Concentration estimation device, concentration estimation method, and program

Abstract

To estimate an oxygen concentration in a heating furnace.SOLUTION: A concentration estimation device for estimating an oxygen concentration in a heating furnace has: a model creation unit that creates a model for estimating the oxygen concentration in the heating furnace with an oxygen concentration measurement value y(t) in the heating furnace at each time t within a predetermined period in the past and predetermined n (n is a predetermined integer of 1 or more) physical quantity measurement values x1(t), x2(t), ..., xn(t) related to the heating furnace as learning data; and an oxygen concentration estimation unit that calculates an estimation value of the oxygen concentration in the heating furnace with the n physical quantity measurement values x1(t'), x2(t'), ..., Xn(t') at a time t' for estimation of the oxygen concentration.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a concentration estimation device, a concentration estimation method, and a program. [Background technology]

[0002] In order to achieve both energy saving and pollution prevention in a heating furnace, it is important to control the oxygen (O2) concentration inside the heating furnace, and for this purpose, various methods are used to measure the oxygen concentration. For example, the oxygen concentration inside the heating furnace is measured using a zirconia-type oxygen concentration meter (see Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] "Instrumentation Tips | Zirconia Oxygen Analyzer" (Internet)<URL:https: / / www.m-system.co.jp / mstoday / plan / mame / b_sensor / 9407 / index.html> Summary of the Invention [Problem to be solved by the invention]

[0004] Generally, to achieve both energy conservation and pollution prevention, it is necessary to operate a heating furnace at a low oxygen concentration, but when using a zirconia oxygen analyzer, it is necessary to operate it at a certain oxygen concentration. This is because if the oxygen concentration becomes too low, incomplete combustion occurs, and the zirconia element causes an oxidation reaction using carbon monoxide, a combustible gas, and unburned combustible fuel as a catalyst, which can result in an erroneous measurement in which the measured oxygen concentration becomes close to zero.

[0005] An embodiment of the present invention has been made in view of the above points, and aims to estimate the oxygen concentration in a heating furnace. [Means for solving the problem]

[0006] In order to achieve the above object, a concentration estimation device according to one embodiment is an oxygen concentration estimation device that estimates an oxygen concentration in a heating furnace, and calculates an oxygen concentration measurement value y(t) in the heating furnace at each time t within a predetermined period in the past and a predetermined number n (n is a predetermined integer of 1 or more) of physical quantity measurement values ​​x1(t), x2(t), . . . , x n a model creation unit that creates a model for estimating the oxygen concentration in the heating furnace using the n physical quantity measurement values ​​x1(t'), x2(t'), . . . , x at time t' that is the target of oxygen concentration estimation; n and an oxygen concentration estimation unit that calculates an estimated value of the oxygen concentration in the heating furnace using (t') and the model. [Effects of the Invention]

[0007] The oxygen concentration in the heating furnace can be estimated. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram showing an example of the overall configuration of a heating furnace control system according to an embodiment of the present invention; [Figure 2] 3A and 3B are diagrams schematically showing the relationship between the excess air ratio and heat loss and the relationship between the excess air ratio and thermal efficiency. [Figure 3] FIG. 10 is a diagram schematically illustrating an example of installation of an oxygen concentration meter and peripheral sensors in a heating furnace. [Figure 4] FIG. 4 is a diagram schematically showing the relationship between the air-fuel ratio and the oxygen concentration measurement value. [Figure 5] FIG. 1 is a diagram illustrating an example of a hardware configuration of an oxygen concentration estimation device according to an embodiment of the present invention. [Figure 6] FIG. 2 is a diagram illustrating an example of a functional configuration of the oxygen concentration estimation device according to the present embodiment. [Figure 7] 4 is a flowchart illustrating an example of an oxygen concentration estimation process in the first embodiment. [Figure 8] 10 is a flowchart illustrating an example of an oxygen concentration estimation process in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] An embodiment of the present invention will be described below, which will be described as follows: A heating furnace control system 1 including an oxygen concentration estimation device 10 that estimates the oxygen concentration in a heating furnace.

