JP6206368B2 - Blast furnace state estimation apparatus and blast furnace state estimation method - Google Patents

Description

  The present invention relates to a blast furnace internal state estimation device and a blast furnace internal state estimation method for estimating an internal state of a blast furnace.

  Conventionally, in the ironmaking field, estimation of the in-furnace state of a blast furnace in which charged materials such as ore and coke are charged into the furnace to produce pig iron is performed. For example, Patent Documents 1 and 2 or Non-Patent Document 1 disclose conventional techniques related to estimation of the in-furnace state of a blast furnace.

  The prior art disclosed in Patent Document 1 is based on the state of the cohesive zone root in the furnace based on the spatial distribution state and temporal change of pressure data or temperature data by a plurality of sensors installed on the outer surface of the blast furnace. That is, the figure of the part in contact with the blast furnace wall in the cohesive zone is estimated. The prior art disclosed in Patent Document 2 is based on the changes in temperature distribution and gas flow velocity distribution at the top of the furnace immediately before charging the charge into the furnace of the blast furnace, and the layer thickness of the charge in the furnace. The distribution is estimated. The prior art disclosed in Non-Patent Document 1 divides a blast furnace model simulating the inside of a blast furnace into a two-dimensional mesh as a symmetric model about the axis in the height direction of the blast furnace. In the furnace, calculate the flow of solids (ores and coke, etc. that are charged into the furnace), the flow of liquid such as hot metal, gas flow, heat transfer, and chemical reaction. The state is estimated.

Japanese Patent No. 4094290 JP-A-9-287008

"Development of blast furnace operation simulator and application to hot metal silicon reduction", Kawasaki Steel Technical Report, Vol. 29, 1997, no. 1, p. 30-36

  By the way, when performing operation management of a blast furnace that operates pig iron manufacturing (hereinafter abbreviated as “operational blast furnace” as appropriate), it is important to estimate the in-furnace state of the blast furnace in operation in real time and with high accuracy. It is. However, in the prior art described in Patent Document 1 described above, the shape of the cohesive zone in the furnace is estimated from the measured values of the temperature and pressure on the outer surface of the blast furnace. It is difficult to perform online estimation with high accuracy for estimating the state in real time.

  On the other hand, the prior art described in Non-Patent Document 1 described above is for estimating in advance the result of the operation change of the blast furnace before the actual operation of the blast furnace, for example, when applying a new operation mode to the blast furnace. Usually, it is used outside the blast furnace iron making process line (offline). In the prior art described in Non-Patent Document 1, there is a possibility that the input data given to the blast furnace model such as the layer thickness distribution of the charge cannot be obtained using the operating blast furnace. It is difficult to estimate the in-furnace condition of a blast furnace in operation.

  Moreover, in the prior art described in Patent Document 2 described above, a method for estimating the layer thickness distribution of the charge in the furnace from each change in the temperature distribution and gas flow velocity distribution at the top of the blast furnace is shown. In reality, there is no means for stably measuring the gas flow velocity distribution. Even if it can be measured in the future, the influence of the surface shape is much larger than the layer thickness distribution, and it is difficult to use it for layer thickness distribution estimation. Furthermore, since the estimation is based on external information of the blast furnace such as temperature distribution and gas flow velocity distribution at the top of the furnace, highly accurate in-core estimation cannot be expected.

  The present invention has been made in view of the above circumstances, and provides a blast furnace state estimation device and a blast furnace state estimation method capable of accurately estimating the in-furnace state of a blast furnace during operation on-line. With the goal.

  In order to solve the above-described problems and achieve the object, an apparatus for estimating the state of a blast furnace according to the present invention includes a furnace for the blast furnace during operation of the blast furnace for producing pig iron by charging a charge in the furnace. When measuring at least the gas component of the in-furnace gas flowing along the furnace diameter direction of the blast furnace, collecting measurement data by the gas measuring unit, and producing the pig iron from the charge The actual data of the gas component used is calculated based on the collected measurement data, and the charging of the charge in the furnace of the operating blast furnace is performed based on the calculated actual data of the gas component. An estimation processing unit that estimates a state and estimates an in-furnace state of the blast furnace during operation based on the estimated charging state of the charge.

  In the blast furnace state estimation apparatus according to the present invention, in the above invention, the estimation processing unit estimates a state of a cohesive zone formed in the furnace of the blast furnace in operation as the furnace state. It is characterized by that.

  Moreover, in the blast furnace state estimation device according to the present invention, in the above invention, the estimation processing unit calculates a gas utilization rate distribution of the in-furnace gas along the furnace radial direction as the actual data of the gas component. Then, the charged state of the charge is estimated using the calculated gas utilization rate distribution.

  Further, in the blast furnace internal state estimation apparatus according to the present invention, in the above invention, the estimation processing unit is configured as the charge state of the charge along the furnace radial direction of the ore and coke as the charge. It is characterized in that the layer thickness distribution is estimated.

  In the blast furnace state estimation apparatus according to the present invention, in the above invention, the estimation processing unit uses the principal component analysis and the multiple regression analysis, and the charge state of the charge from the actual data of the gas component Is estimated.

  In the blast furnace internal state estimation method according to the present invention, during the operation of the blast furnace in which the charge is charged into the furnace to produce pig iron, at least the gas component of the furnace gas flowing in the furnace of the blast furnace is Gas measurement step for measuring along the furnace radial direction of the blast furnace, and measurement data by the gas measurement step were collected, and actual data of the gas component used when manufacturing the pig iron from the charge was collected. Based on the data collection calculation step calculated based on the measurement data, and the actual data of the gas component calculated by the data collection calculation step, the charging state of the charge in the furnace of the blast furnace in operation A first estimating step for estimating; a second estimating step for estimating an in-furnace state of the blast furnace in operation based on a charging state of the charge estimated in the first estimating step; Characterized in that it comprises a.

  Further, in the blast furnace state estimation method according to the present invention, in the above invention, the second estimation step is the state of the cohesive zone formed in the furnace of the blast furnace in operation as the furnace state. It is characterized by estimating.

  Further, in the blast furnace state estimation method according to the present invention, in the above invention, the data collection and calculation step includes, as the actual data of the gas component, a gas utilization rate distribution of the furnace gas along the furnace radial direction. In the first estimation step, the charging state of the charge is estimated using the gas utilization rate distribution calculated in the data collection calculation step.

  Further, in the blast furnace internal state estimation method according to the present invention, in the above invention, the first estimation step includes the charging state of the charge, and the ore and coke as the charge are in the furnace radial direction. The layer thickness distribution along the line is estimated.

