A method and device for self-correcting boiler parameters of a twin mechanism model of a coal-fired unit

Abstract

The present invention discloses a method and device for self-calibrating parameters of a twin mechanism model of a coal-fired unit. The method comprises: establishing a twin mechanism model and an offline calibration mechanism model of the target unit according to design parameters using the Modelica language; a data platform collecting data from a DCS to provide data services for offline calibration of heat transfer coefficients of each heat exchange surface of the boiler, online calibration of heat transfer scaling factors of each heat exchange surface of the boiler, and twin applications; setting the heat transfer scaling factors of each heat exchange surface of the unit twin mechanism model and the offline calibration mechanism model to intermediate values ​​of the operating state; the offline calibration mechanism model, relying on historical data, employs a differential evolution algorithm to determine the optimal heat transfer coefficients of each heat exchange surface of the boiler; and based on the optimal heat transfer coefficients of each heat exchange surface of the boiler, employing a fuzzy control algorithm, online and sequentially cyclically calibrating the heat transfer scaling factors of each heat exchange surface of the twin mechanism model of the unit. The present invention also provides corresponding devices and equipment.

Description

Technical Field

[0001] The present invention belongs to the field of thermal power digital twins, and specifically relates to a method and device for self-correcting boiler parameters of a twin mechanism model of a coal-fired unit. Background Art

[0002] With the advancement of thermal power generation technology, power plants are increasingly demanding more intelligent unit control. Digital twin technology, as an effective means of enhancing the level of unit intelligence, holds significant application value. Thermal power units, as typical complex systems, are characterized by numerous equipment parameters, high levels of nonlinearity, and strong coupling within production processes. Currently, modeling approaches for thermal power units are primarily categorized into two main categories: mechanism-based modeling and data-based modeling. On the one hand, building an accurate digital twin model for a unit based on mechanism-based analysis is inherently time-consuming and challenging. Therefore, traditional mechanism-based modeling inevitably requires several simplifying assumptions to ignore some of the unit's higher-order dynamic characteristics (often referred to as unmodeled dynamics). Furthermore, unit equipment inevitably experiences characteristic shifts or performance degradation during actual operation, leading to discrepancies between the model and the equipment. On the other hand, with mathematical modeling, the accuracy of the data model is overly dependent on data quality and lacks the relationships between the parameters of the mechanism-based formulas. Factors such as insufficient data volume, excessive data noise, and incomplete coverage can all lead to reduced model generalization performance, making it difficult to accurately describe unit dynamic characteristics.

[0003] Thermal power units are typically complex systems characterized by numerous equipment parameters, high levels of nonlinearity, and strong coupling between production processes. This is particularly true for the boiler. Fly ash particles generated during fuel combustion deposit on boiler heat exchange surfaces (such as the superheater, reheater, and economizer), reducing heat transfer efficiency, increasing exhaust gas temperatures, increasing fan power consumption, and causing surface overheating. If parameter self-calibration is not addressed, online twinning based on precise plant models can lead to significant parameter deviations, which become more severe with extended operation.

[0004] Traditional tuning methods lack in-depth research into the physical processes of boilers. Most studies focus on describing coordinated operations, relying on big data and operating conditions. They fail to provide effective mechanistic correction methods. This is particularly true for the complex heat exchange processes of boilers, where viable offline and online correction methods are lacking. Consequently, the resulting twin models fail to objectively represent the characteristics of the twin boiler in real time. Achieving twin operation between the model and the unit requires analyzing the composition of boiler parameters, classifying them, and establishing self-correction methods. This ensures twin accuracy and reflects the actual unit's operating status, a prerequisite for implementing twin applications. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and device for self-correction of boiler parameters of a twin mechanism model of a coal-fired unit. By establishing a twin mechanism model of the unit and an offline correction mechanism model, offline and online self-correction are adopted to realize self-correction of boiler parameters of the twin mechanism model of the coal-fired unit.

[0006] The present invention is achieved by adopting the following technical solutions:

[0007] A method for self-calibration of boiler parameters in a twin mechanism model of a coal-fired unit, comprising:

[0008] According to the design parameters, the twin mechanism model and offline correction mechanism model of the target unit are established using Modelica language;

[0009] The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat transfer surface of the boiler, online correction of the heat transfer scaling factor of each heat transfer surface of the boiler, and twin applications;

[0010] The heat transfer fouling factors of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model are set to the intermediate values ​​of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler.

[0011] Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to cyclically correct the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit online.

[0012] A further improvement of the present invention is that, according to the design parameters, the twin mechanism model and the offline correction mechanism model of the target unit are established using the Modelica language, including:

[0013] According to the design parameters, the twin mechanism model of the target unit is established using the Modelica language. Based on the layout of the temperature and pressure measurement points of the target unit's boiler, the boiler body model is divided into regions, and the corresponding offline correction mechanism model is established.

