NO X Concentration prediction method, sinter manufacturing method, and control device

Abstract

The present invention provides a method for predicting NOx concentration in sintering exhaust gas, which takes into account raw material segregation in a raw material loading layer X The present invention provides a method for predicting NOx concentration in sintering exhaust gas, which takes into account raw material segregation in a raw material loading layer X The present invention provides a method for predicting NOx concentration in sintering exhaust gas, which takes into account raw material segregation in a raw material loading layer X The present invention provides a method for predicting NOx concentration in sintering exhaust gas, which takes into account raw material segregation in a raw material loading layer X The present invention provides a method for predicting NOx concentration in sintering exhaust gas, which takes into account raw material segregation in a raw material loading layer X The present invention provides a method for predicting NOx concentration in sintering exhaust gas, which takes into account raw material segregation in a raw material loading layer X The present invention provides a method for predicting NOx concentration in sintering exhaust gas, which takes into account raw material segregation in a raw material loading layer

Description

Technical Field

[0001] This invention relates to a method for predicting NO in sintering exhaust gas discharged from a sintering machine. X NO concentration X Concentration prediction methods, control devices, and the use of NO X A method for manufacturing sintered ore using concentration prediction methods. Background Technology

[0002] Sinter, one of the raw materials used in blast furnace ironmaking, is produced by mixing and granulating iron ore (the main raw material), byproducts including limestone (CaO) and silica (SiO2), return ore, and solid fuel, with a few percent of water added. The resulting mixture is then sintered using the heat of combustion of the solid fuel. The process involves igniting the surface of a raw material packing layer containing granulated quasi-particles and drawing air from below, causing the solid fuel in the packing layer to burn from top to bottom, thus sintering the raw material.

[0003] At this time, the sintering exhaust gas drawn from below the sintering machine contains combustion products from the filling tank, therefore the sintering exhaust gas contains nitrogen oxides (NOx). X NO in sintering exhaust gas X Most of the NO produced is generated by the oxidation of nitrogen contained in fuel. X From the perspective of air pollution prevention and control, NO X Emissions are limited. During the production of sintered ore, it is necessary to reduce and manage the NO content in sintering exhaust gases. X The emissions of NO in sintering exhaust gas. X The NO content varies depending on various factors during the sintering operation, such as the type of ore used, the amount of fuel used, and the absorption conditions. Therefore, the NO content in sintering exhaust gas... X Concentrations are monitored and managed to keep them below local NO levels. X The management value (target value) of the emission limit.

[0004] Thus, NO, as one of the environmental pollutants X Therefore, it is necessary to control its emissions within the managed limits. Thus, efforts to reduce NO2 in sintering waste gas during the sintering process have been ongoing. X These measures.

[0005] Patent Document 1 discloses a modified carbon material for sinter manufacturing, coated with a material containing 36% by mass or more Ca derived from limestone-based raw materials. According to Patent Document 1, for modified carbon materials with a thickness of 1 mm or more, the coating contains 4–42 g / m² of Ca; for modified carbon materials with a thickness of 0.25 mm or more but less than 1 mm, the coating contains 5–20 g / m² of Ca. By introducing a coating layer that melts at high temperatures onto the surface of the carbon material, combustion of the carbon material at low temperatures can be suppressed, thereby inhibiting NO production.X The emissions.

[0006] Patent document 2 discloses a method for suppressing NO in sintering exhaust gas by setting the particle size of the carbon material used during sintering to a specified value or higher. X The method for generating NO. According to Patent Document 2, by directly using coarse-grained carbon materials with an average particle size of 2 mm or more, or an average particle size of 1 mm or more, as fuel without high-temperature heat treatment, NO can be suppressed. X The emissions.

[0007] It also proposes methods for predicting NO when operating conditions change during sinter production. X Increased emissions and prevention of NO X Methods to increase emissions. Patent document 3 discloses a technique where, when changing the composition ratio of various iron ore grades, if the crystal water content of the changed iron ore increases, the proportion of low-NOx anthracite is increased according to the increase in crystal water content. According to patent document 3, by increasing the proportion of low-NOx anthracite, NO emissions accompanying the increase in crystal water can be prevented in advance. X Emissions increased.

[0008] We have also been trying to use mathematical models to suppress NO. X Increased emissions. Patent document 4 discloses a method that uses a combustion model in a sintered layer that considers the combustion reaction of pulverized coke particles, as well as material and heat budgets, to evaluate NO emissions based on the ratio of CO to O2 concentration in the gas film of pulverized coke particles during combustion. X The amount of NO produced. According to Patent Document 4, using this model to calculate the temperature of the sintered layer, the CO concentration and O2 concentration in the combustion exhaust gas, it is possible to evaluate NO production. X The amount of NO produced. Patent document 4 also describes the evaluation of fuel-type NO using a mathematical model based on combustion principles. X The amount of NO produced can be investigated to explore ways to inhibit NO production. X Various factors that contribute to the quantity.

[0009] Existing technical documents

[0010] Patent documents

[0011] Patent Document 1: Japanese Patent No. 5621653

[0012] Patent Document 2: Japanese Patent Application Publication No. 53-37103

[0013] Patent Document 3: Japanese Patent Application Publication No. 2022-129696

[0014] Patent Document 4: Japanese Patent No. 5447192 Summary of the Invention

[0015] Previously, as disclosed in Patent Documents 1 and 2, many methods for reducing NO in sintering exhaust gas have been developed. X Content, inhibition of NO X Technology related to emissions. As shown in Patent Document 3, for NO... X Several relevant operational factors were generated, their impact was clarified, and methods for preventing NO in advance were developed. X Methods to increase production.

[0016] However, NO X The amount of NO produced is not only determined by the combustion temperature, particle size, or iron ore crystallization water content of the carbon material shown in Patent Documents 1-3, but is also affected by various factors such as the amount of carbon material used as fuel, the granulation moisture content, the permeability of the raw material layer, and the thickness of the raw material layer. Therefore, in these technologies, the ability to predict future NO production is crucial. X The increase or decrease in NO X The actual increase in NO emissions will be carried out before NO X Operations aimed at suppressing emissions increases before they rise have limitations.

[0017] Patent document 4 discloses a mathematical model based on combustion reaction to describe NO. X The model for the amount of NO produced. However, the model disclosed in Patent Document 4 does not consider the segregation of raw materials in the height direction of the raw material loading layer. Therefore, for NO X The prediction of NO production has limitations. Although the model disclosed in Patent Document 4 can estimate the NO production in the pot test, it is not feasible. X The amount of NO produced is unknown, but it is difficult to estimate the amount of NO discharged from the actual sintering machine. X The emissions.

[0018] In actual sintering machines, depending on the type, properties, and granulation conditions of the raw materials charged into the raw material layer, segregation of various raw materials, primarily fuel, will occur. This segregation within the raw material layer in an actual sintering machine affects the concentration of NO in the sintering exhaust gas. X The content-related temperature distribution within the sintering layer has a significant impact. Therefore, the following challenge exists: in actual sintering machines, if raw material segregation within the loading layer is not considered, it is difficult to accurately predict the NO content in the sintering exhaust gas. X concentration.

[0019] This invention was made in view of the problems of this prior art, and its object is to provide a method for predicting NO in sintering exhaust gas by taking into account raw material segregation within the raw material loading layer. X NO concentration X Concentration prediction method and control device. Another object of the present invention is to provide a method for using NO. X A method for manufacturing sintered ore using concentration prediction methods.

[0020] The methods used to solve the above problems are as follows.

[0021] [1] A NO X A concentration prediction method is used to predict the NO concentration in sintering exhaust gas discharged from the sintering machine. X The concentration refers to the concentration of water added to sintering raw materials containing iron and carbon, resulting in granulated particles that are then sintered.

[0022] The above NO X Concentration prediction methods include:

[0023] The raw material information acquisition step involves obtaining raw material information including the particle size, composition, and proportion of each raw material contained in the above-mentioned sintering raw materials.

[0024] The granulation conditions acquisition step involves acquiring granulation conditions that include the moisture content of the sintering raw materials when granulating the granulated particles from the aforementioned sintering raw materials.

