Method and system for controlling outlet steam pressure of steam jet mixer

Abstract

The invention discloses a control method and system for outlet steam pressure of a steam jet type mixer, and relates to the technical field of steam jet type mixers.The control method comprises the following steps that real-time operation parameters and structure parameters of the steam jet type mixer are obtained; constructing an outlet pressure prediction model based on the historical operation data, and inputting the real-time operation parameters and the structure parameters into the prediction model to obtain an outlet pressure prediction value; the method is technically characterized by comprising the steps that an outlet pressure prediction model comprising a working condition feature recognition layer, a pressure change trend prediction layer and a pressure deviation early warning layer is constructed, and the model reduces prediction errors through iterative training by collecting full-cycle operation data of past three years; real-time operation parameters and structure parameters are input into the prediction model, so that an outlet pressure prediction value and a change trend graph can be obtained in advance; and on the basis, the control adjusting quantity is obtained by combining the outlet pressure deviation value and the development trend, so that the adjustment is more predictive.

Description

Technical Field

[0001] The present invention relates to the technical field of steam jet mixers, and in particular to a method and system for controlling the outlet steam pressure of a steam jet mixer. Background Art

[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 production requirements. During actual operation, the stability of the steam pressure at the mixer outlet directly affects the quality and efficiency of subsequent production processes.

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

[0004] In addition, the existing control methods lack the prediction of future pressure change trends and comprehensive evaluation of the regulation effects. They are unable to adopt targeted regulation strategies according to different working conditions, nor can they dynamically optimize the 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 prediction and optimization capabilities. Summary of the Invention

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for controlling the steam pressure at the outlet of a steam jet mixer, the control method comprises the following steps: Acquiring real-time operating parameters and structural parameters of the steam jet mixer, wherein the real-time operating parameters include the steam pressure entering the mixer, the steam flow entering the mixer, the flow rate used for 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 injecting steam, the length of the cavity for mixing steam, and the inclination angle of the steam diffusion module; Based on the historical operating data of the equipment, an outlet pressure prediction model is constructed, which includes an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer. The outlet pressure prediction value and the pressure change trend graph are obtained by inputting real-time operating parameters and structural parameters into the prediction model. Calculate the outlet pressure deviation value based on the actual pressure at the mixer outlet and the outlet pressure prediction value, and judge the deviation development direction based on the pressure change trend graph; 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. The dynamic characteristic parameters of the mixer include pressure response time, flow regulation sensitivity and the coefficient of mutual influence between various parameters, namely the parameter coupling coefficient. Generate control instructions and execute control operations according to the required control adjustment amount, the control operation including a coordinated adjustment mechanism based on the deviation type of the steam flow entering the mixer, the flow rate for cooling water and the pressure compensation of the cavity for mixing steam; Perform a multi-dimensional effect evaluation on the actual pressure at the mixer outlet after the control operation is performed to obtain an evaluation value of the pressure control quality; According to the evaluation value of the pressure control quality, dynamic feedback optimization is performed on the adjustment amount to be controlled, and the parameter weights of each module in the outlet pressure prediction model are updated at the same time.

[0006] Furthermore, the steps of constructing an outlet pressure prediction model including an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer based on the historical operating data of the equipment include: Collect full-cycle operation data from the past three years, including the equipment start-up and shutdown phases, stable operation phases, and load mutation phases. The data volume for each phase should not be less than 30% of the total data volume. The full-cycle operation data obtained is the historical operation data. Extract features from the equipment's historical operating data 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 the adjustment amount and pressure changes; In the working condition feature recognition layer, a feature matrix containing 12 typical working conditions is established, and the current operating condition type is determined by the degree of matching between real-time parameters and the feature matrix; In the pressure change trend prediction layer, a phased prediction algorithm is used 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; In the pressure deviation warning layer, three levels of warning thresholds are set, namely warning thresholds. When the predicted deviation reaches different thresholds, the corresponding warning signal is triggered; The prediction model is iteratively trained using six consecutive months of actual operating data.

[0007] Furthermore, the step of obtaining the required control adjustment amount according to the outlet pressure deviation value, the deviation development trend and the mixer dynamic characteristic parameters includes: Deviation levels are divided according to the absolute value of the outlet pressure deviation, the rate of change, and the direction of the deviation development. Deviation levels include small deviations, gradually increasing deviations, sudden changes, and continuously accumulating deviations, namely, micro-deviations, progressive deviations, sudden changes, and cumulative deviations. For different deviation levels and corresponding working conditions, the corresponding strategies in the preset adjustment strategy library are activated: when the deviation is small, the single parameter fine-tuning mode is adopted; when the deviation is gradually increasing, the dual parameter coordinated adjustment mode is adopted; when the deviation changes suddenly, the emergency response mode including the parameter adjustment priority is activated; when the deviation is continuously accumulating, the compensation adjustment mode including the equipment status correction coefficient is activated; Calculate the basic adjustment amount of each adjustment parameter, and make cross corrections based on the coefficients of mutual influence between the parameters to obtain the corrected inlet steam flow adjustment amplitude, the cooling water flow adjustment rate, and the pressure compensation opening degree of the mixed steam cavity, that is, the mixed steam cavity is the mixing chamber; The energy efficiency of the modified adjustment parameters is optimized according to the energy consumption coefficient under the current working conditions to form the final control adjustment amount.

[0008] Furthermore, the steps of performing the control operation include: Establish a dynamic response model of the adjustment parameters to determine the order of steam flow regulation entering the mixer, cooling water flow regulation, and pressure compensation of the mixing steam cavity, that is, the order of pressure compensation of the mixing steam cavity; According to a preset time sequence, an opening degree adjustment instruction for the steam flow control valve entering the mixer, an opening degree adjustment instruction for the flow control valve for cooling water, and an opening degree instruction for the pressure compensation valve of the mixed steam cavity are sequentially sent, wherein the opening degree of the pressure compensation valve of the mixed steam cavity is in a step-by-step increasing relationship with the deviation level; During the adjustment process, the dynamic response data of each actuator is collected in real time, including the time of valve action and the gradient of pressure change; According to the difference between the dynamic response data and the pre-set response standard, the execution parameters of the subsequent adjustment instructions are corrected in real time.

