Self-adaptive feedback control method and system for coating thickness of hot galvanizing

Abstract

The invention discloses a self-adaptive feedback control method for coating thickness of hot galvanizing. The self-adaptive feedback control method comprises the following steps: S1, data detection; s2, data processing; s3, data analysis; s4, data collection; s5, performing data comparison; and S6, data correction. The invention further discloses a coating thickness self-adaptive feedback control system for hot galvanizing. According to the invention, the technical problem that the set value of the air knife needs to be manually adjusted according to the data of the thickness gauge when the display of the data measured by the thickness gauge and the target value generate an error is solved.

Description

Technical Field

[0001] The invention relates to an automatic control technology for hot-dip galvanizing, and more specifically to a method and system for adaptively feedback controlling the coating thickness of hot-dip galvanizing. Background Art

[0002] Hot-dip galvanizing is a common anti-corrosion protection measure, usually used for the surface treatment of metal products. It is a layer of zinc coating formed by immersing the metal product in a heated zinc solution to cause a chemical reaction between zinc and the metal surface. Hot-dip galvanizing has good corrosion resistance and can effectively prevent metal products from oxidation, corrosion and wear. Zinc has a high electronegativity, so the zinc coating formed on the metal surface can be used as a cathodic protection layer to prevent the metal from being corroded by oxidants. Hot-dip galvanizing is widely used in steel products, pipelines, building materials, automotive parts and other fields. It not only provides excellent corrosion resistance, but also extends the service life of metal products.

[0003] At present, due to the relatively backward control technology of hot-dip galvanizing coating thickness in China, the main control method of major steel mills is still manual measurement combined with traditional PID control. Since manual measurement is cumbersome, the coating thickness on the hot-dip galvanizing production line is based on the actual data measured by the thickness gauge. In order to avoid the influence of temperature and improve the detection accuracy, the thickness gauge is often installed 100-240m after the zinc pot. Since it is semi-manual operation, when the operator finds that the zinc layer thickness is different from the target value from the data measured by the thickness gauge, it is necessary to manually adjust the set value of the air knife according to the data of the thickness gauge. Because the measurement of the thickness gauge will also have a certain lag problem, when the operator adjusts the air knife according to the data of the thickness gauge, the zinc layer thickness on the product surface will still have an error with the target value, affecting the quality of the product. Summary of the invention

[0004] In view of the defects existing in the prior art, the purpose of the present invention is to provide a method and system for adaptive feedback control of hot-dip galvanizing coating thickness, so as to solve the technical problem that when the data measured by the thickness gauge shows an error with the target value, the setting value of the air knife needs to be manually adjusted according to the data of the thickness gauge.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A first aspect of the present invention provides a method for adaptive feedback control of hot-dip galvanizing coating thickness, comprising the following steps:

[0007] S1, data detection;

[0008] S2, data processing;

[0009] S3, data analysis;

[0010] S4, data collection;

[0011] S5. Data comparison;

[0012] S6. Data correction.

[0013] Preferably, the data detection in step S1 specifically includes:

[0014] The distance sensor is used to obtain the distance between the air knife and the strip and the surface information of the strip in real time, and transmit the detection data to the control terminal.

[0015] Preferably, the data processing in step S2 specifically includes:

[0016] The control terminal processes the detection data transmitted by the distance sensor to establish a zinc layer thickness prediction model.

[0017] Preferably, establishing the zinc layer thickness prediction model specifically includes:

[0018] The actual distance between the air knife and the surface of the strip is calculated according to the formula D=sqrt[(x1-x2)^2+(y1-y2)^2+(z1-z2)^2], and a data-driven prediction model for the zinc layer thickness is established based on the actual distance, the jet pressure of the air knife on the strip, and the speed of the strip.

[0019] Preferably, the data analysis in step S3 specifically includes:

[0020] The zinc layer thickness prediction model is used to predict the surface zinc layer thickness of the strip in the current state in real time based on the characteristic values ​​of the inclination and flatness of the strip during the production process, thereby achieving initial measurement of the zinc layer thickness and transmitting the predicted value of the initial measurement to the processing host.

[0021] Preferably, the data collection in step S4 specifically includes:

[0022] The thickness of the zinc layer on the surface of the strip is measured by a thickness gauge, and the measured value is transmitted to the processing host.