  Further, in the blast furnace state estimation method according to the present invention, in the above invention, the first estimation step uses the principal component analysis and the multiple regression analysis to determine the charge of the charge from the actual data of the gas component. The on-state is estimated.

  According to the present invention, since the estimation is based on actual information such as gas utilization rate that is actually inserted into the furnace and measured, the in-furnace state of the operating blast furnace can be estimated online with high accuracy. .

FIG. 1 is a diagram illustrating a configuration example of a blast furnace state estimation apparatus according to an embodiment of the present invention. FIG. 2 is a diagram for explaining forward estimation processing for estimating the gas utilization rate distribution of the in-furnace gas from the charge layer thickness distribution of the ore and coke charged in the furnace for each position in the furnace radial direction. FIG. 3 is a flowchart showing an example of an input estimation model construction method according to the embodiment of the present invention. FIG. 4 is a diagram illustrating creation of a charge layer thickness distribution set using a default distribution of charge layer thickness. FIG. 5 is a flowchart showing an example of the blast furnace state estimation method according to the embodiment of the present invention. FIG. 6 is a diagram for explaining an estimation process for online estimation of a charged state of a charged material in an operating blast furnace. FIG. 7 is a diagram for explaining an estimation process for online estimation of the in-furnace state of the blast furnace in operation. FIG. 8 is a flowchart showing an example of a method for displaying data such as an online estimation result of the in-furnace state. FIG. 9 is a diagram illustrating an example of an online estimation result of the in-furnace state acquired by the in-blast furnace state estimation method according to the embodiment of the present invention. FIG. 10 is a diagram illustrating another example of the online estimation result of the in-furnace state acquired by the in-blast furnace state estimation method according to the embodiment of the present invention. FIG. 11: is a figure which shows the further another example of the online estimation result of the in-furnace state acquired with the blast furnace state estimation method concerning embodiment of this invention.

  Exemplary embodiments of a blast furnace state estimation apparatus and a blast furnace state estimation method according to the present invention will be described below in detail with reference to the accompanying drawings. Note that the present invention is not limited to the embodiment. Moreover, the drawings are schematic, and it should be noted that the relationship between the dimensions of each element, the ratio of each element, and the like may differ from the actual ones. Even between the drawings, there are cases in which portions having different dimensional relationships and ratios are included. Moreover, in each drawing, the same code | symbol is attached | subjected to the same component.

(Blast furnace state estimation device)
First, the configuration of the blast furnace state estimation apparatus according to the embodiment of the present invention will be described. FIG. 1 is a diagram illustrating a configuration example of a blast furnace state estimation apparatus according to an embodiment of the present invention. As shown in FIG. 1, the blast furnace internal state estimation device 1 includes a gas sampler 2 that analyzes a high-temperature gas (hereinafter referred to as furnace gas) flowing through a furnace of a blast furnace 100 that is an operation management target, An estimation processing unit 3 that performs various processes for estimating the state, a determination processing unit 4 that performs an abnormal value determination regarding parameters of the estimation process of the in-furnace state, and a storage unit 5 that stores various data in an updatable manner. . The blast furnace internal state estimation device 1 relates to a thermometer 6 that measures the furnace temperature of the blast furnace 100, a pressure gauge 7 that measures the gas pressure of the blast furnace 100, an input unit 8 that inputs various information, and a blast furnace 100. The display part 9 which displays various information, and the control part 10 which controls each structure part of the blast furnace state estimation apparatus 1 are provided.

  The gas sampler 2 functions as a gas measuring unit that measures the in-furnace gas of the blast furnace 100. Specifically, as shown in FIG. 1, the gas sampler 2 has an elongated cylindrical sonde 2 a and is disposed at an upper portion of the blast furnace 100 in the height direction. As shown in FIG. 1, the sonde 2a has a furnace from the side wall of the blast furnace 100 toward the center position CP in the furnace radial direction so as to be longitudinal in the radial direction in the furnace of the blast furnace 100 (hereinafter referred to as the furnace radial direction). Inserted inside. The gas sampler 2 collects in-furnace gas for each coordinate position in the furnace radial direction of the blast furnace 100 (hereinafter referred to as the furnace radial direction position) using the sonde 2a arranged at a predetermined position in the furnace as described above. Gas analysis is performed on each sample of the in-furnace gas at each radial position. Thereby, the gas sampler 2 supplies at least the gas component of the in-furnace gas flowing through the furnace of the blast furnace 100 during the operation of the blast furnace 100 in which the charge 110 is charged into the furnace to produce the pig iron 115. Measure along the furnace diameter direction. In the present embodiment, the gas sampler 2 measures the component ratio (gas component ratio) and gas temperature of the in-furnace gas for each furnace radial direction position in the furnace of the operating blast furnace 100. Each time, the gas sampler 2 transmits the obtained measurement data of the gas component ratio and the gas temperature to the estimation processing unit 3.

Here, the in-furnace gas measured by the gas sampler 2 includes, for example, carbon monoxide, carbon dioxide, hydrogen, nitrogen, and the like. The gas sampler 2 has such a component ratio of the gas in the furnace as the concentration [volume%] of each gas component in the furnace gas, specifically, the gas component concentration [CO] of carbon monoxide, carbon dioxide Gas component concentration [CO ₂ ], hydrogen gas component concentration [H ₂ ], nitrogen gas component concentration [N ₂ ] and the like are measured.

  The estimation processing unit 3 estimates the in-furnace state of the blast furnace 100 in operation. Specifically, the estimation processing unit 3 collects measurement data from the gas sampler 2 described above, and results data of gas components in the furnace gas used when the blast furnace 100 manufactures pig iron 115 from the charge 110. Is calculated based on the collected measurement data. In the present embodiment, the estimation processing unit 3 calculates the gas utilization rate distribution of the in-furnace gas along the furnace diameter direction of the blast furnace 100 as the actual data of the gas component. Each time, the estimation processing unit 3 stores the obtained calculation data of the gas utilization rate distribution in the storage unit 5. The estimation processing unit 3 stores the measurement data collected from the gas sampler 2 in the storage unit 5.

This gas utilization rate distribution is a distribution of the utilization rate of the in-furnace gas in the production of pig iron 115 by the blast furnace 100, that is, the distribution of the gas utilization rate η for each position in the furnace radial direction in the blast furnace 100. The gas utilization rate η is a ratio of carbon components of carbon monoxide and carbon dioxide, which are gas components in the furnace gas used for producing pig iron 115, that is, a ratio [CO ₂ ] / ([CO]. + [CO ₂ ]). Such a gas utilization rate η can be applied to estimate the progress of the iron reduction reaction in the furnace of the blast furnace 100 and is actually measured at a portion close to the cohesive zone in the furnace (measurement by the gas sampler 2). Therefore, it is a very important index for estimating the in-furnace state of the blast furnace 100, specifically, for estimating the state of the cohesive zone in the furnace.

