CFD-AI-based boiler water-steam side two-dimensional mechanism modeling method and system

Abstract

The application discloses a CFD-AI-based boiler water side two-dimensional mechanism modeling method and system, relates to the technical field of model optimization, and comprises the following steps: acquiring the total types of sensors, calculating the data transmission delay coefficient and the data transmission instability coefficient of each type of sensor, comparing the data transmission delay coefficient and the data transmission instability coefficient with a preset data transmission delay coefficient threshold and a preset data transmission delay coefficient threshold respectively, judging whether the data transmission of the sensor is delayed and stable, and taking corresponding processing measures; the processed sensor transmits the boiler operation parameters in real time, continuously adjusts and optimizes the model of the water side, and controls the specific value of the boiler operation parameters, so that the sensor data transmission delay and instability can be optimized and processed, the processed sensor transmits the boiler operation parameters in real time, continuously adjusts and optimizes the model of the water side, simulates the state of the boiler operation, controls the specific value of the boiler operation parameters, reduces the influence on real-time control decision, and ensures the optimization effect.

Description

Technical Field

[0001] The present invention relates to the technical field of model optimization, and in particular to a two-dimensional mechanism modeling method and system for the steam-water side of a boiler based on CFD-AI. Background Art

[0002] Based on the boiler combustion CFD-AI model and data-driven model, the steam-water measurement unit model is discretely modeled to varying degrees based on the granularity of the CFD-AI simulation, including the combustion zone, burnout zone, water-cooled wall zone, screen pass zone, high-pass / high-in zone, and low-pass / low-in zone. This ensures that the DCS wall temperature measurement points are distributed in different discrete areas and is integrated with the FMU model formed by order reduction of the CFD simulation results. The boundary conditions of the boiler combustion model and steam-water model are determined, including fuel supply, air inlet, steam-water supply, outlet, etc. The importance of the boundary conditions of the boiler combustion model and steam-water model, including fuel supply, air inlet, steam-water supply and outlet, is determined. The boundary conditions need to take into account the actual situation and match the mathematical model to ensure the accuracy of the simulation results.

[0003] Generally speaking, this approach involves directly coupling the CFD-AI model with the system model, directly participating in the system model's debugging. This means that the accuracy of the entire pulverized coal side is disregarded, while the steam-water side model is continuously adjusted and optimized. The system model and components are created using the lumped parameter method. This allows for precise simulation and real-time optimization of the thermodynamic behavior of various boiler regions (such as the combustion zone, water wall area, and superheater). By coupling the refined boundary conditions provided by the CFD-AI model with the system model (lumped parameter method), operating parameters on the steam-water side, such as water flow rate and cooling water temperature, can be continuously adjusted and optimized to ensure that the boiler's temperature and pressure distribution remain within safe ranges while improving overall operating efficiency. This approach enables the boiler control system to perform adaptive adjustments during actual operation, achieving more precise control, prediction, and fault prevention.

[0004] In the above optimization process, real-time optimization relies on accurate sensor data, including parameters such as temperature, pressure, and flow rate. However, in actual boiler operation, the collection of this data may be affected by sensor data transmission delays and instability, which may affect real-time control decisions and thus affect the optimization effect of the system. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and provide a two-dimensional mechanism modeling method and system for the steam-water side of a boiler based on CFD-AI.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The two-dimensional mechanism modeling method of the boiler steam-water side based on CFD-AI includes the following steps:

[0008] During boiler operation, the total types of sensors are obtained, and for each type of sensor, the data transmission time of each sensor and the preset data transmission time interval are obtained to obtain the data transmission delay coefficient of the sensor;

[0009] For each type of sensor, the data transmission time of each sensor is obtained, and the data transmission instability coefficient of the sensor is obtained according to the data transmission time of each sensor;

[0010] Compare the sensor's data transmission delay coefficient with a preset data transmission delay coefficient threshold to determine whether the sensor's data transmission is delayed. If there is no delay, directly output the sensor data; if there is an extension, compensate for the missing data and then output it;

[0011] Compare the sensor's data transmission instability coefficient with a preset data transmission instability coefficient threshold to determine whether the sensor's data transmission is stable; if the transmission is stable, proceed to the next step; if the transmission is unstable, process the sensor data and then proceed to the next step;

[0012] The processed sensors transmit boiler operating parameters in real time, and continuously adjust and optimize the steam-water side model to simulate the boiler operating status and control the specific values ​​of the boiler operating parameters.

