Dynamic digital twin system for condenser performance based on online operation data and establishment method thereof

Abstract

The invention relates to a condenser performance dynamic digital twinning establishment method based on online operation data, which comprises the following steps of: a, acquiring operation data of a condenser through a real-time data acquisition module, and carrying out validity identification on the data; b, the cooling water amount is corrected through a data correction module, and operation parameters used for thermal comparison calculation are calibrated so as to obtain corrected accurate thermal loads; c, the current heat transfer coefficient and heat exchange efficiency are calculated through a heat transfer coefficient and heat exchange efficiency calculation module; d, the assumed cleaning coefficient Fc is replaced with the heat exchange efficiency Evs of the comprehensive field factors through a heat exchange capacity evaluation module, so that the current heat exchange capacity of the condenser is obtained, and real-time dynamic performance digital twinning is achieved; e, the jump reduction condition of the heat exchange efficiency Evs is detected through an anomaly detection module so as to recognize other problems occurring in the condenser system; and f, tracking the performance attenuation process of the condenser through a performance tracking module, and giving a safety or maintenance operation prompt. According to the scheme, application of condenser performance digital twinning scenes such as real-time operation performance monitoring and analysis and operation and maintenance performance evaluation is realized. Particularly, the heat transfer performance of the condenser is dynamically tracked, and the system has remarkable advantages in the aspects of improving the use efficiency of equipment, saving energy and reducing emission.

Description

Technical Field

[0001] The present invention relates to the technical field of condensers, in particular to a dynamic digital twin system for condenser performance based on on-line operation data and a method for establishing the same. Background Art

[0002] As a device that determines the operating performance of a condensing steam turbine, the heat transfer performance of a condenser directly determines the operating efficiency of the steam turbine. The design of the condenser is often based on relatively harsh operating conditions, and the parameters under the design conditions will not appear when the device is put into operation. Therefore, the heat transfer performance of the condenser cannot be intuitively verified during project acceptance. The condenser provides condensation of the exhaust steam for the steam turbine and creates the required vacuum conditions for the steam turbine exhaust. The heat transfer capacity of the condenser is restricted by its own heat transfer efficiency and structure, and is also affected by the tightness of the condenser system installation and the capacity of the vacuum pumping equipment. Its comprehensive heat transfer efficiency needs to be re-evaluated on-site during operation.

[0003] How to evaluate the performance of a condenser in operation and to real-time evaluate the heat transfer efficiency of a condenser operating under different conditions. Currently, the theoretical calculation of the operating conditions performance based on the assumed conditions at the design end (the condenser performance curve that has not been effectively corrected) is still used.

[0004] Because this calculation cannot simulate the structural performance of the condenser and the operating conditions of the system, it cannot finally express the heat transfer efficiency and condensation capacity of the condenser in real time. Even less can it give a performance feedback of a true digital twin of the heat transfer capacity according to the actual situation of the condenser, such as the cleanliness of the heat exchange tubes, the actual pressure loss of the internal flow field, and the relevant constraints of the system. Summary of the Invention

[0005] The present invention proposes a method for establishing a dynamic digital twin of condenser performance based on on-line operation data, which solves the above problems existing in the prior art during use.

[0006] The technical solution of the present invention is realized as follows:

[0007] A dynamic digital twin system for condenser performance based on on-line operation data, characterized by comprising:

[0008] a. A real-time data acquisition module, which is used to obtain the operation data of the condenser and identify the validity of the data, fully considering the influence of factors such as subcooling degree, so as to accurately collect the heat load of the condenser condensation;

[0009] b. A data correction module, which is used to correct the cooling water flow rate through the inlet and outlet water temperatures of the cooling water and calibrate the operating parameters of the cooling water flow rate for thermal comparison calculation, so as to obtain an accurate heat load after correction;

[0010] c. A heat transfer coefficient and heat exchange efficiency calculation module, which is used to calculate the current heat transfer coefficient and heat exchange efficiency according to real-time data. The specific calculation formulas are as follows: Us = fx(As, LMTD, Q),

[0011] Evs = fx(U1, fm, fw, Us),

[0012] where Evs is the heat exchange efficiency of the condenser;

[0013] d. A heat exchange capacity evaluation module, which is used to replace the assumed cleaning coefficient Fc with the heat exchange efficiency Evs that comprehensively considers on-site factors to obtain the current heat exchange capacity of the condenser, and calculate the condenser performance parameters based on the Evs data corresponding to the steam turbine exhaust parameters adjusted by the user to achieve real-time dynamic performance digital twin.