[0023] Preferably, the data comparison in step S5 specifically includes:

[0024] The processing host receives the measured value of the thickness gauge and the predicted value of the control terminal, compares the measured value with the predicted value, calculates the prediction error of the zinc layer thickness prediction model, and then uses the prediction error to calculate the correction value required by the zinc layer thickness prediction model, and transmits the correction value to the adaptive controller.

[0025] Preferably, the data correction in step S6 specifically includes:

[0026] The correction value is stored by the adaptive controller and transmitted to the control terminal. The control terminal adjusts the actual distance between the air knife and the surface of the strip according to the correction value so that the predicted value is close to the measured value.

[0027] Preferably, when the actual distance between the air knife and the surface of the strip changes, the control terminal will recalculate the zinc layer thickness on the strip surface in the current state according to the zinc layer thickness prediction model, and transmit it to the processing host for comparison with the actual value measured by the thickness gauge, and recalculate the prediction error of the zinc layer thickness prediction model, and use the prediction error to calculate the required correction value, and transmit the correction value to the adaptive controller, which stores the correction value and transmits it to the control terminal, and the control terminal adjusts the set value of the air knife according to the correction value, so that the predicted value of the zinc layer thickness of the strip is close to the actual value measured by the thickness gauge.

[0028] Preferably, in the data collection of step S4, the thickness of the surface zinc layer of the strip is measured manually using a thickness gauge, and the measured value is transmitted to the processing host.

[0029] The second aspect of the present invention provides a hot dip galvanizing coating thickness adaptive feedback control system, comprising:

[0030] A distance sensor is arranged near the air knife to obtain the distance between the air knife and the surface of the strip;

[0031] A thickness gauge, used to measure the thickness of the zinc layer on the surface of the strip;

[0032] A control terminal receives the transmission data of the distance sensor and establishes a zinc layer thickness prediction model;

[0033] A processing host receives the measured value of the thickness gauge and the predicted value of the control terminal, compares them, calculates the prediction error of the zinc layer thickness prediction model, and then uses the prediction error to calculate the correction value required by the zinc layer thickness prediction model;

[0034] An adaptive controller, used for storing the correction value and transmitting the correction value to the control terminal, and the control terminal then adjusts the actual distance between the air knife and the surface of the strip according to the correction value so that the predicted value is close to the measured value;

[0035] The hot-dip galvanizing coating thickness adaptive feedback control method provided in the first aspect of the present invention is realized by the hot-dip galvanizing coating thickness adaptive feedback control system.

[0036] Preferably, the control terminal includes:

[0037] An analysis module, used for receiving and processing the data of the distance sensor;

[0038] The control module is used to control and adjust the actual distance between the air knife and the strip.

[0039] Preferably, the adaptive controller comprises:

[0040] A self-learning module, for processing the new correction value transmitted by the processing host;

[0041] The PID controller is used to send instructions and data to the control terminal.

[0042] The present invention provides a method and system for adaptive feedback control of hot-dip galvanizing coating thickness, which has the following beneficial effects:

[0043] 1) The present invention compares the actual value measured by the thickness gauge of the processing host with the predicted value of the zinc layer thickness prediction model, calculates the prediction error of the zinc layer thickness prediction model, calculates the correction value required by the zinc layer thickness prediction model using the prediction error of the zinc layer thickness prediction model, and transmits the correction value to the adaptive controller, stores the correction value transmitted by the processing host through the adaptive controller and sends instructions and data to the control terminal, receives the instructions and data sent by the adaptive controller through the control terminal and adjusts the set value of the air knife according to the data, and through the above steps, when it is detected that the zinc layer thickness on the surface of the product has an error with the target value, the correction value required for the error can be automatically calculated, and the set value of the air knife can be adjusted according to the correction value, thereby reducing the error between the zinc layer thickness on the surface of the product and the target value and improving the quality of the product.

[0044] 2) The present invention establishes a data-driven zinc layer thickness prediction model through the actual distance between the air knife and the strip surface, the air knife jet pressure exerted on the strip, and the strip speed. Based on the characteristic values ​​of the strip's inclination and flatness during the production process, the zinc layer thickness on the strip surface in the current state is predicted in real time, thereby reducing the actual measurement delay.

