A method and apparatus for measuring the flow rate of condensate feedwater systems oriented towards edge computing

Abstract

This invention discloses a method and device for measuring the flow rate of a condensate feedwater system oriented towards edge computing. The method includes the following steps: Step S1, extracting pump operating parameters under multiple typical operating conditions from a historical database of an industrial internet platform; Step S2, performing simulation modeling of the condensate feedwater system, and constructing a nonlinear mapping relationship between the pipe resistance coefficient and flow rate by combining the pipe geometry, fluid properties, and pump mechanical characteristics, thus obtaining the pipe resistance equation; Step S3, simultaneously solving the pipe resistance equation and the centrifugal pump head-flow characteristic equation using the Newton-Raphson iterative method to obtain the theoretically calculated flow rate value; Step S4, comparing the theoretically calculated flow rate value with the real-time measured flow rate value, and dynamically adjusting the model parameters to ensure that the model error is less than a set threshold. This invention achieves high accuracy in measuring the flow rate of the condensate feedwater system.

Description

Technical Field

[0001] This invention relates to the field of feedwater flow measurement technology for the secondary loop of nuclear power plants, and in particular to a method and apparatus for measuring the flow of condensate feedwater systems oriented towards edge computing. Background Technology

[0002] The primary function of the secondary loop main feedwater system in a nuclear power plant is to supply water to the steam generator and maintain its level. Simultaneously, the steam generator uses heat generated by the reactor to heat the feedwater and produce steam to drive the turbine generator. Feedwater is delivered via the main feedwater pump and the start-up feedwater pump. Excessive flow can lead to an excessively high steam generator level, affecting the quality of the generated steam; excessive moisture in the steam can damage the turbine blades. Conversely, insufficient flow can result in an excessively low steam generator level, preventing the adequate removal of heat generated by the reactor, wasting power, and potentially causing core overheating.

[0003] Traditional flow measurement in nuclear power plant feedwater branches currently relies primarily on hardware sensors, such as electromagnetic flowmeters and differential pressure transmitters. This presents several problems: 1. Sensors are susceptible to high-temperature and high-pressure environments during long-term operation, leading to decreased accuracy; 2. Due to the long operating cycles of nuclear power plants, sensor malfunctions necessitate shutdowns for maintenance, impacting overall operational efficiency and incurring high maintenance costs; 3. Limited placement of flow sensors, requiring flowmeter installations to meet a 10D upstream and 5D downstream straight pipe section requirement, results in insufficient effective measurement point coverage; 4. Flow measurement in feedwater systems is significantly affected by fluctuations in operating conditions, such as temperature and pressure, necessitating specially designed flow sensors to withstand extreme working conditions. This means that traditional flow sensors may not meet the requirements.

[0004] Some have attempted to use ultrasonic flow meters, but these are also limited by factors such as installation conditions. Others have tried to calculate flow using pump and pipeline characteristics. However, most people assume the pipeline resistance to be constant, which is a simplified form derived from the Darcy-Weisbach Equation in fluid mechanics.

[0005]

[0006] The above formula can only be achieved under relatively ideal conditions, such as in the seawater circulation pipelines of nuclear power plants. It's important to understand that the pipeline resistance KADM is not a universal constant; its value depends on whether the flow is laminar or turbulent, fluid properties, and pipeline geometry and conditions, such as roughness and valve opening. Traditional static resistance coefficient assumptions do not reflect actual pipeline resistance, leading to low accuracy in calculated flow rates. Summary of the Invention

[0007] In view of this, it is necessary to provide a method and device for measuring the flow rate of condensate feedwater systems oriented towards edge computing, so as to effectively solve the technical problem of low flow rate measurement accuracy in condensate feedwater systems.

[0008] This invention provides a flow measurement method for condensate feedwater systems oriented towards edge computing, comprising the following steps:

[0009] Step S1: Extract pump unit operating parameters under multiple typical working conditions based on the historical database of the industrial internet platform;

[0010] Step S2: Simulate and model the condensate supply system. Combine the pipeline geometry, fluid properties, and pump mechanical characteristics to construct a nonlinear mapping relationship between the pipeline resistance coefficient and flow rate, and obtain the pipeline resistance equation.

