System performance monitoring device and system performance monitoring method

Abstract

To evaluate system performance highly accurately by separating actual fluctuation of a system and an instrument error.SOLUTION: A system performance monitoring device 1 includes: an input unit 11 that inputs measurement values of a plurality of instruments installed in a system; and a measurement error reduction unit 12 configured to calculate a correction value in which an error of measurement values of the plurality of instruments is reduced based on the measurement values of the plurality of instruments and a constraint condition obtained by modeling a characteristic of the system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a system performance monitoring device and a system performance monitoring method. [Background technology]

[0002] In thermal power plants and nuclear power plants, there is a growing need to quantitatively monitor system functions in order to streamline plant maintenance.

[0003] Patent Document 1 describes an invention in which true values ​​are estimated based on the actual measured values ​​of detector signals output from a plurality of detectors provided in a plant, using true value estimation models. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-338049 Summary of the Invention [Problem to be solved by the invention]

[0005] In the case of a plant's seawater cooling system, the state of the system changes due to fluctuations in seawater temperature. Even when the state changes in this way, it is important to distinguish between instrument error and actual fluctuations in order to monitor system performance with high accuracy.

[0006] Another technology that has been put into practical use to reduce measurement errors is data validation and reconciliation (DVR). DVR technology is a measurement error reduction method that calibrates instrument errors using information from redundant existing sensors and constraints to obtain reliable values.

[0007] DVR technology requires redundancy of measurement values ​​and constraints, but due to the difficulty of setting these up, there have been no examples of DVR technology being applied to system monitoring. Specifically, monitoring system flow rate, pump performance, and heat exchanger performance using DVR technology requires a combination of remotely measured data from a process computer and on-site instrument data measured at specified locations in the system during patrol inspections.

[0008] However, DVR technology is based on the premise of heat balance at a given time, and since the time at which field instrument data is acquired is not the same, DVR technology cannot be applied to plants as is. Therefore, it was not possible to apply DVR technology to plants and evaluate system performance by separating actual fluctuations from instrument errors. Therefore, an object of the present invention is to evaluate system performance with high accuracy by separating actual fluctuations from instrument errors. [Means for solving the problem]

[0009] In order to solve the above-mentioned problems, the system performance monitoring device of the present invention is And measure remotely Multiple remote Instrument readings and measurements of field instruments installed at predetermined locations and acquired by workers. an input means for inputting the an estimation means for calculating an estimated value of the field meter at a predetermined time based on constraints that model the characteristics of the system and time-series data of the measurement values ​​of the remote meter; The aforementioned remote Measurement values ​​of the instrument, the constraints and the estimated value of the field meter calculated by the estimation means. and a measurement error reduction means for calculating a correction value that reduces the error in the measurement value of the instrument based on the above.

[0010] The system performance monitoring method of the present invention is And measure remotely Multiple remote Instrument readings and the measured values ​​of field instruments installed at predetermined locations in the system and acquired by workers are input by an input means. accepting an input; calculating an estimated value of the field meter at a predetermined time from constraints that model the characteristics of the system and time-series data of the measurement values ​​of the remote meter input from the input means; The aforementioned The remote input received by the input means Measurement values ​​of the instrument, the above constraints , and the field instrument estimate and a step in which the measurement error reducing means calculates a correction value that reduces the error in the measurement value of the instrument based on the calculated value. Other means will be described in the detailed description of the invention. [Effects of the Invention]