[0045] 3) The present invention repeatedly establishes a new zinc layer thickness prediction model, calculates the required correction value through the new zinc layer thickness prediction model, and stores the new correction value through a self-learning module, so that the zinc layer thickness prediction model can continuously adapt to the new unit status. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic flow chart of the coating thickness adaptive feedback control method of the present invention;

[0047] Figure 2 It is a schematic diagram of the framework structure of the coating thickness adaptive feedback control system of the present invention. DETAILED DESCRIPTION

[0048] In order to better understand the above technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0049] Combination Figure 1 As shown, the present invention provides a hot-dip galvanizing coating thickness adaptive feedback control method, comprising the following steps:

[0050] S1. Data detection: The distance sensor is used to obtain the distance between the air knife and the strip and the surface information of the strip in real time, and transmits the detected data to the control terminal. The distance sensor is installed near the air knife;

[0051] S2, data processing, receiving the data transmitted by the distance sensor through the control terminal and establishing a zinc layer thickness prediction model, the control terminal processes the data transmitted by the distance sensor and displays the processed data in the zinc layer thickness prediction model, and calculates the actual distance between the air knife and the strip surface according to the formula D=sqrt[(x1-x2)^2+(y1-y2)^2+(z1-z2)^2], and establishes a data-driven zinc layer thickness prediction model according to the actual distance between the air knife and the strip surface, the jet pressure of the air knife on the strip, and the speed of the strip;

[0052] A group of distance sensors are set up to measure the upper and lower surfaces of the strip (non-contact measurement), and the number of sensors in each group is determined according to the width of the strip. The width of the production line strip is generally in the range of 800 to 1500 mm. According to the detection capability and range of the distance sensor, multiple distance sensors are set up on the upper and lower surfaces respectively;

[0053] S3. Data analysis: The zinc layer thickness prediction model is used to predict the zinc layer thickness on the strip surface in real time based on the characteristic values ​​of the strip inclination and flatness during the production process, thereby achieving the initial measurement of the zinc layer thickness and transmitting the initial measurement prediction value to the processing host;

[0054] S4, data collection, measuring the zinc layer thickness on the strip surface by a thickness gauge, and transmitting the measured value to the processing host;

[0055] S5, data comparison, by processing the actual measured value transmitted by the thickness gauge received by the host and the predicted value transmitted by the control terminal, and comparing the actual measured value transmitted by the thickness gauge with the predicted value transmitted by the control terminal, calculating the prediction error of the zinc layer thickness prediction model, and then using the prediction error of the zinc layer thickness prediction model to calculate the correction value required by the zinc layer thickness prediction model, and transmitting the correction value to the adaptive controller;

[0056] S6. Data correction: The correction value is stored by the adaptive controller and transmitted to the control terminal. The control terminal adjusts the actual distance between the air knife and the strip surface according to the correction value, so that the predicted value of the zinc layer thickness is close to the actual value measured by the thickness gauge. When the actual distance between the air knife and the strip surface changes, the control terminal re-establishes the zinc layer thickness prediction model according to the actual distance between the air knife and the strip surface, the air knife jet pressure on the strip, the strip speed and multiple working condition data, and calculates the zinc layer thickness on the strip surface in the current state through the zinc layer thickness prediction model again, and transmits it to the processing host for comparison with the actual value measured by the thickness gauge, and calculates the prediction error of the zinc layer thickness prediction model again, and uses the prediction error of the zinc layer thickness prediction model to calculate the correction value required by the zinc layer thickness prediction model, and transmits the correction value to the adaptive controller. The correction value is stored by the adaptive controller and transmitted to the control terminal. The control terminal adjusts the set value of the air knife according to the correction value, so that the predicted value of the zinc layer thickness is close to the actual value measured by the thickness gauge.

[0057] In the above step S4, after the thickness gauge measures the zinc layer thickness on the surface of the strip, manual measurement can also be used. The zinc layer thickness is measured manually, and the true value of the manual measurement is uploaded to the processing host. The processing host compares the predicted value transmitted by the control terminal with the true value of the manual measurement, and calculates the prediction error of the zinc layer thickness prediction model. The prediction error of the zinc layer thickness prediction model is used to calculate the correction value required by the zinc layer thickness prediction model, and the correction value is transmitted to the adaptive controller. The adaptive controller stores the correction value and transmits it to the control terminal. The control terminal adjusts the set value of the air knife according to the correction value, so that the predicted value of the zinc layer thickness is close to the actual value measured by the thickness gauge.

