A method for monitoring the state data of the hydrofluoric acid purification process

Abstract

The present application relates to the technical field of data monitoring, and discloses a method for monitoring the state data of the hydrofluoric acid purification process. The method includes: collecting the state data of the hydrofluoric acid purification equipment in the mixing and reaction tank, evaporator, rectification column and absorption tower of the hydrofluoric acid purification system; performing state evaluation through a fuzzy logic monitoring model to obtain a real-time state evaluation result; calculating the equipment state transition probability, and at the same time identifying the time-varying delay characteristics of the temperature control of the mixing and reaction tank, the pressure response of the rectification column and the concentration adjustment of the absorption tower to obtain the state prediction information of the hydrofluoric acid purification equipment; performing adaptive switching among the normal mode, anti-fluctuation mode, rapid recovery mode and emergency protection mode to obtain a control strategy for the characteristics of the hydrofluoric acid purification process. The present application effectively identifies the asymmetric characteristics of parameter changes in the hydrofluoric acid purification process, improves the detection accuracy of abnormal states, and ensures the safe and stable operation of the hydrofluoric acid purification process.

Description

Technical Field

[0001] This application relates to the technical field of data monitoring, and particularly to a method for monitoring the state data of the hydrofluoric acid purification process. Background Art

[0002] The hydrofluoric acid purification process involves multiple key equipment such as mixing and reaction tanks, evaporators, distillation columns, and absorption towers. The process flow is complex and has strong non-linear and time-varying characteristics. The traditional purification process relies on empirical operation and PID control systems with fixed parameters, and cannot cope with process parameter fluctuations, sudden changes in equipment status, and different production stage differentiation requirements. Especially the asymmetric response characteristics shown in the parameter rising and falling stages often lead to product purity fluctuations, increased energy consumption, and safety hazards.

[0003] Existing equipment status monitoring technologies generally have problems such as incomplete data collection, insufficient preprocessing, and single monitoring models. Especially in high-risk processes such as hydrofluoric acid purification, the judgment of parameter fluctuations often uses a single threshold method, which cannot distinguish different characteristics and risks in the parameter rising process and the falling process. At the same time, the time-varying delay problems in key links such as the temperature control of the mixing and reaction tank, the pressure response of the distillation column, and the concentration adjustment of the absorption tower are ignored, resulting in lagging control responses, increased fluctuations, and even chain reactions. In addition, there is no effective linkage mechanism between equipment status and control strategies, and the control method cannot be intelligently adjusted according to the real-time status of the equipment, making the system slow to respond to abnormal situations. Summary of the Invention

[0004] This application provides a method for monitoring the state data of the hydrofluoric acid purification process. This application effectively identifies the asymmetric characteristics of parameter changes in the hydrofluoric acid purification process, improves the detection accuracy of abnormal states, and ensures the safe and stable operation of the hydrofluoric acid purification process.

[0005] In a first aspect, this application provides a method for monitoring the state data of the hydrofluoric acid purification process, and the method for monitoring the state data of the hydrofluoric acid purification process includes:

[0006] Collect the original data of the equipment operation status of the mixing and reaction tank, evaporator, distillation column, and absorption tower in the hydrofluoric acid purification system, and preprocess the original data of the equipment operation status to obtain the hydrofluoric acid purification equipment status data;

[0007] Input the hydrofluoric acid purification equipment status data into a fuzzy logic monitoring model for status evaluation to obtain a real-time status evaluation result;

[0008] Calculate the equipment status transition probability according to the real-time status evaluation result, and at the same time identify the time-varying delay characteristics of the temperature control of the mixing and reaction tank, the pressure response of the distillation column, and the concentration adjustment of the absorption tower to obtain the hydrofluoric acid purification equipment status prediction information;

[0009] Based on the predicted information of the hydrofluoric acid purification equipment status, an adaptive switch is made among the normal mode, anti-fluctuation mode, rapid recovery mode and emergency protection mode to obtain a control strategy for the characteristics of the hydrofluoric acid purification process.

[0010] In the technical solution provided by this application, by installing a variety of sensors in the mixing and reaction tank, evaporator, rectification column and absorption tower and setting different sampling frequencies, combined with an improved Kalman filtering algorithm and a 3σ outlier recognition mechanism, high-quality data acquisition of the key parameters in the hydrofluoric acid purification process is realized, and the problems of incomplete data and large noise interference in the traditional technology are solved. A dual fuzzy logic monitoring model is adopted, using a triangular membership function to process the rising interval and a Gaussian membership function to process the falling interval, effectively identifying the asymmetric characteristics of parameter changes in the hydrofluoric acid purification process, improving the detection accuracy of abnormal states, especially the recognition ability of key abnormal conditions such as sudden changes in the pressure of the rectification column and concentration deviation of the absorption tower. By establishing an equipment state transition probability matrix and a periodic time-varying delay compensation mechanism, combined with fast Fourier transform analysis and a feedforward-feedback combined compensator, the time-varying delay problems existing in the temperature control of the mixing and reaction tank, pressure response of the rectification column and concentration adjustment of the absorption tower in the hydrofluoric acid purification process are solved, and an accurate prediction of the evolution trend of the equipment state is realized. Based on the predicted information of the equipment state, an adaptive switch is realized among the normal mode, anti-fluctuation mode, rapid recovery mode and emergency protection mode, combined with a mode switching condition matrix and a smooth transition mechanism, enhancing the adaptability of the control system to different hydrofluoric acid purification working conditions and avoiding the limitations of traditional single control strategies under different working conditions. By closely combining equipment state monitoring and control strategies, differential monitoring and predictive control are implemented on the key equipment in the hydrofluoric acid purification process, realizing early intervention in potential risks, preventing state deterioration and chain reactions, and ensuring the safe and stable operation of the hydrofluoric acid purification process. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0012] Figure 1 It is a schematic diagram of an embodiment of the method for monitoring the state data of the hydrofluoric acid purification process in the embodiments of this application. Detailed Embodiments

[0013] The embodiment of the present application provides a method for monitoring the status data of the hydrofluoric acid purification process. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order different from that shown or described here. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0014] For ease of understanding, the specific process of the embodiment of the present application will be described below. Please refer to Figure 1 , an embodiment of the method for monitoring the status data of the hydrofluoric acid purification process in the embodiment of the present application includes:

[0015] Step S101, collect the original data of the equipment operation status of the mixing and reaction tank, evaporator, rectifying column and absorption tower in the hydrofluoric acid purification system, and preprocess the original data of the equipment operation status to obtain the status data of the hydrofluoric acid purification equipment;

[0016] It can be understood that the execution subject of the present application can be a hydrofluoric acid purification process status data monitoring system, or a terminal or a server, and specific limitations are not made here. The embodiment of the present application takes the server as the execution subject as an example for illustration.

