WO2023189248A1 - 造粒粒子の粒度分布推定方法、造粒粒子製造方法及び造粒粒子製造装置 - Google Patents

造粒粒子の粒度分布推定方法、造粒粒子製造方法及び造粒粒子製造装置 Download PDF

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WO2023189248A1
WO2023189248A1 PCT/JP2023/008397 JP2023008397W WO2023189248A1 WO 2023189248 A1 WO2023189248 A1 WO 2023189248A1 JP 2023008397 W JP2023008397 W JP 2023008397W WO 2023189248 A1 WO2023189248 A1 WO 2023189248A1
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Prior art keywords
particle size
granulated particles
size distribution
granulator
granulated
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PCT/JP2023/008397
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English (en)
French (fr)
Japanese (ja)
Inventor
健太 竹原
隆英 樋口
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Ｊｆｅスチール株式会社
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Application filed by Ｊｆｅスチール株式会社 filed Critical Ｊｆｅスチール株式会社
Priority to JP2023544611A priority Critical patent/JP7626235B2/ja
Priority to KR1020247025621A priority patent/KR20240132327A/ko
Priority to CN202380030394.2A priority patent/CN118946673A/zh
Publication of WO2023189248A1 publication Critical patent/WO2023189248A1/ja

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    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22B—PRODUCTION AND REFINING OF METALS; PRETREATMENT OF RAW MATERIALS
    • C22B1/00—Preliminary treatment of ores or scrap
    • C22B1/14—Agglomerating; Briquetting; Binding; Granulating
    • C22B1/16—Sintering; Agglomerating
    • C22B1/20—Sintering; Agglomerating in sintering machines with movable grates
    • C22B1/205—Sintering; Agglomerating in sintering machines with movable grates regulation of the sintering process
    • C—CHEMISTRY; METALLURGY
    • C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
    • C22B—PRODUCTION AND REFINING OF METALS; PRETREATMENT OF RAW MATERIALS
    • C22B1/00—Preliminary treatment of ores or scrap
    • C22B1/14—Agglomerating; Briquetting; Binding; Granulating
    • C22B1/16—Sintering; Agglomerating
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/02—Investigating particle size or size distribution

Definitions

  • the present disclosure relates to a method for estimating the particle size distribution of granulated particles, a method for producing granulated particles, and an apparatus for producing granulated particles.
  • Granulated particles are particles obtained by granulating a sintering raw material into granules using a granulator such as a drum mixer.
  • the sintering raw material After the sintering raw material is granulated into granulated particles, it is charged into a pallet of a sintering machine that produces sintered ore. In the sintering process using a sintering machine, it is important to improve air permeability. Since the air permeability in a sintering machine depends on the size and porosity of the granulated particles, it is required to accurately estimate the particle size distribution of the granulated particles.
  • Patent Document 1 discloses target raw material particle size distribution information indicating the particle size distribution of the target raw material, and target raw material particle size distribution information indicating the particle size distribution of the target raw material in pseudo particles (granulated particles) granulated with a plurality of raw materials including the target raw material. Based on the raw material availability information and the pseudo particle distribution information that indicates the particle size distribution of the pseudo particles in the height direction of the sintered material layer in which the pseudo particles are stacked, the height direction of the target material in the sintered material layer is An apparatus for estimating a distribution is disclosed.
  • the particle size distribution of granulated particles also depends on the blending conditions of raw materials, the water content during granulation, etc.
  • the estimation method described in Patent Document 1 does not take into account the blending conditions of raw materials and the water content during granulation, and if the blending conditions of raw materials and the water content during granulation change, the estimation method described in Patent Document 1 It was difficult to estimate the distribution in the height direction with high accuracy.
  • An object of the present disclosure is to provide a method for estimating the particle size distribution of granulated particles, a method for producing granulated particles, and an apparatus for producing granulated particles, which can accurately estimate the particle size distribution of granulated particles.