[0010] <Overall configuration of heating furnace control system 1> An example of the overall configuration of a heating furnace control system 1 according to this embodiment is shown in Fig. 1. As shown in Fig. 1, the heating furnace control system 1 according to this embodiment includes an oxygen concentration estimation device 10, a control device 20, an adjustment device 30, a heating furnace 40, an oxygen concentration meter 50, and a peripheral sensor 60.

[0011] The heating furnace 40 is, for example, a facility or device used in various plants (petrochemical plants, various manufacturing plants, etc.), and consumes fuel and oxygen to heat an object to be heated (for example, a fluid such as oil) through furnace combustion. Note that the heating furnace 40 is not limited to these, and may be, for example, a boiler, an incinerator, etc.

[0012] The oxygen concentration meter 50 is a device that measures the oxygen concentration inside the heating furnace 40. In the following, it is assumed that a laser oxygen concentration meter 501 and a zirconia oxygen concentration meter 502 are installed in the heating furnace 40 as the oxygen concentration meter 50. However, the zirconia oxygen concentration meter 502 does not have to be installed. In the following, it is also assumed that the oxygen concentration meter 50 (the laser oxygen concentration meter 501 and the zirconia oxygen concentration meter 502) will be removed from the heating furnace 40 after the performance data described below has been created and saved.

[0013] Furthermore, the oxygen concentration meter 50 transmits the measurement value (hereinafter also referred to as the oxygen concentration measurement value) measured at each time t to the oxygen concentration estimation device 10. Hereinafter, the oxygen concentration measurement value measured by the laser oxygen concentration meter 501 at time t is defined as y1(t), and the oxygen concentration measurement value measured by the zirconia oxygen concentration meter 502 is defined as y2(t).

[0014] The laser oximeter 501 is an oximeter that uses laser absorption spectroscopy. The laser oximeter 501 has a light-emitting unit that emits laser light and a light-receiving unit that receives the laser light, and measures the oxygen concentration from the amount of laser light lost between the light-emitting unit and the light-receiving unit.

[0015] The peripheral sensors 60 are various devices that measure various physical quantities around or in the vicinity of the heating furnace 40 and various physical quantities of equipment related to the heating furnace 40. The peripheral sensors 60 also transmit measurement values ​​(hereinafter also referred to as physical quantity measurement values) measured at each time t to the oxygen concentration estimation device 10. Hereinafter, the total number of peripheral sensors 60 is n, and when the peripheral sensors 60 are not distinguished from one another, they are referred to as "peripheral sensors 60," and when the peripheral sensors 60 are to be distinguished from one another, they are referred to as "peripheral sensor 601," "peripheral sensor 602," ..., "peripheral sensor 60." n For i=1, 2, . . . , n, at time t, the peripheral sensor 60 i The physical quantity measured at x i (t). Here, examples of physical quantities measured by the peripheral sensor 60 include the temperature inside the heating furnace 40, the temperature of the object to be heated, the pressure inside the heating furnace 40, the flow rate of fuel, the flow rate of the object to be heated, and the flow rate of exhaust gas. However, these physical quantities are merely examples, and the physical quantities measured by the peripheral sensor 60 are not limited to these. For example, the outside air temperature, wind speed, etc. may also be measured as physical quantities.

[0016] At each time t for which performance data is to be created and stored, the oxygen concentration estimation device 10 receives oxygen concentration measurement values ​​y1(t) and y2(t) from each oxygen concentration meter 50, and also receives physical quantity measurement values ​​x1(t), x2(t), . . . , x from each peripheral sensor 60. n (t) and creates and saves performance data from these measurement values.

[0017] Furthermore, the oxygen concentration estimation device 10 receives physical quantity measurement values ​​x1(t), x2(t), . . . , x from the surrounding sensors 60 at each time t for which the oxygen concentration is to be estimated. n(t) is received, and the oxygen concentration estimate at time t is calculated from these measurements.

