Control device and control method

Abstract

By using indicators that precede changes in the control indicators, changes in disturbance estimates related to the control indicators can be quickly predicted, enabling the execution of control operations. [Solution] The system includes a state observation quantity estimation unit that estimates the state variables and first disturbance value related to the first preceding process by inputting a first observation quantity output in response to an operation to a disturbance estimation observer, a virtual process that is connected in parallel with the first and second preceding processes and outputs disturbances superimposed on the second observation quantity in response to the first disturbance value, and estimates a virtual disturbance value which is a disturbance value superimposed on the second observation quantity due to the first disturbance value, based on a state-space model of the virtual process, and inputs the second observation quantity output in response to an operation and the virtual disturbance value to the disturbance estimation observer to estimate the state variables and second disturbance value related to the second preceding process, and an operation quantity determination unit that determines the operation quantity for the next operation based on the estimation results of the state observation quantity estimation unit.

Description

Technical Field

[0001] The present invention relates to a control device and a control method, and more particularly to a control device and a control method capable of promptly predicting a change in a disturbance estimated value related to a management index and executing a control operation by using an index that precedes a change in the management index. Background Art

[0002] In recent blast furnace processes, there is a growing trend toward the use of inexpensive iron ore and operation under conditions of a low reducing agent ratio, and the need to stabilize the furnace condition of blast furnaces is increasing.

[0003] However, operation using inexpensive low-quality raw materials, low reducing agents and the like tends to lead to unstable furnace conditions, and may cause fluctuations in production and a decrease in furnace heat. For example, it is known that various types of disturbances occur due to charged raw materials such as iron ore and coke, causing fluctuations in blast furnace operation. Therefore, in order to realize stable blast furnace operation, it is necessary to perform operation in consideration of these disturbances.

[0004] Further, in blast furnace operation, various operations such as material charging and condition changing are performed on the blast furnace. In order to optimize blast furnace operation, control is performed such that changes occurring as a result of operations are observed as observed variables, and the observed variables are fed back to adjust the manipulated variable for the next operation.

[0005] For example, a change in the amount of tapped iron produced as a result of an operation is observed and used as a management index, and the manipulated variable is adjusted so that the management index is always positioned between an upper threshold and a lower threshold.

[0006] For example, a disturbance estimation observer that estimates a disturbance superimposed on an observed amount of tapped iron is known (see Non-Patent Document 1).

[0007] Further, for example, Patent Document 1 discloses a technique capable of detecting a future decrease in molten iron temperature that is difficult to predict with a physical model and presenting an action for increasing the molten iron temperature.

[0008] Furthermore, the accuracy of the control model (control parameters) and the accuracy of the disturbances applied to the control model are also important for other applications such as process optimization and anomaly diagnosis. Patent document 2 proposes a method for simultaneously estimating control model parameters and disturbances, which allows for simultaneous and more accurate estimation of disturbances and parameters.

[0009] In process control during blast furnace operation, model predictive control using such technologies is employed (see Non-Patent Document 2). [Prior art documents] [Patent Documents]

[0010] [Patent Document 1] Patent No. 7522999 [Patent Document 2] Japanese Patent Publication No. 2016-181247 [Non-patent literature]

[0011] [Non-Patent Document 1] Ikeda, Fujisaki: Multivariable System Control (Corona Publishing Co.) [Non-Patent Document 2] J. Maciejowski: Model Predictive Control (Tokyo Denki University Press) [Overview of the Initiative] [Problems that the invention aims to solve]

[0012] However, various disturbances are superimposed on the observed values ​​of the pig iron production rate (Pmax), which is a typical management indicator for blast furnace operation. If control operations are carried out using these observed values ​​as they are, there is a risk that operational fluctuations may actually be exacerbated.

[0013] In particular, since Pmax is calculated from measured values ​​tracking the amount of charge falling into the furnace, it has a large amount of measurement noise, and is often used as a management indicator for blast furnace operation after some kind of time smoothing (for example, moving average processing).

[0014] On the other hand, since the breathability index has less measurement noise compared to Pmax, the degree of time smoothing is often less.

[0015] One aspect of the present invention aims to realize a technology that enables the rapid prediction of changes in disturbance estimates related to a control indicator using an indicator that precedes changes in the control indicator, and enables the execution of control operations. [Means for solving the problem]

[0016] To solve the above problems, a control device according to one aspect of the present invention is a control device for controlling a process, wherein the process includes a preceding process that outputs an observable quantity that can be observed with a relatively fast time response to an operation or disturbance, and a succeeding process that outputs an observable quantity that can be observed with a relatively slow time response to an operation or disturbance, wherein a first observable quantity that can be observed with a relatively fast time response is output by the first preceding process, and a second observable quantity that can be observed with a relatively slow time response is output by the second preceding process, wherein the first preceding process, the second preceding process and the succeeding process are connected in series, and the control device outputs in response to an operation. The system includes a state observation quantity estimation unit that estimates the state variables and first disturbance value related to the first preceding process by inputting the first observed quantity to a disturbance estimation observer, and a state observation quantity estimation unit that estimates a virtual disturbance value which is a disturbance value superimposed on the second observed quantity due to the first disturbance value, based on a state-space model of a virtual process which is a virtual process that is coupled in parallel with the first preceding process and the second preceding process and outputs a disturbance superimposed on the second observed quantity in accordance with the first disturbance value, and estimates the state variables and second disturbance value related to the second preceding process by inputting the second observed quantity output in accordance with the operation and the virtual disturbance value to the disturbance estimation observer, and an operation quantity determination unit that determines the operation quantity for the next operation based on the estimation result of the state observation quantity estimation unit.

