Cooling tower circulating water digital twinning control system

Abstract

The invention relates to the technical field of digital twinning, in particular to a cooling tower circulating water digital twinning control system which comprises a data collection module, a data processing module, an influence analysis module, a predictive maintenance module, a strategy construction module and a monitoring and feedback module. According to the system, the wind speed, the water flow speed and the temperature difference are monitored in real time, accurate stress monitoring data are generated, then the reliability and accuracy of the data are optimized through time sequence analysis and anomaly detection, and three-dimensional modeling and coupling analysis are combined, so that the system can more accurately evaluate the influence of the wind load and the water flow on the cooling tower structure; the response capability and prediction accuracy of the system are improved, the water flow speed and the water inlet temperature can be adjusted in real time to optimize operation parameters, the energy efficiency is remarkably improved, the energy consumption is reduced, and the optimization of the structural stability and the operation efficiency is further ensured by the system through continuous state monitoring and performance evaluation.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a cooling tower circulating water digital twin control system. Background Art

[0002] The field of digital twin technology involves creating a virtual model of a physical entity to simulate and analyze its performance and behavior in real time. This technology uses the Internet of Things, big data, machine learning, and simulation technology to collect real-time data from equipment to build a digital mirror that is updated synchronously with the actual physical entity. Digital twins can be used to predict maintenance, optimize operations, and improve product design. They are widely used in industries such as manufacturing, automobiles, construction, and medical. Through digital twins, companies can test changes without affecting actual operations, predict equipment failures, and improve the overall efficiency and performance of the system by simulating different operating scenarios.

[0003] Among them, the cooling tower circulating water digital twin control system refers to the use of digital twin technology to monitor and control the performance of the circulating water system in the cooling tower. This system can realize real-time monitoring, analysis and optimization of the operating status of the cooling tower. By simulating different operating conditions, it can predict and adjust key parameters such as the temperature and flow rate of the circulating water, improve energy efficiency, and reduce energy consumption and maintenance costs. The purpose of this control system is mainly to improve the operating efficiency and reliability of the industrial cooling system and ensure the optimal performance of the cooling tower under different environmental and load conditions.

[0004] Existing digital twin technology mainly focuses on the synchronization and simulation of real-time data, but lacks in-depth analysis and prediction of the long-term impact on structural health. As a result, it is difficult for existing systems to adjust and optimize in a timely manner when faced with complex and changing environmental and load conditions, which will increase operation and maintenance costs and shorten equipment life. For example, under different environmental conditions, failure to adjust operating parameters in real time leads to inefficient cooling or overcooling, affecting the energy efficiency and safe operation of the entire system. Data analysis in existing technologies fails to fully utilize the collected multi-source data, resulting in insufficient decision support and fault prediction, which is particularly evident under high load or extreme conditions, causing premature system failure or operational failure. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and propose a cooling tower circulating water digital twin control system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a cooling tower circulating water digital twin control system comprises:

[0007] The data collection module uses a variety of sensors built into the cooling tower to monitor and record wind speed, water flow rate and temperature difference data in real time, perform digital twinning and formatting, and generate real-time stress monitoring data;

[0008] The data processing module uses the real-time stress monitoring data to perform synchronous detection and identify abnormal data points, and performs time series analysis on normal data to obtain calibrated stress data;

[0009] The impact analysis module uses the calibrated stress data to perform three-dimensional modeling of the cooling tower, calculate the combined impact of wind load and water flow on the structure, perform stress and strain distribution calculations, and obtain coupling analysis results;

[0010] The predictive maintenance module performs failure mode analysis based on the coupling analysis results, identifies the change trend of structural stress, evaluates the structural health of the cooling tower circulating water, and obtains structural health prediction results;

[0011] The strategy building module uses the structural health prediction results to adjust the cooling water flow rate and water inlet temperature in real time, and obtains the control strategy adjustment plan by simulating differentiated operating parameters and performing digital twin control;

[0012] The monitoring and feedback module implements the control strategy adjustment plan, continuously monitors the status of the cooling tower circulating water, analyzes the performance and structural stability, and evaluates the effect of the adjustment plan to generate health status monitoring results.

[0013] As a further solution of the present invention, the real-time stress monitoring data includes monitoring data of internal pressure, vibration level and temperature gradient of the cooling tower, the calibrated stress data includes wind speed, water flow velocity and temperature difference data adjusted after time series analysis, the coupling analysis results include quantitative evaluation of the impact on the structural stability of the cooling tower and factor interaction analysis, the structural health prediction results include life expectancy prediction, fatigue damage assessment and failure probability calculation of key components, the control strategy adjustment scheme includes optimization setting of operating parameters, flow rate adjustment, temperature adjustment range and operating time window, and the health status monitoring results include real-time performance indicators, failure frequency and repair time statistics.

