A PLC-based flow monitoring system for a pit furnace

Abstract

The application relates to the field of shaft furnaces, and discloses a PLC-based flow monitoring system for a shaft furnace, which comprises a flow sensor, a PLC control unit, a man-machine interface and a communication module; the flow sensor is installed on a medium output pipeline of the shaft furnace and is used for collecting flow data of the medium in real time; the PLC control unit receives flow data transmitted by the flow sensor and performs a fuzzy PID algorithm to obtain a control variable, and controls the opening and closing degree of an electromagnetic valve based on the control variable to regulate and control the flow of the medium of the shaft furnace; the man-machine interface is used for displaying a flow curve, an alarm state and historical data; the fuzzy PID algorithm comprises a proportional term, an integral term and a differential term; the proportional term is obtained based on the difference between a flow set value and real-time flow data; and the flow set value is adjusted based on the flow data and pressure data. Through the technical scheme, the coordinated control of multiple parameters of the shaft furnace is realized, and the flow control precision is improved.

Description

Technical Field

[0001] This application relates to the field of pit furnaces, and more particularly to a PLC-based pit furnace flow monitoring system. Background Technology

[0002] Traditional pit furnaces rely heavily on manual adjustment or simple mechanical valves for flow control, resulting in slow response, low accuracy, and an inability to adjust in real time. For example, steam flow control in a steam treatment pit furnace depends on manual observation of the settling hopper, which easily leads to high energy consumption and poor process stability. While existing technologies include PLC-based flow monitoring systems, their adaptability to the special structure of pit furnaces is insufficient, and there is a lack of optimized solutions for the coordinated control of multiple parameters within the furnace.

[0003] Solving this technical problem is a technical challenge that needs to be overcome by those skilled in the art. Summary of the Invention

[0004] This application provides a PLC-based flow monitoring system for a pit furnace, which at least partially solves the above-mentioned technical problems.

[0005] To achieve the above objectives, a PLC-based well furnace flow monitoring system is provided, comprising: a flow sensor, a PLC control unit, a human-machine interface, and a communication module;

[0006] The flow sensor is installed in the medium output pipeline of the pit furnace to collect the flow data of the medium in real time.

[0007] The PLC control unit receives the flow data transmitted by the flow sensor and executes a fuzzy PID algorithm to obtain a control quantity. Based on the control quantity, it controls the opening and closing degree of the solenoid valve to regulate the flow rate of the medium in the pit furnace.

[0008] The human-computer interaction interface is used to display the flow rate curve, alarm status, and historical data;

[0009] The communication module is used to enable communication between the PLC control unit and the host computer or mobile terminal;

[0010] The PLC control unit is also used to acquire temperature data sent by a temperature sensor installed inside the pit furnace and pressure data sent by a pressure sensor installed in the medium output pipeline; the fuzzy PID algorithm includes a proportional term, an integral term, and a derivative term; the proportional term is obtained based on the difference between the flow rate setpoint and the real-time flow rate data; the flow rate setpoint is adjusted based on the flow rate data and the pressure data;

[0011] The PLC control unit also includes a time window adjustment module; the time window adjustment module is used for:

[0012] The fluctuation frequency of temperature data was calculated using the Fast Fourier Transform spectral analysis method.

[0013] When the fluctuation frequency of the temperature data is greater than the first frequency, the time window is... The duration is shortened; when the fluctuation frequency of the temperature data is less than the second frequency, the time window is shortened. Extend the duration; maintain the time window when the fluctuation frequency of temperature data is between the second and first frequencies. The frequency remains unchanged; the second frequency is less than the first frequency.

[0014] Optionally, the flow rate setpoint is adjusted based on the flow rate data and pressure data, including:

[0015] The PLC control unit acquires the flow data collected by the flow sensor. Temperature data collected by temperature sensor Pressure data collected by pressure sensors ;

[0016] Calculate the temperature data The temperature deviation from the preset temperature value T and the pressure data The magnitude of the pressure deviation from the preset pressure value P;

[0017] When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, the standard flow rate is calculated. ;in, ;

[0018] The PLC control unit controls the standard flow rate value. The standard flow rate value is obtained by performing a first-order low-pass filter. ;

[0019] The PLC control unit retrieves the current flow rate setting value; if the standard flow rate value is... Less than the current flow rate setting and pressure data The pressure value P or the temperature data is greater than the preset pressure value P. If the temperature exceeds the preset value T, the current flow rate setting will be lowered; the reduction will increase with the increase of the pressure deviation or temperature deviation. If the standard flow rate value... The pressure data is greater than the current flow setting value. Less than the preset pressure value P or temperature data If the current flow rate is less than the preset temperature value T, the current flow rate setting will be increased; the increase will increase as the pressure deviation or temperature deviation increases.

