Nuclear island heat exchanger allowance early warning method
By establishing a predictive model and using meteorological and water temperature data to calculate margin prediction values, the problem of abnormal operation of the nuclear island system in high-temperature environments has been solved, enabling early warning and optimized operation of nuclear safety, and improving the safety and economic benefits of nuclear power plants.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-24
- Publication Date
- 2026-04-07
Smart Images
Figure CN121809328A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of seawater monitoring technology in nuclear power plants, and in particular to a method for early warning of margin in nuclear island heat exchangers. Background Technology
[0002] Global temperatures are rising significantly, a conclusion supported by several independent datasets from global and regional stations. Simultaneously, global ocean surface temperatures continue to rise. In recent years, seawater temperatures at nuclear power plant sites in southern regions have increased significantly compared to the original design phase. As the final cold source for removing heat from the reactor core, these temperature changes inevitably impact the safety and operation of the units.
[0003] Every summer and autumn, the air and sea temperatures in the area where the CPR1000 nuclear power plant is located in southern China are higher than normal, and the temperature in the various buildings of the power plant also rises. This leads to a deterioration in the operating environment of the high-temperature sensitive electromechanical equipment in the nuclear island system, increasing the possibility of abnormal operation of the nuclear island system equipment and posing a threat to the safe and stable operation of the unit. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to provide a method for early warning of the margin of a nuclear island heat exchanger.
[0005] The technical solution adopted by this invention to solve its technical problem is: a method for early warning of margin in nuclear island heat exchangers, comprising the following steps: Step S10: Obtain meteorological forecast data and water temperature monitoring data; Step S20: Based on meteorological forecast data and water temperature monitoring data, calculate and obtain water temperature prediction data; Step S30: Compare the water temperature monitoring data with the water temperature prediction data to determine the rationality of the water temperature prediction data; Step S40: Obtain the difference between the design temperature value of the nuclear island system and the predicted water temperature data to obtain the margin prediction value; Step S50: Determine whether the predicted margin value is a warning margin value; if so, issue a warning.
[0006] Optionally, step S10 includes: The meteorological forecast data is obtained by acquiring marine meteorological forecast data of the nuclear power plant's sea area and meteorological data of the nuclear power plant's site.
[0007] Optionally, step S10 includes: The intake and outlet water temperatures of the nuclear island system are obtained as the water temperature monitoring data.
[0008] Optionally, step S20 includes: Develop a predictive model; Based on the meteorological forecast data and the water temperature monitoring data, the water temperature prediction data is obtained using the prediction model.
[0009] Optionally, establishing the prediction model includes: Based on fluid dynamics and the aforementioned meteorological forecast data, a model of the sea fluid in the nuclear power plant area was established, resulting in the first seawater prediction model. The seawater temperature AI intelligent model was trained using the meteorological forecast data to obtain a second seawater temperature prediction model. Based on the prediction results of the first seawater prediction model and the second seawater temperature prediction model, a prediction model for the SEC side inlet temperature of the SEC / RRI heat exchanger is established. Based on the water temperature monitoring data, a prediction model for the RRI side outlet temperature of the first SEC / RRI heat exchanger is established. A thermal-hydraulic model is established based on the design characteristics of the heat exchanger. Using the prediction results of the RRI side outlet temperature prediction model of the first SEC / RRI heat exchanger and the thermal-hydraulic model, a prediction model for the RRI side outlet temperature of the second SEC / RRI heat exchanger is established. The intelligent model for downstream heat exchanger outlet temperature is trained using the water temperature monitoring data to obtain a downstream outlet temperature prediction model.