  Moreover, the estimation process part 3 estimates the charging state of the charge 110 in the furnace of the operating blast furnace 100 based on the performance data of the gas component calculated as mentioned above. Specifically, the estimation processing unit 3 uses the distribution of the gas utilization rate η calculated as the actual data of the gas component described above for each position in the radial direction of the furnace, that is, the gas utilization rate distribution, to charge the blast furnace 100 in the furnace. The charging state of the object 110 is estimated. At this time, the estimation processing unit 3 estimates the charging state of the charge 110 in the furnace from the above-described gas component result data using principal component analysis and multiple regression analysis. In this Embodiment, the estimation process part 3 is a layer along the furnace radial direction of the ore and coke which are the charging materials 110 charged in the furnace of the blast furnace 100 as the charging state of the charging material 110 mentioned above. Estimate the thickness distribution (hereinafter referred to as the charge layer thickness distribution). Each time, the estimation processing unit 3 stores the obtained estimation data of the charge layer thickness distribution in the storage unit 5.

  Furthermore, the estimation processing unit 3 is based on the charged state of the charged material 110 estimated as described above, specifically, the charged material layer thickness distribution at each furnace radial direction position in the furnace of the blast furnace 100, The in-furnace state of the blast furnace 100 in operation is estimated. At this time, the estimation processing unit 3 estimates the state (for example, shape and temperature distribution) of the cohesive zone formed in the furnace of the operating blast furnace 100 as the in-furnace state. Each time, the estimation processing unit 3 stores the obtained estimation data of the in-furnace state, specifically, the estimation data of the state of the cohesive zone in the furnace in the storage unit 5.

  The determination processing unit 4 performs an abnormal value determination process for determining whether or not the input variable of the estimation process by the estimation processing unit 3 is an abnormal value. In the present embodiment, the determination processing unit 4 is the actual data of the gas component calculated by the estimation processing unit 3 as an input variable of the estimation processing, specifically, for each furnace radial direction position in the furnace of the operating blast furnace 100. An abnormal value determination process is performed for the gas utilization rate distribution. If the gas utilization rate distribution is not an abnormal value as a result of the abnormal value determination process, the determination processing unit 4 notifies the estimation processing unit 3 that the gas utilization rate distribution is not an abnormal value, and also notifies the estimation processing unit 3 of the in-furnace state. The execution of the estimation process is permitted. On the other hand, if the gas utilization rate distribution is an abnormal value as a result of the abnormal value determination process, the determination processing unit 4 notifies the estimation processing unit 3 of the abnormal value and notifies the estimation processing unit 3 of the furnace. The execution of the estimation process for the internal state is prohibited.

  The storage unit 5 is configured using a non-volatile memory or the like, and stores (accumulates) various data such as data necessary for estimating the in-furnace state of the blast furnace 100 and data used for operation management of the blast furnace 100. Specifically, as shown in FIG. 1, the storage unit 5 stores the input estimation model 5a, the blast furnace model 5b, and the performance data 5c in an updatable manner.

  The input estimation model 5a is an input variable (charge layer thickness distribution in the furnace) given to the blast furnace model 5b from the actual data of the gas components in the furnace of the blast furnace 100 in operation (gas utilization distribution of the gas in the furnace). Is a simulation model for deriving The blast furnace model 5b divides the blast furnace 100 into a large number of fine meshes in the height direction and the furnace radial direction, and for each mesh, mass transfer, fluid flow, heat transfer in the furnace of the blast furnace 100 is performed by a direct difference method or the like. This is a simulation model for calculating the reaction and simulating the in-furnace state of the blast furnace 100 during operation. In the present embodiment, the blast furnace model 5b is based on the input variable derived by the input estimation model 5a, that is, the charge layer thickness distribution in the furnace of the blast furnace 100, and the fusion zone in the furnace of the operating blast furnace 100. The estimated data of the state (shape and temperature distribution, etc.) is derived. The actual data 5c is actual operational data based on the measurement data obtained from the blast furnace 100 in operation by the gas sampler 2 or the like. The actual data 5c includes, for example, the gas temperature of the gas in the furnace, each gas component ratio (each gas component concentration), the gas utilization rate η and its distribution (gas utilization rate distribution) for each position in the furnace radial direction. The actual data 5c includes furnace body temperature measurement data by a thermometer 6 to be described later, gas pressure measurement data by a pressure gauge 7, and the like.

  For example, as shown in FIG. 1, the thermometer 6 is provided on the wall of the blast furnace 100 and continuously or intermittently measures the furnace body temperature, which is the temperature of the furnace body of the blast furnace 100 in operation. The thermometer 6 transmits the obtained measurement data of the furnace body temperature to the control unit 10 every time the furnace body temperature is measured. The pressure gauge 7 continuously or intermittently measures the gas pressure in the operating blast furnace 100. In the present embodiment, the pressure gauge 7 uses, for example, the pressure of the high temperature gas (hot air) supplied into the furnace of the blast furnace 100, the pressure of the gas in the furnace of the blast furnace 100, etc. as the gas pressure of the blast furnace 100 in operation. taking measurement. The pressure gauge 7 transmits the obtained gas pressure measurement data to the control unit 10 each time the gas pressure is measured.

  The input unit 8 is configured using an input device such as a keyboard and a mouse, and inputs various types of information to the control unit 10 in response to an input operation by an operator. As input information by the input unit 8, for example, information for setting parameters necessary for the estimation processing by the estimation processing unit 3 described above (specifications of the blast furnace 100), instruction information for instructing the control unit 10, etc. Is mentioned.

  The display unit 9 is configured using a display device such as a liquid crystal display and displays data instructed to be displayed by the control unit 10. Examples of data displayed by the display unit 9 include estimated data of the in-furnace state of the blast furnace 100 in operation, various measurement data of the blast furnace 100, operation result data of the blast furnace 100, and the like.

  The control unit 10 is configured using a CPU that executes a program, a ROM that stores the program, and a RAM that temporarily stores calculation data and the like. The control unit 10 controls each operation of the gas sampler 2, the estimation processing unit 3, the storage unit 5, and the display unit 9, and inputs / outputs signals between the components of the blast furnace state estimation device 1. Control. Specifically, the control unit 10 controls the measurement timing of the gas sampler 2, controls arithmetic processing and estimation processing by the estimation processing unit 3, stores data in the storage unit 5, and stores data from the storage unit 5. Control reading. Further, the control unit 10 controls the display content and display period of data on the display unit 9 based on the input information of the input unit 8.