[0020] The PLC control unit transmits the adjusted flow rate setting to the human-machine interface, where the adjusted flow rate setting and adjustment timestamp are displayed.

[0021] Optionally, the process of adjusting the flow rate setting also includes:

[0022] When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, a waiting time window is triggered. ;

[0023] When the temperature deviation or pressure deviation is within the time window When the flow rate continuously exceeds the corresponding preset threshold, the standard flow rate value is calculated and the flow rate setting value is adjusted; the time window Adjustments were made based on the characteristics of the pit furnace process.

[0024] Optionally, the time window adjustment module is further configured to:

[0025] The fluctuation frequency of the pressure data was calculated using the Fast Fourier Transform spectral analysis method.

[0026] When the fluctuation frequency of the pressure data is greater than the third frequency, the time window will be... The duration is shortened; when the fluctuation frequency of the pressure data is less than the fourth frequency, the time window is shortened. Extend the duration; maintain the time window when the fluctuation frequency of pressure data is between the fourth and third frequencies. Unchanged; the fourth frequency is less than the third frequency.

[0027] Optionally, when executing the fuzzy PID algorithm, the method further includes:

[0028] Obtain the comparison results of temperature deviation amplitude and corresponding preset threshold, and the comparison results of pressure deviation amplitude and corresponding preset threshold;

[0029] When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, the weight coefficient of the proportional term is set to be greater than the weight coefficient of the integral term.

[0030] When the temperature deviation or pressure deviation is lower than the corresponding preset threshold, the weight coefficient of the proportional term is set to be less than the weight coefficient of the integral term.

[0031] The proportional term output and integral term output are calculated based on the adjusted weighting coefficients. The proportional term output is the product of the proportional term weighting coefficient and the difference between the flow setpoint and the real-time flow data. The integral term output is the product of the integral term weighting coefficient and the integral of the difference between the flow setpoint and the real-time flow data.

[0032] Optionally, the system further includes an edge gateway; the edge gateway communicates with the PLC control unit; the edge gateway is used to: construct a physical correlation model among the flow data from the flow sensor, the temperature data from the temperature sensor, and the pressure data from the pressure sensor; the physical correlation model is based on the hydrodynamic relationship of the medium flow in the well furnace;

[0033] The theoretical flow rate is calculated based on the temperature data from the temperature sensor and the pressure data from the pressure sensor.

[0034] Based on the theoretical flow rate value and the flow data from the flow sensor. The deviation between the two is obtained by comparison;

[0035] If the deviation is greater than the preset consistency threshold, the deviation between the change trend of the data of each sensor and the historical mean within the first time period is calculated, and a confidence score is assigned to the data output by each sensor.

[0036] The sensor with the lowest confidence score is marked as suspicious and a status warning is issued on the human-machine interface.

[0037] Optionally, the edge gateway is further configured to:

[0038] Once a sensor is marked as suspicious, the current output value of the sensor marked as suspicious and its trend of change over a second time period are obtained.

[0039] Based on the physical correlation model and data from two other normal sensors, the theoretical expected value of the sensor marked as being in a suspicious state was calculated.

[0040] Calculate the residual between the measured value and the theoretical expected value of the sensor marked as a suspicious state;

[0041] Determine whether the residual is less than the preset recovery threshold for N consecutive periods and the standard deviation of the rate of change is lower than the threshold.

[0042] If all conditions are met, the sensor is determined to have recovered its health, a status update command is sent to the PLC control unit, and a prompt indicating that the sensor status has recovered is displayed on the human-machine interface.

[0043] Optionally, the edge gateway is further configured to:

[0044] Based on historical flow data, temperature change rate, and pressure change rate, an adaptive Kalman filter algorithm is used to predict the flow change trend within the next T time period.

[0045] Based on the predicted flow rate change trend, the optimal adjustment timing and opening degree of the solenoid valve are calculated using a dynamic programming algorithm. The dynamic programming algorithm includes: defining the state space as the current flow rate deviation, the predicted flow rate trend slope, and the current opening degree of the solenoid valve; defining the decision variable as the solenoid valve opening adjustment amount; and defining the objective function as the weighted sum of the solenoid valve adjustment energy consumption and the flow control error.