[0025] The granulation result estimation step involves inputting the aforementioned raw material information and granulation conditions into a granulation estimation model to estimate the granulation result, including the particle size and composition of the granulated particles at each particle size. The granulation estimation model includes a particle size estimation model and a composition estimation model. The particle size estimation model takes the particle size of the carbon-containing raw material, the proportion of each raw material in the sintering raw material, and the moisture content as input, and outputs the content of the granulated particles in one of multiple particle size partitions. The composition estimation model takes the particle size of the carbon-containing raw material, the proportion of each raw material in the sintering raw material, and the moisture content as input, and outputs the content of a specific component of the granulated particles in one of multiple particle size partitions.

[0026] The loading condition acquisition step involves acquiring the loading conditions when the granulated particles are loaded into the sintering machine to form a raw material loading layer.

[0027] The loading result estimation step, based on the above granulation results and loading conditions, uses a loading estimation model to estimate the particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer. The loading estimation model represents the content proportion of granulated particles of each particle size in each loading partition when the height direction of the raw material loading layer is divided into multiple loading partitions.

[0028] The firing result estimation step, based on at least one of the aforementioned particle size segregation and composition segregation, uses a firing estimation model to estimate the sintering temperature within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer. The firing estimation model includes a heat transfer model capable of calculating the sintering temperature at a specified location within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer.

[0029] NO X The concentration prediction step uses the sintering temperature within the raw material loading layer, the exhaust gas flow rate directly below the raw material loading layer, and the amount of carbon-containing raw material used per unit time in the sintering raw materials to predict the NO concentration in the sintering exhaust gas. X concentration.

[0030] [2] According to the NO described in [1] X Concentration prediction method, wherein, in the above NO X In the concentration prediction step, the following formula (1) is used to predict the NO in the sintering waste gas. X concentration.

[0031] NO X =(A×(T-273)+B)×M×N / (100×14×U)···(1)

[0032] In the above formula (1), NO X NO in the above sintering waste gas X Concentration (ppm), T is the highest sintering temperature (K) in the above-mentioned raw material loading layer, M is the amount of carbon-containing raw material used per unit time in the above-mentioned sintering raw material (ton / h), N is the nitrogen content ratio (mass%) of the carbon-containing raw material in the above-mentioned sintering raw material, U is the exhaust gas flow rate (mol / h) directly below the above-mentioned raw material loading layer, A (K -1 ), B (-) are constants.

[0033] [3] A method for manufacturing sintered ore, comprising:

[0034] Manufacturing conditions determination steps, determining the NO produced by [1] or [2] X NO concentration prediction method for sintering exhaust gas X Manufacturing conditions that ensure the concentration meets the predetermined target value, and

[0035] The sinter manufacturing step involves manufacturing sinter under the manufacturing conditions determined in the above-mentioned manufacturing condition determination step.

[0036] [4] A control device for predicting NO in sintering exhaust gas discharged from a sintering machine. XThe concentration refers to the concentration of water added to sintering raw materials containing iron and carbon, resulting in granulated particles that are then sintered.

[0037] The control device has:

[0038] The raw material information acquisition department acquires raw material information including the particle size, composition, and proportion of each raw material contained in the aforementioned sintering raw materials.

[0039] The granulation condition acquisition unit acquires granulation conditions including the moisture content of the sintering raw material when granulating the granulated particles from the aforementioned sintering raw material.

[0040] The granulation result estimation unit, by inputting the aforementioned raw material information and granulation conditions into a granulation estimation model, estimates the granulation result, including the particle size of the granulated particles and the composition of each particle size. The granulation estimation model includes a particle size estimation model and a composition estimation model. The particle size estimation model takes the particle size of the carbon-containing raw material, the proportion of each raw material contained in the sintering raw material, and the moisture content as input, and outputs the content of the granulated particles in one of multiple particle size partitions. The composition estimation model takes the particle size of the carbon-containing raw material, the proportion of each raw material contained in the sintering raw material, and the moisture content as input, and outputs the content of a specific component of the granulated particles in one of multiple particle size partitions.

[0041] The loading condition acquisition unit acquires the loading conditions when the granulated particles are loaded into the sintering machine to form a raw material loading layer.

[0042] The loading result estimation unit, based on the above granulation results and loading conditions, uses a loading estimation model to estimate the particle size segregation and composition segregation of the granulated particles in the height direction of the raw material loading layer. The loading estimation model represents the content ratio of granulated particles of each particle size in each loading partition when the height direction of the raw material loading layer is divided into multiple loading partitions.

[0043] The sintering result estimation unit, based on at least one of the aforementioned particle size segregation and composition segregation, uses a sintering estimation model to estimate the sintering temperature within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer. The sintering estimation model includes a heat transfer model capable of calculating the sintering temperature at a specified location within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer.

[0044] NO XThe concentration prediction unit uses the sintering temperature inside the raw material loading layer, the exhaust gas flow rate directly below the raw material loading layer, and the amount of carbon-containing raw material used per unit time in the sintering raw materials to predict the NO concentration in the sintering exhaust gas. X concentration.

[0045] [5] According to the control device described in [4], wherein the above-mentioned NO X The concentration prediction unit uses the following formula (1) to predict the NO in sintering exhaust gas. X concentration.

[0046] NO X =(A×(T-273)+B)×M×N / (100×14×U)···(1)

[0047] In equation (1) above, NO X NO in the above sintering waste gas X Concentration (ppm), T is the highest sintering temperature (K) in the above-mentioned raw material loading layer, M is the amount of carbon-containing raw material used per unit time in the above-mentioned sintering raw material (ton / h), N is the nitrogen content ratio (mass%) of the carbon-containing raw material in the above-mentioned sintering raw material, U is the exhaust gas flow rate (mol / h) directly below the above-mentioned raw material loading layer, A (K -1 ), B (-) are constants.

[0048] [6] According to the control device described in [4] or [5], it further comprises determining that NO X NO concentration prediction of sintering exhaust gas X The manufacturing conditions determination section determines the manufacturing conditions under which the concentration meets the predetermined target value.

[0049] In the NO involved in this invention X In the concentration prediction method and control device, the segregation of raw materials in the raw material loading layer is used to estimate the concentration of NO in the sintering waste gas. X The concentration of the raw material is controlled by the sintering temperature within the loading layer and the exhaust gas flow rate directly below the loading layer. Therefore, the NO content involved in this invention... X Concentration prediction methods and control devices can be used to predict NO in sintering exhaust gas by taking into account raw material segregation within the raw material loading layer. X NO concentration X Concentration prediction method and control device. Attached Figure Description

[0050] Figure 1 This is a schematic diagram illustrating an example of the configuration of a sintering apparatus that includes the control device involved in this embodiment.

[0051] Figure 2 This is a schematic diagram illustrating an example of the configuration of a control device.

[0052] Figure 3 This indicates that the NO involved in this implementation method X A flowchart illustrating a concentration prediction method and an example of a method for manufacturing sinter using this prediction method.

[0053] Figure 4 This is a diagram illustrating an example of the loading result when granulated particles with a specified particle size distribution are loaded into a tray under specified loading conditions.

[0054] Figure 5 This is a diagram showing an example of the estimation results of the sintering temperature in the raw material loading layer and the exhaust gas volume directly below the raw material loading layer by the sintering result estimation department.

[0055] Figure 6 This indicates the NO in sintering exhaust gas. X A graph showing the time shift between the actual increase / decrease in concentration and the predicted increase / decrease.

[0056] Figure 7 It means Figure 6 The NO shown X A scatter plot showing the correlation between the actual increase or decrease in concentration and the predicted increase or decrease. Detailed Implementation

[0057] The present invention will now be described through embodiments thereof. These embodiments represent preferred examples of the invention and are not intended to limit the scope of the invention in any way.

[0058] Figure 1 This is a schematic diagram illustrating a configuration example of a sintering apparatus 10 including the control device 12 according to this embodiment. The control device 12 is capable of implementing the NO according to this embodiment. X A device for concentration prediction methods.