  On the other hand, the blast furnace 100 is an operation management target for which the in-furnace state during operation is estimated by the in-blast furnace state estimation apparatus 1 according to the present embodiment. The blast furnace 100 is a huge counter-current moving bed reactor, and as shown in FIG. 1, a furnace top bunker 101 is provided at the top of the furnace, and tuyere 102 is provided at the bottom of the furnace.

  The top bunker 101 is charged with the charge 110 from the top of the blast furnace 100 to the inside (inside the furnace). Here, the charge 110 is either ore (for example, iron ore, sintered ore, lump ore, or the like) that is an iron raw material mainly composed of iron oxide and coke. The furnace top bunker 101 sequentially charges the charge 110 into the furnace so that the ore layers 111 and the coke layers 112 are alternately formed from the top of the blast furnace 100 toward the bottom of the furnace, thereby the ore. An in-furnace mixture 113 having a multilayer structure of the layer 111 and the coke layer 112 is formed. The in-furnace mixture 113 includes a lump, a fusion zone, a dripping zone, and the like in addition to the multilayer structure of the ore layer 111 and the coke layer 112 shown in FIG.

  The blast furnace 100 circulates high-temperature gas blown from the tuyere 102 as in-furnace gas from the bottom of the furnace to the long side of the furnace, and each charge 110 (ore and Coke) is burned with the gas in the furnace. The blast furnace 100 uses the reducing gas containing carbon monoxide generated thereby to reduce iron oxide contained in the ore in each charge 110 in the furnace to produce pig iron 115. The pig iron 115 manufactured in this way becomes molten iron in a molten state, flows to the lower part of the blast furnace 100, and is then sent out of the blast furnace 100.

(Input estimation model)
Next, the input estimation model 5a for estimating the input variables of the blast furnace model 5b that simulates the in-furnace state of the blast furnace 100 in operation in the present embodiment will be described. The input estimation model 5a uses the distribution of gas utilization rate η (gas utilization rate distribution) for each position in the furnace radial direction in the furnace of the operating blast furnace 100 as an input variable, and estimates the input variable of the blast furnace model 5b from this input variable. Deriving data. In the present embodiment, the estimated data obtained by the input estimation model 5a, that is, the input variables of the blast furnace model 5b, each charge 110 (specifically, ore and The layer thickness distribution (charge layer thickness distribution) for each position in the radial direction of the coke) is used. Each state in the furnace of these ores and cokes changes according to the layer thickness ratio of each layer of ores and cokes (for example, the ore layer 111 and the coke layer 112 shown in FIG. 1) formed in the furnace. In this way, the charge layer thickness distribution of the ore and coke in the furnace radial direction position in the furnace is used as an input variable, and by performing multiple regression analysis, the furnace radial direction in the blast furnace 100 in operation The gas utilization rate distribution for each position can be estimated.

FIG. 2 is a diagram for explaining forward estimation processing for estimating the gas utilization rate distribution of the in-furnace gas from the charge layer thickness distribution of the ore and coke charged in the furnace for each position in the furnace radial direction. As shown in FIG. 2, the charge layer thickness distribution x _i of the charging ore and coke Ro径direction position every the furnace, when the layer thickness at any Ro径direction position is changed, the layer thickness The gas utilization rate distribution y _i of each furnace radial direction position of the in-furnace gas is affected with the change in the furnace radial direction position as the center. Regression coefficient a multiple regression analysis in the forward estimation processing to the gas utilization rate distribution y _i from charge layer thickness distribution x _i is to the gas utilization rate distribution y _i of the charge layer thickness distribution x _i It has a meaning like an influence coefficient indicating influence, and is expressed as a component of a matrix A of m × m (m is a positive integer) shown below. In such a multiple regression analysis, when the charge layer thickness distribution x _i is expressed as a component of an m × 1 matrix X and the gas utilization rate distribution y _i is expressed as a component of an m × 1 matrix Y, As shown in Expression (1), the matrix Y representing the gas utilization rate distribution y _i is calculated by the product of the matrix A representing the multiple regression coefficient a and the matrix X representing the charge layer thickness distribution x _i .

Therefore, in order to perform the reverse estimation process for estimating the charge layer thickness distribution x _i represented by the matrix X from the gas utilization rate distribution y _i represented by the matrix Y, the multiple regression coefficient a is usually set to An inverse matrix A ⁻¹ of the matrix A to be expressed may be obtained. However, since each data (gas utilization rate distribution y _i and charge layer thickness distribution x _i ) to be processed in this multiple regression analysis is a distribution system, the gas utilization rate distribution y _i and the charge layer thickness distribution Since the correlation between the components increases as the adjacency relationship between the components of x _i increases, the inverse matrix A ⁻¹ cannot be obtained. For this reason, principal component analysis is applied to each component of the gas utilization rate distribution y _i and the charge layer thickness distribution x _i .

Each component of the gas utilization rate distribution y _i and the charge layer thickness distribution x _i can be compressed to an appropriate dimension such as two dimensions by principal component analysis. A multiple regression analysis is performed on the gas utilization rate distribution y _i and the charge layer thickness distribution x _i after dimensional compression by the principal component analysis, whereby the charge layer thickness distribution x is calculated from the gas utilization rate distribution y _i. An inverse influence coefficient B that enables estimation of _i can be obtained. Such inverse influence coefficient B is data representing the input estimation model 5a for estimating the input variable of the blast furnace model 5b described above, and is stored in the storage unit 5 (see FIG. 1) as the input estimation model 5a.

(How to build an input estimation model)
Next, a method for constructing the above-described input estimation model 5a will be described. FIG. 3 is a flowchart showing an example of an input estimation model construction method according to the embodiment of the present invention. The construction method of the input estimation model 5a in the present embodiment is a method in which the above-described blast furnace state estimation device 1 (see FIG. 1) constructs the input estimation model 5a offline.

Specifically, in this method of construction, blast furnace state estimating apparatus 1, as shown in FIG. 3, first, the initial and the input variables of the blast furnace model 5b for simulating blast furnace 100 charge layer thickness distribution x _i Set (step S101). In step S <b> 101, the estimation processing unit 3 inserts the charge for each position in the furnace radial direction based on data such as a distribution record of a preset charge layer thickness (ore and coke layer thickness) in the furnace radial direction. Initial data of the layer thickness distribution x _i , that is, a default distribution x _D is set. The default distribution x _D is the distribution data indicating the initial setting value of the charge layer thickness in the furnace for each Ro径direction position.