[0017] To solve the above problems, a control method according to one aspect of the present invention is a control method for a control device that controls a process, wherein the process includes a preceding process that outputs an observable quantity that can be observed with a relatively fast time response to an operation or disturbance, and a succeeding process that outputs an observable quantity that can be observed with a relatively slow time response to an operation or disturbance, wherein a first observable quantity that can be observed with a relatively fast time response is output by the first preceding process, and a second observable quantity that can be observed with a relatively slow time response is output by the second preceding process, wherein the first preceding process, the second preceding process and the succeeding process are connected in series, and the control device method outputs in response to an operation. The process includes a state observation estimation step in which the state variables and a first disturbance value relating to the first preceding process are estimated by inputting the first observed quantity into a disturbance estimation observer, and a virtual disturbance value which is a disturbance value superimposed on the second observed quantity due to the first disturbance value, based on a state-space model of a virtual process which is coupled in parallel with the first preceding process and the second preceding process and outputs a disturbance superimposed on the second observed quantity in accordance with the first disturbance value, and the state variables and a second disturbance value relating to the second preceding process are estimated by inputting the second observed quantity output in accordance with the operation and the virtual disturbance value into a disturbance estimation observer, and an operation quantity determination step in which an operation quantity relating to the next operation is determined based on the estimation result of the state observation estimation step. [Effects of the Invention]

[0018] According to one aspect of the present invention, by using an indicator that precedes the change in the control indicator, changes in the disturbance estimate related to the control indicator can be quickly predicted, and control operations can be performed. [Brief explanation of the drawing]

[0019] [Figure 1] This diagram illustrates the overview of the blast furnace process. [Figure 2]This figure shows the relationship between the blast furnace process and the manipulated and observed quantities. [Figure 3] This graph shows the time-dependent changes in the breathability index and Pmax. [Figure 4] This figure illustrates the overview of the operational inputs in the blast furnace process corresponding to the state-space models of equations (3) and (4). [Figure 5] This diagram illustrates the process and operation input overview by breaking down the preceding process P1 in Figure 4 into preceding processes P1-1 and P1-2. [Figure 6] This diagram illustrates the process and operation input overview, including the virtual process P3 which is coupled in parallel with the preceding processes P1-1 and P1-2. [Figure 7] This block diagram shows an example of a functional configuration of a control device that controls the blast furnace process. [Figure 8] This is a flowchart illustrating an example of the control process flow. [Figure 9] This diagram illustrates the ability of the estimated values ​​to track changes in disturbances. [Figure 10] This is a block diagram illustrating the physical configuration of a computer used as a control device. [Modes for carrying out the invention]

[0020] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings. First, an overview of the blast furnace process will be given.

[0021] Figure 1 is a diagram illustrating the blast furnace process. In the blast furnace process, as shown in Figure 1, sintered ore and coke are charged alternately into the blast furnace 101 from the top of the furnace, and hot air and reducing agents such as pulverized coal are blown in from the tuyeres at the bottom of the furnace. The pulverized coal and coke are gasified by this hot air, and high-temperature reducing gases such as carbon monoxide and hydrogen are blown up into the furnace, melting the sintered ore and removing oxygen. The molten iron comes into contact with the carbon in the coke and is reduced, becoming molten iron containing slightly less than 5% carbon, which accumulates in the molten metal reservoir at the bottom of the furnace.

[0022] This molten iron is removed from a tap located on the side of the furnace bottom and transported to the next steelmaking process. Additionally, gases such as coke oven gas (COG) or natural gas (NG) may be blown in through the tuyeres (or other tuyeres).

[0023] Furthermore, in blast furnace operation, operational management indicators are set, and the time evolution of these operational management indicators is monitored. By performing operational operations on the blast furnace process using corresponding control variables, the furnace conditions are controlled. Therefore, setting appropriate operational management indicators is extremely important for the stable operation of the blast furnace. Details of operational management indicators will be described later, but operational management indicators include indicators that should be kept at set target values ​​(control variables in this embodiment) and indicators that are mainly used to grasp changes in furnace conditions (observed variables in this embodiment).

[0024] Figure 2 shows the relationship between the blast furnace process and the manipulated variable u and the observed variable y. As shown in Figure 2, the blast furnace process takes the manipulated variable u as input and outputs the observed variable y. The state of the blast furnace process is denoted as x.

[0025] The manipulated quantity u is a value related to the airflow rate and the amount of pulverized coal (pulverized coal injection amount PCI or pulverized coal ratio PCR), etc.

[0026] The observed quantities y include molten iron temperature, gas composition (e.g., CO concentration, CO2 concentration, N2 concentration, H2 concentration), gas utilization rate (e.g., CO utilization rate, H2 utilization rate), carbon solution loss (CSL), CO utilization rate, H2 utilization rate, amount of iron tapped, molten iron Si, and molten iron temperature.

[0027] In recent years, blast furnace processes have seen a shift towards using inexpensive iron ore and operating under low reducing agent ratio conditions, leading to a growing need to stabilize blast furnace conditions.

[0028] However, operating with inexpensive, low-quality raw materials or low-reducing agents can easily lead to unstable furnace conditions, causing fluctuations in production volume and a decrease in furnace heat. For example, it is known that various types of disturbances can occur due to charging materials such as iron ore and coke, causing fluctuations in blast furnace operation. Therefore, in order to achieve stable blast furnace operation, it is necessary to operate while taking these disturbances into consideration.