[0009] Further, multi-dimensional effect evaluation steps include: Establish an evaluation index system based on four dimensions: pressure stability, adjustment timeliness, energy loss rate, and equipment loss; Calculate the fluctuation range of outlet pressure after adjustment, the time to reach a stable state, the steam consumption per unit pressure adjustment and the number of valve operations; Perform weighted calculation on each indicator according to the pre-set importance to obtain the comprehensive pressure control quality assessment value; Set the qualified range of evaluation values ​​under different working conditions, and when the evaluation value is within the qualified range, it is determined that the adjustment is effective.

[0010] Further, the steps of dynamic feedback optimization include: When the evaluation value of the pressure control quality is within the qualified range, the matching relationship between the current working condition characteristics and the adjustment strategy is extracted and stored in the optimal strategy library; 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 to determine the dominant factors affecting pressure control through parameter sensitivity analysis; 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 timing, and adjusting the compensation weight; A policy iteration model is established, and the control logic is optimized as a whole after completing n effective adjustments, so that the long-term control error is gradually reduced. Among them, n∈[80, 120], generally defaulted to 100.

[0011] A control system for steam pressure at the outlet of a steam jet mixer, the control system comprising: a parameter acquisition module for acquiring real-time operating parameters and structural parameters of the steam jet mixer, wherein the real-time operating parameters include the steam pressure entering the mixer, the steam flow entering the mixer, the flow rate used for cooling water, the steam temperature entering the mixer, and the actual pressure at the mixer outlet; and the structural parameters include the diameter of the nozzle for injecting steam, the length of the cavity for mixing steam, and the inclination angle of the steam diffusion module; A multi-layer prediction model module is used to construct an outlet pressure prediction model based on the historical operating data of the equipment, which includes an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer. The prediction model is input with real-time operating parameters and structural parameters to obtain the outlet pressure prediction value and pressure change trend graph; Deviation comprehensive analysis module, used to calculate the outlet pressure deviation value based on the actual pressure at the mixer outlet and the outlet pressure prediction value, and judge the deviation development direction in combination with the pressure change trend graph; An intelligent adjustment amount calculation module is used to obtain the required control adjustment 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 coefficient of mutual influence between various parameters; A coordinated execution module is used to generate control instructions and execute control operations according to the required control adjustment amount, and the control operations include a coordinated adjustment mechanism based on the deviation type of the steam flow entering the mixer, the flow rate for cooling water, and the pressure compensation of the cavity for mixing steam; A multi-dimensional evaluation module is used to perform a multi-dimensional effect evaluation on the actual pressure at the mixer outlet after the control operation is performed to obtain an evaluation value of the pressure control quality; The dynamic optimization module is used to perform dynamic feedback optimization on the adjustment amount to be controlled according to the evaluation value of the pressure control quality, and update the parameter weights of each module in the outlet pressure prediction model.

[0012] Furthermore, the multi-layer prediction model module includes: 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 mutation phase, that is, historical operation data; The characteristic parameter extraction unit is used to extract characteristics of the historical operation data of the equipment and identify key characteristic parameters such as the fluctuation frequency of the parameters entering the mixer, the pressure response lag time, and the degree of correlation between the adjustment amount and the pressure change; The operating condition identification unit is used to establish a feature matrix containing 12 typical operating conditions in the operating condition feature identification layer, and determine the current operating condition type by the degree of matching between real-time parameters and the feature matrix; The segmented prediction unit is used to adopt 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; The three-level warning unit is used to set three-level warning thresholds in the pressure deviation warning layer, and trigger corresponding warning signals when the predicted deviation reaches different thresholds; The model training unit is used to iteratively train the prediction model using six consecutive months of actual operation data.

[0013] Furthermore, the intelligent adjustment amount calculation module includes: The deviation level classification unit is used to classify the deviation level according to the absolute value, change rate and development trend of the outlet pressure deviation. The deviation levels include small deviation, gradually increasing deviation, sudden change deviation and continuously accumulated deviation. A strategy matching unit is used to match corresponding regulation strategies from a preset regulation strategy library according to different deviation levels and corresponding working condition types; The parameter correction unit is used to calculate the basic adjustment amount of each adjustment parameter, and perform cross correction based on the coefficients of mutual influence between the parameters to obtain the corrected inlet steam flow adjustment amplitude, the cooling water flow adjustment rate, and the pressure compensation opening degree of the mixed steam cavity; The energy efficiency optimization unit is used to optimize the energy efficiency of the modified adjustment parameters according to the energy consumption coefficient under the current working conditions to form the final control adjustment amount.

[0014] Furthermore, the dynamic optimization module includes: The optimal strategy storage unit is used to extract the matching relationship between the current working 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; The root cause analysis unit is used to initiate the root cause tracing analysis process when the pressure control quality assessment value falls below the lower limit of the qualified range, and to determine 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 coefficients of mutual influence between various parameters, optimizing the adjustment timing, and adjusting the compensation weight; The iterative optimization unit is used to establish a strategy iteration model. After completing 100 effective adjustments, the control logic is optimized as a whole to gradually reduce the long-term control error.

[0015] The present invention provides a method and system for controlling the outlet steam pressure of a steam jet mixer, which has the following beneficial effects: First, an outlet pressure prediction model was constructed, consisting of an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer. This model collects full-cycle operating data from the past three years and uses iterative training to reduce prediction errors. Furthermore, by inputting real-time operating and structural parameters into the prediction model, the predicted outlet pressure value and change trend graph are obtained in advance. Based on this, the control adjustment variable is derived by combining the outlet pressure deviation value and its development trend, making the adjustment more predictive. Secondly, this process from prediction to deviation analysis to precise adjustment effectively reduces blind adjustments, brings the outlet pressure closer to the target value, and significantly improves control accuracy. Furthermore, based on the absolute value, change rate, and development trend of the outlet pressure deviation value, deviations are classified into four levels: small deviation, gradually increasing deviation, sudden deviation, and continuously accumulating deviation. Furthermore, a characteristic matrix of typical operating conditions is established in the operating condition feature recognition layer, and the current operating condition type is determined through real-time parameter matching. For different deviation levels and operating condition types, corresponding strategies are matched from a control strategy library. This targeted adjustment model enables the system to flexibly respond to various complex operating condition changes and comprehensively enhances its adaptability to different operating conditions.