[0017] Specifically, different types of sensors are installed on the key equipment of the hydrofluoric acid purification system, including temperature sensors, pressure sensors, flow sensors and concentration analyzers. These sensors are used to monitor the core parameters of each equipment respectively. Among them, the temperature sensors (PT100 type) are installed on the top of the mixing and reaction tank, evaporator and rectification column to obtain the temperature change situation. The pressure sensors (YB-151 type) are arranged at the top of the rectification column, the inlet and outlet of the absorption tower and in the mixing and reaction tank to monitor the pressure fluctuations of each key equipment. The flow sensors (LZB-50 type) are installed in the evaporator outlet and the absorption tower circulation pipeline to detect the change of hydrofluoric acid flow. The concentration analyzer (CM-230 type) is used to monitor the hydrofluoric acid concentration at the outlet of the absorption tower and upload the data in real time. In order to realize the effective management and synchronization of sensor data, the monitoring point layout is carried out to ensure that the data acquisition covers the key parameters of all key equipment. At the same time, different sampling frequencies are set. For example, the temperature data is sampled once every 5 seconds, the pressure data is sampled once every 3 seconds, the flow data is sampled once every 2 seconds, and the concentration data is sampled once every 10 seconds, so as to capture the real-time changes of equipment operation in the hydrofluoric acid purification process to the greatest extent. After the data acquisition is completed, all the collected data will be collected and synchronously calibrated through an industrial-grade edge computing unit (MCU-E520). The edge computing unit is responsible for the preliminary processing of data, including data format conversion, timestamp unification and data synchronization between devices, to avoid data misalignment problems caused by different sampling frequencies of different sensors. This process also includes using an improved Kalman filtering algorithm to filter the data to eliminate the measurement noise in the data and improve the accuracy of the data. At the same time, an outlier identification mechanism is introduced in the data collection stage, and the 3σ principle is used to identify outliers. That is, when the current sampling value of a certain parameter deviates from the average value of the previous 10 sampling points by more than 3 times the standard deviation, this point is marked as an outlier, and the data is corrected by interpolation of the front and back values, so as to ensure the smoothness of the data. The system operation parameter data set is transmitted through the industrial Ethernet protocol to ensure the real-time and accuracy of the data. The data transmission rate is maintained above 10Mbps, and the transmission delay needs to be controlled within 50ms to ensure the high-speed transmission of key data between the equipment and the central monitoring system. A dual data storage mechanism is adopted to store the system operation parameter data set in the local edge device and the central server. Among them, the local edge device stores the high-frequency data of the recent 72 hours for short-term data traceability and analysis, while the central server stores the compressed long-term historical data and ensures that the data retention period is not less than 3 years. The central monitoring system conducts real-time analysis and storage of the received data to form the original data of the complete equipment operation status. Filtering processing and outlier identification are carried out on the original data of the equipment operation status. The filtering processing adopts an improved Kalman filtering algorithm to eliminate the measurement noise that appears and improve the data quality.Meanwhile, the outlier recognition mechanism adopts the 3σ principle and ensures the timely recognition of data anomalies in different process states by dynamically adjusting the anomaly recognition threshold. The data after filtering and outlier recognition is the status data of the hydrofluoric acid purification equipment.

[0018] Classify the original data of the equipment operation status. According to the different data sources, the data is divided into the temperature data of the mixing and reaction tank, the temperature data at the outlet of the evaporator, the pressure data of the rectification column, and the concentration data of the absorption tower, and independent management and analysis are carried out for different data types. Filter the classified original parameter data. Adopt the Kalman filter algorithm to eliminate the measurement noise in the data through the iterative mechanism of prediction and update, and obtain parameter data that is smoother and more in line with the actual operation of the equipment. Calculate the fluctuation benchmark for the parameter data after noise elimination to provide a reference standard for subsequent outlier recognition. By calculating the average value and standard deviation of the data over a period of time, establish the fluctuation benchmark for each type of parameter. The fluctuation benchmark can reflect the change range of each equipment parameter under normal operation. Based on the fluctuation benchmark, mark the abnormal points for the parameter data after eliminating the measurement noise. Adopt the 3-fold standard deviation principle to judge the abnormal points. When the change amplitude of a certain data point exceeds the normal fluctuation range, the system automatically marks this point as an abnormal point to form an abnormal point marking set. Perform linear interpolation processing on each abnormal point in the abnormal point marking set to eliminate the influence of abnormal data on the system status monitoring. Linear interpolation estimates the reasonable value of the abnormal point through the adjacent valid data points before and after, thereby replacing the abnormal point, making the corrected data maintain smoothness and continuity, and avoiding misjudgment or malfunction of the control algorithm caused by abnormal data. Perform piecewise linear fitting and data downsampling on the corrected parameter data. Piecewise linear fitting analyzes the data change trend, divides the data into different segments, and fits the data in each segment to enable the system to identify the data change characteristics in different stages. When the data change rate is lower than the set change threshold, the system automatically performs data downsampling to reduce the data volume, calculation pressure, and storage requirements. Obtain the status data of the hydrofluoric acid purification equipment.

[0019] Step S102: Input the status data of the hydrofluoric acid purification equipment into the fuzzy logic monitoring model for status evaluation to obtain the real-time status evaluation result;

[0020] Specifically, calculate the parameter change rate based on the status data of the hydrofluoric acid purification equipment. For example, calculate the change rate of the temperature in the mixing and reaction tank, the change rate of the temperature at the outlet of the evaporator, the change rate of the pressure at the top of the distillation column, and the change rate of the concentration at the outlet of the absorption tower. Then, divide the equipment status data into a parameter rising interval data set and a parameter falling interval data set according to the parameter change rate. Perform fuzzy processing on the parameter rising interval data set to convert the continuously changing numerical values into fuzzy set input values. The fuzzy processing uses a triangular membership function, defining five fuzzy sets: {extremely small, relatively small, medium, relatively large, extremely large}. According to the normal operating range of the equipment and the range of parameter change rates, fuzzy the trend of parameter changes to form the fuzzy input values for the rising interval. At the same time, perform fuzzy processing on the parameter falling interval data set. The data in the falling interval uses a Gaussian membership function because there are mutations and non-linear changes during the falling process, and a more sensitive membership function is used to capture these changes to obtain the fuzzy input values for the falling interval. Input the fuzzy input values for the rising interval and the falling interval into the fuzzy logic monitoring model for inference calculation. Map and infer the parameter changes by establishing a 45-rule fuzzy rule base. The fuzzy inference rule takes the form of "if the change rate is X and the current value is Y, then the equipment status is Z", covering various states such as normal, slightly abnormal, moderately abnormal, severely abnormal, and dangerous. The preliminary result obtained from the inference calculation is the first fuzzy state output information. Determine the adaptive factor according to the equipment operation time and batch, and adjust the first fuzzy state output information. The value range of the adaptive factor is between 0.8 and 1.2, and it is dynamically adjusted according to the length of the equipment operation time and the process changes of the batch, so as to improve the accuracy and sensitivity of the fuzzy logic inference result. The adjusted fuzzy state output information is the second fuzzy state output information. Since different equipment has different importance levels in the hydrofluoric acid purification process, weight the second fuzzy state output information through the equipment importance weight coefficient matrix to make the abnormal states of key equipment receive higher sensitivity and priority. After completing the weighting process, defuzzify the fuzzy state information to map the fuzzy set back to the specific numerical space to obtain the real-time status evaluation result.