[0011] Step S3: Combine the pipeline resistance equation and the centrifugal pump head-flow characteristic equation, and solve the equation set using the Newton-Raphson iterative method to obtain the theoretical calculated flow rate value;

[0012] Step S4: Compare the theoretically calculated flow rate with the real-time measured flow rate, and dynamically adjust the model parameters to ensure that the model error is less than the set threshold.

[0013] Preferably, step S1 specifically comprises:

[0014] Real-time operating condition data is acquired and stored from the DCS system. The real-time operating condition data is dynamically mapped to the simulation model through timestamp alignment. A multi-operating condition parameter set for the pump group is constructed based on the real-time operating condition data. The multi-operating condition dataset is then cleaned and clustered to generate a standardized training dataset.

[0015] Preferably, step S2 specifically comprises:

[0016] Using the BNL simulation platform, thermodynamic boundary conditions and fluid physics parameters from historical data were integrated to establish a numerical simulation model of the condensate feedwater system, which includes pump mechanical characteristics, pipeline geometry, and thermo-hydraulic coupling effects. The pipeline resistance equation was obtained by analyzing the numerical simulation model.

[0017] Preferably, step S3 specifically comprises:

[0018] Combining the aforementioned pipeline resistance equation with the corresponding centrifugal pump head-flow characteristic equation, we obtain a set of nonlinear equations:

[0019]

[0020] ;

[0021] in, For centrifugal pump head, For centrifugal pump flow rate, The head-flow characteristic equation for a centrifugal pump is as follows: This is the pipe resistance coefficient. The equation for pipe resistance is as follows: It is quality flow. For density, To obtain the pipeline pressure drop between two pressure measuring points containing a centrifugal pump;

[0022] The total pressure difference of the pump pipeline system is obtained, and the initial flow rate value is set. The centrifugal pump includes a condensate pump and a feed water pump. The head-flow characteristic equation of the condensate pump is interpolated in two dimensions using the parametric equation method, and the pipeline resistance equation and the head-flow characteristic equation of the feed water pump are interpolated in one dimension to obtain the theoretically calculated flow rate value.

[0023] Preferably, step S4 specifically comprises:

[0024] The system acquires real-time flow rate values ​​measured by sensors, calculates the difference between the theoretically calculated flow rate value and the real-time flow rate value, analyzes the model accuracy and evaluates the model reliability based on the difference, dynamically adjusts the model parameters of the numerical simulation model of the condensate feedwater system based on the model accuracy and model reliability to keep the model error less than a set threshold, and records the error data.

[0025] Preferred options also include:

[0026] The theoretically calculated flow rate is fed back to the nuclear power plant's industrial internet platform. Based on the theoretically calculated flow rate, fault mode matching is used to identify system anomalies, locate faults, and achieve anomaly diagnosis.

[0027] Preferred options also include:

[0028] An embedded real-time computing engine is built, which interacts with the DCS system through the OPC UA protocol. The soft measurement results of traffic, model confidence and anomaly diagnosis signals are integrated into the human-machine interface to realize embedded monitoring of soft measurement.

[0029] Preferred options also include:

[0030] Based on the deviation analysis between the theoretically calculated flow rate and the real-time measured flow rate, the variable frequency control parameters of the centrifugal pump are adjusted to maintain optimal thermal efficiency.

[0031] The present invention also provides a flow measurement device for a condensate feedwater system oriented towards edge computing, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements the flow measurement method for a condensate feedwater system oriented towards edge computing.

[0032] Compared with existing technologies, the advantages of this invention are as follows: This invention proposes a soft measurement method for feedwater flow rate based on dynamic coupling modeling of centrifugal pump characteristics and pipeline resistance. Unlike the traditional static resistance coefficient assumption, this invention collects multi-dimensional operating parameters of the pump group, combines the full-condition characteristic curve of the centrifugal pump with pipeline resistance simulation modeling, constructs a simulation model to solve for the flow rate of the secondary loop condensate feedwater, and achieves accurate reconstruction of flow parameters through dynamic resistance compensation mechanism and multi-parameter fusion modeling, controlling the soft measurement error of flow rate within a small range and meeting the refined control requirements of the secondary loop feedwater system. Even in scenarios where traditional flowmeters fail in nuclear power systems, this invention can still achieve continuous and stable monitoring of feedwater flow rate. Attached Figure Description

[0033] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0034] Figure 1 is a flowchart of an embodiment of a condensate feedwater system flow measurement method for edge computing provided by the present invention;

[0035] Figure 2a yes Figure 1 A schematic diagram of the structure of a condensate pump in an embodiment of the condensate supply system shown in the illustration.

