A method and system for controlling the outlet steam pressure of a steam jet mixer

Abstract

This invention discloses a method and system for controlling the outlet steam pressure of a steam jet mixer, relating to the field of steam jet mixer technology. The control method comprises the following steps: acquiring real-time operating parameters and structural parameters of the steam jet mixer; constructing an outlet pressure prediction model based on historical operating data, and obtaining the predicted outlet pressure value by inputting the real-time operating parameters and structural parameters into the prediction model; the key technical points are: constructing an outlet pressure prediction model that includes an operating condition characteristic identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. This model reduces prediction errors by collecting full-cycle operating data from the past three years and iteratively training it; and by inputting the real-time operating parameters and structural parameters into the prediction model, the predicted outlet pressure value and its change trend can be obtained in advance; based on this, the control adjustment amount is obtained by combining the outlet pressure deviation value and its development trend, making the adjustment more predictive.

Description

Technical Field

[0001] This invention relates to the field of steam jet mixer technology, specifically to a method and system for controlling the outlet steam pressure of a steam jet mixer. Background Technology

[0002] Steam jet mixers are widely used in industrial production. Their main function is to mix steam with different parameters to obtain steam that meets the production requirements. In actual operation, the stability of the steam pressure at the mixer outlet directly affects the quality and efficiency of subsequent production processes.

[0003] Currently, the control of the outlet steam pressure of steam jet mixers mostly adopts a simple single-parameter adjustment method, that is, only the inlet steam flow or cooling water flow is adjusted according to the deviation between the actual value and the set value of the outlet pressure. This control method has obvious limitations. Since the operation of the mixer is affected by multiple parameters and there are interactions between these parameters, single-parameter adjustment is difficult to quickly and accurately stabilize the outlet pressure within the target range. It is prone to problems such as large pressure fluctuations and adjustment lag, which affect the stability of production and energy utilization efficiency.

[0004] Furthermore, existing control methods lack the ability to predict future pressure change trends and comprehensively evaluate the regulation effect. They cannot adopt targeted regulation strategies based on different operating conditions, nor can they dynamically optimize control parameters, resulting in poor control accuracy and adaptability. Therefore, there is an urgent need for a steam jet mixer outlet steam pressure control method and system that can comprehensively consider the influence of multiple parameters and has predictive and optimization capabilities. Summary of the Invention

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

[0006] A method for controlling the outlet steam pressure of a steam jet mixer, comprising the following steps:

[0007] The real-time operating parameters and structural parameters of the steam jet mixer are obtained. The real-time operating parameters include the steam pressure entering the mixer, the steam flow rate entering the mixer, the flow rate of cooling water, the steam temperature entering the mixer, and the actual pressure at the mixer outlet. The structural parameters include the diameter of the nozzle for jetting steam, the length of the mixing steam chamber, and the tilt angle of the steam diffusion module.

[0008] An outlet pressure prediction model is constructed based on the equipment's historical operating data, which includes a working condition feature identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. By inputting real-time operating parameters and structural parameters into the prediction model, the predicted outlet pressure value and the trend graph of pressure change are obtained.

[0009] The outlet pressure deviation is calculated based on the actual pressure at the mixer outlet and the predicted outlet pressure, and the trend of the deviation is judged by combining the trend graph of pressure change.

[0010] The required control adjustment amount is obtained based on the outlet pressure deviation value, the deviation development trend and the dynamic characteristic parameters of the mixer. Among them, the dynamic characteristic parameters of the mixer include pressure response time, flow regulation sensitivity and the coefficients of mutual influence between various parameters, i.e., parameter coupling coefficients.

[0011] Control commands are generated and control operations are executed as needed, including a coordinated regulation mechanism for the steam flow rate entering the mixer based on the deviation type, the flow rate for cooling water, and the pressure compensation of the mixing steam chamber.

[0012] A multi-dimensional effect evaluation of the actual pressure at the mixer outlet after the control operation is performed is conducted to obtain the evaluation value of the pressure control quality.

[0013] Based on the evaluation value of pressure control quality, the required control adjustment amount is dynamically optimized through feedback, and the parameter weights of each module in the outlet pressure prediction model are updated at the same time.

[0014] Furthermore, the steps of constructing an outlet pressure prediction model based on the equipment's historical operating data, including a working condition characteristic identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer, include:

[0015] Collect full-cycle operation data from the past three years, including equipment start-up and shutdown, stable operation, and load change phases. The data volume of each phase shall not be less than 30% of the total data volume. The obtained full-cycle operation data is the historical operation data.

[0016] Feature extraction is performed on the historical operating data of the equipment to identify key characteristic parameters such as the fluctuation frequency of parameters entering the mixer, pressure response lag time, and the degree of correlation between adjustment amount and pressure change;

[0017] A feature matrix containing 12 typical working conditions is established in the working condition feature recognition layer. The current operating condition type is determined by the degree of matching between real-time parameters and the feature matrix.

[0018] In the pressure change trend prediction layer, a phased prediction algorithm is adopted to establish short-term, medium-term and long-term prediction sub-models for the outlet pressure in the next 5 seconds, 15 seconds and 30 seconds, respectively.

[0019] Three levels of warning thresholds are set in the pressure deviation warning layer, i.e., warning thresholds. When the predicted deviation reaches different thresholds, the corresponding warning signal is triggered.

[0020] The prediction model was iteratively trained using six consecutive months of actual operating data.

[0021] Furthermore, the steps for obtaining the required control adjustment amount based on the outlet pressure deviation value, the deviation development trend, and the dynamic characteristic parameters of the mixer include:

[0022] Deviation levels are classified according to the absolute value, rate of change, and trend of deviation. These deviation levels include minor deviation, gradually increasing deviation, sudden change deviation, and continuously accumulating deviation, namely, micro-deviation, gradual deviation, abrupt deviation, and cumulative deviation.

[0023] For different deviation levels and corresponding operating conditions, the corresponding strategies in the preset adjustment strategy library are activated: when the deviation is small, a single parameter fine-tuning mode is used; when the deviation is gradually increasing, a dual parameter coordinated adjustment mode is used; when the deviation changes suddenly, an emergency response mode including parameter adjustment priority is activated; when the deviation is continuously accumulating, a compensation adjustment mode including equipment status correction coefficient is activated.

[0024] Calculate the basic adjustment amount of each adjustment parameter, and perform cross correction by combining the coefficients of mutual influence between each parameter to obtain the corrected inlet steam flow rate adjustment range, the cooling water flow rate adjustment rate, and the pressure compensation opening degree of the mixing steam chamber, that is, the mixing steam chamber is the mixing chamber.

[0025] Based on the energy consumption coefficient under the current operating conditions, the modified adjustment parameters are optimized for energy efficiency to form the final control adjustment amount.

[0026] Furthermore, the steps for performing control operations include:

[0027] Establish a dynamic response model for the adjustment parameters to determine the sequential relationship between the steam flow rate adjustment entering the mixer, the cooling water flow rate adjustment, and the pressure compensation of the mixing steam cavity, i.e., the sequential relationship of the pressure compensation of the mixing steam cavity.

[0028] According to a pre-set time sequence, the opening degree adjustment instructions for the steam flow regulating valve entering the mixer, the opening degree adjustment instructions for the cooling water flow regulating valve, and the opening degree instructions for the pressure compensation valve of the mixing steam chamber are sent sequentially. Among them, the opening degree of the pressure compensation valve of the mixing steam chamber is in a step-like increasing relationship with the deviation level.