[0014] Preferably, it further includes:

[0015] a. An anomaly detection module, which is used to detect sudden decreases in the heat exchange efficiency Evs to identify other problems in the condenser system;

[0016] b. A performance tracking module, which is used to track the attenuation process of the condenser performance and issue safety or maintenance operation prompts based on the recorded data.

[0017] A method for establishing a dynamic digital twin of condenser performance based on online operation data, the establishment method includes the following steps:

[0018] a. Obtain the operation data of the condenser through the real-time data acquisition module and identify the validity of the data;

[0019] b. Correct the cooling water volume through the data correction module and calibrate the operation parameters for thermal comparison calculation to obtain an accurate heat load after correction; c. Calculate the current heat transfer coefficient and heat exchange efficiency through the heat transfer coefficient and heat exchange efficiency calculation module;

[0020] d. Replace the assumed cleaning coefficient Fc with the heat exchange efficiency Evs that comprehensively considers on-site factors through the heat exchange capacity evaluation module to obtain the current heat exchange capacity of the condenser and achieve real-time dynamic performance digital twin;

[0021] e. Detect sudden decreases in the heat exchange efficiency Evs through the anomaly detection module to identify other problems in the condenser system;

[0022] f. Track the attenuation process of the condenser performance through the performance tracking module and issue safety or maintenance operation prompts.

[0023] Optimally, when the user adjusts the production capacity, resulting in changes in the exhaust steam parameters of the steam turbine, the performance parameters of the condenser calculated using the current heat transfer efficiency Evs data obtained through performance tracking can dynamically reflect the condensation capacity of the condenser.

[0024] Optimally, the current heat transfer coefficient and heat transfer efficiency in calculation step (d) are as follows:

[0025] Us = fx(As, LMTD, Q)

[0026] Evs = fx(U1, fm, fw, Us)

[0027] At this time, Evs is the current heat transfer efficiency of the condenser, and the relative heat transfer efficiency with respect to the assumed clean coefficient Fc can be calculated based on this value. As can be seen from the above solutions, this solution has the following characteristics:

[0028] 1. The heat load calculation uses measured data and identifies corrections, fully considering the additional heat load caused by additional factors.

[0029] 2. Upgrade the capacity check of the condenser to a performance check, forming a basis for recordable and identifiable performance changes.

[0030] 3. With the real-time heat transfer efficiency, the true performance of the existing condenser can be evaluated, and the operation and maintenance plan proposed to the user will be more reliable and timely.

[0031] 4. The dynamic performance digital twin is a brand-new concept, which is a correction of the performance that is not limited to the structural attributes of the condenser itself, and is a digital twin that is completely consistent with the on-site performance of the condenser. It is not a theoretical reference performance.

[0032] 5. The performance evaluation and condensation capacity feedback are more intuitive and effective.

[0033] In summary, the beneficial effects of the present invention are as follows:

[0034] The method of the present invention is applicable to the digital delivery of condensers in scenarios such as power plants, chemical plants, and other devices that require high-efficiency condensation equipment; performance acceptance of devices; real-time operation performance monitoring and analysis, and operation and maintenance performance evaluation. In particular, dynamically tracking the heat transfer performance of the condenser has significant advantages in improving the use efficiency of equipment and energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0036] Figure 1 This is a schematic diagram of an application example of a method for establishing a dynamic digital twin of the performance of a condenser based on online operation data in the present invention.

[0037] Figure 2 This is a schematic diagram of the operating parameters for thermal comparison calculation.

[0038] Figure 3 This is a schematic diagram for checking operation and maintenance parameters. Detailed implementation manners

[0039] Next, in combination with the accompanying drawings in the embodiments of the present invention Figures 1-3 , the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] Embodiment

[0041] As Figures 1 to 3 As shown, this embodiment discloses a dynamic digital twin system of the performance of a condenser based on online operation data and its establishment method. First, the current situation is analyzed. The specific current situation is as follows:

[0042] 1. The design of the condenser is based on the given design conditions and the heat transfer coefficient under specific conditions defined according to relevant standards (generally according to the HEI condenser standard) and the set heat transfer coefficient conversion parameter (generally using a cleanliness factor of about 85%) for thermal calculation to obtain the heat transfer area and structural parameters of the condenser. The formula is:

