A Steam Emission Control Method Based on Pilot Valve Regulation

Abstract

This invention discloses a steam emission control method based on pilot valve regulation, specifically relating to the field of steam emission technology. The method includes determining the actual pressure, desired pressure, and set pressure of the main valve, comparing the relationship between the actual and desired pressures of the main valve to determine the hysteresis between the actual and desired pressures in the time delay information, obtaining the delay factor of the delay transfer function model of the steam system PID controller within the monitoring period, determining the response delay of the steam system in the time delay information, and modeling the time series data of the actual pressure using an ARMA model to determine the changing trend of the actual pressure of the main valve in the pressure change information, thereby assessing the potential risks of the steam system. This invention helps ensure stable operation of the system under various changing environments and avoids over-adjustment or control failure of the pilot valve.

Description

Technical Field

[0001] This invention relates to the field of steam emission technology, and more specifically, to a steam emission control method based on pilot valve regulation. Background Technology

[0002] Existing technologies typically employ advanced control techniques such as PID control or model predictive control (MPC). These methods enable pilot valves to control the pressure of the steam system more stably, especially when steam demand fluctuates significantly. However, in practical applications, steam systems face many challenges due to factors such as steam demand fluctuations, control system lag, and sensor response delays. In particular, when demand fluctuates significantly, it is often difficult to detect potential risks in a timely manner, which may lead to system failure or instability.

[0003] To address the two aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a steam emission control method based on pilot valve regulation to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A steam emission control method based on pilot valve regulation specifically includes the following steps:

[0007] S1: Collect data from various pressure sensors in the steam system to determine the actual pressure, desired pressure, and set pressure of the pilot valve. By comparing the relationship between the actual pressure and the desired pressure of the main valve, determine the hysteresis between the actual pressure and the desired pressure of the main valve in the time delay information.

[0008] S2: The delay transfer function model of the steam system PID controller during the monitoring period, obtain the delay factor of the delay transfer function model, and determine the response delay of the steam system in the time delay information;

[0009] S3: By using the ARMA model to model the time series data of actual pressure, predict and analyze the changing trend of actual pressure, and determine the changing trend of the actual pressure of the main valve in the pressure change information;

[0010] S4: By comprehensively analyzing time delay information and pressure change information, an early warning assessment model is constructed to determine the potential risks of the steam system, and the set pressure of the pilot valve is adjusted according to the magnitude of the potential risks.

[0011] In a preferred embodiment, comparing the relationship between the actual pressure of the main valve and the desired pressure of the main valve includes:

[0012] Based on the pressure sensor in the steam pipeline, the actual pressure of the main valve is determined, and the actual pressure of the main valve during the monitoring period is marked as follows: Where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of the actual pressure collected during the monitoring period;

[0013] Obtain the desired pressure of the main valve and mark the desired pressure of the main valve as: ;

[0014] The relationship between the actual pressure and the desired pressure of the main valve is analyzed using a cross-correlation function. The cross-correlation coefficient between the actual pressure and the desired pressure of the main valve is determined, and the calculation formula is as follows: ;in, The cross-correlation coefficient between the actual pressure and the desired pressure of the main valve is given by τ, where τ is the lag time and is a positive integer.

[0015] In a preferred embodiment, determining the hysteresis between the actual pressure and the desired pressure of the main valve in the time delay information includes:

[0016] The hysteresis between the actual pressure and the expected pressure of the main valve in the time delay information is represented by the correlation coefficient.

[0017] Set a threshold for cross-correlation coefficients and a threshold for lag time. Compare the cross-correlation coefficients at different lag times within the threshold for ... , where i = 1, 2, 3, ..., I, I is a positive integer, and i is the number of the cross-correlation coefficient at different lag times that are greater than the cross-correlation coefficient threshold;

[0018] The correlation coefficient is calculated using the following formula: ;in, The correlation coefficient, The threshold for cross-correlation coefficients.