[0014] The twin mechanism model of the unit was established, including the boiler, steam turbine, and electrical full-process mechanism models;

[0015] The offline correction mechanism model is divided into furnace area, horizontal flue area and tail flue area mechanism models according to regions. The offline correction mechanism model should be completely consistent with the corresponding area of ​​the unit twin mechanism model.

[0016] A further improvement of the present invention is that the data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat transfer surface of the boiler, online correction of the heat transfer scaling factor of each heat transfer surface of the boiler, and twin applications, including:

[0017] The data platform collects data in real time from the OPC Server of the DCS system of the target unit through a single isolation network gateway. While forwarding the data in real time, it saves historical data. The DCS instructions in the real-time data serve as input instructions for the twin mechanism model of the unit. The historical data saved by the platform is used for self-correction of the heat transfer coefficients of each heat exchange surface of the boiler, and the correction results are synchronously updated to the corresponding heat exchange surfaces of the twin mechanism model of the unit. The outlet temperature of each heat exchange surface of the boiler in the real-time data is used for the on-line correction of the heat transfer fouling factor of each heat exchange surface of the boiler in the twin mechanism model of the unit. The calculation results of the twin mechanism model of the unit are output to the front-end display module for display.

[0018] A further improvement of the present invention is that the DCS instructions in the real-time data are used as input instructions for the twin mechanism model of the unit, including:

[0019] Feed water flow, coal volume, air volume, total turbine valve control instruction, medium pressure valve control instruction, condensate pump frequency, circulating water temperature, circulating water control instruction, high and low water level adjustment instruction, deaerator water level adjustment instruction, each section extraction valve control instruction, etc.

[0020] A further improvement of the present invention is that the heat transfer scaling factors of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model are set to the intermediate values ​​of the operating state. The offline correction mechanism model relies on historical data and adopts a differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler, including:

[0021] Determine the stability and classify the historical data stored in the data platform;

[0022] According to the design specifications and historical data, the heat transfer coefficient range of each heat transfer surface and the ratio between multiple internal heat transfer coefficient groups are determined to constrain the heat transfer coefficient of each heat transfer surface of the boiler;

[0023] Historical data is used as the boundary of the offline correction mechanism model. The difference between the outlet steam temperature of the offline correction mechanism model and the corresponding steam temperature in the historical data of the target unit is evaluated. The differential evolution algorithm is used to optimize the heat transfer coefficient, and the obtained optimal heat transfer coefficient is synchronously updated to the twin mechanism model of the unit.

[0024] A further improvement of the present invention is to determine the stability and classify the historical data stored in the data platform, including:

[0025] The load range covers 25%-100%, and the stability judgment conditions are: (1) The load of the target unit changes negatively within a minute less than 2MW and the duration is greater than 10 minutes, (2) the change value of the total steam turbine valve command per minute is less than 0.2, (3) Ambient temperature deviation Within 3℃.

[0026] A further improvement of the present invention is that, based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, a fuzzy control algorithm is used to perform online cyclic correction of the heat transfer scaling factor of each heat transfer surface of the twin mechanism model boiler of the unit in sequence, including:

[0027] After completing the offline calibration to determine the optimal heat transfer coefficient for each section of the boiler's heat transfer surface, the outlet temperature of each heat transfer surface of the target unit's boiler and the outlet temperature of the heat transfer surface corresponding to the unit's twin mechanism model are collected in real time. Using a fuzzy control algorithm, the heat transfer scaling factor of each heat transfer surface of the twin mechanism model boiler is self-calibrated online.

[0028] The heat transfer fouling factor correction of each heat exchange surface of the twin mechanism model boiler of the unit is carried out according to the flue gas flow, that is, the order of furnace vertical flue - horizontal flue - tail flue. The heat exchange surface of the boiler is calibrated at preset intervals and calibrated one by one.

[0029] A further improvement of the present invention is that the outlet temperature of each heat exchange surface of the target unit's boiler and the outlet temperature of the heat exchange surface corresponding to the unit's twin mechanism model are collected in real time, and a fuzzy control algorithm is used to self-calibrate the heat exchange scaling factor of each heat exchange surface of the twin mechanism model boiler online, including:

[0030] Error E is the outlet temperature of the boiler heat exchange surface of the target unit Corresponding model heat exchange surface outlet temperature The difference calculation, E= - , Error change EC target unit boiler heat exchange surface outlet temperature Changes and speed of change Classify and establish the fuzzy control set of heat exchange fouling factor, and dynamically correct the boiler heat exchange fouling factor in real time.

[0031] A coal-fired unit twin mechanism model boiler parameter self-correction device, comprising:

[0032] The model building module uses the Modelica language to build the twin mechanism model and offline correction mechanism model of the target unit according to the design parameters;

[0033] Data acquisition module: The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat exchange surface of the boiler, online correction of the heat transfer scaling factor of each heat exchange surface of the boiler, and twin applications;

[0034] The calculation module sets the heat transfer scaling factor of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model to the intermediate value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler.