[0036] Figure 2b yes Figure 1 A schematic diagram of the structure of a water pump in an embodiment of the condensate water supply system shown in the illustration;

[0037] Figure 3 yes Figure 1 The flowchart of one embodiment of the soft measurement algorithm shown in the example is an example of the algorithm.

[0038] Figure 4 yes Figure 1 A schematic diagram of one embodiment of the simulation platform shown in the example;

[0039] Figure 5 yes Figure 1 A fitting curve of the pipe resistance coefficient in one embodiment shown in the diagram;

[0040] Figure 6a yes Figure 1 A comparison diagram of theoretically calculated flow rate values ​​and real-time measured flow rate values ​​in the illustrated embodiment;

[0041] Figure 6b yes Figure 1 A graph showing the measurement error value in one embodiment of the example;

[0042] Figure 7 yes Figure 1The illustrated embodiment is a schematic diagram of an embedded monitoring platform. Detailed Implementation

[0043] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0044] Example 1

[0045] Please see Figure 1 The flow measurement method for a condensate feedwater system oriented towards edge computing in this embodiment specifically includes the following steps:

[0046] Step S1: Extract pump unit operating parameters under multiple typical working conditions based on the historical database of the industrial internet platform;

[0047] Step S2: Simulate and model the condensate supply system. Combine the pipeline geometry, fluid properties, and pump mechanical characteristics to construct a nonlinear mapping relationship between the pipeline resistance coefficient and flow rate, and obtain the pipeline resistance equation.

[0048] Step S3: Combine the pipeline resistance equation and the centrifugal pump head-flow characteristic equation, and solve the equation set using the Newton-Raphson iterative method to obtain the theoretical calculated flow rate value;

[0049] Step S4: Compare the theoretically calculated flow rate with the real-time measured flow rate, and dynamically adjust the model parameters to ensure that the model error is less than the set threshold.

[0050] This embodiment addresses the flow calculation error problem caused by ignoring the dynamic changes in pipeline resistance in the prior art, and provides an online soft measurement method and dynamic optimization method based on fitting the pipeline resistance coefficient with historical data and combining pump characteristics and fluid parameters.

[0051] Specifically, in this embodiment, the structure of the nuclear power plant condensate pump system includes sensor arrangement, such as... Figure 2a As shown: Condensate flows from the condenser (CND), branches through pipelines into multiple condensate pumps, is heated by the shaft seal heater, and then sent to the deaerator. When the unit load is low, the condensate recirculation valve is opened to return some condensate to the condenser hot well, ensuring that the condensate pump flow rate does not fall below its minimum allowable value. Valves are installed on the pipelines to control flow rate and direction, and pressure gauges and thermometers are provided to monitor pressure and temperature, ensuring normal system operation. The nuclear power plant feedwater pump system structure includes sensor placement, such as... Figure 2bAs shown: After condensate enters the deaerator for deoxygenation, it is pressurized by the feedwater pump to become feedwater, which is then reheated and sent to the steam generator. When the unit load is low, the feedwater recirculation valve will be opened to return a portion of the feedwater to the deaerator, ensuring that the feedwater pump flow rate does not fall below its minimum allowable value. The feedwater system is equipped with various monitoring and control devices to monitor parameters such as feedwater flow rate, pressure, and temperature in real time, and automatically adjust system operation accordingly to ensure a stable supply and quality of feedwater.