[0046] Record and compare the optimal adjustment timing and opening / closing degree with the actual adjustment time and actual opening / closing degree.

[0047] If the difference between either of the two is greater than the corresponding allowable difference, it will be displayed on the human-computer interaction interface.

[0048] Optionally, the edge gateway is also used to: estimate the viscosity coefficient of the medium in real time based on the temperature and pressure data of the medium inside the well furnace when calculating the opening degree of the solenoid valve;

[0049] Adjust the maximum value of the solenoid valve opening degree according to the viscosity coefficient of the medium; when the viscosity of the medium increases, decrease the maximum value of the adjustment value to avoid valve jamming or slow response; when the viscosity of the medium decreases, increase the maximum value of the adjustment value to improve control sensitivity.

[0050] In summary, the embodiments of this application, through the above technical solutions, achieve coordinated control of multiple parameters of the pit furnace and improve the accuracy of flow control.

[0051] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a system block diagram of a PLC-based well furnace flow monitoring system provided in an exemplary embodiment of this application;

[0054] Figure 2 This is a schematic diagram of a PLC-based well furnace flow monitoring system provided in an exemplary embodiment of this application;

[0055] Explanation of reference numerals in the attached diagram: 01, Flow sensor; 102, PLC control unit; 103, Human-machine interface; 104, Communication module. Detailed Implementation

[0056] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0057] This application provides a PLC-based flow monitoring system for a pit furnace. Please refer to Figures 1 and 2. The PLC-based flow monitoring system for a pit furnace provided in this application includes:

[0058] Flow sensor, PLC control unit, human-machine interface and communication module;

[0059] The flow sensor is installed in the medium output pipeline of the pit furnace to collect the flow data of the medium in real time.

[0060] The PLC control unit receives the flow data transmitted by the flow sensor and executes a fuzzy PID algorithm to obtain a control quantity. Based on the control quantity, it controls the opening and closing degree of the solenoid valve to regulate the flow rate of the medium in the pit furnace.

[0061] The human-computer interaction interface is used to display the flow rate curve, alarm status, and historical data;

[0062] The communication module is used to enable communication between the PLC control unit and the host computer or mobile terminal;

[0063] The PLC control unit is also used to acquire temperature data sent by a temperature sensor installed inside the pit furnace and pressure data sent by a pressure sensor installed in the medium output pipeline; the fuzzy PID algorithm includes a proportional term, an integral term, and a derivative term; the proportional term is obtained based on the difference between the flow rate setpoint and the real-time flow rate data; the flow rate setpoint is adjusted based on the flow rate data and the pressure data;

[0064] The PLC control unit also includes a time window adjustment module; the time window adjustment module is used for:

[0065] The fluctuation frequency of temperature data was calculated using the Fast Fourier Transform spectral analysis method.

[0066] When the fluctuation frequency of the temperature data is greater than the first frequency, the time window is... The duration is shortened; when the fluctuation frequency of the temperature data is less than the second frequency, the time window is shortened. Extend the duration; maintain the time window when the fluctuation frequency of temperature data is between the second and first frequencies. The frequency remains unchanged; the second frequency is less than the first frequency.

[0067] Specifically, flow sensors are installed in the medium output pipeline of the pit furnace to collect real-time flow data. Electromagnetic flow meters or vortex flow meters can be selected based on the type of medium inside the pit furnace. The PLC control unit is the core control component of the entire system; this system uses a Siemens S7-1200 series PLC. The fuzzy PID algorithm combines the advantages of fuzzy control and PID control. The PID control algorithm includes a proportional term (P), an integral term (I), and a derivative term (D). The proportional term is derived from the difference between the flow setpoint and the real-time flow data, enabling rapid response to deviations and reducing errors. The integral term eliminates static errors and improves the system's control accuracy. The derivative term predicts the trend of deviation changes and initiates control in advance to avoid excessive system fluctuations. The flow setpoint is not fixed but adjusted based on real-time collected flow and pressure data, making the flow control more closely match the actual operating conditions of the pit furnace.