[0013] A further improvement of the present invention is that the data transmission delay coefficient of the sensor is obtained by obtaining the data transmission time of each sensor and the preset data transmission time interval, including:

[0014] Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence;

[0015] Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence;

[0016] The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer;

[0017] Mark the preset data transmission time interval as , the data transmission time interval Compare it with the preset data transmission time interval and set the data transmission time interval greater than the preset data transmission time interval Data transmission interval Re-label and mark , =1, 2, 3, 4, ..., , is an integer;

[0018] Calculate the data transmission delay coefficient of the sensor. The calculation formula is: Where, is the data transmission delay coefficient.

[0019] A further improvement of the present invention is that comparing the data transmission delay coefficient of the sensor with a preset data transmission delay coefficient threshold, and determining whether the data transmission of the sensor is delayed includes:

[0020] If the data transmission delay coefficient of the sensor is less than the preset data transmission delay coefficient threshold, it means that there is no delay in the data transmission of the sensor. The boiler operating parameters are directly transmitted in real time according to the sensor, and the steam-water side model is continuously adjusted and optimized to simulate the boiler operation status and control the specific values ​​of the boiler operating parameters.

[0021] If the data transmission delay coefficient of the sensor is not less than the preset data transmission delay coefficient threshold, it means that the data transmission of the sensor is delayed. The missing real-time data is generated through the interpolation algorithm to fill the information gap caused by the delay; and the real-time data smoothing algorithm is introduced to reduce the sensor data transmission delay.

[0022] A further improvement of the present invention is that the data transmission instability coefficient of the sensor is obtained according to the data transmission time of each sensor, including:

[0023] Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence;

[0024] Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence;

[0025] The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer;

[0026] Calculate the data transmission instability coefficient , the calculation formula is:

[0027]

[0028] in, For the average value of the transmission time interval sequence, the obtained expression is: .

[0029] The further improvement of the present application is that the data transmission instability coefficient of the sensor is compared with the preset data transmission instability coefficient threshold value to determine whether the data transmission of the sensor is stable, and corresponding processing measures are taken, including:

[0030] If the data transmission instability coefficient of the sensor is less than the preset data transmission instability coefficient threshold value, it indicates that the data transmission of the sensor is not unstable, and the real-time transmission of the boiler operation parameters of the sensor is directly performed, and the model of the steam-water side is continuously adjusted and optimized to simulate the state of the boiler operation and control the specific value of the boiler operation parameter;

[0031] If the data transmission instability coefficient of the sensor is not less than the preset data transmission instability coefficient threshold value, it indicates that the data transmission of the sensor is unstable, and the sensor data is processed by using the moving average method and Kalman filtering, and the sampling frequency of the internal operation parameters of the boiler is reduced.

[0032] The CFD-AI-based two-dimensional mechanism modeling system of the boiler steam-water side includes:

[0033] The first data acquisition module is used to acquire the total type of the sensor during the operation of the boiler, and for each type of sensor, the transmission data time of each sensor and the preset data transmission time interval are acquired to obtain the data transmission delay coefficient of the sensor;

[0034] The second data acquisition module is used to acquire the transmission data time of each sensor for each type of sensor, and the data transmission instability coefficient of the sensor is obtained according to the transmission data time of each sensor;

[0035] The first comparison module is used to compare the data transmission delay coefficient of the sensor with the preset data transmission delay coefficient threshold value to determine whether the data transmission of the sensor is delayed, and if there is no delay, the sensor data is directly output; if there is a delay, the missing data is compensated and then output;

[0036] The second comparison module is used to compare the data transmission instability coefficient of the sensor with the preset data transmission instability coefficient threshold value to determine whether the data transmission of the sensor is stable; if the transmission is stable, the next step is executed; if the transmission is unstable, the sensor data is processed and then the next step is executed;

[0037] The execution module is used to continuously adjust and optimize the model of the steam-water side by using the processed real-time transmission of the sensor to the boiler operation parameters, simulate the state of the boiler operation, and control the specific value of the boiler operation parameter.