[0014] As a further solution of the present invention, the data collection module includes:

[0015] The data recording submodule monitors wind speed, water flow rate and temperature difference data in real time from multiple sensors built into the cooling tower, smoothes wind speed fluctuations, and adjusts water flow rate to obtain sensor data sets;

[0016] The data integration submodule uses the data twin technology to mirror the data through the sensor data set, and performs data formatting at the same time, transforms the unstructured data, and integrates the data to obtain a formatted data set;

[0017] The structural stress analysis submodule uses the formatted data set to perform stress distribution analysis on the structured data, predicts stress anomalies in key pressure-bearing parts according to the model, analyzes and calculates structural stress, and obtains real-time stress monitoring data.

[0018] As a further solution of the present invention, the data processing module includes:

[0019] The abnormal identification submodule uses the real-time stress monitoring data to identify the abnormal peak of the data point frequency, determines the data points that deviate from the normal range in combination with the threshold setting, and marks the abnormalities to generate an abnormal data index;

[0020] The data screening submodule screens normal data and performs trend analysis based on the abnormal data index, identifies seasonal adjustment factors, and evaluates the impact of cyclical fluctuations to obtain time series analysis results;

[0021] The data adjustment submodule utilizes the time series analysis results to eliminate errors by smoothing the data, calibrate and optimize the consistency of the data, and obtain calibrated stress data.

[0022] As a further solution of the present invention, the impact analysis module includes:

[0023] The model building submodule optimizes the support structure and the contact surface based on the calibrated stress data and the real-time structural size and shape of the cooling tower to generate a three-dimensional structural model;

[0024] The dynamic response analysis submodule simulates the force of wind load on the three-dimensional model through the three-dimensional structural model, and analyzes the influence of water flow on the structure in combination with the movement direction of water flow, integrates wind load and water flow data to analyze the dynamic response of the cooling tower, and obtains the combined load influence analysis result;

[0025] The stress-strain analysis submodule utilizes the combined load impact analysis results and adopts finite element analysis technology to mesh the three-dimensional model, calculate the stress response of each mesh, analyze the distribution of stress and strain, and obtain coupling analysis results.

[0026] As a further solution of the present invention, the formula of the finite element analysis technology is as follows:

[0027]

[0028] Among them, σ is the stress distribution value in the three-dimensional model, E represents the elastic modulus of the material, ν represents the proportional relationship between the lateral contraction and longitudinal elongation of the material under stress, ∈ x represents the strain along the x-axis, ∈ y represents the strain along the y-axis.

[0029] As a further solution of the present invention, the predictive maintenance module includes:

[0030] The mode analysis submodule identifies potential failure points of the cooling tower structure based on the coupling analysis results, determines the frequency and impact of the failure mode through data comparison and analysis, and generates failure mode identification results;

[0031] The trend identification submodule uses the failure mode identification result to track and evaluate the change trend of the structural stress, calculate the speed and acceleration of the stress change, and obtain the stress change trend analysis result;

[0032] The structural integrity assessment submodule quantitatively assesses the integrity of the cooling tower circulating water structure based on the stress change trend analysis results, predicts the service life in combination with environmental factors and operating conditions, and obtains structural health prediction results.

[0033] As a further solution of the present invention, the strategy building module includes:

[0034] The parameter adjustment submodule adjusts the cooling water flow rate and the water inlet temperature in real time based on the structural health prediction results, and optimizes the parameter settings by monitoring the feedback data to match the cyclically changing environment and the cooling tower conditions to obtain the optimized parameter settings;

[0035] The scheme comparison submodule uses the optimization parameter settings to simulate differentiated operating parameters in a virtual environment, analyzes the impact of differentiated parameter settings on cooling efficiency and structural safety, determines the optimal operating scheme through multi-scheme comparison, and generates operation simulation analysis results;

[0036] The virtual model synchronization submodule uses a genetic algorithm to perform real-time synchronization between the cooling tower and the virtual model through the operation simulation analysis results, executes real-time control strategy testing, and obtains a control strategy adjustment plan.

[0037] As a further solution of the present invention, the formula of the genetic algorithm is as follows:

[0038]

[0039] Where f is the performance optimization coefficient of the cooling tower, T i Represents the real-time temperature inside the cooling tower, T set Represents the set target temperature, C p represents specific heat capacity, P cool Represents cooling power, W rate stands for water flow rate and ABS stands for absolute value.