[0021] Set the universe ranges of temperature, pressure, flow rate, and concentration to ensure that the parameter change rate and the change of the current parameter value are within a reasonable monitoring range. The universe space of the parameter change rate is obtained through statistical analysis of the parameter change conditions of the equipment during normal operation. At the same time, determine the normal operation ranges of temperature, pressure, flow rate, and concentration to form the universe space of the parameter value. Uniformly divide the universe space of the parameter change rate into five preset levels, and these five levels correspond to the five fuzzy sets of "extremely small, relatively small, medium, relatively large, extremely large". To achieve fuzzification, construct five triangular membership functions, and the vertex coordinates of these functions are located at both ends and the midpoint of the universe respectively. For example, for the temperature change rate, define five intervals of extremely small, relatively small, medium, relatively large, and extremely large from the lowest to the highest point. The vertices of the triangular membership functions are located at the midpoints of each level interval and drop to zero at both endpoints, forming a continuous fuzzy membership distribution. Similarly, uniformly divide the universe space of the parameter value into the same five levels and construct five triangular membership functions, and the vertex coordinates of these functions are located at the midpoints of each interval to obtain the fuzzy membership function set of the parameter value. Input each pair of parameter change rates in the parameter increase interval dataset into the fuzzy membership function set of the parameter change rate for calculation to obtain the change rate membership value matrix. During the calculation process, determine which interval range of the membership function the current change rate falls into, and then calculate the membership value of the change rate according to the triangular membership function. According to the linear change relationship of the membership function, obtain the belonging degree of the change rate in different membership functions. Similarly, input the current parameter value in the parameter increase interval dataset into the fuzzy membership function set of the parameter value for calculation to obtain the parameter value membership value matrix. For the fuzzification of the current parameter value, adopt the same mechanism as the change rate. By judging which interval the parameter value falls into and calculating the corresponding membership value, form the parameter value membership matrix. Combine the change rate membership value matrix and the parameter value membership value matrix, and combine them by weighted summation or taking the minimum membership degree of the two membership matrices to ensure that each pair of change rates and current values can be mapped into a fuzzy input value matrix to form the fuzzy input value of the increase interval.

[0022] Step S103: Calculate the equipment state transition probability according to the real-time state evaluation result, and at the same time identify the time-varying delay characteristics of the temperature control of the mixing and reaction tank, the pressure response of the rectification column, and the concentration adjustment of the absorption tower to obtain the state prediction information of the hydrofluoric acid purification equipment;

[0023] Specifically, based on the real-time status evaluation results, the operating status of each device is classified, and the current status data is divided into five statuses: "normal, slightly abnormal, moderately abnormal, severely abnormal, and dangerous". The classified data is stored in the device status classification dataset. According to the device status classification dataset, the transfer situation of the operating status of each device to other statuses is statistically analyzed. By analyzing the frequency of each device transferring from one status to another over a past period of time and performing normalization processing, the device status transfer probability is obtained, which reflects the possibility of the device transferring between different statuses. For example, the probability of the rectification column pressure transferring from the normal status to the slightly abnormal status is greater than the probability of directly jumping from the normal status to the severely abnormal status. The device status transfer probability is dynamically updated to ensure that the device status evolution law always reflects the actual status under the current process conditions. The dynamic update method uses the sliding time window method to statistically analyze the status transfer data within a recent period of time (such as 500 hours), and perform an update every 100 hours to ensure that the status transfer matrix is always in the latest state, forming a dynamic transfer matrix that reflects the device status change law. The hydrofluoric acid purification device status data is input into the fast Fourier transform analysis algorithm to analyze the delay periods of the temperature control of the mixing and reaction tank, the pressure response of the rectification column, and the concentration adjustment of the absorption tower. The fast Fourier transform converts the data in the time domain into a frequency domain signal, thereby identifying the main delay period characteristics of different devices, which reflects the time lag of the device's response after receiving a control signal. According to the analyzed delay period characteristics, a feedforward-feedback combined delay compensation model is constructed to eliminate the influence of time-varying delay on the device status prediction. The structure of the compensator includes a feedforward part for predicting the future status change of the system and a feedback part for real-time correction of the control error. By combining the feedforward and feedback compensation mechanisms, control adjustments are made in advance within the delay period, thereby reducing the interference of the delay on the system status. The compensation model can dynamically adjust the compensation parameters according to the delay characteristics of different devices, so as to maintain a high control accuracy under different delay periods and ensure the accuracy of the device status prediction. The dynamic transfer matrix is combined with the delay compensation model to form a device status prediction mechanism by calculating the status probability distribution at a future moment and taking the delay factor into account. The status probability distribution at a future moment is predicted based on the current status and the status transfer matrix. After combining with the delay compensation model, the status change deviation caused by the control signal delay is corrected, so that the prediction result is closer to the actual operating status of the device, and the hydrofluoric acid purification device status prediction information is obtained.

[0024] Step S104: Based on the hydrofluoric acid purification device status prediction information, adaptively switch between the normal mode, anti-fluctuation mode, rapid recovery mode, and emergency protection mode to obtain a control strategy for the characteristics of the hydrofluoric acid purification process.

[0025] Specifically, a control mode switching condition matrix is constructed based on the device status prediction information. This matrix maps the status classification of the device to the control mode and determines the optimal control mode under different statuses. When the device status gradually transfers from the normal status to the abnormal status, the control mode switching condition matrix dynamically determines the current optimal control mode type according to the device status evaluation result, the state transition probability, and the status prediction information after delay compensation. The conditions in the matrix include the category to which the current status belongs, and also comprehensively consider the evolution trend of future statuses, the degree of abnormality, and the possibility of status recovery, ensuring that the appropriate control mode can be switched at the initial stage of status abnormality to prevent the abnormality from further deteriorating. Select the control algorithm parameters corresponding to the device status based on the currently applicable control mode type. Among them, the PID control algorithm is used in the normal mode, which is applicable when the device is in the normal status. After the PID parameters are optimized, they can maintain the device running stably under the best working conditions. When the device status transfers to a slightly abnormal or fluctuating trend, the system switches to the anti-fluctuation mode and uses the fuzzy PID control algorithm to suppress fluctuations and stabilize the device operation by adjusting the PID parameters in real time. If the status further deteriorates and enters the moderately abnormal status, the system switches to the rapid recovery mode and uses the model predictive control strategy to achieve the rapid return of the device to the normal status by predicting future statuses and optimizing the control input in advance. When the device status enters the severely abnormal or dangerous status, the system immediately switches to the emergency protection mode and uses the robust H∞ control algorithm, which has anti-interference ability and robustness to ensure the basic safe operation of the device under extreme conditions. During the process of control mode switching, in order to avoid sudden changes in control signals and system instability caused by control switching, the switching process of the control algorithm is smoothed. Input the control algorithm parameters corresponding to the current device status into the state observer. The state observer estimates the current operating status of the device by fusing the actual measurement data and the model prediction data. At the same time, calculate the output values of the old and new control algorithms respectively to obtain the dual-algorithm control quantity during control mode switching. The dual-algorithm control quantity is weighted averaged through a transition function to form a smooth control output signal. The transition function adopts an exponential smoothing form and is dynamically adjusted according to the time process and the state change speed during the switching process to ensure the continuity of the control signal during the switching period and avoid instability of the device operation caused by sudden changes. After completing the control mode switching and applying the new control algorithm, evaluate the performance of the smoothed control output signal. By setting control mode effectiveness evaluation indicators, including the integral of absolute error, the total change of the control signal, and the system stabilization time, etc., comprehensively evaluate the actual control effect of the current control strategy. The performance evaluation result is used to judge the effectiveness of the current control mode and is input as feedback information into the control mode switching condition matrix.If the evaluation result shows that the current control mode fails to effectively suppress the abnormal state or restore the device to the normal operating state, the system further optimizes the control mode switching conditions according to the feedback result, adjusts the thresholds and switching logics in the switching condition matrix, and forms a control strategy that continuously self-optimizes and adapts.