[0068] The human-machine interface (HMI) serves as the medium for interaction and information exchange between the system and the user. It displays flow curves, alarm statuses, and historical data, and also supports remote parameter settings by staff. In this system, the monitoring interface is designed using PROFACE configuration software. Staff can intuitively understand the changes in the flow rate of the medium in the pit furnace through this interface. When an anomaly occurs, the alarm status will be displayed on the interface in a timely manner, allowing staff to quickly become aware of the situation. The communication module enables communication between the PLC control unit and the host computer or mobile terminal. It supports multiple communication protocols such as Modbus and Profibus, and allows for the selection of wireless or wired communication methods according to actual needs, thereby expanding the system's IoT functionality. Through the communication module, staff can obtain system operating data from a host computer or mobile terminal located far from the pit furnace site, achieving remote monitoring. They can also remotely send control commands to the PLC control unit, further reducing manual on-site intervention.

[0069] Through the above scheme, the PLC control unit combines multiple parameters such as temperature and pressure, and dynamically adjusts the flow setpoint and controls the opening and closing degree of the solenoid valve through a fuzzy PID algorithm. Compared with the existing PLC flow monitoring system that only controls a single parameter or has insufficient adaptability, this scheme achieves coordinated control of multiple parameters of the pit furnace, improving the accuracy of flow control. The flow setpoint can be optimized according to the actual working conditions, making the flow control more suitable for the actual working conditions of the pit furnace.

[0070] In some embodiments, the flow rate setpoint is adjusted based on the flow rate data and pressure data, including:

[0071] The PLC control unit acquires the flow data collected by the flow sensor. Temperature data collected by temperature sensor Pressure data collected by pressure sensors ;

[0072] Calculate the temperature data The temperature deviation from the preset temperature value T and the pressure data The magnitude of the pressure deviation from the preset pressure value P;

[0073] When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, the standard flow rate is calculated. ;in, ;

[0074] The PLC control unit controls the standard flow rate value. The standard flow rate value is obtained by performing a first-order low-pass filter. ;

[0075] The PLC control unit retrieves the current flow rate setting value; if the standard flow rate value is... Less than the current flow rate setting and pressure data The pressure value P or the temperature data is greater than the preset pressure value P. If the temperature exceeds the preset value T, the current flow rate setting will be lowered; the reduction will increase with the increase of the pressure deviation or temperature deviation. If the standard flow rate value... The pressure data is greater than the current flow setting value. Less than the preset pressure value P or temperature data If the current flow rate is less than the preset temperature value T, the current flow rate setting will be increased; the increase will increase as the pressure deviation or temperature deviation increases.

[0076] The PLC control unit transmits the adjusted flow rate setting to the human-machine interface, where the adjusted flow rate setting and adjustment timestamp are displayed.

[0077] Specifically, the core function of a pit furnace is to achieve heat treatment of workpieces within a specific temperature environment. When the furnace temperature is too high, the excess heat needs to be removed by increasing the flow rate of the medium to prevent performance degradation due to overheating. When the temperature is too low, the flow rate of the cooling medium needs to be reduced or the flow rate of the heating-related medium needs to be increased to ensure that the heat treatment temperature meets the process requirements. The pressure of the medium output pipeline directly reflects the medium delivery status. Excessive pressure may exceed the pipeline's pressure-bearing limit, causing safety hazards such as leakage and pipeline damage. In this case, the flow rate needs to be increased to reduce the pressure accumulated in the pipeline. With traditional fixed flow rate settings, when the temperature is too high, the fixed flow rate cannot remove heat in time, easily causing the workpiece to overheat and become unusable; when the pressure is too low, the fixed flow rate cannot guarantee the supply of medium, easily leading to a decrease in process efficiency.

[0078] In this system, the PLC control unit adjusts the flow setpoint based on flow rate, temperature, and pressure, solving the problem of poor adaptability between fixed setpoints and dynamic operating conditions. By collecting real-time operating data and calculating the deviation range, adjustments are initiated when parameters deviate from the threshold, avoiding system instability caused by frequent adjustments. First-order low-pass filtering eliminates instantaneous data interference, ensuring the stability of setpoint adjustments and avoiding misadjustments caused by false fluctuations. Furthermore, the greater the temperature deviation, the greater the flow adjustment range, which can quickly bring the furnace temperature back to the process range.

[0079] In some embodiments, the process of adjusting the flow rate setting also includes:

[0080] When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, a waiting time window is triggered. ;

[0081] When the temperature deviation or pressure deviation is within the time window When the flow rate continuously exceeds the corresponding preset threshold, the standard flow rate value is calculated and the flow rate setting value is adjusted; the time window Adjustments were made based on the characteristics of the pit furnace process.