Subsequently, blast furnace state estimating apparatus 1, based on the default distribution x _D set in step S101, to create a charge layer thickness distribution set Xs (step S102). FIG. 4 is a diagram illustrating creation of a charge layer thickness distribution set using a default distribution of charge layer thickness. In step S102, the estimation processing unit 3, the values of charge layer thickness of each Ro径direction position in the default distribution x _D, preset value content, is respectively increased or decreased change. Thereby, as shown in FIG. 4, the estimation processing unit 3 has a plurality of charge layer thickness distributions x _{1 to} x _{m in} which the value of the charge layer thickness at some positions in the furnace radial direction differs among the distribution data. Create The estimation processing unit 3 uses the plurality of charge layer thickness distributions x _{1 to} x _m as one set of distribution data, and creates a charge layer thickness distribution set Xs composed of the one set of distribution data.

Next, the blast furnace internal state estimation device 1 uses the charge layer thickness distribution set Xs created in step S102 to provide a gas utilization rate distribution corresponding to the plurality of charge layer thickness distributions x _{1 to} x _m described above. A set Ys is created (step S103). In step S103, the estimation processing unit 3 uses the blast furnace model 5b stored in the storage unit 5 in advance, and uses a plurality of the charge layer thickness distributions x _{1 to} x _m included in the charge layer thickness distribution set Xs. Using each of them as an input variable of the blast furnace model 5b, calculation processing for estimating the gas utilization rate distribution y _i is performed. Thus, the estimation processing unit 3 calculates a plurality of gas utilization index y ₁ ~y _m corresponding to the plurality of charge layer thickness distribution x ₁ ~x _m. The estimation processing unit 3 uses the plurality of gas usage rate distributions y _{1 to} ym calculated as described _above as one set of distribution data, and creates a gas usage rate distribution set Ys including the one set of distribution data.

  Thereafter, the blast furnace state estimation apparatus 1 constructs an input estimation model 5a based on the charge layer thickness distribution set Xs created in step S102 and the gas utilization rate distribution set Ys created in step S103 ( Step S104), the process is terminated.

In step S104, the estimation processing unit 3 determines that the plurality of charge layer thickness distributions x _{1 to} x _m included in the charge layer thickness distribution set Xs and the plurality of gas utilization rates included in the gas utilization rate distribution set Ys. for each of the distribution y ₁ ~y _m, it performs principal component analysis. Thus, the estimation processing unit 3 compresses each component of the plurality of charge layer thickness distribution x ₁ ~x _m and the gas utilization factor distribution y ₁ ~y _m to the appropriate dimensions. Next, estimation processing unit 3, to charge layer thickness distribution x ₁ ~x _m and gas utilization index y ₁ ~y _m after the dimension compression, as described above performs a multiple regression analysis, thereby derives the inverse influence coefficient B that allows the estimation of the charge layer thickness distribution x ₁ ~x _m from the gas utilization rate distribution y ₁ ~y _m. Thereafter, the estimation processing unit 3 stores the inverse influence coefficient B derived in this way in the storage unit 5 as the input estimation model 5a.

(Blast furnace state estimation method)
Next, a blast furnace state estimation method according to an embodiment of the present invention will be described. FIG. 5 is a flowchart showing an example of the blast furnace state estimation method according to the embodiment of the present invention. The blast furnace internal state estimation method according to the present embodiment uses the above-described blast furnace internal state estimation device 1 (see FIG. 1) and performs each processing step of steps S201 to S208 shown in FIG. This is a method for estimating the in-furnace state of the blast furnace 100 online.

  Specifically, in this blast furnace state estimation method, as shown in FIG. 5, the blast furnace state estimation apparatus 1 first sets parameters for estimation processing for estimating the in-furnace state of the operating blast furnace 100 ( Step S201). In step S201, the estimation processing unit 3 sets parameters necessary for the blast furnace model 5b used for the estimation process of the in-furnace state. Examples of parameters set for the blast furnace model 5b include specifications of the blast furnace 100 (in-furnace dimensions such as an inner diameter) and the like. Such parameter information is input to the control unit 10 by the input unit 8 and transmitted from the control unit 10 to the estimation processing unit 3, for example.

  Subsequently, the blast furnace internal state estimation device 1 determines whether it is time to collect data from the operating blast furnace 100 (step S202). In step S <b> 202, the control unit 10 determines that the time elapses as the data collection timing from the blast furnace 100 every time a predetermined time elapses from the previous data collection timing for the blast furnace 100.

  Specifically, the control unit 10 measures an elapsed time from the previous data collection timing for the blast furnace 100 based on, for example, a reference signal (clock signal) or the like. If the elapsed time from the previous data collection timing has not reached the preset time, the control unit 10 determines that it is not the data collection timing (No in step S202), waits for the passage of time, and also executes step S202. Repeat the process. On the other hand, when the elapsed time from the previous data collection timing for the blast furnace 100 reaches a preset time, the control unit 10 determines the timing of this elapsed time as the current data collection timing for the blast furnace 100 (step S202). , Yes), the process proceeds to step S203.

  When it is determined in step S202 that the current timing is the data collection timing, the blast furnace state estimation apparatus 1 measures the gas data in the blast furnace 100 as data collected from the blast furnace 100 (step S203).

  In step S203, the control unit 10 controls the gas sampler 2 to measure data during operation of the blast furnace 100 at the data collection timing determined in step S202. The gas sampler 2 is configured to control at least the gas in the furnace flowing through the furnace of the blast furnace 100 during operation of the blast furnace 100 in which the charged material is manufactured by charging the furnace with the charge based on the control of the control unit 10. Components are measured along the furnace radial direction. At this time, the gas sampler 2 is configured such that the component ratio (gas component concentration) of each gas component such as carbon monoxide and carbon dioxide in the furnace gas and the gas for each position in the furnace radial direction of the blast furnace 100 in operation. Measure the temperature. The gas sampler 2 transmits the obtained measurement data of the gas component concentration and the gas temperature for each position in the furnace radial direction to the estimation processing unit 3.

Subsequently, the blast furnace internal state estimation device 1 collects the measurement data of the in-furnace gas and calculates the actual data in the operating blast furnace 100 (step S204). In step S204, the estimation processing unit 3 determines each measurement data of the in-furnace gas in step S203, specifically, each position in the furnace radial direction of each gas component (carbon monoxide, carbon dioxide, etc.) contained in the in-furnace gas. Each measurement data of gas component concentration and gas temperature is collected from the gas sampler 2. Next, the estimation processing unit 3 obtains the actual data of the gas components used when producing the pig iron 115 from the charge 110 such as ore and coke shown in FIG. 1 based on the collected measurement data. calculate. At this time, the estimation processing unit 3 calculates the gas utilization rate distribution y _i of the in-furnace gas along the furnace radial direction as the actual data of the gas component described above. Specifically, the estimation processing unit 3 sets the gas component ratio of carbon monoxide and carbon dioxide used for manufacturing pig iron 115, that is, the ratio ([CO ₂ ] / ([CO] + [CO ₂ ])). Based on this, a gas utilization rate distribution y _i for each position in the furnace radial direction is calculated.

