but... Figure 7F The accuracy was significantly improved to R = 0.71. Figure 7G and Figure 7H The accuracy was also improved to R=0.69 and R=0.66, respectively. These results indicate that high-precision particle size measurement can be achieved in either laser distance measurement or camera image capture.

[0126] Corrections can also be made based on raw material conditions, or by using corrections based on both raw material conditions and operating conditions instead of corrections based solely on operating conditions. Alternatively, other indicators representing the raw material packing state, such as width expansion, can be used instead of layer thickness.

[0127] A method for effectively utilizing this embodiment in operation will be described.

[0128] In sintering plants, it is generally desirable for granulated particles to be large in size, consistently uniform in size over time, and with consistent particle diameters. However, due to variations in the properties of the raw materials before granulation, such as the unpredictable influence of introduced moisture, even with a constant operating rate, the particle size after granulation can deviate over time, sometimes being smaller than desired. This results in unstable or reduced aeration when loading the granulated particles into the sintering machine, raising concerns about producing low-quality sinter.

[0129] By using the information processing method described in this embodiment, the particle size of the granulated particles can be consistently determined. The particle size can be controlled to the desired size by changing the operating factors affecting the particle size, thus reducing particle size deviation. Specifically, in the sinter manufacturing method, the ratio of at least one of the three raw materials—iron-containing raw material, by-product raw material, and carbon-containing raw material—used at each observation time output in S5 is considered for adjustment. According to this sinter manufacturing method, by controlling the particle size after granulation, the aeration is stable when the granulated particles are loaded into the sintering machine, enabling the production of high-quality sinter. Alternatively, in the sinter manufacturing method, the index Zi output at each observation time in S5 is considered for adjustment of at least one of the three operating conditions: the rotational speed of the granulator, the residence time, and the addition of moisture. According to this sinter manufacturing method, the particle size can also be controlled to the desired size, enabling the production of high-quality sinter in the same way.

[0130] The method of feeding the granulated particles into the sintering machine is also important. After being stored in the buffer hopper, the granulated particles are fed into the sintering machine's pallet via a cylindrical feeder and chute or cylindrical chute. Ideally, the particle size distribution in the height direction of the granulated particles piled on the pallet should not change over time. However, if the particle size deviation changes after granulation, the particle size distribution in the height direction of the granulated particles piled on the pallet will change if the feeding method is not changed. This can easily lead to instability or reduction in the aeration of the sintering machine. Instability or reduction in aeration can result in a decrease in the quality of the produced sinter.

[0131] In the sinter manufacturing method, the index Zi output at each observation moment in S5 is considered to adjust at least one of the three operating conditions of the raw material loading device: the speed of the cylindrical feeder, the chute angle, and the speed of the cylindrical chute. According to this sinter manufacturing method, the variation of particle size deviation in the height direction of the raw material piled on the tray of the sintering machine over time is adjusted to be constant, thereby stabilizing the aeration. The result is the production of high-quality sinter.

[0132] Another method for effectively utilizing this implementation method in operation will be described.

[0133] In a blast furnace, iron raw materials, primarily composed of iron oxide, are alternately charged from the top. Hot air blown in from the tuyeres at the bottom of the furnace combusts the coke, and the reducing gas containing the generated CO reduces the iron oxide in the sinter or lump ore, thereby producing pig iron. Therefore, to ensure the blast furnace's ventilation, it is crucial to determine the particle size, shape, or size distribution of the raw materials charged into the furnace in a manner that ensures the formation of voids between the coke particles.

[0134] If the information processing method described in this embodiment is used, the particle size, particle shape, or particle size distribution of the raw material can be accurately measured on the conveyor 11 before blast furnace charging, and the blast furnace operation method, i.e., the reflection of the amount or distribution of raw material charged, can be implemented, thereby ensuring the stable operation of the blast furnace's ventilation. That is, in the blast furnace operation method, the operating conditions of the blast furnace with the charged raw material are adjusted by considering the index Zi at each observation time output in S5. Specifically, by adjusting the air flow rate, which represents the amount of hot blast delivered from the blast furnace tuyeres, or the coke ratio, which represents the amount of coke input for producing about 1 ton of molten iron, according to the particle size, the ventilation of the blast furnace can be stabilized.

[0135] Another method for effectively utilizing this implementation method in operation will be described.

[0136] To ensure proper ventilation within the blast furnace, the coke used must be of high strength and uniform in both strength and particle size. To produce high-strength, uniformly sized coke, the bulk density of the coal charged into the coking furnace needs to be increased to ensure strong contact between coal particles during heating and dry distillation. Therefore, optimizing the particle size of the coal charged into the coking furnace is crucial. When pulverizing the coal, pulverization conditions must be selected to achieve the target particle size and minimize particle size deviation. Thus, in the coke manufacturing method, the rotational speed of the coal pulverizer is adjusted using the index Zi output at each observation moment in S5. Specifically, after measuring the particle size distribution of the coal feedstock in front of and behind the pulverizer using the information processing method described in this embodiment, the pulverized coal particle size can be adjusted to the target particle size by controlling the motor current or hammer rotation speed of the pulverizer. The result is the production of high-strength, high-quality coke.