  • a method for estimating particle size distribution of granulated particles includes: A method for estimating the particle size distribution of granulated particles using a granulated particle manufacturing apparatus equipped with a granulator that granulates granulated particles from a sintered raw material containing a plurality of raw materials, the method comprising: obtaining granulation conditions including a blending ratio of the plurality of raw materials contained in the sintering raw material and a water content of the sintering raw material when the granulator granulates the granulated particles; , Based on the granulation conditions, calculating parameters of a distribution function that fits the particle size distribution of the granulated particles granulated by the granulator; estimating the particle size distribution using the distribution function and the parameters; including.
  • a granulated particle manufacturing method manufactures granulated particles using the above particle size distribution estimation method.
  • a granulated particle manufacturing device includes: a granulator that granulates granulated particles from a sintered raw material containing a plurality of raw materials; a control device that estimates the particle size distribution of the granulated particles granulated by the granulator; Equipped with The control device includes: Obtaining granulation conditions including the blending ratio of the plurality of raw materials contained in the sintering raw material and the water content of the sintering raw material when the granulator granulates the granulated particles, Based on the granulation conditions, calculate parameters of a distribution function that fits the particle size distribution of the granulated particles granulated by the granulator, The particle size distribution is estimated using the distribution function and the parameters.
  • the particle size distribution of granulated particles can be estimated with high accuracy.
  • FIG. 1 is a diagram schematically showing a configuration example of a granulated particle manufacturing apparatus according to an embodiment of the present disclosure.
  • 1 is a diagram schematically showing a configuration example of a control device according to an embodiment of the present disclosure. It is a flowchart which shows the example of a procedure of the particle size distribution estimation method of granulated particles concerning one embodiment of this indication.
  • FIG. 3 is a diagram showing measured values of particle size distribution in Examples.
  • FIG. 3 is a diagram showing the correlation between measured values and calculated values of particle size distribution in Examples.
  • FIG. 1 is a diagram schematically showing a configuration example of a granulated particle manufacturing apparatus 1 according to an embodiment of the present disclosure.
  • the granulated particle manufacturing apparatus 1 includes a control device 10, a plurality of blending tanks 20-1 to 20-3, a moisture supply device 30, and a granulator 40.
  • the blending tanks 20-1 to 20-3 may be simply referred to as "blending tank 20" when there is no particular need to distinguish them.
  • the granulated particle manufacturing apparatus 1 is provided with three blending tanks 20-1 to 20-3 is described as an example, but the granulated particle manufacturing apparatus 1 is equipped with a mixing tank
  • the number of 20 is not limited to three.
  • the number of blending tanks 20 included in the granulated particle manufacturing apparatus 1 may be two or four or more.
  • the granulated particle manufacturing apparatus 1 is an apparatus that manufactures granulated particles from a sintered raw material containing a plurality of raw materials.
  • the plurality of raw materials may include, for example, an iron-containing raw material, a CaO (calcium oxide)-containing raw material, coke powder, and the like.
  • the iron-containing raw material may be, for example, iron ore.
  • the CaO-containing raw material may be limestone, for example.
  • the plurality of raw materials included in the sintering raw material are an iron-containing raw material, a CaO-containing raw material, and coke powder.
  • the granulated particles produced by the granulated particle production apparatus 1 are, for example, transported to a sintering machine and sintered in the sintering machine to become sintered ore. Sintered ore is used in the iron making process.
  • the control device 10 can communicate with the blending tanks 20-1 to 20-3, the water supply device 30, and the granulator 40.
  • the control device 10 controls the blending tanks 20-1 to 20-3, the water supply device 30, and the granulator 40.
  • the broken lines connecting the control device 10, the blending tanks 20-1 to 20-3, the moisture supply device 30, and the granulator 40 indicate that the control device 10 connects the blending tanks 20-1 to 20-3, This indicates that communication is possible with the moisture supply device 30 and the granulator 40, and that the control device 10 can control the blending tanks 20-1 to 20-3, the moisture supply device 30, and the granulator 40.