[0059] The sintering equipment 10 is an apparatus capable of producing sintered ore from sintering raw materials containing iron-containing and carbon-containing raw materials. The sintering equipment 10 includes a control device 12, a granulator 14, a sintering machine 16, a crusher 18, a cooler 20, and a screening device 22. The granulator 14 granulates the sintering raw materials containing iron-containing and carbon-containing raw materials into granulated particles. Granulation water is added to the sintering raw materials during granulation by the granulator 14. The sintering raw materials may further contain calcium oxide (CaO) as a by-product.

[0060] The granulated particles obtained by granulation by granulator 14 are conveyed to sintering machine 16. Granulator 14 is any granulator capable of producing granulated particles. Granulator 14 is, for example, a drum mixer.

[0061] The sintering machine 16 can be any sintering machine capable of sintering granulated particles. For example, the sintering machine 16 is a belt sintering machine. The sintering machine 16 has a sintering raw material supply device 24, a tray 26, an ignition furnace 28, and a bellows 29. The sintering raw material supply device 24 loads the granulated particles supplied by the granulator 14 into the tray 26.

[0062] Tray 26 is an endlessly movable tray. Granulated particles are fed into tray 26 from sintering raw material supply device 24, forming a raw material loading layer. Ignition furnace 28 ignites the carbon-containing raw materials contained in the surface layer of the raw material loading layer formed on tray 26.

[0063] The bellows 29 draws air from the raw material loading layer formed on the tray 26 downwards. As the air from the raw material loading layer is drawn downwards by the bellows 29, the combustion and molten material within the raw material loading layer move downwards. Thus, by the downward movement of the combustion and molten material within the raw material loading layer, the raw material loading layer is sintered. Through sintering of the raw material loading layer, a sintered cake is obtained from it.

[0064] The crusher 18 crushes the sinter cake supplied from the sintering machine 16. The crusher 18 feeds the crushed sinter cake to the cooler 20. The cooler 20 cools the crushed sinter cake supplied from the crusher 18. The cooled sinter cake crushed material is then fed to the screening device 22.

[0065] The screening device 22 screens the crushed material from the sinter cake cooled by the cooler 20 according to the particle size of the crushed material. The screening device 22 separates the crushed material from the sinter cake into, for example, sinter with a particle size of 5 mm or more and return ore with a particle size of less than 5 mm. Thus, sinter is finally produced through screening by the screening device 22. The return ore screened by the screening device 22 can be incorporated into the sintering raw materials and reused as raw material for sinter.

[0066] The control device 12 acquires information about the raw materials contained in the sintering raw materials supplied to the granulator 14, namely, raw material information. The control device 12 acquires granulation conditions including information about the moisture content of the sintering raw materials when the granulator 14 granulates the sintering raw materials into granulated particles.

[0067] Based on the acquired raw material information and granulation conditions, the control device 12 estimates the granulation result of the granulated particles granulated by the granulator 14. The granulation result estimated by the control device 12 includes the particle size of the granulated particles and the composition of each particle size. The control device 12 acquires the loading conditions when the sintering raw material supply device 24 loads the granulated particles into the tray 26 to form the raw material loading layer.

[0068] Based on the estimated granulation results and the acquired loading conditions, control device 12 estimates the particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer formed on tray 26. Based on the estimated particle size segregation and compositional segregation, control device 12 estimates the sintering temperature within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer. Alternatively, control device 12 may estimate the sintering temperature and exhaust gas flow rate within the raw material loading layer based on either particle size segregation or compositional segregation.

[0069] Control device 12 uses the estimated sintering temperature and exhaust gas flow rate within the raw material loading layer to predict the NO content in the sintering exhaust gas discharged from sintering machine 16. X Concentration. Therefore, the control device 12 can predict NO in the sintering exhaust gas by taking into account raw material segregation within the raw material loading layer. X A device for determining the concentration of NO. Furthermore, control device 12 determines the concentration of NO in the predicted sintering exhaust gas. X Sintered ore manufacturing conditions are set to meet predetermined target concentrations, and these manufacturing conditions are established in each unit of the sintering equipment 10. This allows for the simultaneous satisfaction of NO concentration in the sintering exhaust gas. X The target concentration is used to produce sintered ore.

[0070] Next, the control device 12 will be described. Figure 2 This is a schematic diagram showing an example of the structure of the control device 12. The control device 12 can be a general-purpose computer such as a workstation or personal computer, or it can be a dedicated computer configured to perform the functions of the control device 12 of the sintering equipment 10. The control device 12 includes a control unit 30, an input unit 32, an output unit 34, a storage unit 36, and a communication unit 38.

[0071] The control unit 30 reads programs, data, etc., stored in the storage unit 36 ​​and executes various functions. The control unit 30 controls the granulator 14, sintering machine 16, crusher 18, cooler 20, and screening device 22. By executing programs read from the storage unit 36, the control unit 30 functions as a raw material information acquisition unit 40, a granulation condition acquisition unit 42, a granulation result estimation unit 44, a loading condition acquisition unit 46, a loading result estimation unit 48, a sintering result estimation unit 50, and a NO... X The concentration prediction unit 52 and the manufacturing condition determination unit 54 perform their functions.

[0072] The input unit 32 includes one or more input interfaces for detecting user input and acquiring input information based on user operations. The input unit 32 may include, for example, physical keys, electrostatic capacitive keys, or a display. The output unit 34 includes one or more output interfaces for outputting information to notify the user. The output unit 34 may include, for example, a display that outputs image information, or a speaker that outputs sound information.

[0073] Storage unit 36 ​​may be, for example, flash memory, hard disk, optical storage, etc. A portion of storage unit 36 ​​may be external to control device 12. In this case, a portion of storage unit 36 ​​may be a hard disk, memory card, etc., connected to control device 12 via any interface. Storage unit 36 ​​stores programs used by control unit 30 to perform various functions, data used by those programs, etc.

[0074] The communication unit 38 includes at least one of a communication module for wired communication and a communication module for wireless communication. The control device 12 can communicate with other terminal devices, etc., via the communication unit 38.

[0075] Next, the raw material information acquisition unit 40, the granulation condition acquisition unit 42, the granulation result estimation unit 44, the loading condition acquisition unit 46, the loading result estimation unit 48, the firing result estimation unit 50, and NO are analyzed. X The processing performed by the concentration prediction unit 52 will be explained.

[0076] The raw material information acquisition unit 40 acquires information about the raw materials contained in the sintering raw material supplied to the granulator 14, namely, raw material information. The raw material information includes information about the particle size, composition, and proportion of each raw material contained in the sintering raw material. For example, when the sintering raw material contains iron-containing raw material, CaO-containing raw material, and carbon-containing raw material, the raw material information includes information about the particle size, composition, and proportion of each of the iron-containing raw material, CaO-containing raw material, and carbon-containing raw material.

[0077] Iron-containing raw materials include, for example, iron ore. CaO-containing raw materials include, for example, limestone. Carbon-containing raw materials include, for example, pulverized coke. The particle size information for each raw material includes information on the proportion of each predetermined particle size distribution. The composition information for each raw material includes, for example, information on at least one of the following: carbon (C) concentration, moisture concentration, calcium oxide (CaO) concentration, and alumina (Al2O3) concentration.

[0078] The raw material information acquisition unit 40 can acquire raw material information through input operations performed by the operator on the input unit 32. The operator can obtain raw material information in advance by performing sieving, chemical analysis, etc. on each raw material.

[0079] The raw material information acquisition unit 40 can also acquire raw material information by receiving raw material information input by the operator to other terminal devices via the communication unit 38. The raw material information acquisition unit 40 outputs the acquired raw material information to the granulation condition acquisition unit 42.

[0080] The granulation condition acquisition unit 42 acquires granulation conditions including information on the moisture content of the sintering raw material when the granulator 14 granulates the raw material into granules. The granulation condition acquisition unit 42 can acquire the granulation conditions from the granulator 14 or through input operations by the operator to the input unit 32.