[0029] During the adjustment process, dynamic response data of various actuators are collected in real time, including valve action time and pressure change gradient;

[0030] Based on the difference between the dynamic response data and the pre-set counter-response standard, the execution parameters of subsequent adjustment commands are corrected in real time.

[0031] Further, multi-dimensional effect evaluation steps include:

[0032] An evaluation index system is established from four dimensions: pressure stability, regulation timeliness, energy loss rate, and equipment wear degree.

[0033] Calculate the fluctuation range of the outlet pressure after adjustment, the time to reach a steady state, the steam consumption per unit pressure adjustment, and the number of valve actuations.

[0034] The various indicators are weighted according to their pre-set importance to obtain the comprehensive pressure control quality assessment value;

[0035] Set acceptable ranges for evaluation values ​​under different working conditions. When the evaluation value is within the acceptable range, the adjustment is considered effective.

[0036] Further, the steps for dynamic feedback optimization include:

[0037] When the pressure control quality assessment value is within the qualified range, the matching relationship between the current operating condition characteristics and the adjustment strategy is extracted and stored in the optimal strategy library;

[0038] When the assessment value of pressure control quality is lower than the lower limit of the qualified range, the root cause tracing analysis process is initiated, and the dominant factors affecting pressure control are identified through parameter sensitivity analysis.

[0039] Adjust the calculation logic of the control adjustment amount according to the type of dominant factor, including correcting the coefficients of mutual influence between various parameters, optimizing the adjustment sequence, and adjusting the compensation weight;

[0040] Establish a strategy iteration model. After every n effective adjustments, perform an overall optimization of the control logic to gradually reduce the long-term control error. Here, n∈[80,120], and is generally defaulted to 100.

[0041] A control system for the outlet steam pressure of a steam jet mixer, the control system comprising:

[0042] The parameter acquisition module is used to acquire the real-time operating parameters and structural parameters of the steam jet mixer. The real-time operating parameters include the steam pressure entering the mixer, the steam flow rate entering the mixer, the flow rate of cooling water, the steam temperature entering the mixer, and the actual pressure at the mixer outlet. The structural parameters include the diameter of the nozzle for jetting steam, the length of the mixing steam chamber, and the tilt angle of the steam diffusion module.

[0043] The multi-layer prediction model module is used to build an outlet pressure prediction model based on the equipment's historical operating data. This model includes a working condition feature identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. By inputting real-time operating parameters and structural parameters into the prediction model, the predicted outlet pressure value and the trend graph of pressure change can be obtained.

[0044] The deviation analysis module is used to calculate the outlet pressure deviation based on the actual pressure at the mixer outlet and the predicted outlet pressure, and to judge the trend of deviation development by combining the pressure change trend graph.

[0045] The intelligent regulation calculation module is used to obtain the required control regulation amount based on the outlet pressure deviation value, the deviation development trend and the dynamic characteristic parameters of the mixer. The dynamic characteristic parameters of the mixer include pressure response time, flow regulation sensitivity and the coefficients of mutual influence between various parameters.

[0046] The collaborative execution module is used to generate control commands and execute control operations as needed, including a collaborative regulation mechanism for the steam flow rate entering the mixer based on the deviation type, the flow rate for cooling water, and the pressure compensation of the mixing steam chamber.

[0047] The multi-dimensional evaluation module is used to evaluate the actual pressure at the mixer outlet after the control operation is performed in multiple dimensions, and obtain the evaluation value of the pressure control quality.

[0048] The dynamic optimization module is used to dynamically optimize the control adjustment amount based on the evaluation value of the pressure control quality, and at the same time update the parameter weights of each module in the outlet pressure prediction model.

[0049] Furthermore, the multi-layer prediction model module includes:

[0050] The full-cycle data storage unit is used to store the full-cycle operation data of the past three years, including the equipment start-up and shutdown phase, stable operation phase, and load change phase, i.e., historical operation data;

[0051] The feature parameter extraction unit is used to extract features from the historical operating data of the equipment and identify key feature parameters such as the fluctuation frequency of the parameters entering the mixer, the pressure response lag time, and the correlation between the adjustment amount and pressure change.

[0052] The working condition identification unit is used to establish a feature matrix containing 12 typical working conditions in the working condition feature identification layer, and to determine the current operating condition type by the degree of matching between real-time parameters and the feature matrix.

[0053] The segmented prediction unit is used to employ a phased prediction algorithm in the pressure change trend prediction layer to establish short-term, medium-term, and long-term prediction sub-models for the outlet pressure in the next 5 seconds, 15 seconds, and 30 seconds, respectively.

[0054] The three-level early warning unit is used to set three levels of early warning thresholds in the pressure deviation early warning layer. When the predicted deviation reaches different thresholds, the corresponding early warning signal is triggered.

[0055] The model training unit is used to iteratively train the prediction model using six consecutive months of actual operating data.

[0056] Furthermore, the intelligent adjustment calculation module includes:

[0057] The deviation level classification unit is used to classify the deviation level according to the absolute value, rate of change and trend of deviation of the outlet pressure deviation value. The deviation level includes small deviation, gradually increasing deviation, sudden change deviation and continuous accumulation deviation.

[0058] The strategy matching unit is used to match the corresponding adjustment strategy from the preset adjustment strategy library for different deviation levels and corresponding working conditions.

[0059] The parameter correction unit is used to calculate the basic adjustment amount of each adjustment parameter, and perform cross correction by combining the coefficients of mutual influence between the parameters to obtain the corrected inlet steam flow rate adjustment range, the cooling water flow rate adjustment rate, and the pressure compensation opening degree of the mixing steam chamber.

[0060] The energy efficiency optimization unit is used to optimize the energy efficiency of the corrected adjustment parameters based on the energy consumption coefficient under the current operating conditions, and to form the final control adjustment amount.

[0061] Furthermore, the dynamic optimization module includes:

[0062] The optimal strategy storage unit is used to extract the matching relationship between the current operating condition characteristics and the adjustment strategy when the evaluation value of the pressure control quality is within the qualified range, and store it in the optimal strategy library.

[0063] The root cause analysis unit is used to initiate the root cause tracing analysis process when the pressure control quality assessment value is lower than the lower limit of the qualified range, and to identify the dominant factors affecting pressure control through parameter sensitivity analysis.

[0064] The logic adjustment unit is used to adjust the calculation logic of the control adjustment amount according to the type of the dominant factor, including correcting the coefficients of mutual influence between various parameters, optimizing the adjustment sequence, and adjusting the compensation weight.

[0065] The iterative optimization unit is used to establish a strategy iterative model. After every 100 effective adjustments, the control logic is optimized as a whole to gradually reduce long-term control errors.