[0029] Furthermore, in blast furnace operation, various operations are performed on the blast furnace, such as adding materials and changing conditions. In order to optimize blast furnace operation, control is carried out by observing the changes resulting from these operations as observed quantities and using these observed quantities as feedback to adjust the quantities of operations to be performed in the next operation.

[0030] For example, changes in the amount of molten metal produced as a result of operations are observed and used as a control indicator, and the manipulated variable is adjusted so that the control indicator always falls between the upper and lower thresholds. However, various disturbances are superimposed on the observed value of the amount of molten metal produced (Pmax), which is a typical control indicator for blast furnace operation, and if control operations are performed using this observed value as is, there is a risk that it may actually exacerbate operational fluctuations.

[0031] In particular, since Pmax is calculated from measured values ​​tracking the amount of charge falling into the furnace, it has a large amount of measurement noise, and is often used as a management indicator for blast furnace operation after some kind of time smoothing (for example, moving average processing).

[0032] On the other hand, since the aeration index has less measurement noise compared to Pmax, the degree of time smoothing is often smaller. More specifically, the aeration index may be, for example, the blowing pressure, or measured shaft pressure in the circumferential and vertical directions, the K value representing the aeration resistance inside the furnace, or an index calculated as a statistical quantity based on these. Alternatively, an index such as the stave heat load, furnace body heat load, or shaft heat load (temperature change of equipment for cooling the blast furnace) may be used as the aeration index.

[0033] Due to these circumstances, the ventilation index often appears to change before the control index Pmax. Figure 3 is a graph showing the time evolution of the ventilation index and Pmax. Here, the ventilation index is set to the airflow pressure with the sign reversed.

[0034] In the graph in Figure 3, the horizontal axis represents time, and the vertical axis represents the amount of Pmax or the ventilation index. The changes in Pmax and the ventilation index over time are shown as line graphs.

[0035] As shown in Figure 3, Pmax often rises after the ventilation index rises and falls after the ventilation index falls. In other words, the ventilation index often changes before Pmax.

[0036] In this embodiment, ventilation indicators such as air pressure and K value are used as indicators that precede changes in the Pmax disturbance, which is a control indicator, to quickly predict changes in the Pmax disturbance estimate. In this way, it becomes possible to quickly execute control operations based on the Pmax disturbance estimate, and future fluctuations in furnace heat can be suppressed in advance.

[0037] In this embodiment, the models that can calculate the aeration index, Pmax, and furnace heat index as outputs are those that can be represented by the state-space models of equations (1) and (2) below.

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[0038] If it can be represented by a linear model, the state-space models of equations (1) and (2) can be represented by the state-space models of equations (3) and (4) below.

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[0039] Here, in the state-space models of equations (3) and (4), the blast furnace process can be approximated as a process close to a series connection. Figure 4 is a diagram illustrating the overview of the operational inputs in the blast furnace process corresponding to the state-space models of equations (3) and (4).

[0040] As shown in Figure 4, in controlling the operational inputs during blast furnace operation, Model Predictive Control (MPC) is used to estimate disturbances and states using a disturbance estimation observer, and estimates of disturbances d0^, d1^, and state variables x^ of the blast furnace process are calculated. Here, x^, d0^, and d1^ are represented with a caret (^) above x and d, respectively. Characters with carets will be represented similarly from now on.

[0041] Based on the calculated disturbance estimates d0^, d1^, and x^ of the blast furnace process state variables, the MPC determines the manipulated variable u and applies the manipulated input to the blast furnace process. The target value r may be any value between the upper and lower thresholds of the control indicators, for example.

[0042] The operation corresponding to the manipulated quantity u affects the blast furnace process. The blast furnace process includes a preceding process P1, which is a fast reaction to operations and disturbances, and a succeeding process P2, which is a slow reaction. The preceding process P1 is a process in which the time taken to obtain a response to operations and disturbances is relatively short, while the succeeding process P2 is a process in which the time taken to obtain a response to operations and disturbances is relatively long.

[0043] The preceding process P1 alters aeration indicators such as blower pressure, shaft pressure measurements in the circumferential and vertical directions, K value, and shaft thermal load. Furthermore, the preceding process P1 alters factors such as the amount of iron tapped (Pmax), CO utilization rate, and carbon solution loss (CSL). The subsequent process P2 alters factors such as molten iron Si and molten iron temperature.

[0044] The preceding process P1 corresponds to chemical reaction processes such as the reduction reaction of ore and the consumption reaction of coke, while the subsequent process P2 roughly corresponds to the heat transfer and heat transfer phenomena of the reaction heat generated in the preceding process P1. In the example in Figure 4, the state variable x related to Pmax changed by the preceding process P1 is shown. Pmax However, it is being supplied to the subsequent process P2.

[0045] Observable quantities related to indicators that change due to the preceding process P1 (e.g., aeration index, Pmax, etc.) can be observed relatively shortly after the operation or disturbance occurs, and such observable quantities can be said to be observable with a relatively fast time response to the operation or disturbance. On the other hand, observable quantities related to indicators that change due to the subsequent process P2 (e.g., molten iron temperature) can be observed relatively long after the operation or disturbance occurs, and such observable quantities can be said to be observable with a relatively slow time response to the operation or disturbance.

[0046] In the example in Figure 4, the manipulated variable u, disturbance d0, and disturbance d1 are supplied to the preceding process P1, and the manipulated variable u is supplied to the subsequent process P2. Although disturbances may also be superimposed on the observed variables of the subsequent process P2, this is omitted here.