[0011] According to the present invention, it is possible to evaluate system performance with high accuracy by separating actual fluctuations from instrument errors. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a block diagram of a system performance monitoring device according to an embodiment of the present invention. [Figure 2] FIG. 10 is a diagram showing the relationship between the measured values ​​and errors of each instrument. [Figure 3] 10 is a flowchart of a system performance monitoring process. [Figure 4] 10 is a flowchart of a process for reducing an error in a measurement value. [Figure 5] 10 is a graph showing estimated values ​​of a physical model. [Figure 6] 10 is a graph showing deviation correction. [Figure 7] 1 is a graph showing DVR ratings. [Figure 8] FIG. 10 is a diagram showing measurement data before applying DVR evaluation. [Figure 9] FIG. 10 shows estimates of measurement data after applying DVR ratings. [Figure 10] FIG. 1 is a diagram showing a system including a pump and a pressure sensor. [Figure 11] 1 is a graph showing the relationship between flow rate and head. [Figure 12] 10 is a graph showing a method for detecting pump deterioration. [Figure 13] FIG. 1 is a diagram showing a system including a heat exchanger. [Figure 14] FIG. 10 is a diagram showing a system relating to pressure loss characteristics. [Figure 15] 10 is a graph showing the relationship between flow rate and pressure loss. [Figure 16] FIG. 2 is a diagram showing a system related to a turbine. [Figure 17] 1 is a graph showing the relationship between flow rate and stage pressure. [Figure 18] FIG. 2 is a diagram showing a system relating to a valve. [Figure 19]10 is a graph showing the relationship between flow rate and valve opening degree. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. FIG. 1 is a block diagram of a system performance monitoring device 1 according to this embodiment. A system performance monitoring device 1 is connected to a pipe 2 so as to be able to remotely receive data from a plurality of flow meters 3a to 3d installed on the pipe 2. The pipe 2 also has a field flow meter 4a that is temporarily installed at a predetermined location by an operator during patrol. The system performance monitoring device 1 is configured to include an input unit 11, a measurement error reduction unit 12, a field meter measurement error setting unit 13, an estimation unit 14, a deterioration detection unit 15, and a memory unit 16. The memory unit 16 stores a physical model 161 in which the constraints of the system including the pipe 2 are modeled.

[0014] The input unit 11 inputs the measured values ​​of the plurality of flow meters 3a to 3d installed in the piping 2. Here, the piping 2 is a system, and the plurality of flow meters 3a to 3d are instruments. The on-site flow meter 4a is an on-site instrument that measures on-site instrument data at a predetermined location. The on-site flow meter 4a is used by workers to obtain the indicated value at a predetermined location when they make a patrol inspection at the site, etc. The measured values ​​of the plurality of flow meters 3a to 3d have a predetermined error from the true value.

[0015] The measurement error reduction unit 12 calculates an estimated value of the flow rate in the pipe 2 by reducing the errors in the measurement values ​​of the plurality of flow meters 3a-3d through DVR evaluation based on the measurement values ​​of the plurality of flow meters 3a-3d and constraint conditions that model the characteristics of the system. The measurement error reduction unit 12 inputs the field instrument data of the field flow meter 4a along with the signal data and constraint conditions of the flow meters 3a-3d, and calculates an estimated value by reducing the errors in the measurement values ​​of the plurality of flow meters 3a-3d. Note that the error in the measurement value here refers to the deviation between the measurement value and the true value.

[0016] After inspecting the system, the worker acquires field meter data multiple times using the field flow meter 4a. The field meter measurement error setting unit 13 calculates the average measurement value and measurement error of the field flow meter 4a based on the acquired data multiple times. The estimation unit 14 estimates the measurement data value at the evaluation time at the location of the on-site flow meter 4a from the constraint conditions and the time-series data of the signals of each meter. Here, the on-site flow meter 4a is a on-site meter that is used by workers to measure predetermined locations during patrol inspections.

[0017] The deterioration detection unit 15 detects deterioration of a system based on the measured values ​​of the system. The deterioration detection unit 15 issues an alarm when it detects deterioration of the system. Note that when it detects deterioration of a system, the deterioration detection unit 15 may switch from this system to another system. This allows the plant to continue operating by switching from the deteriorated system to another healthy system.

[0018] The storage unit 16 is a large-capacity storage device such as a hard disk, and stores a physical model 161 of this system.

[0019] This invention focuses on the fact that at least one data item in a system is continuous measurement data received remotely, and that field instrument data is associated with remote measurement data via a physical model, etc. The problem of this embodiment is addressed in three phases: (1) physical model construction, (2) deviation correction, and (3) DVR performance evaluation.