[0082] Specifically, by setting a continuous monitoring time to determine whether the deviation is a persistent anomaly, false deviations caused by instantaneous interference can be effectively filtered out, avoiding unnecessary adjustments to the flow setting value triggered by a single false alarm. Moreover, in this application, the time window duration is set according to the process characteristics of the pit furnace, such as the medium type and heat treatment process requirements, which improves the system's adaptability to various types of pit furnaces.

[0083] In some embodiments, the time window adjustment module is further configured to:

[0084] The fluctuation frequency of the pressure data was calculated using the Fast Fourier Transform spectral analysis method.

[0085] When the fluctuation frequency of the temperature data is greater than the first frequency, the time window is... The duration is shortened; when the fluctuation frequency of the temperature data is less than the second frequency, the time window is shortened. Extend the duration; maintain the time window when the fluctuation frequency of temperature data is between the second and first frequencies. Unchanged; the second frequency is less than the first frequency;

[0086] Specifically, the temperature data collected by the temperature sensor and the pressure data collected by the pressure sensor are analyzed by the Fast Fourier Transform spectral analysis method to calculate the fluctuation frequency of the temperature data and the fluctuation frequency of the pressure data. The higher the fluctuation frequency, the more frequent the temperature or pressure changes and the worse the operating stability. The lower the fluctuation frequency, the smoother the temperature or pressure changes and the better the operating stability.

[0087] The time window adjustment module has a built-in preset first frequency and second frequency. The frequency values ​​of the first frequency and second frequency are set according to the operating characteristics of different heat treatment processes of pit furnace. For example, in the carburizing process, the furnace temperature needs to be kept stable, so F1=0.5Hz and F2=0.1Hz can be set; in the steam treatment process, the temperature is easily affected by the steam supply fluctuation, so F1=1Hz and F2=0.3Hz can be set, and the calculated temperature fluctuation frequency is compared with F1 and F2.

[0088] When the temperature fluctuation frequency is greater than F1, it indicates that the temperature data fluctuates frequently. If the original time window duration is maintained, it may not be able to capture the continuous anomaly in time. At this time, the time window adjustment module shortens the time window duration to improve the response speed to the real anomaly under high-frequency fluctuation conditions and avoid adjustment lag due to the window being too long.

[0089] When the temperature fluctuation frequency is less than F2, it indicates that the temperature data fluctuates smoothly. If the time window is too short, it may misjudge minor instantaneous interference as a continuous anomaly, triggering unnecessary adjustments to the flow rate setpoint. In this case, the time window adjustment module will extend the time window duration to enhance the filtering capability of instantaneous interference under low-frequency fluctuation conditions and avoid ineffective adjustments.

[0090] When F2 ≤ temperature fluctuation frequency ≤ F1: This indicates that the temperature data fluctuation is within the stable range allowed by the process, and the time window adjustment module maintains the current time window duration unchanged. Pressure fluctuation frequency is similar and will not be elaborated upon here.

[0091] In some embodiments, when executing the fuzzy PID algorithm, the method further includes:

[0092] Obtain the comparison results of temperature deviation amplitude and corresponding preset threshold, and the comparison results of pressure deviation amplitude and corresponding preset threshold;

[0093] When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, the weight coefficient of the proportional term is set to be greater than the weight coefficient of the integral term.

[0094] When the temperature deviation or pressure deviation is lower than the corresponding preset threshold, the weight coefficient of the proportional term is set to be less than the weight coefficient of the integral term.

[0095] The proportional term output and integral term output are calculated based on the adjusted weighting coefficients. The proportional term output is the product of the proportional term weighting coefficient and the difference between the flow setpoint and the real-time flow data. The integral term output is the product of the integral term weighting coefficient and the integral of the difference between the flow setpoint and the real-time flow data.

[0096] Specifically, the PLC control unit acquires the temperature deviation and pressure deviation amplitudes. Based on the comparison results, the weighting coefficients of the proportional term (P) and integral term (I) in the fuzzy PID algorithm are set differently. When the temperature deviation exceeds the preset temperature threshold or the pressure deviation exceeds the preset pressure threshold, it indicates that the current operating condition deviates significantly from the process standard. In this case, the system's response speed should be improved first to quickly reduce the deviation. The PLC control unit sets the proportional term weighting coefficient to be greater than the integral term weighting coefficient. For example, the proportional term weighting coefficient is set to 0.6 and the integral term weighting coefficient is set to 0.4. By enhancing the effect of the proportional term, the solenoid valve can quickly adjust its opening and closing degree, rapidly changing the medium flow rate to alleviate the operating condition deviation.