[0038] A further improvement of the present invention is that, in the first data acquisition module, obtaining the data transmission delay coefficient of each sensor by using the time of data transmission of each sensor and the preset data transmission time interval includes:

[0039] Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence;

[0040] Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence;

[0041] The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer;

[0042] Mark the preset data transmission time interval as , the data transmission time interval Compare it with the preset data transmission time interval and set the data transmission time interval greater than the preset data transmission time interval Data transmission interval Re-label and mark , =1, 2, 3, 4, ..., , is an integer;

[0043] Calculate the data transmission delay coefficient of the sensor. The calculation formula is: Where, is the data transmission delay coefficient.

[0044] A further improvement of the present invention is that, in the first comparison module, comparing the data transmission delay coefficient of the sensor with a preset data transmission delay coefficient threshold, and determining whether the data transmission of the sensor is delayed includes:

[0045] If the data transmission delay coefficient of the sensor is less than the preset data transmission delay coefficient threshold, it means that there is no delay in the data transmission of the sensor. The boiler operating parameters are directly transmitted in real time according to the sensor, and the steam-water side model is continuously adjusted and optimized to simulate the boiler operation status and control the specific values ​​of the boiler operating parameters.

[0046] If the data transmission delay coefficient of the sensor is not less than the preset data transmission delay coefficient threshold, it means that the data transmission of the sensor is delayed. The missing real-time data is generated through the interpolation algorithm to fill the information gap caused by the delay; and the real-time data smoothing algorithm is introduced to reduce the sensor data transmission delay.

[0047] A further improvement of the present invention is that, in the second data acquisition module, obtaining the data transmission instability coefficient of the sensor according to the data transmission time of each sensor includes:

[0048] Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence;

[0049] Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence;

[0050] The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer;

[0051] Calculate the data transmission instability coefficient , the calculation formula is:

[0052]

[0053] in, is the average value of the transmission time interval series, and the expression is: .

[0054] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the two-dimensional mechanism modeling method of the boiler steam-water side based on CFD-AI.

[0055] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0056] The present invention proposes a two-dimensional mechanism modeling method for the steam-water side of a boiler based on CFD-AI. During boiler operation, the total types of sensors are obtained. For each type of sensor, the data transmission delay coefficient and the data transmission instability coefficient of the sensor are calculated, and compared with the preset data transmission delay coefficient threshold and the preset data transmission delay coefficient threshold, respectively, to determine whether the data transmission of the sensor is delayed and whether it is stable, and take corresponding treatment measures; the processed sensors transmit the boiler operating parameters in real time, and continuously adjust and optimize the model of the steam-water side, simulate the state of boiler operation, and control the specific values ​​of the boiler operating parameters, which can optimize the sensor data transmission delay and instability. The processed sensors transmit the boiler operating parameters in real time, and continuously adjust and optimize the model of the steam-water side, simulate the state of boiler operation, and control the specific values ​​of the boiler operating parameters, thereby reducing the impact on real-time control decisions and ensuring the optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 This is a flow chart of the two-dimensional mechanism modeling method of the boiler steam-water side based on CFD-AI.

[0059] Figure 2 This is the structural block diagram of the two-dimensional mechanism modeling system of the boiler steam-water side based on CFD-AI. DETAILED DESCRIPTION

[0060] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0061] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0062] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0063] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0064] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0065] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0066] Example 1

[0067] The embodiment of the present invention provides a two-dimensional mechanism modeling method for the steam-water side of a boiler based on CFD-AI. Figure 1 , Figure 1 A flow chart of a two-dimensional mechanism modeling method for the steam-water side of a boiler based on CFD-AI provided in an embodiment of the present invention. The method includes the following steps:

[0068] During boiler operation, the total types of sensors are obtained, and for each type of sensor, the data transmission time of each sensor and the preset data transmission time interval are obtained to obtain the data transmission delay coefficient of the sensor;

[0069] For each type of sensor, the data transmission time of each sensor is obtained, and the data transmission instability coefficient of the sensor is obtained according to the data transmission time of each sensor;

[0070] Compare the data transmission delay coefficient of the sensor with the preset data transmission delay coefficient threshold to determine whether the data transmission of the sensor is delayed and take corresponding processing measures;

[0071] Compare the sensor's data transmission instability coefficient with a preset data transmission instability coefficient threshold to determine whether the sensor's data transmission is stable and take corresponding processing measures;

[0072] The processed sensors transmit boiler operating parameters in real time, and continuously adjust and optimize the steam-water side model to simulate the boiler operating status and control the specific values ​​of the boiler operating parameters.