[0066] This invention provides a method and system for controlling the outlet steam pressure of a steam jet mixer, which has the following advantages:

[0067] 1. First, an outlet pressure prediction model is constructed, comprising a working condition feature identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. This model reduces prediction errors through iterative training by collecting full-cycle operating data from the past three years. Furthermore, by inputting real-time operating parameters and structural parameters into the prediction model, the predicted outlet pressure value and its trend graph can be obtained in advance. Based on this, control adjustment quantities are obtained by combining the outlet pressure deviation value and its development trend, making the adjustment more predictive. Second, through this process from prediction to deviation analysis to precise adjustment, blind adjustment is effectively reduced, bringing the outlet pressure closer to the target value and significantly improving control accuracy. In addition, based on the absolute value, rate of change, and development trend of the outlet pressure deviation, the deviation is divided into four levels: small deviation, gradually increasing deviation, sudden change deviation, and continuously accumulating deviation. Simultaneously, a feature matrix of typical working conditions is established in the working condition feature identification layer, and the current working condition type is determined through real-time parameter matching. For different deviation levels and working condition types, corresponding strategies are matched from the adjustment strategy library. This targeted adjustment model enables the system to flexibly respond to various complex working condition changes, comprehensively enhancing its adaptability to different operating conditions.

[0068] 2. During the acquisition of control adjustment quantities, the basic adjustment quantities of each adjustment parameter are calculated and cross-corrected. Then, the energy efficiency of the adjustment parameters is optimized in conjunction with the energy consumption coefficient under the current operating conditions. This is to reduce the steam consumption per unit pressure adjustment while ensuring control effectiveness, effectively improving energy utilization efficiency and optimizing energy consumption. Secondly, the control effect is evaluated from four aspects, including pressure stability and adjustment timeliness, through a multi-dimensional evaluation module to obtain an evaluation value of pressure control quality. When the evaluation value is within the qualified range, the matching relationship between the current operating conditions and the adjustment strategy is stored in the optimal strategy library. When the evaluation value is below the qualified range, the root cause analysis process is initiated to identify the dominant factors affecting control and adjust the calculation logic. At the same time, after a certain number of effective adjustments, the control logic is optimized as a whole. This continuous process from evaluation to feedback to iterative optimization enables the system to continuously improve its performance and maintain a good control state in the long term.

[0069] 3. Due to the adoption of a coordinated adjustment mechanism that compensates for the steam flow rate entering the mixer, the flow rate of cooling water, and the chamber pressure of the mixed steam; when sending control commands, the relevant valves are adjusted sequentially according to the preset timing sequence, and the dynamic response data of the actuators are collected in real time. The subsequent parameters are corrected based on the difference between the data and the preset standard. This multi-parameter coordinated adjustment and dynamic correction process avoids the limitations of single-parameter adjustment and reduces pressure fluctuations. As can be seen from actual operation, the fluctuation range of the outlet pressure after adjustment is significantly reduced, and the time to reach a stable state is significantly shortened, providing a reliable guarantee for the stable operation of subsequent production processes. Attached Figure Description

[0070] Figure 1 This is a simplified flowchart of the overall structure of the present invention;

[0071] Figure 2 This is a complete flowchart of the overall structure of the present invention. Detailed Implementation

[0072] 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.

[0073] Example: Please refer to Figure 1 This embodiment provides a method for controlling the outlet steam pressure of a steam jet mixer. The steps of the control method are as follows:

[0074] S1. Obtain the real-time operating parameters and structural parameters of the steam jet mixer. The real-time operating parameters include the steam pressure entering the mixer, the steam flow rate entering the mixer, the flow rate of cooling water, the steam temperature entering the mixer, and the actual pressure at the mixer outlet. The structural parameters include the diameter of the nozzle for jetting steam, the length of the mixing steam chamber, and the tilt angle of the steam diffusion module.

[0075] S2. Based on the historical operating data of the equipment, construct an outlet pressure prediction model that includes a working condition feature identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. By inputting real-time operating parameters and structural parameters into the prediction model, the predicted value of the outlet pressure and the trend graph of pressure change are obtained.

[0076] S3. Calculate the outlet pressure deviation value based on the actual pressure at the mixer outlet and the predicted outlet pressure value, and judge the trend of the deviation by combining the pressure change trend graph.

[0077] S4. Obtain the required control adjustment amount based on the outlet pressure deviation value, the deviation development trend and the mixer dynamic characteristic parameters. Among them, the mixer dynamic characteristic parameters include pressure response time, flow regulation sensitivity and the coefficients of mutual influence between various parameters.

[0078] S5. Generate control commands and execute control operations as needed, including a coordinated adjustment mechanism for the steam flow rate entering the mixer based on the deviation type, the flow rate for cooling water, and the pressure compensation of the mixing steam chamber.

[0079] S6. Evaluate the actual pressure at the mixer outlet after the control operation is performed in multiple dimensions to obtain the evaluation value of the pressure control quality.

[0080] S7. Based on the evaluation value of pressure control quality, perform dynamic feedback optimization of the control adjustment amount, and update the parameter weights of each module in the outlet pressure prediction model.

[0081] As shown in steps S1-S7 above, during the operation of the steam jet mixer, real-time operating parameters such as the steam pressure entering the mixer, the steam flow rate entering the mixer, the flow rate of cooling water, the steam temperature entering the mixer, and the actual pressure at the mixer outlet are obtained in real time through pressure detection devices, flow detection devices, and temperature detection devices installed on the inlet pipe, pressure detection devices on the outlet pipe, and flow detection devices on the cooling water pipe. At the same time, structural parameters such as the diameter of the jet steam nozzle, the length of the mixing steam cavity, and the tilt angle of the steam diffusion module are extracted from the mixer's design data and equipment introduction.

[0082] Complete operational data from the past three years, including equipment start-up, shutdown, stable operation, and sudden load changes, was collected. After processing this data, an outlet pressure prediction model was constructed, comprising a condition characteristic identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. Real-time operating parameters and structural parameters were input into this prediction model to obtain the predicted outlet pressure value and a trend graph of pressure changes. The actual pressure at the mixer outlet was subtracted from the predicted outlet pressure value to obtain the outlet pressure deviation value. Combined with the pressure change trend graph, the deviation was analyzed to determine whether it was gradually increasing, remaining stable, or gradually decreasing, thus determining the trend of deviation development. Based on the outlet pressure deviation value, the deviation development trend, and the dynamic characteristic parameters of the mixer, such as pressure response time, flow regulation sensitivity, and the coefficients of mutual influence between various parameters, the required control adjustment amount was calculated. Control commands were generated according to the required control adjustment amount, and control operations were implemented, including coordinated adjustment of the steam flow rate entering the mixer, the flow rate of cooling water, and the pressure compensation of the mixing steam cavity.

[0083] The actual pressure at the mixer outlet after the control operation is evaluated from multiple aspects, such as pressure stability and timely adjustment, to obtain an assessment value of pressure control quality. Based on this assessment value, the required control adjustment quantity is dynamically optimized, and the parameter weights of each module in the outlet pressure prediction model are updated. The formula for calculating the outlet pressure deviation is: Outlet pressure deviation = Actual pressure at the mixer outlet - Predicted outlet pressure value. The actual pressure at the mixer outlet is the instantaneous pressure value measured in real time by the pressure detection device in the outlet pipeline, and the predicted outlet pressure value is the pressure estimate value at the corresponding time calculated by the outlet pressure prediction model.

[0084] In a specific implementation process, the steps of constructing an outlet pressure prediction model based on the equipment's historical operating data, including a working condition characteristic identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer, include:

[0085] S201. Collect full-cycle operation data from the past three years, including equipment start-up and shutdown phases, stable operation phases, and load change phases, with the data volume for each phase not less than 30% of the total data volume;

[0086] S202. Extract features from the historical operating data of the equipment to identify key characteristic parameters such as the fluctuation frequency of parameters entering the mixer, pressure response lag time, and the degree of correlation between adjustment amount and pressure change.