[0020] (1) Physical model construction In the physical model construction phase, the plant manager constructs a physical model 161, which is a constraint condition for DVR evaluation to link measurement data with field instrument data, such as heat balance, pump QH characteristics, heat balance equations and heat transfer equations for heat exchangers, and route pressure drop-flow rate relationship equations. The plant manager stores this physical model 161 in advance in the memory unit 16 of the system performance monitoring device 1.

[0021] (2) Deviation correction In the deviation correction phase, an operator acquires field instrument data immediately after an equipment inspection when instrument and equipment abnormalities can be ignored, and inputs the data to the system performance monitoring device 1. The system performance monitoring device 1 then combines the field instrument data and the measurement data to correct the deviation of the physical model 161, and matches the characteristics of the physical model 161 to the sound state of the actual equipment. The operator also acquires this field instrument data multiple times from each field instrument and inputs it to the system performance monitoring device 1. The system performance monitoring device 1 calculates the standard deviation from the variance in the field instrument data, and sets the error of the field instrument.

[0022] (3) DVR performance evaluation In the DVR performance evaluation phase, the system performance monitoring device 1 interpolates the values ​​at the same time of each field instrument data separately acquired during the patrol inspection through a time series curve created from the remote measurement data and the physical model 161. The system performance monitoring device 1 further setting The DVR evaluation is performed based on the constraints of the physical model 161 from the on-site instrument data and remote measurement data with time interpolated using the above-mentioned method. The system performance monitoring device 1 calculates the system flow rate from the deviation between the estimated value of the flow rate, etc. obtained by the physical model characteristics tailored to the actual equipment and the DVR evaluation value, and monitors the deterioration of the equipment, etc.

[0023] According to the present invention, it is possible to appropriately monitor system functions without installing additional instruments. Furthermore, according to the present invention, it is possible to evaluate system performance with high accuracy by separating actual fluctuations from instrument errors. Furthermore, it is possible to rationalize inspection intervals by quantitatively monitoring pump performance degradation and heat exchanger performance degradation, which are important for system function monitoring, based on actual equipment operating data.

[0024] FIG. 2 shows the relationship between the measurement values ​​and errors of each instrument. The graphs, from left to right, show flow meters 3a, 3b, 3c, and 3d. The dashed lines and hatching in the graphs indicate the frequency distribution of the measured values ​​and errors of flow meters 3a, 3b, 3c, and 3d. The solid line in the left graph shows the distribution of estimated values ​​after DVR correction using the measured values ​​of flow meters 3a only. The solid line in the second graph from the left shows the distribution of estimated values ​​after DVR correction using the measured values ​​of flow meters 3a and 3b. The solid line in the third graph from the left shows the distribution of estimated values ​​after DVR correction using the measured values ​​of flow meters 3a, 3b, and 3c. The solid line in the fourth graph from the left shows the distribution of estimated values ​​after DVR correction using the measured values ​​of flow meters 3a, 3b, 3c, and 3d. The vertical axis of the graph shows the measured values ​​or estimated values ​​after DVR correction. The horizontal axis of the graph shows the frequency with which the values ​​are measured.

[0025] The origin, where the thick dashed horizontal axis and the vertical axis intersect, is the true value. As shown in the legend, the dashed lines and hatching indicate the distribution of the measured values ​​of each instrument. The solid line indicates the distribution of the estimated values ​​after DVR correction.

[0026] The difference between the peak of the distribution of the measured value and the true value is the bias error e1. The spread of the distribution of the estimated value is the random error e2. The sum of the bias error e1 and the random error e2 is the maximum error of the measured value.

[0027] As shown in Figure 2, the bias error and random error of each meter are dispersed from the graph of flow meter 3a on the left to the graph of flow meter 3d on the right. In contrast, the bias error and random error of the estimated value after DVR correction both become smaller as the redundancy of the measurement values ​​increases.