[0019] In a preferred embodiment, determining the response delay of the steam system in the time delay information includes:

[0020] The response delay of the steam system in the time delay information is represented by the response delay coefficient;

[0021] Determine the delay transfer function model of the PID controller for the steam system during the monitoring period, obtain the delay factor of the delay transfer function model, and express the delay transfer function model as follows: ;in, For the delayed transfer function model, , , ... For higher-order terms, It is a delay factor;

[0022] The delay time of the delay transfer function model within the monitoring period is marked as: m = 1, 2, 3, ..., M, where M is a positive integer and m is the number of the delay time of the PID controller delay transfer function model collected at different times within the monitoring period.

[0023] Determine the mean and standard deviation of the delay time for the delay transfer function model, and label the mean and standard deviation of the delay time for the delay transfer function model as: and ,in, , ;

[0024] The response delay factor is calculated using the following formula: ;in, This represents the response delay coefficient.

[0025] In a preferred embodiment, determining the trend of the actual pressure change in the main valve from the pressure change information includes:

[0026] The trend of actual pressure change in the main valve in the pressure change information is represented by the pressure time series deviation coefficient.

[0027] Using the time series of actual pressure collected in the previous monitoring period as the training set, the ARMA model is trained to determine the order P and Q of the ARMA model, resulting in the following expression for the trained ARMA model: Where a is a constant term, The actual pressure during the previous monitoring period is represented by r = 1, 2, 3, ..., R, where R is a positive integer; p = 1, 2, 3, ..., P, where p is the order number P of the ARMA model; and q = 1, 2, 3, ..., Q, where q is the order number Q of the ARMA model. These are the coefficients of the MA component in the ARMA model. These are the coefficients of the AR component in the ARMA model. It is a white noise term;

[0028] After obtaining the trained ARMA model, the actual stress within the current detection time period is predicted using the trained ARMA model. The expression for predicting the actual stress within the current detection time period is: ;in, To predict the actual pressure during the nth data collection in the current time period, For known observations in time series data, For prediction errors in time series data;

[0029] The predicted actual pressure for the current monitoring period is marked as: The pressure time series deviation coefficient is determined by calculating the residual between the predicted actual pressure during the current monitoring period and the actual pressure of the main valve during the actual monitoring period. The calculation formula is as follows: ;in, This is the pressure time series deviation coefficient.

[0030] In a preferred embodiment, an early warning assessment model is constructed, including:

[0031] By comprehensively analyzing time delay information and pressure change information, a weighted calculation is performed on the correlation coefficient, response delay coefficient, and pressure time series deviation coefficient to construct an early warning assessment model and generate an early warning assessment coefficient. The formula for calculating the early warning assessment coefficient is as follows: ;in, This is the early warning assessment coefficient. , , These are the proportional coefficients for the correlation coefficient, response delay coefficient, and pressure time series deviation coefficient, respectively. , , All are greater than 0.

[0032] In a preferred embodiment, determining the potential risks of the steam system includes:

[0033] Set a threshold for the early warning evaluation coefficient. Compare the early warning evaluation coefficient within the monitoring period with the threshold. If the early warning evaluation coefficient is greater than the threshold, an early warning signal is generated. If the early warning evaluation coefficient is less than the threshold, no signal is generated.

[0034] The technical effects and advantages of this invention are as follows:

[0035] This invention collects data from various pressure sensors in a steam system, analyzes the relationship between the actual pressure and the desired pressure of the main valve, and thus obtains time delay information and hysteresis. Combined with the delay transfer function model of a PID controller, it quantifies the response delay of the steam system. Using an ARMA model, it models the time series of the actual pressure to predict pressure change trends. Finally, by comprehensively analyzing time delay and pressure change information, it constructs an early warning assessment model to promptly assess potential system risks. Based on the magnitude of the risk, it adjusts the set pressure of the pilot valve to ensure the main valve pressure remains stable within the target range. This invention helps ensure stable system operation under various changing environments and avoids over-adjustment or control failure of the pilot valve. Attached Figure Description