[0087] S203. Establish a feature matrix containing 12 typical working conditions in the working condition feature recognition layer, and determine the current operating condition type by the degree of matching between real-time parameters and the feature matrix.

[0088] S204. A phased prediction algorithm is adopted in the pressure change trend prediction layer to establish short-term, medium-term and long-term prediction sub-models for the outlet pressure in the next 5 seconds, 15 seconds and 30 seconds, respectively.

[0089] S205. Set three-level early warning thresholds in the pressure deviation early warning layer. When the predicted deviation reaches different thresholds, the corresponding early warning signal is triggered.

[0090] S206. The prediction model is iteratively trained using six consecutive months of actual operating data.

[0091] As shown in steps S201-S206 above, collect full-cycle operation data covering the equipment start-up and shutdown phase, stable operation phase, and load change phase over the past three years. The data volume for each phase is no less than 30% of the total data volume. This data includes detailed parameters such as inlet steam pressure, inlet steam flow rate, cooling water flow rate, and outlet pressure for each phase. Process the collected historical operation data, use data cleaning technology to remove outliers, and then use feature extraction algorithms to extract key feature parameters such as the fluctuation frequency of the parameters entering the mixer (i.e., the number of times the parameters change per unit time), the pressure response lag time (i.e., the interval between the pressure changes after parameter adjustment), and the correlation between the adjustment amount and the pressure change (i.e., the amount of pressure change when the adjustment amount changes by a unit value).

[0092] In the operating condition feature identification layer, a feature matrix containing 12 typical operating conditions is established. Each operating condition corresponds to a set of parameter range combinations. By matching real-time parameters with this feature matrix, the matching degree is calculated to determine the current operating condition type. In the pressure change trend prediction layer, a phased prediction algorithm is adopted to construct short-term, medium-term, and long-term prediction sub-models for the outlet pressure in the next 5 seconds, 15 seconds, and 30 seconds, respectively. The short-term prediction sub-model is based on the parameter change trend of the most recent 10 seconds and predicts using a linear fitting algorithm. The medium-term prediction sub-model combines the operating data patterns of the past 1 minute and predicts using a moving average algorithm. The long-term prediction sub-model refers to the operating condition change patterns over a longer time range and predicts using a regression analysis algorithm.

[0093] In the pressure deviation early warning layer, three levels of early warning thresholds are set: Level 1 is ±2% of the target pressure, Level 2 is ±5%, and Level 3 is ±8%. When the predicted deviation reaches the corresponding threshold, the corresponding audible and visual early warning signal is triggered by the control system. The prediction model is iteratively trained using six consecutive months of actual operating data. By continuously adjusting the weight parameters in the prediction algorithm, the accuracy of the prediction model is improved. The prediction error is calculated as follows: Prediction Error = (Predicted Outlet Pressure Value - Actual Outlet Pressure) / Actual Outlet Pressure × 100%, where the predicted outlet pressure value is the pressure value output by the prediction model, and the actual outlet pressure is the real pressure value measured by the outlet pressure detection device.

[0094] In a specific implementation process, the steps for obtaining the required control adjustment amount based on the outlet pressure deviation value, the deviation development trend, and the dynamic characteristic parameters of the mixer include:

[0095] S401. Based on the absolute value, rate of change, and trend of deviation of the export pressure deviation, the deviation level is classified, including small deviation, gradually increasing deviation, sudden change deviation, and continuous accumulation deviation.

[0096] S402. For different deviation levels and corresponding operating conditions, activate the corresponding strategies in the preset adjustment strategy library: when the deviation is small, use the single parameter fine-tuning mode; when the deviation is gradually increasing, use the dual parameter coordinated adjustment mode; when the deviation changes suddenly, activate the emergency response mode that includes parameter adjustment priority; when the deviation is continuously accumulating, activate the compensation adjustment mode that includes equipment status correction coefficient.

[0097] S403. Calculate the basic adjustment amount of each adjustment parameter, and perform cross correction by combining the coefficients of mutual influence between each parameter to obtain the corrected inlet steam flow rate adjustment range, the cooling water flow rate adjustment rate, and the pressure compensation opening degree of the mixing steam cavity.

[0098] S404. Optimize the energy efficiency of the corrected adjustment parameters based on the energy consumption coefficient under the current working conditions to form the final control adjustment amount.

[0099] As shown in steps S401-S404 above, based on the absolute value, rate of change, and trend of the outlet pressure deviation, the deviation is divided into four levels: minor deviation, gradually increasing deviation, sudden change deviation, and continuously accumulating deviation. Minor deviation is defined as an absolute value less than 2% of the target pressure and a rate of change less than 0.01 MPa / minute; gradually increasing deviation is defined as an absolute value between 2% and 5% and a continuous increase at a rate of 0.01-0.05 MPa / minute; sudden change deviation is defined as an absolute value greater than 5% and a change exceeding 0.05 MPa within 10 seconds; continuously accumulating deviation is defined as a slow accumulation over a period of more than one hour with an absolute value gradually exceeding 5%. For different deviation levels and corresponding operating conditions, adjustments are made from preset settings... Select appropriate adjustment strategies from the strategy library. For example, when the deviation is small and the operation is stable, use a single-parameter fine adjustment mode, adjusting only the cooling water flow rate. When the deviation gradually increases and the operation is under load fluctuation, use a dual-parameter coordinated adjustment mode for both inlet steam flow rate and cooling water flow rate, with the two adjusting in opposite directions. Calculate the basic adjustment amount for each adjustment parameter. The basic adjustment amount is determined based on the ratio between the deviation value and the target pressure. For example, when the deviation is 3% of the target pressure, the basic adjustment amount is set to a 3% reduction in inlet steam flow rate. Then, combine the coefficients of mutual influence between various parameters for cross-correction to obtain the corrected adjustment parameters. The coefficients of mutual influence can be determined experimentally. For example, the influence coefficient of the change in inlet steam flow rate on the cooling water flow rate is 0.2.

[0100] Finally, based on the energy consumption coefficient under the current operating conditions (0.8 for high load and 0.3 for low load), the energy efficiency of the corrected adjustment parameters is optimized to determine the final control adjustment amount. The basic adjustment amount is corrected using the formula: Corrected adjustment amount = Basic adjustment amount × (1 + Parameter mutual influence coefficient), where the parameter mutual influence coefficient is the influence ratio between different parameters obtained through experiments. The energy efficiency optimized adjustment amount = Corrected adjustment amount × (1 - Energy consumption coefficient × 0.1), where the energy consumption coefficient is a value between 0 and 1 set according to the energy consumption under different operating conditions.

[0101] In a specific implementation process, the steps for performing control operations include:

[0102] S501. Establish a dynamic response model for the adjustment parameters and determine the sequential relationship between the actions of steam flow rate adjustment entering the mixer, cooling water flow rate adjustment, and pressure compensation of the mixing steam chamber.