[0081] The moisture content of the sintering raw materials during granulation by the granulator 14 is determined by the moisture content of each raw material in the sintering raw materials and the amount of granulation water added to the granulator 14. The moisture content of each raw material can be measured, for example, using an infrared moisture meter.

[0082] The granulation conditions acquired by the granulation condition acquisition unit 42 only need to include information on the moisture content of the sintering raw material when the granulator 14 granulates the raw material. Alternatively, it may include information on other conditions set by the granulator 14 during the granulation process. For example, the granulation conditions may further include information on the granulator 14's fill power, rotational speed, and residence time. The granulation condition acquisition unit 42 outputs the granulation conditions and the raw material information acquired from the raw material information acquisition unit 40 to the granulation result estimation unit 44.

[0083] The granulation result estimation unit 44 estimates the granulation result of the granulated particles granulated by the granulator 14 based on the raw material information and granulation conditions obtained from the granulation condition acquisition unit 42. The granulation result estimated by the control device 12 includes the particle size of the granulated particles and the composition of each particle size.

[0084] When estimating granulation results, the granulation result estimation unit 44 uses a granulation estimation model that includes a particle size estimation model and a composition estimation model. The particle size estimation model and the composition estimation model can be stored in the storage unit 36. The particle size estimation model is a machine learning model that, when inputting the proportions of the raw materials in the sintering raw materials and the moisture content of the sintering raw materials during granulation, outputs the content of granulated particles in one of multiple particle size partitions, representing a learned particle size distribution.

[0085] The granulation result estimation unit 44 estimates the content of granulated particles in a particle size partition by inputting the mixing ratio of each raw material and granulation conditions, including the moisture content of the sintering raw material during granulation, into a particle size estimation model read from the storage unit 36. The content of granulated particles in a particle size partition is an example of the particle size of the granulated particles. The particle size of the granulated particles can also be expressed by other indicators.

[0086] The particle size partition can be, for example, three levels. When the particle size partition has three levels, it can be divided into greater than 8 mm, 2.8 mm to 8.0 mm, and less than 2.8 mm. The particle size partition is not limited to three levels; it can be divided into more particle size partitions. The granulation result estimation unit 44 repeats the same process for other particle size partitions to estimate the content of granulated particles in each particle size partition. By estimating the content of granulated particles in all particle size partitions, the granulation result estimation unit 44 can determine the particle size distribution of the granulated particles.

[0087] The composition estimation model is a machine learning model that, when given the proportions of the raw materials in the sintering raw materials and the moisture content of the raw materials during granulation, outputs the content of a specific component in one of the particle size partitions of the granulated particles, which is divided into multiple particle size partitions.

[0088] The granulation result estimation unit 44 estimates the specific component content of granulated particles in a particle size partition by inputting the mixing ratio of each raw material and the granulation conditions, including the moisture content of the sintering raw material during granulation, into a component estimation model read from the storage unit 36. The granulation result estimation unit 44 repeats the same process for other particle size partitions to estimate the specific component content of granulated particles in each partition. By estimating the specific component content of granulated particles in all particle size partitions, the granulation result estimation unit 44 can determine the specific component content of the granulated particles. The granulation result estimation unit 44 outputs the estimated content of granulated particles in each particle size partition and the specific component content of granulated particles in each particle size partition to the loading result estimation unit 48.

[0089] The particle size estimation model and the composition estimation model are pre-generated using machine learning on a large dataset consisting of a set of actual input values ​​and actual output values, and stored in storage unit 36. The inputs to the particle size estimation model and the composition estimation model may include the particle size of other raw materials. The inputs to the particle size estimation model and the composition estimation model may also include granulation conditions such as the rotational speed and residence time of the granulator 14.

[0090] The above description illustrates the use of machine learning models as granularity estimation models and component estimation models. However, granularity estimation models and component estimation models can also be multiple regression models. In this case, the inputs of the machine learning model become the explanatory variables of the multiple regression model, and the outputs of the machine learning model become the target variables of the multiple regression model. In the multiple regression model, a large dataset consisting of actual input values ​​and actual output values ​​is also used to pre-calculate the parameters of the multiple regression model, which can be stored in the storage unit 36.

[0091] When the granularity estimation model and the component estimation model are machine learning models, they can be updated using subsequently acquired datasets through machine learning. When the granularity estimation model and the component estimation model are multiple regression models, the parameters of the multiple regression model can be updated using subsequently acquired datasets.

[0092] The loading condition acquisition unit 46 acquires the loading conditions when the sintering raw material supply device 24 loads granulated particles into the tray 26 to form the raw material loading layer. The loading condition acquisition unit 46 can acquire the loading conditions from the sintering machine 16 or through input operations by the operator on the input unit 32.

[0093] The loading conditions acquired by the loading condition acquisition unit 46 include, for example, the speed of the tray 26, the opening of the secondary channel of the sintering raw material supply device 24, and the angle of the chute of the sintering raw material supply device 24. The loading condition acquisition unit 46 outputs the acquired loading conditions to the loading result estimation unit 48.

[0094] The storage unit 36 ​​stores a table showing the proportion of granulated particles of each size in each of the multiple loading zones that divide the raw material loading layer in the height direction, based on the particle size distribution and loading conditions of each granulated particle. If the loading result estimation unit 48 obtains the granulation result of the granulated particles from the granulation result estimation unit 44 and the loading conditions from the loading condition acquisition unit 46, it reads the aforementioned table corresponding to the particle size distribution and loading conditions from the storage unit 36. Here, the granulation result estimated by the granulation result estimation unit 44 refers to the content of granulated particles in each particle size zone and the content of specific components of granulated particles in each particle size zone.

[0095] The proportion of granulated particles of each size in each of the multiple loading zones in the height direction of the raw material loading layer can be calculated in advance for each loading zone by performing a simulation of the sintering raw material supply device 24 of the sintering machine 16 using the Discrete Element Method (DEM). Therefore, by performing the above simulation for the particle size distribution and loading conditions of each granulated particle, a table representing the proportion of granulated particles of each size in each loading zone can be obtained in advance. Thus, the proportion of granulated particles of each size can be estimated for each loading zone under the particle size distribution and loading conditions. The table representing the proportion of granulated particles of each size in each loading zone can be obtained in advance and stored in the storage unit 36.

[0096] The loading result estimation unit 48 estimates the particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer formed on the tray 26 based on the granulation result obtained from the granulation result estimation unit 44 and the loading conditions obtained from the loading condition acquisition unit 46. The loading result estimation unit 48 estimates the particle size segregation of the granulated particles in the height direction of the raw material loading layer using a table showing the content ratio of granulated particles of each particle size in each loading zone.

[0097] The composition of each particle size of the granulated particles is estimated by the granulation result estimation unit 44. Therefore, the loading result estimation unit 48 can also estimate the compositional segregation of the granulated particles in the height direction of the raw material loading layer using the particle size segregation of the granulated particles in the height direction and the composition of each particle size in each loading zone.

[0098] A table showing the proportion of granulated particles of each particle size in each loading partition is an example of a loading estimation model. As a loading estimation model, a machine learning model or a multiple regression model can also be used, which outputs the proportion of granulated particles of each particle size in each loading partition when the content, composition, and loading conditions of granulated particles in each particle size partition are input.

[0099] The loading result estimation unit 48 outputs the estimated particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer to the firing result estimation unit 50. Based on the particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer obtained from the loading result estimation unit 48, the firing result estimation unit 50 estimates the sintering temperature within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer. Alternatively, the firing result estimation unit 50 may estimate the sintering temperature and exhaust gas flow rate within the raw material loading layer based on either particle size segregation or compositional segregation.

[0100] The firing result estimation unit 50 uses, for example, the heat transfer model disclosed in Reference 1 shown below to estimate the sintering temperature and exhaust gas flow rate within the raw material loading layer. The exhaust gas flow rate (amount of gas molecules) directly below the raw material loading layer is estimated. The exhaust gas flow rate can be calculated from the pressure loss of the fluid in each control volume disclosed in Reference 1.