[0010] Optionally, the step of obtaining the water temperature prediction data using the prediction model based on the meteorological forecast data and the water temperature monitoring data includes: The seawater temperature at the first predicted plant site is obtained by calculating the meteorological forecast data using the first seawater prediction model. The second seawater temperature prediction model is used to calculate the meteorological forecast data to obtain the second predicted seawater temperature at the plant site; Based on the first and second predicted seawater temperatures at the plant site, the inlet temperature of the nuclear island system is predicted using the SEC / RRI heat exchanger SEC side inlet temperature prediction model, and the SEC / RRI heat exchanger SEC side inlet temperature is obtained. Based on the RRI side outlet temperature prediction model of the first SEC / RRI heat exchanger, combined with the water temperature monitoring data and the SEC side inlet temperature of the SEC / RRI heat exchanger, the predicted outlet temperature of the first SEC / RRI heat exchanger on the RRI side is obtained. Based on the SEC side inlet temperature of the SEC / RRI heat exchanger and the water temperature monitoring data, the predicted RRI side outlet temperature of the second SEC / RRI heat exchanger is obtained by using the thermal-hydraulic model. The outlet temperature of the nuclear island system is predicted using the RRI side outlet temperature prediction model of the second SEC / RRI heat exchanger, and the predicted RRI side outlet temperature is obtained. Based on the predicted outlet temperature of the RRI side and the water temperature monitoring data, the predicted outlet temperature of the downstream user's heat exchanger is obtained by using the downstream outlet temperature prediction model. The water temperature prediction data includes one or more of the following: the first predicted plant site seawater temperature, the second predicted plant site seawater temperature, the SEC side inlet temperature of the SEC / RRI heat exchanger, the RRI side outlet predicted temperature of the first SEC / RRI heat exchanger, the RRI side outlet predicted temperature of the second SEC / RRI heat exchanger, the RRI side outlet predicted temperature, and the downstream user heat exchanger outlet predicted temperature.
[0011] Optionally, step S30 includes: The water temperature monitoring data is compared with the SEC side inlet temperature of the SEC / RRI heat exchanger. If the difference between the two is within a first preset range, the SEC side inlet temperature of the SEC / RRI heat exchanger is reasonable. Determine whether the difference between the water temperature monitoring data and the predicted temperature at the RRI outlet is within a second preset range. If it is, then the predicted temperature at the RRI outlet is reasonable.
[0012] Optionally, step S40 includes: The margin prediction value is obtained by calculating the difference between the design temperature value and the SEC side inlet temperature of the SEC / RRI heat exchanger, the predicted RRI side outlet temperature, and the predicted outlet temperature of the downstream user heat exchanger.
[0013] Optionally, the design temperature values include: the seawater design temperature value, the RRI design temperature value, and the downstream heat outlet temperature alarm value of the nuclear island system; The difference between the seawater temperature value and the predicted inlet temperature on the SEC side of the SEC / RRI heat exchanger, the predicted outlet temperature on the RRI side, and the predicted outlet temperature of the downstream user's heat exchanger is calculated to obtain the margin prediction value, including: The first margin prediction value is obtained by calculating the seawater design temperature value and the SEC side inlet temperature of the SEC / RRI heat exchanger. The second margin prediction value is obtained by calculating the RRI design temperature value and the RRI side outlet predicted temperature. The third margin prediction value is obtained by calculating the downstream heat outlet temperature alarm value and the downstream user heat exchanger outlet predicted temperature. The margin prediction value includes the first margin prediction value, the second margin prediction value, and the third margin prediction value.
[0014] Optionally, step S50 includes: Determine whether the first margin prediction value, the second margin prediction value, and the third margin prediction value are all 0; if they are 0, issue an early warning.
[0015] The implementation of this invention has the following beneficial effects: This invention uses meteorological forecast data and water temperature monitoring data to predict the inlet and outlet water temperatures of the nuclear island system, and then calculates corresponding margin prediction values using these inlet and outlet water temperatures. Further analysis of these margin prediction values allows for early detection of risks, buys crucial time for emergency response, and significantly improves nuclear safety levels. Attached Figure Description
[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of a method for early warning of the margin of a nuclear island heat exchanger in one embodiment. Detailed Implementation
[0017] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.
[0019] This invention provides a method for early warning of margin in nuclear island heat exchangers, such as... Figure 1 As shown, it includes the following steps: Step S10: Obtain meteorological forecast data and water temperature monitoring data.