[0097] When the temperature deviation is lower than the preset temperature threshold and the pressure deviation is lower than the preset pressure threshold, it indicates that the current operating condition is close to the process standard, the deviation is small and tends to be stable. At this time, it is necessary to prioritize improving the control accuracy of the system to avoid flow fluctuations caused by excessive adjustment, which would disrupt process stability. At this time, the PLC control unit should set the proportional term weight coefficient to be smaller than the integral term weight coefficient. For example, the proportional term weight coefficient can be set to 0.3 and the integral term weight coefficient can be set to 0.7. By enhancing the effect of the integral term, small static deviations can be gradually eliminated, and flow oscillations caused by excessive proportional term effect can be avoided. This is more suitable for operating conditions with different deviation levels.

[0098] In some embodiments, the system further includes an edge gateway; the edge gateway communicates with the PLC control unit; the edge gateway is used to: construct a physical correlation model among the flow data from the flow sensor, the temperature data from the temperature sensor, and the pressure data from the pressure sensor; the physical correlation model is based on the hydrodynamic relationship of the medium flow in the well furnace;

[0099] The theoretical flow rate is calculated based on the temperature data from the temperature sensor and the pressure data from the pressure sensor.

[0100] Based on the theoretical flow rate value and the flow data from the flow sensor. The deviation between the two is obtained by comparison;

[0101] If the deviation is greater than the preset consistency threshold, the deviation between the change trend of the data of each sensor and the historical mean within the first time period is calculated, and a confidence score is assigned to the data output by each sensor.

[0102] The sensor with the lowest confidence score is marked as suspicious and a status warning is issued on the human-machine interface.

[0103] Specifically, when a sensor malfunctions or displays abnormal data, such as a flow sensor experiencing measurement deviation due to pipe scaling or a temperature sensor experiencing data drift due to aging, the system may fail to identify the problem in a timely manner and execute control commands based on erroneous data, leading to inaccurate flow regulation. This system constructs a physical correlation model of flow rate, temperature, and pressure based on the fluid dynamics of the medium flow in a well furnace using an edge gateway. It calculates the theoretical flow rate using temperature and pressure data and judges the consistency of sensor data by the deviation from the actual collected flow rate data. When the deviation exceeds a threshold, it analyzes the trend of each sensor's data change over a first time period and assigns a confidence score, identifying the suspicious sensor with the lowest confidence score. The status of the suspicious sensor is then displayed as a real-time warning on the human-machine interface, allowing staff to quickly handle the situation and reducing the process risks caused by abnormal sensor data.

[0104] In some embodiments, the edge gateway is further configured to:

[0105] Once a sensor is marked as suspicious, the current output value of the sensor marked as suspicious and its trend of change over a second time period are obtained.

[0106] Based on the physical correlation model and data from two other normal sensors, the theoretical expected value of the sensor marked as being in a suspicious state was calculated.

[0107] Calculate the residual between the measured value and the theoretical expected value of the sensor marked as a suspicious state;

[0108] Determine whether the residual is less than the preset recovery threshold for N consecutive periods and the standard deviation of the rate of change is lower than the threshold.

[0109] If all conditions are met, the sensor is determined to have recovered its health, a status update command is sent to the PLC control unit, and a prompt indicating that the sensor status has recovered is displayed on the human-machine interface.

[0110] In some embodiments, the edge gateway is further configured to:

[0111] Based on historical flow data, temperature change rate, and pressure change rate, an adaptive Kalman filter algorithm is used to predict the flow change trend within the next T time period.

[0112] Based on the predicted flow rate change trend, the optimal adjustment timing and opening degree of the solenoid valve are calculated using a dynamic programming algorithm. The dynamic programming algorithm includes: defining the state space as the current flow rate deviation, the predicted flow rate trend slope, and the current opening degree of the solenoid valve; defining the decision variable as the solenoid valve opening adjustment amount; and defining the objective function as the weighted sum of the solenoid valve adjustment energy consumption and the flow control error.

[0113] Record and compare the optimal adjustment timing and opening / closing degree with the actual adjustment time and actual opening / closing degree.

[0114] If the difference between either of the two is greater than the corresponding allowable difference, it will be displayed on the human-computer interaction interface.