[0073] Based on the two-dimensional mechanism modeling method of the boiler steam-water side based on CFD-AI provided by the embodiment of the present invention, through the above method, in actual boiler operation, the sensor data transmission delay and instability can be optimized, and the processed sensors transmit the boiler operating parameters in real time, and continuously adjust and optimize the steam-water side model to simulate the state of boiler operation and control the specific values ​​of the boiler operating parameters, thereby reducing the impact on real-time control decisions and ensuring the optimization effect.

[0074] In one embodiment, obtaining the data transmission time of each sensor and the preset data transmission time interval to obtain the data transmission delay coefficient of the sensor includes:

[0075] Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence;

[0076] Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence;

[0077] The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer;

[0078] Mark the preset data transmission time interval as , the data transmission time interval The data transmission time interval is compared with the preset data transmission time interval, and the data transmission time interval that is greater than the preset data transmission time interval is Re-label and mark , =1, 2, 3, 4, ..., , is an integer;

[0079] Calculate the data transmission delay coefficient of the sensor. The calculation formula is: Where, is the data transmission delay coefficient.

[0080] It should be noted that all sensor types (such as temperature sensors, pressure sensors, flow sensors, etc.) in the boiler system are identified, and the transmission data time of each type of sensor is recorded in real time, and the preset data transmission time interval (such as 0.5 seconds, 1 second, etc.) is stored. The specific preset data transmission time interval depends on the actual situation and is not limited or elaborated.

[0081] It should be noted that the sensor data transmission delay coefficient measures the impact of sensor data transmission delay during actual boiler operation. A larger sensor data transmission delay coefficient is more likely to affect real-time control decisions, and thus, the system's optimization effectiveness. This is because real-time control systems rely on sensor data to provide accurate and timely boiler operating parameters, such as temperature, pressure, and flow. These parameters are core inputs to control algorithms and optimization strategies. Excessive data transmission delay can prevent the control system from responding promptly to changes in the boiler's operating state, leading to delayed decision adjustments, affecting the balance of temperature and pressure distribution, and even potentially posing safety hazards to boiler operation. Furthermore, accumulated delays can interfere with the dynamic optimization process, resulting in reduced combustion efficiency, increased energy consumption, and even weakening the system's sensitivity to abnormal conditions, significantly reducing the effectiveness of fault prediction and prevention. Therefore, timely assessment and optimization of sensor data transmission delay is crucial to ensuring system stability and improving overall operational efficiency.

[0082] In one embodiment, obtaining the data transmission instability coefficient of each sensor according to the data transmission time of each sensor includes:

[0083] Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence;

[0084] Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence;

[0085] The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer;

[0086] Calculate the data transmission instability coefficient , the calculation formula is:

[0087]

[0088] in, is the average value of the transmission time interval series, and the expression is: .

[0089] It should be noted that the sensor data transmission instability coefficient is used to measure the impact of unstable sensor data transmission during actual boiler operation. A larger sensor data transmission instability coefficient is more likely to affect real-time control decisions, and thus, the system's optimization effectiveness. This is because real-time control systems rely on stable sensor data input to adjust boiler operating parameters in a timely manner. Unstable transmission intervals can lead to uneven distribution of collected data points, compromising data continuity and integrity. This not only impairs the control system's accurate perception of the boiler's operating status but can also cause model prediction bias and feedback delays in the optimization algorithm, thus affecting the dynamic adjustment of operating parameters. Furthermore, unstable data transmission can result in missing or redundant information at critical moments, reducing the accuracy of fault prediction and system optimization, and further increasing safety risks in boiler operation. Therefore, assessing and mitigating sensor data transmission instability is crucial to ensuring efficient and safe system operation.