[0052] against Figure 2a and Figure 2b The condensate feedwater system first undergoes real-time parameter acquisition: based on the industrial internet platform, specifically the Data History Package database, multi-dimensional parameters such as pump operating speed, inlet and outlet pressure, and medium temperature are collected. Multiple sets of typical operating conditions are extracted, including pump operating parameters under transient processes such as reactor start-up and shutdown, and peak shaving, providing a multi-dimensional data foundation for subsequent model construction. Secondly, system simulation modeling is performed: using the BNL simulation platform, thermodynamic boundary conditions and fluid property parameters from historical data are integrated to establish a full-system numerical simulation model encompassing pump mechanical characteristics, pipeline geometry, and thermo-hydraulic coupling effects. Thirdly, resistance modeling: combining the centrifugal pump's full-condition characteristic curves with pipeline resistance simulation modeling, a dynamic pipeline resistance coefficient-flow mapping relationship is established based on historical data and the simulation model, constructing a dynamic resistance curve. Next, flow rate calculation: based on the centrifugal pump head-flow characteristic equation. Solve the pipeline resistance equations simultaneously By solving the system of equations using Newton's iterative method, the soft measurement value of the flow rate is obtained, and the theoretical calculated flow rate is output. Finally, online verification: compare the calculated traffic. With measuring flow rate The model parameters are dynamically adjusted to keep the error ≤2%, and the error data is recorded to ensure the accuracy of measurement traceability across the entire operating range.

[0053] The software algorithm flow of this embodiment is as follows: Figure 3 As shown, pressure, temperature, and speed measurement data are read from the real-time time-series data library of the industrial internet platform. After preprocessing, the pump's operation is determined by the pump outlet pressure. If the pump is not running, the system returns a status indicating that the pump is not running and displays this information on the front end, while also storing it in the industrial internet platform's time-series data history library. If the pump is running, the measurement data is input into the calculation model to determine whether the fluid state is laminar or turbulent, and the equations are solved using Newton's iteration method. Once the calculation results converge, they are output to the front end display and stored in the industrial internet platform's time-series data history library.

[0054] This embodiment overcomes the limitations of traditional flow measurement devices in the complex operating conditions of the secondary loop condensate and feedwater systems of nuclear power plants. Through multi-parameter fusion modeling, it achieves accurate reconstruction of flow parameters, enabling continuous and stable monitoring of feedwater flow even in scenarios where traditional flowmeters fail, such as the high-radiation environment of the nuclear island, high-temperature and high-pressure media, and pipeline impurity accumulation. This method not only replaces the measurement function of physical flowmeters but also constructs a dynamically adaptive digital twin model of the hydraulic system by real-time acquisition of pump current-head characteristic curves and pipeline pressure differential-flow characteristic data.

[0055] Compared with existing technologies, this embodiment significantly improves measurement accuracy: through dynamic resistance compensation mechanism and multi-parameter coupled modeling, the flow soft measurement error can be controlled within ±2%, meeting the refined control requirements of the secondary water supply system; optimized life cycle cost: reducing reliance on high-precision physical flow meters, hardware investment costs are reduced by more than 10%; supports online parameter updates and model self-calibration, avoiding downtime maintenance due to sensor failure, reducing annual maintenance costs by about 20%; intelligent operation and maintenance upgrade: the embedded monitoring platform integrates real-time flow prediction, equipment health assessment and anomaly early warning functions, which can identify potential risks such as pipe scaling and pump efficiency decline in advance, realizing the transformation from "passive inspection" to "proactive maintenance".

[0056] This embodiment can replace physical flow meters: in complex operating conditions such as high temperature and pressure, high radiation, or insufficient straight pipe sections, it can replace traditional physical sensors such as electromagnetic flow meters and differential pressure transmitters to achieve non-invasive flow monitoring. It can also operate in parallel with existing flow meters to achieve redundancy verification: by comparing multi-source data, the reliability of the measurement system is improved, and an independent verification channel is provided for nuclear safety-grade control systems.

[0057] Specifically, step S1 is as follows:

[0058] Real-time operating condition data is acquired and stored from the DCS system. The real-time operating condition data is dynamically mapped to the simulation model through timestamp alignment. A multi-operating condition parameter set for the pump group is constructed based on the real-time operating condition data. The multi-operating condition dataset is then cleaned and clustered to generate a standardized training dataset.

[0059] This embodiment employs multi-source heterogeneous data fusion acquisition technology. Sensor data from the nuclear power system is acquired and processed through a distributed control system (DCS). The DCS connects to various sensors via interfaces to collect real-time data such as temperature, pressure, flow rate, and radiation levels, which are then stored in the industrial internet platform database. Timestamp alignment enables dynamic mapping between operating condition data and simulation models. Based on the historical database of the nuclear power plant's industrial internet platform, a multi-condition parameter set for pump units covering transient processes such as reactor start-up and shutdown, and peak shaving is constructed. Standardized training datasets are generated through data cleaning and operating condition clustering. Specific pump unit operating condition parameters include inlet pressure... Export pressure Inlet medium temperature outlet medium temperature Rotation speed and measured flow rate .