[0040] As a further solution of the present invention, the monitoring and feedback module includes:

[0041] The operating parameter application submodule adopts the control strategy adjustment scheme to adjust the flow rate and inlet water temperature of the cooling tower circulating water in actual operation, applies the new operating parameters in real time through digital twin control, verifies that the strategy is executed according to the predetermined goals, and obtains the strategy implementation results;

[0042] The state analysis submodule continuously monitors the temperature, flow rate and key indicators of the cooling tower circulating water based on the results of the strategy implementation, analyzes the real-time status of the water circulation, identifies signs of deviation from normal operation, and generates water circulation monitoring data;

[0043] The effect evaluation submodule evaluates the performance and structural stability of water circulation cooling through the water circulation monitoring data, analyzes the impact of strategy adjustment on operation efficiency and operation health, and obtains health status monitoring results.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, real-time monitoring and analysis are performed through advanced data collection and processing logic, which significantly improves the system's responsiveness and prediction accuracy. By real-time monitoring of wind speed, water flow velocity and temperature difference, accurate stress monitoring data is generated, and then the reliability and accuracy of the data are optimized through time series analysis and anomaly detection. Combined with three-dimensional modeling and coupling analysis, the system can more accurately evaluate the impact of wind load and water flow on the cooling tower structure, improve the effectiveness of predictive maintenance, and adjust the water flow velocity and water inlet temperature in real time to optimize operating parameters, significantly improve energy efficiency and reduce energy consumption. Through continuous status monitoring and performance evaluation, the system further ensures the optimization of structural stability and operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0048] Figure 3 It is a flow chart of the data collection module of the present invention;

[0049] Figure 4 It is a flow chart of the data processing module of the present invention;

[0050] Figure 5 This is a flow chart of the impact analysis module of the present invention;

[0051] Figure 6 This is a flow chart of the predictive maintenance module of the present invention;

[0052] Figure 7 A flow chart of the strategy building modules of the present invention;

[0053] Figure 8 It is a flow chart of the monitoring and feedback module of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] See also Figure 1 to Figure 2 , a cooling tower circulating water digital twin control system includes:

[0057] The data collection module uses a variety of sensors built into the cooling tower to monitor and record wind speed, water flow rate and temperature difference data in real time, perform digital twinning, data verification and formatting, and perform signal filtering to eliminate noise, generating real-time stress monitoring data;

[0058] The data processing module uses real-time stress monitoring data to perform synchronous detection and identify abnormal data points, and conducts time series analysis on normal data to obtain calibrated stress data;

[0059] The impact analysis module uses calibrated stress data to perform three-dimensional modeling of the cooling tower, calculate the combined impact of wind load and water flow on the structure, record the structural response through iterative solution, perform stress and strain distribution calculations, and obtain coupling analysis results;

[0060] The predictive maintenance module performs failure mode analysis based on the coupling analysis results, identifies the changing trend of structural stress, evaluates the structural health of the cooling tower circulating water, and obtains structural health prediction results;

[0061] The strategy building module uses the structural health prediction results to adjust the cooling water flow rate and inlet water temperature in real time. By simulating differentiated operating parameters, the impact on the structure is quantified, and digital twin control is performed to obtain the control strategy adjustment plan.

[0062] The monitoring and feedback module implements the control strategy adjustment plan, continuously monitors the status of the cooling tower circulating water, analyzes the performance and structural stability through real-time data, and evaluates the effect of the adjustment plan to generate health status monitoring results.

[0063] Real-time stress monitoring data include monitoring data of internal pressure, vibration level and temperature gradient of cooling tower. Calibrated stress data include wind speed, water flow velocity and temperature difference data adjusted through time series analysis. Coupling analysis results include quantitative evaluation of the impact on cooling tower structural stability and factor interaction analysis. Structural health prediction results include life expectancy prediction, fatigue damage assessment and failure probability calculation of key components. Control strategy adjustment plan includes optimization setting of operating parameters, flow rate adjustment, temperature adjustment range and operating time window. Health status monitoring results include real-time performance indicators, failure frequency and repair time statistics.

[0064] See also Figure 2 , 3 , the data collection modules include:

[0065] The data recording submodule monitors wind speed, water flow rate and temperature difference data in real time from multiple sensors built into the cooling tower, smoothes wind speed fluctuations, and adjusts water flow rate to obtain the sensor data set. The execution process is as follows;

[0066] Monitor the internal environment of the cooling tower, collect wind speed, water flow rate and temperature difference data in real time, collect data based on multiple sensors, smooth out wind speed fluctuations, adjust water flow rate to optimize performance, perform real-time data processing for each monitored parameter, such as wind speed, water flow rate, and temperature difference, use smoothing algorithms to reduce random fluctuations in data, improve data stability and reliability, and adjust water flow rate to cope with different working environments to ensure cooling efficiency. Through a series of processing, a sensor data set is obtained.