[0043] As = fx(Q, U, LMTD)

[0044] Where U = fx(U1, Fw, Fm, Fc)

[0045] U1, Fw, and Fm are all the design condition values in the standard, and Fc is the assumed cleanliness factor (that is, the conversion value of the heat transfer coefficient)

[0046] Q is the heat load, and LMTD is the logarithmic mean temperature difference, which are the calculated values of the design input conditions. According to these conditions, the heat transfer area As of a condenser (including specific structural parameters) can be determined

[0047] After the area of the condenser is determined, the exhaust steam temperature that the condenser can reach under this cleanliness factor can be calculated inversely according to different heat loads and operating conditions (mainly the inlet and outlet water temperatures and the cooling water volume), and the exhaust steam pressure can be obtained.

[0048] 2. Based on the heat transfer area and structural characteristics of the designed condenser, the heat transfer capacity (exhaust steam pressure) of the condenser under different operating conditions (hypothetical cleanliness factor, hypothetical system tightness, and perfect internal structure) is calculated inversely as the response of the digital delivery of the condenser. It is based on the results of standard theoretical calculations.

[0049] 3. Because it is impossible to accurately evaluate the cleanliness of the condenser heat exchange tubes and the influence of operating parameters under the relevant conditions of the existing condenser structure and system, the design assumption values (cleanliness factor) used in theoretical calculations often do not match the actual operating results. The difference in heat transfer efficiency cannot be quantified and fed back, and a performance evaluation of the actual state cannot be given based on operating data. This digital twin performance symmetry rate cannot be dynamically updated and corrected. The operating performance parameters it provides are also distorted and have limited reference value.

[0050] 4. The condenser is a special heat exchanger. For the same parameters, different manufacturers give different structural solutions. Even if the main structures are the same (area and tube structure parameters), the heat transfer efficiency will vary greatly due to differences in the internal structure (flow field scheme). At the same time, for exactly the same condenser, the performance will also be different due to differences in installation and other equipment in the system.

[0051] 5. Since there may be additional heat loads generated due to various factors during on-site operation and some operating parameters cannot be accurately obtained, it is necessary to effectively identify relevant operating parameters and consider the influence of various factors on operating parameters. The existing results based on theoretical calculations cannot monitor and feedback the complete heat transfer performance of the condenser.

[0052] 6. The existing solutions only feedback the condensation capacity under hypothetical conditions and cannot feedback the heat transfer efficiency. It is impossible to intuitively judge the performance state of the condenser based on changing operating parameters.

[0053] These are all technical problems that cannot be overcome by existing technologies for realizing dynamic performance digital twins.

[0054] This solution proposes a dynamic digital twin system for the performance of a condenser based on on-line operating data and a method for establishing the same. The purpose of this implementation method is to achieve accurate evaluation and dynamic digital twin of the condenser performance through real-time monitoring and data analysis. By using measured data and identifying and correcting, fully considering additional factors, the accuracy and practicability of the condenser performance verification are improved, and a more reliable and timely operation and maintenance plan is provided for users.

[0055] The technical solutions achieved are as follows:

[0056] 1. There are two strategies for obtaining accurate heat loads corrected through real-time monitoring data:

[0057] (a) By obtaining real-time data, identifying the validity of the data, fully considering the influence of factors such as subcooling degree, accurately collecting the heat load of condenser condensation, and correcting the cooling water volume through the inlet and outlet water temperatures of the cooling water. And calibrate the operating parameters such as the cooling water flow rate used for thermal comparison calculation. As Figure 1 shown by the steam turbine parameters, the designed water volume of the condenser is 15668 m 3 / h. By checking the inlet and outlet water temperatures, the actual water volume can be calculated as 15221.6 m 3 / h, and the flow rate inside the heat exchange tubes is obtained as 2.238 m / s through the structural parameters. At the same time, the corrected heat load parameters are calculated according to parameters such as the subcooling degree.

[0058] (b) When the exhaust steam parameters of the steam turbine are uncertain, the heat load can be obtained through accurate cooling water parameters, and the operating parameters used for thermal comparison calculation can be calibrated. As Figure 2 shown.

[0059] 2. Calculate the current heat transfer coefficient and heat exchange efficiency (the actual fouling factor in Figure 1 ) using the following formula:

[0060] Us = fx(As, LMTD, Q)

[0061] Evs = fx(U1, fm, fw, Us)

[0062] At this time, Evs is the heat exchange efficiency (actual fouling factor) of the current condenser, and the relative heat exchange efficiency relative to the assumed fouling factor Fc can be calculated according to this value.