[0036] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0037] Figure 1 This is a schematic flowchart of a steam emission control method based on pilot valve regulation according to the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Example 1

[0040] Figure 1 This is a flowchart illustrating the steps of a steam emission control method based on pilot valve regulation according to the present invention, specifically including the following steps:

[0041] S1: Collect data from various pressure sensors in the steam system to determine the actual pressure, desired pressure, and set pressure of the pilot valve. By comparing the relationship between the actual pressure and the desired pressure of the main valve, determine the hysteresis between the actual pressure and the desired pressure of the main valve in the time delay information.

[0042] S2: The delay transfer function model of the steam system PID controller during the monitoring period, obtain the delay factor of the delay transfer function model, and determine the response delay of the steam system in the time delay information;

[0043] S3: By using the ARMA model to model the time series data of actual pressure, predict and analyze the changing trend of actual pressure, and determine the changing trend of the actual pressure of the main valve in the pressure change information;

[0044] S4: By comprehensively analyzing time delay information and pressure change information, an early warning assessment model is constructed to determine the potential risks of the steam system, and the set pressure of the pilot valve is adjusted according to the magnitude of the potential risks.

[0045] By using pilot valves to control the pressure and flow rate of the steam system, the stability and reliability of the system are ensured, and excessively high or low pressures can threaten equipment and system safety. In the steam system, the main function of the pilot valve is to adjust the opening degree of the main valve according to the set pressure. The set pressure of the pilot valve affects the actual pressure of the main valve. By adjusting the pressure of the pilot valve, the opening degree of the main valve can be controlled, thereby indirectly controlling the steam flow rate and system pressure.

[0046] In a steam system, the pilot valve's set pressure, the main valve's actual pressure, and the main valve's desired pressure are closely related control parameters. These parameters ensure that the system can adjust the steam flow rate according to demand while maintaining stable system pressure. The pilot valve's set pressure is used to adjust the main valve's pressure control point. It is dynamically adjusted based on the error between the main valve's actual pressure and its desired pressure. When the system's actual pressure is lower than the desired set pressure, the pilot valve's set pressure increases to open the main valve, allowing for a larger steam flow. Conversely, when the actual pressure exceeds the desired pressure, the set pressure decreases, thereby reducing the steam flow rate.

[0047] The actual pressure of the main valve refers to the pressure in the system measured by multiple pressure sensors. It is affected by various factors such as pipeline resistance, steam flow, and equipment status. By continuously monitoring the actual pressure of the main valve and comparing it with the expected pressure, the error is calculated. If the error is large, the control system will adjust the opening of the main valve by adjusting the set pressure of the pilot valve, thereby adjusting the actual pressure to approach the expected value.

[0048] The desired pressure of the main valve is the pressure value under ideal conditions. It is usually set by the control system according to load demand, process requirements or safety standards. The actual pressure is driven to approach the desired value by adjusting the set pressure of the pilot valve.

[0049] In steam systems, PID controllers and MPC (model predictive control) are used to meet the response to large changes in steam demand and improve the performance of the steam system. The PID controller adjusts the pilot valve by measuring the error between the actual pressure and the expected pressure of the main valve, and the MPC predicts changes in steam demand and pressure and makes adjustments in advance based on the prediction results.

[0050] MPC predicts future demand changes and system dynamics, and sets the desired pressure for the main valve in advance. MPC can better match the steam demand. The PID controller adjusts the control of the pilot valve based on the error between the desired pressure and the actual pressure of the main valve.

[0051] It should be noted that MPC is typically used to handle system dynamics with regularity or known trends. Therefore, it is very suitable for predicting and controlling regular demand changes. However, for situations with large demand fluctuations, MPC may have difficulty accurately predicting the future state of the system, resulting in large control errors.