[0067] The data integration submodule uses the data twin technology to mirror the data through the sensor data set, and performs data formatting at the same time, transforms the unstructured data, and integrates the data to obtain the execution process of the formatted data set as follows;

[0068] Responsible for processing the collected sensor data sets, using data twin technology to create a digital mirror of the data, performing data replication, enhancing data reliability and accessibility, performing data formatting, and converting the collected unstructured data into a structured format to facilitate further data analysis and integration. In this way, the data is reorganized and optimized, enhancing the efficiency and accuracy of data processing, and a formatted data set is obtained after integration.

[0069] The structural stress analysis submodule uses the formatted data set to perform stress distribution analysis on the structured data, predicts stress anomalies in key pressure-bearing parts based on the model, analyzes and calculates structural stress, and obtains the execution process of real-time stress monitoring data as follows;

[0070] Use the formatted data set to perform stress distribution analysis according to the formula Calculate the stress anomaly of the key pressure-bearing parts. a represents stress, F a Represents the force, A a Represents the stress area. Detailed explanation of the formula and the process of formula calculation: Considering the stress σ a is the force F a With the force area A a The ratio of the force F a The structural response data obtained by sensor monitoring can be used to calculate the stress area A. a is a known structural parameter. For example, if F is measured under experimental conditions a = 500 Newtons and A a = 0.1 square meters, then the stress σ a Can be Such calculations not only provide a quantitative description of the stress state of the pressure-bearing parts of the structure, but also help predict stress anomalies so that corresponding maintenance measures can be taken.

[0071] See also Figure 2 , 4 , the data processing module includes:

[0072] The anomaly identification submodule uses real-time stress monitoring data to identify the abnormal peak of the data point frequency, and combines the threshold setting to determine the data points that deviate from the normal range, and marks them as abnormal. The execution process of generating the abnormal data index is as follows;

[0073] Using real-time stress monitoring data, by identifying abnormal peaks in the frequency of data points, according to the formula Calculate the data points that deviate from the normal range. In the formula, F b represents frequency, T b Represents the time interval. Detailed explanation of the formula and the derivation process of the formula calculation: Abnormal frequency F b The data point time interval T b For example, if the interval between two consecutive data points is T b The measured value is 0.02 seconds, so the abnormal frequency F b Can be Calculation. Frequencies above the normal operating frequency, such as 10Hz, are flagged as anomalies. The formula helps identify and flag frequency anomalies in the data for further processing.

[0074] The data screening submodule screens normal data and performs trend analysis based on abnormal data indexes, identifies seasonal adjustment factors, and evaluates the impact of cyclical fluctuations to obtain the time series analysis results. The execution process is as follows;

[0075] Based on the abnormal data index, data points marked as abnormal are removed, and then trend analysis is performed on the remaining normal data to identify seasonal adjustment factors in the data. The adjustment factors reflect the specific patterns of data changes with seasons, evaluate how seasonal factors affect the cyclical fluctuations of data, and reflect the true trend and cyclical changes of the data after considering seasonal adjustments, provide support for subsequent decision-making, and obtain time series analysis results.

[0076] The data adjustment submodule uses the time series analysis results to eliminate errors by smoothing the data, calibrating and optimizing the consistency of the data, and obtaining the calibrated stress data. The execution process is as follows;

[0077] Based on the results of time series analysis, random errors in the recording process are eliminated through data smoothing. The processing method reduces data noise by applying moving average or smoothing technology, and then calibrates the data, improves data consistency, and more accurately reflects the actual stress state. It optimizes the quality and availability of the data, is more suitable for further analysis and application, and obtains calibrated stress data.

[0078] See also Figure 2 , 5 , the impact analysis module includes:

[0079] The model building submodule optimizes the support structure and contact surface based on the calibrated stress data and the real-time structural size and shape of the cooling tower to generate a three-dimensional structural model. The execution process is as follows;

[0080] Based on the calibrated stress data, the real-time structural size and shape of the cooling tower are taken into consideration to optimize the structure, and the supporting structure and contact surface are improved. Guided by data, the process involves comprehensive consideration of various structural parameters and actual working conditions to ensure that the model can truly reflect the physical properties and working environment of the structure, improve the application value and engineering applicability of the model, and generate a three-dimensional structural model.