  • control device 10 The details of the configuration and functions of the control device 10 will be described later.
  • the blending tank 20 supplies raw materials contained in the sintering raw materials to the granulator 40.
  • the amount of raw material that the blending tank 20 supplies to the granulator 40 is controlled by the control device 10.
  • the blending tanks 20-1 to 20-3 each supply different types of raw materials to the granulator 40.
  • the blending tank 20-1 supplies the iron-containing raw material to the granulator 40.
  • the blending tank 20-2 supplies the CaO-containing raw material to the granulator 40.
  • the blending tank 20-3 supplies coke powder to the granulator 40.
  • the moisture supply device 30 supplies moisture to be added to the raw material to the granulator 40 when the blending tanks 20-1 to 20-3 supply the raw material to the granulator 40.
  • the amount of moisture that the moisture supply device 30 supplies to the granulator 40 is controlled by the control device 10.
  • the granulator 40 granulates granulated particles from a sintered raw material containing a plurality of raw materials.
  • the sintering raw material includes a plurality of raw materials supplied from the blending tanks 20-1 to 20-3 and moisture supplied from the moisture supply device 30.
  • the sintering raw material supplied to the granulator 40 includes an iron-containing raw material, a CaO-containing raw material, coke powder, and water.
  • the granulator 40 may be any granulator capable of producing granulated particles, and may be a drum mixer, for example.
  • the granulated particles granulated by the granulator 40 are, for example, transported to a sintering machine and used as a raw material for manufacturing sintered ore.
  • FIG. 2 is a diagram schematically showing a configuration example of the control device 10 according to an embodiment of the present disclosure.
  • the control device 10 may be a general-purpose computer such as a workstation or a personal computer, or may be a dedicated computer configured to function as the control device 10 of the granulated particle manufacturing device 1.
  • the control device 10 includes a control section 11, an input section 12, an output section 13, a storage section 14, and a communication section 15.
  • the control unit 11 includes at least one processor, at least one dedicated circuit, or a combination thereof.
  • the processor is a general-purpose processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), or a dedicated processor specialized for specific processing.
  • the dedicated circuit is, for example, an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).
  • the control unit 11 reads programs, data, etc. stored in the storage unit 14 and executes various functions.
  • the control unit 11 controls the blending tank 20, the water supply device 30, and the granulator 40.
  • the control unit 11 causes the control unit 11 to function as a raw material information acquisition unit 111, a granulation condition acquisition unit 112, a particle size distribution estimation unit 113, and a guidance information acquisition unit 114 by executing the program read from the storage unit 14. be able to.
  • the processing executed by the raw material information acquisition unit 111, the granulation condition acquisition unit 112, the particle size distribution estimation unit 113, and the guidance information acquisition unit 114 will be described later.
  • the input unit 12 includes one or more input interfaces that detect user input and obtain input information based on user operations.
  • the input unit 12 includes, for example, physical keys, capacitive keys, a touch screen provided integrally with the display of the output unit 13, a microphone that accepts voice input, and the like.
  • the output unit 13 includes one or more output interfaces that output information and notify the user.
  • the output unit 13 includes, for example, a display that outputs information as an image, a speaker that outputs information as audio, and the like.
  • the display included in the output unit 13 may be, for example, an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, or the like.
  • the storage unit 14 is, for example, a flash memory, a hard disk, an optical memory, or the like. A part of the storage unit 14 may be located outside the control device 10. In this case, part of the storage unit 14 may be a hard disk, a memory card, etc. connected to the control device 10 via an arbitrary interface.
  • the storage unit 14 stores programs for the control unit 11 to execute each function, data used by the programs, and the like.
  • the communication unit 15 includes at least one of a communication module that supports wired communication and a communication module that supports wireless communication.
  • the control device 10 can communicate with other terminal devices via the communication unit 15.