[0026] In the embodiment of the present application, by installing various sensors on the mixing and reaction tank, evaporator, rectification column and absorption tower and setting different sampling frequencies, combined with the improved Kalman filtering algorithm and 3σ outlier recognition mechanism, high-quality data acquisition of the key parameters in the hydrofluoric acid purification process is realized, and the problems of incomplete data and large noise interference in the traditional technology are solved. The dual fuzzy logic monitoring model is adopted, the triangular membership function is used to process the rising interval, and the Gaussian membership function is used to process the falling interval, effectively identifying the asymmetric characteristics of the parameter changes in the hydrofluoric acid purification process, improving the detection accuracy of state anomalies, especially the recognition ability of key abnormal conditions such as the sudden change of the rectification column pressure and the concentration deviation of the absorption tower. By establishing the equipment state transition probability matrix and the periodic time-varying delay compensation mechanism, combined with the fast Fourier transform analysis and the feedforward-feedback combined compensator, the time-varying delay problems existing in the temperature control of the mixing and reaction tank, the pressure response of the rectification column and the concentration adjustment of the absorption tower in the hydrofluoric acid purification process are solved, and the accurate prediction of the equipment state evolution trend is realized. Based on the equipment state prediction information, adaptive switching is realized among the normal mode, anti-fluctuation mode, fast recovery mode and emergency protection mode, and combined with the mode switching condition matrix and the smooth transition mechanism, the adaptability of the control system to different hydrofluoric acid purification working conditions is enhanced, and the limitations of the traditional single control strategy under different working conditions are avoided. By closely combining equipment state monitoring and control strategies, differential monitoring and predictive control are implemented on the key equipment in the hydrofluoric acid purification process, early intervention on potential risks is realized, the state deterioration and chain reaction are prevented, and the safe and stable operation of the hydrofluoric acid purification process is guaranteed.

[0027] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0028] Install temperature sensors, pressure sensors, flow sensors and concentration analyzers on the mixing and reaction tank, evaporator, rectification column and absorption tower in the hydrofluoric acid purification system to obtain the monitoring point layout of the hydrofluoric acid purification system;

[0029] Collect the real-time data of each monitoring point of the hydrofluoric acid purification system through the monitoring point layout of the hydrofluoric acid purification system to obtain the mixing and reaction tank temperature data, evaporator outlet temperature data, rectification column pressure data and absorption tower concentration data;

[0030] Collect and synchronously calibrate the temperature data of the mixing and reaction tank, the temperature data at the outlet of the evaporator, the pressure data of the distillation column, and the concentration data of the absorption tower using an industrial-grade edge computing unit to obtain a dataset of system operation parameters;

[0031] Transmit the dataset of system operation parameters to the central monitoring system via the industrial Ethernet protocol and store it in the local edge device and the central server to obtain the original data of the equipment operation status;

[0032] Perform filtering processing and outlier identification on the original data of the equipment operation status to obtain the status data of the hydrofluoric acid purification equipment.

[0033] Specifically, different types of sensors are installed on key equipment such as mixing tanks, evaporators, distillation towers and absorption towers to monitor the important process parameters of each equipment in real time. For the mixing tank, it is necessary to monitor the temperature, pressure and flow changes during the reaction process. A PT100 temperature sensor is set in the mixing tank to monitor the reaction temperature, a YB-151 pressure sensor is used to monitor the reaction pressure, and an LZB-50 flow sensor is set at the inlet and outlet of the reaction material to monitor the inlet and outlet flow. For the evaporator, it is necessary to monitor the outlet temperature to ensure the temperature control accuracy of the hydrofluoric acid gasification process. A PT100 temperature sensor is set at the outlet of the evaporator to accurately collect temperature data during the evaporation process. As the core equipment in the hydrofluoric acid purification process, the distillation tower needs to monitor the pressure, temperature and flow changes at the top and bottom of the tower. Therefore, a YB-151 pressure sensor and a PT100 temperature sensor are set at the top and bottom of the distillation tower respectively, and an LZB-50 flow sensor is installed at the feed and discharge ports of the distillation tower to grasp the operating status of the distillation tower. The outlet hydrofluoric acid concentration of the absorption tower is a key indicator of the system operation effect. A CM-230 concentration analyzer is installed at the outlet of the absorption tower to monitor the concentration changes of the absorption tower in real time. After completing the layout of the monitoring points, the operating data of each key equipment in the hydrofluoric acid purification system is collected in real time through the monitoring points to obtain the mixing tank temperature data, evaporator outlet temperature data, distillation tower pressure data and absorption tower concentration data. The data collection adopts a layered collection mechanism, and different types of sensors are set with different sampling frequencies to capture subtle changes in the operating status of the equipment. The mixing tank temperature data is collected every 5 seconds to reflect the dynamic change trend of the reaction temperature; the evaporator outlet temperature data also uses a 5-second sampling cycle to ensure that the temperature fluctuation information of the evaporation process is obtained in time; the distillation tower pressure data is collected at a higher frequency of 3 seconds / time; the absorption tower concentration data is a key parameter of the purification process, and the concentration analysis data is collected every 10 seconds to ensure that the concentration change can reflect the operation status of the absorption tower in a timely manner. After these real-time data are collected by sensors, they are directly input into the industrial-grade edge computing unit for data collection and synchronous calibration. The industrial edge computing unit (MCU-E520) is responsible for data collection and synchronization calibration. It aggregates the raw data from different sensors and performs time stamp calibration to ensure that the data from different collection points maintain time consistency. Since different devices and sensors have different sampling periods, in order to avoid data dislocation and time drift, the edge computing unit uses a high-precision time synchronization protocol to time align data with different sampling frequencies. At the same time, during the data collection stage, the edge computing unit converts the data format and unifies the data from different sensors into a standard data format for subsequent data processing and analysis. The edge computing unit also performs preliminary data cleaning on the data, including deleting duplicate data, correcting invalid data, and performing data integrity checks to ensure the integrity and consistency of the system operation parameter data set.After the data aggregation is completed, a dataset of operating parameters is generated, which includes key parameters such as the temperature data of the mixing and reaction tank, the temperature data at the outlet of the evaporator, the pressure data of the distillation column, and the concentration data of the absorption tower. The system operating parameter dataset is transmitted through the industrial Ethernet protocol, which has the advantages of fast data transmission speed, large bandwidth, and strong anti-interference ability, ensuring that a large amount of real-time data can be stably and efficiently transmitted to the central monitoring system. During the data transmission process, data is packaged through the TCP / IP protocol, and a data encryption mechanism is adopted to ensure the security of data during transmission. The industrial Ethernet protocol guarantees that the data transmission bandwidth is above 10 Mbps and controls the data transmission delay within 50 ms, ensuring that the system operating data is uploaded to the central monitoring system in a timely manner. To ensure the reliability of data and prevent data loss, a dual data storage mechanism is adopted, and the data is stored in both the local edge device and the central server simultaneously. The local edge device stores high-frequency data within the most recent 72 hours for timely data compensation in case of network interruption, while the central server stores compressed long-term historical data with a data storage period of no less than 3 years for subsequent status analysis and trend prediction. After the data aggregation, transmission, and storage are completed, the original data of the equipment operating status is filtered and outlier identification is performed to obtain the status data of the hydrofluoric acid purification equipment. The filtering process denoises the data through an improved Kalman filtering algorithm. The Kalman filter dynamically predicts the current data based on historical data and fuses the measured data with the predicted data, thereby effectively eliminating the random noise in the data and improving the smoothness of the data. During the filtering process, outlier identification is performed, and the 3σ principle is used to mark abnormal data, that is, when a data point deviates from the mean of historical data by more than 3 times the standard deviation, it is marked as an abnormal point, and data correction is performed through interpolation or data fitting. The data is subjected to piecewise linear fitting and data downsampling. The part with a data change rate lower than the threshold is downsampled, thereby reducing the data volume and the computational pressure, and finally obtaining the status data of the hydrofluoric acid purification equipment.