[0081] The dynamic response analysis submodule simulates the force of wind load on the three-dimensional model through the three-dimensional structural model, and analyzes the influence of water flow on the structure in combination with the movement direction of water flow. The dynamic response of the cooling tower is analyzed by integrating wind load and water flow data. The execution process of obtaining the combined load influence analysis result is as follows;

[0082] The three-dimensional structural model is used to simulate the force of wind load on the model, and the influence of water flow on the structure is analyzed. According to the formula F a=ma, calculate the dynamic response of the cooling tower. In the formula, F a represents the force, m represents the mass, and a represents the acceleration. Detailed explanation of the formula and the calculation process of the formula: In actual operation, the force F a It can be obtained by quantifying the dynamic effects of wind load and water flow on the structure. For example, if the mass m of the 3D model is 5000 kg, and the average acceleration a obtained from the analysis of wind load and water flow data is 0.5 m / s2, then the force F a F a =5000×0.5=2500 Newton. In this way, the maximum and minimum forces on the cooling tower under different environmental conditions can be calculated to evaluate the stability and safety of the structure.

[0083] The stress-strain analysis submodule uses the combined load influence analysis results and finite element analysis technology to mesh the three-dimensional model, calculate the stress response of each mesh, analyze the distribution of stress and strain, and obtain the coupled analysis results. The execution process is as follows;

[0084] The formula for the finite element analysis technique is as follows:

[0085]

[0086] Among them, σ is the stress distribution value in the three-dimensional model, E represents the elastic modulus of the material, ν represents the proportional relationship between the lateral contraction and longitudinal elongation of the material under stress, ∈ x represents the strain along the x-axis, ∈ y represents the strain along the y-axis.

[0087] formula:

[0088]

[0089] Among them, the determination of each parameter is as follows:

[0090] E (elastic modulus): obtained through standard material testing, for example, steel is approximately 210 GPa.

[0091] ν (Poisson's ratio): obtained through material testing. For most metal materials, the Poisson's ratio is between 0.25 and 0.35.

[0092] ∈ x (strain along the x-axis) and ∈ y (Strain along the y-axis): In finite element analysis, the strain value is calculated from the node displacement results.

[0093] The parameters of the steel are set as follows: E = 210 GPa, ν = 0.3, ∈ x = 0.001 (tensile strain of 1 thousandth), ∈ y=-0.0003 (compressive strain along the y-axis due to Poisson's effect).

[0094] Substitute into the formula to calculate:

[0095]

[0096] The results show that under the given material properties and strain conditions, the calculated stress is 210MPa, which means that the material is in a safe working state under this loading condition, because the yield strength of most steels is much higher than this value. This stress level can help engineers determine the safety and potential risk points of the structure, and then provide a basis for structural design and optimization.

[0097] See also Figure 2 , 6 , the predictive maintenance module includes:

[0098] The mode analysis submodule identifies the potential failure points of the cooling tower structure based on the coupling analysis results, determines the frequency and impact of the failure mode through data comparison and analysis, and generates the execution process of the failure mode identification results as follows;

[0099] The potential failure points of the cooling tower structure are identified by coupling analysis results. According to the formula Calculate the frequency of the failure mode. Where, f b represents the frequency of the failure mode, k represents the structural stiffness, and m represents the mass. Detailed explanation of the formula and the process of formula calculation: The formula is based on the vibration analysis and relates the failure frequency of the structure to the stiffness and mass of the structure. Set the stiffness of the cooling tower failure point k = 10,000 Newtons / meter, and the mass m = 5,000 kilograms, then the failure mode frequency f b Can be Calculated in Hertz. The frequency indicates the natural vibration frequency of the failure mode under normal conditions. By comparing the frequency with the monitored frequency in actual operation, the impact and characteristics of the failure mode can be further confirmed.

[0100] The trend identification submodule uses the failure mode identification results to track and evaluate the change trend of structural stress, calculate the speed and acceleration of stress change, and obtain the stress change trend analysis results. The execution process is as follows;

[0101] Using the failure mode identification results, the speed and acceleration of stress change are calculated by tracking and evaluating the structural stress data. In particular, the stress change rate is obtained by comparing the stress change values ​​in different time periods. According to the trend of stress change over time, the acceleration is further calculated to identify the trend of rapid stress changes in the short term or gradual accumulation in the long term, revealing the dynamic characteristics of the pressure changes that the cooling tower structure is subjected to during operation, providing a reliable basis for further predicting the long-term health status of the structure, and obtaining the stress change trend analysis results.