[0115] Specifically, the edge gateway uses an adaptive Kalman filter algorithm, combined with historical flow data, temperature change rate, and pressure change rate, to predict the flow change trend within the next T time period. Based on the predicted trend, a dynamic programming algorithm is used to calculate the optimal adjustment timing and opening / closing degree with the goal of minimizing the weighted sum of solenoid valve regulation energy consumption and flow control error. By comparing the optimal adjustment parameters with the actual adjustment parameters, when the difference exceeds the allowable range, it is displayed on the human-machine interface, which makes it easy for staff to promptly identify the shortcomings of the adjustment strategy and selectively optimize it.

[0116] In some embodiments, the edge gateway is further configured to: estimate the viscosity coefficient of the medium in real time based on the temperature and pressure data of the medium inside the well furnace when calculating the opening degree of the solenoid valve;

[0117] Adjust the maximum value of the solenoid valve opening degree according to the viscosity coefficient of the medium; when the viscosity of the medium increases, decrease the maximum value of the adjustment value to avoid valve jamming or slow response; when the viscosity of the medium decreases, increase the maximum value of the adjustment value to improve control sensitivity.

[0118] It should be noted that the PLC-based well furnace flow monitoring system provided in this embodiment of the invention is used to execute all the process steps of the PLC-based well furnace flow monitoring system in the above embodiment. The working principles and beneficial effects of the two correspond one-to-one, so they will not be described again.

[0119] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0120] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0121] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0122] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A PLC-based flow monitoring system for a pit furnace, characterized in that, include: Flow sensor, PLC control unit, human-machine interface and communication module; The flow sensor is installed in the medium output pipeline of the pit furnace to collect the flow data of the medium in real time. The PLC control unit receives the flow data transmitted by the flow sensor and executes a fuzzy PID algorithm to obtain a control quantity. Based on the control quantity, it controls the opening and closing degree of the solenoid valve to regulate the flow rate of the medium in the pit furnace. The human-computer interaction interface is used to display the flow rate curve, alarm status, and historical data; The communication module is used to enable communication between the PLC control unit and the host computer or mobile terminal; The PLC control unit is also used to acquire temperature data sent by a temperature sensor installed inside the pit furnace and pressure data sent by a pressure sensor installed in the medium output pipeline; the fuzzy PID algorithm includes a proportional term, an integral term, and a derivative term; the proportional term is obtained based on the difference between the flow rate setpoint and the real-time flow rate data; the flow rate setpoint is adjusted based on the flow rate data and the pressure data; The PLC control unit also includes a time window adjustment module; the time window adjustment module is used for: The fluctuation frequency of temperature data was calculated using the Fast Fourier Transform spectral analysis method. When the fluctuation frequency of the temperature data is greater than the first frequency, the time window is... The duration is shortened; when the fluctuation frequency of the temperature data is less than the second frequency, the time window is shortened. Extend the duration; maintain the time window when the fluctuation frequency of temperature data is between the second and first frequencies. Unchanged; the second frequency is less than the first frequency; The flow rate setpoint is adjusted based on the flow rate data and pressure data, including: The PLC control unit acquires the flow data collected by the flow sensor. Temperature data collected by temperature sensor Pressure data collected by pressure sensors ; Calculate the temperature data The temperature deviation from the preset temperature value T and the pressure data The pressure deviation from the preset pressure value P; When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, the standard flow rate is calculated. ;in, ; The PLC control unit controls the standard flow rate value. The standard flow rate value is obtained by performing a first-order low-pass filter. ; The PLC control unit retrieves the current flow rate setting value; if the standard flow rate value is... Less than the current flow rate setting and pressure data The pressure value P or the temperature data is greater than the preset pressure value P. If the temperature exceeds the preset value T, the current flow rate setting will be lowered; the reduction will increase with the increase of the pressure deviation or temperature deviation. If the standard flow rate value... The pressure data is greater than the current flow setting value. Less than the preset pressure value P or temperature data If the current flow rate is less than the preset temperature value T, the current flow rate setting will be increased; the increase will increase as the pressure deviation or temperature deviation increases. The PLC control unit transmits the adjusted flow rate setting to the human-machine interface, where the adjusted flow rate setting and adjustment timestamp are displayed.

2. The system according to claim 1, characterized in that, The process of adjusting the flow rate setting also includes: When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, a waiting time window is triggered. ; When the temperature deviation or pressure deviation is within the time window When the flow rate continuously exceeds the corresponding preset threshold, the standard flow rate value is calculated and the flow rate setting value is adjusted; the time window Adjustments were made based on the process characteristics of the pit furnace.