[0047] The values ​​of indicators such as aeration index, Pmax, gas utilization rate, and CSL, which have changed due to the preceding process P1, are fed back to the Model Predictive Control (MPC) as observed quantities. That is, based on the observed quantities, disturbance and state estimation is performed using a disturbance estimation observer, and estimated values ​​of disturbances and blast furnace state variables are recalculated.

[0048] The disturbance estimate d0^ is calculated by inputting the value of the ventilation index, which is an observed quantity corresponding to the preceding process P1, into the disturbance estimation observer. The disturbance estimate d1^ is calculated by inputting the value of Pmax, for example, which is an observed quantity corresponding to the preceding process P1, into the disturbance estimation observer.

[0049] The estimated state variable x^ of the blast furnace process is calculated by merging the estimated state variable x0^, which is obtained by inputting the values ​​of the aeration index and Pmax corresponding to the preceding process P1 into the disturbance estimation observer, with the estimated state variable x1^.

[0050] Then, based on the calculated disturbance estimates d0^, d1^, and x^ of the blast reactor process state variables, a new manipulated variable u is determined by model predictive control, and the operation input is performed. In this way, the changes resulting from the operation are observed as observed variables, and the manipulated variable for the next operation is adjusted by feeding back these observed variables.

[0051] In this way, the blast furnace process is controlled so that a predetermined indicator (for example, the value of Pmax) is kept close to a preset target value. For example, the blast furnace process is controlled so that the value of a predetermined indicator is always between a preset upper limit and lower limit of the control range.

[0052] When the blast furnace process corresponding to the state-space models of equations (3) and (4) can be approximated as a series combination of a preceding process P1 and a succeeding process P2, as shown in Figure 4, then the coefficient matrices A and C of the state-space models of equations (3) and (4) can be interpreted as having a characteristic block structure. That is, the coefficient matrices A and C each have the block structure shown in equation (5).

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[0053] Figure 5 is a diagram illustrating the process and operational inputs by decomposing the preceding process P1 in Figure 4 into preceding process P1-1 and preceding process P1-2. Here, it is assumed that an aeration index is obtained as the observable quantity corresponding to preceding process P1-1, and that indicators such as gas composition, carbon solution loss, CO utilization rate, H2 utilization rate, and pig iron production (Pmax) are obtained as the observable quantities corresponding to preceding process P1-2. In the following, Pmax will be mainly used as an example to explain the observable quantity corresponding to preceding process P1-2.

[0054] In the example in Figure 5, the manipulated variable u and disturbance d0 are supplied to the preceding process P1-1, the manipulated variable u and disturbance d1 are supplied to the preceding process P1-2, and furthermore, the state variable x related to the ventilation index is supplied from the preceding process P1-1 to the preceding process P1-2. Pressure It is being supplied.

[0055] If the blast furnace process can be approximated as a series combination of preceding processes P1-1, P1-2, and subsequent process P2, as shown in Figure 5, then the coefficient matrices A and C of the state-space model in equations (3) and (4) can be interpreted as having a different characteristic block structure. That is, coefficient matrices A and C each have the block structure shown in equation (6).

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[0056] Furthermore, in the following sections, we will consider each element of the matrix in equation (5). A 11 =Apressure , C 11 =C pressure , A 22 =A Pmax , C 22 =C Pmax described by replacement.

[0057] The temporal change of the ventilation index can be expressed by a state space model represented by formula (7) and formula (8). [Math.]] ...(7) [Math.]] ...(8) Here, x pressure (k) is a state variable, u(k) is a manipulated variable, y pressure (k) is a measured value of the ventilation index, A pressure , B pressure , and C pressure represent coefficient matrices of the discrete-time state space model.

[0058] In formula (7) and formula (8), formulation is performed on the assumption that an output disturbance d0(k) is superimposed to change the observed value of the ventilation index. However, in practice, the disturbance related to the ventilation index is considered to affect Pmax through changes in the state variable x pressure related to the ventilation index.

[0059] Therefore, in order to reconcile the fact that disturbance affects Pmax through changes in the state variable x pressure related to the ventilation index with the formulation that assumes the observed value of the ventilation index changes due to superimposed output disturbance d0(k), formula transformation is performed based on the block structure of coefficient matrix A and coefficient matrix C and formula (7) and formula (8). Accordingly, for ζ which is the temporal change of the influence of disturbance on Pmax through changes in the state variable x pressure related to the ventilation index, ζ dA state-space model with (k) as the observable quantity can be represented by equations (9) and (10), which are driven by d0(k).

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[0060] In the example in Figure 6, the manipulated variable u and disturbance d0 are supplied to the preceding process P1-1, the manipulated variable u and disturbance d1 are supplied to the preceding process P1-2, and furthermore, the state variable x related to the ventilation index is supplied from the preceding process P1-1 to the preceding process P1-2. pressure The disturbance d0 supplied to the preceding process P1-1 is also supplied to the virtual process P3, and the observed quantity ζ output by the virtual process P3 is also supplied. d (k) is superimposed on the observed quantity (e.g., Pmax) output by the preceding process P1-2 when the disturbance d1 is applied.

[0061] That is, the observable quantity ζ d (k) can be considered a disturbance that corresponds to the output disturbance d0(k) and is output by the virtual process P3, and is superimposed on the observed quantity of Pmax, and the virtual disturbance ζ d We will refer to this as (k).

[0062] The only disturbance superimposed on the observed quantities of the preceding process P1-1 is disturbance d0, whereas the disturbances superimposed on the observed quantities of the preceding process P1-2 are disturbance d1 and virtual disturbance ζ. d This is the sum of (k) and (1). Virtual disturbance ζ d (k) can also be described as a disturbance that affects both preceding process P1-1 and preceding process P1-2.