[0102] The structural integrity assessment submodule quantitatively assesses the integrity of the cooling tower circulating water structure based on the stress change trend analysis results, and predicts the service life in combination with environmental factors and operating conditions. The execution process of obtaining the structural health prediction results is as follows;

[0103] Based on the stress change trend analysis results, combined with the operating conditions and environmental factors of the cooling tower, the overall stress distribution of the circulating water system of the structure is quantitatively evaluated, and the stress peak and fatigue limit of the key components of the cooling tower under different operating conditions are calculated. The future change trend is predicted based on the stress change data, and the service life of the cooling tower is estimated. The structural health prediction results are obtained through analysis.

[0104] See also Figure 2 , 7 , the strategy building blocks include:

[0105] The parameter adjustment submodule adjusts the cooling water flow rate and inlet water temperature in real time based on the structural health prediction results. It tunes the parameter settings by monitoring the feedback data to match the cyclically changing environment and cooling tower conditions. The execution process of the optimized parameter settings is as follows;

[0106] Based on the structural health prediction results, the cooling water flow rate and inlet water temperature are adjusted in real time, monitored through feedback data, and parameter settings are adjusted according to the monitoring data to better adapt to the current environmental conditions and the operating conditions of the cooling tower. During the real-time adjustment process, the system will use the intelligent control mechanism to continuously optimize the parameter settings according to different feedback indicators, such as changes in water flow rate, temperature rise or fall, to ensure maximum cooling efficiency and ensure structural stability, and obtain optimized parameter settings.

[0107] The scheme comparison submodule uses the optimization parameter setting to simulate the differentiated operation parameters in the virtual environment, analyzes the impact of the differentiated parameter setting on the cooling efficiency and structural safety, determines the optimal operation scheme through multi-scheme comparison, and generates the operation simulation analysis results. The execution process is as follows;

[0108] By utilizing optimized parameter settings, different operating parameter combinations are tested in a virtual simulation environment, focusing on analyzing the impact of each operating parameter on cooling efficiency and structural safety. Each set of differentiated parameters such as flow rate, temperature change, etc. will affect the overall performance of the cooling tower. The simulation results obtained through simulation are used to compare the advantages and disadvantages of each solution, especially under extreme conditions, to analyze whether the adjustment of parameters will affect the long-term stability of the structure or cause a decrease in cooling efficiency. After a series of comparisons, the optimal operating solution is determined and the operation simulation analysis results are generated.

[0109] The virtual model synchronization submodule uses genetic algorithm to perform real-time synchronization between the cooling tower and the virtual model through the operation simulation analysis results, and performs real-time control strategy testing to obtain the execution process of the control strategy adjustment plan as follows;

[0110] The formula of genetic algorithm is as follows:

[0111]

[0112] Where f is the performance optimization coefficient of the cooling tower, T i Represents the real-time temperature inside the cooling tower, T set Represents the set target temperature, C p represents specific heat capacity, P cool Represents cooling power, W rate stands for water flow rate and ABS stands for absolute value.

[0113] formula:

[0114]

[0115] The various parameters in the formula are obtained through precise measurement and data collection, as follows:

[0116] T i (Current internal temperature): Monitor the temperature inside the cooling tower in real time through the temperature sensor. Set the temperature measured at a time point to 35°C.

[0117] T set (Target set temperature): The temperature set according to cooling demand, set the set temperature to 30℃.

[0118] C p (Specific heat capacity of water): The standard value is 4.186 kJ / kg / K, which is obtained by looking up the Material Properties Handbook.

[0119] P cool (Cooling power): The power consumption of the cooling system is measured through the energy consumption monitoring system and the current power is set to 5kW.

[0120] W rate(Water flow rate): Measure the water flow rate through the flow meter and set the flow rate to 0.05m 3 / s.

[0121] Substituting specific values, the calculation is as follows:

[0122] 1. Calculate the absolute value of the temperature difference and find the square root:

[0123]

[0124] 2. Calculate the numerator:

[0125]

[0126] 3. Calculate the denominator:

[0127] P cool ·W rate =5·0.05=0.25kW / s

[0128] 4. Divide the numerator by the denominator to get the optimized value:

[0129]

[0130] The calculation results represent the performance optimization coefficient of the cooling tower under the given settings and environmental conditions. The numerical results show that based on the current control parameters, an energy conversion efficiency of 37.452 kJ / K can be achieved for every 1 kW of cooling power consumed. This shows that after the control strategy is adjusted, energy can be used more efficiently, which plays a key role in the performance management and optimization of the cooling tower.