[0137] This disclosure is not limited to the embodiments described above. For example, two or more blocks shown in the block diagram may be merged, or a block may be divided. For two or more steps shown in the flowchart, they may be executed in parallel or in a different order, depending on the processing capacity of the device executing each step or as needed, instead of being executed sequentially as described. Furthermore, modifications can be made without departing from the spirit of this disclosure.

[0138] Explanation of reference numerals in the attached figures

[0139] 10… Measurement system; 11… Conveyor; 12… Stacked raw materials; 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, characterized in that, Equipped with a control unit, The control unit acquires observation data at each observation time by using sensors to observe the surface of the stacked raw materials that are being transported sequentially. The control unit uses the acquired observation data to calculate an index related to the particle size of each particle included in the stockpiled raw material at each observation time. The control unit obtains measurement data at each measurement time corresponding to each observation time by measuring the state of the accumulated raw materials. The control unit uses the acquired measurement data to correct the calculated indicators for the observation times corresponding to each measurement time. The control unit outputs corrected indicators for each observation time.

2. The information processing device according to claim 1, characterized in that, The control unit calibrates the index based on the size of the stacked raw material, which is determined from the measured data.

3. The information processing device according to claim 2, characterized in that, The dimensions of the stacked raw materials include the layer thickness of the stacked raw materials.

4. The information processing apparatus according to claim 3, characterized in that, The control unit acquires distance data obtained by measuring the distance from the sensor to the surface of the piled raw material as the observation data. The control unit also uses the distance data as the measurement data.

5. The information processing apparatus according to claim 1, characterized in that, The control unit acquires camera images obtained by photographing the surface of the piled raw materials as the measurement data. The control unit corrects the index based on the brightness of the camera image.

6. The information processing apparatus according to claim 5, characterized in that, The control unit also uses the camera images as the observation data.

7. The information processing apparatus according to claim 1, characterized in that, The control unit acquires data obtained by measuring the conveying amount of the stockpiled raw materials as the measurement data.

8. The information processing apparatus according to claim 1, characterized in that, The control unit acquires data obtained by measuring the composition of the stockpiled raw materials as the measurement data.

9. The information processing apparatus according to claim 8, characterized in that, The composition of the stockpiled raw materials includes the moisture content of the stockpiled raw materials.

10. An information processing method, characterized in that, include: The information processing device obtains observation data at each observation moment by using sensors to observe the surface of the stacked raw materials that are being transported in sequence; The information processing device uses the acquired observation data to calculate an index related to the particle size of each particle included in the stockpiled raw material at each observation time. The information processing device obtains measurement data at each measurement time corresponding to each observation time by measuring the state of the piled raw materials. The information processing device uses the acquired measurement data to correct the calculated indicators corresponding to the observation times at each measurement time; and The information processing device outputs corrected indicators for each observation time.

11. A method for manufacturing sintered ore, characterized in that, include: The proportion of at least one of the three raw materials—iron-containing raw materials, auxiliary raw materials, and carbon-containing raw materials—that are used as sintering raw materials are adjusted using the index output at each observation time by the information processing method of claim 10.

12. A method for manufacturing sintered ore, characterized in that, include: The operating conditions of the granulator are adjusted using the indexes output at each observation time by the information processing method of claim 10, which are the rotational speed, residence time, and addition of water.

13. A method for manufacturing sintered ore, characterized in that, include: The operating conditions of at least one of the three operating conditions of the raw material loading device—the speed of the cylindrical feeder, the chute angle, and the speed of the cylindrical chute—are adjusted using the index output at each observation moment by the information processing method of claim 10.

14. A blast furnace operation method, characterized in that, include: The operating conditions of the blast furnace being loaded with raw materials are adjusted using the indicators output at each observation time by the information processing method of claim 10.

15. A method for manufacturing coke, characterized in that, include: The rotational speed of the coal pulverizer is adjusted using the index output at each observation moment by the information processing method of claim 10.

16. A program, characterized in that, The computer performs the following actions: The observation data is obtained at each observation time by using sensors to observe the surface of the stacked raw materials that are being transported in sequence. The acquired observation data was used to calculate an index related to the particle size of each particle included in the stockpiled raw material at each observation time. Measurement data is obtained at each measurement time corresponding to each observation time by measuring the state of the accumulated raw materials. The obtained measurement data was used to correct the calculated indicators for the observation times corresponding to each measurement time; and Output corrected metrics for each observation time.

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