[0063] Virtual disturbance ζ d By using (k), the time evolution of Pmax output by process P1-2 can be represented by the state-space model expressed by equations (11) and (12).

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[0064] The measured value of the ventilation index is given by y in equation (8) pressure By substituting (k) and the manipulated quantity related to the performed operation into u(k) in equation (7), and applying the disturbance estimation observer to the state-space model represented by equations (7) and (8), the disturbance d0(k) and the state variable x are obtained. pressure (k) can be estimated. That is, the estimated value of the disturbance d0^(k) and the estimated value of the state variable x pressure ^(k) is calculated.

[0065] By substituting the estimated disturbance d0(k) obtained from the above estimation into d0(k) in equation (9), and applying the disturbance estimation observer to the state-space model represented by equations (9) and (10), the virtual disturbance ζ d (k) can be estimated, that is, the estimated value ζ of the virtual disturbance. d^(k) is calculated.

[0066] The measured value of Pmax and the virtual disturbance ζ obtained from the above estimation. d (k) is an estimate of y in equation (12) Pmax and ζ d By substituting (k) into and, substituting the manipulated quantity related to the performed operation into u(k) in equation (11), and applying the disturbance estimation observer to the state space model represented by equations (11) and (12), the disturbance d1(k) and the state variable x Pmax (k) can be estimated. That is, the estimated value of the disturbance d1^(k) and the estimated value of the state variable x Pmax ^(k) is calculated.

[0067] And the estimated value x pressure ^(k) and estimated value x Pmax By merging with ^(k), the estimated value of the state variable x^(k) is calculated.

[0068] In the case of Figure 6, in the control of the operation input, disturbance and state estimation is performed using a disturbance estimation observer through process control. As a result, the estimated value of the disturbance d0^ and the estimated value of the virtual disturbance ζ are obtained. d The estimated values ​​of the disturbance d1^ and the state variable x^ are calculated.

[0069] The calculated estimated disturbance d0^ and the estimated hypothetical disturbance ζ d Based on the estimated disturbance d1^ and the estimated state variable x^, the MPC determines the manipulated variable u and applies an operational input to the blast reactor process. This performs a control operation to maintain a predetermined control indicator (e.g., an observed quantity of the preceding process P1-2) at the target value r. The target value r may be any value between the upper and lower thresholds of the control indicator.

[0070] The values ​​of indicators such as the ventilation index, Pmax, gas utilization rate, and CSL, which have been changed by the preceding process P1, are fed back to the MPC as observed quantities. That is, based on the observed quantities, disturbance and state estimation is performed using a disturbance estimation observer, and estimated values ​​of disturbances and state variables are recalculated.

[0071] In this embodiment, the estimated value d0^ of the disturbance related to the preceding process P1-1 and the estimated value ζ of the virtual disturbance that commonly affects both the preceding process P1-1 and the preceding process P1-2 are used. d Since ^ and are supplied to the MPC, it becomes possible to more accurately estimate disturbances superimposed on Pmax, for example.

[0072] (Example of a control device's functional configuration) The control device according to this embodiment includes a state observation quantity estimation unit that inputs a first observation quantity (e.g., ventilation index) output in response to an operation to a disturbance estimation observer to estimate a state variable and a first disturbance value related to a first preceding process (e.g., preceding process P1-1), a virtual process (e.g., virtual process P3) which is coupled in parallel with the first and second preceding processes, and which outputs a disturbance superimposed on a second observation quantity in response to the first disturbance value, and estimates a virtual disturbance value which is a disturbance value superimposed on a second observation quantity (e.g., Pmax) due to the first disturbance value, based on a state-space model of the virtual process, and inputs the second observation quantity and the virtual disturbance value output in response to an operation to the disturbance estimation observer to estimate a state variable and a second disturbance value related to a second preceding process (e.g., preceding process P1-2), and an operation quantity determination unit that determines the operation quantity for the next operation based on the estimation results of the state observation quantity estimation unit.

[0073] Figure 7 is a block diagram showing an example of the functional configuration of a control device for controlling a blast furnace process. The control device 200 shown in Figure 7 is a device that controls the blast furnace process by observing operational indicators. As shown in Figure 7, the control device 200 has a control quantity calculation unit 201 and an operation control unit 202.

[0074] (Control variable calculation unit) The control variable calculation unit 201 includes an estimation unit 211 and a manipulated variable calculation unit 212. The control variable calculation unit 201 is supplied with an observed variable and a manipulated variable. The observed variable is, for example, the observed variable y of the ventilation index. pressure , the observed quantity y of Pmax PmaxThis corresponds to, for example, the control input u output from the MPC.

[0075] (Estimation Department) The estimation unit 211 estimates disturbances and the state of the blast furnace using a disturbance estimation observer based on various observed quantities and manipulated quantities input for the process, and outputs estimated values. The estimation unit 211 is a functional block that corresponds to, for example, the disturbance estimation observer included in the process control shown in Figure 6.

[0076] The estimation unit 211 estimates, for example, the disturbance d0^ and the virtual disturbance ζ. d Calculate the estimated disturbance d1^ and the estimated state variable x^.

[0077] (Disturbance model of the disturbance estimation observer) Generally, when performing estimation using the disturbance estimation observer of the estimation unit 211, it is necessary to specify disturbance models that represent the time evolution of d0(k) and d1(k). A constant-value disturbance model can usually be specified as the disturbance model that represents the time evolution of d1(k).