[0060] Specifically, step S2 is as follows:

[0061] Using the Bonuli simulation platform (BNL), thermodynamic boundary conditions and fluid physics parameters from historical data were integrated to establish a numerical simulation model of the condensate feedwater system, including pump mechanical characteristics, pipeline geometry, and thermo-hydraulic coupling effects. The pipeline resistance equation was obtained by analyzing the numerical simulation model.

[0062] This embodiment employs thermo-hydraulic coupling simulation modeling technology, using BNL's PowerBuilder thermodynamic system modeling software for modeling. It establishes a dynamic resistance coefficient-flow rate mapping model by combining pipe geometry, fluid properties, and pump mechanical characteristics. This enables high-precision digital characterization of the resistance characteristics of complex pipeline systems.

[0063] PowerBuilder is a widely used simulation modeling tool for thermal power generating units, wind farms, photovoltaic power plants, and other generating units. PowerBuilder divides the actual thermal system equipment or equipment combinations of a power plant into modules and establishes its mass, energy, and momentum conservation equations. By analyzing the connection relationships between the various modules, the software automatically identifies the fluid network topology hidden in the thermal system and automatically completes the solution process for large-scale fluid networks.

[0064] Figure 4 The diagram shows the BNL simulation platform. The two ends of the condensate pump calculation are the condenser pressure and the condensate main pressure, respectively. The condenser pressure corresponds to P319_N4 in the diagram, and the condensate main pressure corresponds to P319_N9 in the diagram. The three condensate pumps are located between the two nodes. The piping system between the two nodes can be simulated by combining pipes and valves.

[0065] Figure 5 To fit the pipeline resistance coefficient curve, including laminar and turbulent curves, and combining the condensate main flow rate, condensate pump characteristics, and simulation verification, the KADM value of the pipeline system coefficient is calculated, resulting in the curve of the pipeline resistance coefficient KADM changing with flow rate, divided into laminar and turbulent curves. At low flow rates, such as during reactor start-up and shutdown, the pipeline is mainly laminar, while in steady state, it is mainly turbulent.

[0066] Specifically, step S3 is as follows:

[0067] Combining the aforementioned pipeline resistance equation with the corresponding centrifugal pump head-flow characteristic equation, we obtain a set of nonlinear equations:

[0068]

[0069] ;

[0070] in, For centrifugal pump head, For centrifugal pump flow rate, The head-flow characteristic equation for a centrifugal pump is as follows: This is the pipe resistance coefficient. The equation for pipe resistance is as follows: It is quality flow. For density, To obtain the pipeline pressure drop between two pressure measuring points containing a centrifugal pump;

[0071] The total pressure difference of the pump pipeline system is obtained, and the initial flow rate value is set. The centrifugal pump includes a condensate pump and a feed water pump. The head-flow characteristic equation of the condensate pump is interpolated in two dimensions using the parametric equation method, and the pipeline resistance equation and the head-flow characteristic equation of the feed water pump are interpolated in one dimension to obtain the theoretically calculated flow rate value.

[0072] This embodiment uses the Newton-Raphson iterative method to solve for the flow rate, with a convergence threshold set at ±0.1%. In the secondary loop condensate feedwater system of a nuclear power plant, there are two sets of pumps: condensate pumps and feedwater pumps. Generally, not all pumps are running. Pump operation can be determined by the pump outlet pressure and speed. The characteristic equations of the running pumps are then used. and the corresponding pipeline system characteristic equations A system of simultaneous nonlinear equations is established. Given the total pressure difference in the pump-pipeline system, the nonlinear equations are solved using the Newton-Raphson iterative method. An initial guessed flow rate is set. When solving the head-flow characteristic equation of the variable frequency feedwater pump, two-dimensional interpolation is performed using the parametric equation method. One-dimensional interpolation is used when solving the head-flow characteristic equation of the condensate pump and the pipeline resistance equation. The pipeline resistance curve requires calculation of the Reynolds number from the initial guessed flow rate to determine whether to use laminar or turbulent flow lines. Finally, the real-time theoretical flow rate is calculated. .