[0018]

number

[0019] That is, the oxygen concentration estimation device 10 estimates the oxygen concentration by calculating the physical quantity measurement values ​​x1(t), x2(t), . . . , x n It functions as a software sensor that calculates an estimated oxygen concentration value y^(t) from (t). Hereinafter, the model for estimating oxygen concentration will be referred to as the oxygen concentration estimation model and represented by f. In addition, below, the time t at which the oxygen concentration is estimated will be represented as "t'" to distinguish it from the time t at which actual data is created and saved.

[0020] The control device 20 calculates the optimal manipulated variable for the adjustment device 30 based on the oxygen concentration estimated value y^(t') received from the oxygen concentration estimation device 10, and outputs a control command including the manipulated variable to the adjustment device 30. The adjustment device 30 is an air damper (or air valve) that adjusts the amount of air (oxygen) flowing into the heating furnace 40, and a fuel valve that adjusts the amount of fuel flowing into the heating furnace 40. The manipulated variable of the air damper is its opening / closing angle (or opening / closing amount), while the manipulated variable of the air valve is its opening / closing angle (or opening / closing amount).

[0021] The control device 20 may output a control command to both the air damper and the fuel valve, or may output a control command to either one of them (i.e., either the air damper or the fuel valve). In particular, for example, if the fuel flow rate is constant during operation of the heating furnace 40, the control device 20 may output a control command only to the air damper.

[0022] Here, the heat loss and thermal efficiency within the heating furnace 40 fluctuate depending on the amount of fuel and air flowing into the heating furnace 40, and as a result, the oxygen concentration within the heating furnace 40 also fluctuates. For this reason, the control device 20 calculates the manipulated variable for the adjustment device 30 using a known control technique so as to optimize the heat loss and thermal efficiency within the heating furnace 40, and outputs the manipulated variable to the adjustment device 30. There are various known control techniques such as this, and an example is the technique described in Japanese Patent No. 6135831.

[0023] In general, the relationship between the excess air ratio and heat loss, and the relationship between the excess air ratio and thermal efficiency, are shown in Figure 2. In Figure 2, the vertical axis represents heat loss or thermal efficiency, and the horizontal axis represents the excess air ratio. The excess air ratio is the ratio of the amount of air actually flowing into the heating furnace 40 to the theoretical amount of air required for combustion per unit of fuel. In Figure 2, line 1001 represents heat loss due to excess air, and curve 1002 represents heat loss due to incomplete combustion. As shown by line 1001, the greater the excess air ratio is above 1, the greater the heat loss, as heat escapes and heats the excess air. On the other hand, as shown by curve 1002, a small excess air ratio causes incomplete combustion, resulting in greater heat loss due to CO generation, and when it exceeds a certain threshold, smoke is generated. Also in Figure 2, curve 2001 represents the thermal efficiency of the heating furnace 40. As shown by curve 2001, thermal efficiency is maximized in region D, which includes an excess air ratio where the heat loss due to excess air and the heat loss due to incomplete combustion are approximately the same, and decreases as the excess air ratio moves away from region D. Therefore, control device 20 calculates the manipulated variable of adjustment device 30 so that the heat loss and thermal efficiency are within region D, and outputs a control command including the manipulated variable to adjustment device 30.

[0024] The overall configuration of the heating furnace control system 1 shown in FIG. 1 is an example, and is not limited to this. For example, various facilities, equipment, devices, etc. not shown may be included.

[0025] <Example of installation of oxygen concentration meter 50 and peripheral sensor 60 in heating furnace 40> An example of the installation of the oxygen concentration meter 50 and the peripheral sensor 60 relative to the heating furnace 40 is shown in Fig. 3. In the example shown in Fig. 3, a laser oxygen concentration meter 501 and a zirconia oxygen concentration meter 502 are installed. In addition, in the example shown in Fig. 3, an exhaust gas flow meter 601, a heated object flow meter 602, a fuel supply flow meter 603, an in-furnace pressure meter 604, an in-furnace thermometer 605, an in-furnace thermometer 606, and a heated object thermometer 607 are installed as peripheral sensors 60.

[0026] It goes without saying that the example of installation of the oxygen concentration meter 50 and the peripheral sensor 60 shown in FIG. 3 is just an example, and the present invention is not limited to this.