[0020] Specifically, meteorological forecast data and sea temperature monitoring data include real-time meteorological forecasts for the nuclear power plant area and future (e.g., 72-hour) sea temperature predictions issued by national or local public meteorological stations via the internet, as well as real-time meteorological data obtained from meteorological stations located at the nuclear power plant site. The meteorological data obtained from meteorological stations located at the nuclear power plant site includes historical meteorological data monitored by these stations.
[0021] Step S20: Based on meteorological forecast data and water temperature monitoring data, calculate and obtain water temperature prediction data.
[0022] The specific calculation process can be accomplished using a pre-set mathematical model. This model comprehensively considers various factors affecting water temperature changes, such as meteorological conditions like air temperature, wind speed, wind direction, and precipitation, as well as marine environmental factors like ocean currents and tides. By inputting acquired meteorological forecast data and water temperature monitoring data into this mathematical model, and through a series of complex calculations and analyses, relatively accurate water temperature prediction data can be obtained. Furthermore, to ensure the accuracy of the calculation results, the mathematical model can be periodically calibrated and optimized. Based on the comparison between actual water temperature monitoring data and predicted data, the parameters in the model can be adjusted and corrected.
[0023] Step S30: Compare the water temperature monitoring data with the water temperature prediction data to determine the rationality of the water temperature prediction data.
[0024] By comparing and analyzing actual water temperature monitoring data with model data for water temperature prediction, the accuracy and rationality of the prediction data can be verified and evaluated. By comparing the differences between the monitored and predicted values one by one, it can be determined whether there are systematic biases or errors in the prediction model, thereby judging the reliability of the prediction results and providing a basis for subsequent data correction and model optimization.
[0025] Step S40: Obtain the difference between the design temperature value of the nuclear island system and the water temperature prediction data to obtain the margin prediction value.
[0026] The difference between the design temperature value of the nuclear island system and the predicted water temperature data is obtained, and the margin prediction value is calculated based on this difference, thereby realizing the assessment and prediction of the system's safety performance.
[0027] Step S50: Determine whether the margin prediction value is a warning margin value; if so, issue a warning.
[0028] The predicted margin value is compared and analyzed with the pre-set warning margin value. If the predicted margin value is greater than the safety margin threshold, it indicates that the nuclear island heat exchanger has sufficient safety margin under the current operating conditions and can operate normally. If the predicted margin value is less than or equal to the safety margin threshold, it indicates that the safety margin of the nuclear island heat exchanger is insufficient, and there may be potential safety risks. In this case, a warning signal needs to be issued to remind relevant personnel to take timely measures, such as adjusting operating parameters and carrying out equipment maintenance, to ensure the safe and stable operation of the nuclear island system.
[0029] This invention utilizes meteorological forecast data and water temperature monitoring data to predict the inlet and outlet water temperatures of the nuclear island system. Based on these temperatures, corresponding margin prediction values are calculated. The determination of whether an early warning is needed is then based on these margin prediction values. This allows for early detection of risks, buys crucial time for emergency response, and significantly improves nuclear safety levels.
[0030] In one embodiment, step S10 includes: Meteorological forecast data is obtained by acquiring marine meteorological forecast data of the nuclear power plant's sea area and meteorological data of the nuclear power plant site.
[0031] Understandably, marine meteorological forecast data can be future predictions of seawater temperature in the nuclear power plant area. Site meteorological data can be obtained from real-time monitoring data acquired by meteorological stations located at the nuclear power plant site. Both marine meteorological forecast data and site meteorological data include wind speed, wind direction, temperature, humidity, air pressure, and rainfall.
[0032] In one embodiment, step S10 includes: The intake and outlet water temperatures of the nuclear island system are obtained as water temperature monitoring data.
[0033] Specifically, the intake water temperature includes the monitored values of the intake water temperature for critical plant water systems. The water temperature on the plate heat exchanger side of the cooling water heat exchanger in the critical plant water systems is obtained through the nuclear power plant's real-time information monitoring system. The outlet water temperature includes the monitored values of the RRI (Remotely Receiving Inlet) outlet temperature of the cooling water heat exchanger. The RRI outlet temperature of the cooling water heat exchanger is obtained through the nuclear power plant's real-time information monitoring system.
[0034] In one embodiment, step S20 includes: Develop a predictive model.