[0101] The information input into the heat transfer model can be the thickness of the raw material loading layer, the velocity of the tray 26, the temperature inside the furnace 28, the component concentration inside the raw material loading layer, the porosity inside the raw material loading layer, and the exhaust gas flow rate. The thickness of the raw material loading layer, the velocity of the tray 26, and the temperature inside the furnace 28 can use the set values ​​of the sintering machine 16. The component concentration and porosity inside the raw material loading layer are calculated using values ​​estimated by the loading result estimation unit 48 based on particle size segregation and component segregation. The exhaust gas flow rate can use the value measured in the sintering machine 16. The temperature inside the furnace 28 is determined by the gas flow rate inside the furnace 28; therefore, the gas flow rate inside the furnace 28 can also be input into the heat transfer model instead of the temperature.

[0102] Reference 1: Koichiro Ohno et al., Influence of coke combustion rate on the estimated temperature distribution within the sintering layer in numerical simulation, Iron and Steel, Vol. 101, 2015, No. 1, pp. 19-24

[0103] The firing result estimation unit 50, using the aforementioned heat transfer model, can calculate the sintering temperature at a predetermined location within the raw material loading layer and the exhaust gas temperature directly below the raw material loading layer according to a set time scale. The time scale can be, for example, 1 second. The firing result estimation unit 50 outputs the estimated sintering temperature and exhaust gas volume to the NO... XConcentration Prediction Section 52. NO X The concentration prediction unit 52 uses the sintering temperature estimated by the sintering result estimation unit 50 and the exhaust gas flow rate directly below the raw material loading layer to predict the NO concentration in the sintering exhaust gas. X concentration.

[0104] NO X Concentration prediction unit 52 uses, for example, the following formula (1) to predict NO in sintering exhaust gas. X Concentration. The following formula (1), which includes constants A and B, can be pre-stored in the storage unit 36 ​​by the operator's input operation on the input unit 32.

[0105] NO X =(A×(T-273)+B)×M×N / (100×14×U)···(1)

[0106] In the above formula (1), NO X NO in sintering waste gas X Concentration (ppm). T is the highest sintering temperature (K) within the raw material loading layer. M is the amount of carbon-containing raw material used per unit time in the sintering raw material (ton / h). N is the nitrogen content (mass %) of the carbon-containing raw material in the sintering raw material. U is the exhaust gas flow rate (moles / h) directly below the raw material loading layer. A (K) -1 ), B (-) are constants.

[0107] Constant A is preferably a constant of 0.033 to 0.036, and constant B is preferably a constant of 52 to 54. As N, the nitrogen content in the carbon-containing raw material of the sintering raw material can be predetermined and the determined value can be used. Generally, the nitrogen content in the carbon-containing raw material is in the range of 0.5% to 2.5% by mass. Therefore, any value within this range can be used instead of the aforementioned determined value.

[0108] When using the above formula (1) to predict NO X At concentrations, NO X The concentration prediction unit 52 uses the estimated sintering temperature and exhaust gas volume within the raw material loading layer to determine the highest sintering temperature and the exhaust gas volume directly below the raw material loading layer. X The concentration prediction unit 52 uses the carbon content of the sintering raw material obtained by the raw material information acquisition unit 40 to calculate the amount of carbon-containing raw material used per unit time. Thus, since the highest sintering temperature in the raw material loading layer, the exhaust gas flow rate directly below the raw material loading layer, and the amount of carbon-containing raw material used per unit time can be determined in the above formula (1), NO... X Concentration prediction unit 52 can use the above formula (1) to predict NO in sintering exhaust gas X concentration.

[0109] Instead of the above equation (1), NO X The concentration prediction unit 52 can also use a learned machine learning model or a multiple regression model to predict NO in sintering exhaust gas. X Concentration. At this point, the completed machine learning model or multiple regression model takes the highest sintering temperature in the raw material loading layer, the exhaust gas flow rate directly below the raw material loading layer, and the amount of carbon-containing raw material used per unit time as inputs, and the NO concentration in the sintering exhaust gas as input. X The model outputs concentration.

[0110] Thus, in the control device 12, the segregation of raw materials within the raw material loading layer is used to estimate the NO content in the sintering exhaust gas. X The concentration of the raw material is controlled by the sintering temperature inside the loading layer and the exhaust gas flow rate directly below the loading layer. Therefore, the control device 12 and the NO emission control implemented in this embodiment are also considered. X Concentration prediction methods have emerged to predict NO in sintering exhaust gas by considering raw material segregation within the raw material loading layer. X Concentration control device 12 and NO X Concentration prediction method. Furthermore, NO in sintering exhaust gas is predicted by considering raw material segregation within the raw material loading layer. X The concentration can accurately predict NO concentration in sintering exhaust gas. X concentration.

[0111] Next, the processing performed by the manufacturing condition determination unit 54 will be explained. The manufacturing condition determination unit 54 determines the NO... X NO in sintering exhaust gas predicted by concentration prediction unit 52 X The concentration of NO in the sintering exhaust gas meets the predetermined target. X Manufacturing conditions for achieving the target concentration value. The manufacturing conditions determination unit 54 sets the determined manufacturing conditions to the manufacturing conditions of each device constituting the sintering equipment 10, and manufactures sintered ore under these manufacturing conditions. This allows for the reduction of NO in the sintering exhaust gas. X The sinter is produced while the concentration meets the target value.

[0112] Figure 3 This indicates that the NO involved in this implementation method X A flowchart illustrating a concentration prediction method and an example of a method for manufacturing sinter using this prediction method. Figure 3 The process performed by the manufacturing conditions determination unit 54 is described in detail. Figure 3 The process shown is initiated, for example, by receiving a start instruction from the operator via the input unit 32.

[0113] The raw material information acquisition unit 40 acquires information about the raw materials contained in the sintering raw materials supplied to the granulator 14, i.e., raw material information (step S101). This step S101 is the raw material information acquisition step. The raw material information acquisition unit 40 outputs the acquired raw material information to the granulation condition acquisition unit 42.

[0114] The granulation condition acquisition unit 42 acquires granulation conditions (step S102) including information on the moisture content of the sintering raw material when the granulator 14 granulates the raw material into particles. This process in step S102 is the granulation condition acquisition step. The granulation condition acquisition unit 42 outputs the raw material information and granulation conditions to the granulation result estimation unit 44.

[0115] Based on the acquired raw material information and granulation conditions, the granulation result estimation unit 44 estimates the granulation result of the granulated particles granulated by the granulator 14 (step S103). The granulation result estimated by the granulation result estimation unit 44 includes the particle size of the granulated particles and the composition of each particle size. This step S103 is the granulation result estimation step. The granulation result estimation unit 44 outputs the estimated granulation result of the granulated particles to the loading result estimation unit 48.

[0116] The loading condition acquisition unit 46 acquires the loading conditions when the sintering raw material supply device 24 loads granulated particles into the tray 26 to form a raw material loading layer (step S104). This step S104 is the loading condition acquisition step. The loading condition acquisition unit 46 outputs the acquired loading conditions to the loading result estimation unit 48.

[0117] The loading result estimation unit 48 uses the granulation results of the granulated particles obtained from the granulation result estimation unit 44 and the loading conditions obtained from the loading condition acquisition unit 46 to estimate the particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer formed on the tray 26 (step S105). This step S105 is the loading result estimation step. The loading result estimation unit 48 outputs the particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer to the firing result estimation unit 50.

[0118] The sintering result estimation unit 50 uses at least one of particle size segregation and composition segregation of the granulated particles in the height direction of the raw material loading layer to estimate the sintering temperature and exhaust gas flow rate within the raw material loading layer (step S106), and uses them to determine the maximum sintering temperature and the exhaust gas flow rate directly below the raw material loading layer. This step S106 is the sintering result estimation step. The sintering result estimation unit 50 outputs the determined maximum sintering temperature and the exhaust gas flow rate directly below the raw material loading layer to the NO. X Concentration prediction section 52.