3. The system according to claim 2, characterized in that, The time window adjustment module is also used for: The fluctuation frequency of the pressure data was calculated using the Fast Fourier Transform spectral analysis method. When the fluctuation frequency of the pressure data is greater than the third frequency, the time window will be... The duration is shortened; when the fluctuation frequency of the pressure data is less than the fourth frequency, the time window is shortened. Extend the duration; maintain the time window when the fluctuation frequency of pressure data is between the fourth and third frequencies. Unchanged; the fourth frequency is less than the third frequency.

4. The system according to claim 3, characterized in that, When executing the fuzzy PID algorithm, the method further includes: Obtain the comparison results of temperature deviation amplitude and corresponding preset threshold, and the comparison results of pressure deviation amplitude and corresponding preset threshold; When the temperature deviation or pressure deviation exceeds the corresponding preset threshold, the weight coefficient of the proportional term is set to be greater than the weight coefficient of the integral term. When the temperature deviation or pressure deviation is lower than the corresponding preset threshold, the weight coefficient of the proportional term is set to be less than the weight coefficient of the integral term. The proportional term output and integral term output are calculated based on the adjusted weighting coefficients. The proportional term output is the product of the proportional term weighting coefficient and the difference between the flow setpoint and the real-time flow data. The integral term output is the product of the integral term weighting coefficient and the integral of the difference between the flow setpoint and the real-time flow data.

5. The system according to claim 1, characterized in that, The system also includes an edge gateway; the edge gateway communicates with the PLC control unit; the edge gateway is used to: construct a physical correlation model between the flow data from the flow sensor, the temperature data from the temperature sensor, and the pressure data from the pressure sensor; the physical correlation model is based on the hydrodynamic relationship of the medium flow in the well furnace; The theoretical flow rate is calculated based on the temperature data from the temperature sensor and the pressure data from the pressure sensor. Based on the theoretical flow rate value and the flow data from the flow sensor. The deviation between the two is obtained by comparison; If the deviation is greater than the preset consistency threshold, the deviation between the change trend of the data of each sensor and the historical mean within the first time period is calculated, and a confidence score is assigned to the data output by each sensor. The sensor with the lowest confidence score is marked as suspicious and a status warning is issued on the human-machine interface.

6. The system according to claim 5, characterized in that, The edge gateway is also used for: Once a sensor is marked as suspicious, the current output value of the sensor marked as suspicious and its trend of change over a second time period are obtained. Based on the physical correlation model and data from two other normal sensors, the theoretical expected value of the sensor marked as being in a suspicious state was calculated. Calculate the residual between the measured value and the theoretical expected value of the sensor marked as a suspicious state; Determine whether the residual is less than the preset recovery threshold for N consecutive periods and the standard deviation of the rate of change is lower than the threshold. If all conditions are met, the sensor is determined to have recovered its health, a status update command is sent to the PLC control unit, and a prompt indicating that the sensor status has recovered is displayed on the human-machine interface.

7. The system according to claim 6, characterized in that, The edge gateway is also used for: Based on historical flow data, temperature change rate, and pressure change rate, an adaptive Kalman filter algorithm is used to predict the flow change trend within the next T time period. Based on the predicted flow rate change trend, the optimal adjustment timing and opening degree of the solenoid valve are calculated using a dynamic programming algorithm. The dynamic programming algorithm includes: defining the state space as the current flow rate deviation, the predicted flow rate trend slope, and the current opening degree of the solenoid valve; defining the decision variable as the solenoid valve opening adjustment amount; and defining the objective function as the weighted sum of the solenoid valve adjustment energy consumption and the flow control error. Record and compare the optimal adjustment timing and opening / closing degree with the actual adjustment time and actual opening / closing degree. If the difference between either of the two is greater than the corresponding allowable difference, it will be displayed on the human-computer interaction interface.

8. The system according to claim 7, characterized in that, The edge gateway is also used to: estimate the viscosity coefficient of the medium in real time based on the temperature and pressure data of the medium inside the well furnace when calculating the opening degree of the solenoid valve; Adjust the maximum value of the solenoid valve opening degree according to the viscosity coefficient of the medium; when the viscosity of the medium increases, decrease the maximum value of the adjustment value to avoid valve jamming or slow response; when the viscosity of the medium decreases, increase the maximum value of the adjustment value to improve control sensitivity.

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