[0035] The correction module, based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, adopts a fuzzy control algorithm to perform online cyclic correction of the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit.

[0036] A further improvement of the present invention is that, in the model building module, the twin mechanism model and the offline correction mechanism model of the target unit are established using the Modelica language according to the design parameters, including:

[0037] According to the design parameters, the twin mechanism model of the target unit is established using the Modelica language. Based on the layout of the temperature and pressure measurement points of the target unit's boiler, the boiler body model is divided into regions, and the corresponding offline correction mechanism model is established.

[0038] The twin mechanism model of the unit was established, including the boiler, steam turbine, and electrical full-process mechanism models;

[0039] The offline correction mechanism model is divided into furnace area, horizontal flue area and tail flue area mechanism models according to regions. The offline correction mechanism model should be completely consistent with the corresponding area of ​​the unit twin mechanism model.

[0040] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0041] The twin modeling and application of the boiler system is the most complex core system in the entire coal-fired unit twin system. Realizing the self-correction of the boiler parameters of the twin mechanism model of the coal-fired unit is one of the key links and core issues in the twin application of the coal-fired unit. The method and device for self-correction of the boiler parameters of the twin mechanism model of the coal-fired unit provided by the present invention can realize the self-correction of the boiler parameters of the twin mechanism model of the coal-fired unit, and realize real-time access to the unit DCS instructions under the load (25%-100%) working condition of the target unit, and synchronize the operation with the unit. The dynamic accuracy of the model can reach within 5%. Through the application of the twin mechanism model of the coal-fired unit, the ash fouling condition of the heat exchange surface of the boiler of the target unit can be monitored in real time, and soot blowing can be optimized; at the same time, it provides a solid foundation and strong support for improving the control performance of the unit, optimizing production strategies, predicting the health status of equipment, improving the energy efficiency of the unit, and reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The following schematically shows a flow chart of a method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to an embodiment of the present invention.

[0043] Figure 2 The structure diagram of a method for self-correcting boiler parameters of a twin mechanism model of a coal-fired unit according to an embodiment of the present invention is schematically shown.

[0044] Figure 3The figure schematically shows a flow chart of offline optimization of heat transfer coefficients of various heat transfer surfaces of a twin mechanism model boiler of a coal-fired unit according to an embodiment of the present invention.

[0045] Figure 4 The structure block diagram of a coal-fired unit twin mechanism model boiler parameter self-correction device according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0046] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0047] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] Example 1

[0049] The present invention provides a method for self-calibration of boiler parameters in a twin mechanism model of a coal-fired unit, comprising:

[0050] According to the design parameters, the twin mechanism model and offline correction mechanism model of the target unit are established using Modelica language;

[0051] The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat transfer surface of the boiler, online correction of the heat transfer scaling factor of each heat transfer surface of the boiler, and twin applications;

[0052] The heat transfer fouling factors of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model are set to the intermediate values ​​of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler.

[0053] Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to cyclically correct the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit online.

[0054] Example 2

[0055] In the present invention, by analyzing the characteristics of twin modeling and twin application of coal-fired unit boilers, the unit twin mechanism model (FMU) and static optimization model (FMU) are established using the Modelica language, and the self-calibration of the boiler parameters of the twin mechanism model of the coal-fired unit is realized through offline and online self-calibration methods.

[0056] like Figure 1 The following schematically illustrates a method and device flow chart for self-calibrating boiler parameters for a twin mechanism model of a coal-fired unit according to an embodiment of the present invention. The method comprises:

[0057] S11. Establish the twin mechanism model and offline correction mechanism model of the target unit using the Modelica language according to the design parameters;

[0058] S12. The data platform collects data from the DCS and provides data services for offline calibration of the heat transfer coefficients of each boiler heat exchange surface, online calibration of the heat transfer scaling factors of each boiler heat exchange surface, and twin applications.

[0059] S13. Set the heat transfer fouling factor of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model to the intermediate value of the operating state. The offline correction mechanism model uses historical data and a differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler.

[0060] S14. Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to cyclically correct the heat transfer fouling factors of each heat transfer surface of the twin mechanism model of the unit online.

[0061] Through the above implementation method, a twin mechanism model of the unit and an offline correction mechanism model are established, and offline and online self-correction are adopted to realize self-correction of boiler parameters of the twin mechanism model of the coal-fired unit.

[0062] In the embodiments provided herein, a twin mechanism model for the entire unit is established, encompassing the boiler, turbine, and electrical process models. The offline correction mechanism model is divided into three regional models: the furnace region, the horizontal flue region, and the tail flue region. The offline correction mechanism model must be fully consistent with the corresponding regions of the twin mechanism model for the unit.