[0016] 2. In the process of obtaining the control adjustment amount, the basic adjustment amount of each adjustment parameter is calculated and after cross-correction, the energy efficiency of the adjustment parameter will be optimized in combination with the energy consumption coefficient under the current operating conditions; so as to reduce the steam consumption of unit pressure adjustment while ensuring the control effect, effectively improve the energy utilization efficiency, and achieve energy consumption optimization; secondly, the control effect is evaluated from four aspects such as pressure stability and adjustment timeliness through a multi-dimensional evaluation module to obtain the evaluation value of the pressure control quality; and when the evaluation value is in 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 lower than the qualified range, the root cause analysis process is started to determine the dominant factors affecting the control and adjust the calculation logic; at the same time, after completing 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 own performance and maintain a good control state for a long time.

[0017] 3. Due to the adoption of a coordinated adjustment mechanism for the steam flow entering the mixer, the flow used for cooling water, and the cavity pressure compensation of the mixed steam; when sending control instructions, the relevant valves are adjusted in sequence according to the preset timing, and the dynamic response data of the actuators are collected in real time, and the subsequent parameters are corrected according to the difference between the data and the preset standards; this process of multi-parameter coordinated adjustment and dynamic correction avoids the limitations of single parameter adjustment and reduces pressure fluctuations; through actual operation, it can be seen that the fluctuation range of the outlet pressure after adjustment is significantly reduced, and the time to reach a stable state is significantly shortened, providing reliable guarantee for the stable operation of subsequent production processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A simplified flow chart of the overall structure of the present invention; Figure 2 It is a complete flow chart of the overall structure of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] Example: See Figure 1 This embodiment provides a method for controlling the steam pressure at the outlet of a steam jet mixer. The steps of the control method are as follows: S1. Acquiring real-time operating parameters and structural parameters of a steam jet mixer, wherein 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; and the structural parameters include the diameter of the steam jet nozzle, the length of the steam mixing cavity, and the inclination angle of the steam diffusion module; S2. Build an outlet pressure prediction model based on the historical operating data of the equipment, which includes an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer. Input real-time operating parameters and structural parameters into the prediction model to obtain the outlet pressure prediction value and pressure change trend graph; S3. Calculate the outlet pressure deviation value based on the actual pressure at the mixer outlet and the outlet pressure prediction value, and determine the deviation development trend based on the pressure change trend graph; S4. Obtaining a required control adjustment amount based on the outlet pressure deviation value, the deviation development trend, and the mixer dynamic characteristic parameters, wherein the mixer dynamic characteristic parameters include pressure response time, flow rate adjustment sensitivity, and the coefficient of mutual influence between various parameters; S5. Generate a control instruction and execute a control operation based on the required control adjustment amount, wherein the control operation includes a coordinated adjustment mechanism for the steam flow entering the mixer, the flow rate for cooling water, and the pressure compensation of the chamber for mixing steam based on the deviation type; S6. Perform a multi-dimensional effect evaluation on the actual pressure at the mixer outlet after the control operation is performed to obtain an evaluation value of the pressure control quality; S7. Dynamically feedback optimize the adjustment amount to be controlled based on the evaluation value of the pressure control quality, and update the parameter weights of each module in the outlet pressure prediction model.

[0021] As shown in the above steps S1-S7, when the steam jet mixer is in operation, the real-time operating parameters such as the steam pressure entering the mixer, the steam flow entering the mixer, the flow used for cooling water, the steam temperature entering the mixer, and the actual pressure at the mixer outlet are obtained in real time through the pressure detection device, flow detection device, temperature detection device installed in the inlet pipe, the pressure detection device in the outlet pipe, and the flow detection device in the cooling water pipeline. At the same time, the structural parameters such as the diameter of the nozzle for spraying steam, the length of the cavity for mixing steam, and the inclination angle of the steam diffusion module are extracted from the design data and equipment introduction of the mixer.

[0022] Complete operating data from the past three years, including equipment start-up and shutdown, stable operation, and sudden load changes, is collected. After processing this data, an outlet pressure prediction model is constructed, which includes an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer. Real-time operating parameters and structural parameters are input into the prediction model to obtain the outlet pressure prediction value and the pressure change trend graph; the outlet pressure deviation value is obtained by subtracting the outlet pressure prediction value from the actual pressure at the mixer outlet, and the pressure change trend graph is used to analyze whether the deviation is gradually increasing, remaining stable, or gradually decreasing, to determine the deviation development direction; based on the outlet pressure deviation value, the deviation development direction, and the dynamic characteristic parameters of the mixer, such as pressure response time, flow regulation sensitivity, and the coefficient of mutual influence between various parameters, the required control adjustment amount is calculated; control instructions are generated according to the required control adjustment amount, and control operations are implemented, including coordinated adjustment of the steam flow entering the mixer, the flow used for cooling water, and the pressure compensation of the mixed steam cavity.

[0023] The actual pressure at the mixer outlet after the control operation is executed is evaluated from multiple aspects such as pressure stability and adjustment timeliness to obtain an evaluation value of the pressure control quality. The adjustment amount to be controlled is then dynamically feedback optimized based on the evaluation value, and the parameter weights of each module in the outlet pressure prediction model are updated at the same time. The outlet pressure deviation numerical calculation formula is: outlet pressure deviation numerical value = actual pressure at the mixer outlet - outlet pressure prediction value, where the actual pressure at the mixer outlet is the instantaneous pressure value obtained by real-time measurement by the pressure detection device of the outlet pipeline, and the outlet pressure prediction value is the pressure estimate value at the corresponding moment calculated by the outlet pressure prediction model.

[0024] In a specific implementation process, the steps of constructing an outlet pressure prediction model including an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer based on historical operating data of the equipment include: S201. Collect full-cycle operation data from the past three years, including equipment startup and shutdown phases, stable operation phases, and load sudden change phases. The data volume for each phase should not be less than 30% of the total data volume. S202, extracting features from the historical operating data of the device to identify key characteristic parameters such as the fluctuation frequency of the parameters entering the mixer, the pressure response lag time, and the degree of correlation between the adjustment amount and the pressure change; S203, establishing a feature matrix containing 12 typical working conditions in the working condition feature recognition layer, and determining the current operating condition type based on the degree of matching between the real-time parameters and the feature matrix; S204. In the pressure change trend prediction layer, a phased prediction algorithm is used 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. S205. Setting three levels of warning thresholds in the pressure deviation warning layer, triggering corresponding warning signals when the predicted deviation reaches different thresholds; S206 , iteratively training the prediction model using actual operation data for six consecutive months.