[0090] In one embodiment, comparing the data transmission delay coefficient of the sensor with a preset data transmission delay coefficient threshold to determine whether the data transmission of the sensor is delayed includes:

[0091] If the data transmission delay coefficient of the sensor is less than the preset data transmission delay coefficient threshold, it means that there is no delay in the data transmission of the sensor. The boiler operating parameters are directly transmitted in real time according to the sensor, and the steam-water side model is continuously adjusted and optimized to simulate the boiler operation status and control the specific values ​​of the boiler operating parameters.

[0092] If the data transmission delay coefficient of the sensor is not less than the preset data transmission delay coefficient threshold, it means that the data transmission of the sensor is delayed and corresponding processing measures need to be taken.

[0093] In one embodiment, if the data transmission delay coefficient of the sensor is not less than a preset data transmission delay coefficient threshold, it indicates that the data transmission of the sensor is delayed and corresponding processing measures need to be taken, specifically:

[0094] The missing real-time data is generated through interpolation algorithm to fill the information gap caused by delay; and the real-time data smoothing algorithm is introduced to reduce the sensor data transmission delay.

[0095] It should be noted that if the delay coefficient is not less than the preset threshold, it indicates a significant delay in sensor data transmission. A series of measures are required to minimize the impact of this delay on system optimization and control. Specific measures include generating missing data through interpolation algorithms or using historical data trend predictions. For example, if a boiler's pressure sensor experiences a transmission delay, the control system may receive data at longer intervals, making it impossible for the system to grasp the boiler's pressure status in real time. In this case, an interpolation algorithm can be used to infer the pressure value during the delay period based on previous and subsequent pressure data. Alternatively, regression analysis can be used to predict the pressure value during this period using the changing trends of historical data. This ensures that the control system can still make reasonable regulatory decisions even without real-time data input.

[0096] Enabling redundant sensors or backup data channels: Imagine a boiler's fuel flow sensor experiences a significant data transmission delay, impacting combustion control accuracy. To prevent a single sensor failure or delay from impacting system control, the boiler can be configured with multiple redundant sensors or backup data channels. When a delay occurs in the primary sensor's data transmission, the system automatically switches to the backup sensor or channel, ensuring the control system continues to receive accurate data input, thus maintaining normal boiler operation.

[0097] Introducing a real-time data smoothing algorithm: Suppose a boiler's temperature sensor experiences unstable data transmission, resulting in significant fluctuations in the collected data. In this case, a real-time data smoothing algorithm can effectively smooth out these sudden fluctuations, ensuring a more stable and continuous data input for the system, thereby preventing control decision errors caused by delayed data. This allows the control system to more smoothly regulate the boiler's state without being affected by transmission delays.

[0098] The implementation of these treatment measures can effectively alleviate the problems caused by sensor data transmission delays and ensure the reliability and stability of the boiler control system in real-time operation.

[0099] In one embodiment, the data transmission instability coefficient of the sensor is compared with a preset data transmission instability coefficient threshold to determine whether the data transmission of the sensor is stable, and corresponding processing measures are taken, including:

[0100] If the data transmission instability coefficient of the sensor is less than the preset data transmission instability coefficient threshold, it means that the data transmission of the sensor is not unstable. The boiler operating parameters are directly transmitted in real time according to the sensor, and the steam-water side model is continuously adjusted and optimized to simulate the boiler operation state and control the specific values ​​of the boiler operating parameters.

[0101] If the data transmission instability coefficient of the sensor is not less than the preset data transmission instability coefficient threshold, it means that the data transmission of the sensor is unstable and corresponding processing measures need to be taken.

[0102] In one embodiment, if the data transmission instability coefficient of the sensor is not less than a preset data transmission instability coefficient threshold, it indicates that the data transmission of the sensor is unstable and corresponding processing measures need to be taken, specifically:

[0103] The sliding average method and Kalman filter are used to process the sensor and reduce the sampling frequency of the boiler's internal operating parameters.

[0104] It should be noted that the sliding average method is a simple and effective smoothing technique. By averaging sensor data within a time window, it can effectively reduce the impact of short-term fluctuations and outliers. Boiler sensor data often experiences sudden interference or short-term fluctuations. The sliding average method can eliminate these transient fluctuations, smoothing the data and reducing the impact of unstable sensor data on the boiler control system. For example, if a boiler's pressure sensor generates transient abnormal data due to external noise, the sliding average method can "suppress" these abnormal data by averaging multiple samples, thereby ensuring more stable system decisions. Furthermore, the sliding average method is computationally simple and low-computation, making it suitable for boiler control systems that require real-time response and effectively reducing the negative impact of data fluctuations on control effectiveness.