[0058] Combination Figure 2 As shown, the present invention also provides a hot-dip galvanizing coating thickness adaptive feedback control system, comprising:

[0059] A distance sensor 1 is provided near the air knife 2 to obtain the distance between the air knife 2 and the surface of the strip 3;

[0060] A thickness gauge 4, used to measure the thickness of the zinc layer on the surface of the strip 3;

[0061] The control terminal 5 receives the transmission data of the distance sensor 1 and establishes a zinc layer thickness prediction model. The control terminal 5 processes the data transmitted by the distance sensor 1 and displays the processed data in the zinc layer thickness prediction model. The actual distance between the air knife 2 and the surface of the strip 3 is calculated according to the formula D=sqrt[(x1-x2)^2+(y1-y2)^2+(z 1-z2)^2], and the data-driven zinc layer thickness prediction model is established according to the actual distance between the air knife 2 and the surface of the strip 3, the jet pressure of the air knife 2 on the strip 3, and the speed of the strip 3;

[0062] Assume that the surface distances of the strip detected by the 1st to nth distance sensors on the upper surface are Xt1, Xt2, Xt3, ..., Xtn, respectively, and the surface distances of the strip detected by the 1st to nth distance sensors on the lower surface are Xb1, Xb2, Xb3, ..., Xbn, respectively. The height position of the distance sensor itself remains unchanged. The Y coordinates of the corresponding measuring points along the length direction of the strip are recorded. The Y coordinates of the upper and lower surfaces of the sensor data collection are aligned.

[0063] Then at the point Y of the strip length, the cross-sectional condition is expressed as: {Xt1-Xb1, Xt2-Xb2, Xt3-Xb3…, Xtn-Xbn}, and by subtracting the corresponding plate thickness H, we can get the zinc layer thickness prediction model.

[0064] The processing host 6 receives the measured value of the thickness gauge 4 and the predicted value of the control terminal 5, compares them, calculates the prediction error of the zinc layer thickness prediction model, and then uses the prediction error to calculate the correction value required by the zinc layer thickness prediction model;

[0065] The adaptive controller 7 is used to store the correction value and transmit the correction value to the control terminal 5. The control terminal 5 then adjusts the actual distance between the air knife 2 and the surface of the strip 3 according to the correction value, so that the predicted value of the control terminal 5 is close to the actual measured value of the thickness gauge 4.

[0066] The hot-dip galvanizing coating thickness adaptive feedback control method of the present invention is realized by the hot-dip galvanizing coating thickness adaptive feedback control system of the present invention.

[0067] The control terminal 5 includes:

[0068] An analysis module 501 is used to receive and process data transmitted by the distance sensor 1;

[0069] The control module 502 is used to control and adjust the actual distance between the air knife 2 and the strip 3 .

[0070] The data transmitted by the distance sensor 1 is received and processed by the analysis module 501, and the processed data is displayed in the zinc layer thickness prediction model. A data-driven zinc layer thickness prediction model is established based on the actual distance between the air knife 2 and the surface of the strip 3, the jet pressure of the air knife 2 on the strip 3, the thermal map distribution of the zinc layer thickness on the surface of the strip 3, and the speed of the strip 3.

[0071] The control module 502 receives the data transmitted by the adaptive controller 7 and adjusts the setting value of the air knife 2 according to the data.

[0072] The adaptive controller 7 comprises:

[0073] A self-learning module 701 for processing new correction values ​​transmitted by the processing host 6;

[0074] The PID controller 702 is used to send instructions and data to the control terminal 5 .

[0075] The new correction data transmitted by the processing host 6 is stored by the self-learning module 701, so that the self-learning module 701 can continuously store the new correction data and transmit the correction data to the PID controller 702 when an error occurs in the predicted value.

[0076] The PID controller 702 sends instructions and data to the control terminal 5, and the control module 502 of the control terminal 5 receives the instructions and data transmitted by the PID controller 702 and adjusts the set value of the air knife 2, so that the zinc layer thickness prediction model can continuously adapt to the new unit status.