[0063] 3. By replacing Fc with the heat exchange efficiency Evs considering comprehensive on-site factors, the current heat exchange capacity of the condenser can be obtained. When the user adjusts the production capacity and inputs the operating parameters required by the steam turbine, the condenser performance parameters calculated using the Evs data ( Figure 3 shown, "corrected fouling factor") are dynamic condensation capacity results, which are real-time dynamic performance digital twins.

[0064] 4. EVs will change due to changes in the state of the heat exchange tubes and will also jump and decrease due to other problems in the system. It will not make mistakes in performance judgment due to changes in load and cooling water conditions, and can reflect the current state of the condenser and track the attenuation process of its performance. The abnormal situation of the jump and decrease can be identified by tracking the recorded data, and safety or maintenance operation prompts can be issued (see the conclusion part of Figure 1 / Figure 2 ).

[0065] This solution will be described in detail for the implementation method:

[0066] I. Implementation steps

[0067] (1) Data collection and preprocessing

[0068] Acquire the operating data of the condenser in real time, including key parameters such as cooling water inlet and outlet temperature, flow rate, pressure, etc.

[0069] Identify the validity of data, eliminate outliers, and ensure the accuracy and reliability of data.

[0070] Taking full account of the influence of additional factors such as subcooling, the data is corrected to accurately attribute the heat load of the condenser condensation.

[0071] (2) Heat load calculation and correction

[0072] Two strategies are used to calculate heat loads:

[0073] a. Correct the cooling water volume through real-time data, and calibrate the operating parameters such as cooling water flow rate used for thermal comparison calculation to obtain the corrected and accurate heat load. Figure 1 Turbine parameter priority mode

[0074] b. Use accurate cooling water parameters to directly calculate heat load and calibrate related operating parameters. Figure 2 Operation parameter priority mode

[0075] Compare the results of the two strategies and choose the more accurate method as the basis for subsequent calculations.

[0076] (3) Calculation of heat transfer coefficient and heat exchange efficiency

[0077] Based on real-time data and corrected heat load,

[0078] The current heat transfer coefficient Us is calculated using the formula Us=fx(As,LMTD,Q).

[0079] The heat exchange efficiency Evs is calculated using the formula Evs=fx(U1,fm,fw,Us).

[0080] The relative heat transfer efficiency relative to the assumed cleanliness factor Fc is calculated based on the Evs value to evaluate whether the current performance of the condenser meets the design value.

[0081] (4) Dynamic digital twin and performance evaluation

[0082] The heat transfer efficiency Evs based on comprehensive on-site factors is used to replace the assumed cleanliness factor Fc to obtain the current heat transfer capacity of the condenser. Figure 3 (shown)

[0083] When users adjust production capacity, Evs data is used to calculate the dynamic condensing capacity of the condenser, realizing real-time dynamic performance digital twin.

[0084] Monitor the changes of Evs in real time, track the attenuation process of the condenser performance, identify abnormal conditions through the recorded data, and issue safety or maintenance operation tips. (See Figure 3 the conclusion section)

[0085] II. Performance Feedback and Optimization Suggestions

[0086] Evaluate the true performance of the condenser based on the results of dynamic digital twin, and provide users with reliable operation and maintenance solutions.

[0087] For performance attenuation or abnormal conditions, dynamically put forward optimization suggestions or maintenance measures to ensure the stable operation of the condenser.

[0088] III. Implementation Effects

[0089] The heat load calculation uses measured data and identifies corrections, improving the accuracy and reliability of the calculation.

[0090] Upgrade the capacity check of the condenser to performance check, forming a basis for recordable and identifiable performance changes, which helps users better understand the actual operation status of the condenser.

[0091] Calculate the heat transfer efficiency in real time, evaluate the true performance of the condenser, and provide users with more reliable and timely operation and maintenance solutions.

[0092] Achieved dynamic performance digital twin, which is completely consistent with the on-site performance of the condenser, providing users with more intuitive and effective performance evaluation and condensation capacity feedback.

[0093] Through performance tracking and anomaly detection, potential problems are discovered and handled in a timely manner to ensure the safe and stable operation of the condenser.