[0073] Specifically, step S4 is as follows:

[0074] The system acquires real-time flow rate values ​​measured by sensors, calculates the difference between the theoretically calculated flow rate value and the real-time flow rate value, analyzes the model accuracy and evaluates the model reliability based on the difference, dynamically adjusts the model parameters of the numerical simulation model of the condensate feedwater system based on the model accuracy and model reliability to keep the model error less than a set threshold, and records the error data.

[0075] By comparing theoretically calculated flow rates with real-time measured flow rates, the error is calculated and the model accuracy is analyzed. Statistical methods, such as mean square error and standard deviation, are used to evaluate the reliability of the model.

[0076] Specifically, it also includes:

[0077] The theoretically calculated flow rate is fed back to the nuclear power plant's industrial internet platform. Based on the theoretically calculated flow rate, fault mode matching is used to identify system anomalies, locate faults, and achieve anomaly diagnosis.

[0078] This embodiment feeds back the calculated flow rate to the industrial internet platform after calculation, providing a data foundation for fault diagnosis and operational efficiency optimization, and supporting subsequent diagnostic and optimization functions. Deviation analysis between predicted and actual flow rates is performed, and system anomalies are identified through fault mode matching. This quickly locates potential faults and enables anomaly diagnosis, assisting maintenance personnel in identifying equipment anomalies such as pump efficiency degradation, valve jamming, or partial pipeline blockage.

[0079] Specifically, it also includes:

[0080] An embedded real-time computing engine is built, which interacts with the DCS system through the OPC UA protocol. The soft measurement results of traffic, model confidence and anomaly diagnosis signals are integrated into the human-machine interface to realize embedded monitoring of soft measurement.

[0081] This embodiment develops an embedded real-time computing engine based on traffic calculation. It achieves millisecond-level data interaction with the power plant's DCS system through the OPC UA protocol, integrates real-time traffic soft measurement, and integrates traffic soft measurement results, model confidence, and anomaly diagnostic signals into the human-machine interface (HMI). It supports multi-dimensional data visualization and provides multi-dimensional visualization monitoring and closed-loop control interfaces through the HMI interface, forming an embedded intelligent monitoring platform.

[0082] Specifically, it also includes:

[0083] Based on the deviation analysis between the theoretically calculated flow rate and the real-time measured flow rate, the variable frequency control parameters of the centrifugal pump are adjusted to maintain optimal thermal efficiency.

[0084] Based on the deviation analysis between the predicted flow rate and the actual operating parameters, the frequency conversion adjustment parameters of the feedwater pump can be automatically optimized to ensure that the secondary loop thermal system always maintains the best thermal efficiency, thereby achieving refined energy-saving control while ensuring the safe operation of the nuclear power unit.

[0085] To verify this embodiment, the soft measurement method provided in this embodiment is compared with the existing technology for measuring flow rate. Figure 6a and Figure 6b This is a comparison chart of soft-measured flow rate and measured flow rate. Figure 6aThis display shows the flow rate change over time during startup. The dashed line represents the calculated value from the soft-sensor model, and the solid line represents the measured value from the flow meter. When the pump is not running, the calculated value is 0. At other times, the error between the measured and calculated values ​​is small. Figure 6b As shown.

[0086] Figure 7 This is a schematic diagram of an embedded monitoring platform, showing the construction of the front-end large screen of the condensate water supply system calculation model, including the process. Figure 1 The system consists of five sections: flowchart 2, regulating valve, feedwater pump, and condensate pump. The feedwater pump section displays the status monitoring of four physical quantities: head, flow rate, power, and efficiency, historical fault information, as well as three sets of characteristic curves and operating points for the feedwater pump. The three sets of characteristic curves are flow rate-head, flow rate-power consumption, and flow rate-efficiency.

[0087] Example 2

[0088] This embodiment provides a flow measurement device for a condensate feedwater system oriented towards edge computing, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the flow measurement method for a condensate feedwater system oriented towards edge computing as described in Embodiment 1.

[0089] The condensate feedwater system flow measurement device provided in this embodiment is used to implement the condensate feedwater system flow measurement method based on edge computing. Therefore, the condensate feedwater system flow measurement device also possesses the technical effects of the condensate feedwater system flow measurement method based on edge computing, and will not be described in detail here.