[0119] NO XThe concentration prediction unit 52 uses the highest sintering temperature determined by the sintering result estimation unit 50 and the exhaust gas flow rate directly below the raw material loading layer to predict the NO concentration in the sintering exhaust gas. X Concentration (step S107). Step S107 addresses the treatment of NO in sintering waste gas. X Concentration prediction step. Thus, the NO involved in this embodiment... X The concentration prediction method consists of the following steps: raw material information acquisition, granulation condition acquisition, granulation result estimation, loading condition acquisition, loading result estimation, calcination result estimation, and NO concentration prediction. X Concentration prediction steps.

[0120] NO X The concentration prediction unit 52 will predict the NO concentration in the sintering waste gas. X The concentration is output to the manufacturing condition determination unit 54. The manufacturing condition determination unit 54 determines the concentration of NO. X The concentration prediction unit 52 predicts the NO in sintering waste gas X Does the concentration meet the predetermined target value (step S108)? NO in sintering exhaust gas X The target concentration value is pre-stored by the operator in the storage unit 36 ​​via the input unit 32.

[0121] Manufacturing condition determination unit 54 reads NO from storage unit 36 X The target concentration will be determined by NO. X The concentration prediction unit 52 predicts the NO in sintering waste gas X Concentration and NO X Comparison of target concentration values. When the predicted NO concentration in the sintering exhaust gas... X Concentration of NO X When the concentration is below the target value, the manufacturing conditions determination unit 54 determines the predicted NO concentration in the sintering exhaust gas. X The concentration meets the target value (step S108: Yes). On the other hand, when the predicted NO concentration in the sintering exhaust gas... X Concentration higher than NO X When the concentration target value is reached, the manufacturing condition determination unit 54 determines the predicted NO concentration in the sintering exhaust gas. X The concentration does not meet the target value (step S108: No).

[0122] When judging the NO in sintering exhaust gas X If the predicted concentration does not meet the target value (step S108: No), the manufacturing condition determination unit 54 changes the method used to predict NO in sintering exhaust gas. XConcentration of sintered ore manufacturing conditions (step S109). Manufacturing condition determination unit 54, for example, changes the sintering result estimation unit 50 to estimate the sintering temperature in the raw material loading layer and the raw material loading layer thickness, the speed of tray 26, the temperature in ignition furnace 28, or the gas flow rate of ignition furnace 28.

[0123] The manufacturing condition determination unit 54 returns the process to step S106, and performs steps S106 to S108 again under the changed manufacturing conditions. This process of steps S106 to S109 is repeated until the predicted NO content in the sintering exhaust gas is determined in step S108. X The concentration continues until the target value is met. Therefore, the predicted NO concentration in the sintering exhaust gas can be determined. X Manufacturing conditions (firing conditions) that meet the concentration target value. A unit for changing firing conditions, pre-determined for each firing condition item to be changed.

[0124] For the manufacturing condition determination unit 54, instead of the aforementioned manufacturing conditions, the loading conditions obtained by the loading condition acquisition unit 46 can be changed. In this case, the manufacturing condition determination unit 54 executes steps S105 to S108 again under the changed loading conditions. Figure 3 (The dashed arrow). Steps S105 to S109 are repeated until step S108 determines that the predicted NO in the sintering waste gas... X The concentration continues until the target value is met. Therefore, the predicted NO concentration in the sintering exhaust gas can be determined. X The concentration is determined to meet the manufacturing conditions (loading conditions) that meet the target value. A unit of loading condition change is made, and each loading condition item to be changed is predetermined.

[0125] For the manufacturing condition determination unit 54, instead of the aforementioned manufacturing conditions, the moisture content of the sintering raw material obtained by the granulation condition acquisition unit 42 can be changed. When the granulation conditions obtained by the granulation condition acquisition unit 42 include granulation conditions such as the rotational speed and residence time of the granulator 14, these granulation conditions can be changed during the processing in step S109. At this time, the manufacturing condition determination unit 54 performs the processing in steps S103 to S108 again under the changed granulation conditions. Figure 3 (The dotted arrow). Repeat steps S103 to S109 until step S108 determines that the predicted NO in the sintering waste gas... X The concentration continues until the target value is met. Therefore, the predicted NO concentration in the sintering exhaust gas can be determined. X The concentration was determined to meet the target value under the manufacturing conditions (granulation conditions). A unit of granulation condition was changed, with each granulation condition item to be changed pre-determined. When NO in the sintering exhaust gas was within all combinations of manufacturing conditions... XIf the concentration is not determined to meet the target value, the situation or error can be displayed on the output unit 34, and the process can be terminated.

[0126] On the other hand, when judging the NO in sintering exhaust gas X When the predicted concentration meets the target value (step S108: Yes), the manufacturing condition determination unit 54 will use NO... X The manufacturing conditions for concentration prediction were determined to meet the NO requirements. X Manufacturing conditions for the concentration target value (step S110). The process of step S110 is the manufacturing condition determination step. The manufacturing condition determination unit 54 sets the determined manufacturing conditions as the manufacturing conditions for each device of the sintering equipment 10. Then, sintered ore is manufactured in the sintering equipment 10 with the manufacturing conditions set (step S111). The process of step S111 is the sintered ore manufacturing step.

[0127] In this way, by changing various manufacturing conditions, the NO in the sintering exhaust gas can be predicted. X The concentration can determine the NO concentration in the sintering exhaust gas. X Manufacturing conditions that meet the target concentration. Then, by manufacturing sinter in sintering equipment 10 reflecting the determined manufacturing conditions, it is possible to meet the NO concentration requirements in the sintering exhaust gas. X The target concentration is used to produce sintered ore.

[0128] Next, the experimental results confirming the granulation results are presented. Table 1 below shows the particle size distribution and actual carbon concentration of the granulated particles after granulation under various sintering raw material and moisture content conditions. In the example shown in Table 1, pulverized coke was used as the carbon-containing raw material.

[0129]

[0130] In the granulation experiment, sintering raw materials were granulated under 24 granulation conditions from T1 to T24 to obtain granulated particles. Table 1 shows the mixing ratio, coke particle size, and moisture content as granulation conditions from T1 to T24. The actual values ​​show the particle size distribution and carbon concentration of each particle size.

[0131] In the blending ratios shown in Table 1, raw material A is iron ore from South America. Raw material B is iron ore from South America of a different grade than raw material A. Raw material C is iron ore from Australia. Regarding the particle size of pulverized coke, "-2mm" indicates the proportion of pulverized coke with a particle size less than 2mm. "-1mm" indicates the proportion of pulverized coke with a particle size less than 1mm. Pulverized coke with a particle size less than 2mm refers to pulverized coke that passes through a sieve with a 2mm aperture, and pulverized coke with a particle size less than 1mm refers to pulverized coke that passes through a sieve with a 1mm aperture.

[0132] Regarding the particle size distribution of granulated particles, "+8.0" indicates the proportion of granulated particles with a diameter greater than 8.0 mm. "2.8-8.0" indicates the proportion of granulated particles with a diameter between 2.8 mm and 8.0 mm. "-2.8" indicates the proportion of granulated particles with a diameter less than 2.8 mm.

[0133] Granulated particles with a diameter greater than 8.0 mm are sieved through an 8.0 mm sieve. Granulated particles with a diameter between 2.8 mm and 8.0 mm are sieved through an 8.0 mm sieve to the undersize and then sieved through a 2.8 mm sieve to the oversize. Granulated particles with a diameter less than 2.8 mm are sieved through a 2.8 mm sieve to the undersize.

[0134] Regarding the carbon concentration for each particle size, "+8.0" indicates the carbon concentration of granulated particles with a diameter greater than 8.0 mm. "2.8-8.0" indicates the carbon concentration of granulated particles with a diameter between 2.8 mm and 8.0 mm. "-2.8" indicates the carbon concentration of granulated particles with a diameter less than 2.8 mm.

[0135] The particle size estimation model and the composition estimation model were generated using the actual values ​​shown in Table 1, and were used to estimate the granulation particle content and carbon concentration in each particle size partition. The estimations were confirmed using both machine learning models and multiple regression models.