[0035] Water temperature prediction data is obtained using a prediction model based on meteorological forecast data and water temperature monitoring data.
[0036] This invention integrates weather forecasts and real-time water temperature data to proactively predict the inlet and outlet water temperatures and safety margins of the nuclear island cooling system, achieving a fundamental shift from "passive response" to "proactive early warning." This not only enables early risk detection and buys crucial time for emergency response, significantly improving nuclear safety, but also optimizes operational strategies, avoiding unnecessary load reductions or shutdowns, and improving economic efficiency while ensuring safety. Furthermore, it promotes the intelligent upgrading of nuclear power plant operation and maintenance, making decision-making more scientific and operations more reliable, comprehensively enhancing the nuclear power plant's ability to respond to environmental changes and fulfill its safety responsibilities.
[0037] In one embodiment, building a prediction model includes: Based on fluid dynamics and meteorological forecast data, a model of the seawater in the nuclear power plant area was established, resulting in the first seawater prediction model.
[0038] In some scenarios, the sea area involved in nuclear power plants is typically set as a relatively large region, with a simulated area often reaching a rectangular area of 300 km by 300 km. To accurately simulate and predict meteorological and marine environmental changes in this sea area, the required meteorological forecast data covers several key elements. These include: tidal harmonic constant data to describe tidal variations; wind field data reflecting wind speed and direction changes; pressure field data characterizing atmospheric pressure distribution; sea surface thermal radiation flux data to analyze sea surface heat exchange processes; initial temperature-salinity field data describing seawater temperature and salinity distribution at the initial moment of the simulation; and open-boundary temperature-salinity data to set the seawater temperature and salinity conditions at the model boundary. Substituting these data into the first seawater prediction model enables accurate simulation and prediction of seawater temperature from the surface to the bottom, and from the past to the future.
[0039] The AI-powered model for seawater temperature was trained using meteorological forecast data to obtain a second seawater temperature prediction model.
[0040] Specifically, historical meteorological data monitored by meteorological stations located at nuclear power plant sites are used to train an AI-powered intelligent prediction model, resulting in a second seawater temperature prediction model. The micro-scale meteorological characteristics of the nuclear power plant site (such as local circulation under topographic forcing and sea-land breeze front processes) contained in the data monitored by these meteorological stations are not extrapolable. The time span and sampling frequency together determine the model's ability to capture rare extreme events and long-term climate trends, thus making the predictions of the second seawater temperature prediction model more accurate.
[0041] Based on the prediction results of the first seawater prediction model and the second seawater temperature prediction model, a prediction model for the SEC side inlet temperature of the SEC / RRI heat exchanger is established.
[0042] In some scenarios, neural network prediction models, such as Long Short-Term Memory (LSTM) networks, are established using the prediction results of the first and second seawater temperature prediction models. These neural network prediction models can further improve the prediction accuracy of seawater inlet temperature by learning complex patterns and long-term dependencies in historical data.
[0043] Based on water temperature monitoring data, a prediction model for the RRI side outlet temperature of the first SEC / RRI heat exchanger was established.
[0044] Specifically, historical data on the intake and outlet water temperatures of the nuclear island system are used to establish an AI-powered intelligent prediction model for the RRI (Refrigerant Regulator) side outlet temperature of the equipment cooling water heat exchanger.
[0045] A thermal-hydraulic model is established based on the design characteristics of the heat exchanger.
[0046] Based on the design characteristics of the equipment cooling water heat exchanger, a thermo-hydraulic model of the equipment cooling water heat exchanger is established.
[0047] Using the prediction results of the RRI side outlet temperature prediction model and the thermal-hydraulic model of the first SEC / RRI heat exchanger, a prediction model for the RRI side outlet temperature of the second SEC / RRI heat exchanger is established.
[0048] Specifically, a neural network prediction model is established using the prediction results of the RRI side outlet temperature prediction model and the thermal-hydraulic model of the first SEC / RRI heat exchanger.