[0103] S502. According to the preset timing sequence, the opening degree adjustment command of the steam flow regulating valve entering the mixer, the opening degree adjustment command of the cooling water flow regulating valve, and the opening degree command of the pressure compensation valve of the mixing steam chamber are sent sequentially. Among them, the opening degree of the pressure compensation valve of the mixing steam chamber is in a step-like increasing relationship with the deviation level.

[0104] S503. During the adjustment process, collect dynamic response data of various actuators in real time, including valve action time and pressure change gradient;

[0105] S504. Based on the difference between the dynamic response data and the pre-set counter-response standard, the execution parameters of subsequent adjustment commands are adjusted in real time. Further, the multi-dimensional effect evaluation steps include:

[0106] S505. An evaluation index system is established from four dimensions: pressure stability, regulation timeliness, energy loss rate, and equipment wear degree.

[0107] S506. Calculate the fluctuation range of the outlet pressure after adjustment, the time to reach a steady state, the steam consumption per unit pressure adjustment, and the number of valve actions.

[0108] S507. Weight the various indicators according to the pre-set importance to obtain the comprehensive pressure control quality assessment value;

[0109] S508. Set the acceptable range of the evaluation value under different working conditions. When the evaluation value is within the acceptable range, the adjustment is deemed effective.

[0110] As shown in steps S501-S508 above, a dynamic response model is established. By analyzing the speed and degree of the impact of each parameter adjustment on the outlet pressure, the order of action of steam flow rate regulation entering the mixer taking precedence over cooling water flow rate regulation and pressure compensation of the mixing steam cavity after the two is determined. The response time of steam flow rate regulation on pressure is 5 seconds, cooling water flow rate regulation is 10 seconds, and pressure compensation is 15 seconds.

[0111] Following the aforementioned sequence, instructions are sent sequentially to adjust the opening degree of the steam flow regulating valve entering the mixer, the cooling water flow regulating valve, and the pressure compensation valve of the mixing steam chamber. The opening degree of the pressure compensation valve of the mixing steam chamber increases in a stepwise manner with the deviation level: 10% opening for minor deviations, 20% for gradually increasing deviations, 30% for sudden changes in deviations, and 40% for continuously accumulating deviations. The valve opening degree is achieved by controlling the rotation angle of the valve motor. During the adjustment process, dynamic response data of various actuators are collected in real time, such as valve action time: the time from receiving the instruction to reaching the specified opening degree. Pressure change gradient: The amount of pressure change per unit time, etc. These data are compared with the preset response standard. For example, if the valve action time is no more than 5 seconds, the pressure change gradient is 0.02-0.05 MPa / second. If there is a difference, the execution parameters of subsequent adjustment commands are corrected in real time by the control system. For example, if the valve action time is too long, the drive current is increased to speed up the action. The valve opening degree is calculated as follows: Valve opening degree = Percentage corresponding to the deviation level × (1 + Pressure change gradient × 0.05), where the percentage corresponding to the deviation level is the basic opening ratio set for each deviation level, and the pressure change gradient is the actual measured pressure change rate.

[0112] In a specific implementation process, the steps of dynamic feedback optimization include:

[0113] S701. When the evaluation value of pressure control quality is within the qualified range, extract the matching relationship between the current operating condition characteristics and the adjustment strategy, and store it in the optimal strategy library.

[0114] S702. When the assessment value of pressure control quality is lower than the lower limit of the qualified range, the root cause tracing analysis process is initiated, and the dominant factors affecting pressure control are determined through parameter sensitivity analysis.

[0115] S703. Adjust the calculation logic of the control adjustment amount according to the type of the dominant factor, including correcting the coefficients of mutual influence between various parameters, optimizing the adjustment sequence, and adjusting the compensation weight;

[0116] S704. Establish a strategy iteration model. After every 100 effective adjustments, perform an overall optimization of the control logic to gradually reduce long-term control errors.

[0117] As shown in steps S701-S704 above, an evaluation index system is established from four dimensions: pressure stability, regulation timeliness, energy loss rate, and equipment wear degree. Pressure stability is measured by the pressure fluctuation amplitude within 10 minutes after adjustment, with a higher score for smaller fluctuation amplitude; regulation timeliness is measured by the time required from detecting deviation to pressure stabilization, with a higher score for shorter time; energy loss rate is measured by the steam consumption per unit pressure regulation, with a higher score for lower consumption; and equipment wear degree is measured by the number of valve actions, with a higher score for fewer actions. The fluctuation amplitude of outlet pressure after adjustment, the time to reach a stable state, the steam consumption per unit pressure regulation, and the number of valve actions are calculated and weighted according to the following weights: pressure stability (40%), regulation timeliness (30%), energy consumption ratio (20%), and equipment wear degree (10%). The comprehensive pressure control quality evaluation value is obtained, with each index having a maximum score of 100 points.

[0118] The evaluation values ​​are set within acceptable ranges for different operating conditions. The acceptable range is 80-100 points for stable operation, 70-100 points for load fluctuation, and 60-100 points for equipment start-up and shutdown. When the evaluation value is within the acceptable range, the adjustment is deemed effective. The formula involved is: Comprehensive evaluation value = Pressure stability score × 40% + Adjustment timeliness score × 30% + Energy consumption ratio score × 20% + Equipment wear score × 10%. The scores for each item are calculated based on the comparison between actual measurement data and standard data.

[0119] A control system for the outlet steam pressure of a steam jet mixer, the control system comprising:

[0120] The parameter acquisition module is used to acquire the real-time operating parameters and structural parameters of the steam jet mixer. The real-time operating parameters include the steam pressure entering the mixer, the steam flow rate entering the mixer, the flow rate of cooling water, the steam temperature entering the mixer, and the actual pressure at the mixer outlet. The structural parameters include the diameter of the nozzle for jetting steam, the length of the mixing steam chamber, and the tilt angle of the steam diffusion module.

[0121] The multi-layer prediction model module is used to construct an outlet pressure prediction model based on the equipment's historical operating data. This model includes a working condition characteristic identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. Real-time operating parameters and structural parameters are input into this prediction model to obtain the predicted outlet pressure value and a trend graph of pressure changes. The multi-layer prediction model module includes:

[0122] The full-cycle data storage unit is used to store the full-cycle operation data of the past three years, including the equipment start-up and shutdown phase, stable operation phase, and load change phase;

[0123] The feature parameter extraction unit is used to extract features from the historical operating data of the equipment and identify key feature parameters such as the fluctuation frequency of the parameters entering the mixer, the pressure response lag time, and the correlation between the adjustment amount and pressure change.

[0124] The working condition identification unit is used to establish a feature matrix containing 12 typical working conditions in the working condition feature identification layer, and to determine the current operating condition type by the degree of matching between real-time parameters and the feature matrix.

[0125] The segmented prediction unit is used to employ a phased prediction algorithm in the pressure change trend prediction layer to establish short-term, medium-term, and long-term prediction sub-models for the outlet pressure in the next 5 seconds, 15 seconds, and 30 seconds, respectively.

[0126] The three-level early warning unit is used to set three levels of early warning thresholds in the pressure deviation early warning layer. When the predicted deviation reaches different thresholds, the corresponding early warning signal is triggered.

[0127] The model training unit is used to iteratively train the prediction model using six consecutive months of actual operating data.

[0128] The deviation analysis module is used to calculate the outlet pressure deviation based on the actual pressure at the mixer outlet and the predicted outlet pressure, and to judge the trend of deviation development by combining the pressure change trend graph.