[0027] <Relationship between air-fuel ratio and oxygen concentration measurement> The air-fuel ratio is the ratio of air to fuel (air / fuel). The relationship between the air-fuel ratio and the measured oxygen concentration is shown in Figure 4. In Figure 4, the vertical axis represents the air-fuel ratio or oxygen concentration, and the horizontal axis represents time. As shown in Figure 4, when the air-fuel ratio falls below a certain value (i.e., when the amount of oxygen becomes small), the measurement value of the zirconia oxygen concentration meter 502 falls near 0, resulting in an erroneous measurement. This is because when the oxygen concentration becomes too low, incomplete combustion occurs, and as a result, the zirconia element undergoes an oxidation reaction using carbon monoxide and unburned combustible fuel as a catalyst. On the other hand, the laser oxygen concentration meter 501 does not experience such erroneous measurements.

[0028] Therefore, in this embodiment, when the measurement value of the zirconia oxygen analyzer 502 is close to 0 (that is, when |y2(t)|<ε for a certain small value ε>0), the measurement value of the laser oxygen analyzer 501 is used as training data (correct data) to create the oxygen concentration estimation model f. Note that when the measurement value of the zirconia oxygen analyzer 502 is not close to 0, either y1(t) or y2(t) may be used as training data, or the average value of y1(t) and y2(t) may be used as training data.

[0029] <Hardware Configuration of Oxygen Concentration Estimation Device 10> An example of the hardware configuration of the oxygen concentration estimation device 10 according to this embodiment is shown in Fig. 5. As shown in Fig. 5, the oxygen concentration estimation device 10 according to this embodiment includes an input device 101, a display device 102, an external I / F 103, a communication I / F 104, a processor 105, and a memory device 106. These pieces of hardware are connected to each other via a bus 107 so as to be able to communicate with each other.

[0030] The input device 101 is, for example, a keyboard, a mouse, a touch panel, various physical buttons, etc. The display device 102 is, for example, a display, a display panel, etc. Note that the oxygen concentration estimation device 10 does not necessarily have to include at least one of the input device 101 and the display device 102, for example.

[0031] The external I / F 103 is an interface with an external device such as a recording medium 103a. Examples of the recording medium 103a include a CD (Compact Disc), a DVD (Digital Versatile Disk), an SD memory card (Secure Digital memory card), and a USB (Universal Serial Bus) memory card.

[0032] The communication I / F 104 is an interface for connecting the oxygen concentration estimation device 10 to a communication network. The processor 105 is, for example, a variety of arithmetic devices such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The memory device 106 is, for example, a variety of storage devices such as an SSD (Solid State Drive), RAM (Random Access Memory), ROM (Read Only Memory), and flash memory.

[0033] 5 is an example, and the oxygen concentration estimation device 10 may have other hardware configurations. For example, the oxygen concentration estimation device 10 may have multiple processors 105 and multiple memory devices 106, or may have various types of hardware other than the hardware shown in the figure.

[0034] <Functional configuration of the oxygen concentration estimation device 10> An example of the functional configuration of the oxygen concentration estimation device 10 according to this embodiment is shown in Fig. 6. As shown in Fig. 6, the oxygen concentration estimation device 10 according to this embodiment includes a performance data creation unit 201, a model creation processing unit 202, and an oxygen concentration estimation unit 203. These units are realized, for example, by a processor 105 executing one or more programs installed in the oxygen concentration estimation device 10. The oxygen concentration estimation device 10 according to this embodiment also includes a performance data storage unit 204. The storage unit 204 is realized, for example, by the memory device 106, but may also be realized by a database server connected via a communications network.