[0105] Application of Kalman filter and its reasons:

[0106] The Kalman filter is a recursive filtering algorithm based on a dynamic system model that can more accurately estimate sensor data in the presence of noise and unstable data. Compared to the sliding average method, the Kalman filter combines sensor measurements with the system's dynamic model to smooth unstable data by estimating the optimal value of the current system state. The Kalman filter is particularly suitable for processing data that is subject to system state fluctuations and is affected by noise. In boiler control systems, the Kalman filter can automatically distinguish sensor noise from true signals by continuously adjusting the filter weights, thereby improving data accuracy. Through the Kalman filter, the boiler system can predict the future system state based on the current unstable data and make corresponding adjustments based on the prediction results, ensuring that various boiler parameters such as pressure and temperature operate within a stable range.

[0107] Reasons and benefits for reducing the sampling frequency of boiler internal operating parameters:

[0108] When sensors become unstable, the system often experiences frequent data fluctuations. This can cause the boiler control system to overreact and frequently adjust control parameters, impacting overall system stability. Reducing the sampling frequency can reduce unnecessary adjustments caused by data fluctuations and prevent the system from making excessive control decisions when data is unstable. By reducing the sampling frequency, the system can process data more smoothly, reducing the suddenness of system responses and allowing control strategies to adjust over a longer period of time, thereby achieving more stable boiler operation. For example, in a temperature control system, an excessively high sampling frequency can cause the boiler thermostat to adjust frequently, resulting in excessive fluctuations. By reducing the sampling frequency, the boiler control system can avoid frequent adjustments, maintaining a more stable boiler temperature and ensuring more efficient and safe operation. Furthermore, reducing the sampling frequency can reduce the system's computational burden and increase processing speed. This can effectively improve the system's overall responsiveness, especially when data from multiple sensors is unstable.

[0109] By combining the sliding average method, Kalman filtering, and adjusting the sampling frequency, the boiler control system can better maintain system stability when faced with unstable sensor data, ensuring that the boiler operates efficiently and safely in various operating environments.

[0110] It should be noted that the processed sensors transmit the boiler's operating parameters in real time and continuously adjust and optimize the steam-water side model, simulating the boiler's operating status and controlling the specific values ​​of the boiler's operating parameters. Specifically, by coupling the boundary conditions provided by the CFD-AI model with the system model, the system will continuously adjust the boiler's operating parameters based on the real-time data, especially optimizing the steam-water side control. This includes adjusting key parameters such as water flow rate and cooling water temperature. Through precise simulation and adjustment, the system can better control the temperature and pressure distribution inside the boiler, avoiding dangerous conditions such as overheating or overcooling, and ensuring that the boiler operates in a safe and stable state.

[0111] Example 2

[0112] like Figure 2 As shown, the two-dimensional mechanism modeling system of the boiler steam-water side based on CFD-AI provided by the present invention includes:

[0113] The first data acquisition module is used to acquire the total types of sensors during boiler operation, and for each type of sensor, acquire the time of data transmission of each sensor and a preset data transmission time interval to obtain the data transmission delay coefficient of the sensor;

[0114] a second data acquisition module, configured to acquire, for each type of sensor, a data transmission time of each sensor, and obtain a data transmission instability coefficient of the sensor according to the data transmission time of each sensor;

[0115] The first comparison module is used to compare the data transmission delay coefficient of the sensor with a preset data transmission delay coefficient threshold to determine whether the data transmission of the sensor is delayed. If there is no delay, the sensor data is directly output; if there is a delay, the missing data is compensated and then output;

[0116] The second comparison module is used to compare the data transmission instability coefficient of the sensor with a preset data transmission instability coefficient threshold to determine whether the data transmission of the sensor is stable; if the transmission is stable, the next step is executed; if the transmission is unstable, the sensor data is processed and the next step is executed;

[0117] The execution module is used to transmit the processed sensor data to the boiler operating parameters in real time, continuously adjust and optimize the steam-water side model, simulate the boiler operating status, and control the specific values ​​of the boiler operating parameters.