[0131] See also Figure 2 , 8 , the monitoring and feedback modules include:

[0132] The operating parameter application submodule adopts the control strategy adjustment scheme to adjust the flow rate and inlet water temperature of the cooling tower circulating water in actual operation, and applies the new operating parameters in real time through digital twin control to verify that the strategy is executed according to the predetermined goals. The execution process of obtaining the strategy implementation results is as follows;

[0133] Based on the control strategy adjustment plan, the circulating water flow rate and inlet water temperature of the cooling tower are adjusted through the automation system in actual operation, and the digital twin technology is used to monitor and feedback the new operating parameters in real time. The digital twin technology can replicate and analyze the operating status of the cooling tower in real time to ensure that the new parameter settings match the actual operating conditions. The control strategy will verify whether it is executed according to the predetermined goals based on the feedback data, and make immediate adjustments based on the difference between the goals and the actual operation to obtain the strategy implementation results.

[0134] Based on the results of the strategy implementation, the status analysis submodule continuously monitors the temperature, flow rate and key indicators of the cooling tower circulating water, analyzes the real-time status of the water cycle, identifies signs of deviation from normal operation, and generates the execution process of water cycle monitoring data as follows;

[0135] Based on the results of strategy implementation, the temperature, flow rate and key operating indicators of the cooling tower circulating water are continuously monitored, the real-time status of the system is analyzed, and the fluctuations in temperature and flow rate during the water circulation process, as well as potential abnormal signs, are detected. By comparing operating data and real-time monitoring data, signals that deviate from normal operating conditions are identified to ensure that the cooling tower can be adjusted in time to cope with changes in operation, and whether the current operating status meets the expected operating standards is determined to generate water circulation monitoring data.

[0136] The effect evaluation submodule evaluates the performance and structural stability of water circulation cooling through water circulation monitoring data, analyzes the impact of strategy adjustment on operation efficiency and operation health, and obtains the execution process of health status monitoring results as follows;

[0137] Through water circulation monitoring data, the performance of water circulation cooling and the stability of the cooling tower structure are evaluated, focusing on analyzing the impact of current strategy adjustments on system operating efficiency, especially the long-term impact on cooling effects and equipment health. By evaluating key indicators such as stress distribution and temperature changes, it is confirmed whether there are potential hidden dangers, and combined with the analysis results, the health status monitoring data of the cooling tower is obtained to ensure that the system can maintain long-term stable operation and achieve optimal performance status, and obtain health status monitoring results.

[0138] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A cooling tower circulating water digital twin control system, characterized in that: The system comprises: The data collection module uses a variety of sensors built into the cooling tower to monitor and record wind speed, water flow rate and temperature difference data in real time, perform digital twinning and formatting, and generate real-time stress monitoring data; The data processing module uses the real-time stress monitoring data to perform synchronous detection and identify abnormal data points, and performs time series analysis on normal data to obtain calibrated stress data; The impact analysis module uses the calibrated stress data to perform three-dimensional modeling of the cooling tower, calculate the combined impact of wind load and water flow on the structure, perform stress and strain distribution calculations, and obtain coupling analysis results; The predictive maintenance module performs failure mode analysis based on the coupling analysis results, identifies the change trend of structural stress, evaluates the structural health of the cooling tower circulating water, and obtains structural health prediction results; The strategy building module uses the structural health prediction results to adjust the cooling water flow rate and water inlet temperature in real time, and obtains the control strategy adjustment plan by simulating differentiated operating parameters and performing digital twin control; The monitoring and feedback module implements the control strategy adjustment plan, continuously monitors the status of the cooling tower circulating water, analyzes the performance and structural stability, and evaluates the effect of the adjustment plan to generate health status monitoring results.

2. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The real-time stress monitoring data includes monitoring data of the internal pressure, vibration level and temperature gradient of the cooling tower. The calibrated stress data includes wind speed, water flow velocity and temperature difference data adjusted after time series analysis. The coupling analysis results include quantitative evaluation of the impact on the stability of the cooling tower structure and analysis of factor interaction. The structural health prediction results include life expectancy prediction, fatigue damage assessment and failure probability calculation of key components. The control strategy adjustment plan includes optimization setting of operating parameters, flow rate adjustment, temperature adjustment range and operating time window. The health status monitoring results include real-time performance indicators, failure frequency and repair time statistics.

3. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The data collection module includes: The data recording submodule monitors wind speed, water flow rate and temperature difference data in real time from multiple sensors built into the cooling tower, smoothes wind speed fluctuations, and adjusts water flow rate to obtain sensor data sets; The data integration submodule uses the data twin technology to mirror the data through the sensor data set, and performs data formatting at the same time, transforms the unstructured data, and integrates the data to obtain a formatted data set; The structural stress analysis submodule uses the formatted data set to perform stress distribution analysis on the structured data, predicts stress anomalies in key pressure-bearing parts according to the model, analyzes and calculates structural stress, and obtains real-time stress monitoring data.

4. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The data processing module comprises: The abnormal identification submodule uses the real-time stress monitoring data to identify the abnormal peak of the data point frequency, determines the data points that deviate from the normal range in combination with the threshold setting, and marks the abnormalities to generate an abnormal data index; The data screening submodule screens normal data and performs trend analysis based on the abnormal data index, identifies seasonal adjustment factors, and evaluates the impact of cyclical fluctuations to obtain time series analysis results; The data adjustment submodule utilizes the time series analysis results to eliminate errors by smoothing the data, calibrate and optimize the consistency of the data, and obtain calibrated stress data.

5. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The impact analysis module includes: The model building submodule optimizes the support structure and the contact surface based on the calibrated stress data and the real-time structural size and shape of the cooling tower to generate a three-dimensional structural model; The dynamic response analysis submodule simulates the force of wind load on the three-dimensional model through the three-dimensional structural model, and analyzes the influence of water flow on the structure in combination with the movement direction of water flow, integrates wind load and water flow data to analyze the dynamic response of the cooling tower, and obtains the combined load influence analysis result; The stress-strain analysis submodule utilizes the combined load impact analysis results and adopts finite element analysis technology to mesh the three-dimensional model, calculate the stress response of each mesh, analyze the distribution of stress and strain, and obtain coupling analysis results.

6. The cooling tower circulating water digital twin control system according to claim 5, characterized in that: The formula for the finite element analysis technique is as follows: Among them, σ is the stress distribution value in the three-dimensional model, E represents the elastic modulus of the material, ν represents the proportional relationship between the lateral contraction and longitudinal elongation of the material under stress, ∈ x represents the strain along the x-axis, ∈ y represents the strain along the y-axis.

7. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The predictive maintenance module includes: The mode analysis submodule identifies potential failure points of the cooling tower structure based on the coupling analysis results, determines the frequency and impact of the failure mode through data comparison and analysis, and generates failure mode identification results; The trend identification submodule uses the failure mode identification result to track and evaluate the change trend of the structural stress, calculates the speed and acceleration of the stress change, and obtains the stress change trend analysis result; The structural integrity assessment submodule quantitatively assesses the integrity of the cooling tower circulating water structure based on the stress change trend analysis results, predicts the service life in combination with environmental factors and operating conditions, and obtains structural health prediction results.

8. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The strategy building module includes: The parameter adjustment submodule adjusts the cooling water flow rate and the water inlet temperature in real time based on the structural health prediction results, and optimizes the parameter settings by monitoring the feedback data to match the cyclically changing environment and the cooling tower conditions to obtain the optimized parameter settings; The scheme comparison submodule uses the optimization parameter settings to simulate differentiated operating parameters in a virtual environment, analyzes the impact of differentiated parameter settings on cooling efficiency and structural safety, determines the optimal operating scheme through multi-scheme comparison, and generates operation simulation analysis results; The virtual model synchronization submodule uses a genetic algorithm to perform real-time synchronization between the cooling tower and the virtual model through the operation simulation analysis results, executes real-time control strategy testing, and obtains a control strategy adjustment plan.

9. The cooling tower circulating water digital twin control system according to claim 8, characterized in that: The formula of the genetic algorithm is as follows: Where f is the performance optimization coefficient of the cooling tower, T i Represents the real-time temperature inside the cooling tower, T set Represents the set target temperature, C p represents specific heat capacity, P cool Represents cooling power, W rate stands for water flow rate and ABS stands for absolute value.

10. The cooling tower circulating water digital twin control system according to claim 1, characterized in that: The monitoring and feedback module includes: The operating parameter application submodule adopts the control strategy adjustment scheme to adjust the flow rate and inlet water temperature of the cooling tower circulating water in actual operation, applies the new operating parameters in real time through digital twin control, verifies that the strategy is executed according to the predetermined goals, and obtains the strategy implementation results; The state analysis submodule continuously monitors the temperature, flow rate and key indicators of the cooling tower circulating water based on the results of the strategy implementation, analyzes the real-time status of the water circulation, identifies signs of deviation from normal operation, and generates water circulation monitoring data; The effect evaluation submodule evaluates the performance and structural stability of water circulation cooling through the water circulation monitoring data, analyzes the impact of strategy adjustment on operation efficiency and operation health, and obtains health status monitoring results.

Images