[0049] This neural network prediction model employs advanced deep learning algorithms to deeply fuse and optimize the outputs of the RRI side outlet temperature prediction model and the thermal-hydraulic model for the first SEC / RRI heat exchanger. Through training with a large amount of historical data, the model can automatically capture potential patterns and complex relationships within the data, thereby more accurately predicting the changing trend of the second outlet temperature and improving the accuracy and stability of the prediction.
[0050] A smart model for downstream heat exchanger outlet temperature was trained using water temperature monitoring data to obtain a downstream outlet temperature prediction model.
[0051] In some scenarios, the outlet water temperature of the nuclear island system includes the monitored outlet temperature of the equipment cooling water heat exchanger on the RRI side and the monitored outlet temperature of the RRI downstream users. Using the monitored outlet temperature values of the equipment cooling water heat exchanger on the RRI side and the monitored outlet temperature values of the RRI downstream users, a downstream outlet temperature prediction model is established for each RRI downstream user heat exchanger.
[0052] In one embodiment, water temperature prediction data is obtained using a prediction model based on meteorological forecast data and water temperature monitoring data, including: The seawater temperature at the first predicted plant site was obtained by calculating the meteorological forecast data using the first seawater prediction model.
[0053] The tidal harmonic constant data, wind field, pressure field, sea surface thermal radiation flux, initial temperature and salinity field, and open boundary temperature and salinity data from the meteorological forecast data were substituted into the first seawater prediction model to carry out calculations and obtain the seawater temperature at the first predicted plant site.
[0054] The second seawater temperature prediction model was used to calculate the seawater temperature at the second predicted plant site based on meteorological forecast data.
[0055] Specifically, the wind speed, wind direction, temperature, humidity, air pressure, and rainfall from the historical meteorological monitoring data of the nuclear power plant site in the meteorological forecast data are substituted into the second seawater temperature prediction model to obtain the second predicted seawater temperature at the plant site.
[0056] Based on the seawater temperatures of the first and second predicted plant sites, the inlet temperature of the nuclear island system is predicted using the SEC / RRI heat exchanger SEC side inlet temperature prediction model, thus obtaining the SEC / RRI heat exchanger SEC side inlet temperature.
[0057] A neural network prediction model (SEC / RRI heat exchanger SEC side inlet temperature prediction model) was established using the first and second predicted seawater temperatures at the plant site. This model was then used to predict the inlet temperature of the plate heat exchanger side of the equipment's cooling water heat exchanger. It is understood that the inlet temperature of the plate heat exchanger side of the nuclear island system is the same as the seawater temperature.
[0058] Based on the RRI side outlet temperature prediction model of the first SEC / RRI heat exchanger, combined with water temperature monitoring data and the SEC side inlet temperature of the SEC / RRI heat exchanger, the predicted outlet temperature of the RRI side of the first SEC / RRI heat exchanger is obtained.
[0059] The monitored values of the SEC side inlet temperature and RRI side outlet temperature of the SEC / RRI heat exchanger are input into the RRI side outlet temperature prediction model of the first SEC / RRI heat exchanger, and the predicted RRI side outlet temperature is used as the predicted RRI side outlet temperature of the first SEC / RRI heat exchanger.
[0060] Based on the monitoring data of the SEC side inlet temperature and water temperature of the SEC / RRI heat exchanger, the predicted outlet temperature of the RRI side of the second SEC / RRI heat exchanger is obtained by using a thermal-hydraulic model.
[0061] Input the monitored values of the SEC side inlet temperature and RRI side outlet temperature of the SEC / RRI heat exchanger into the thermal-hydraulic model to calculate the predicted outlet temperature result on the RRI side (predicted outlet temperature on the RRI side of the second SEC / RRI heat exchanger).
[0062] The outlet temperature of the nuclear island system was predicted using the RRI side outlet temperature prediction model of the second SEC / RRI heat exchanger, and the predicted RRI side outlet temperature was obtained.
[0063] Based on the predicted RRI-side outlet temperatures of the first and second SEC / RRI heat exchangers, a prediction model for the RRI-side outlet temperature of the second SEC / RRI heat exchanger, primarily based on a neural network prediction model, is established. The predicted RRI-side outlet temperature is then predicted using this model to obtain the predicted RRI-side outlet temperature.