[0028] FIG. 3 is a flowchart of the system performance monitoring process. First, the input unit 11 inputs the measured values ​​of a plurality of meters installed in the piping 2, which is a system (step S10). Then, the input unit 11 determines whether or not there is on-site meter data measured by the on-site flow meter 4a (step S11). If there is field instrument data (Yes), the input unit 11 accepts the input of the field instrument data (step S12) and proceeds to step S13. In step S12, the field instrument measurement error setting unit 13 sets an estimated value and error of the field instrument data from the field instrument data acquired multiple times. If there is no field instrument data (No), the input unit 11 proceeds to step S15.

[0029] The estimation unit 14 determines whether the acquired field instrument data is at the evaluation time (step S13). If the acquisition time of the field instrument data is not at the evaluation time (No), the estimation unit 14 estimates the field instrument data at the evaluation time measured by the field flow meter 4a from the time series data of the measurement values ​​of multiple meters installed in the system (step S14), and proceeds to step S15. If it is at the evaluation time (Yes), the estimation unit 14 proceeds to step S15.

[0030] In step S15, the measurement error reduction unit 12 calculates estimated values ​​in which the errors in the measurement values ​​of the multiple meters are reduced based on constraint conditions that model the characteristics of the system. Here, the measurement error reduction unit 12 calculates estimated values ​​in which the errors in the measurement values ​​are calibrated using DVR evaluation.

[0031] In step S16, the deterioration detection unit 15 determines whether the system has deteriorated. If the deterioration detection unit 15 determines that the system has deteriorated (Yes), it issues an alarm that the system has deteriorated (step S17) and ends the processing in Fig. 3. If the deterioration detection unit 15 determines that the system has not deteriorated (No), it ends the processing in Fig. 3.

[0032] The estimation unit 14 of this embodiment estimates the error by performing multiple measurements. setting Using the obtained field instrument data, an estimated value is calculated from the set physical model and remotely measured data. Furthermore, the estimation unit 14 interpolates the estimated value of the field instrument data at the DVR evaluation time from the time series curve. Using the interpolated estimated value of the field instrument data, the remotely measured data, and the physical model, an error reduction evaluation is performed by the measurement error reduction means, and the system flow rate is estimated with high accuracy and the equipment performance is monitored.

[0033] FIG. 4 is a flowchart of the process for reducing errors in the measurement values. First, the input unit 11 inputs measurement values ​​of a plurality of meters installed in the system (step S20). Here, the input unit 11 inputs measurement values ​​of the flow meters 3a to 3d shown in FIG. 1. Then, an operator measures field instrument data multiple times using the field flow meter 4a (step S21) and inputs the acquired field instrument data to the system performance monitoring device 1. The input unit 11 accepts the input of the field instrument data (step S22).

[0034] The field instrument measurement error setting unit 13 calculates the field instrument data from the field instrument data acquired multiple times after the system inspection. Mistake Calculate the difference. The estimation unit 14 calculated Estimates of field instrument data, The field instrument measurement error setting unit 13 calculates The process of FIG. 4 is terminated when estimated values ​​of parameters such as system flow rate are calculated based on the errors, the measurement values ​​of flow meters 3a to 3d, which are multiple instruments that measure remotely, and constraints that model the characteristics of the system (step S23).

[0035] 《Data Validation Reconciliation (DVR) Explained》 In continuous process plants such as oil refineries, petrochemical plants, general chemical plants, and power plants, real-time measurements of flow rate, temperature, pressure, and liquid level are required to understand the operating state of the process. These measurements inevitably contain errors. Data validation reconciliation (DVR) is a technique for obtaining better estimates of the operating state of a process by applying a physical model 161 that describes the material and energy balances to these raw measurements. In other words, it utilizes the redundancy of the number of measurement points for the physical model 161 to obtain a set of estimates that satisfy all the relationship equations of the physical model 161 and minimize the amount of correction to the original measurements. This section explains an example of reducing instrument errors using field instruments Y and Z measured at the system site.

[0036] Figure 5 is a graph showing the estimated values ​​of a physical model of a certain system. The vertical axis of the graph represents the system flow rate. The horizontal axis of the graph represents time. The solid line in the graph indicates the estimated value of the physical model. The dashed line indicates the true value. The estimated value of the physical model has a certain deviation from the true value due to various error factors.