[0078] As a disturbance model representing the time evolution of d0(k), a ramp-shaped disturbance model assuming d0...=0 is preferable because it improves the ability of the disturbance estimate to track changes in the time series trend. Here, d0... is represented by d0 with two dots (...) above it.

[0079] Furthermore, if it is known in advance that a specific oscillation period occurs for d0(k), then the disturbance model obtained by time discretizing the sine wave model in equation (13) may be specified as the disturbance model representing the time evolution of d0(k), where ω is the angular frequency.

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[0080] (Operation amount calculation section) The manipulated variable calculation unit 212 calculates the manipulated variable based on the estimated values ​​of the disturbance and the state variables supplied from the estimation unit 211. The manipulated variable calculation unit 212 is, for example, a functional block corresponding to the MPC included in the process control in Figure 6. That is, the manipulated variable u is calculated by the manipulated variable calculation unit 212.

[0081] The manipulated quantity u may include, for example, at least one of the airflow rate, oxygen enrichment rate, or pulverized coal injection rate.

[0082] The target value r may be stored, for example, in the internal memory of the manipulated variable calculation unit 212.

[0083] (Operation Control Unit) The operation control unit 202 controls the execution of operations on the blast furnace process. The operation control unit 202 performs the above-described control so that operations are executed according to the operation amount calculated by the operation amount calculation unit 212.

[0084] (Control process flow) Next, an example of a control process executed by the control device 200 according to this embodiment will be described. Figure 8 is a flowchart illustrating an example of the control process flow.

[0085] In step S101, the estimation unit 211 calculates estimates of disturbances and state variables from the observed quantities of process P1-1.

[0086] In this case, for example, the measured value of the ventilation index is given by y in equation (8). pressureBy substituting (k) and the manipulated quantity related to the performed operation into u(k) in equation (7), and applying the disturbance estimation observer to the state-space model represented by equations (7) and (8), the disturbance d0(k) and the state variable x are obtained. pressure (k) is estimated. That is, the estimated disturbance d0^(k) and the estimated state variable x pressure ^(k) is calculated.

[0087] In step S102, the estimation unit 211 calculates an estimated value of a virtual disturbance from the estimated value of the disturbance in process P1-1.

[0088] At this point, for example, by substituting the estimated value of the disturbance d0(k) obtained as a result of the processing in step 101 into d0(k) in equation (9), and applying the disturbance estimation observer to the state space model represented by equations (9) and (10), a virtual disturbance ζ is obtained. d (k) is estimated, that is, the estimated value of the virtual disturbance ζ d ^(k) is calculated.

[0089] In step S103, the estimation unit 211 calculates estimated values ​​of disturbance and state from the observed values ​​of process P1-2 and the estimated values ​​of the virtual disturbance.

[0090] At this time, for example, the measured value of Pmax and the virtual disturbance ζ obtained as a result of the processing in step S102 are used. d The estimated value of (k) and y in equation (12) Pmax and ζ d By substituting (k) into and, substituting the manipulated quantity related to the performed operation into u(k) in equation (11), and applying the disturbance estimation observer to the state space model represented by equations (11) and (12), the disturbance d1(k) and the state variable x Pmax (k) can be estimated. That is, the estimated value of the disturbance d1^(k) and the estimated value of the state variable x Pmax ^(k) is calculated.

[0091] In step S104, the estimation unit 211 calculates a state variable by merging the estimated value of the state variable obtained as a result of the processing in step S102 and the estimated value of the state variable obtained as a result of the processing in step S103.

[0092] In this case, for example, the estimated value x pressure ^(k) and estimated value x Pmax By merging with ^(k), the estimated value of the state variable x^(k) is calculated.

[0093] In step S105, the manipulated variable calculation unit 212 calculates the estimated disturbance d0^(k) calculated in step S101 and the estimated virtual disturbance ζ calculated in step S102. d The manipulated variable u is calculated using ^(k), the estimated disturbance d1^(k) calculated in step S103, and the estimated state variable x^(k) calculated in step S104.

[0094] At this time, for example, a pre-set model calculation is performed to calculate the manipulated quantity u. The manipulated quantity u may be values ​​related to the airflow rate, oxygen enrichment rate, or pulverized coal amount (pulverized coal injection amount PCI or pulverized coal ratio PCR).

[0095] In step S106, the operation control unit 202 controls the execution of the operation related to the manipulated quantity u calculated in step S105.

[0096] Furthermore, the control variables related to the operations whose execution was controlled as a result of the processing in step S106 are fed back to the control variable calculation unit 201, and the processing in steps S101 to S106 is repeatedly executed. The control processing is executed in this manner.

[0097] Figure 9 illustrates the tracking ability of the estimated values ​​in response to changes in disturbances. In this figure, the horizontal axis represents the number of calculations, and the vertical axis represents the estimated value, showing the true value of disturbance d0, the estimated value of disturbance d1, and the virtual disturbance ζ. d The change in the estimated value is shown by the broken line.

[0098] For simplicity, here, the change in disturbance d0 is directly represented as disturbance d1, and the virtual disturbance ζ d This assumes that the following will be reflected: In other words, if a disturbance d0 with a value of 1 is superimposed on the observable of process P1-1, then a disturbance d1 with a value of 1 is superimposed on process P1-2.

[0099] As shown in Figure 9, the true value of the disturbance d0 changes in a stepwise manner when the first calculation (calculation of the estimated value) is performed, changing from 0 to 1, and then remaining at 1. Case 1 in Figure 9 shows the change of disturbance d1 over time in Figure 5, and Case 2 in Figure 9 shows the virtual disturbance ζ in Figure 6. d This shows the change in the disturbance d1 over time.