[0094] In summary, through steps such as real-time monitoring data, calculating the heat transfer coefficient and heat transfer efficiency, and constructing a dynamic digital twin model, the implementation method realizes the accurate evaluation and dynamic management of the condenser performance. This method improves the accuracy and practicability of condenser performance evaluation, provides users with more reliable and timely operation and maintenance solutions, and helps ensure the safe and stable operation of the condenser.

[0095] Combined with actual cases for illustration:

[0096] Reference Figure 1 , through this technical solution, the conclusions that can be drawn are: the performance of the condenser is seriously poor, the inside of the tubes is severely scaled, the internal exhaust resistance increases, the degree of subcooling is too large, and the water resistance is too large.

[0097] Reference Figure 2 , through this technical solution, the conclusions that can be drawn are: the performance of the condenser is seriously poor, the inside of the tubes is severely scaled, the internal exhaust resistance increases, the degree of subcooling is too large, and the water resistance is too large.

[0098] Reference Figure 3 , it can be concluded through this technical solution that the current heat transfer performance of the condenser can meet the requirements of the exhaust steam parameters (the exhaust steam parameter requirements of the steam turbine after production capacity adjustment).

[0099] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A dynamic digital twin system of condenser performance based on online operation data, characterized by include: a. Real-time data acquisition module, used to obtain the operation data of the condenser and identify the validity of the data, taking into full consideration the influence of subcooling, so as to accurately collect the heat load of the condenser; b. Data correction module, used to correct the cooling water volume by the cooling water inlet and outlet temperatures, and calibrate the cooling water flow rate operating parameters used for thermal comparison calculation to obtain the corrected accurate heat load; c. Heat transfer coefficient and heat exchange efficiency calculation module, used to calculate the current heat transfer coefficient and heat exchange efficiency based on real-time data. The specific calculation formula is as follows: Us = fx (As, LMTD, Q), Evs=fx(U1,fm,fw,Us), Where Evs is the heat exchange efficiency of the condenser; d. Heat exchange capacity evaluation module, which is used to replace the assumed cleanliness coefficient Fc with the heat exchange efficiency Evs of comprehensive on-site factors to obtain the current heat exchange capacity of the condenser, and calculate the condenser performance parameters using Evs data according to the turbine exhaust parameters of the user's adjusted production capacity, to achieve real-time dynamic performance digital twin.

2. A dynamic digital twin system of condenser performance based on online operation data according to claim 1, characterized in that: Also includes: a. Anomaly detection module, used to detect the sudden decrease of heat exchange efficiency Evs, so as to identify other problems in the condenser system; b. Performance tracking module, used to track the degradation process of condenser performance and issue safety or maintenance operation prompts based on the recorded data.

3. A method for establishing a dynamic digital twin of condenser performance based on online operation data, characterized in that: Including any one of claims 1 and 2, the establishment method comprises the following steps: a. Obtain the operating data of the condenser through the real-time data acquisition module and identify the validity of the data; b. Correct the cooling water volume through the data correction module and calibrate the operating parameters used for thermal comparison calculation to obtain the corrected accurate heat load; c. Calculate the current heat transfer coefficient and heat transfer efficiency through the heat transfer coefficient and heat transfer efficiency calculation module; d. Use the heat transfer efficiency Evs of comprehensive field factors to replace the assumed cleanliness coefficient Fc through the heat transfer capacity evaluation module to obtain the current heat transfer capacity of the condenser and realize real-time dynamic performance digital twin; e. Detect the jump reduction of heat exchange efficiency Evs through the abnormal detection module to identify other problems in the condenser system; f. Track the degradation process of condenser performance through the performance tracking module and issue safety or maintenance operation prompts.

4. The method for establishing a dynamic digital twin of condenser performance based on online operation data according to claim 3 is characterized in that It also includes: according to the turbine exhaust parameters of the user's adjusted production capacity, the condenser performance parameters are calculated using the heat exchange efficiency Evs data to dynamically reflect the condensing capacity of the condenser.

5. A method for establishing a dynamic digital twin of condenser performance based on online operation data according to claim 3 or 4, characterized in that Also includes: Calculate the current heat transfer coefficient and heat transfer efficiency in step (d) as follows: Us=fx(As,LMTD,Q) Evs=fx(U1,fm,fw,Us) At this time, Evs is the heat transfer efficiency of the condenser, and the relative heat transfer efficiency relative to the assumed cleanliness factor Fc can be calculated based on this value.

Images