[0052] The monitoring period is set to monitor the actual steam demand and avoid overshoot or oscillation caused by sudden changes in demand. The time delay information and pressure change information of the steam system are collected during the monitoring period. The lag between the actual pressure and the expected pressure of the main valve in the time delay information is represented by the correlation coefficient. The response delay of the steam system in the time delay information is represented by the response delay coefficient. The change trend of the actual pressure of the main valve in the pressure change information is represented by the pressure time series deviation coefficient.

[0053] It should be noted that the monitoring period is a specific time period, which is set by staff according to the actual situation.

[0054] The correlation coefficient is used to analyze the relationship between the actual pressure and the expected pressure of the main valve. The advantages of the correlation coefficient include:

[0055] By using the relevant correlation coefficient, the relationship between the actual pressure and the expected pressure of the main valve can be detected over a period of time, and it can be determined whether the system exhibits atypical or irregular behavior.

[0056] It can determine the effectiveness of control algorithms such as PID or MPC in regulating the current steam system and whether the control algorithm can enhance the stability of the control system.

[0057] Based on the results of the autocorrelation anomaly system, it is possible to determine whether the current fluctuation is within the normal range, and then decide whether the control strategy needs to be adjusted to prevent the system from over-adjusting. This can reduce oscillations caused by lag, over-adjustment, and other reasons by adjusting the parameters of the control algorithm.

[0058] The logic for obtaining the relevant correlation coefficient is as follows: Based on the pressure sensor in the steam pipeline, determine the actual pressure of the main valve, and mark the actual pressure of the main valve during the monitoring period as follows: Where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of the actual pressure collected during the monitoring period;

[0059] Obtain the desired pressure of the main valve and mark the desired pressure of the main valve as: ;

[0060] It should be noted that the desired pressure of the main valve is a continuous dynamic adjustment process. The desired pressure of the main valve is determined by the MPC and the actual situation. Generally, when the steam demand increases, the desired pressure of the main valve increases, and when the steam demand decreases, the desired pressure of the main valve decreases.

[0061] The relationship between the actual pressure and the desired pressure of the main valve is analyzed using a cross-correlation function. The cross-correlation coefficient between the actual pressure and the desired pressure of the main valve is determined, and the calculation formula is as follows: ;in, The cross-correlation coefficient between the actual pressure and the desired pressure of the main valve is given by τ, where τ is the lag time and is a positive integer.

[0062] Set a threshold for cross-correlation coefficients and a threshold for lag time. Compare the cross-correlation coefficients at different lag times within the threshold for ... , where i = 1, 2, 3, ..., I, I is a positive integer, and i is the number of the cross-correlation coefficient at different lag times that are greater than the cross-correlation coefficient threshold;

[0063] It should be noted that the cross-correlation coefficient threshold and the lag time threshold are set by professionals in the field. The cross-correlation coefficient threshold is used to determine that there is a strong correlation between the expected pressure and the actual pressure of the main valve. The lag time threshold is the lag of the system response time, which usually reflects the lag of the steam system in changing the actual pressure according to the expected pressure. The lag time threshold is usually a small value to ensure the timeliness of the steam system response.

[0064] The correlation coefficient is calculated using the following formula: ;in, The correlation coefficient, The threshold for cross-correlation coefficients.

[0065] As can be seen from the formula, the larger the correlation coefficient, the more correlated the actual pressure and the expected pressure of the main valve are, indicating that the pilot valve controls the main valve better, the current demand changes may be more regular, and the system can adapt to changes in steam demand.

[0066] By determining the delay transfer function model of the steam system using a PID controller during the monitoring period, the delay factor of the delay transfer function model is obtained. The response delay coefficient is then determined by calculating the volatility of the delay factor. The advantages of the response delay coefficient include:

[0067] While other parameters (such as pressure, flow rate, temperature, etc.) can provide information about the current state of the system, they may not fully capture the system's delay characteristics when responding to changes in demand. The response delay coefficient directly reflects the system's hysteresis effect and its fluctuations, and therefore can more accurately reveal the system's stability.