[0025] As shown in the above steps S201-S206, full-cycle operation data covering the equipment start-up and shutdown stages, stable operation stages, and load mutation stages in the past three years are collected, and the data volume of each stage is not less than 30% of the total data volume. These data include detailed parameters such as the inlet steam pressure, inlet steam flow, cooling water flow, and outlet pressure of each stage; the collected historical operation data are processed, and data cleaning technology is used to remove outliers. Then, the key characteristic parameters such as the fluctuation frequency of the parameters entering the mixer (i.e., the number of parameter changes per unit time), the pressure response lag time (i.e., the interval time for the pressure to start changing after the parameter adjustment), and the degree of correlation between the adjustment amount and the pressure change (i.e., the change in pressure when the adjustment amount changes by unit value) are extracted through the feature extraction algorithm.

[0026] In the operating condition feature identification layer, a feature matrix containing 12 typical working conditions is established. Each operating condition corresponds to a set of parameter range combinations. The current operating condition type is determined by matching the real-time parameters with the feature matrix and calculating the matching degree. 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 in the last 10 seconds and is predicted by a linear fitting algorithm. The medium-term prediction sub-model is based on the operating data pattern of the past 1 minute and is predicted by a sliding average algorithm. The long-term prediction sub-model refers to the operating condition change pattern over a longer time range and uses a regression analysis algorithm for prediction.

[0027] In the pressure deviation warning layer, three levels of warning thresholds are set. The first level warning is when the deviation reaches ±2% of the target pressure, the second level warning is ±5%, and the third level warning is ±8%. When the predicted deviation reaches the corresponding threshold, the corresponding sound and light warning signal is triggered by the control system; the prediction model is iteratively trained with actual operation data for six consecutive months, and the accuracy of the prediction model is improved by continuously adjusting the weight parameters in the prediction algorithm. The prediction error calculation formula is: Prediction error = (predicted value of outlet pressure - actual outlet pressure) / actual outlet pressure × 100%, where the predicted value of outlet pressure 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.

[0028] In a specific implementation process, the step of obtaining the required control adjustment amount according to the outlet pressure deviation value, the deviation development trend and the mixer dynamic characteristic parameters includes: S401, classifying the deviation level according to the absolute value, change rate, and deviation development trend of the outlet pressure deviation value, wherein the deviation level includes slight deviation, gradually increasing deviation, sudden change deviation, and continuously accumulating deviation; S402. Activate corresponding strategies from a preset adjustment strategy library for different deviation levels and corresponding operating conditions: When the deviation is small, adopt a single parameter fine-tuning mode; when the deviation gradually increases, adopt a dual-parameter coordinated adjustment mode; when the deviation changes suddenly, activate an emergency response mode with parameter adjustment priority; when the deviation accumulates continuously, activate a compensation adjustment mode with an equipment status correction coefficient; S403. Calculate the basic adjustment amount of each adjustment parameter, perform cross correction based on the coefficients of mutual influence between the parameters, and obtain the corrected inlet steam flow adjustment amplitude, the cooling water flow adjustment rate, and the pressure compensation opening degree of the mixed steam cavity; S404: Optimize the energy efficiency of the corrected adjustment parameters according to the energy consumption coefficient under the current working conditions to form the final control adjustment amount.

[0029] As shown in the above steps S401-S404, according to the absolute value of the outlet pressure deviation, the rate of change and the development trend of the deviation, the deviation is divided into four levels: small deviation, gradually increasing deviation, sudden change deviation and continuously accumulated deviation. Among them, small deviation means that the absolute value is less than 2% of the target pressure and the rate of change is lower than 0.01MPa / minute; gradually increasing deviation means that the absolute value is between 2% and 5% and it continues to increase at a rate of 0.01-0.05MPa / minute; suddenly changing deviation means that the absolute value is greater than 5% and the change amplitude exceeds 0.05MPa within 10 seconds; continuously accumulated deviation means that it accumulates slowly over a period of more than 1 hour and the absolute value gradually exceeds 5%; according to different deviation levels and corresponding working conditions, the preset adjustment Select an appropriate regulation strategy from the regulation strategy library. For example, when there is a small deviation and the operation is stable, a single parameter fine adjustment mode is adopted, and only the cooling water flow is adjusted. When the deviation gradually increases and the load fluctuates, a dual-parameter coordinated adjustment mode of the inlet steam flow and the cooling water flow is adopted, and the two adjustment directions are opposite. Calculate the basic regulation amount of each regulation parameter. The basic regulation amount is determined according to the proportional relationship between the deviation value and the target pressure. For example, when the deviation is 3% of the target pressure, the basic regulation amount is set to a 3% reduction in the inlet steam flow. Then, cross-correction is performed based on the mutual influence coefficients between the various parameters to obtain the corrected regulation parameters. Among them, the mutual influence coefficients can be determined through experiments. For example, the influence coefficient of the inlet steam flow change on the cooling water flow is 0.2.

[0030] Finally, based on the energy consumption coefficient under the current operating conditions, which is 0.8 at high load and 0.3 at low load, the energy efficiency of the corrected adjustment parameters is optimized to determine the final control adjustment amount. The basic adjustment amount correction formula is: 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 adjustment amount after energy efficiency optimization = Corrected adjustment amount × (1-Energy consumption coefficient × 0.1), and the energy consumption coefficient is a value between 0 and 1 set according to the energy consumption under different operating conditions.