Thereafter, the blast furnace state estimation apparatus 1 determines whether or not the actual data of the gas component calculated in step S204 is an abnormal value (step S205). In step S <b> 205, the determination processing unit 4 obtains the actual data of the gas component calculated by the estimation processing unit 3 as described above, specifically, the gas utilization rate distribution y _i for each position in the furnace radial direction from the estimation processing unit 3. get. The determination processing unit 4 compares the gas usage rate η at each furnace radial direction position in the acquired gas usage rate distribution y _i with a preset threshold value, and based on the result of this comparison processing, this gas usage rate It is determined whether or not the distribution y _i is an abnormal value.

If the determination processing unit 4 determines that the gas utilization rate distribution y _i is an abnormal value as a result of the comparison process described above (Yes in step S205), the determination processing unit 4 invalidates the abnormal gas utilization rate distribution y _i . The estimation processing unit 3 is prohibited from executing the estimation process. In this case, the blast furnace internal state estimation device 1 returns to the above-described step S202 without executing steps S206 to S208 described later, and repeats the processing after step S202.

On the other hand, if the determination processing unit 4 determines that the gas utilization rate distribution y _i is not an abnormal value as a result of the comparison process described above (No in step S205), the gas utilization rate distribution y _i is not an abnormal value ( Is notified to the estimation processing unit 3 and the estimation processing unit 3 is allowed to execute the estimation process. In this case, the estimation processing unit 3 stores the normal gas utilization rate distribution y _i in the storage unit 5 as performance data 5c. The blast furnace internal state estimating device 1 loads the charged material 110 in the furnace of the operating blast furnace 100 based on the normal gas utilization rate distribution y _i , that is, the normal performance data of the gas component calculated in step S204. The on-state is estimated online (step S206).

In step S206, the estimation processing unit 3 reads out the gas utilization rate distribution y _i calculated in step S204 described above from actual data 5c in the storage unit 5, using the read gas utilization index y _i, of the blast furnace 100 The charging state of the charge 110 in the furnace is estimated online. At this time, the estimation processing unit 3 sets the charge distribution of the ore and the coke as the charge 110 in the furnace radial direction, that is, the charge at each position in the furnace radial direction, as the charge state of the charge 110. The layer thickness distribution x _i is estimated online.

FIG. 6 is a diagram for explaining an estimation process for online estimation of a charged state of a charged material in an operating blast furnace. Hereinafter, the process of step S206 will be specifically described with reference to FIG. In step S206, the estimation processing unit 3 acquires the plurality of gas utilization index y ₁ ~y _m based on the gas utilization rate distribution y _i read from the actual data 5c in the storage unit 5, the plurality of gas utilization rate creating a distribution y ₁ consisting ~y _m gas utilization index set Ys. The estimation processing unit 3 uses the principal component analysis and the multiple regression analysis to estimate the charging state of the charge 110 from the gas utilization rate distribution set Ys that is the actual data of the gas components described above. That is, as described above, the estimation processing unit 3 reads the input estimation model 5a previously constructed by the principal component analysis and the multiple regression analysis from the storage unit 5, and uses this input estimation model 5a, as shown in FIG. The charge layer thickness distribution set Xs is estimated from the utilization rate distribution set Ys.

Here, the charge layer thickness distribution set Xs is a set of a plurality of charge layer thickness distributions x _{1 to} x _m indicating the charge state of the charge 110 in the furnace of the operating blast furnace 100. Distribution data. As shown in FIG. 6, the estimation processing unit 3 gives the input estimation model 5a with the gas utilization rate distribution set Ys as an input variable, so that the charge layer thickness as the charge state of the charge 110 described above is obtained. A distribution set Xs is calculated. The estimation processing unit 3 is an estimation result of the charging state of the charging 110 based on the plurality of charging layer thickness distributions x _{1 to} x _m included in such a charging layer thickness distribution set Xs. The charge layer thickness distribution x _i for each furnace radial position is obtained online.

After executing step S206 described above, the blast furnace state estimation apparatus 1 performs online estimation of the in-furnace state of the operating blast furnace 100 based on the charge state of the charge 110 estimated in step S206 (step S207). In step S207, the estimation processing unit 3 forms in the furnace of the operating blast furnace 100 based on the charge layer thickness distribution x _i for each position in the furnace radial direction, which is the estimation result of the charge state of the charge 110. The state (shape, temperature distribution, etc.) of the cohesive zone is estimated online as the in-furnace state of the blast furnace 100.

FIG. 7 is a diagram for explaining an estimation process for online estimation of the in-furnace state of the blast furnace in operation. Hereinafter, the process of step S207 will be specifically described with reference to FIG. In step S207, the estimation processing unit 3 reads the blast furnace model 5b from the storage unit 5, and sets the parameters in step S201 described above for the read blast furnace model 5b. At this time, the estimation processing unit 3 may set the measurement data of the gas temperature for each position in the furnace radial direction collected from the gas sampler 2 as a parameter in the blast furnace model 5b. Thereafter, the estimation processing unit 3 includes a plurality of charge layer thickness distributions x _{1 to} x _m based on the above-described charge layer thickness distribution x _i for each furnace radial direction position, that is, a charge layer thickness distribution set Xs. Is used as an input variable, and in-furnace estimation data 123 of the blast furnace 100 is calculated by the blast furnace model 5b as shown in FIG.

  Here, the in-furnace estimation data 123 includes the in-furnace state of the operating blast furnace 100, specifically, the shape and temperature distribution of the in-furnace mixture 113 (see FIG. 1) due to the charge 110 in the furnace, and the like. This is estimated data indicating a state by a pattern such as a color or a boundary line thereof. In the in-furnace estimation data 123, as shown in FIG. 7, as shown in FIG. 7, the fusion result indicating the estimation result of the state (shape, temperature distribution, etc.) of the fusion zone in the furnace of the operating blast furnace 100 is shown by a pattern, a boundary line, and the like Band estimation data 125 is included. As shown in FIG. 7, the estimation processing unit 3 gives the blast furnace model 5b as the in-furnace state of the operating blast furnace 100 by giving the charge layer thickness distribution set Xs as an input variable. Estimated data 123 is calculated. In this way, the estimation processing unit 3 determines the estimation result of the in-furnace state of the blast furnace 100 indicated by the in-furnace estimation data 123, particularly the shape and temperature distribution of the cohesive zone indicated by the cohesive zone estimation data 125. Obtain status estimation results online.