[0068] Hysteresis usually causes the controller (such as the PID controller) to respond late, which in turn causes large errors or control instability. By analyzing the response delay coefficient, the hysteresis of the system when facing changes in external demand can be effectively identified.

[0069] The response delay coefficient provides feedback for the adjustment of PID controllers or MPC controllers, and can help identify the system's delay model and optimize the transfer function and control strategy.

[0070] The logic for obtaining the response delay coefficient is as follows: determine the delay transfer function model of the steam system PID controller within the monitoring time period, obtain the delay factor of the delay transfer function model, and the delay transfer function model is expressed as: ;in, For the delayed transfer function model, , , ... For higher-order terms, It is a delay factor;

[0071] The delay time of the delay transfer function model within the monitoring period is marked as: m = 1, 2, 3, ..., M, where M is a positive integer and m is the number of the delay time of the PID controller delay transfer function model collected at different times within the monitoring period.

[0072] Determine the mean and standard deviation of the delay time for the delay transfer function model, and label the mean and standard deviation of the delay time for the delay transfer function model as: and ,in, , ;

[0073] It should be noted that the delay transfer function model of the PID controller of the steam system changes continuously with the changes in steam demand during the monitoring period. Therefore, the delay factor also changes with time and demand. This change reflects the lag characteristics of the control process when the system responds to different changes in steam demand.

[0074] The response delay factor is calculated using the following formula: ;in, This represents the response delay coefficient.

[0075] As can be seen from the formula, the larger the response delay coefficient, the more significant the difference in response speed at different time points within the monitoring period. This may lead to frequent over-adjustment and under-adjustment, inaccurate control signals, and consequently affect equipment performance. It also indicates that the degree of mismatch between steam supply and demand may be higher.

[0076] By using an ARMA model to model time-series data of actual pressure, the changing trends of actual pressure can be predicted and analyzed. By training an ARMA model based on actual pressure, the deviation and error between the model and actual pressure are identified, and the pressure time-series deviation coefficient is obtained. The advantages of the pressure time-series deviation coefficient include:

[0077] ARMA models can capture the autoregressive (AR) and moving average (MA) characteristics in time series data, thereby accurately describing the historical behavior and potential patterns of the system. This makes it possible to predict actual stress based on historical data and thus optimize control strategies.

[0078] ARMA models can capture long-term dependencies and short-term fluctuations in time series data, and are particularly suitable for data with time correlation (such as periodic fluctuations, trends, etc.), and can more accurately reflect the dynamic characteristics of steam systems.

[0079] ARMA models provide the ability to predict future behavior under actual pressure, which is especially important for advanced control strategies such as MPC. By combining ARMA models with PID control, system response can be predicted and adjusted more accurately, improving control precision and response speed.

[0080] The logic for obtaining the pressure time series deviation coefficient is as follows: using the actual pressure time series collected in the previous monitoring period as the training set, the ARMA model is trained to determine the order P and Q of the ARMA model, resulting in the trained ARMA model expression: Where a is a constant term, The actual pressure during the previous monitoring period is represented by r = 1, 2, 3, ..., R, where R is a positive integer; p = 1, 2, 3, ..., P, where p is the order number P of the ARMA model; and q = 1, 2, 3, ..., Q, where q is the order number Q of the ARMA model. These are the coefficients of the MA component in the ARMA model. These are the coefficients of the AR component in the ARMA model. It is a white noise term;

[0081] After obtaining the trained ARMA model, the actual stress within the current detection time period is predicted using the trained ARMA model. The expression for predicting the actual stress within the current detection time period is: ;in, To predict the actual pressure during the nth data collection in the current time period, For known observations in time series data, For prediction errors in time series data;

[0082] The predicted actual pressure for the current monitoring period is marked as: The pressure time series deviation coefficient is determined by calculating the residual between the predicted actual pressure during the current monitoring period and the actual pressure of the main valve during the actual monitoring period. The calculation formula is as follows: ;in, This is the pressure time series deviation coefficient.