[0034] In a specific embodiment, the process of performing filtering processing and outlier identification on the original data of the equipment operating status to obtain the status data of the hydrofluoric acid purification equipment may specifically include the following steps:

[0035] Classify the original data of the equipment operating status to obtain the classified original parameter data, and use the Kalman filtering algorithm to filter the classified original parameter data to obtain the parameter data after eliminating the measurement noise;

[0036] Calculate the average value and standard deviation of the parameter data after eliminating the measurement noise to obtain the parameter fluctuation reference value;

[0037] Based on the parameter fluctuation reference value, perform outlier marking on the parameter data after eliminating measurement noise to obtain an outlier marking set;

[0038] Perform linear interpolation processing on each outlier in the outlier marking set to obtain the parameter data after outlier correction;

[0039] Perform piecewise linear fitting and data downsampling on the parameter data after outlier correction to obtain the status data of the hydrofluoric acid purification equipment.

[0040] Specifically, the original data of the device operation status is classified. According to different data sources and monitoring parameters, the data is divided into four independent data sets: the temperature data of the mixing and reaction tank, the temperature data at the outlet of the evaporator, the pressure data of the rectification column, and the concentration data of the absorption tower. The classified original parameter data is refined according to the characteristics of the device operation parameters and aligned in terms of data time according to the sampling period, so that subsequent data processing can maintain time consistency and prevent time drift of parameters of different devices during the data analysis process. The classified original parameter data is filtered. The Kalman filtering algorithm is used to dynamically estimate the data, and the measurement noise in the data is eliminated through the mechanism of prediction and update. The Kalman filter dynamically models the device operation status according to the state equation and measurement equation of the system, and uses historical data to predict the current data to obtain the optimal estimated value, making the filtered data smoother and closer to the real state. During the data filtering process, the Kalman filter continuously updates the state estimated value and corrects the prediction result according to the newly collected data. The dynamic update mechanism enables the filtered data to effectively remove random noise, reduce measurement errors, and improve the stability of the data. The data after filtering is the parameter data after eliminating the measurement noise. Calculate the average value and standard deviation of the parameter data after eliminating the measurement noise to obtain the fluctuation reference value of each parameter. Based on the calculated parameter fluctuation reference value, mark the abnormal points of the parameter data after eliminating the measurement noise and generate an abnormal point marking set. The 3σ principle is adopted for abnormal point identification, that is, when a data point deviates from the parameter average value by more than 3 times the standard deviation, the data point is marked as an abnormal point and stored in the abnormal point marking set. By correcting the marked data points, the data anomalies caused by measurement errors or sudden interferences are effectively eliminated. Perform linear interpolation on the marked abnormal points to correct the abnormal data points. Linear interpolation estimates the abnormal points by using the adjacent valid data points before and after, and replaces the abnormal points with corrected values that conform to the data change trend. After completing the abnormal value correction, in order to improve the efficiency of data processing and avoid storing a large amount of redundant data, perform piecewise linear fitting and data downsampling on the corrected parameter data to obtain the status data of the hydrofluoric acid purification equipment. Piecewise linear fitting analyzes the data change trend, divides the data into multiple continuous intervals, and fits the data in each interval, so that the data change within each interval is accurately represented by a linear model. When performing data downsampling, according to the set change rate threshold, downsample the data segments with a change rate lower than the set threshold, thereby reducing the data volume and improving the efficiency of data storage and processing.

[0041] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0042] Calculate the parameter change rate based on the status data of the hydrofluoric acid purification equipment, and divide the status data of the hydrofluoric acid purification equipment into a parameter rising interval data set and a parameter falling interval data set according to the parameter change rate;

[0043] Perform fuzzification processing on the parameter rising interval data set to obtain the fuzzy input value in the rising interval;

[0044] Perform fuzzification processing on the parameter falling interval data set to obtain the fuzzy input value in the falling interval;

[0045] Input the fuzzy input value in the rising interval and the fuzzy input value in the falling interval into the fuzzy logic monitoring model for inference calculation to obtain the first fuzzy state output information;

[0046] Determine the adaptive factor according to the equipment operation time and batch, and adjust the first fuzzy state output information according to the adaptive factor to obtain the second fuzzy state output information;

[0047] Through the equipment importance weight coefficient matrix, perform weighted processing and defuzzification on the second fuzzy state output information to obtain the real-time status evaluation result.

[0048] Specifically, monitor the status data of key equipment in the hydrofluoric acid purification process. The data includes the temperature data of the mixing and reaction tank, the outlet temperature data of the evaporator, the pressure data of the distillation column, and the outlet concentration data of the absorption tower. By performing time series analysis on these data, calculate the change rates between consecutive time points for each parameter, such as the temperature change rate, pressure change rate, flow rate change rate, and concentration change rate, etc. And divide the data into an ascending interval data set and a descending interval data set according to the positive and negative directions of the change rates. The calculation of the parameter change rate is achieved through the difference method or the sliding window method to capture the minute changes of each key parameter during the operation of the equipment. The ascending interval data set contains the data when the parameter shows an upward trend, reflecting the state of system heating, increasing pressure, or rising flow rate, while the descending interval data set contains the data when the parameter is in a downward trend, reflecting the state of equipment cooling, decreasing pressure, or reducing flow rate. Perform fuzzification on the ascending interval data set of the parameter, converting the continuously changing numerical data into fuzzy set input values. The fuzzification process uses the triangular membership function or the Gaussian membership function, and sets the domain range of the membership function according to the normal operation interval of different parameters. The parameter change rate is divided into five fuzzy sets, including "extremely small, relatively small, medium, relatively large, extremely large". The vertices of the membership functions of each fuzzy set are located at both ends and the midpoint of the domain, effectively reflecting the trend and amplitude of data changes. During the fuzzification process, perform fuzzy mapping on each parameter change rate and calculate its membership degree values in each fuzzy set to form the fuzzy input values of the ascending interval. At the same time, perform fuzzification on the descending interval data set of the parameter. However, due to the more complex non-linear changes during the descending process, the Gaussian membership function is used for the data fuzzification of the descending interval to more sensitively capture the sudden changes and fluctuating changes occurring during the descending process, obtaining the fuzzy input values of the descending interval. Input the fuzzy input values of the ascending interval and the descending interval into the fuzzy logic monitoring model for inference calculation. The fuzzy logic monitoring model contains a fuzzy rule base consisting of 45 fuzzy rules. The fuzzy rules perform inference mapping on different parameter change situations in the form of "if the change rate is X and the current value is Y, then the equipment status is Z", covering status categories such as normal, slightly abnormal, moderately abnormal, severely abnormal, and dangerous. During the inference process, the fuzzy model performs fuzzy inference on the fuzzy inputs of the ascending interval and the descending interval, and through the fuzzy inference mechanism, combines and matches the fuzzy sets of the input variables to generate the first fuzzy state output information. Since the operating state of the equipment has time-varying characteristics, the first fuzzy state output information is not fixed, but dynamically adjusted with factors such as the operating time of the equipment and batch changes. To improve the accuracy of the inference results, determine the adaptive factor according to the operating time and batch of the equipment, and adjust the first fuzzy state output information according to the adaptive factor.The adaptive factor is a dynamically adjustable parameter, whose value fluctuates between 0.8 and 1.2 and is dynamically updated according to the running time and batches of different devices, enabling the fuzzy logic monitoring model to automatically adjust the sensitivity and response speed of the inference process as the device state changes. After the adjustment of the adaptive factor is completed, the second fuzzy state output information is obtained. The second fuzzy state output information is weighted and defuzzified through the device importance weight coefficient matrix. The weight coefficient matrix weights the parameter states according to the importance of different devices. For example, the distillation column and the absorption column, as the core devices in the hydrofluoric acid purification process, have higher weight coefficients in the weight matrix, while the weight of the mixing reactor and the evaporator is relatively low. The defuzzification process uses the weighted average method or the maximum membership degree method to convert the fuzzy state information into a specific numerical form, obtaining the final real-time state evaluation result, which reflects the current operating state of the device.