[0090] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of the present invention.

Claims

1. A method for measuring the flow rate of a condensate feedwater system oriented towards edge computing, characterized in that, Includes the following steps: Step S1: Based on the historical database of the industrial internet platform, extract pump set operating parameters under multiple typical working conditions; Step S2: Simulate and model the condensate supply system. Combine the pipeline geometry, fluid properties, and pump mechanical characteristics to construct a nonlinear mapping relationship between the pipeline resistance coefficient and flow rate, and obtain the pipeline resistance equation. Step S3: Combine the pipeline resistance equation and the centrifugal pump head-flow characteristic equation, and solve the equation set using the Newton-Raphson iterative method to obtain the theoretical calculated flow rate value; Step S4: Compare the theoretically calculated flow rate with the real-time measured flow rate, and dynamically adjust the model parameters to ensure that the model error is less than the set threshold. Step S2 specifically involves: A simulation platform was used to integrate thermodynamic boundary conditions and fluid physical parameters from historical data to establish a numerical simulation model of the condensate feedwater system, which includes pump mechanical characteristics, pipeline geometry, and thermo-hydraulic coupling effects. The pipeline resistance equation was obtained by analyzing the numerical simulation model. Step S3 specifically involves: Combining the aforementioned pipeline resistance equation with the corresponding centrifugal pump head-flow characteristic equation, we obtain a set of nonlinear equations: ； in, For centrifugal pump head, For centrifugal pump flow rate, The centrifugal pump speed, The head-flow characteristic equation for a centrifugal pump is as follows: This is the pipe resistance coefficient. The equation for pipe resistance is as follows: It is quality flow. For density, The pressure drop across the pipeline between the two pressure measuring points is given, and the centrifugal pump is located between the two pressure measuring points. The total pressure difference of the pump pipeline system is obtained, and the initial flow rate value is set. The centrifugal pump includes a condensate pump and a feed water pump. The head-flow characteristic equation of the condensate pump is interpolated in two dimensions using the parametric equation method, and the pipeline resistance equation and the head-flow characteristic equation of the feed water pump are interpolated in one dimension to obtain the theoretically calculated flow rate value.

2. The method for measuring the flow rate of a condensate feedwater system based on edge computing according to claim 1, characterized in that, Step S1 specifically involves: Real-time operating condition data is acquired and stored from the DCS system. The real-time operating condition data is dynamically mapped to the simulation model through timestamp alignment. A multi-condition parameter set for the pump group is constructed based on the real-time operating condition data. The multi-condition parameter set is then cleaned and clustered to generate a standardized training dataset.

3. The method for measuring the flow rate of a condensate feedwater system based on edge computing according to claim 1, characterized in that, Step S4 specifically involves: The system acquires real-time flow rate values ​​measured by sensors, calculates the difference between the theoretically calculated flow rate value and the real-time flow rate value, analyzes the model accuracy and evaluates the model reliability based on the difference, dynamically adjusts the model parameters of the numerical simulation model of the condensate feedwater system based on the model accuracy and model reliability to keep the model error less than a set threshold, and records the error data.

4. The method for measuring the flow rate of a condensate feedwater system based on edge computing according to claim 1, characterized in that, Also includes: The theoretically calculated flow rate is fed back to the nuclear power plant's industrial internet platform. Based on the theoretically calculated flow rate, fault mode matching is performed to identify system anomalies, locate faults, and achieve anomaly diagnosis.

5. The method for measuring the flow rate of a condensate feedwater system based on edge computing according to claim 1, characterized in that, Also includes: An embedded real-time computing engine is built, which interacts with the DCS system through the OPC UA protocol. The soft measurement results of traffic, model confidence and anomaly diagnosis signals are integrated into the human-machine interface to realize embedded monitoring of soft measurement.

6. The method for measuring the flow rate of a condensate feedwater system based on edge computing according to claim 1, characterized in that, Also includes: Based on the deviation analysis between the theoretically calculated flow rate and the real-time measured flow rate, the variable frequency control parameters of the centrifugal pump are adjusted to maintain optimal thermal efficiency.

7. A flow measurement device for a condensate feedwater system oriented towards edge computing, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the edge computing-oriented condensate feedwater system flow measurement method as described in any one of claims 1-6.

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