  • the raw material information acquisition unit 111 acquires information regarding the raw materials that each blending tank 20 supplies to the granulator 40.
  • the raw material information acquisition unit 111 acquires information such as the ingredients of the raw material, the particle size of the raw material, and the blending amount of the raw material.
  • the information on the ingredients of the raw material is information on the ingredient composition of the raw material.
  • the information on the particle size of the raw material is information indicating the particle size of the raw material.
  • the information on the blended amount of raw materials is information indicating the amount of raw materials that the blending tank 20 supplies to the granulator 40.
  • the raw material information acquisition unit 111 determines the raw material components, Obtain information on particle size of raw materials and blending amount of raw materials.
  • the raw material information acquisition unit 111 may acquire information regarding the raw materials that each blending tank 20 supplies to the granulator 40 by inputting information to the input unit 12 by an operator. In addition, the raw material information acquisition unit 111 acquires information regarding the raw materials that each blending tank 20 supplies to the granulator 40 by receiving information input by an operator into another terminal device via the communication unit 15. You may. Further, the raw material information acquisition unit 111 may acquire information on the blended amount of raw materials from the setting values set in the blending tank 20.
  • the granulation condition acquisition unit 112 acquires information on raw material components, raw material particle size, and raw material blending amount from the raw material information acquisition unit 111 as information regarding the raw materials that each blending tank 20 supplies to the granulator 40. .
  • the granulation condition acquisition unit 112 calculates the blending ratio of the plurality of raw materials included in the sintering raw material based on the acquired information on the blending amount of each raw material.
  • the granulation condition acquisition unit 112 acquires information on the water content of the sintering raw material when the granulator 40 granulates the granulated particles.
  • the moisture content of the sintered raw material when the granulator 40 granulates the granulated particles is determined from the moisture content of each raw material and the moisture supplied from the moisture supply device 30 to the granulator 40 .
  • the moisture content of each raw material is measured using, for example, an infrared moisture meter.
  • the granulation condition acquisition unit 112 may acquire the information on the water content through an input operation to the input unit 12 by an operator. Further, the granulation condition acquisition unit 112 may acquire the information on the water content by receiving information input by an operator into another terminal device via the communication unit 15. Furthermore, the granulation condition acquisition unit 112 may acquire the information on the moisture content from a setting value set in the moisture supply device 30.
  • the granulation condition acquisition unit 112 acquires information on the space factor of the granulator 40, the rotation speed of the granulator 40, and the estimated residence time in the granulator 40 as information regarding the granulator 40.
  • the granulation condition acquisition unit 112 may acquire information regarding the granulation machine 40 through an input operation to the input unit 12 by an operator. Further, the granulation condition acquisition unit 112 may acquire information regarding the granulator 40 by receiving information input by an operator into another terminal device via the communication unit 15. Further, the granulation condition acquisition unit 112 may acquire information regarding the granulator 40 from the setting values set in the granulator 40.
  • the granulation condition acquisition unit 112 outputs the acquired granulation conditions to the particle size distribution estimation unit 113.
  • the granulation conditions that the granulation condition acquisition unit 112 outputs to the particle size distribution estimation unit 113 include at least the blending ratio of a plurality of raw materials included in the sintering raw material and the granulation conditions when the granulation machine 40 granulates the granulated particles. This includes the moisture content of the sintering raw material.
  • the granulation conditions may further include information on the ingredients of the raw material, the particle size of the raw material, the space factor of the granulator 40, the rotation speed of the granulator 40, and the estimated residence time in the granulator 40.
  • the particle size distribution estimation unit 113 estimates the particle size distribution of the granulated particles to be granulated by the granulator 40 based on the granulation conditions acquired from the granulation condition acquisition unit 112. First, the particle size distribution estimating unit 113 calculates parameters of a distribution function that fits the particle size distribution of the granulated particles granulated by the granulator 40 based on the granulation conditions acquired from the granulation condition acquisition unit 112. . Next, the particle size distribution estimating unit 113 substitutes the calculated parameters into a distribution function that fits the particle size distribution of the granulated particles to be granulated by the granulator 40, and performs granulation using the distribution function to which the parameters have been substituted. The particle size distribution of the granulated particles granulated by the granulator 40 is estimated.