[0129] The intelligent regulation calculation module is used to obtain the required control regulation amount based on the outlet pressure deviation value, the deviation development trend, and the mixer's dynamic characteristic parameters. These dynamic characteristic parameters include pressure response time, flow regulation sensitivity, and coefficients of interaction between various parameters. The intelligent regulation calculation module includes:

[0130] The deviation level classification unit is used to classify the deviation level according to the absolute value, rate of change and trend of deviation of the outlet pressure deviation value. The deviation level includes small deviation, gradually increasing deviation, sudden change deviation and continuous accumulation deviation.

[0131] The strategy matching unit is used to match the corresponding adjustment strategy from the preset adjustment strategy library for different deviation levels and corresponding working conditions.

[0132] The parameter correction unit is used to calculate the basic adjustment amount of each adjustment parameter, and perform cross correction by combining the coefficients of mutual influence between the parameters to obtain the corrected inlet steam flow rate adjustment range, the cooling water flow rate adjustment rate, and the pressure compensation opening degree of the mixing steam chamber.

[0133] The energy efficiency optimization unit is used to optimize the energy efficiency of the corrected adjustment parameters based on the energy consumption coefficient under the current operating conditions, and to form the final control adjustment amount.

[0134] The collaborative execution module is used to generate control commands and execute control operations as needed, including a collaborative regulation mechanism for the steam flow rate entering the mixer based on the deviation type, the flow rate for cooling water, and the pressure compensation of the mixing steam chamber.

[0135] The multi-dimensional evaluation module is used to evaluate the actual pressure at the mixer outlet after the control operation is performed in multiple dimensions, and obtain the evaluation value of the pressure control quality.

[0136] The dynamic optimization module is used to dynamically optimize the control adjustment quantity based on the evaluation value of pressure control quality, and simultaneously update the parameter weights of each module in the outlet pressure prediction model; the dynamic optimization module includes:

[0137] The optimal strategy storage unit is used to extract the matching relationship between the current operating condition characteristics and the adjustment strategy when the evaluation value of the pressure control quality is within the qualified range, and store it in the optimal strategy library.

[0138] The root cause analysis unit is used to initiate the root cause tracing analysis process when the pressure control quality assessment value is lower than the lower limit of the qualified range, and to identify the dominant factors affecting pressure control through parameter sensitivity analysis.

[0139] The logic adjustment unit is used to adjust the calculation logic of the control adjustment amount according to the type of the dominant factor, including correcting the coefficients of mutual influence between various parameters, optimizing the adjustment sequence, and adjusting the compensation weight.

[0140] The iterative optimization unit is used to establish a strategy iterative model. After every 100 effective adjustments, the control logic is optimized as a whole, so that the long-term control error is gradually reduced.

[0141] In the specific implementation process, when the evaluation value of pressure control quality is within the qualified range, the matching relationship between the current operating condition characteristics (such as inlet steam pressure, flow rate, temperature and other parameter values) and the adjustment strategy (adjusted parameters and adjustment amount) is extracted and stored in the optimal strategy library database of the control system. This database adopts a relational database structure, which is convenient for subsequent query and call.

[0142] When the assessed value is lower than the lower limit of the qualified range, the root cause tracing analysis process is initiated. By comparing the differences of various parameters under normal and abnormal operating conditions, and combining the results of parameter sensitivity analysis, the dominant factors affecting pressure control are identified, such as sensor measurement errors, actuator aging, and changes in parameter coupling relationships. Based on the type of dominant factor, the calculation logic of the control adjustment amount is adjusted. For example, when there is a sensor measurement error, the parameter measurement value is corrected; when the actuator is aging, the adjustment amount is increased; when the parameter coupling relationship changes, the parameter mutual influence coefficient is corrected, and the adjustment sequence is optimized. After every 100 effective adjustments, the control logic is optimized as a whole. By analyzing the adjustment data of the first 100 adjustments, the average adjustment effect of each parameter is calculated, and the adjustment strategy and parameter settings are improved to gradually reduce the long-term control error. The formula involved is: Control logic optimization coefficient = 1 - (Average control error of the first 100 adjustments × 0.01), where the average control error of the first 100 adjustments is the arithmetic mean of the control errors of each of the first 100 adjustments. This coefficient is used to correct the adjustment range in subsequent control logic.

[0143] The parameter acquisition module consists of sensors and a data processing component. The sensors include an inlet steam pressure sensor, an inlet steam flow sensor, an inlet steam temperature sensor, an outlet pressure sensor, and a cooling water flow sensor. The data processing component uses a PLC controller to filter, amplify, and classify the data collected by the sensors, and extracts structural parameters from the mixer design document to provide accurate data for subsequent stages. The multi-layer prediction model module includes a full-cycle data storage unit, a feature parameter extraction unit (software module for running feature extraction algorithms), a working condition identification unit (database storing feature matrices of 12 typical working conditions and matching algorithm module), a segmented prediction unit (module for running short-term, medium-term, and long-term prediction algorithms), a three-level early warning unit (early warning threshold setting and signal triggering module), and a model training unit (model parameter adjustment and training algorithm module), respectively realizing data storage, feature extraction, and working condition identification. The system includes functions for pressure prediction, early warning signals, and model training; a deviation comprehensive analysis module consisting of an operational deviation calculation algorithm and an operational trend judgment algorithm, which calculates deviation values ​​and analyzes the deviation development trend through the slope and curvature of trend graphs; an intelligent regulation calculation module including a deviation level classification unit (deviation level judgment algorithm and standards), a strategy matching unit (regulation strategy library database and matching algorithm), a parameter correction unit (basic regulation calculation and correction algorithm), and an energy efficiency optimization unit (energy consumption coefficient table and optimization algorithm), used to determine the regulation amount that needs to be controlled; and a collaborative execution module consisting of an instruction conversion unit (module that converts regulation amount into actuator electrical signals), an instruction generation unit (module that generates control instructions sequentially), a synchronous execution unit (PLC output module that sends instructions), and a feedback monitoring unit (input module that receives actuator feedback signals), which completes the conversion, generation, sending, and execution feedback of control instructions.

[0144] The multi-dimensional evaluation module includes a sensor and data transmission module for collecting evaluation index data and a module for running evaluation value calculation algorithms. It evaluates the control effect from multiple aspects and obtains evaluation values. The dynamic optimization module consists of a database for storing optimal strategies, a module for running root cause analysis algorithms, a module for adjusting control logic parameters, and a module for running iterative optimization algorithms. It realizes the functions of strategy storage, root cause analysis, logic adjustment, and iterative optimization.

[0145] The full-cycle data storage unit uses solid-state drives to store full-cycle operation data covering the past three years, including the equipment start-up and shutdown phase (daily operation data during equipment start-up and shutdown), the stable operation phase (continuous data during normal equipment operation), and the load surge phase (process data when load changes by more than 20%). The data is stored in a structured file format in chronological order.