[0031] In a specific implementation process, the steps of performing the control operation include: S501, establishing a dynamic response model of adjustment parameters to determine the order of steam flow regulation entering the mixer, cooling water flow regulation, and pressure compensation of the cavity for mixing steam; S502: Sending, in accordance with a pre-set timing sequence, an opening degree adjustment instruction for the steam flow control valve entering the mixer, an opening degree adjustment instruction for the cooling water flow control valve, and an opening degree instruction for the pressure compensation valve of the mixed steam cavity, wherein the opening degree of the pressure compensation valve of the mixed steam cavity increases in a step-by-step manner with respect to the deviation level; S503. During the adjustment process, real-time dynamic response data of each actuator is collected, including valve action time and pressure change gradient; S504: According to the difference between the dynamic response data and the pre-set response standard, the execution parameters of the subsequent adjustment instructions are modified in real time. Further, the multi-dimensional effect evaluation step includes: S505. Establish an evaluation index system based on four dimensions: pressure stability, adjustment timeliness, energy loss rate, and equipment loss rate; S506. Calculate the fluctuation range of the outlet pressure after adjustment, the time to reach a stable state, the steam consumption per unit pressure adjustment, and the number of valve operations; S507, performing weighted calculation on each indicator according to a preset importance to obtain a comprehensive pressure control quality evaluation value; S508. Set qualified intervals for evaluation values ​​under different working conditions, and determine that the adjustment is effective when the evaluation value is within the qualified interval.

[0032] As shown in the above steps S501-S508, a dynamic response model is established. By analyzing the speed and degree of the impact of each parameter adjustment on the outlet pressure, it is determined that the steam flow rate adjustment entering the mixer takes precedence over the cooling water flow rate adjustment, and the pressure compensation of the mixed steam cavity is performed after the two. The response time of the steam flow rate adjustment on the pressure is 5 seconds, the cooling water flow rate adjustment is 10 seconds, and the pressure compensation is 15 seconds.

[0033] According to the above timing, the opening degree adjustment instructions of the steam flow control valve entering the mixer, the flow control valve for cooling water and the pressure compensation valve of the mixed steam cavity are sent in turn. The opening degree of the pressure compensation valve of the mixed steam cavity increases in a step-by-step manner with the deviation level. It opens 10% for small 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, the dynamic response data of each actuator is collected in real time, such as the 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. Compare these data with the pre-set response standards. For example, the valve action time does not exceed 5 seconds, and the pressure change gradient is 0.02-0.05MPa / second. If there is a difference, the execution parameters of the subsequent adjustment instructions are corrected in real time through the control system. For example, when the valve action time is too long, the drive current is increased to speed up the action speed. The valve opening degree is calculated as follows: valve opening degree = percentage corresponding to 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 actually measured pressure change rate.

[0034] In a specific implementation process, the steps of dynamic feedback optimization include: S701: When the evaluation value of the pressure control quality is within the qualified range, the matching relationship between the current working condition characteristics and the adjustment strategy is extracted and stored in the optimal strategy library; S702: When the pressure control quality assessment value is lower than the lower limit of the qualified range, the root cause tracing analysis process is initiated to determine the dominant factors affecting pressure control through parameter sensitivity analysis; S703, adjusting 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 timing, and adjusting the compensation weight; S704: Establish a strategy iteration model, and optimize the control logic as a whole after completing 100 effective adjustments, so as to gradually reduce the long-term control error.

[0035] As shown in the above steps S701-S704, an evaluation index system is established from four dimensions: pressure stability, adjustment timeliness, energy loss rate and equipment loss degree. Pressure stability is measured by the pressure fluctuation amplitude within 10 minutes after adjustment. The smaller the fluctuation amplitude, the higher the score; adjustment timeliness is measured by the time required from the discovery of deviation to pressure stabilization. The shorter the time, the higher the score; energy loss rate is measured by the steam consumption per unit pressure adjustment. The lower the consumption, the higher the score; equipment loss degree is measured by the number of valve actions. The fewer the number, the higher the score; the fluctuation amplitude of the outlet pressure after adjustment, the time to reach a stable state, the steam consumption per unit pressure adjustment and the number of valve actions are calculated, and weighted calculation is performed according to the weights of pressure stability (40%), adjustment timeliness (30%), energy consumption ratio (20%) and equipment loss (10%) to obtain a comprehensive pressure control quality evaluation value. The full score for each indicator is 100 points.

[0036] The qualified range of evaluation values ​​under different working conditions is set. The qualified range under stable operating conditions is 80-100 points, under load fluctuation conditions is 70-100 points, and during the equipment start-up and shutdown stages is 60-100 points. When the evaluation value is within the qualified range, the adjustment is determined to be effective. The formula involved is comprehensive evaluation value = pressure stability score × 40% + adjustment timeliness score × 30% + energy consumption ratio score × 20% + equipment loss score × 10%, where each score is calculated based on the comparison between actual measurement data and standard data.