[0100] In the example in Figure 9, the estimated value of the disturbance d1, shown as Case 1, gradually increases from 0, and the estimated value of the disturbance d1 calculated in the 14th or 15th calculation is 1. On the other hand, the virtual disturbance ζ shown as Case 2 d The estimated value of the disturbance d1 gradually increases from a value of 0, reaching the virtual disturbance ζ in the 8th or 9th calculation. d The estimated value of the disturbance d1 is 1.

[0101] In other words, it takes time for the estimated value of the disturbance d1 to approach the true value, but the virtual disturbance ζ d It takes relatively little time for the estimated value of the disturbance d1 to approach the true value.

[0102] (Effects of the embodiment) In this embodiment, ventilation indicators such as airflow pressure and K value are used as indicators that precede changes in the control indicator Pmax, allowing for rapid prediction of changes in the Pmax disturbance estimate and execution of control operations. This makes it possible to quickly execute control operations based on the Pmax disturbance estimate, thereby suppressing future furnace heat fluctuations in advance.

[0103] Furthermore, in this embodiment, the estimated value of the virtual disturbance ζ dSince the manipulated variable is determined using ^, it becomes possible to perform control operations that predict changes in the Pmax disturbance more quickly and accurately.

[0104] (Other embodiments) In the embodiments described above, the present invention was explained in the context of its application to a blast furnace. However, the application is not limited to blast furnaces, and the present invention may also be applied to shaft furnaces used for direct reduction ironmaking, for example.

[0105] <Examples of implementation using software> The function of the control device 200 is a program that causes the device to function as a computer, and this can be realized by a program that causes each block of the device to function as a computer.

[0106] Figure 10 is a block diagram illustrating the physical configuration of a computer used as a control device 200. As shown in Figure 10, the control device 200 can be configured by a computer comprising a bus 510, a processor 501, a main memory 502, an auxiliary memory 503, a communication interface 504, and an input / output interface 505. The processor 501, main memory 502, auxiliary memory 503, communication interface 504, and input / output interface 505 are connected to each other via the bus 510. An input device 506 and an output device 507 are connected to the input / output interface 505.

[0107] Processor 501 can include, for example, a CPU (Central Processing Unit), a microprocessor, a digital signal processor, a microcontroller, or a combination thereof.

[0108] For the main memory 502, for example, semiconductor RAM (random access memory) can be used.

[0109] For example, the auxiliary memory 503 may be flash memory, an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a combination thereof. The auxiliary memory 503 stores a program that causes the processor 501 to execute the operations of the control device 200 described above. The processor 501 loads the program stored in the auxiliary memory 503 onto the main memory 502 and executes each instruction contained in the loaded program.

[0110] The communication interface 504 is an interface that connects to a network.

[0111] The input / output interface 505 can be, for example, a USB interface, a short-range communication interface such as infrared or Bluetooth®, or a combination thereof.

[0112] For input devices 506, for example, a keyboard, mouse, touchpad, microphone, or a combination thereof may be used. For output devices 507, for example, a display, printer, speaker, or a combination thereof may be used.

[0113] When the functions of the control device 200 are realized by a program that causes the device to function as a computer, the functions described in each of the above embodiments are realized by executing the above program using the processor 501 and the main memory 502.

[0114] The above program may be recorded on one or more computer-readable recording media, not temporary ones. These recording media may or may not be provided by the above device. In the latter case, the program may be supplied to the above device via any wired or wireless transmission medium.

[0115] Furthermore, some or all of the functions of each of the above blocks can also be realized by logic circuits. For example, an integrated circuit in which logic circuits that function as each of the above control blocks are formed is also included in the scope of the present invention.

[0116] Furthermore, some or all of the functions of each of the above blocks may operate on the above device, or they may operate on other devices (e.g., edge computers, cloud servers, etc.).

[0117] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included within the technical scope of the present invention.

[0118] 〔summary〕 A control device according to Embodiment 1 of the present invention is a control device for controlling a process, wherein the process includes a preceding process that outputs an observable quantity observable with a relatively fast time response to an operation or disturbance, and a succeeding process that outputs an observable quantity observable with a relatively slow time response to an operation or disturbance, wherein a first observable quantity observable with a relatively fast time response is output by the first preceding process, and a second observable quantity observable with a relatively slow time response is output by the second preceding process, wherein the first preceding process, the second preceding process, and the succeeding process are connected in series, and the control device outputs the first A control device comprising: a state observation quantity estimation unit that estimates a state variable and a first disturbance value related to the first preceding process by inputting an observed quantity into a disturbance estimation observer; a virtual disturbance value that is a disturbance value superimposed on the second observed quantity due to the first disturbance value, based on a state-space model of a virtual process that is coupled in parallel with the first preceding process and the second preceding process, and outputs a disturbance superimposed on the second observed quantity in accordance with the first disturbance value; and an operation quantity determination unit that estimates an operation quantity related to the second preceding process by inputting the second observed quantity output in accordance with the operation and the virtual disturbance value into a disturbance estimation observer; and an operation quantity determination unit that determines an operation quantity for the next operation based on the estimation result of the state observation quantity estimation unit.

[0119] In the control device according to embodiment 2 of the present invention, in embodiment 1 described above, the first observed quantity is an aeration index, and the second observed quantity is at least one of the following: gas composition, carbon solution loss, CO utilization rate, H2 utilization rate, and pig iron production (Pmax).