[0146] The feature parameter extraction unit processes historical operating data using data analysis software running on an industrial control computer. It uses Fourier transform to extract the fluctuation frequency of inlet parameters, time series analysis to extract the pressure response delay time, and correlation analysis to extract key feature parameters such as the degree of correlation between regulation and pressure changes. The extraction results are stored in the form of a data table. The operating condition identification unit has a built-in feature matrix database of 12 typical operating conditions. Each operating condition includes the range values ​​of parameters such as inlet steam pressure, flow rate, and temperature. It uses the Euclidean distance algorithm to calculate the Euclidean distance between the real-time parameters and the parameters of each operating condition in the feature matrix. The smaller the distance, the higher the matching degree. This determines the type of current operating condition. When the matching degree is greater than 0.8, it is identified as the corresponding operating condition.

[0147] The segmented prediction unit employs a neural network-based algorithm. The short-term prediction sub-model takes the parameter data from the most recent 10 seconds as input, contains 3 hidden layers, and outputs the stress value for the next 5 seconds. The medium-term prediction sub-model takes the parameter data from the most recent 1 minute as input, contains 5 hidden layers, and outputs the stress value for the next 15 seconds. The long-term prediction sub-model takes the parameter data from the most recent 10 minutes as input, contains 7 hidden layers, and outputs the stress value for the next 30 seconds. Each sub-model is trained using a backpropagation algorithm. The three-level warning unit compares the prediction deviation value with the three-level warning threshold. When the first-level warning threshold is reached, a yellow light and a low-volume alarm are triggered; when the second-level warning threshold is reached, an orange light and a medium-volume alarm are triggered. When the level 3 warning threshold is reached, a red light and high volume alarm are triggered, and an alarm signal is sent to the control system. The model training unit acquires data from actual operation every day and uses the gradient descent algorithm to iteratively train the neural network weight parameters in the prediction model. Each training iteration is 1000 times to gradually reduce the prediction error and ensure prediction accuracy. The Euclidean distance matching degree is calculated as follows: Matching degree = 1 - (Euclidean distance between real-time parameters and feature matrix / maximum Euclidean distance), where the Euclidean distance between real-time parameters and feature matrix is ​​the Euclidean distance between the real-time parameter vector and the working condition parameter vector in the feature matrix, and the maximum Euclidean distance is the maximum Euclidean distance value between all working conditions.

[0148] The deviation level classification unit classifies deviations into four levels based on the absolute value, rate of change, and trend of the deviation, according to preset standards (such as the judgment criteria for minor deviations) and through program logic, and outputs level identifiers (numbers 1-4 correspond to the four levels respectively). The strategy matching unit has a built-in adjustment strategy library database. Each strategy in the database contains the deviation level, operating condition type, and corresponding adjustment model. Based on the deviation level identifier and the operating condition type output by the operating condition identification unit, the unit retrieves and matches the corresponding adjustment strategy through a database query statement. The retrieval uses an exact matching model.

[0149] The parameter correction unit calculates the basic adjustment amount based on the matched control strategy and the ratio of deviation value to adjustment amount. For example, for every 1% increase in deviation, the basic adjustment amount increases by 2%. Then, it performs cross-correction on the basic adjustment amount using the parameter coupling coefficient matrix (a 4×4 matrix containing the influence coefficients between parameters) to obtain the corrected control parameters. The parameter coupling coefficient matrix is ​​obtained by fitting experimental data. The energy efficiency optimization unit has a built-in energy consumption coefficient table for different operating conditions (e.g., 0.8 for high load, 0.5 for medium load, and 0.3 for low load). Based on the current operating condition, it queries the corresponding coefficient and optimizes the corrected control parameters to minimize energy consumption while meeting control requirements, thus determining the final control adjustment amount. The parameter coupling correction formula is: Corrected parameter = Basic adjustment amount + Σ(Other parameters × Coupling coefficient), where other parameters refer to other control parameter values ​​that affect the current parameter, and the coupling coefficient is the corresponding value in the parameter coupling coefficient matrix.

[0150] The optimal strategy storage unit uses MySQL database software. When the evaluation value is within the acceptable range, the current operating condition characteristics (represented as an array of parameter values) and the corresponding adjustment strategy (adjustment parameters and adjustment amounts) are stored as a record in the database. The database table structure includes operating condition parameter fields and adjustment strategy fields for easy subsequent querying and retrieval. When the evaluation value is below the acceptable range, the root cause analysis unit starts the analysis program. By comparing the average value, variance, and other statistical measures of parameters under normal and abnormal operating conditions, it identifies parameters with significant differences. Combined with the parameter sensitivity analysis results (parameters with high sensitivity are given priority), it determines the dominant factors affecting pressure control. For example, the parameter with the highest parameter sensitivity analysis coefficient is the dominant factor. The logic adjustment unit adjusts the calculation logic of the control adjustment amount according to the type of dominant factor. For example, when the dominant factor is sensor error, the compensation coefficient of the parameter measurement value is corrected; when the dominant factor is actuator aging, the adjustment is increased. The correction coefficients for the quantity are adjusted, and the corresponding coefficients in the parameter coupling coefficient matrix are also adjusted to optimize the order of parameter adjustments, prioritizing the adjustment of parameters with greater impact. The iterative optimization unit establishes a strategy iterative model. After every 100 effective adjustments, the control logic is optimized as a whole. By calculating the average adjustment error and adjustment time of each parameter in the first 100 adjustments, a genetic algorithm is used to optimize the weight parameters and thresholds in the control logic, so that the long-term control error is gradually reduced. After each optimization, the control logic is tested and verified to ensure the optimization effect. Among them, the parameter sensitivity analysis coefficient = (parameter change / initial parameter value) / (pressure change / initial pressure value), where the parameter change refers to the change in the adjustment parameter, the initial parameter value is the parameter value before adjustment, the pressure change is the change in pressure after adjustment, and the initial pressure value is the pressure value before adjustment. The larger the coefficient, the more sensitive the parameter is to the influence of pressure.

[0151] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. 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.

[0152] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0153] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes 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.

Claims

1. A method for controlling the outlet steam pressure of a steam jet mixer, characterized in that, The control method involves the following steps: Obtain the real-time operating parameters and structural parameters of the steam jet mixer. The real-time operating parameters include inlet steam pressure, inlet steam flow rate, cooling water flow rate, inlet steam temperature, and actual outlet pressure. An outlet pressure prediction model is constructed based on historical operating data of the equipment. Real-time operating parameters and structural parameters are input into the prediction model to obtain the predicted outlet pressure value. The outlet pressure prediction model, constructed based on historical operating data, includes a working condition characteristic identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. Collect historical operating data covering equipment start-up and shutdown phases, stable operation phases, and load change phases; extract features from historical operating data to identify key characteristic parameters, including the frequency of inlet parameter fluctuations, pressure response lag time, and the correlation strength between adjustment amount and pressure change; establish a feature matrix containing typical operating conditions in the operating condition feature identification layer, and determine the current operating condition type by the matching degree between real-time parameters and the feature matrix. In the pressure change trend prediction layer, a segmented prediction algorithm is adopted to establish short-term, medium-term and long-term prediction sub-models for outlet pressure; in the pressure deviation early warning layer, three-level early warning thresholds are set, and corresponding early warning signals are triggered when the prediction deviation reaches different thresholds; the model is iteratively trained using actual operating data; The deviation value of export pressure is calculated based on the actual export pressure and the predicted export pressure. The control adjustment amount is obtained based on the outlet pressure deviation value, the deviation development trend and the dynamic characteristic parameters of the mixer. Among them, the dynamic characteristic parameters of the mixer include pressure response time, flow regulation sensitivity and parameter coupling coefficient. Generate control commands based on the control adjustment amount and execute control operations; A multi-dimensional effect evaluation of the actual outlet pressure after the execution of control operations is conducted to obtain the pressure control quality evaluation value. The control adjustment is dynamically optimized based on the pressure control quality assessment value, and the hierarchical parameter weights of the outlet pressure prediction model are updated synchronously.