[0083] As can be seen from the formula, the larger the pressure time series deviation coefficient, the greater the fluctuation in steam demand may be. The model may not be able to react in time, which may mean that the system's feedback mechanism has not adjusted the pilot valve or main valve pressure in a timely or accurate manner, resulting in the steam system being unable to respond quickly to changes in demand or external disturbances.

[0084] By comprehensively analyzing time delay information and pressure change information, a weighted calculation is performed on the correlation coefficient, response delay coefficient, and pressure time series deviation coefficient to construct an early warning assessment model and generate an early warning assessment coefficient. The formula for calculating the early warning assessment coefficient is as follows: ;in, This is the early warning assessment coefficient. , , These are the proportional coefficients for the correlation coefficient, response delay coefficient, and pressure time series deviation coefficient, respectively. , , All are greater than 0.

[0085] A warning evaluation coefficient threshold is set, and the warning evaluation coefficient within the monitoring period is compared with the warning evaluation coefficient threshold. If the warning evaluation coefficient is greater than the warning evaluation coefficient threshold, a warning signal is generated, and the operator adjusts the set pressure of the pilot valve to ensure that the main valve pressure can be stabilized within the target range. If the warning evaluation coefficient is less than the warning evaluation coefficient threshold, no signal is generated, indicating that the steam system can meet the response to large changes in steam demand by using PID controller and MPC (model predictive control).

[0086] This invention collects data from various pressure sensors in a steam system, analyzes the relationship between the actual pressure and the desired pressure of the main valve, and thus obtains time delay information and hysteresis. Combined with the delay transfer function model of a PID controller, it quantifies the response delay of the steam system. Using an ARMA model, it models the time series of the actual pressure to predict pressure change trends. Finally, by comprehensively analyzing time delay and pressure change information, it constructs an early warning assessment model to promptly assess potential system risks. Based on the magnitude of the risk, it adjusts the set pressure of the pilot valve to ensure the main valve pressure remains stable within the target range. This invention helps ensure stable system operation under various changing environments and avoids over-adjustment or control failure of the pilot valve.

[0087] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0088] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0089] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0090] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0091] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0093] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A steam emission control method based on pilot valve regulation, characterized in that, Specifically, the following steps are included: S1: Collect data from various pressure sensors in the steam system to determine the actual pressure, desired pressure, and set pressure of the pilot valve. By comparing the relationship between the actual pressure and the desired pressure of the main valve, determine the hysteresis between the actual pressure and the desired pressure of the main valve in the time delay information. S2: The delay transfer function model of the steam system PID controller during the monitoring period, obtain the delay factor of the delay transfer function model, and determine the response delay of the steam system in the time delay information; S3: By using the ARMA model to model the time series data of actual pressure, predict and analyze the changing trend of actual pressure, and determine the changing trend of the actual pressure of the main valve in the pressure change information; S4: By comprehensively analyzing time delay information and pressure change information, an early warning assessment model is constructed to determine the potential risks of the steam system, and the set pressure of the pilot valve is adjusted according to the magnitude of the potential risks.

2. The steam emission control method based on pilot valve regulation according to claim 1, characterized in that, Compare the relationship between the actual pressure of the main valve and the expected pressure of the main valve, including: Based on the pressure sensor in the steam pipeline, the actual pressure of the main valve is determined, and the actual pressure of the main valve during the monitoring period is marked as follows: Where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of the actual pressure collected during the monitoring period; Obtain the desired pressure of the main valve and mark the desired pressure of the main valve as: ; The relationship between the actual pressure and the desired pressure of the main valve is analyzed using a cross-correlation function. The cross-correlation coefficient between the actual pressure and the desired pressure of the main valve is determined, and the calculation formula is as follows: ;in, The cross-correlation coefficient between the actual pressure and the desired pressure of the main valve is given by τ, where τ is the lag time and is a positive integer.