[0077] Example 1

[0078] refer to Figure 1 and Figure 2 The first embodiment of the present invention is a method for adaptive feedback control of hot-dip galvanizing coating thickness, which specifically includes the following steps:

[0079] S1, data detection, obtaining the distance between the air knife 2 and the strip 3 and the surface information of the strip 3 in real time through the distance sensor 1, and transmitting the detected data to the control terminal 5, the distance sensor 1 is installed near the air knife 2;

[0080] S2. Data processing: The control terminal 5 receives the data transmitted by the distance sensor 1 and establishes a zinc layer thickness prediction model. The control terminal 5 processes the data transmitted by the distance sensor 1 and displays the processed data in the zinc layer thickness prediction model. The actual distance between the air knife 2 and the surface of the strip 3 is calculated according to the formula D=sqrt[(x1-x2)^2+(y1-y2)^2+(z1-z2)^2], and a data-driven zinc layer thickness prediction model is established according to the actual distance between the air knife 2 and the surface of the strip 3, the jet pressure of the air knife 2 on the strip 3, and the speed of the strip 3. The control terminal 5 includes an analysis module 501, which is used to receive and process the data transmitted by the distance sensor 1. The analysis module 501 receives and processes the data transmitted by the distance sensor 1, and displays the processed data in the zinc layer thickness prediction model, obtains the thermal map distribution of the zinc layer thickness on the surface of the strip 3 and the coordinates (x2, y2, z2) of the strip 3, and puts the coordinates of the air knife 2 and the strip 3 into the formula D=sqrt[(x1-x2)^2+(y1-y2)^2+(z1-z2)^2] to calculate the actual distance between the air knife 2 and the surface of the strip 3, and establishes a data-driven zinc layer thickness prediction model according to the actual distance between the air knife 2 and the surface of the strip 3, the jet pressure of the air knife 2 on the strip 3, and the speed of the strip 3;

[0081] S3, data analysis, through the zinc layer thickness prediction model according to the current strip 3 is subjected to the jet pressure of the air knife 2 and the speed of the strip 3, and then based on the characteristic values ​​of the inclination and flatness of the strip 3 in the production process, the zinc layer thickness on the surface of the strip 3 in the current state is predicted in real time, thereby realizing the initial measurement of the zinc layer thickness, and the predicted value of the initial measurement is transmitted to the processing host 6, and the data of the distance sensor 1 is processed by the analysis module 501 to obtain the gas pressure and the speed of the strip 3 and the characteristic values ​​of the current inclination and flatness of the strip 3. The specific calculation formula is lnZ=k0+k1lnD+k2lnV-k4lnP, where Z is the zinc layer thickness, D is the actual distance between the air knife 2 and the strip 3, V is the speed of the strip 3, and P is the jet pressure of the air knife 2;

[0082] S4, data collection, measuring the thickness of the zinc layer on the surface of the strip 3 by the thickness gauge 4, and transmitting the measured value to the processing host 6;

[0083] S5, data comparison, receiving the measured value transmitted by the thickness gauge 4 and the predicted value transmitted by the control terminal 5 through the processing host 6, and comparing the measured value transmitted by the thickness gauge 4 with the predicted value transmitted by the control terminal 5, calculating the prediction error of the zinc layer thickness prediction model, and then using the prediction error of the zinc layer thickness prediction model to calculate the correction value required by the zinc layer thickness prediction model, and transmitting the correction value to the adaptive controller 7, the adaptive controller 7 includes a self-learning module 701, which is used to store the new correction data transmitted by the processing host 6; the PID controller 702, which is used to send instructions and data to the control terminal 5, and store the new correction data transmitted by the processing host 6 through the self-learning module 701, so that the self-learning module 701 can continuously store new correction data, and transmit the correction data to the PID controller 702 when an error occurs in the predicted value;

[0084] S6, data correction, the correction value is stored by the adaptive controller 7 and transmitted to the control terminal 5, the control terminal 5 will adjust the actual distance between the air knife 2 and the surface of the strip 3 according to the correction value, so that the predicted value of the zinc layer thickness is close to the actual value measured by the thickness gauge 4, when the actual distance between the air knife 2 and the surface of the strip 3 changes, the control terminal 5 will re-establish the zinc layer thickness prediction model according to the actual distance between the air knife 2 and the surface of the strip 3 and the air knife 2 jet pressure on the strip 3 and the speed of the strip 3, and calculate again through the zinc layer thickness prediction model The zinc layer thickness on the surface of the strip 3 in the current state is calculated, and it is transmitted to the processing host 6 for comparison with the actual value measured by the thickness gauge 4, and the prediction error of the zinc layer thickness prediction model is calculated again, and the correction value required by the zinc layer thickness prediction model is calculated by using the prediction error of the zinc layer thickness prediction model, and the correction value is transmitted to the adaptive controller 7, and the correction value is stored by the adaptive controller 7 and transmitted to the control terminal 5, and the control terminal 5 adjusts the set value of the air knife 2 according to the correction value, so that the predicted value of the zinc layer thickness is close to the actual value measured by the thickness gauge 4;