[0037] Figure 6 is a graph showing deviation correction. The vertical axis of the graph represents the system flow rate. The horizontal axis of the graph represents time. Time tc is the timing when the equipment was inspected. The dashed-dotted line in the graph indicates an estimated value estimated using a physical model set up from remotely measured values. The solid line in the graph indicates an estimated value estimated from deviation-corrected values ​​estimated from the physical model. The dashed line indicates the true value. Area 51 is a plurality of field measurement data measured by a worker using field instruments at a certain patrol time. Area 52 is a plurality of field measurement data measured by a worker using field instruments at a different patrol time. By using this plurality of field measurement data and applying deviation correction to the estimated values ​​from the physical model, it is possible to estimate a value close to the true value.

[0038] FIG. 7 is a graph showing DVR evaluation. The vertical axis of the graph represents the system flow rate. The horizontal axis of the graph represents time. Time tc is the timing when the worker inspected the equipment. Time te is the timing when the DVR evaluation was performed.

[0039] The solid line on the graph represents the true value. The circular icon represents the field measurement data measured by a worker using field instrument Y. The fine dashed line on the upper side is a data estimation curve based on the field measurement data measured by a worker using field instrument Y. This data estimation curve allows the field instrument data at the location of field instrument Y at time te, which is the DVR evaluation timing, to be estimated.

[0040] The triangle icon indicates the field measurement data measured by field instrument Z. The rough dashed line below the solid line is a data estimation curve based on the field measurement data measured by field instrument Z. This data estimation curve allows the field measurement data at the location of field instrument Z at the DVR evaluation timing to be estimated.

[0041] According to this embodiment, it is now possible to monitor the performance of systems other than the thermal cycle, such as the auxiliary cooling water system and seawater system, which was previously difficult, without installing additional instruments. Furthermore, by quantitatively monitoring the deterioration of pump and heat exchanger performance, it is possible to rationalize inspection intervals.

[0042] <Example of measuring pipe flow rate using a flow meter> FIG. 8 is a diagram showing measurement data before applying DVR evaluation. There is a simple system as shown in Figure 8, and flow meters 31a to 31c are installed in the piping of each system to measure the mass flow rate. The measured value A of flow meter 31a is 50 t / h. The measured value B of flow meter 31b is 25 t / h. The measured value C of flow meter 31c is 20 t / h.

[0043] The error of the flow meter 31a is ±10%, the error of the flow meter 31b is ±5%, and the error of the flow meter 31c is ±3%.

[0044] Pipe A branches into pipe B and pipe C. In this case, the equation A=B+C holds as the equation that represents the material balance (physical model). Therefore, there are three variables and one equation that defines their relationship, so if there are 3-1=2 measurement points, the flow rate at the other measurement points can theoretically be calculated. In other words, the degrees of freedom of this process are 2. If all three variables are measured, there is one more measurement value than the degrees of freedom of the process, so the measurement redundancy is 1.

[0045] On the other hand, actual flow measurements, due to their uncertainty, do not satisfy the process material balance equation. This situation is common in system measurements. DVR evaluation takes advantage of this measurement redundancy to reduce the uncertainty of individual measurements while obtaining best estimates that satisfy the physical model. These best estimates can also be used to calculate process performance indicators and estimate unmeasured variables.

[0046] DVR evaluation involves finding the best estimates of process variables using the least squares method as follows when there are more measurement points than the degrees of freedom of the process (redundant measurement points exist), as in the above example: Since a physical model generally becomes a nonlinear simultaneous equation, a nonlinear least squares problem is solved. Equation (1) is the basic equation for the consistency.

number

[0047] Equation (2) is an equation related to the constraints. The constraints are configured from a physical model of the system. Here, the physical model is a turbine model, etc.

number

[0048] FIG. 9 shows the estimated values ​​of the measurement data after applying the DVR evaluation. The estimated value of the measurement data after applying DVR evaluation for flow meter 31a is 46.27 t / h. The estimated value of the measurement data after applying DVR evaluation for flow meter 31b is 25.93 t / h. The estimated value of the measurement data after applying DVR evaluation for flow meter 31c is 20.34 t / h.