[0063] As an embodiment of the present invention, according to the different heat exchange modes of each heat exchange surface of the coal-fired boiler, the heat exchange modes can be divided into three categories: radiation heat exchange, convection heat exchange, and radiation-convection heat exchange. By deeply analyzing the calculation formulas when the mechanism model adopts different heat exchange modes, the parameters that need to be optimized are derived. The formula used in one embodiment of the present invention is as follows:

[0064] 1) When the model uses radiation heat transfer:

[0065] Heat flow output by the model's radiation heat transfer The calculation formula is as follows:

[0066]

[0067]

[0068]

[0069] ---Formula 1

[0070] in, The Stefan-Boltzmann constant is 5.67E-8 W / (m 2 ·K 4 );

[0071] is the radiation view factor (calculated by formula 2);

[0072] is the heat exchange fouling factor;

[0073] emissivity of the upper space;

[0074] Corrected heat transfer area.

[0075]

[0076]

[0077] Radiation view factor

[0078]

[0079]

[0080] ---Formula 2

[0081] Gas temperature weighting coefficient ---Formula 3

[0082] Wall temperature weighting coefficient ---Formula 4

[0083] Soot load ---Formula 5

[0084] Coal particle load ---Formula 6

[0085] Fly ash loading ---Formula 7

[0086] in, is the fly ash content in flue gas;

[0087] is the density of smoke;

[0088] is the fuel content in the flue gas;

[0089] Coke particle absorbance ---Formula 8

[0090] Fly ash particle absorbance ---Formula 9

[0091] Suspended particle radiation coefficient

[0092]

[0093] ---Formula 10

[0094] When the model adopts the radiation heat transfer method, according to the above, it can be concluded that when the model parameters are self-calibrated, it is only necessary to determine the heat transfer fouling factor and the radiation coefficient of the upper space value.

[0095] 2) When the model uses convection heat transfer:

[0096] Model convective heat transfer is selected based on temperature difference calculation ,available , the heat transfer formula is:

[0097] ---Formula 11

[0098] in ， Indicates the heat exchange surface of all tube bundles in the furnace;

[0099] is the heat exchange temperature difference;

[0100] is the heat transfer coefficient;

[0101] is the heat exchange fouling factor;

[0102] By the state of flue gas ( , , ) to obtain the physical properties of the flue gas, such as density ( ), dynamic viscosity of gas molecules ( ), the Prandtl constant of gas molecules ( ), thermal conductivity of gas molecules ( )

[0103] ---Formula 12

[0104] Forward cross-sectional area

[0105] Orthogonal alignment ratio

[0106] Horizontal alignment ratio

[0107] Tube bundle outer diameter

[0108] Number of tube bundle rows,

[0109] Flue gas velocity ---Formula 13

[0110] Corrected Reynolds number ---Formula 14

[0111] Among them, the characteristic length

[0112] Laminar Nusselt number

[0113] Turbulent Nusselt number ---Formula 15

[0114] Nusselt number ---Formula 16

[0115] Tube bundle arrangement correction factor

[0116] ---Formula 17

[0117] Constrained Nusselt number

[0118] ---Formula 18

[0119] Heat transfer coefficient ---Formula 19

[0120] When the model adopts convection heat transfer, according to the above formula, it can be obtained that when the model parameters are self-calibrated, it is only necessary to determine the heat transfer fouling factor and heat transfer coefficient .

[0121] 3) When the model uses radiation convection heat transfer:

[0122] Calculate and select based on temperature difference ,available , for specific formulas and details, please refer to the heat exchange module.

[0123] For convective heat transfer, For radiation heat transfer

[0124] ---Formula 20

[0125] The heat flux of radiation heat transfer is

[0126]

[0127] ---Formula 21

[0128] Among them, the emissivity of the tube is Radiation coefficient of the upper space in radiation heat transfer formula 1 ;

[0129] is the total heat exchange area of ​​the tube bundle;

[0130] The Stefan-Boltzmann constant is 5.67E-8 W / (m 2 ·K 4 );

[0131] Emissivity of furnace gases and particulate matter and absorbance Reference.

[0132] The heat flux of convective heat transfer is

[0133] ---Formula 22

[0134] Among them, the heat transfer coefficient Refer to the heat transfer coefficient of convective heat transfer formula 11 .

[0135] The heat transfer of the entire pipe is

[0136] ---Formula 23

[0137] When the model adopts radiation convection heat transfer, according to the above formula, it can be obtained that when the model parameters are self-calibrated, it is only necessary to determine the heat transfer fouling factor and emissivity and heat transfer coefficient 2.

[0138] In this embodiment, when the radiation heat exchange method is adopted, the heat transfer coefficient is the radiation coefficient of the upper space. When convection heat transfer is used, the heat transfer coefficient is When radiation convection heat transfer is used, the heat transfer coefficient is the radiation rate and heat transfer coefficient 2.