[0037] A control system for steam pressure at the outlet of a steam jet mixer, the control system comprising: a parameter acquisition module for acquiring real-time operating parameters and structural parameters of the steam jet mixer, wherein the real-time operating parameters include the steam pressure entering the mixer, the steam flow entering the mixer, the flow rate used for cooling water, the steam temperature entering the mixer, and the actual pressure at the mixer outlet; and the structural parameters include the diameter of the nozzle for injecting steam, the length of the cavity for mixing steam, and the inclination angle of the steam diffusion module; The multi-layer prediction model module is used to construct an outlet pressure prediction model based on the historical operation data of the equipment, which includes an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer. The prediction model is input with real-time operating parameters and structural parameters to obtain the outlet pressure prediction value and pressure change trend graph. The multi-layer prediction model module includes: Full-cycle data storage unit, used to store full-cycle operation data of the past three years, including equipment start-up and shutdown phases, stable operation phases, and load mutation phases; The characteristic parameter extraction unit is used to extract characteristics of the historical operation data of the equipment and identify key characteristic parameters such as the fluctuation frequency of the parameters entering the mixer, the pressure response lag time, and the degree of correlation between the adjustment amount and the pressure change; The operating condition identification unit is used to establish a feature matrix containing 12 typical operating conditions in the operating condition feature identification layer, and determine the current operating condition type by the degree of matching between real-time parameters and the feature matrix; The segmented prediction unit is used to adopt 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; The three-level warning unit is used to set three-level warning thresholds in the pressure deviation warning layer, and trigger corresponding warning signals when the predicted deviation reaches different thresholds; A model training unit, used to iteratively train the prediction model using six consecutive months of actual operation data; Deviation comprehensive analysis module, used to calculate the outlet pressure deviation value based on the actual pressure at the mixer outlet and the outlet pressure prediction value, and judge the deviation development direction in combination with the pressure change trend graph; The intelligent adjustment amount calculation module is used to obtain the required control adjustment 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 coefficient of mutual influence between various parameters. The intelligent adjustment amount calculation module includes: The deviation level classification unit is used to classify the deviation level according to the absolute value, change rate and development trend of the outlet pressure deviation. The deviation levels include small deviation, gradually increasing deviation, sudden change deviation and continuously accumulated deviation. A strategy matching unit is used to match corresponding regulation strategies from a preset regulation strategy library according to different deviation levels and corresponding working condition types; The parameter correction unit is used to calculate the basic adjustment amount of each adjustment parameter, and perform cross correction based on the coefficients of mutual influence between the parameters to obtain the corrected inlet steam flow adjustment amplitude, the cooling water flow adjustment rate, and the pressure compensation opening degree of the mixed steam cavity; The energy efficiency optimization unit is used to optimize the energy efficiency of the modified adjustment parameters according to the energy consumption coefficient under the current working conditions to form the final control adjustment amount; A coordinated execution module is used to generate control instructions and execute control operations according to the required control adjustment amount, and the control operations include a coordinated adjustment mechanism based on the deviation type of the steam flow entering the mixer, the flow rate for cooling water, and the pressure compensation of the cavity for mixing steam; A multi-dimensional evaluation module is used to perform a multi-dimensional effect evaluation on the actual pressure at the mixer outlet after the control operation is performed to obtain an evaluation value of the pressure control quality; The dynamic optimization module is used to dynamically feedback optimize the control adjustment amount according to the evaluation value of the pressure control quality, and update the parameter weights of each module in the outlet pressure prediction model. The dynamic optimization module includes: The optimal strategy storage unit is used to extract the matching relationship between the current working 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; The root cause analysis unit is used to initiate the root cause tracing analysis process when the pressure control quality assessment value falls below the lower limit of the qualified range, and to determine 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 coefficients of mutual influence between various parameters, optimizing the adjustment timing, and adjusting the compensation weight; The iterative optimization unit is used to establish a strategy iteration model. After completing 100 effective adjustments, the control logic is optimized as a whole to gradually reduce the long-term control error.

[0038] During the specific implementation process, when the evaluation value of the pressure control quality is within the qualified range, the matching relationship between the current operating conditions (such as the inlet steam pressure, flow, temperature and other parameter values) and the regulation strategy (adjusted parameters and adjustment amounts) is extracted and stored in the optimal strategy library database of the control system. The database adopts a relational database structure to facilitate subsequent query and call.

[0039] When the assessment value falls below the lower limit of the qualified interval, the root cause tracing analysis process is initiated. By comparing the differences in various parameters under normal and abnormal operating conditions and combining them with the results of parameter sensitivity analysis, the dominant factors affecting pressure control, such as sensor measurement error, actuator aging, and changes in parameter coupling relationships, are determined. Based on the type of dominant factor, the calculation logic for the required control adjustment amount is adjusted. For example, parameter measurement values ​​are corrected for sensor measurement error, adjustment amounts are increased for actuator aging, and parameter interaction coefficients and adjustment sequences are optimized for changes in parameter coupling relationships. After every 100 effective adjustments, the control logic is optimized. By analyzing the data from the first 100 adjustments, the average adjustment effect of each parameter is calculated. The adjustment strategy and parameter settings are then refined to gradually reduce the long-term control error. The formula involved is: control logic optimization coefficient = 1 - (average control error of the first 100 times × 0.01), where the average control error of the first 100 times is the arithmetic mean of the control errors of each of the first 100 adjustments. This coefficient is used to correct the adjustment amplitude in subsequent control logic.

[0040] The parameter acquisition module is composed of sensors and data processing components. The sensors include inlet steam pressure sensor, inlet steam flow sensor, inlet steam temperature sensor, outlet pressure sensor and cooling water flow sensor. The data processing component adopts PLC controller to filter, amplify and classify the data collected by the sensors, and extract structural parameters from the mixer design document to provide accurate data for subsequent links; the multi-layer prediction model module includes full-cycle data storage unit, feature parameter extraction unit: software module for running feature extraction algorithm, working condition identification unit: database and matching algorithm module for storing 12 typical working condition feature matrices, segmented prediction unit: module for running short-term, medium-term and long-term prediction algorithms, three-level warning unit: warning threshold setting and signal triggering module and model training unit: model parameter adjustment and training algorithm module, which respectively realize data storage, feature extraction and working condition identification , pressure prediction, early warning signal and model training functions; the deviation comprehensive analysis module is composed of an operation deviation calculation algorithm and an operation trend judgment algorithm, which calculates the deviation value and analyzes the deviation development trend through the slope and curvature of the trend graph; the intelligent adjustment quantity calculation module includes a deviation level division unit: deviation level judgment algorithm and standard, strategy matching unit: adjustment strategy library database and matching algorithm, parameter correction unit: basic adjustment quantity calculation and correction algorithm and energy efficiency optimization unit: energy consumption coefficient table and optimization algorithm, which are used to determine the adjustment quantity that needs to be controlled; the collaborative execution module is composed of an instruction conversion unit: a module that converts the adjustment quantity into an electrical signal of the actuator, an instruction generation unit: a module that generates control instructions in sequence, a synchronous execution unit: a PLC output module that sends instructions and a feedback monitoring unit: an input module that receives feedback signals from the actuator, which completes the conversion, generation, sending and execution feedback of the control instructions.

[0041] The multi-dimensional evaluation module includes a sensor and data transmission module for collecting evaluation index data and a module for running the evaluation value calculation algorithm, which evaluates the control effect in many aspects and obtains the evaluation value; the dynamic optimization module consists of a database for storing the optimal strategy, a module for running the root cause analysis algorithm, a module for adjusting the control logic parameters and a module for running the iterative optimization algorithm, realizing the functions of strategy storage, root cause analysis, logic adjustment and iterative optimization.

[0042] The full-cycle data storage unit uses a solid-state hard drive to store full-cycle operating data for the past three years, covering the equipment start-up and shutdown phase (operating data of the equipment start-up and shutdown process every day), the stable operation phase (continuous data during normal equipment operation), and the load mutation phase (process data when the load changes by more than 20%). The data is stored in chronological order in a structured file format.