[0085] When the actual distance between the air knife 2 and the surface of the strip 3 or other working conditions are constantly changing, the control terminal 5 will continuously establish a new zinc layer thickness prediction model according to the actual distance between the air knife 2 and the surface of the strip 3, the air knife 2 jet pressure on the strip 3, and the speed of the strip 3, and calculate the zinc layer thickness on the surface of the strip 3 in the current state through the new zinc layer thickness prediction model, and transmit it to the processing host 6 for comparison with the actual measured value measured by the thickness gauge 4, calculate the error value of the new zinc layer thickness prediction model, and use the error value of the new zinc layer thickness prediction model to correct the predicted value of the zinc layer thickness prediction model, and transmit the corrected data to the adaptive controller 7, and store the corrected data through the adaptive controller 7 and transmit it to the control terminal 5, and continuously adjust the actual distance between the air knife 2 and the surface of the strip 3 through the control terminal 5, the adaptive controller 7 includes a self-learning module 701, which is used to store the new corrected data transmitted by the processing host 6; a PID controller 702, which is used to send the control terminal 5 Instructions and data, the new correction data transmitted by the processing host 6 is stored through the self-learning module 701, the zinc layer thickness on the surface of the strip 3 is predicted by continuously establishing a new zinc layer thickness prediction model, and compared with the actual measured value detected by the thickness gauge 4, the prediction error value of the zinc layer thickness prediction model is calculated, and the predicted value of the zinc layer thickness on the surface of the strip 3 is corrected by the error value, and the corrected data value is transmitted to the self-learning module 701 of the adaptive controller 7, so that the self-learning module 701 can continuously store new correction data, and transmit the correction data to the PID controller 702 when an error occurs in the predicted value, and send instructions and data to the control terminal 5 through the PID controller 702, and receive the instructions and data transmitted by the PID controller 702 through the control module 502 of the control terminal 5 and adjust the set value of the air knife 2, so that the zinc layer thickness prediction model can continuously adapt to new working conditions (strip speed, zinc pot temperature, etc.) and strip characteristics (strip specifications, flatness, inclination, etc.).

[0086] Example 2

[0087] refer to Figure 1 and Figure 2 The second embodiment of the hot-dip galvanizing coating thickness adaptive feedback control method specifically includes the following steps:

[0088] S1, data detection, obtaining the distance between the air knife 2 and the strip 3 and the surface information of the strip 3 in real time through the distance sensor 1, and transmitting the detected data to the control terminal 5, the distance sensor 1 is installed near the air knife 2;

[0089] S2. Data processing: The control terminal 5 receives the data transmitted by the distance sensor 1 and establishes a zinc layer thickness prediction model. The control terminal 5 processes the data transmitted by the distance sensor 1 and displays the processed data in the zinc layer thickness prediction model. The actual distance between the air knife 2 and the surface of the strip 3 is calculated according to the formula D=sqrt[(x1-x2)^2+(y1-y2)^2+(z1-z2)^2], and a data-driven zinc layer thickness prediction model is established according to the actual distance between the air knife 2 and the surface of the strip 3, the jet pressure of the air knife 2 on the strip 3, and the speed of the strip 3. The control terminal 5 includes an analysis module 501, which is used to receive and process the data transmitted by the distance sensor 1. The analysis module 501 receives and processes the data transmitted by the distance sensor 1, and displays the processed data in the zinc layer thickness prediction model, obtains the thermal map distribution of the zinc layer thickness on the surface of the strip 3 and the coordinates (x2, y2, z2) of the strip 3, and puts the coordinates of the air knife 2 and the strip 3 into the formula D=sqrt[(x1-x2)^2+(y1-y2)^2+(z1-z2)^2] to calculate the actual distance between the air knife 2 and the surface of the strip 3, and establishes a data-driven zinc layer thickness prediction model according to the actual distance between the air knife 2 and the surface of the strip 3, the jet pressure of the air knife 2 on the strip 3, and the speed of the strip 3;