[0136] In the case of using machine learning models for estimation, particle size estimation and composition estimation models were used. The particle size estimation model, given the input particle size of pulverized coke, the proportion of sintering raw materials, and the moisture content during granulation, outputs the particle content of granulated particles in each particle size region. The composition estimation model, given the input particle size of pulverized coke, the proportion of sintering raw materials, and the moisture content during granulation, outputs the carbon concentration of granulated particles at each particle size.

[0137] On the other hand, when using a multiple regression model for estimation, the particle size estimation model uses the particle size of pulverized coke, the proportion of sintering raw materials, and the moisture content during granulation as explanatory variables, and the granulated particle content of each particle size zone as the target variable. The composition estimation model uses the particle size of pulverized coke, the proportion of sintering raw materials, and the moisture content during granulation as explanatory variables, and the carbon concentration of each particle size as the target variable.

[0138] The correlation coefficients between the estimated and actual values ​​when using machine learning models and multiple regression models are shown in Table 2 below. For the case of using machine learning models, the correlation coefficients when using neural networks and when using C&R Trees (Classification and Regression Trees) are shown.

[0139]

[0140] As shown in Table 2, high correlation coefficients were obtained regardless of the model used. This result confirms that by using a machine learning model or a multiple regression model that has been trained, the particle size of granulated particles and the concentration of specific components at each particle size can be estimated with high accuracy.

[0141] Next, the assumptions about the loading results will be explained. Figure 4 This is a graph illustrating an example of the loading result when granulated particles with a specified particle size distribution are loaded into tray 26 under specified loading conditions. For example... Figure 4 As shown, in this embodiment, for each section in the height direction of the raw material loading layer, the estimated content ratio of granulated particles with particle sizes of +8.0 mm and -2.8 mm and the carbon content ratio are determined.

[0142] Figure 4 The proportions of granulated particles of each size in each partition along the height direction of the raw material loading layer, as shown in (a) and (b), can be determined through DEM-based simulations of the particle size distribution and loading conditions for each granulated particle. Based on the carbon concentration of granulated particles of each size estimated by the composition estimation model, and the proportions of particles of each size in each partition along the height direction of the raw material loading layer, the following can also be calculated: Figure 4 (c) shows the carbon content of each zone in the height direction of the raw material loading layer.

[0143] Thus, by performing DEM-based simulations, the proportion of granulated particles of each size along the height direction of the raw material loading layer can be determined. Therefore, if DEM simulations are performed in advance for the particle size distribution and loading conditions of each granulated particle, and a table showing the proportion of granulated particles of each size along the height direction of the raw material loading layer is created, the loading results can be predicted using this table.

[0144] Next, the estimation of the firing results will be explained. Figure 5 This is a graph representing an example of the estimated sintering temperature within the raw material loading layer and the exhaust gas volume directly below the raw material loading layer, as shown in the sintering result estimation section 50. (Example:) Figure 5As shown, in the heat transfer model of this embodiment, the raw material loading layer is divided into multiple micro-units. In each unit region, the temperature distribution within the loading layer and the exhaust gas flow rate distribution directly below the loading layer are calculated using a firing estimation model that includes the heat transfer model. The exhaust gas flow rate directly below the loading layer is calculated using the pressure loss within each control volume disclosed in Reference 1. Assuming the coke's occurrence state in the quasi-particles, S'-type and P-type are assumed for calculation. Therefore, the exhaust gas flow rate and temperature within the loading layer at each position along the machine length direction can be estimated. By using... Figure 4 The carbon content distribution shown can be calculated by setting the carbon content for each micro-unit, taking into account the temperature distribution within the loading layer and the exhaust gas volume directly below the loading layer, considering the segregation within the loading layer.

[0145] Example

[0146] Next, the NO content in the sintering exhaust gas during the actual sintering process of producing sintered ore using raw material ratios of 1 to 3 was confirmed. X The actual value of the concentration and the NO in the sintering waste gas X Examples illustrating the correlation coefficient R between predicted concentration values ​​are provided below. The formulation and amount of sintering raw materials used in the examples are shown in Table 3 below.

[0147]

[0148] The raw materials shown in Table 3, combined with sintering raw materials 1 to 3, are granulated using granulator 14 and loaded onto tray 26 of sintering machine 16 to form a raw material loading layer. The sinter is then fired in sintering machine 16 to produce sintered ore. For the production of this sintered ore, the temperature within the raw material loading layer and the exhaust gas flow rate directly below the loading layer are estimated using granulation result estimation unit 44, loading result estimation unit 48, and firing result estimation unit 50. Furthermore, the estimated raw material loading layer temperature and exhaust gas flow rate directly below the loading layer are used to predict the NO content in the sintering exhaust gas. X Concentration of NO in sintering exhaust gas X The concentration was predicted using the following formula (1).

[0149] NO X =(A×(T-273)+B)×M×N / (100×14×U)···(1)

[0150] In the above formula (1), NO X NO in sintering waste gas X Concentration (ppm). T is the highest sintering temperature (K) within the raw material loading layer. M is the amount of carbon-containing raw material used per unit time in the sintering raw material (ton / h). N is the nitrogen content (mass %) of the carbon-containing raw material in the sintering raw material. U is the exhaust gas flow rate (moles / h) directly below the raw material loading layer. A (K)-1 ), B (-) are constants.

[0151] In this embodiment, T uses the highest estimated temperature within the raw material loading layer. M uses the amount of carbon-containing raw material used per unit time, calculated from the carbon content of the sintering raw material. N uses 1.75. U uses the total estimated exhaust gas flow rate directly below the loading layer in each wind box. A uses a constant of -0.020 to -0.036, and B uses a constant of 51 to 55. The NO in the sintering exhaust gas... X The correlation coefficient R between the actual and estimated concentrations, as well as the difference between the average actual and estimated values, are shown in Tables 4 and 5 below.

[0152]

[0153]

[0154] As shown in Tables 4 and 5, by setting A to -0.036 to -0.020 and B to 51 to 55, regardless of which of the raw material combinations 1 to 3, NO X Estimated concentration and NO X The correlation coefficients of the actual concentration values ​​all reached above 0.70. This result confirms that by using the above formula (1) with A set to -0.036 to -0.020 and B set to 51 to 55, it is possible to predict NO in sintering waste gas with high accuracy. X concentration.

[0155] On the other hand, observe the NO in the sintering exhaust gas X In Invention Examples 1 to 5, where A is set to -0.020, the difference between the actual and predicted concentrations exceeds 100 ppm. Although a smaller A results in lower NO concentrations in the sintering exhaust gas... X The smaller the difference between the actual and predicted concentration values, the better. However, in Invention Examples 6 to 10 where A is set to -0.030, the difference exceeds 35 ppm in most compounding materials.

[0156] In contrast, in Invention Examples 12-14, 17-19, and 22-24, where A is set to -0.036 to -0.033 and B is set to 52 to 54, the NO in the sintering waste gas... X The difference between the actual and predicted concentration values ​​is less than 35 ppm. These results confirm that, when using the above formula (1), A is preferably -0.036 to -0.020, more preferably -0.036 to -0.030, and even more preferably -0.036 to -0.033. B is confirmed to be preferably 51 to 55, more preferably 52 to 54.

[0157] In particular, in the above formula (1), it is preferable to set A to -0.036 to -0.033 and B to 52 to 54. It has been confirmed that by setting A and B within this range, the NO content in the sintering exhaust gas can be reduced. X The correlation coefficient between the actual and predicted concentration values ​​is above 0.7, and the difference between the actual and predicted values ​​can be controlled below 35 ppm.

[0158] Figure 6 This indicates that NO in sintering exhaust gas X A graph showing the time transition between actual and predicted increases / decreases in concentration. Figure 6 In the diagram, the horizontal axis represents elapsed time (h), and the vertical axis represents the NO content in the sintering exhaust gas. X The increase or decrease in concentration (ppm). Figure 6 Example 17 of the invention is shown, in which A is set to -0.035 and B is set to 53, and the NO content in the sintering exhaust gas when using raw material formulation 1 is shown. X The amount of increase or decrease in concentration.