[0064] Based on the predicted outlet temperature of the RRI side and water temperature monitoring data, the predicted outlet temperature of the downstream user's heat exchanger is obtained by using the downstream outlet temperature prediction model.
[0065] The predicted outlet temperature of the RRI and the real-time monitoring value of the heat exchanger of the downstream user of the RRI are substituted into the downstream outlet temperature prediction model to predict the outlet temperature of the heat exchanger of the downstream user of the RRI, thus obtaining the predicted outlet temperature of the heat exchanger of the downstream user.
[0066] The water temperature forecast data includes one or more of the following: the first predicted plant site seawater temperature, the second predicted plant site seawater temperature, the SEC side inlet temperature of the SEC / RRI heat exchanger, the predicted RRI side outlet temperature of the first SEC / RRI heat exchanger, the predicted RRI side outlet temperature of the second SEC / RRI heat exchanger, the predicted RRI side outlet temperature, and the predicted outlet temperature of the downstream user's heat exchanger.
[0067] In one embodiment, step S30 includes: Compare the water temperature monitoring data with the SEC side inlet temperature of the SEC / RRI heat exchanger. If the difference between the two is within the first preset range, then the SEC side inlet temperature of the SEC / RRI heat exchanger is reasonable.
[0068] Specifically, the water intake temperature monitoring value of the plate heat exchanger side of the equipment cooling water heat exchanger is compared with the inlet temperature of the SEC side of the SEC / RRI heat exchanger. If the difference between the two is within the first preset range, then the inlet temperature of the SEC side of the SEC / RRI heat exchanger is reasonable.
[0069] Determine whether the difference between the water temperature monitoring data and the predicted temperature at the RRI outlet is within a second preset range. If it is, then the predicted temperature at the RRI outlet is reasonable.
[0070] The monitored RRI side outlet temperature of the equipment cooling water heat exchanger is compared with the predicted RRI side outlet temperature. If the difference between the two is within the second preset range, the predicted RRI side outlet temperature is reasonable.
[0071] In one embodiment, step S40 includes: The margin prediction value is obtained by calculating the difference between the design temperature value and the predicted inlet temperature on the SEC side of the SEC / RRI heat exchanger, the predicted outlet temperature on the RRI side, and the predicted outlet temperature of the downstream user's heat exchanger.
[0072] Based on the obtained margin prediction value, it is further determined whether it is within the preset safety margin range. If the margin prediction value is within the safety margin range, it indicates that the current operating status of the nuclear island heat exchanger is normal, and no warning operation is required; if the margin prediction value exceeds the safety margin range, the warning mechanism is triggered, and a warning message is sent to the relevant operators, prompting them to check and maintain the nuclear island heat exchanger in a timely manner to avoid possible failures or accidents and ensure the safe and stable operation of the nuclear island system.
[0073] In one embodiment, the design temperature values include: the seawater design temperature value of the nuclear island system, the RRI design temperature value, and the downstream heat outlet temperature alarm value.
[0074] The design temperature values encompass several key parameters, specifically including the seawater design temperature for the nuclear island system, the design temperature values related to the RRI system, and the heat outlet temperature alarm values for downstream heat exchange processes. This temperature data is crucial for ensuring the safe operation of the nuclear power plant and the stability of the thermal system.
[0075] The difference between the seawater temperature value and the predicted inlet temperature on the SEC side of the SEC / RRI heat exchanger, the predicted outlet temperature on the RRI side, and the predicted outlet temperature of the downstream user's heat exchanger is calculated to obtain the margin prediction value, including: The first margin prediction value is obtained by calculating the seawater design temperature value and the SEC side inlet temperature of the SEC / RRI heat exchanger.
[0076] Seawater design temperature values include SEC system safety limits or nuclear island system seawater design temperature values.
[0077] Based on the safety limits of the SEC system or the seawater design temperature of the nuclear island system and the SEC side inlet temperature of the SEC / RRI heat exchanger, the predicted margin value of the SEC side inlet temperature (the first margin prediction value) is calculated.