[0049] The error of flow meter 31a is ±5.04 %in The error of the flow meter 31b is ±4.51%. The error of the flow meter 31c is ±2.90%.

[0050] In the examples of Figures 8 and 9, if the "uncertainty" of each measurement value can be estimated, it becomes possible to perform the DVR evaluation described above. The DVR evaluation yields estimated values ​​at each measurement point that satisfy the material balance and have smaller uncertainties than the original measurement values.

[0051] In this way, when there are more measurement points than the process model and its degrees of freedom, DVR evaluation makes it possible to obtain more reliable measurement point estimates that satisfy the process model by providing the uncertainty of the measurement values ​​at each measurement point.In addition, it also makes it possible to calculate process performance indicators and unmeasured variables, which can be calculated as functions of the obtained estimates, along with their uncertainties.

[0052] In the examples of Figures 8 and 9, even if one of the flow rate measurement points becomes unmeasurable due to a meter failure or other reason, measurement redundancy still remains, so it is possible to perform DVR evaluation. In other words, it is possible to estimate the flow rate at the unmeasurable measurement point as an unmeasured variable. However, in this case, the uncertainty of each estimated value will be larger overall than if there were no meter failure.

[0053] Below, we will explain an example of calculating the flow rate from other measured values ​​via a physical model, which makes it possible to calculate the flow rate at locations where no flow meter is installed.

[0054] <<Example of measuring flow rate from pump performance curve>> In this embodiment, the system is a pipe including a pump. The deterioration detection unit 15 can detect the deterioration of the pump from the deviation between the discharge flow rate calculated from the pump performance curve and the system flow rate calculated by the measurement error reduction unit 12.

[0055] FIG. 10 is a diagram showing a system including a pump and a pressure sensor. A pump 22 is installed in the pipe 2, and a pressure sensor 32a is installed downstream of the pump 22, and a pressure sensor 32b is installed downstream of the pump 22.

[0056] FIG. 11 is a graph showing the relationship between the flow rate and the head. The vertical axis of the graph represents the head. The horizontal axis of the graph represents the flow rate. Here, the head refers to the differential pressure between the inlet and outlet of pump 22. The differential pressure can be calculated from the difference between the measurement value of pressure sensor 32a and the measurement value of pressure sensor 32b. The lower the differential pressure, the greater the flow rate, and here the relationship is as shown in equation (3).

number

[0057] When the differential pressure is ΔP1, the flow rate Q1 can be calculated by substituting the differential pressure ΔP1 into equation (3).

[0058] FIG. 12 is a graph showing a method for detecting pump deterioration. The solid line indicates the QH characteristics of the pump 22 in a healthy state, and the dashed line indicates the QH characteristics of the pump 22 in a deteriorated state.

[0059] If there is a deviation between the system flow evaluation value Q2 obtained by DVR evaluation using various physical models and the flow rate Q1 discharged by a healthy pump 22, which is estimated from the differential pressure at the pump inlet and outlet using a physical model of the pump 22 performance curve, the QH characteristic curve of the pump 22 is deemed to be depressed, and pump deterioration is detected.

[0060] <<Example of determining flow rate from heat transfer characteristics of a heat exchanger>> In this embodiment, the measurement error reducing unit 12 calculates the flow rate of the cooling water using the definition equation of the heat exchange amount of the heat exchanger as a constraint condition, and the deterioration detecting unit 15 detects the deterioration of this heat exchanger.

[0061] FIG. 13 is a diagram showing a system including the heat exchanger 23. Heat exchanger 23 exchanges heat between cooling water flowing through pipe 2a installed horizontally in the figure and cooling water flowing through pipe 2b installed vertically in the figure. The flow rate of pipe 2a is G1.

[0062] Thermometer 33a is installed at the inlet of pipe 2a, and thermometer 33b is installed at the outlet. The measurement value of thermometer 33a is T1. The measurement value of thermometer 33b is T2. Thermometer 33c is installed at the inlet of pipe 2b, and thermometer 33d is installed at the outlet. The measurement value of thermometer 33c is T3. The measurement value of thermometer 33d is T4.