  • the particle size distribution estimation unit 113 may calculate the parameters using a parameter estimation model corresponding to each parameter.
  • the parameter estimation model may be stored in the storage unit 14.
  • the parameter estimation model may be a multiple regression model, a trained machine learning model, etc.
  • the parameter estimation model is a multiple regression model, for example, the blending ratio of a plurality of raw materials included in the sintering raw material and the water content of the sintering raw material when the granulator 40 granulates the granulated particles are determined. It may be a multiple regression model in which the explanatory variable is an explanatory variable and one of the parameters of the distribution function is an objective variable.
  • the parameter estimation model is a machine learning model, for example, the blending ratio of a plurality of raw materials included in the sintering raw material and the water content of the sintering raw material when the granulator 40 granulates the granulated particles are determined. It may be a trained machine learning model that takes as input and outputs one of the parameters of the distribution function.
  • Such a multiple regression model or machine learning model is based on the mixing ratio of multiple raw materials included in the sintered raw material, the moisture content of the sintered raw material when the granulator 40 granulates the granulated particles, and the distribution.
  • Each parameter of the multiple regression model may be determined in advance using a data set including the actual values of the parameters of the function and stored in the storage unit 14.
  • the trained machine learning model also calculates the mixing ratio of multiple raw materials contained in the sintered raw material, the moisture content of the sintered raw material when the granulator 40 granulates the granulated particles, and the distribution function.
  • Machine learning may be performed in advance using a data set including one actual value of each parameter and stored in the storage unit 14.
  • the actual values of the parameters of the distribution function can be determined by fitting the distribution function to the curve of the particle size distribution of granulated particles whose particle size distribution is known.
  • the distribution function that the particle size distribution estimating unit 113 fits to the particle size distribution of the granulated particles may be a Rosin-Rammler distribution function, a normal distribution function, a binomial distribution function, or the like. If the distribution function is a distribution function that is distributed only in the first quadrant, it is possible to prevent negative values from appearing in the distribution of the granulated particles.
  • the Rosin Rammler distribution function is a distribution function that is distributed only in the first quadrant.
  • the distribution function is a rosin Rammler distribution function
  • the rosin Rammler distribution function can be expressed as in the following equation (1).
  • d p indicates the particle size of the granulated particles
  • ⁇ indicates the particle size characteristic index
  • n indicates the uniform number.
  • the particle size distribution estimation unit 113 calculates ⁇ and n shown in equation (1) as parameters.
  • the particle size distribution estimating unit 113 After calculating the parameters ⁇ and n, the particle size distribution estimating unit 113 substitutes the parameters ⁇ and n into the rosin Rammler distribution function shown in equation (1) to estimate the particle size distribution of the granulated particles granulated by the granulator 40. be able to.
  • the particle size distribution estimating unit 113 may output the estimated particle size distribution of the granulated particles to the output unit 13 and display it on the output unit 13.
  • the particle size distribution estimation unit 113 calculates the mean value, standard deviation, skewness, and kurtosis as parameters.
  • the particle size distribution estimation unit 113 After calculating the mean value, standard deviation, skewness, and kurtosis, which are the parameters of the normal distribution function, the particle size distribution estimation unit 113 substitutes the mean value, standard deviation, skewness, and kurtosis into the normal distribution function, and performs granulation.
  • the particle size distribution of the granulated particles granulated by the machine 40 can be estimated.
  • the particle size distribution estimation unit 113 calculates the probability P and the number of trials N as parameters.
  • the particle size distribution estimating unit 113 After calculating the probability P and the number of trials N, which are parameters of the binomial distribution function, the particle size distribution estimating unit 113 substitutes the probability P and the number of trials N into the binomial distribution function, and calculates the granules produced by the granulator 40.