[0078] The specific formula is as follows: [(T7-TSEC_pre) / T7]*100%; In the formula, T7 is the design temperature of the seawater in the nuclear island system; TSEC_pre is the inlet temperature of the SEC side of the SEC / RRI heat exchanger.
[0079] The second margin prediction value is obtained by calculating the RRI design temperature value and the RRI side outlet prediction temperature.
[0080] RRI design temperature values include the RRI side operating limits of the SEC / RRI heat exchanger or the RRI design temperature values of the nuclear island system.
[0081] Based on the RRI side operating limit of the SEC / RRI heat exchanger or the RRI design temperature value of the nuclear island system and the predicted RRI side outlet temperature, the predicted RRI side outlet temperature margin (second margin prediction value) is calculated.
[0082] The specific formula is as follows: [(TRRI-TRRI_pre) / TRRI]*100%.
[0083] In the formula, TRRI is the RRI design temperature value of the nuclear island system; TRRI_pre is the predicted outlet temperature of the RRI side.
[0084] The third margin prediction value is obtained by calculating the downstream heat outlet temperature alarm value and the predicted outlet temperature of the downstream user's heat exchanger.
[0085] Based on the alarm value of the hot side outlet temperature of the heat exchanger of the downstream user of RRI (downstream hot outlet temperature alarm value) and the predicted outlet temperature of the heat exchanger of the downstream user, the predicted margin value of the heat exchanger of the downstream user of RRI (third margin prediction value) is calculated.
[0086] The specific formula is as follows: [(TAlarm-TRRI_user_pre) / TAlarm]*100%; In the formula, TAlarm is the downstream heat outlet temperature alarm value; TRRI_user_pre is the predicted outlet temperature of the downstream user's heat exchanger.
[0087] The margin forecast includes the first margin forecast, the second margin forecast, and the third margin forecast.
[0088] In one embodiment, step S50 includes: Determine whether the first, second, and third margin forecast values are 0; if they are 0, issue an early warning.
[0089] Furthermore, if an alarm is triggered, it is recommended to run the contingency plan.
[0090] The above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.
Claims
1. A method for early warning of margin in a nuclear island heat exchanger, characterized in that, Includes the following steps: Step S10: Obtain meteorological forecast data and water temperature monitoring data; Step S20: Based on meteorological forecast data and water temperature monitoring data, calculate and obtain water temperature prediction data; Step S30: Compare the water temperature monitoring data with the water temperature prediction data to determine the rationality of the water temperature prediction data; Step S40: Obtain the difference between the design temperature value of the nuclear island system and the predicted water temperature data to obtain the margin prediction value; Step S50: Determine whether the predicted margin value is a warning margin value; If so, an early warning will be issued.
2. The early warning method according to claim 1, characterized in that, Step S10 includes: The meteorological forecast data is obtained by acquiring marine meteorological forecast data of the nuclear power plant's sea area and meteorological data of the nuclear power plant's site.
3. The early warning method according to claim 1, characterized in that, Step S10 includes: The intake and outlet water temperatures of the nuclear island system are obtained as the water temperature monitoring data.
4. The early warning method according to claim 1, characterized in that, Step S20 includes: Develop a predictive model; Based on the meteorological forecast data and the water temperature monitoring data, the water temperature prediction data is obtained using the prediction model.
5. The early warning method according to claim 4, characterized in that, The establishment of the prediction model includes: Based on fluid dynamics and the aforementioned meteorological forecast data, a model of the sea fluid in the nuclear power plant area was established, resulting in the first seawater prediction model. The seawater temperature AI intelligent model was trained using the meteorological forecast data to obtain a second seawater temperature prediction model. Based on the prediction results of the first seawater prediction model and the second seawater temperature prediction model, a prediction model for the SEC side inlet temperature of the SEC / RRI heat exchanger is established. Based on the water temperature monitoring data, a prediction model for the RRI side outlet temperature of the first SEC / RRI heat exchanger is established. A thermal-hydraulic model is established based on the design characteristics of the heat exchanger. Using the prediction results of the RRI side outlet temperature prediction model of the first SEC / RRI heat exchanger and the thermal-hydraulic model, a prediction model for the RRI side outlet temperature of the second SEC / RRI heat exchanger is established. The intelligent model for downstream heat exchanger outlet temperature is trained using the water temperature monitoring data to obtain a downstream outlet temperature prediction model.