[0159] exist Figure 6 In the middle, the solid line represents NO. X The actual increase or decrease in concentration over time is represented by the dashed line, which indicates NO. X The time shift of the predicted increase or decrease in concentration. For example... Figure 6 As shown, NO in sintering waste gas X The actual increase or decrease in concentration over time tends to be in good agreement with the predicted increase or decrease over time.

[0160] Figure 7 To indicate Figure 6 The NO shown X A graph showing the correlation between actual and predicted increases / decreases in concentration. Figure 7 In the middle, the horizontal axis represents the NO content in the sintering waste gas. X The predicted increase or decrease in concentration, with the vertical axis representing NO in sintering waste gas. X The actual increase or decrease in concentration. For example... Figure 7 As shown, NO in sintering waste gas X The correlation coefficient R between the predicted increase / decrease in concentration and the actual increase / decrease is as high as 0.71. This result confirms that by using the NO involved in this embodiment… X The concentration prediction method can predict NO in sintering waste gas with high accuracy. X concentration.

[0161] Symbol Explanation

[0162] 10 Sintering Equipment

[0163] 12 Control devices

[0164] 14 Granulator

[0165] 16 Sintering Machine

[0166] 18 Crusher

[0167] 20 Cooler

[0168] 22 Screening device

[0169] 24 Sintering raw material supply device

[0170] 26 pallets

[0171] 28 Ignition Furnace

[0172] 29. Bellows

[0173] 30 Control Department

[0174] 32 Input Section

[0175] 34 Output Section

[0176] 36 Storage Department

[0177] 38 Ministry of Communications

[0178] 40 Raw Material Information Acquisition Department

[0179] 42 Granulation Condition Acquisition Department

[0180] 44 Granulation Result Estimation Section

[0181] 46 Loading Condition Acquisition Unit

[0182] 48 Loading result estimation section

[0183] 50 Estimated Firing Results

[0184] 52 NO X Concentration Prediction Department

[0185] 54 Manufacturing Conditions Determination Department

Claims

1. A type of NO X A concentration prediction method is used to predict the NO concentration in sintering exhaust gas discharged from the sintering machine. X The concentration refers to the concentration of the sintering machine, which is a sintering machine used to sinter granulated particles obtained by adding water to sintering raw materials containing iron-containing and carbon-containing raw materials. The NO X Concentration prediction methods include: The raw material information acquisition step involves acquiring raw material information including the particle size, composition, and proportion of each raw material contained in the sintering raw material. The granulation conditions acquisition step involves acquiring granulation conditions that include the moisture content of the sintering raw material when granulating the granulated particles from the sintering raw material. The granulation result estimation step involves inputting the raw material information and granulation conditions into a granulation estimation model to estimate the granulation result, including the particle size and composition of the granulated particles at each particle size. The granulation estimation model includes a particle size estimation model and a composition estimation model. The particle size estimation model takes the particle size of the carbon-containing raw material, the proportions of the raw materials in the sintering raw material, and the moisture content as input, and outputs the content of the granulated particles in one of multiple particle size partitions. The composition estimation model takes the particle size of the carbon-containing raw material, the proportions of the raw materials in the sintering raw material, and the moisture content as input, and outputs the content of a specific component of the granulated particles in one of multiple particle size partitions. The loading condition acquisition step involves acquiring the loading conditions when the granulated particles are loaded into the sintering machine to form a raw material loading layer. The loading result estimation step involves using a loading estimation model, based on the granulation results and loading conditions, to estimate the particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer. The loading estimation model represents the proportion of granulated particles of each particle size in each loading partition when the height direction of the raw material loading layer is divided into multiple loading partitions. The firing result estimation step, based on at least one of the particle size segregation and the composition segregation, uses a firing estimation model to estimate the sintering temperature within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer. The firing estimation model includes a heat transfer model capable of calculating the sintering temperature at a specified location within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer. NO X The concentration prediction step uses the sintering temperature within the raw material loading layer, the exhaust gas flow rate directly below the raw material loading layer, and the amount of carbon-containing raw material used per unit time in the sintering raw material to predict the NO concentration in the sintering exhaust gas. X concentration.

2. The NO according to claim 1 X Concentration prediction methods, among which, In the NO X In the concentration prediction step, the following formula (1) is used to predict the NO in the sintering waste gas. X concentration: NO X =(A×(T-273)+B)×M×N / (100×14×U)···(1) In the above formula (1), NO X NO in the sintering waste gas X Concentration, in ppm; T, the highest sintering temperature in the raw material loading layer, in K; M, the amount of carbon-containing raw material used per unit time in the sintering raw material, in ton / h; N, the nitrogen content of the carbon-containing raw material in the sintering raw material, in mass%; U, the exhaust gas flow rate directly below the raw material loading layer, in mol / h; A(K -1 ), B (-) are constants.

3. A method for manufacturing sintered ore, comprising: The manufacturing conditions determination step determines the NO produced by claim 1 or 2. X NO concentration prediction method for sintering exhaust gas X Manufacturing conditions that ensure the concentration meets the predetermined target value, and The sinter manufacturing step involves manufacturing sinter under the manufacturing conditions determined in the manufacturing condition determination step.

4. A control device for predicting NO in sintering exhaust gas discharged from a sintering machine. X The concentration refers to the concentration of the sintering machine, which is a sintering machine used to sinter granulated particles obtained by adding water to sintering raw materials containing iron-containing and carbon-containing raw materials. The control device has: The raw material information acquisition unit acquires raw material information including the particle size, composition, and proportion of each raw material contained in the sintering raw material. The granulation condition acquisition unit acquires granulation conditions including the moisture content of the sintering raw material when granulating the granulated particles from the sintering raw material. The granulation result estimation unit, by inputting the raw material information and granulation conditions into a granulation estimation model, estimates the granulation result, including the particle size and composition of the granulated particles at each particle size. The granulation estimation model includes a particle size estimation model and a composition estimation model. The particle size estimation model takes the particle size of the carbon-containing raw material, the proportion of each raw material in the sintering raw material, and the moisture content as input, and outputs the content of the granulated particles in one of multiple particle size partitions. The composition estimation model takes the particle size of the carbon-containing raw material, the proportion of each raw material in the sintering raw material, and the moisture content as input, and outputs the content of a specific component of the granulated particles in one of multiple particle size partitions. The loading condition acquisition unit acquires the loading conditions when the granulated particles are loaded into the sintering machine to form a raw material loading layer. The loading result estimation unit, based on the granulation results and the loading conditions, uses a loading estimation model to estimate the particle size segregation and compositional segregation of the granulated particles in the height direction of the raw material loading layer. The loading estimation model represents the content proportion of granulated particles of each particle size in each loading partition when the height direction of the raw material loading layer is divided into multiple loading partitions. The sintering result estimation unit, based on at least one of the particle size segregation and the composition segregation, uses a sintering estimation model to estimate the sintering temperature within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer. The sintering estimation model includes a heat transfer model capable of calculating the sintering temperature at a specified location within the raw material loading layer and the exhaust gas flow rate directly below the raw material loading layer. NO X The concentration prediction unit uses the sintering temperature within the raw material loading layer, the exhaust gas flow rate directly below the raw material loading layer, and the amount of carbon-containing raw material used per unit time in the sintering raw material to predict the NO concentration in the sintering exhaust gas. X concentration.

5. The control device according to claim 4, wherein, The NO X The concentration prediction unit uses the following formula (1) to predict the NO in sintering exhaust gas. X concentration: NO X =(A×(T-273)+B)×M×N / (100×14×U)···(1) In the above formula (1), NO X NO in the sintering waste gas X Concentration, in ppm; T, the highest sintering temperature in the raw material loading layer, in K; M, the amount of carbon-containing raw material used per unit time in the sintering raw material, in ton / h; N, the nitrogen content of the carbon-containing raw material in the sintering raw material, in mass%; U, the exhaust gas flow rate directly below the raw material loading layer, in mol / h; A(K -1 ), B (-) are constants.

6. The control device according to claim 4 or 5, wherein, Further, it has the ability to determine that NO X NO concentration prediction of sintering exhaust gas X The manufacturing conditions determination section determines the manufacturing conditions under which the concentration meets the predetermined target value.

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