  • the particle size distribution of particles can be estimated.
  • the guidance information acquisition unit 114 does not estimate the particle size distribution of the granulated particles granulated by the granulator 40, but sets the particle size distribution of the granulated particles granulated by the granulator 40 to a target particle size distribution. Calculate the granulation conditions for
  • the guidance information acquisition unit 114 calculates granulation conditions based on the input parameters. For example, when the distribution function that fits the particle size distribution is a rosin Rammler distribution function, parameters ⁇ and n of the rosin Rammler distribution function having a shape corresponding to the target particle size distribution are input to the guidance information acquisition unit 114. For example, the guidance information acquisition unit 114 inputs various granulation conditions to the parameter estimation model, and determines whether the output parameters match the input parameters. The guidance information acquisition unit 114 performs this operation for the parameters ⁇ and n, and sets the granulation conditions for which it is determined that the output parameters match the input parameters for both the parameters ⁇ and n to the target particle size distribution.
  • the guidance information acquisition unit 114 calculates the granulation conditions based on the input parameters ⁇ and n.
  • the granulation conditions calculated by the guidance information acquisition unit 114 include, for example, the blending ratio of a plurality of raw materials included in the sintering raw material, and the moisture content of the sintering raw material when the granulator 40 granulates the granulated particles. may include.
  • the guidance information acquisition unit 114 may acquire the parameters ⁇ and n by input operations to the input unit 12 by the operator. Further, the guidance information acquisition unit 114 may acquire the parameters ⁇ and n by receiving information input by an operator into another terminal device via the communication unit 15.
  • the guidance information acquisition unit 114 outputs the calculated granulation conditions to the output unit 13.
  • the output unit 13 outputs the granulation conditions acquired from the guidance information acquisition unit 114. For example, when the output unit 13 includes a display, the output unit 13 displays the granulation conditions acquired from the guidance information acquisition unit 114 on the display.
  • the operator who visually confirms the granulation conditions displayed on the display can manufacture granulated particles with a target particle size distribution by setting the granulation conditions and operating the granulated particle manufacturing apparatus 1.
  • the guidance information acquisition unit 114 may automatically set the calculated granulation conditions as the granulation conditions when operating the granulated particle manufacturing apparatus 1. Thereby, the granulated particle manufacturing apparatus 1 automatically sets the granulation conditions for making the particle size distribution of the granulated particles granulated by the granulator 40 into the target particle size distribution, and can be manufactured.
  • control part 11 has the function of the guidance information acquisition part 114
  • the control part 11 does not have the function of the guidance information acquisition part 114
  • the control unit 11 has the function of the guidance information acquisition unit 114, it becomes easier to manufacture granulated particles with the target particle size distribution, so the control unit 11 has the function of the guidance information acquisition unit 114. It is preferable that you do so.
  • the particle size distribution estimation method executed by the granulated particle manufacturing apparatus 1 according to the present embodiment will be described with reference to the flowchart shown in FIG. 3.
  • step S101 the control unit 11 of the control device 10 determines the blending ratio of a plurality of raw materials included in the sintering raw material and the water content of the sintering raw material when the granulator 40 granulates the granulated particles. Obtain the granulation conditions including.
  • step S102 the control unit 11 calculates parameters of a distribution function that fits the particle size distribution of the granulated particles to be granulated by the granulator 40, based on the acquired granulation conditions.
  • the distribution function may be a Rosin-Rammler distribution function, a normal distribution function, a binomial distribution function, etc.
  • step S103 the control unit 11 substitutes the calculated parameters into a distribution function that fits the particle size distribution of the granulated particles, and uses the distribution function to determine the particle size distribution of the granulated particles to be granulated by the granulator 40. Estimate. The control unit 11 may output the estimated particle size distribution of the granulated particles to the output unit 13 and cause the output unit 13 to display it.