6. The early warning method according to claim 5, characterized in that, The step of obtaining the water temperature prediction data using the prediction model based on the meteorological forecast data and the water temperature monitoring data includes: The seawater temperature at the first predicted plant site is obtained by calculating the meteorological forecast data using the first seawater prediction model. The second seawater temperature prediction model is used to calculate the meteorological forecast data to obtain the second predicted seawater temperature at the plant site; Based on the first and second predicted seawater temperatures at the plant site, the inlet temperature of the nuclear island system is predicted using the SEC / RRI heat exchanger SEC side inlet temperature prediction model, and the SEC / RRI heat exchanger SEC side inlet temperature is obtained. Based on the RRI side outlet temperature prediction model of the first SEC / RRI heat exchanger, combined with the water temperature monitoring data and the SEC side inlet temperature of the SEC / RRI heat exchanger, the predicted outlet temperature of the first SEC / RRI heat exchanger on the RRI side is obtained. Based on the SEC side inlet temperature of the SEC / RRI heat exchanger and the water temperature monitoring data, the predicted RRI side outlet temperature of the second SEC / RRI heat exchanger is obtained by using the thermal-hydraulic model. The outlet temperature of the nuclear island system is predicted using the RRI side outlet temperature prediction model of the second SEC / RRI heat exchanger, and the predicted RRI side outlet temperature is obtained. Based on the predicted outlet temperature of the RRI side and the water temperature monitoring data, the predicted outlet temperature of the downstream user's heat exchanger is obtained by using the downstream outlet temperature prediction model. The water temperature prediction data includes one or more of the following: the first predicted plant site seawater temperature, the second predicted plant site seawater temperature, the SEC side inlet temperature of the SEC / RRI heat exchanger, the RRI side outlet predicted temperature of the first SEC / RRI heat exchanger, the RRI side outlet predicted temperature of the second SEC / RRI heat exchanger, the RRI side outlet predicted temperature, and the downstream user heat exchanger outlet predicted temperature.
7. The early warning method according to claim 6, characterized in that, Step S30 includes: The water temperature monitoring data is compared with the SEC side inlet temperature of the SEC / RRI heat exchanger. If the difference between the two is within a first preset range, the SEC side inlet temperature of the SEC / RRI heat exchanger is reasonable. Determine whether the difference between the water temperature monitoring data and the predicted temperature at the RRI outlet is within a second preset range. If it is, then the predicted temperature at the RRI outlet is reasonable.
8. The early warning method according to claim 6, characterized in that, Step S40 includes The margin prediction value is obtained by calculating the difference between the design temperature value and the SEC side inlet temperature of the SEC / RRI heat exchanger, the predicted RRI side outlet temperature, and the predicted outlet temperature of the downstream user heat exchanger.
9. The early warning method according to claim 8, characterized in that, The design temperature values include: the seawater design temperature value, the RRI design temperature value, and the downstream heat outlet temperature alarm value of the nuclear island system. The difference between the seawater temperature value and the predicted inlet temperature on the SEC side of the SEC / RRI heat exchanger, the predicted outlet temperature on the RRI side, and the predicted outlet temperature of the downstream user's heat exchanger is calculated to obtain the margin prediction value, including: The first margin prediction value is obtained by calculating the seawater design temperature value and the SEC side inlet temperature of the SEC / RRI heat exchanger. The second margin prediction value is obtained by calculating the RRI design temperature value and the RRI side outlet predicted temperature. The third margin prediction value is obtained by calculating the downstream heat outlet temperature alarm value and the downstream user heat exchanger outlet predicted temperature. The margin prediction value includes the first margin prediction value, the second margin prediction value, and the third margin prediction value.
10. The early warning method according to claim 9, characterized in that, Step S50 includes: Determine whether the first margin prediction value, the second margin prediction value, and the third margin prediction value are all 0; if they are 0, issue an early warning.