Automatic control system and method for inside blowing process of galvanized steel pipe

Abstract

The application discloses a galvanized steel pipe inner blowing process automatic control system and method, belongs to the intelligent control technical field of hot galvanizing process, acquires steel pipe structure parameters and matches historical process models, and outputs initial temperature and pressure values; the temperature change rate of the steel pipe inner cavity is collected, and a thermal state vector is established; the current optimal inner blowing pressure P(t) and pulse frequency f(t) are calculated by combining the fuzzy controller with the initial parameters T0 and P0 and the ΔT / Δt; the PWM driving signal is generated to control the electromagnetic valve and the fan operation, and the blowing rhythm regulation is realized; the pressure falling curve after each blowing is collected, the falling rate dP / dt is calculated, and the zinc liquid detachment efficiency is evaluated; when the deviation exceeds the threshold value, the fuzzy rule base is automatically corrected, the self-learning and optimization of the control parameters are realized; the method improves the inner blowing precision and consistency, has strong adaptability, and can be widely applied to the intelligent manufacturing field of hot galvanized steel pipes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of hot galvanizing process, in particular to an automatic control system and method for an inner blowing process of a galvanized steel pipe. BACKGROUND

[0002] Galvanized steel pipes are widely used in the fields of construction, transportation, and power, etc., and the quality of the inner wall galvanizing directly affects the corrosion resistance and service life. The traditional inner blowing process mainly relies on manual setting of parameters, and a timer is used to drive compressed air to blow the inner wall of the steel pipe to remove residual zinc liquid. However, this method has the following serious defects:

[0003] Firstly, the current inner blowing process lacks adaptive adjustment means for the response mechanism of different steel pipe diameters, wall thicknesses, and zinc liquid viscosity, resulting in incomplete blowing of the zinc tumor on the inner wall of some steel pipes in multiple batch continuous production, forming "cavitation point" micro-corrosion sources, which seriously affect product consistency. Secondly, the existing inner blowing control system generally relies on simple temperature control and constant pressure control, and cannot real-time sense the change of the zinc liquid solidification rate in the steel pipe cavity, which is prone to "zinc liquid back suction" phenomenon under low temperature or low air pressure, causing zinc hanging wire or zinc accumulation at the tail end of the steel pipe, increasing the subsequent cleaning cost.

[0004] Especially in a multi-station automatic hot galvanizing production line, due to the dynamic change of the inner blowing port distance from the zinc liquid surface, the traditional scheme cannot establish a closed-loop adjustment model of the steel pipe inner temperature flow field, resulting in large differences in cleaning degree of steel pipes of different lengths under the same inner blowing parameters, and even inducing the risk of zinc slag spatter of associated equipment, reducing the stability and safety of the whole line. SUMMARY

[0005] The purpose of the present application is to provide an automatic control system and method for an inner blowing process of a galvanized steel pipe to solve the problems in the background art.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme:

[0007] The automatic control method for the inner blowing process of the galvanized steel pipe comprises:

[0008] S1: obtaining the basic parameters of the target steel pipe entering the inner blowing station, matching with the historical galvanizing parameter model in the database, and outputting the corresponding process initial temperature and pressure value pair (T0, P0);

[0009] S2: after the target steel pipe enters the inner blowing position, collecting the temperature gradient data of the inner cavity wall of the steel pipe, combining the set sampling period Δt, and establishing the temperature change rate vector ΔT / Δt;

[0010] S3: taking ΔT / Δt as a dynamic zinc liquid solidification rate index, and calculating the optimal inner blowing pressure P(t) and air flow pulse frequency f(t) at the current time slice t in combination with the obtained T0 and P0.

[0011] S4: generating a driving signal sequence according to P(t) and f(t), and sending the driving signal sequence to the electromagnetic valve and the fan for real-time control of the blowing rhythm;

[0012] S5: collecting a pressure drop curve in the steel pipe cavity after each internal blowing pulse, and calculating an actual drop rate dP / dt as a basis for judging the zinc liquid desorption efficiency;

[0013] S6: comparing the dP / dt with an expected drop curve, and if the deviation is greater than a set threshold ε, then real-time correcting an output interval in the fuzzy rule base, and optimizing P(t+1) and f(t+1) in the next cycle.