[0043] The characteristic parameter extraction unit processes historical operating data through data analysis software running on the industrial control computer, uses Fourier transform to extract the inlet parameter fluctuation frequency, uses time series analysis to extract the pressure response delay time, and uses correlation analysis to extract key characteristic parameters such as the degree of correlation between the adjustment amount and the pressure change. The extraction results are stored in the form of a data table; the operating condition identification unit has a built-in characteristic matrix database of 12 typical operating conditions. Each operating condition contains the range values ​​of parameters such as inlet steam pressure, flow, and temperature. The Euclidean distance algorithm is used to calculate the Euclidean distance between the real-time parameters and the operating condition parameters in the characteristic matrix. The smaller the distance, the higher the matching degree. This is used to judge the type of the current operating condition. When the matching degree is greater than 0.8, it is determined to be the corresponding operating condition.

[0044] The segmented prediction unit uses a neural network-based algorithm. The short-term prediction sub-model inputs parameter data from the last 10 seconds, contains three hidden layers, and outputs stress values ​​for the next 5 seconds. The medium-term prediction sub-model inputs parameter data from the last minute, contains five hidden layers, and outputs stress values ​​for the next 15 seconds. The long-term prediction sub-model inputs parameter data from the last 10 minutes, contains seven hidden layers, and outputs stress values ​​for the next 30 seconds. Each sub-model is trained using a back-propagation algorithm. The three-level warning unit compares the predicted deviation value with the three-level warning threshold. When the first-level warning threshold is reached, a yellow light and low-volume alarm are triggered; when the second-level warning threshold is reached, an orange light and medium-volume alarm are triggered. When the third-level warning threshold is reached, a red light and a high-volume alarm are triggered, and an alarm signal is sent to the control system; the model training unit obtains 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 1,000 times, so that the prediction error is gradually reduced to ensure prediction accuracy; among them, the Euclidean distance matching calculation formula is: 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.

[0045] The deviation level division unit divides the deviation into four levels according to the absolute value, change rate and development direction of the deviation value, and outputs the level identification (numbers 1-4 correspond to the four levels respectively) through program logic judgment according to the preset standards (such as the judgment conditions for small deviations); the strategy matching unit has a built-in adjustment strategy library database. Each strategy in the database contains the deviation level, working condition type and corresponding adjustment model. According to the deviation level identification and the working condition type output by the working condition identification unit, the corresponding adjustment strategy is retrieved through the database query statement, and the retrieval adopts the exact matching model.

[0046] The parameter correction unit calculates the basic adjustment amount based on the matched adjustment strategy and the proportional relationship between the deviation value and the adjustment amount. For example, for every 1% increase in the deviation, the basic adjustment amount increases by 2%. The basic adjustment amount is then cross-corrected in combination with the parameter coupling coefficient matrix (a 4×4 matrix containing the influence coefficients between various parameters) to obtain the corrected adjustment 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 (such as 0.8 at high load, 0.5 at medium load, and 0.3 at low load). It queries the corresponding coefficient based on the current operating condition and optimizes the corrected adjustment parameters to ensure that the adjusted parameters meet the control requirements while minimizing energy consumption, thereby 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" refers to the other adjustment parameter values ​​that affect the current parameter, and the coupling coefficient is the corresponding value in the parameter coupling coefficient matrix.

[0047] The optimal strategy storage unit uses MySQL database software. When the evaluation value is in the qualified range, the current working condition characteristics (represented in the form of parameter value array) and the corresponding adjustment strategy (adjustment parameters and adjustment amount) are stored in the database as a record. The database table structure contains working condition parameter fields and adjustment strategy fields to facilitate subsequent query and call; when the evaluation value is lower than the qualified range, the root cause analysis unit starts the analysis program, and by comparing the parameter mean value, variance and other statistical quantities of normal working conditions and abnormal working conditions, finds out the parameters with significant differences, and combines the parameter sensitivity analysis results (parameters with high sensitivity are given priority) to determine the dominant factor affecting pressure control, such as the parameter with the highest parameter sensitivity analysis coefficient as 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 coefficient of the quantity is corrected, and the corresponding coefficient in the parameter coupling coefficient matrix is ​​corrected at the same time, the order of parameter adjustment is optimized, and the adjustment of parameters with greater influence is given priority; the iterative optimization unit establishes a strategy iteration model, and after completing 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, the 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 pressure.

[0048] 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 in the form of a computer program product. Those skilled in the art will appreciate 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 performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0049] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the objectives of this embodiment based on actual needs.

[0050] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for controlling the steam pressure at the outlet of a steam jet mixer, characterized in that: The control method steps are as follows: Acquire real-time operating parameters and structural parameters of the steam jet mixer, wherein the real-time operating parameters include inlet steam pressure, inlet steam flow, cooling water flow, inlet steam temperature, and actual outlet pressure; Build an outlet pressure prediction model based on the historical operation data of the equipment, and obtain the outlet pressure prediction value by inputting the real-time operation parameters and structural parameters into the prediction model; Calculate the outlet pressure deviation value based on the actual outlet pressure and the outlet pressure prediction value; Obtaining a control adjustment amount based on the outlet pressure deviation value, the deviation development trend, and the mixer dynamic characteristic parameters, wherein the mixer dynamic characteristic parameters include pressure response time, flow regulation sensitivity, and parameter coupling coefficient; Generate control instructions and execute control operations according to control adjustment quantities; Perform a multi-dimensional effect evaluation on the actual outlet pressure after the control operation is performed to obtain the pressure control quality evaluation value; The control adjustment amount is dynamically feedback optimized according to the pressure control quality evaluation value, and the hierarchical parameter weights of the outlet pressure prediction model are updated synchronously.

2. The method for controlling the steam pressure at the outlet of a steam jet mixer according to claim 1, wherein: The outlet pressure prediction model based on historical operating data includes the following: Collect historical operating data covering equipment start-up and shutdown phases, stable operation phases, and load mutation phases; perform feature extraction on historical operating data to identify key characteristic parameters, including inlet parameter fluctuation frequency, pressure response lag time, and the strength of the correlation 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 based on the matching degree between real-time parameters and the feature matrix; A segmented prediction algorithm is used in the pressure change trend prediction layer to establish short-term, medium-term, and long-term prediction sub-models for the outlet pressure respectively. A three-level warning threshold is set in the pressure deviation warning layer, and corresponding warning signals are triggered when the prediction deviation reaches different thresholds. The model is iteratively trained using actual operating data.