  After executing step S207 described above, the blast furnace state estimation apparatus 1 stores the online estimation result of the in-furnace state in step S207 in the storage unit 5 (step S208). In this step S208, the estimation processing unit 3 stores the in-furnace estimation data 123 calculated using the blast furnace model 5b as shown in FIG. 7 as an online estimation result of the in-furnace state of the operating blast furnace 100. Save to. Thereafter, the blast furnace state estimation apparatus 1 returns to step S202 described above and repeats the processing steps after step S202.

(Display of data such as online estimation results)
Next, a method for displaying data such as an online estimation result of the in-furnace state acquired by the in-furnace state estimation method according to the embodiment of the present invention will be described. FIG. 8 is a flowchart showing an example of a method for displaying data such as an online estimation result of the in-furnace state.

  In the data display method according to the present embodiment, the blast furnace state estimation apparatus 1 first performs setting for displaying data such as the above-described online estimation result of the in-furnace state as shown in FIG. S301).

  In step S <b> 301, the input unit 8 inputs instruction information for instructing display setting to the control unit 10 in accordance with the input operation of the worker. The control unit 10 performs display setting of the display unit 9 based on the input information from the input unit 8. At this time, the control unit 10 sets the display content, display period, and the like of the data displayed on the display unit 9.

  For example, in the setting of the display content, the control unit 10 displays the data to be displayed as the online estimation result indicating the in-furnace state of the operating blast furnace 100, the in-reactor estimation data including the cohesive zone estimation data 125 illustrated in FIG. 123 is determined, or whether only the cohesive zone estimation data 125 is determined. More specifically, the control unit 10 determines whether or not to display data indicating the overall state (shape, ore temperature distribution, pressure distribution, etc.) of the in-furnace mixture 113 (see FIG. 1) during operation. Whether or not to display at least one of the shape of the cohesive zone, the ore temperature distribution, the pressure distribution, etc., of the inner mixture 113 is determined. Moreover, the control part 10 determines whether the operation data of the blast furnace 100 are displayed according to the above-mentioned online estimation result.

  After performing step S301 described above, the blast furnace state estimation apparatus 1 displays data based on the display setting in step S301 (step S302), and ends this process. In step S <b> 302, the control unit 10 reads data such as an online estimation result corresponding to the display content set as described above from the storage unit 5. Next, the control unit 10 transmits the read data to the display unit 9 and instructs the display unit 9 for the display period set as described above to control the data display process by the display unit 9.

  The display unit 9 displays various data instructed to be displayed on the screen during the instructed display period based on the control by the control unit 10. As various data displayed by the display unit 9 during this display period, for example, in-furnace estimation data 123 (see FIG. 7) indicating the shape of the entire in-furnace mixture 113 during operation, ore temperature distribution, pressure distribution, and the like. Examples include cohesive zone estimation data 125 (see FIG. 7) indicating the state of the cohesive zone during operation, ore temperature distribution, pressure distribution, and the like, operation data of the blast furnace 100 during operation, and the like.

  Each process of step S301, S302 mentioned above is repeatedly performed whenever it displays various data of display objects, such as an online estimation result of a furnace state, on the display part 9. FIG. By visually recognizing various data displayed on the display unit 9 in this manner, the operator can accurately grasp the in-furnace state of the blast furnace 100 in operation, particularly the shape of the cohesive zone, etc. online. Can do.

  FIG. 9 is a diagram illustrating an example of an online estimation result of the in-furnace state acquired by the in-blast furnace state estimation method according to the embodiment of the present invention. FIG. 10 is a diagram illustrating another example of the online estimation result of the in-furnace state acquired by the in-blast furnace state estimation method according to the embodiment of the present invention. FIG. 11: is a figure which shows the further another example of the online estimation result of the in-furnace state acquired with the blast furnace state estimation method concerning embodiment of this invention.

  9 to 11, the position in the furnace radial direction is a dimensionless expression expressed by the ratio between the furnace diameter (inner diameter) of the blast furnace 100 and the position in the furnace radial direction from the center position CP (see FIG. 1) in the furnace. Is the position (dimensionless furnace radial direction position). The solid lines L1, L3, and L5 are calculated by the input estimation model 5a from the gas utilization rate distribution (actual data of gas components of the in-furnace gas) calculated for each position in the furnace radial direction calculated based on the measurement data by the gas sampler 2. The estimated data of the charge layer thickness distribution for each furnace radial direction position is shown. Solid lines L2, L4, and L6 use the charge layer thickness distribution for each furnace radial direction position estimated by the input estimation model 5a as an input variable, and the gas utilization rate for each furnace radial direction position calculated by the blast furnace model 5b. Distribution estimation data is shown. The broken lines L11, L12, and L13 indicate the actual data of the gas utilization rate distribution for each furnace radial direction position calculated based on the measurement data obtained by the gas sampler 2 as described above.

  As shown in FIGS. 9 to 11, the input variable when calculating the cohesive zone estimation data 125, which is the online estimation result of the in-furnace state, by the blast furnace model 5 b, that is, the estimation of the gas utilization rate distribution for each position in the furnace radial direction. The data (see solid lines L2, L4, and L6) is almost the same as the actual data (broken lines L11, L12, and L14) of the gas utilization rate distribution for each position in the furnace radial direction based on the measurement data. From this, in any case of FIGS. 9-11, it was confirmed by the blast furnace model 5b that the gas utilization factor distribution for every position of a furnace radial direction can be estimated so that it may become substantially the same as the performance data. The cohesive zone estimation data 125 calculated by such a blast furnace model 5b indicates the state of the cohesive zone shape and the like in the furnace of the operating blast furnace 100 in real time and in any case of FIGS. Shown in accuracy.

  The cohesive zone estimation data 125 as shown in FIGS. 9 to 11 is displayed on the display unit 9 shown in FIG. 1, and the display content is visually recognized, so that the status of the cohesive zone during operation of the blast furnace 100 is estimated online. The result can be grasped with high accuracy. For example, by referring to the cohesive zone estimation data 125 shown in FIG. 9, it can be confirmed that the position of the cohesive zone is high in the center portion (arrow portion in FIG. 9) of the blast furnace 100 in the furnace radial direction. By referring to the cohesive zone estimation data 125 shown in FIG. 10, it can be confirmed that the cohesive zone has a shape that sags downward in a portion near the furnace wall of the blast furnace 100 (an arrow portion in FIG. 10). . By referring to the cohesive zone estimation data 125 shown in FIG. 11, it can be confirmed that a part of the layer thickness of the cohesive zone (the arrow portion in FIG. 11) is thickened in the furnace of the blast furnace 100.