3. The steam emission control method based on pilot valve regulation according to claim 2, characterized in that: Determine the hysteresis between the actual pressure and the expected pressure of the main valve in the time delay information, including: The hysteresis between the actual pressure and the expected pressure of the main valve in the time delay information is represented by the correlation coefficient. Set a threshold for cross-correlation coefficients and a threshold for lag time. Compare the cross-correlation coefficients at different lag times within the threshold for ... , where i = 1, 2, 3, ..., I, I is a positive integer, and i is the number of the cross-correlation coefficient at different lag times that are greater than the cross-correlation coefficient threshold; The correlation coefficient is calculated using the following formula: ;in, The correlation coefficient, The threshold for cross-correlation coefficients.

4. The steam emission control method based on pilot valve regulation according to claim 3, characterized in that, Determining the response delay of the steam system in the time delay information includes: The response delay of the steam system in the time delay information is represented by the response delay coefficient; Determine the delay transfer function model of the PID controller for the steam system during the monitoring period, obtain the delay factor of the delay transfer function model, and express the delay transfer function model as follows: ;in, For the delayed transfer function model, , , ... For higher-order terms, It is a delay factor; The delay time of the delay transfer function model within the monitoring period is marked as: m = 1, 2, 3, ..., M, where M is a positive integer and m is the number of the delay time of the PID controller delay transfer function model collected at different times within the monitoring period. Determine the mean and standard deviation of the delay time for the delay transfer function model, and label the mean and standard deviation of the delay time for the delay transfer function model as: and ,in, , ; The response delay factor is calculated using the following formula: ;in, This represents the response delay coefficient.

5. The steam emission control method based on pilot valve regulation according to claim 4, characterized in that, Determine the trend of actual pressure change in the main valve from the pressure change information, including: The trend of actual pressure change in the main valve in the pressure change information is represented by the pressure time series deviation coefficient. Using the time series of actual pressure collected in the previous monitoring period as the training set, the ARMA model is trained to determine the order P and Q of the ARMA model, resulting in the following expression for the trained ARMA model: Where a is a constant term, The actual pressure during the previous monitoring period is represented by r = 1, 2, 3, ..., R, where R is a positive integer; p = 1, 2, 3, ..., P, where p is the order number P of the ARMA model; and q = 1, 2, 3, ..., Q, where q is the order number Q of the ARMA model. These are the coefficients of the MA component in the ARMA model. These are the coefficients of the AR component in the ARMA model. It is a white noise term; After obtaining the trained ARMA model, the actual stress within the current detection time period is predicted using the trained ARMA model. The expression for predicting the actual stress within the current detection time period is: ;in, To predict the actual pressure during the nth data collection in the current time period, For known observations in time series data, For prediction errors in time series data; The predicted actual pressure for the current monitoring period is marked as: The pressure time series deviation coefficient is determined by calculating the residual between the predicted actual pressure during the current monitoring period and the actual pressure of the main valve during the actual monitoring period. The calculation formula is as follows: ;in, This is the pressure time series deviation coefficient.

6. The steam emission control method based on pilot valve regulation according to claim 5, characterized in that, Construct an early warning assessment model, including: By comprehensively analyzing time delay information and pressure change information, a weighted calculation is performed on the correlation coefficient, response delay coefficient, and pressure time series deviation coefficient to construct an early warning assessment model and generate an early warning assessment coefficient. The formula for calculating the early warning assessment coefficient is as follows: ;in, This is the early warning assessment coefficient. , , These are the proportional coefficients for the correlation coefficient, response delay coefficient, and pressure time series deviation coefficient, respectively. , , All are greater than 0.

7. The steam emission control method based on pilot valve regulation according to claim 6, characterized in that, Assessing the potential risks of a steam system includes: Set a threshold for the early warning evaluation coefficient. Compare the early warning evaluation coefficient within the monitoring period with the threshold. If the early warning evaluation coefficient is greater than the threshold, an early warning signal is generated. If the early warning evaluation coefficient is less than the threshold, no signal is generated.

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