[0063] Equation (4) is the first heat transfer equation for this heat exchanger 23.

number

[0064] From equation (4), since the heat transfer coefficient k and the heat transfer area A are known, the logarithmic mean temperature difference θm can be calculated from the measured values ​​T1, T2, T3, and T4, and the heat exchange amount Q can be calculated. Equation (5) is the second heat transfer equation for this heat exchanger 23.

number

[0065] According to the formula (5), the flow rate G1 can be calculated from the heat exchange amount Q calculated by the formula (4) and the inlet / outlet temperature difference ΔT. That is, the measurement error reducing unit 12 calculates the flow rate based on the performance curve of the heat exchanger 23. The performance of the heat exchanger 23 is expressed by the heat transfer coefficient k in equation (4). In the heat transfer characteristic equation (4), if the flow rate G1 is known from other model characteristics and DVR evaluation, the heat exchange amount Q can be found using only equation (5). In equation (4), if Q, A, and θm are known, k can be found, and if the value of k decreases continuously, a decrease in the performance of the heat exchanger 23 (heat transfer tube fouling) can be detected.

[0066] The reason why the input of on-site instrument data is necessary is that the inlet and outlet temperatures of the heat exchanger 23 can only be measured by on-site instruments, and there are cases where an instrument that can measure them remotely is not installed.

[0067] <<Example of calculating flow rate from pressure loss characteristics>> FIG. 14 is a diagram showing a system relating to pressure loss characteristics. A pressure gauge 34a and a pressure gauge 34b are installed in the pipe 24. In this system, a pressure loss occurring region is included between the installation location of the pressure gauge 34a and the installation location of the pressure gauge 34b. The measured value of pressure gauge 34a is P3. The measured value of pressure gauge 34b is P4. The pressure difference between the two is ΔP3. The equation for calculating the pressure loss is shown in equation (6).

number

[0068] FIG. 15 is a graph showing the relationship between the flow rate and the pressure loss. The vertical axis of the graph represents pressure loss, and the horizontal axis of the graph represents flow rate. In this case, the larger the pressure loss, the larger the flow rate. The solid line on the graph can be expressed by the following equation (7).

number

[0069] The flow rate Q3 at that time can be calculated by calculating ΔP3 from the measured values ​​of the pressure gauges 34a and 34b and substituting the calculated value into equation (7). The reason why the field instrument data needs to be input is that the pressure gauges 34a and 34b are field instruments, and there are cases where a remotely measuring instrument is not installed.

[0070] <<Example of measuring flow rate from turbine characteristics>> FIG. 16 is a diagram showing a system related to the turbine 25. The turbine 25 is Step by step and the specified stage flow A quantity Q5 flows, and a predetermined Stage pressure A force P5 is generated.

[0071] Figure 17 shows the flow rate and Stage pressure 10 is a graph showing the relationship between force and temperature. The vertical axis of the graph is the turbine 25 Stage pressureThe horizontal axis of the graph indicates the flow rate. stage flow The quantity Q5 is approximately proportional to the pressure and can be expressed by the following equation (8).

number

[0072] Therefore, Stage pressure From Power P5 stage flow In other words, the measurement error reduction unit 12 calculates the amount Q5 of the turbine 25. Stage pressure Calculate the flow rate based on the force.

[0073] <<Example of measuring flow rate from valve opening>> FIG. 18 is a diagram showing a system relating to the valve 26. The piping is provided with a valve 26. The valve 26 is provided with a valve opening detector 36a. The valve opening detector 36a detects the valve opening degree O. CV A flow meter 36b is provided on the outlet side of this valve 26. The flow meter 36b detects a flow rate Q7.

[0074] FIG. 19 is a graph showing the relationship between the flow rate and the valve opening. The vertical axis of the graph represents the valve opening. The horizontal axis of the graph represents the flow rate. The valve opening and the flow rate satisfy the relationship in equation (9).

number

[0075] Therefore, the measurement error reduction unit 12 CV The flow rate Q7 can be calculated from

[0076] (Variation) The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the described configurations. It is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is also possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0077] The above-described configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware such as an integrated circuit. The above-described configurations, functions, etc. may be realized by software by a processor interpreting and executing a program that realizes each function. Information such as the programs, tables, and files that realize each function can be stored in a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), or on a storage medium such as a flash memory card or a DVD (Digital Versatile Disk).