[0139] like Figure 2 The following schematically illustrates a method and apparatus for self-calibrating boiler parameters of a twin mechanism model of a coal-fired unit according to an embodiment of the present invention. The apparatus comprises:

[0140] ① OPC_Server of the unit DCS system: provides real-time data communication services of the actual unit;

[0141] ②Data platform: data platform and equipment responsible for data storage, forwarding and management;

[0142] ③ Unit twin mechanism model: a full system twin model established based on the twin object unit;

[0143] ④ Offline correction module: This module is used to call historical data offline and use the differential evolution algorithm to optimize the heat transfer coefficient of each heating surface of the boiler. It includes an offline correction mechanism model, offline optimization algorithm and program;

[0144] ⑤Online correction module: an algorithm module that uses fuzzy control algorithm to synchronously correct the heat exchange scaling factor of the boiler heat exchange surface of the twin mechanism model of the unit based on the unit's online real-time data;

[0145] ⑥Front-end display module: front-end display interface, equipment and programs for user management and viewing.

[0146] like Figure 2 As shown, the structural relationship of each module of the present invention includes: the data platform collects data in real time from the OPC Server of the DCS system of the target unit through a single isolation network gateway, and saves historical data while forwarding the data in real time; the DCS instructions in the real-time data are used as input instructions of the twin mechanism model of the unit; the offline correction module calls the historical data saved on the data platform, self-corrects the heat transfer coefficient of each heat exchange surface of the boiler through the differential evolution algorithm and synchronously updates it to the twin mechanism model of the unit; the outlet temperature of each heat exchange surface of the boiler in the real-time data is sent to the online correction module of the twin mechanism model of the unit in real time, and the heat transfer scaling factor of each heat exchange surface of the boiler is corrected online through the fuzzy control algorithm; the above calculation results are output to the front-end display module for display.

[0147] The DCS instructions in the real-time data serve as the input instructions of the twin mechanism model of the unit. The control instructions include: feed water flow, coal volume, air volume, total steam turbine valve instruction, medium pressure valve instruction, condensate pump frequency, circulating water temperature, circulating water control instruction, high and low water level adjustment instruction, deaerator water level adjustment instruction, each section steam extraction valve control instruction, etc.

[0148] By calling on a large amount of historical operating condition data stored in the data platform, the historical operating condition data is judged to be stable and classified. The heat transfer fouling factors of each heat exchange surface of the unit twin mechanism model and the offline correction mechanism model are set to the intermediate value of the operating state. Then, the differential evolution algorithm is used to optimize the boiler heat transfer coefficient and synchronously update it to the unit twin mechanism model.

[0149] Based on the optimal heat transfer coefficient of each heat exchange surface of the boiler, the data of the DCS system of the target unit is collected online, and the difference changes between the outlet temperatures of each heat exchange surface of the target unit's boiler and the outlet temperatures of each heat exchange surface of the corresponding model are synchronously sensed. The changes and change speeds of the outlet temperatures of each heat exchange surface of the target unit's boiler are also sensed, and the heat transfer scaling factors of each heat exchange surface of the twin mechanism model of the unit are corrected online in real time.

[0150] like Figure 3The following schematically illustrates a process flow diagram for offline optimization of the heat transfer coefficients of each heat transfer surface of a twin mechanism model boiler of a coal-fired unit according to an embodiment of the present invention. The method includes:

[0151] The heat transfer fouling factors of each heat transfer surface of the boiler in the twin mechanism model and offline correction mechanism model of the unit are set to the intermediate values ​​of the operating state. The differential evolution algorithm is used to optimize the heat transfer coefficients of the boiler and determine the optimal heat transfer coefficient of each heat transfer surface of the boiler, including:

[0152] S21. Determine the stability and classify the historical data stored in the data platform;

[0153] The historical data are judged to be stable and classified, including: the load range of the target unit covers 25%--100%, and the conditions for judging stability are: (1) the unit load changes negative range per minute less than 2MW and the duration is greater than 10 minutes, (2) the total change value of the turbine valve adjustment instruction per minute is less than 0.2, (3) Ambient temperature deviation Within 3℃, obtain the steady-state operating conditions at different times under various loads in the historical data. The above three conditions are in parallel relationship.

[0154] S22. Based on design specifications and historical data, determine the heat transfer coefficient range for each heat transfer surface and the ratio between multiple internal heat transfer coefficient groups, and constrain the heat transfer coefficients of each heat transfer surface in the boiler;

[0155] S23. The historical data is used as the boundary of the offline correction mechanism model. The difference between the outlet steam temperature of the offline correction mechanism model and the corresponding steam temperature in the historical data of the target unit is evaluated. The differential evolution algorithm is used to optimize the heat transfer coefficient. The obtained optimal heat transfer coefficient is synchronously updated to the twin mechanism model of the unit.

[0156] In one embodiment of the present invention, a method is provided for online self-calibration of the heat exchange scaling factor of each heat exchange surface of the boiler of the unit twin mechanism model by using a fuzzy control algorithm based on the real-time acquisition of the outlet temperature of each heat exchange surface of the target unit boiler and the outlet temperature of the heat exchange surface corresponding to the twin mechanism model of the unit, including:

[0157] Error E is the outlet temperature of the unit boiler heat exchange surface Corresponding model heat exchange surface outlet temperature The difference calculation, E= - , error variation EC unit boiler heat exchange surface outlet temperature Changes and speed of change Classify and establish the fuzzy control set of heat exchange fouling factors, and dynamically correct the heat exchange fouling factors of the boiler in real time.