[0049] In a specific embodiment, the process of performing the step of fuzzifying the parameter rising interval data set to obtain the fuzzy input value of the rising interval may specifically include the following steps:

[0050] Set the domain ranges of temperature, pressure, flow rate, and concentration to obtain the domain space of the parameter change rate, and determine the normal operating intervals of temperature, pressure, flow rate, and concentration to obtain the domain space of the parameter values;

[0051] Divide the domain space of the parameter change rate evenly into five levels according to a preset setting, and construct five triangular membership functions with vertex coordinates located at both ends and the midpoint of the domain to obtain the fuzzy membership function set of the parameter change rate;

[0052] Divide the domain space of the parameter values evenly into five levels according to a preset setting, and construct five triangular membership functions with vertex coordinates at the midpoints of each interval to obtain the fuzzy membership function set of the parameter values;

[0053] Calculate each pair of parameter change rates in the parameter rising interval data set by inputting them into the fuzzy membership function set of the parameter change rate to obtain the change rate membership value matrix. At the same time, input the current parameter value in the parameter rising interval data set into the fuzzy membership function set of the parameter values to obtain the parameter value membership value matrix;

[0054] Combine the change rate membership value matrix and the parameter value membership value matrix to obtain the fuzzy input value of the rising interval.

[0055] Specifically, according to the process characteristics and historical operation data of the equipment, the change ranges of temperature, pressure, flow rate, and concentration are set, and the range of the parameter change rate is defined as the parameter change rate universe space. The universe space of the parameter change rate is set according to the physical characteristics of each equipment. For example, the temperature change rate of the mixing and reaction tank is set from -10°C / min to 10°C / min. At the same time, the normal operation intervals of temperature, pressure, flow rate, and concentration are determined to obtain the parameter value universe space. The parameter change rate universe space is evenly divided into five preset levels to construct a fuzzy membership function set of the parameter change rate. The five levels are "extremely small, relatively small, medium, relatively large, extremely large". The change situation of the parameter change rate is divided into different degrees of change amplitudes, so as to describe the state change of the system more precisely. The membership function is modeled using a triangular membership function. The vertex coordinates of each membership function are located at both ends and the midpoint of the universe, so that the parameter change rate is evenly mapped into each fuzzy set. At the same time, the parameter value universe space is evenly divided into the same five levels, and a fuzzy membership function set of the parameter value is constructed. The fuzzy membership function of the parameter value is also modeled using a triangular membership function. The vertex coordinates of each membership function are set at the midpoint of each interval and gradually decrease to zero at both ends, thus forming a complete fuzzy mapping. Each pair of parameter change rates in the parameter rising interval dataset is input into the fuzzy membership function set of the parameter change rate for calculation to obtain the change rate membership value matrix. For each data point in the parameter rising interval dataset, determine the fuzzy interval to which its parameter change rate belongs, and calculate its membership value in each fuzzy set according to the triangular membership function. The calculation of the membership value is linearly interpolated according to the distance between the parameter change rate and the vertex of the fuzzy set to obtain the change rate membership value matrix, which reflects the change trend of the equipment parameter change rate and quantifies the influence of different change amplitudes on the system state. At the same time, the current parameter value in the parameter rising interval dataset is input into the fuzzy membership function set of the parameter value for calculation to obtain the parameter value membership value matrix. The parameter value membership value matrix forms the parameter value membership matrix by judging the fuzzy interval where the current parameter value is located and calculating the membership degree according to the membership function. Combine the change rate membership value matrix and the parameter value membership value matrix, and perform weighted summation or minimum membership combination on the two to form the fuzzy input value of the rising interval.

[0056] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0057] Based on the real-time state evaluation results, classify the operating states of each equipment to obtain an equipment state classification dataset;

[0058] According to the equipment state classification dataset, calculate the frequency of each equipment operating state transferring to other states and perform normalization processing to obtain the equipment state transition probability;

[0059] Dynamically update the device state transition probability to obtain a dynamic transition matrix that can reflect the law of device state evolution;

[0060] Input the state data of the hydrofluoric acid purification equipment into the fast Fourier transform analysis algorithm to analyze the temperature control delay period of the mixing and reaction tank, the pressure response delay period of the rectification column, and the concentration adjustment delay period of the absorption tower, and obtain the main delay period characteristics of each device;

[0061] According to the main delay period characteristics of each device, construct a feedforward-feedback combined compensator for the transfer function to obtain a delay compensation model for each device;

[0062] Combine the dynamic transition matrix and the delay compensation model, and by calculating the state probability distribution at future times and considering the influence of delay factors, obtain the state prediction information of the hydrofluoric acid purification equipment.

[0063] Specifically, based on the real-time state assessment results, the operating states of each device are classified. The assessment results are obtained by inferring the fuzzy input values of the rising interval and the falling interval through a fuzzy logic monitoring model, reflecting the current state of the device, including the operating states of devices such as the mixing and reaction tank, evaporator, distillation column, and absorption tower. These states are divided into five levels: normal, slightly abnormal, moderately abnormal, severely abnormal, and dangerous state. During the operation of the system, the real-time state assessment results are continuously updated as the device state changes. Therefore, the state data is sorted according to the device classification to form a device state classification data set, recording the state changes of each device at different time periods. According to the device state classification data set, calculate the frequency of each device operating state transferring to other states and perform normalization processing to obtain the device state transition probability, reflecting the possibility of the device evolving from one state to other states. During the calculation process, count the number of times each device transfers from one state to other states in a recent period of time (for example, 1000 time steps), and normalize these frequencies to obtain the state transition probability matrix. For example, if the frequency of the distillation column transferring from the normal state to the slightly abnormal state accounts for 20% of the total transfer frequency, then the transfer probability is 0.2, and these state transition probabilities constitute a preliminary state transition matrix. Since the hydrofluoric acid purification process has non-linear and time-varying characteristics, the change law of the device state will be dynamically adjusted with the change of process conditions, and the update of the device state transition probability needs to be dynamic. Introduce the sliding time window method to dynamically update the device state transition probability. Continuously collect new state change data within the set time window (for example, 500 hours), and update the state transition probability matrix every 100 hours to ensure that the state transition matrix always reflects the evolution law of the current operating state of the device. Through the dynamic update mechanism, a dynamic transition matrix reflecting the evolution law of the device state is generated. Input the hydrofluoric acid purification device state data into the fast Fourier transform analysis algorithm to convert the time-domain data into frequency-domain data, so as to identify the main delay cycle characteristics in the control systems of each device. For example, the delay cycle of the mixing and reaction tank temperature control system reflects the heat transfer time of the reactants during the mixing process, and its main delay cycle is identified through fast Fourier transform analysis; the pressure response delay cycle of the distillation column reflects the time required for the top pressure to reach a steady state after the load changes; the delay cycle of the absorption tower concentration adjustment is related to factors such as liquid absorption efficiency and material transfer speed. According to the main delay cycle characteristics of each device, construct a feedforward-feedback combined compensator of the transfer function to eliminate the system response lag problem caused by the delay and obtain a delay compensation model for each device.The structure of the feedforward-feedback compensator includes a feedforward part and a feedback part. The feedforward part is used to perform control compensation in advance when the system detects a state change, making an advanced adjustment by predicting the future state trend, so as to reduce the impact of delay on the system. The feedback part is used to monitor the actual response of the device state in real time and correct the control quantity according to the actual operating state to form a closed-loop compensation control. The parameters of the compensator are dynamically adjusted according to the delay period of different devices. For example, for the temperature control delay period of the mixing and reaction tank, the feedforward part of the compensator makes an advance compensation according to the temperature change trend in the next 45 minutes, and the feedback part makes real-time correction according to the temperature deviation, so as to ensure that the temperature change can be stably maintained within the set range. The compensation models of the distillation column and the absorption tower are also adjusted according to their respective delay periods, so that the system can maintain a high control accuracy and stability under different working conditions. After combining the dynamic transition matrix and the delay compensation model, by calculating the state probability distribution at future moments and taking the delay factor into account, the state prediction information of the hydrofluoric acid purification equipment is obtained. The state probability distribution is predicted based on the current device state and the dynamic transition matrix. By simulating and calculating the state transition conditions at multiple future time steps, the future state change trend of the device is predicted. The delay compensation model compares the prediction result with the actual operating state through the feedforward-feedback compensation mechanism and corrects the deviation caused by the control signal delay, making the state prediction information more accurate and reliable. Generate device state prediction information, including the future state changes of each device, the operating state trend after delay compensation, and potential abnormal state warnings.