  • FIGS. 4 and 5 show examples in which the particle size distribution is estimated using the granulated particle manufacturing apparatus 1.
  • FIG. 4 is a table showing the particle size distribution of granulated particles and measured values of carbon ratio for each particle size when granulated particles were manufactured under various conditions.
  • FIG. 5 is a graph comparing the measured value of the particle size distribution of the granulated particles with the calculated value of the particle size distribution estimated using the granulated particle manufacturing apparatus 1.
  • a drum mixer with a diameter of 0.3 m and a length of 0.4 m was used as the granulator 40.
  • a total of 8 kg of sintering raw materials mixed at various mixing ratios were used.
  • the moisture supply device 30 added a predetermined amount of moisture to the raw materials supplied from the blending tank 20.
  • the Rosin Rammler distribution function was used as the distribution function.
  • a machine learning model that inputs the blending ratio of raw materials A, B, C and coke breeze, the particle size of coke breeze, and moisture content and outputs the parameter ⁇
  • SPSS machine learning model manufactured by IBM Corporation
  • the parameter ⁇ of the rosin Rammler distribution function is calculated. and n were determined.
  • FIG. 4 shows the mixing ratio, coke powder particle size, and moisture content as conditions. Moreover, the particle size distribution of the granulated particles is shown as a measured value.
  • raw material A is iron ore from South America.
  • Raw material B is iron ore from South America, which is a different brand from raw material A.
  • Raw material C is iron ore from Australia.
  • 2- mm indicates the proportion of coke powder having a diameter of less than 2 mm.
  • -1 mm indicates the proportion of coke breeze having a diameter of less than 1 mm.
  • +8.0 indicates the proportion of granulated particles having a diameter exceeding 8.0 mm.
  • 2.8-8.0 indicates the proportion of granulated particles having a diameter of 2.8 mm or more and 8.0 mm or less.
  • a machine learning model was machine learned using the performance data shown in FIG. 4 to generate a trained machine learning model, and this was used to determine the parameters ⁇ and n. Then, the particle size of the granulated particles was estimated using the Rosin Rammler distribution function with parameters ⁇ and n.
  • FIG. 5 is a graph showing the correlation between the calculated value of the particle size distribution estimated using the granulated particle manufacturing apparatus 1 and the measured value.
  • Figure 5 shows three graphs. From the top, a graph comparing measured values and calculated values for granulated particles with a diameter of over 8.0 mm, measured values and calculated values for granulated particles with a diameter of 2.8 mm or more and 8.0 mm or less 2 is a graph comparing measured values and calculated values for granulated particles having a diameter of less than 2.8 mm.
  • the horizontal axis is the measured value.
  • the vertical axis is the calculated value.
  • the control device 10 includes The granulation conditions including the blending ratio of a plurality of raw materials to be mixed and the water content of the sintered raw material when the granulator 40 granulates the granulated particles are obtained, and the granulation is performed based on the obtained granulation conditions. Parameters of a distribution function that fits the particle size distribution of the granulated particles granulated by the granulator 40 are calculated, and the particle size distribution is estimated using the calculated parameters and distribution function.
  • the granulated particle manufacturing apparatus 1 estimates the particle size distribution of the granulated particles based on the granulation conditions including the blending ratio of a plurality of raw materials and the water content of the sintered raw material.
  • the particle size distribution of the granulated particles can be estimated by considering the blending ratio of the plurality of raw materials and the water content of the sintered raw material. Therefore, the method for estimating the particle size distribution of granulated particles using the granulated particle manufacturing apparatus 1 according to the present embodiment and the granulated particle manufacturing apparatus 1 according to the present embodiment is based on the mixing ratio of a plurality of raw materials and the water content of the sintered raw material. After considering the amount, the particle size distribution of the granulated particles can be estimated with high accuracy.
  • the present disclosure is not limited to the embodiments described above.