  As described above, in the embodiment of the present invention, the furnace flowing through the furnace of the blast furnace during operation of the blast furnace for producing pig iron by charging the furnace with charges such as ore and coke. At least the gas component of the gas is measured along the furnace radial direction, and based on the collected measurement data, the actual data of the gas component used for pig iron production in the furnace gas is calculated, and the calculated actual data of the gas component Based on the above, the charging state of the charge in the furnace of the blast furnace in operation is estimated, and the in-furnace state of the blast furnace in operation is estimated based on the estimated charging state of the charge.

  Therefore, the estimation data of the gas component calculated by the blast furnace model used for estimation of the in-furnace state and the actual data of the gas component calculated based on the measurement data of the operating blast furnace are almost the same. As a charge state of the charge in the inside, the charge layer thickness distribution for each position in the furnace radial direction in the furnace of the operating blast furnace can be estimated in real time and with high accuracy. By providing the estimated data of the charge layer thickness distribution as an input variable to the above blast furnace model, the estimated data of the in-furnace state of the blast furnace can be calculated in real time and with high accuracy. As a result, the in-furnace state of the operating blast furnace can be estimated with high accuracy.

  By using the blast furnace state estimation device and the blast furnace state estimation method according to the embodiment of the present invention, the in-furnace state of the blast furnace in operation, specifically, the shape of the entire in-furnace mixture (charge), Status of ore temperature distribution, pressure distribution, etc., especially the state of the cohesive zone (shape, ore temperature distribution, pressure distribution, etc.) of the in-furnace mixture, in real time and with high accuracy during blast furnace operation be able to. As a result, accurate operation management of the blast furnace becomes possible, and when an abnormality occurs in the furnace of the operating blast furnace, the abnormality in the furnace can be detected at an early stage. Can be addressed as soon as possible.

  In the embodiment described above, the data of the gas in the furnace of the blast furnace 100 is measured by the gas sampler 2 at the timing of data collection. However, the present invention is not limited to this. In the present invention, the gas sampler 2 may always measure the data of the gas in the furnace during the operation period of the blast furnace 100. In this case, the control unit 10 may validate only the data measured at the timing to collect data among all the measurement data by the gas sampler 2, and invalidate the data measured at other timings. The estimation processing unit 3 may perform arithmetic processing using effective measurement data among these measurement data.

  Moreover, in embodiment mentioned above, although data, such as an online estimation result which shows the in-furnace state of the blast furnace 100 in operation, were displayed on the display part 9, this invention is not limited to this. In the present invention, the data such as the above-described online estimation result of the in-furnace state is not limited to the screen display, and may be output by printing on a print medium such as paper, or may be output by both screen display and printing. May be. In this case, the blast furnace state estimation apparatus 1 may include a printing unit instead of the display unit 9 as an example of a data output unit, or may include both the display unit 9 and the printing unit.

  Furthermore, in the above-described embodiment, the thermometer 6 for measuring the furnace body temperature of the blast furnace 100 is provided at one place on the wall portion of the blast furnace 100, but the present invention is not limited to this. One or more thermometers 6 may be disposed on the side wall of the blast furnace 100, or one or more thermometers 6 may be disposed on the lower part of the blast furnace 100. That is, in the present invention, the arrangement location and the number of the thermometers 6 with respect to the blast furnace 100 are not particularly limited.

  Further, the present invention is not limited by the above-described embodiment, and the present invention includes a configuration in which the above-described constituent elements are appropriately combined. In addition, all other embodiments, examples, operation techniques, and the like made by those skilled in the art based on the above-described embodiments are included in the present invention.

DESCRIPTION OF SYMBOLS 1 Blast furnace state estimation apparatus 2 Gas sampler 2a Sonde 3 Estimation processing part 4 Judgment processing part 5 Memory | storage part 5a Input estimation model 5b Blast furnace model 5c Actual data 6 Thermometer 7 Pressure gauge 8 Input part 9 Display part 10 Control part 100 Blast furnace 101 Top bunker 102 Tuyere 110 Charges 111 Ore layer 112 Coke layer 113 In-furnace mixture 115 Pig iron 123 In-core estimated data 125 Cohesive zone estimated data CP Center position

Claims (2)

A gas measuring unit that measures at least the gas component of the gas in the furnace flowing in the furnace of the blast furnace along the radial direction of the furnace during the operation of the blast furnace for producing pig iron by charging the inside of the furnace When,
Collecting measurement data by the gas measurement unit, based on the collected measurement data, as the actual data of the gas component used when manufacturing the pig iron from the charge , along the furnace radial direction The gas utilization rate distribution indicating the pattern shape of the gas utilization rate of the in-furnace gas is calculated, and using the calculated gas utilization rate distribution, as the charged state of the charge in the furnace of the blast furnace in operation , the instrumentation to estimate the thickness distribution shown ore and the layer thickness of the pattern along the furnace radial direction of the coke, the initial charge, based-out the layer thickness distribution of the estimated said charge, said in operation As the in-furnace state of the blast furnace, an estimation processing unit that estimates the state of the cohesive zone formed in the furnace of the blast furnace in operation ;
With
The estimation processing unit estimates the layer thickness distribution of the charge from the gas utilization rate distribution using principal component analysis and multiple regression analysis. A gas measurement step for measuring at least a gas component of the gas in the furnace flowing in the furnace of the blast furnace along the radial direction of the furnace during operation of the blast furnace for producing pig iron by charging a charge in the furnace When,
Collecting measurement data by the gas measurement step, based on the collected measurement data, as actual data of the gas component used when manufacturing the pig iron from the charge , along the furnace radial direction A data collection calculation step for calculating a gas utilization rate distribution indicating a pattern shape of the gas utilization rate of the in-furnace gas ;
Using the gas utilization rate distribution calculated by the data collection calculation step, as the charging state of the charge in the furnace of the blast furnace in operation , the ore and coke as the charge in the furnace radial direction A first estimation step for estimating a layer thickness distribution indicating a pattern shape of the layer thickness along the line ;
Based on the layer thickness distribution of the charge estimated in the first estimating step, the state of the cohesive zone formed in the operating blast furnace furnace is estimated as the operating state of the operating blast furnace. A second estimating step,
Including
It said first estimation step, using principal component analysis and multiple regression analysis, blast furnace state estimation method characterized by estimating the thickness distribution of the charge from the gas utilization rate distribution.

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