[0090] S3, data analysis, through the zinc layer thickness prediction model according to the current strip 3 is subjected to the jet pressure of the air knife 2 and the speed of the strip 3, and then based on the characteristic values ​​of the inclination and flatness of the strip 3 in the production process, the zinc layer thickness on the surface of the strip 3 in the current state is predicted in real time, thereby realizing the initial measurement of the zinc layer thickness, and the predicted value of the initial measurement is transmitted to the processing host 6, and the data of the distance sensor 1 is processed by the analysis module 501 to obtain the gas pressure and the speed of the strip 3 and the characteristic values ​​of the current inclination and flatness of the strip 3. The specific calculation formula is lnZ=k0+k1lnD+k2lnV-k4lnP, where Z is the zinc layer thickness, D is the actual distance between the air knife 2 and the strip 3, V is the speed of the strip 3, and P is the jet pressure of the air knife 2;

[0091] S4, data collection, manually measuring the zinc layer thickness, and then uploading the actual value of the manual measurement to the processing host 6, the processing host 6 compares the predicted value transmitted by the control terminal 5 with the actual value of the manual measurement, and calculates the prediction error of the zinc layer thickness prediction model, and uses the prediction error of the zinc layer thickness prediction model to correct the predicted value of the zinc layer thickness prediction model, and transmits the corrected data to the self-learning module 701 of the adaptive controller 7, and stores the new corrected data through the self-learning module 701. Through the above method, the self-learning module 701 can continuously store new corrected data, and transmit the corrected data to the PID controller 702, and then send instructions and data to the control terminal 5 through the PID controller 702, and receive the instructions and data transmitted by the PID controller 702 through the control module 502 of the control terminal 5 and adjust the set value of the air knife 2, so that the zinc layer thickness prediction model can continuously adapt to the new unit state. Manual measurement of the zinc layer thickness is generally measured using the potential method, usually using a solution to corrode the coating, and there will be a potential mutation when corroding the substrate. The thickness of the coating is inferred by calculating the corrosion time, which is a more accurate measurement method.

[0092] Since the potential measurement method used manually is currently a more accurate measurement method, the actual value measured manually is used as the comparison object in this list;

[0093] The actual value detected by the thickness gauge 4, the real value measured manually, the predicted value calculated by the analysis module 501, and the predicted value after the control module 5 adjusts the setting value of the air knife 2 are compared. The specific comparison data are shown in the following table:

[0094]

[0095] According to the data in the table, it can be seen that when the actual distance between the air knife 2 and the strip 3 changes, the comparison of each group of data, the predicted value, the corrected predicted value, the measured value and the true value, in this table, the speed of the strip 3 and the paint spraying pressure of the air knife 2 are specific values ​​before processing. By changing the actual distance between the air knife 2 and the strip 3, the measured value detected by the thickness gauge 4, the true value measured manually, the predicted value calculated by the analysis module 501 and the predicted value after the control module 5 adjusts the set value of the air knife 2 are compared.

[0096] Error data calculation table of zinc layer thickness between the actual zinc layer thickness measured by thickness gauge 4 and the true zinc layer thickness predicted:

[0097]

[0098] The error data calculation table of the predicted zinc layer thickness after the control module 501 corrects the actual distance between the air knife 2 and the strip 3 and the actual zinc layer thickness measured manually:

[0099]

[0100] Error comparison table of measured zinc layer thickness and corrected predicted zinc layer thickness compared with manual measurement data:

[0101] Error of measured zinc layer thickness Error in predicted zinc layer thickness under adaptive mechanism 0.90 -0.14 0.74 -0.50 -0.48 -0.35 -0.80 0.29

[0102] The above data show that the error of the corrected predicted zinc layer thickness is smaller than the error of the measured zinc layer thickness, which can further reduce the error between the zinc layer thickness on the product surface and the target value, making the product more sophisticated.

[0103] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. A method for adaptive feedback control of hot-dip galvanizing coating thickness, characterized in that: The following steps are involved: S1, data detection; S2, data processing; S3, data analysis; S4, data collection; S5. Data comparison; S6. Data correction.

2. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 1, characterized in that: The data detection step S1 specifically includes: The distance sensor is used to obtain the distance between the air knife and the strip and the surface information of the strip in real time, and transmit the detection data to the control terminal.

3. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 2, characterized in that: The data processing in step S2 specifically includes: The control terminal processes the detection data transmitted by the distance sensor to establish a zinc layer thickness prediction model.

4. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 3, characterized in that: Establishing the zinc layer thickness prediction model specifically includes: The actual distance between the air knife and the surface of the strip is calculated according to the formula D=sqrt[(x1-x2)^2+(y1-y2)^2+(z1-z2)^2], and a data-driven prediction model for the zinc layer thickness is established based on the actual distance, the jet pressure of the air knife on the strip, and the speed of the strip.

5. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 3, characterized in that: The data analysis in step S3 specifically includes: The zinc layer thickness prediction model is used to predict the surface zinc layer thickness of the strip in the current state in real time based on the characteristic values ​​of the inclination and flatness of the strip during the production process, thereby achieving initial measurement of the zinc layer thickness and transmitting the predicted value of the initial measurement to the processing host.

6. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 5, characterized in that: The step S4 data collection specifically includes: The thickness of the zinc layer on the surface of the strip is measured by a thickness gauge, and the measured value is transmitted to the processing host.

7. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 6, characterized in that: The data comparison in step S5 specifically includes: The processing host receives the measured value of the thickness gauge and the predicted value of the control terminal, compares the measured value with the predicted value, calculates the prediction error of the zinc layer thickness prediction model, and then uses the prediction error to calculate the correction value required by the zinc layer thickness prediction model, and transmits the correction value to the adaptive controller.

8. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 7, characterized in that: The data correction in step S6 specifically includes: The correction value is stored by the adaptive controller and transmitted to the control terminal. The control terminal adjusts the actual distance between the air knife and the surface of the strip according to the correction value so that the predicted value is close to the measured value.

9. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 8, characterized in that: When the actual distance between the air knife and the surface of the strip changes, the control terminal will recalculate the zinc layer thickness on the strip surface in the current state according to the zinc layer thickness prediction model, and transmit it to the processing host for comparison with the actual value measured by the thickness gauge, and recalculate the prediction error of the zinc layer thickness prediction model, and use the prediction error to calculate the required correction value, and transmit the correction value to the adaptive controller, which stores the correction value and transmits it to the control terminal. The control terminal adjusts the set value of the air knife according to the correction value, so that the predicted value of the zinc layer thickness of the strip is close to the actual value measured by the thickness gauge.

10. The method for adaptive feedback control of hot-dip galvanizing coating thickness according to claim 6, characterized in that: In the data collection step S4, the thickness of the surface zinc layer of the strip is measured manually using a thickness gauge, and the measured value is transmitted to the processing host.

11. An adaptive feedback control system for hot-dip galvanizing coating thickness, characterized in that: include: A distance sensor is arranged near the air knife to obtain the distance between the air knife and the surface of the strip; A thickness gauge, used to measure the thickness of the zinc layer on the surface of the strip; A control terminal receives the transmission data of the distance sensor and establishes a zinc layer thickness prediction model; A processing host receives the measured value of the thickness gauge and the predicted value of the control terminal, compares them, calculates the prediction error of the zinc layer thickness prediction model, and then uses the prediction error to calculate the correction value required by the zinc layer thickness prediction model; An adaptive controller, used for storing the correction value and transmitting the correction value to the control terminal, and the control terminal then adjusts the actual distance between the air knife and the surface of the strip according to the correction value so that the predicted value is close to the measured value; The hot-dip galvanizing coating thickness adaptive feedback control method as claimed in any one of claims 1 to 10 is realized by the hot-dip galvanizing coating thickness adaptive feedback control system.

12. The hot-dip galvanizing coating thickness adaptive feedback control system according to claim 11, characterized in that: The control terminal comprises: An analysis module, used for receiving and processing the data of the distance sensor; The control module is used to control and adjust the actual distance between the air knife and the strip.

13. The hot-dip galvanizing coating thickness adaptive feedback control system according to claim 11, characterized in that: The adaptive controller comprises: A self-learning module, for processing the new correction value transmitted by the processing host; The PID controller is used to send instructions and data to the control terminal.

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