[0064] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0065] Construct a control mode switching condition matrix according to the state prediction information of the hydrofluoric acid purification equipment;

[0066] Perform real-time calculation on the control mode switching condition matrix to obtain the currently applicable control mode type;

[0067] Select the control algorithm parameters corresponding to the device state based on the currently applicable control mode type. Among them, the PID parameter control algorithm is adopted in the normal mode, the fuzzy PID control algorithm is adopted in the anti-fluctuation mode, the model predictive control strategy is adopted in the fast recovery mode, and the robust H∞ control algorithm is adopted in the emergency protection mode;

[0068] Input the control algorithm parameters corresponding to the device state into the state observer for system current state estimation, and at the same time calculate the output values of the old and new control algorithms to obtain the dual-algorithm control quantity at the time of control mode switching;

[0069] Perform weighted averaging on the dual-algorithm control quantity at the time of control mode switching through the transition function to obtain a smoothly transitioning control output signal;

[0070] The performance of the smoothed transition control output signal is evaluated by using the control mode effectiveness evaluation index to obtain the performance evaluation result, and the performance evaluation result is fed back to the control mode switching condition matrix to obtain the control strategy for the characteristics of the hydrofluoric acid purification process.

[0071] Specifically, according to the device status prediction information, a control mode switching condition matrix is constructed. This matrix maps the status categories of the device to control modes to ensure automatic switching to the optimal control mode under different device statuses. The control mode switching condition matrix is a fifth-order matrix. The status categories include "normal, slightly abnormal, moderately abnormal, severely abnormal, and dangerous", while the control modes include "conventional mode, anti-fluctuation mode, rapid recovery mode, and emergency protection mode". By setting the condition thresholds in the matrix, the appropriate control mode is automatically selected according to the device status prediction information. The control mode switching condition matrix is calculated in real time. By comparing the current status of the device with the predicted status, it is determined whether a mode switch is required, and the type of the currently applicable control mode is obtained. The conditions for control mode switching include multiple factors such as state transition probability, parameter change trend, and device importance. For example, if the system detects that the change amplitude of the concentration at the outlet of the absorption tower exceeds the set threshold and the concentration deviation trend continues to increase, the system will switch to the rapid recovery mode; when the temperature of the mixing and reaction tank shows abnormal fluctuations and the device operation status enters the dangerous area, the system will automatically switch to the emergency protection mode. Through the real-time calculation of the multi-dimensional condition matrix, it is ensured that the system automatically selects the optimal control mode type under different statuses. After determining the type of the currently applicable control mode, the corresponding control algorithm parameters are selected according to different control modes. Among them, the conventional mode is applicable when the device is in a normal state, and the PID control algorithm is used for control. After the PID parameters are precisely tuned, the system operates stably; when the device status changes to slightly abnormal or there is a fluctuation trend, the system switches to the anti-fluctuation mode. In this mode, the fuzzy PID control algorithm is used, and the PID parameters are dynamically adjusted through the fuzzy inference mechanism, so that the control system can better suppress fluctuations and improve the stability of the system; if the device enters the moderately abnormal state, the system selects the rapid recovery mode. This mode adopts the model predictive control strategy, and by predicting the future status and optimizing the control input in advance, the device can quickly return to the normal state; when the device status enters the severely abnormal or dangerous state, the system switches to the emergency protection mode. This mode adopts the robust H∞ control algorithm, which has strong anti-interference ability and robustness and can maintain the basic safe operation of the device under extreme conditions. After the control mode switching is completed, the control algorithm parameters corresponding to the device status are input into the state observer for the current state estimation of the system, and at the same time, the output values of the old and new control algorithms are calculated to ensure the smooth transition of the system during the control mode switching process. The state observer estimates the current state of the system in real time according to the actual measurement data and the device state model, and calculates the control output values at the current moment by combining the old and new control algorithms respectively.For example, when the system switches from the normal mode to the anti-fluctuation mode, the state observer calculates the PID control output value in the normal mode based on the current device state estimation result, and at the same time calculates the fuzzy PID control output value in the anti-fluctuation mode, generating the dual-algorithm control quantities during the control mode switch. These control quantities are used for the subsequent smooth switching process to prevent the control signal from suddenly changing and causing system instability. To avoid system oscillation or instability caused by the sudden change of the control signal during the control mode switch, the dual-algorithm control quantities during the control mode switch are weighted and averaged through a transition function to obtain a smoothly transitioned control output signal. The transition function adopts an exponential smoothing form and is dynamically adjusted according to the time process of the control mode switch and the state change speed. During the weighted averaging process, the output values of the old and new control algorithms are smoothly transitioned according to the current state estimation result and the mode switch progress. For example, at the initial stage of the mode switch, the weight of the new algorithm is low, and as time goes by, the weight of the new algorithm gradually increases until it completely replaces the old algorithm, realizing the seamless switch of the control signal, thereby maintaining the smoothness of the device operation state and the continuity of the control signal. After completing the control mode switch and the smooth transition of the control output signal, the performance of the smoothly transitioned control output signal is evaluated, and the actual control effect of the current control mode is judged through the control mode effectiveness evaluation index. The performance evaluation includes multiple evaluation indexes such as the integral of absolute error, the total change amount of the control signal, and the system stable time. The system comprehensively evaluates the effect of the current control mode according to these evaluation indexes and generates a performance evaluation result. If the performance evaluation result indicates that the current control mode fails to effectively suppress the abnormal state or restore the device to the normal operation state, the system feeds back the performance evaluation result to the control mode switch condition matrix and dynamically adjusts the condition thresholds and switching logics in the matrix to optimize the switching mechanism of the control mode. For example, if the system finds that the anti-fluctuation mode cannot effectively suppress fluctuations in a certain specific abnormal situation, the system automatically reduces the trigger condition threshold for switching to the anti-fluctuation mode, or increases the priority for switching to the fast recovery mode, so that it can switch to a more suitable control mode more quickly the next time a similar state occurs.

[0072] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described system, system, and unit can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0073] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a hydrofluoric acid purification process state data monitoring device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc.