[0035] At each time t for which performance data is to be created and saved, the performance data creation unit 201 receives oxygen concentration measurement values ​​y1(t) and y2(t) from each oxygen concentration meter 50, and also receives physical quantity measurement values ​​x1(t), x2(t), . . . , x from each peripheral sensor 60. n (t), and from these measurements, the actual data {y(t),x1(t),x2(t),...,x n (t)} is created and stored in the performance data storage unit 204. Here, for a certain small value ε>0, at time t where |y2(t)|<ε, y(t)=y1(t) is set. On the other hand, at other times t, either y(t)=y1(t) or y(t)=y2(t) may be set, or y(t)=(y1(t)+y2(t)) / 2 may be set, or y(t)=min(y1(t),y2(t)) or y(t)=max(y1(t),y2(t)) may be set. Note that y(t) is training data.

[0036] In the following, the actual data is z(t):=(y(t),x1(t),x2(t),···,x nAssume that the performance data storage unit 204 stores a performance data set {t(t)|t∈T1} as a time (t)). Here, T1 is a set of times for which performance data is to be created and saved. For example, if performance data is to be created and saved from a certain time t1 to t2, T1 is expressed as T1={t|t1≦t≦t2}.

[0037] The model creation processing unit 202 creates an oxygen concentration estimation model f from the actual data set {z(t)|t∈T1}. Here, the model creation processing unit 202 includes a data acquisition unit 211 and a model creation unit 212. The data acquisition unit 211 acquires the actual data set {z(t)|t∈T2} used to create the oxygen concentration estimation model f from the actual data storage unit 204. Here, T2 is a set of time periods of the actual data used to create the oxygen concentration estimation model f, and T2 ⊆ T1. The model creation unit 212 creates the oxygen concentration estimation model f using the actual data set {z(t)|t∈T2} acquired by the data acquisition unit 211. Note that the oxygen concentration estimation model f is, for example, a statistical model or a machine learning model, and has a parameter θ to be learned.

[0038] The oxygen concentration estimation unit 203 receives physical quantity measurement values ​​x1(t'), x2(t'), . . . , x from the surrounding sensors 60 at each time t' for which the oxygen concentration is to be estimated. n (t'), and receive the measured values ​​of these physical quantities x1(t'), x2(t'), . . ., x n (t') and the oxygen concentration estimation model f to calculate the oxygen concentration estimated value y^(t'). That is, the oxygen concentration estimation unit 203 calculates the oxygen concentration estimated value y^(t') from the physical quantity measured values ​​x1(t'), x2(t'), . . . , x n (t') is the explanatory variable and the oxygen concentration is the target variable, y^(t')=f(x1(t'),x2(t'),···,x n The oxygen concentration estimated value y^(t') at time t' is calculated using the calculated oxygen concentration estimated value y^(t'). The oxygen concentration estimated value y^(t') is output to the control device 20.

[0039] <Oxygen concentration estimation process (Example 1)> The oxygen concentration estimation process in the first embodiment will be described below with reference to Fig. 7. In the following, it is assumed that an actual data set {z(t)|t∈T1} for a certain time period T1 in the past is stored in the actual data storage unit 204. Steps S101 and S102 in Fig. 7 are model creation processes that are performed in advance. Meanwhile, steps S103 and S104 are repeatedly performed for each time t' (i.e., current time t') at which the oxygen concentration is to be estimated.

[0040] The data acquisition unit 211 of the model creation processing unit 202 acquires the performance data set {z(t)|t∈T2} used to create the oxygen concentration estimation model f from the performance data storage unit 204 (step S101). Here, the data acquisition unit 211 may acquire the performance data set {z(t)|t∈T1} itself stored in the performance data storage unit 204 as the performance data set {z(t)|t∈T2}, or may acquire a performance data set consisting of some of the performance data as the performance data set {z(t)|t∈T2}.

[0041] The model creation unit 212 of the model creation processing unit 202 creates an oxygen concentration estimation model f using the actual data set {z(t)|t∈T2} acquired in step S101 (step S102). That is, the model creation unit 212 uses the actual data set {z(t)|t∈T2} as a learning data set and calculates the oxygen concentration estimation value y^(t)=f(x1(t),x2(t), ,x n The parameter θ is learned so as to reduce the error between y(t) and y(t). Here, the oxygen concentration estimation model f can be a linear model such as a multiple regression model, or a nonlinear model such as support vector regression (SVR) or a neural network. In general, the relationship between the oxygen concentration and the temperature inside the heating furnace 40 changes continuously, and the characteristics of each physical quantity inside the heating furnace 40 change depending on the oxygen concentration. Therefore, linear models often result in low estimation accuracy. For this reason, it is particularly preferable to use a nonlinear model as the oxygen concentration estimation model f.