[0158] The classification and judgment method for the error (E) is as follows:

[0159] ① If E <= -20°C, E is Negative Big (NB);

[0160] ② If -20 < E <= -10°C, E is Negative Medium (NM);

[0161] ③ If -10 < E <= -5°C, E is Negative Small (NS);

[0162] ④ If -5 < E <= 5°C, E is Zero (0);

[0163] ⑤ If 5 < E <= 10°C, E is Positive Small (PS);

[0164] ⑥ If 10 < E <= 20°C, E is Positive Medium (PM);

[0165] ⑦ If E > 20°C, E is Positive Big (PB);

[0166] Judgment conditions for the change and change rate of the outlet temperature of the heat exchange surface of the unit:

[0167] ① When > 0, the outlet temperature of the heat exchange surface of the unit rises;

[0168] ② When = 0, the outlet temperature of the heat exchange surface of the unit remains unchanged;

[0169] ③ When < 0, the outlet temperature of the heat exchange surface of the unit drops;

[0170] ④ When > 0, the outlet temperature of the heat exchange surface of the unit changes rapidly;

[0171] ⑤ When = 0, the outlet temperature of the heat exchange surface of the unit changes at a constant speed;

[0172] ⑥ When < 0, the outlet temperature of the heat exchange surface of the unit changes slowly;

[0173] The classification and judgment method for the error change (EC) is as follows:

[0174] ① If "the outlet temperature of the heat exchange surface of the unit rises" & "the outlet temperature of the heat exchange surface of the unit changes rapidly" = true, EC is Negative Big (NB);

[0175] ② If "the outlet temperature of the heat exchange surface of the unit rises" & "the outlet temperature of the heat exchange surface of the unit changes at a constant speed" = true, EC is Negative Medium (NM);

[0176] ③ If "unit heat exchange surface outlet temperature rises" & "unit heat exchange surface outlet temperature slows down" = true, EC negative is small (NS);

[0177] ④ If "the outlet temperature of the unit heat exchange surface remains unchanged" = true, EC is zero (0);

[0178] ⑤ If "unit heat exchange surface outlet temperature drops" & "unit heat exchange surface outlet temperature decelerates" = true, EC is positive (PS);

[0179] ⑥ If "unit heat exchange surface outlet temperature drops" & "unit heat exchange surface outlet temperature changes uniformly" = true, EC is in the middle (PM);

[0180] ⑦ If "unit heat exchange surface outlet temperature drops" & "unit heat exchange surface outlet temperature accelerates change" = true, EC is positive (PB).

[0181] According to the above conditions, the control signal U, i.e. the heat transfer fouling factor, can be established. Fuzzy control rule table for real-time correction.

[0182] Table: Fuzzy control rules for real-time correction of heat exchange fouling factor

[0183]

[0184] As an embodiment of the present invention, the real-time online application of the twin mechanism model of the unit described in the present invention receives real-time instructions from the unit DCS system as the model's control instructions. The control instructions include: feed water flow, coal flow, air volume, total steam turbine valve control instructions, medium pressure valve control instructions, condensate pump frequency, circulating water temperature, circulating water control instructions, high and low water level adjustment instructions, deaerator water level adjustment instructions, and each section steam extraction valve control instructions. After the system is put into operation, the dynamic accuracy of the model is within 5% at 25%-100% of the unit's rated power. Important parameters for comparison include, but are not limited to, the following:

[0185] (1) Generator power;

[0186] (2) Pressure of the outlet main pipe of the water pump;

[0187] (3) Economizer inlet feedwater temperature;

[0188] (4) Economizer inlet feed water pressure;

[0189] (5) Boiler feed water flow rate;

[0190] (6) Water wall outlet header temperature;

[0191] (7) Temperature of the inner wall of the steam-water separator;

[0192] (8) Lower than the inlet steam temperature;

[0193] (9) Lower than outlet steam temperature;

[0194] (10) Steam temperature at the outlet of superheater A;

[0195] (11) Steam temperature at the outlet of superheater B;

[0196] (12) Steam temperature at the inlet of the superheated secondary desuperheater A;

[0197] (13) Steam temperature at the inlet of the superheated secondary desuperheater B;

[0198] (14) Steam temperature at the outlet of the secondary desuperheater A;

[0199] (15) Steam temperature at the outlet of the secondary superheater B;

[0200] (16) Higher than outlet steam temperature;

[0201] (17) Higher than outlet steam pressure;

[0202] (18) Main steam pressure (engine side);

[0203] (19) Main steam temperature;

[0204] (20) Reheat cold section steam temperature;

[0205] (21) Reheat cold section steam pressure;

[0206] (22) Low re-export steam temperature A;