[0118] Example 3

[0119] The present invention 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 two-dimensional mechanism modeling method of the boiler steam-water side based on CFD-AI.

[0120] 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.

[0121] 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 1a system to perform the function specified in the block or blocks.

[0122] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0124] The above presents the basic principles and main features of the present application and the advantages thereof. It is apparent to a person skilled in the art that the present application is not limited to the details of the above exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered as exemplary and non-limiting, and the scope of the present application is defined by the appended claims and not by the above description, and it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the claims. Any reference signs in the claims should not be considered as limiting the claims to which they relate.

[0125] Furthermore, it should be understood that although the present specification is described in terms of embodiments, not every implementation embodies an independent technical solution, and the present specification is described in this way only for the sake of clarity, and a person skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by a person skilled in the art. The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application, and any modification made on the basis of the technical solutions according to the present application falls within the protection scope of the claims of the present application.

Claims

1. A two-dimensional mechanism modeling method for the steam-water side of a boiler based on CFD-AI, characterized by: The following steps are involved: During boiler operation, the total types of sensors are obtained, and for each type of sensor, the data transmission time of each sensor and the preset data transmission time interval are obtained to obtain the data transmission delay coefficient of the sensor; For each type of sensor, the data transmission time of each sensor is obtained, and the data transmission instability coefficient of the sensor is obtained according to the data transmission time of each sensor; Compare the sensor's data transmission delay coefficient with a preset data transmission delay coefficient threshold to determine whether the sensor's data transmission is delayed. If there is no delay, directly output the sensor data; if there is an extension, compensate for the missing data and then output it; Comparing the data transmission instability coefficient of the sensor with a preset data transmission instability coefficient threshold to determine whether the data transmission of the sensor is stable; If the transmission is stable, proceed to the next step; If the transmission is unstable, process the sensor data and proceed to the next step; The processed sensors transmit boiler operating parameters in real time, and continuously adjust and optimize the steam-water side model to simulate the boiler operating status and control the specific values ​​of the boiler operating parameters.

2. The two-dimensional mechanism modeling method of boiler steam-water side based on CFD-AI according to claim 1 is characterized in that: The data transmission delay coefficient of each sensor obtained by obtaining the data transmission time of each sensor and the preset data transmission time interval includes: Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence; Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence; The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer; Mark the preset data transmission time interval as , the data transmission time interval Compare it with the preset data transmission time interval and set the data transmission time interval greater than the preset data transmission time interval Data transmission interval Re-label and mark , =1, 2, 3, 4, ..., , is an integer; Calculate the data transmission delay coefficient of the sensor. The calculation formula is: Where, is the data transmission delay coefficient.

3. The two-dimensional mechanism modeling method of boiler steam-water side based on CFD-AI according to claim 2 is characterized in that: Comparing the sensor's data transmission delay coefficient with a preset data transmission delay coefficient threshold to determine whether the sensor's data transmission is delayed includes: If the data transmission delay coefficient of the sensor is less than the preset data transmission delay coefficient threshold, it means that there is no delay in the data transmission of the sensor. The boiler operating parameters are directly transmitted in real time according to the sensor, and the steam-water side model is continuously adjusted and optimized to simulate the boiler operation status and control the specific values ​​of the boiler operating parameters. If the data transmission delay coefficient of the sensor is not less than the preset data transmission delay coefficient threshold, it means that the data transmission of the sensor is delayed. The missing real-time data is generated through the interpolation algorithm to fill the information gap caused by the delay; and the real-time data smoothing algorithm is introduced to reduce the sensor data transmission delay.

4. The two-dimensional mechanism modeling method of boiler steam-water side based on CFD-AI according to claim 1 is characterized in that: The data transmission instability coefficient of the sensor obtained according to the data transmission time of each sensor includes: Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence; Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence; The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer; Calculate the data transmission instability coefficient , the calculation formula is: in, is the average value of the transmission time interval series, and the expression is: .