[0074] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring the state data of the hydrofluoric acid purification process, characterized in that, Including: Collecting the original data of the operating states of the mixing and reaction tank, evaporator, rectification column, and absorption tower in the hydrofluoric acid purification system, and preprocessing the original data of the operating states of the equipment to obtain the state data of the hydrofluoric acid purification equipment; Inputting the state data of the hydrofluoric acid purification equipment into a fuzzy logic monitoring model for state evaluation to obtain a real-time state evaluation result; Calculating the equipment state transition probability according to the real-time state evaluation result, and simultaneously identifying the time-varying delay characteristics of the temperature control of the mixing and reaction tank, the pressure response of the rectification column, and the concentration adjustment of the absorption tower to obtain the state prediction information of the hydrofluoric acid purification equipment; Specifically including: Based on the real-time state evaluation result, classifying the operating states of each equipment to obtain an equipment state classification data set; According to the equipment state classification data set, calculating the frequency of transfer of each equipment operating state to other states and performing normalization processing to obtain the equipment state transition probability; Dynamically updating the equipment state transition probability to obtain a dynamic transition matrix that can reflect the evolution law of the equipment state; Inputting the state data of the hydrofluoric acid purification equipment into a fast Fourier transform analysis algorithm to analyze the delay periods of the temperature control of the mixing and reaction tank, the pressure response of the rectification column, and the concentration adjustment of the absorption tower to obtain the main delay period characteristics of each equipment; According to the main delay period characteristics of each equipment, constructing a feedforward-feedback combined compensator of the transfer function to obtain a delay compensation model for each equipment; Combining the dynamic transition matrix and the delay compensation model, and by calculating the state probability distribution at a future time and considering the influence of delay factors, obtaining the state prediction information of the hydrofluoric acid purification equipment; Based on the state prediction information of the hydrofluoric acid purification equipment, performing adaptive switching among the normal mode, anti-fluctuation mode, rapid recovery mode, and emergency protection mode to obtain a control strategy for the characteristics of the hydrofluoric acid purification process.

2. The method for monitoring the state data of the hydrofluoric acid purification process according to claim 1, characterized in that, The collecting the original data of the operating states of the mixing and reaction tank, evaporator, rectification column, and absorption tower in the hydrofluoric acid purification system, and preprocessing the original data of the operating states of the equipment to obtain the state data of the hydrofluoric acid purification equipment includes: Installing temperature sensors, pressure sensors, flow sensors, and concentration analyzers on the mixing and reaction tank, evaporator, rectification column, and absorption tower in the hydrofluoric acid purification system to obtain the monitoring point layout of the hydrofluoric acid purification system; Collecting the real-time data of each monitoring point of the hydrofluoric acid purification system through the monitoring point layout of the hydrofluoric acid purification system to obtain the mixing and reaction tank temperature data, evaporator outlet temperature data, rectification column pressure data, and absorption tower concentration data; Using an industrial-grade edge computing unit to collect and synchronously calibrate the mixing and reaction tank temperature data, the evaporator outlet temperature data, the rectification column pressure data, and the absorption tower concentration data to obtain a system operation parameter data set; Transmitting the system operation parameter data set to a central monitoring system through an industrial Ethernet protocol and storing it in a local edge device and a central server to obtain the original data of the equipment operating state; Performing filtering processing and outlier identification on the original data of the equipment operating state to obtain the state data of the hydrofluoric acid purification equipment.

3. The method for monitoring the state data of the hydrofluoric acid purification process according to claim 2, wherein Filtering the original data of the device operation status and identifying outliers to obtain the status data of the hydrofluoric acid purification equipment, including: Classify the original data of the device operation status to obtain the classified original parameter data, and use the Kalman filter algorithm to filter the classified original parameter data to obtain the parameter data after removing measurement noise; Calculate the average value and standard deviation of the parameter data after removing measurement noise to obtain the parameter fluctuation reference value; Based on the parameter fluctuation reference value, mark the abnormal points of the parameter data after removing measurement noise to obtain an abnormal point mark set; Perform linear interpolation on each abnormal point in the abnormal point mark set to obtain the parameter data after outlier correction; Perform piecewise linear fitting and data downsampling on the parameter data after outlier correction to obtain the status data of the hydrofluoric acid purification equipment.

4. The method for monitoring the status data of the hydrofluoric acid purification process according to claim 1, wherein, Input the status data of the hydrofluoric acid purification equipment into the fuzzy logic monitoring model for status evaluation to obtain the real-time status evaluation result, including: Calculate the parameter change rate according to the status data of the hydrofluoric acid purification equipment, and divide the status data of the hydrofluoric acid purification equipment into a parameter rising interval data set and a parameter falling interval data set according to the parameter change rate; Fuzzify the parameter rising interval data set to obtain the fuzzy input value in the rising interval; Fuzzify the parameter falling interval data set to obtain the fuzzy input value in the falling interval; Input the fuzzy input value in the rising interval and the fuzzy input value in the falling interval into the fuzzy logic monitoring model for inference calculation to obtain the first fuzzy state output information; Determine the adaptive factor according to the device operation time and batch, and adjust the first fuzzy state output information according to the adaptive factor to obtain the second fuzzy state output information; Through the device importance weight coefficient matrix, perform weighted processing and defuzzification on the second fuzzy state output information to obtain the real-time status evaluation result.

5. The method for monitoring the state data of the hydrofluoric acid purification process according to claim 4, characterized in that, The fuzzification of the parameter rising interval data set to obtain the fuzzy input value in the rising interval includes: Set the domain range of temperature, pressure, flow rate and concentration to obtain the domain space of the parameter change rate, and determine the normal operation interval of temperature, pressure, flow rate and concentration to obtain the domain space of the parameter value; Uniformly divide the domain space of the parameter change rate into five levels according to a preset, and construct five triangular membership functions with vertex coordinates at both ends and the midpoint of the domain to obtain the fuzzy membership function set of the parameter change rate; Uniformly divide the domain space of the parameter value into five levels according to a preset, and construct five triangular membership functions with vertex coordinates at the midpoint of each interval to obtain the fuzzy membership function set of the parameter value; Input each pair of parameter change rates in the parameter rising interval data set into the fuzzy membership function set of the parameter change rate for calculation to obtain the change rate membership value matrix, and at the same time input the current parameter value in the parameter rising interval data set into the fuzzy membership function set of the parameter value for calculation to obtain the parameter value membership value matrix; Combine the change rate membership value matrix and the parameter value membership value matrix to obtain the fuzzy input value in the rising interval.

6. The method for monitoring the state data of the hydrofluoric acid purification process according to claim 1, wherein Based on the state prediction information of the hydrofluoric acid purification equipment, adaptively switch between the normal mode, anti-fluctuation mode, rapid recovery mode, and emergency protection mode to obtain a control strategy for the characteristics of the hydrofluoric acid purification process, including: Construct a control mode switching condition matrix according to the state prediction information of the hydrofluoric acid purification equipment; Perform real-time calculation on the control mode switching condition matrix to obtain the currently applicable control mode type; Select the control algorithm parameters corresponding to the equipment state based on the currently applicable control mode type, where the normal mode uses a control algorithm with PID parameters, the anti-fluctuation mode uses a fuzzy PID control algorithm, the rapid recovery mode uses a model predictive control strategy, and the emergency protection mode uses a robust H∞ control algorithm; Input the control algorithm parameters corresponding to the equipment state into the state observer for system current state estimation, and at the same time calculate the output values of the old and new control algorithms to obtain the dual-algorithm control quantity during control mode switching; Perform weighted averaging on the dual-algorithm control quantity during control mode switching through a transition function to obtain a smoothly transitioning control output signal; Perform performance evaluation on the smoothly transitioning control output signal through a control mode effectiveness evaluation index to obtain a performance evaluation result, and feedback the performance evaluation result to the control mode switching condition matrix to obtain a control strategy for the characteristics of the hydrofluoric acid purification process.

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