[0078] In each embodiment, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are interconnected. [Explanation of symbols]

[0079] 1 System performance monitoring device 11 Input section (input means) 12 Measurement error reduction unit (measurement error reduction means) 13 Field instrument measurement error setting unit (field instrument measurement error setting means) 14 Estimation part (estimation means) 15 Deterioration detection unit (deterioration detection means) 16 Memory section 161 Physical Model 2 Piping 3a~3d Flow meter (meter) 4a Field flow meter (field instrument)

Claims

1. an input means for inputting measurement values ​​of a plurality of remote meters installed in the system and measuring remotely, and measurement values ​​of field meters installed at predetermined locations and acquired by an operator; an estimation means for calculating an estimated value of the field meter at a predetermined time based on constraints that model the characteristics of the system and time-series data of the measurement values ​​of the remote meter; a measurement error reduction means for calculating a correction value that reduces an error in the measurement value of the meter based on the measurement value of the remote meter, the constraint conditions, and the estimated value of the field meter calculated by the estimation means; A system performance monitoring device comprising:

2. a field instrument measurement error setting means for acquiring measurement values ​​of the field instrument multiple times after inspection of the system and calculating an error in the measurement values ​​of the field instrument; 2. The system performance monitoring device according to claim 1, further comprising:

3. a deterioration detection means for detecting deterioration of the system based on a measurement value of the system; 2. The system performance monitoring device according to claim 1.

4. the system includes a pump; the deterioration detection means detects deterioration of the pump from a deviation between a discharge flow rate calculated from a performance curve of the pump and a system flow rate calculated from the measurement error reduction means; 4. The system performance monitoring device according to claim 3.

5. the system includes a heat exchanger; In the measurement error reduction means, a definition equation of the heat exchange amount of the heat exchanger is set as the constraint condition, The deterioration detection means detects deterioration of the heat exchanger.

4. The system performance monitoring device according to claim 3.

6. The measurement value of the remote meter is any one of the flow rate, temperature, and pressure of the liquid or gas flowing in the system.

2. The system performance monitoring device according to claim 1.

7. the system includes a pump; the measurement error reducing means calculates the flow rate based on a performance curve of the pump; 7. The system performance monitoring device according to claim 6.

8. the system includes a heat exchanger; the measurement error reducing means calculates the flow rate based on a performance curve of the heat exchanger.

7. The system performance monitoring device according to claim 6.

9. The system includes a pressure loss generation region, the measurement error reducing means calculates the flow rate based on the pressure loss in the pressure loss occurring region.

7. The system performance monitoring device according to claim 6.

10. the system includes a turbine; the measurement error reducing means calculates the flow rate based on the stage pressure of the turbine.

7. The system performance monitoring device according to claim 6.

11. the system includes a valve; the measurement error reduction means calculates the flow rate from the valve opening degree of the valve.

7. The system performance monitoring device according to claim 6.

12. The deterioration detection means issues an alarm when it detects deterioration of the system.

4. The system performance monitoring device according to claim 3.

13. the deterioration detection means switches from the system to another system when it detects deterioration of the system; 4. The system performance monitoring device according to claim 3.

14. a step of receiving, via an input means, measurement values ​​of a plurality of remote meters that are installed in the system and perform remote measurements, and measurement values ​​of field meters that are installed at predetermined locations in the system and acquired by an operator; calculating an estimated value of the field meter at a predetermined time from constraints that model the characteristics of the system and time-series data of the measurement values ​​of the remote meter input from the input means; a step in which a measurement error reduction means calculates a correction value that reduces an error in the measurement value of the meter based on the measurement value of the remote meter, the constraint condition, and the estimated value of the field meter received by the input means; A system performance monitoring method comprising:

Images