[0120] In the control device according to embodiment 3 of the present invention, in embodiment 2 described above, the ventilation index is the air supply pressure, or the shaft pressure measurement value in the circumferential and height directions, the K value, and at least one of the stave heat load, furnace body heat load, and shaft heat load.

[0121] In any of the above embodiments 1 to 3, the control device according to embodiment 4 of the present invention is configured such that the manipulated amount includes at least one of the airflow rate, oxygen enrichment rate, or pulverized coal injection rate.

[0122] In the control device according to embodiment 5 of the present invention, in any of embodiments 1 to 4 described above, the disturbance estimation observer is set to a disturbance model obtained by time discretizing a sinusoidal wave model as a disturbance model representing the time evolution of the first disturbance value.

[0123] A control method according to aspect 6 of the present invention is a control method for a control device that controls a process, wherein the process includes a preceding process that outputs an observable quantity that can be observed with a relatively fast time response to an operation or disturbance, and a succeeding process that outputs an observable quantity that can be observed with a relatively slow time response to an operation or disturbance, wherein a first observable quantity that can be observed with a relatively fast time response is output by the first preceding process, and a second observable quantity that can be observed with a relatively slow time response is output by the second preceding process. The first preceding process, the second preceding process, and the subsequent process are connected in series, and the control device method includes a state observation estimation step in which the first observed quantity output in response to an operation is input to a disturbance estimation observer to estimate the state variables and first disturbance value related to the first preceding process, a virtual process connected in parallel with the first preceding process and the second preceding process, which outputs a disturbance superimposed on the second observed quantity in response to the first disturbance value, and estimates a virtual disturbance value which is a disturbance value superimposed on the second observed quantity due to the first disturbance value, based on a state-space model of the virtual process, and inputs the second observed quantity output in response to an operation and the virtual disturbance value to the disturbance estimation observer to estimate the state variables and second disturbance value related to the second preceding process, and an operation quantity determination step in which the operation quantity for the next operation is determined based on the estimation result of the state observation estimation step. [Explanation of Symbols]

[0124] 200 Control device 201 Control Variable Calculation Unit 202 Operation Control Unit 211 Estimation Department 212 Operation amount calculation section

Claims

1. A control device for controlling a process, The process includes a preceding process that outputs an observable quantity that responds relatively quickly to an operation or disturbance, and a succeeding process that outputs an observable quantity that responds relatively slowly to an operation or disturbance. Of the observed quantities output by the preceding process, a first observed quantity that can be observed with a relatively fast time response is output by the first preceding process, and of the observed quantities output by the preceding process, a second observed quantity that can be observed with a relatively slow time response is output by the second preceding process. The first preceding process, the second preceding process, and the subsequent process are assumed to be connected in series. The control device is By inputting the first observed quantity output in response to the operation into the disturbance estimation observer, the state variable related to the first preceding process and the first disturbance value are estimated. A virtual process coupled in parallel with the first and second preceding processes, which outputs a disturbance superimposed on the second observable in response to the first disturbance, estimates a virtual disturbance value which is a disturbance value superimposed on the second observable due to the first disturbance value, based on the state-space model of the virtual process. A state observation quantity estimation unit estimates the state variables and the second disturbance value related to the second preceding process by inputting the second observation quantity output in response to the operation and the virtual disturbance value to a disturbance estimation observer, The system includes an operation quantity determination unit that determines the operation quantity for the next operation based on the estimation result of the state observation quantity estimation unit. Control device.

2. The first observed quantity is an air permeability index, The second set of observed quantities are gas composition, carbon solution loss, CO utilization rate, and H2. ２ At least one of the following: utilization rate and / or pig iron production (Pmax). The control device according to claim 1.

3. The ventilation index is the airflow pressure, or the measured shaft pressure in the circumferential and vertical directions, the K value, and at least one of the stave heat load, furnace body heat load, and shaft heat load. The control device according to claim 2.

4. The aforementioned operating amount includes at least one of the airflow rate, oxygen enrichment rate, or pulverized coal injection rate. The control device according to claim 1.

5. The disturbance estimation observer is configured to use a disturbance model obtained by time-discretizing a sinusoidal wave model as the disturbance model representing the time evolution of the first disturbance value. The control device according to claim 1.

6. A control method for a control device that controls a process, The process includes a preceding process that outputs an observable quantity that responds relatively quickly to an operation or disturbance, and a succeeding process that outputs an observable quantity that responds relatively slowly to an operation or disturbance. Of the observed quantities output by the preceding process, a first observed quantity that can be observed with a relatively fast time response is output by the first preceding process, and of the observed quantities output by the preceding process, a second observed quantity that can be observed with a relatively slow time response is output by the second preceding process. The first preceding process, the second preceding process, and the subsequent process are assumed to be connected in series. The aforementioned control device method is By inputting the first observed quantity output in response to the operation into the disturbance estimation observer, the state variable related to the first preceding process and the first disturbance value are estimated. A virtual process coupled in parallel with the first and second preceding processes, which outputs a disturbance superimposed on the second observable in response to the first disturbance, estimates a virtual disturbance value which is a disturbance value superimposed on the second observable due to the first disturbance value, based on the state-space model of the virtual process. A state observation estimation step in which the state variables related to the second preceding process and the second disturbance value are estimated by inputting the second observation quantity output in response to the operation and the virtual disturbance value into a disturbance estimation observer, The process includes an operation quantity determination step, which determines the operation quantity for the next operation based on the estimation results of the state observation quantity estimation step. Control method.

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