  • a plurality of blocks shown in the block diagram may be integrated, or one block may be divided. Instead of performing the steps in the flowchart in chronological order as described, they may be performed in parallel or in a different order depending on the processing power of the device performing each step or as needed. Other changes can be made without departing from the spirit of the present disclosure.
  • the particle size distribution estimating unit 113 determines the blending ratio of a plurality of raw materials included in the sintering raw material and the moisture content of the sintering raw material when the granulator 40 granulates the granulated particles.
  • the explanation has been given using an example in which the parameters of the distribution function are calculated based on Then, the parameters of the distribution function may be calculated. That is, the granulation conditions may further include the particle size of the coke powder in addition to the blending ratio of the plurality of raw materials and the water content.
  • the particle size distribution estimating unit 113 can estimate the particle size distribution of the granulated particles with higher accuracy by also considering the particle size of the coke powder.
  • the particle size distribution estimating unit 113 may calculate the parameters of the distribution function by considering the particle size of other raw materials instead of or in addition to the particle size of coke breeze. Further, the particle size distribution estimating unit 113 may calculate the parameters of the distribution function based on information on the components of each raw material in addition to the blending ratio and water content of the plurality of raw materials.
  • the particle size distribution estimating unit 113 determines the blending ratio of a plurality of raw materials included in the sintering raw material and the moisture content of the sintering raw material when the granulator 40 granulates the granulated particles.
  • the parameters of the distribution function may be calculated further based on at least one of the ratio, the rotation speed of the granulator 40, and the estimated residence time in the granulator 40.
  • the granulation conditions include at least the space factor of the granulator 40, the rotation speed of the granulator 40, and the estimated residence time in the granulator 40, in addition to the blending ratio and moisture content of a plurality of raw materials. It may further include one.
  • the particle size distribution estimation unit 113 can estimate the particle size distribution of the granulated particles with higher accuracy. It can be estimated.
  • the sintering raw material includes an iron-containing raw material, a CaO-containing raw material, and coke powder
  • the sintering raw material may not include an iron-containing raw material and a coke powder.
  • the sintering raw material does not need to contain a CaO-containing raw material.
  • the guidance information acquisition unit 114 sets the granulation conditions to Let us take as an example a case where granulation conditions including the blending ratio of a plurality of raw materials included in the sintering raw material and the water content of the sintering raw material when the granulator 40 granulates the granulated particles are calculated.
  • the granulation conditions calculated by the guidance information acquisition unit 114 may further include the particle size of the coke powder in addition to the blending ratio and water content of the plurality of raw materials.

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PCT/JP2023/008397 2022-03-28 2023-03-06 造粒粒子の粒度分布推定方法、造粒粒子製造方法及び造粒粒子製造装置 WO2023189248A1 (ja)

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KR1020247025621A KR20240132327A (ko) 2022-03-28 2023-03-06 조립 입자의 입도 분포 추정 방법, 조립 입자 제조 방법 및 조립 입자 제조 장치
CN202380030394.2A CN118946673A (zh) 2022-03-28 2023-03-06 造粒粒子的粒度分布估计方法、造粒粒子制造方法和造粒粒子制造装置

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JPS576500B2 (enrdf_load_stackoverflow) * 1977-04-20 1982-02-05
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JPH03166321A (ja) * 1988-10-27 1991-07-18 Kawasaki Steel Corp 焼結原料の造粒方法及びその装置
JPH06330188A (ja) * 1993-05-21 1994-11-29 Nippon Steel Corp 焼結用原料の水分調整方法
JP2009242829A (ja) * 2008-03-28 2009-10-22 Jfe Steel Corp 焼結鉱の製造方法
JP2014136818A (ja) * 2013-01-16 2014-07-28 Nippon Steel & Sumitomo Metal 高炉用非焼成含炭塊成鉱の製造方法
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JP2016176121A (ja) * 2015-03-20 2016-10-06 株式会社神戸製鋼所 焼結鉱の製造方法

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