[0042] The oxygen concentration estimation unit 203 estimates the oxygen concentration by calculating the physical quantity measurement values ​​x1(t'), x2(t'), . . . , x at the current time t'. n (t') is received (step S103).

[0043] The oxygen concentration estimation unit 203 estimates the physical quantity measurement values ​​x1(t'), x2(t'), . . . , x received in step S103. n (t') and the oxygen concentration estimation model f to calculate the oxygen concentration estimated value y^(t') at the current time t' (step S104). That is, the oxygen concentration estimation unit 203 calculates y^(t')=f(x1(t'),x2(t'), . . . ,x n The oxygen concentration estimated value y^(t') at the current time t' is calculated using the calculated oxygen concentration estimated value y^(t'). This oxygen concentration estimated value y^(t') is output to the control device 20, which controls the manipulated variable for the adjustment device 30. In this way, the oxygen concentration in the heating furnace 40 is controlled.

[0044] <Oxygen concentration estimation process (Example 2)> The oxygen concentration estimation process in the second embodiment will be described below with reference to Fig. 8. In the following, it is assumed that a performance data set {z(t)|t∈T1} for a certain time period T1 in the past is stored in the performance data storage unit 204. Note that steps S201 to S204 in Fig. 8 are repeatedly performed for each time t' (i.e., current time t') at which the oxygen concentration is to be estimated.

[0045] The oxygen concentration estimation unit 203 estimates the oxygen concentration by calculating the physical quantity measurement values ​​x1(t'), x2(t'), . . . , x at the current time t'. n (t') is received (step S201).

[0046] The data acquisition unit 211 of the model creation processing unit 202 acquires the actual data set {z(t)|t∈T2} used to create the oxygen concentration estimation model f from the actual data storage unit 204 (step S202). Here, the data acquisition unit 211 acquires the physical quantity measurement values ​​x1(t'), x2(t'), . . . , x received in the above step S201.n A predetermined number of performance data items are acquired from the performance data storage unit 204 in order of proximity to (t'), and the performance data set consisting of these acquired performance data items is set as performance data set {z(t)|t∈T2}.

[0047] Specifically, the measured physical quantities x1(t'), x2(t'), , x n (t') and actual data z(t) = (y(t),x1(t),x2(t),...,x n (t)) is the distance d(t',t) = ((x1(t')-x1(t)) 2 +(x2(t')-x2(t)) 2 +···+(x n (t')-x n (t)) 2 ) 1 / 2 Then, N (e.g., 100) pieces of z(t) are acquired from the performance data storage unit 204 in ascending order of distance, and a performance data set consisting of these acquired performance data z(t) is designated as performance data set {z(t)|t∈T2}. Note that, although Euclidean distance is used as the distance d in the above, a distance other than Euclidean distance may also be used.

[0048] The model creation unit 212 of the model creation processing unit 202 creates an oxygen concentration estimation model f using the actual data set {z(t)|t∈T2} acquired in step S202 (step S203). That is, the model creation unit 212 uses the actual data set {z(t)|t∈T2} as a learning data set and calculates the oxygen concentration estimation value y^(t)=f(x1(t),x2(t), ,x nThe parameter θ is learned so as to reduce the error between y(t) and y(t). Here, as the oxygen concentration estimation model f, for example, a local linear model such as JIT-PLS (Just-in-time PLS) or LW-PLS (Locally-Weighted PLS) can be adopted. In general, the relationship between the oxygen concentration and the furnace temperature, etc., changes continuously within the heating furnace 40, and the characteristics of each physical quantity within the heating furnace 40 change depending on the oxygen concentration. Therefore, the estimation accuracy of a (global) linear model is often low. For this reason, it is preferable to adopt, as the oxygen concentration estimation model f, a linear model (local linear model) that uses particularly local learning data.