2. The method for controlling the outlet steam pressure of a steam jet mixer according to claim 1, characterized in that, The control adjustment values ​​are obtained based on the outlet pressure deviation, the deviation trend, and the dynamic characteristic parameters of the mixer, including: Deviation levels are classified based on the absolute value, rate of change, and trend of the deviation. These levels include minor deviations, gradual deviations, abrupt deviations, and cumulative deviations. For each deviation level and corresponding operating condition, a pre-defined adjustment strategy from the library is activated: for minor deviations, a single-parameter fine-tuning mode is used; for gradual deviations, a dual-parameter coordinated adjustment mode is used; for abrupt deviations, an emergency response mode with parameter adjustment priorities is activated; and for cumulative deviations, a compensation adjustment mode with equipment condition correction coefficients is activated. Calculate the basic adjustment amount of each adjustment parameter, and perform cross correction by combining the parameter coupling coefficient to obtain the corrected adjustment range of inlet steam flow, cooling water flow rate, and mixing chamber pressure compensation opening degree.

3. The method for controlling the outlet steam pressure of a steam jet mixer according to claim 1, characterized in that, Performing control operations includes: A dynamic response model for the adjustment parameters is established to determine the timing relationship between the inlet steam flow regulation, cooling water flow regulation, and mixing chamber pressure compensation. The opening degree adjustment commands for the inlet steam regulating valve, cooling water regulating valve, and mixing chamber pressure compensation valve are sent sequentially according to a preset timing sequence. The opening degree of the mixing chamber pressure compensation valve increases in a stepwise manner with the deviation level. Dynamic response data of each actuator are collected in real time during the adjustment process. Based on the difference between the dynamic response data and the preset response standard, the execution parameters of subsequent adjustment commands are corrected in real time.

4. The method for controlling the outlet steam pressure of a steam jet mixer according to claim 1, characterized in that, Multi-dimensional performance evaluation includes: An evaluation index system is established from four dimensions: pressure stability, regulation timeliness, energy loss rate, and equipment wear degree. The fluctuation range of outlet pressure after regulation, the time to reach a stable state, the steam consumption per unit pressure regulation, and the number of valve actions are calculated. The various indicators are weighted according to preset weights to obtain a comprehensive pressure control quality evaluation value. The qualified range of the evaluation value under different operating conditions is set. When the evaluation value is within the qualified range, the regulation is deemed effective.

5. The method for controlling the outlet steam pressure of a steam jet mixer according to claim 1, characterized in that, Dynamic feedback optimization includes: When the pressure control quality assessment value is within the acceptable range, the matching relationship between the current operating condition characteristics and the adjustment strategy is extracted and stored in the optimal strategy library; when the pressure control quality assessment value is lower than the lower limit of the acceptable range, the root cause analysis process is initiated, and the dominant factors affecting pressure control are determined through parameter sensitivity analysis. The calculation logic of the control adjustment amount is adjusted according to the type of dominant factor, including correcting the parameter coupling coefficient, optimizing the adjustment timing, and adjusting the compensation weight.

6. A control system for the outlet steam pressure of a steam jet mixer, characterized in that, The control system includes: The parameter acquisition module is used to acquire the real-time operating parameters and structural parameters of the steam jet mixer. The real-time operating parameters include inlet steam pressure, inlet steam flow rate, cooling water flow rate, inlet steam temperature, and actual outlet pressure. The multi-layer prediction model module is used to construct an outlet pressure prediction model based on historical operating data, which includes a working condition characteristic identification layer, a pressure change trend prediction layer, and a pressure deviation early warning layer. The predicted outlet pressure value is obtained by inputting real-time operating parameters and structural parameters into the prediction model. The multi-layer prediction model module includes: A full-cycle data storage unit is used to cover historical operational data, including equipment start-up and shutdown phases, stable operation phases, and load surge phases. The feature parameter extraction unit is used to extract features from historical operating data and identify key feature parameters, including the frequency of fluctuation of inlet parameters, pressure response lag time, and the correlation strength between regulation amount and pressure change. The working condition identification unit is used to establish a feature matrix containing typical working conditions in the working condition feature identification layer, and to determine the current operating condition type by the matching degree between real-time parameters and the feature matrix. The segmented prediction unit is used to establish short-term, medium-term, and long-term prediction sub-models for outlet pressure by employing a segmented prediction algorithm in the pressure change trend prediction layer. The three-level early warning unit is used to set three levels of early warning thresholds in the pressure deviation early warning layer. When the predicted deviation reaches different thresholds, the corresponding early warning signal is triggered. The deviation analysis module is used to calculate the deviation value of the export pressure based on the actual export pressure and the predicted export pressure. The intelligent regulation calculation module is used to obtain the control regulation amount based on the outlet pressure deviation value, the deviation development trend and the dynamic characteristic parameters of the mixer. The dynamic characteristic parameters of the mixer include pressure response time, flow regulation sensitivity and parameter coupling coefficient. The collaborative execution module is used to generate control commands and execute control operations based on the control adjustment amount. The control operations include a collaborative adjustment mechanism based on deviation type of inlet steam flow, cooling water flow and mixing chamber pressure compensation. The multi-dimensional evaluation module is used to evaluate the actual outlet pressure after the execution of control operations from multiple dimensions, and obtain the pressure control quality evaluation value. The dynamic optimization module is used to dynamically optimize the control adjustment quantity based on the pressure control quality assessment value, and synchronously update the hierarchical parameter weights of the outlet pressure prediction model.

7. A control system for the outlet steam pressure of a steam jet mixer according to claim 6, characterized in that, The intelligent regulation calculation module includes: The deviation level classification unit is used to classify the deviation level according to the absolute value, rate of change and trend of deviation of the outlet pressure deviation value. The deviation level includes micro deviation, gradual deviation, abrupt deviation and cumulative deviation. The strategy matching unit is used to match the corresponding adjustment strategy from the preset adjustment strategy library for different deviation levels and corresponding working conditions. The parameter correction unit is used to calculate the basic adjustment amount of each adjustment parameter, and perform cross correction in combination with the parameter coupling coefficient to obtain the corrected inlet steam flow rate adjustment range, cooling water flow rate adjustment rate and mixing chamber pressure compensation opening degree.

8. A control system for the outlet steam pressure of a steam jet mixer according to claim 6, characterized in that, The dynamic optimization module includes: The optimal strategy storage unit is used to extract the matching relationship between the current operating condition characteristics and the adjustment strategy when the pressure control quality assessment value is in the qualified range, and store it in the optimal strategy library. The root cause analysis unit is used to initiate the root cause analysis process when the pressure control quality assessment value is lower than the lower limit of the qualified range, and to identify the dominant factors affecting pressure control through parameter sensitivity analysis. The logic adjustment unit is used to adjust the calculation logic of the control adjustment amount according to the type of dominant factor, including correcting the parameter coupling coefficient, optimizing the adjustment timing, and adjusting the compensation weight.

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