5. The two-dimensional mechanism modeling method of boiler steam-water side based on CFD-AI according to claim 4 is characterized in that: Compare the sensor's data transmission instability coefficient with the preset data transmission instability coefficient threshold to determine whether the sensor's data transmission is stable. Take corresponding measures, including: If the data transmission instability coefficient of the sensor is less than the preset data transmission instability coefficient threshold, it means that the data transmission of the sensor is not unstable. The boiler operating parameters are directly transmitted in real time according to the sensor, and the steam-water side model is continuously adjusted and optimized to simulate the boiler operation state and control the specific values ​​of the boiler operating parameters. If the data transmission instability coefficient of the sensor is not less than the preset data transmission instability coefficient threshold, it means that the data transmission of the sensor is unstable. The sliding average method and Kalman filter are used to process the sensor data, and the sampling frequency of the internal operating parameters of the boiler is reduced.

6. The two-dimensional mechanism modeling system of boiler steam-water side based on CFD-AI is characterized by: include: The first data acquisition module is used to acquire the total types of sensors during boiler operation, and for each type of sensor, acquire the time of data transmission of each sensor and a preset data transmission time interval to obtain the data transmission delay coefficient of the sensor; a second data acquisition module, configured to acquire, for each type of sensor, a data transmission time of each sensor, and obtain a data transmission instability coefficient of the sensor according to the data transmission time of each sensor; The first comparison module is used to compare the data transmission delay coefficient of the sensor with a preset data transmission delay coefficient threshold to determine whether the data transmission of the sensor is delayed. If there is no delay, the sensor data is directly output; if there is a delay, the missing data is compensated and then output; A second comparison module is used to compare the data transmission instability coefficient of the sensor with a preset data transmission instability coefficient threshold to determine whether the data transmission of the sensor is stable; If the transmission is stable, proceed to the next step; If the transmission is unstable, process the sensor data and proceed to the next step; The execution module is used to transmit the processed sensor data to the boiler operating parameters in real time, continuously adjust and optimize the steam-water side model, simulate the boiler operating status, and control the specific values ​​of the boiler operating parameters.

7. The two-dimensional mechanism modeling system of boiler steam-water side based on CFD-AI according to claim 6 is characterized in that: In the first data acquisition module, obtaining the data transmission delay coefficient of each sensor by the time of data transmission of each sensor and the preset data transmission time interval includes: Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence; Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence; The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer; Mark the preset data transmission time interval as , the data transmission time interval Compare it with the preset data transmission time interval and set the data transmission time interval greater than the preset data transmission time interval Data transmission interval Re-label and mark , =1, 2, 3, 4, ..., , is an integer; Calculate the data transmission delay coefficient of the sensor. The calculation formula is: Where, is the data transmission delay coefficient.

8. The two-dimensional mechanism modeling system of boiler steam-water side based on CFD-AI according to claim 7 is characterized in that: In the first comparison module, comparing the data transmission delay coefficient of the sensor with a preset data transmission delay coefficient threshold to determine whether the data transmission of the sensor is delayed includes: If the data transmission delay coefficient of the sensor is less than the preset data transmission delay coefficient threshold, it means that there is no delay in the data transmission of the sensor. The boiler operating parameters are directly transmitted in real time according to the sensor, and the steam-water side model is continuously adjusted and optimized to simulate the boiler operation status and control the specific values ​​of the boiler operating parameters. If the data transmission delay coefficient of the sensor is not less than the preset data transmission delay coefficient threshold, it means that the data transmission of the sensor is delayed. The missing real-time data is generated through the interpolation algorithm to fill the information gap caused by the delay; and the real-time data smoothing algorithm is introduced to reduce the sensor data transmission delay.

9. The two-dimensional mechanism modeling system of boiler steam-water side based on CFD-AI according to claim 6 is characterized in that: In the second data acquisition module, obtaining the data transmission instability coefficient of each sensor according to the data transmission time of each sensor includes: Sequence the timestamps corresponding to the actual transmission data of each sensor to obtain a data timestamp sequence; Calculate the difference between every two adjacent timestamps in the data timestamp sequence to obtain a transmission time interval sequence; The transmission time intervals in the transmission time interval sequence are marked as , A number indicating the order of the transmission time interval in the transmission time interval sequence, =1, 2, 3, 4, ..., , is a positive integer; Calculate the data transmission instability coefficient , the calculation formula is: in, is the average value of the transmission time interval series, and the expression is: .

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the two-dimensional mechanism modeling method of the boiler steam-water side based on CFD-AI according to any one of claims 1 to 5.

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