[0049] The oxygen concentration estimation unit 203 estimates the physical quantity measurement values ​​x1(t'), x2(t'), . . . , x received in step S201. n (t') and the oxygen concentration estimation model f created in step S203 (step S204). That is, the oxygen concentration estimation unit 203 calculates the oxygen concentration estimated value y^(t') at the current time t' from y^(t')=f(x1(t'),x2(t'),...,x n The oxygen concentration estimated value y^(t') at the current time t' is calculated using the calculated oxygen concentration estimated value y^(t'). This oxygen concentration estimated value y^(t') is output to the control device 20, which controls the manipulated variable for the adjustment device 30. In this way, the oxygen concentration in the heating furnace 40 is controlled.

[0050] <Modification> In the above embodiment, the oxygen concentration meter 50 is removed after the performance data is created and saved, but it is also possible to remove, for example, only the laser oxygen concentration meter 501 and not the zirconia oxygen concentration meter 502. In this case, the oxygen concentration measurement value y2(t') of the zirconia oxygen concentration meter 502 may also be used as an explanatory variable.

[0051] Specifically, the actual data is z(t):=(y(t)=y1(t),y2(t),x1(t),x2(t),···,x n (t)), y(t) is the objective variable, y2(t),x1(t),x2(t),...,xn (t) is used as an explanatory variable to create an oxygen concentration estimation model f. Then, when calculating the oxygen concentration estimate, y^(t')=f(y2(t'),x1(t'),x2(t'),...,x n The oxygen concentration estimate y^(t') at the current time t' is calculated using the following equation: y^(t)=g(x1(t),x2(t),...,x n (t)), and if |y2(t')| < ε, then y^(t') = g(x1(t'), x2(t'), , x n (t')) and calculate y^(t')=f(y2(t'),x1(t'),x2(t'),···,x if and only if |y2(t')| ≥ ε. n (t')) may be calculated.

[0052] <Summary> As described above, in the heating furnace control system 1 according to this embodiment, an oxygen concentration estimation model f is created using the measurement values ​​of the laser oxygen analyzer 501, which can stably measure oxygen concentrations even at low oxygen concentrations, as training data, and the oxygen concentration inside the heating furnace 40 is estimated using this oxygen concentration estimation model f. This makes it possible to accurately estimate the oxygen concentration even at low oxygen concentrations using the oxygen concentration estimation model f, instead of using the zirconia oxygen analyzer 502, which may erroneously measure oxygen concentrations at low oxygen concentrations, and makes it possible to achieve optimal control of the heating furnace 40 that achieves both energy conservation and pollution prevention.

[0053] Moreover, after the oxygen concentration estimation model f is created, the laser oxygen analyzer 501 can be removed from the heating furnace 40, so that, for example, the laser oxygen analyzer 501 can be installed in another heating furnace 40 and an oxygen concentration estimation model f can be created in the same way. Therefore, there is no need to prepare a laser oxygen analyzer 501, which is generally expensive, for each heating furnace 40, and the oxygen concentration estimation model f can be created relatively inexpensively.

[0054] The present invention is not limited to the above-described specifically disclosed embodiments, and various modifications, changes, and combinations with known technologies are possible without departing from the scope of the claims. [Explanation of symbols]

[0055] 1. Furnace control system 10. Oxygen concentration estimation device 20 Control device 30 Adjustment equipment 40 Furnace 50 Oxygen concentration meter 60 Peripheral Sensor 101 Input Device 102 Display device 103 External I / F 103a Recording media 104 Communication I / F 105 processors 106 Memory Device 107 Bus 201 Performance Data Creation Department 202 Model creation processing section 203 Oxygen concentration estimation unit 204 Performance data storage unit 211 Data Acquisition Unit 212 Model Creation Department