3. The method for controlling the steam pressure at the outlet of a steam jet mixer according to claim 1, wherein: The control adjustment amount obtained based on the outlet pressure deviation value, deviation development trend and mixer dynamic characteristic parameters includes: Deviation levels are divided according to the absolute value, change rate, and development trend of the outlet pressure deviation value. Deviation levels include micro-deviation, gradual deviation, sudden deviation, and cumulative deviation. For different deviation levels and corresponding working conditions, corresponding strategies in the preset adjustment strategy library are activated: when it is a micro-deviation, a single parameter fine-tuning mode is adopted; when it is a gradual deviation, a dual-parameter collaborative adjustment mode is adopted; when it is a sudden deviation, an emergency response mode including parameter adjustment priority is activated; when it is a cumulative deviation, a compensation adjustment mode including an equipment status correction coefficient is activated. The basic adjustment amount of each adjustment parameter is calculated, and cross correction is performed in combination with the parameter coupling coefficient to obtain the corrected inlet steam flow adjustment amplitude, cooling water flow adjustment rate and mixing chamber pressure compensation opening degree.

4. The method for controlling the steam pressure at the outlet of a steam jet mixer according to claim 1, wherein: Execution control operations include: A dynamic response model for the adjustment parameters is established to determine the timing relationship between the inlet steam flow rate adjustment, cooling water flow rate adjustment, and mixing chamber pressure compensation. In accordance with the preset timing, the opening degree adjustment instructions for the inlet steam control valve, the cooling water control valve, and the mixing chamber pressure compensation valve are sent in sequence, with the opening degree of the mixing chamber pressure compensation valve increasing in a step-by-step manner. Dynamic response data for each actuator is 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 the subsequent adjustment instructions are corrected in real time.

5. The method for controlling the steam pressure at the outlet of a steam jet mixer according to claim 1, wherein: Multi-dimensional effect evaluation includes: An evaluation index system is established from four dimensions: pressure stability, regulation timeliness, energy loss rate and equipment loss rate; the fluctuation amplitude of the outlet pressure after regulation, the time to reach a stable state, the steam consumption per unit pressure regulation and the number of valve operations are calculated; each indicator is weighted according to the preset weight to obtain a comprehensive pressure control quality evaluation value; the qualified range of the evaluation value under different working conditions is set, and when the evaluation value is within the qualified range, the regulation is determined to be effective.

6. The method for controlling the steam pressure at the outlet of a steam jet mixer according to claim 1, wherein: Dynamic feedback optimization includes: When the pressure control quality assessment value is within the qualified range, the matching relationship between the current working 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 qualified range, the root cause tracing analysis process is initiated to determine the dominant factors affecting pressure control through parameter sensitivity analysis; 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.

7. A steam jet mixer outlet steam pressure control system, characterized in that: The control system includes: A parameter acquisition module is used to obtain real-time operating parameters and structural parameters of the steam jet mixer, wherein the real-time operating parameters include inlet steam pressure, inlet steam flow, cooling water flow, inlet steam temperature, and actual outlet pressure; A multi-layer prediction model module is used to construct an outlet pressure prediction model based on historical operating data, which includes an operating condition feature recognition layer, a pressure change trend prediction layer, and a pressure deviation warning layer. The outlet pressure prediction value is obtained by inputting real-time operating parameters and structural parameters into the prediction model; Deviation comprehensive analysis module, used to calculate the outlet pressure deviation value based on the actual outlet pressure and the outlet pressure prediction value; An intelligent adjustment amount calculation module is used to obtain the control adjustment amount based on the outlet pressure deviation value, the deviation development trend and the dynamic characteristic parameters of the mixer, where the dynamic characteristic parameters of the mixer include pressure response time, flow regulation sensitivity and parameter coupling coefficient; A collaborative execution module is used to generate control instructions and execute control operations based on the control adjustment amount, and the control operations include a collaborative adjustment mechanism of the inlet steam flow, cooling water flow and mixing chamber pressure compensation based on the deviation type; A multi-dimensional evaluation module is used to perform a multi-dimensional effect evaluation on the actual outlet pressure after the control operation is performed to obtain a pressure control quality evaluation value; The dynamic optimization module is used to perform dynamic feedback optimization on the control adjustment amount according to the pressure control quality evaluation value, and synchronously update the hierarchical parameter weights of the outlet pressure prediction model.

8. A steam jet mixer outlet steam pressure control system according to claim 7, characterized in that: The multi-layer prediction model module includes: Full-cycle data storage unit, used to cover historical operation data including equipment start-up and shutdown stages, stable operation stages, and load mutation stages; The characteristic parameter extraction unit is used to extract characteristics of historical operation data and identify key characteristic parameters including the inlet parameter fluctuation frequency, pressure response lag time, and the correlation strength between the adjustment amount and pressure change; The operating condition identification unit is used to establish a characteristic matrix containing typical operating conditions in the operating condition feature identification layer, and determine the current operating condition type by the matching degree between the real-time parameters and the characteristic matrix; The segmented prediction unit is used to use a segmented 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; The three-level warning unit is used to set three-level warning thresholds in the pressure deviation warning layer, and trigger corresponding warning signals when the predicted deviation reaches different thresholds.

9. A steam jet mixer outlet steam pressure control system according to claim 7, characterized in that: The intelligent adjustment amount calculation module includes: Deviation level classification unit, used to classify deviation levels according to the absolute value, change rate and deviation development trend of the outlet pressure deviation value, where the deviation levels include micro deviation, gradual deviation, sudden deviation and cumulative deviation; A strategy matching unit is used to match corresponding regulation strategies from a preset regulation strategy library according to different deviation levels and corresponding working condition types; 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 adjustment amplitude, cooling water flow adjustment rate and mixing chamber pressure compensation opening degree.

10. The steam jet mixer outlet steam pressure control system according to claim 7, characterized in that: The dynamic optimization module includes: The optimal strategy storage unit is used to extract the matching relationship between the current working condition characteristics and the adjustment strategy when the pressure control quality evaluation value is within the qualified range, and store it in the optimal strategy library; The root cause analysis unit is used to start the root cause tracing analysis process when the pressure control quality assessment value is lower than the lower limit of the qualified range, and determine 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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