[0207] (23) Low re-export steam temperature B;

[0208] (24) High re-inlet steam temperature A;

[0209] (25) High re-inlet steam temperature B;

[0210] (26) High re-export steam pressure;

[0211] (27) High re-export steam temperature;

[0212] (28) Reheating hot section steam temperature;

[0213] (29) Reheating hot section steam pressure;

[0214] (30) Extraction steam pressure of each section;

[0215] (31) Extraction steam temperature of each section;

[0216] (32) High and low inlet and outlet temperatures;

[0217] (33) High and low inlet and outlet pressures;

[0218] (34) Deaerator pressure;

[0219] (35) Deaerator temperature;

[0220] (36) Condensate temperature;

[0221] (37) Condenser vacuum;

[0222] (38) Circulating water temperature.

[0223] Example 3

[0224] like Figure 4 As shown, the present invention provides a coal-fired unit twin mechanism model boiler parameter self-correction device, comprising:

[0225] The model building module uses the Modelica language to build the twin mechanism model and offline correction mechanism model of the target unit according to the design parameters;

[0226] Data acquisition module: The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat exchange surface of the boiler, online correction of the heat transfer scaling factor of each heat exchange surface of the boiler, and twin applications;

[0227] The calculation module sets the heat transfer scaling factor of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model to the intermediate value of the operating state. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler.

[0228] The correction module, based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, adopts a fuzzy control algorithm to perform online cyclic correction of the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit.

[0229] Example 4

[0230] This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method for self-correction of boiler parameters of a twin mechanism model of a coal-fired unit.

[0231] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0232] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0233] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0234] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0235] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0236] In addition, it should be understood that although this specification describes the embodiments, not every embodiment contains only one independent technical solution. This description is for clarity only. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for the purpose of illustrating the technical concept of the present invention and cannot be used to limit the scope of protection of the present invention. Any changes made based on the technical solution in accordance with the technical concept proposed by the present invention fall within the scope of protection of the claims of the present invention.

Claims

1. A method for self-calibration of boiler parameters in a twin mechanism model of a coal-fired unit, characterized in that: include: According to the design parameters, the twin mechanism model and offline correction mechanism model of the target unit are established using the Modelica language, including: according to the design parameters, the twin mechanism model of the target unit is established using the Modelica language, and according to the layout of the temperature and pressure measurement points of the target unit boiler, the boiler body model is divided into regions, and the corresponding offline correction mechanism model is established; the established unit twin mechanism model includes the boiler, steam turbine, and electrical full process mechanism models; the offline correction mechanism model is divided into furnace area, horizontal flue area, and tail flue area mechanism models by region, and the offline correction mechanism model should be completely consistent with the corresponding area of ​​the unit twin mechanism model; The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat transfer surface of the boiler, online correction of the heat transfer scaling factor of each heat transfer surface of the boiler, and twin applications; The heat transfer fouling factors of each heat transfer surface of the boiler in the twin mechanism model and the offline correction mechanism model of the unit are set to the intermediate values ​​of the operating status. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler, including: judging the stability and classifying the historical data stored in the data platform; determining the heat transfer coefficient range of each heat transfer surface and the ratio between multiple groups of internal heat transfer coefficients according to the design specifications and historical data, and constraining the heat transfer coefficient of each heat transfer surface of the boiler; using historical data as the boundary of the offline correction mechanism model, and evaluating the difference between the outlet steam temperature of the offline correction mechanism model and the corresponding steam temperature in the historical data of the target unit, the differential evolution algorithm is used to optimize the heat transfer coefficient, and the obtained optimal heat transfer coefficient is synchronously updated to the twin mechanism model of the unit; Based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, the fuzzy control algorithm is used to cyclically correct the heat transfer scaling factors of each heat transfer surface of the twin mechanism model of the unit in sequence, including: After completing the offline calibration to determine the optimal heat transfer coefficient of each section of the boiler heat transfer surface, the outlet temperature of each heat transfer surface of the target unit boiler and the outlet temperature of the heat transfer surface corresponding to the twin mechanism model of the unit are collected in real time. The fuzzy control algorithm is used to self-calibrate the heat transfer scaling factor of each heat transfer surface of the twin mechanism model boiler of the unit online, including: the error E is calculated using the outlet temperature of the heat transfer surface of the target unit boiler Corresponding model heat exchange surface outlet temperature The difference calculation, E= - , Error change EC target unit boiler heat exchange surface outlet temperature Changes and speed of change Classify and establish the fuzzy control set of heat exchange scaling factor, and dynamically correct the boiler heat exchange scaling factor in real time; The heat transfer fouling factor correction of each heat exchange surface of the twin mechanism model boiler of the unit is carried out according to the flue gas flow, that is, the order of furnace vertical flue - horizontal flue - tail flue. The heat exchange surface of the boiler is calibrated at preset intervals and calibrated one by one.

2. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 1, characterized in that: The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat exchange surface of the boiler, online correction of the heat transfer scaling factor of each heat exchange surface of the boiler, and twin applications, including: The data platform collects data in real time from the OPC Server of the DCS system of the target unit through a single isolation network gateway. While forwarding the data in real time, it saves historical data. The DCS instructions in the real-time data serve as input instructions for the twin mechanism model of the unit. The historical data saved by the platform is used for self-correction of the heat transfer coefficients of each heat exchange surface of the boiler, and the correction results are synchronously updated to the corresponding heat exchange surfaces of the twin mechanism model of the unit. The outlet temperature of each heat exchange surface of the boiler in the real-time data is used for the on-line correction of the heat transfer fouling factor of each heat exchange surface of the boiler in the twin mechanism model of the unit. The calculation results of the twin mechanism model of the unit are output to the front-end display module for display.

3. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 2, characterized in that: The DCS instructions in the real-time data serve as input instructions for the unit twin mechanism model, including: Feed water flow, coal volume, air volume, total steam turbine valve control instruction, medium pressure valve control instruction, condensate pump frequency, circulating water temperature, circulating water control instruction, high and low water level adjustment instruction, deaerator water level adjustment instruction, and each section steam extraction valve control instruction.

4. The method for self-calibration of boiler parameters of a twin mechanism model of a coal-fired unit according to claim 1, characterized in that: Determine the stability and classify the historical data stored in the data platform, including: The load range covers 25%-100%, and the stability judgment conditions are: (1) The load of the target unit changes negatively within a minute less than 2MW and the duration is greater than 10 minutes, (2) the total change value of the turbine valve adjustment instruction per minute is less than 0.2, (3) Ambient temperature deviation Within 3℃.

5. A coal-fired unit twin mechanism model boiler parameter self-correction device, characterized in that: include: The model establishment module uses the Modelica language to establish the twin mechanism model and offline correction mechanism model of the target unit according to the design parameters, including: establishing the twin mechanism model of the target unit according to the Modelica language according to the design parameters, dividing the boiler body model by area according to the layout of the temperature and pressure measurement points of the target unit boiler, and establishing the corresponding offline correction mechanism model; the established unit twin mechanism model includes the boiler, steam turbine, and electrical full-process mechanism models; the offline correction mechanism model is divided into furnace area, horizontal flue area, and tail flue area mechanism models by area, and the offline correction mechanism model should be completely consistent with the corresponding area of ​​the unit twin mechanism model; Data acquisition module: The data platform collects data from the DCS and provides data services for offline correction of the heat transfer coefficient of each heat exchange surface of the boiler, online correction of the heat transfer scaling factor of each heat exchange surface of the boiler, and twin applications; The calculation module sets the heat transfer fouling factors of each heat transfer surface of the boiler in the twin mechanism model of the unit and the offline correction mechanism model as the intermediate values ​​of the operating status. The offline correction mechanism model relies on historical data and adopts the differential evolution algorithm to determine the optimal heat transfer coefficient of each heat transfer surface of the boiler, including: judging the stability and classification of the historical data stored in the data platform; determining the heat transfer coefficient range of each heat transfer surface and the ratio between multiple groups of internal heat transfer coefficients according to the design specifications and historical data, and constraining the heat transfer coefficient of each heat transfer surface of the boiler; using historical data as the boundary of the offline correction mechanism model, and evaluating the difference between the outlet steam temperature of the offline correction mechanism model and the corresponding steam temperature in the historical data of the target unit, the differential evolution algorithm is used to optimize the heat transfer coefficient, and the obtained optimal heat transfer coefficient is synchronously updated to the twin mechanism model of the unit; The correction module, based on the optimal heat transfer coefficient of each heat transfer surface of the boiler, adopts fuzzy control algorithm to perform online cyclic correction of the heat transfer scaling factor of each heat transfer surface of the twin mechanism model boiler of the unit in sequence, including: After completing the offline calibration to determine the optimal heat transfer coefficient of each section of the boiler heat transfer surface, the outlet temperature of each heat transfer surface of the target unit boiler and the outlet temperature of the heat transfer surface corresponding to the twin mechanism model of the unit are collected in real time. The fuzzy control algorithm is used to self-calibrate the heat transfer scaling factor of each heat transfer surface of the twin mechanism model boiler of the unit online, including: the error E is calculated using the outlet temperature of the heat transfer surface of the target unit boiler Corresponding model heat exchange surface outlet temperature The difference calculation, E= - , Error change EC target unit boiler heat exchange surface outlet temperature Changes and speed of change Classify and establish the fuzzy control set of heat exchange scaling factor, and dynamically correct the boiler heat exchange scaling factor in real time; The heat transfer fouling factor correction of each heat exchange surface of the twin mechanism model boiler of the unit is carried out according to the flue gas flow, that is, the order of furnace vertical flue - horizontal flue - tail flue. The heat exchange surface of the boiler is calibrated at preset intervals and calibrated one by one.

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