Claims

1. An oxygen concentration estimation device for estimating an oxygen concentration in a heating furnace, The oxygen concentration measurement value y(t) in the heating furnace at each time t within a predetermined period in the past and a predetermined number n (n is a predetermined integer of 1 or more) of physical quantity measurement values ​​x related to the heating furnace are calculated. １ (t), x ２ (t), ..., x ｎ a model creation unit that creates a model for estimating the oxygen concentration in the heating furnace using (t) as learning data; The n physical quantity measurement values ​​x at time t' for which the oxygen concentration is to be estimated １ (t'), x ２ (t'), ..., x ｎ an oxygen concentration estimation unit that calculates an estimated value of the oxygen concentration in the heating furnace using (t') and the model; and the heating furnace includes a laser oxygen concentration meter that is installed so as to be removable after the learning data is created; The oxygen concentration measurement value y(t) is a measurement value y 1 (t) of the oxygen concentration inside the heating furnace measured by the laser oxygen analyzer.

2. The heating furnace is further equipped with a zirconia oxygen concentration meter, The oxygen concentration in the heating furnace measured by the zirconia oxygen concentration meter is expressed as y ２ As (t), For a predetermined value ε>0, |y ２ If (t)|<ε, then y(t)=y １ (t), |y ２ If (t) | ≥ ε, then y(t) = y １ (t), y(t)=y ２ (t),y(t)=(y １ (t) + y ２ (t)) / 2, y(t)=min(y １ (t), y ２ (t)), or y(t)=max(y １ (t), y ２ 2. The concentration estimation device according to claim 1, wherein the concentration estimation device is one of the following:

3. The model creation unit The n physical quantity measurement values ​​x at time t' for which the oxygen concentration is to be estimated １ (t'), x ２ (t'), ..., x ｎ (t'), and the n physical quantity measurement values ​​x at each time t within a predetermined period in the past １ (t), x ２ (t), ..., x ｎ Calculate the distance d(t', t) from (t) The oxygen concentration measurement value y(t) and the n physical quantity measurement values ​​x(t) corresponding to a predetermined number of t's in ascending order of the distance d(t', t) are calculated. １ (t), x ２ (t), ..., x ｎ 3. The concentration estimation device according to claim 1, wherein the model is created using (t) and (t) as learning data.

4. An oxygen concentration estimation device for estimating the oxygen concentration in a heating furnace, The oxygen concentration measurement value y(t) in the heating furnace at each time t within a predetermined period in the past and a predetermined number n (n is a predetermined integer of 1 or more) of physical quantity measurement values ​​x related to the heating furnace are calculated. １ (t), x ２ (t), ..., x ｎ a model creation procedure for creating a model for estimating the oxygen concentration in the heating furnace using (t) as learning data; The n physical quantity measurement values ​​x at time t' for which the oxygen concentration is to be estimated １ (t'), x ２ (t'), ..., x ｎ an oxygen concentration estimation step of calculating an estimated value of the oxygen concentration in the heating furnace using (t') and the model; Run the heating furnace includes a laser oxygen concentration meter that is installed so as to be removable after the learning data is created; The concentration estimation method, wherein the oxygen concentration measurement value y(t) is a measurement value y 1 (t) of the oxygen concentration inside the heating furnace measured by the laser oxygen analyzer.

5. An oxygen concentration estimation device that estimates the oxygen concentration in a heating furnace, The oxygen concentration measurement value y(t) in the heating furnace at each time t within a predetermined period in the past and a predetermined number n (n is a predetermined integer of 1 or more) of physical quantity measurement values ​​x related to the heating furnace are calculated. １ (t), x ２ (t), ..., x ｎ a model creation procedure for creating a model for estimating the oxygen concentration in the heating furnace using (t) as learning data; The n physical quantity measurement values ​​x at time t' for which the oxygen concentration is to be estimated １ (t'), x ２ (t'), ..., x ｎ an oxygen concentration estimation step of calculating an estimated value of the oxygen concentration in the heating furnace using (t') and the model; Execute the heating furnace includes a laser oxygen concentration meter that is installed so as to be removable after the learning data is created; The oxygen concentration measurement value y(t) is a measurement value y 1 (t) of the oxygen concentration inside the heating furnace measured by the laser oxygen analyzer.

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