[0014] Preferably, the S1 comprises:

[0015] The length L and the inner diameter D of the target steel pipe are obtained, and the wall thickness T is synchronously collected to form a basic parameter group (L, D, T);

[0016] The production batch number and the material identification of the target steel pipe are read to generate an extended parameter set M;

[0017] The basic parameter group (L, D, T) and the extended parameter set M are taken as an input feature vector, which is input into a pre-constructed historical process parameter database, and a K-neighbor algorithm is used for multi-dimensional similarity comparison;

[0018] Based on the similarity score, the top three historical records with the highest similarity are extracted, and the optimal initial temperature and pressure value pair (T0, P0) is calculated as the initial control parameter of the internal blowing process of the current steel pipe by a weighted average strategy.

[0019] Preferably, the S2 comprises:

[0020] An infrared temperature sensor array is arranged at equal intervals on an annular positioning frame around the outlet of the internal blowing nozzle, and each sensor array includes a plurality of infrared probes arranged along the axial direction of the steel pipe;

[0021] Each infrared probe synchronously collects the inner wall surface temperature value of the corresponding monitoring point at a sampling period of Δt seconds ;

[0022] The least square method is used to fit the temperature sequence of each group of collection points to obtain a corresponding temperature spatial gradient distribution curve, and the temperature change rate between adjacent two sampling points is calculated in the time axis direction ;

[0023] The values of all monitoring points are constructed into a temperature change rate vector . .

[0024] Preferably, S3 comprises:

[0025] A fuzzy control model is constructed based on the temperature change rate vector ΔT / Δt and the initial temperature-pressure value pair (T0, P0);

[0026] The fuzzy control model presets several input fuzzy variables, including preset membership functions, and output variables: airflow pressure increase / decrease amplitude ΔP and pulse frequency adjustment factor Δf;

[0027] According to the current input variable membership degree, inference is carried out through the fuzzy rule base to output the internal blowing pressure adjustment value P(t) and the airflow pulse frequency f(t) at the current time slice t;

[0028] If the ΔT / Δt change trend fluctuation exceeds the set threshold η for consecutive multiple periods, the membership function parameters are adjusted and optimized.

[0029] Preferably, S4 comprises:

[0030] The output internal blowing pressure P(t) and pulse frequency f(t) are taken as control targets and are subjected to digital signal coding processing;

[0031] A PWM control signal is generated by using a duty cycle modulation method, wherein the signal pulse width is proportional to P(t) and the pulse interval is inversely proportional to f(t);

[0032] The PWM control signal is synchronously output to a variable frequency driver and an electromagnetic valve control to respectively control the fan output air pressure and the electromagnetic valve rhythm opening.

[0033] Preferably, S5 comprises:

[0034] The data of the internal cavity pressure change with time are recorded to form a complete pressure drop curve P(t);

[0035] The P(t) curve is subjected to smoothing processing by using a sliding average filtering algorithm, and the drop slope, i.e. the drop rate dP / dt, is extracted by polynomial curve fitting;

[0036] The dP / dt is compared with a set standard reference drop rate value to judge whether the current internal blowing reaches the expected zinc liquid desorption efficiency, and if it is less than the standard value, a parameter correction suggestion is triggered.

[0037] Preferably, S6 comprises:

[0038] The actually measured internal cavity pressure drop rate dP / dt is compared with the expected reference curve dP / dt s at the same kind of working condition to calculate a deviation function ΔE(t);

[0039] If the ΔE(t) exceeds the set deviation threshold ε, the rule correction mechanism of the fuzzy controller is triggered, and the historical record of the current input feature and output response is extracted;

[0040] Based on the current deviation direction and amplitude, the fuzzy membership function boundary of the corresponding output variable in the fuzzy rule base is adjusted, and the control output interval is corrected.

[0041] The updated rule base is used for parameter calculation in the next control period, and the corrected P(t+1) and f(t+1) values are output.

[0042] The application also provides a galvanized steel pipe inner blowing process automatic control system, which comprises:

[0043] The parameter acquisition module acquires the basic parameters of the target steel pipe entering the inner blowing station, matches the historical galvanizing parameter model in the database, and outputs the corresponding process initial temperature and pressure value pair (T0, P0).

[0044] The temperature change data acquisition module acquires the temperature gradient data of the inner cavity wall surface of the target steel pipe after the target steel pipe enters the inner blowing position, combines the set sampling period Δt, and establishes a temperature change rate vector ΔT / Δt.

[0045] The calculation module takes ΔT / Δt as a dynamic zinc liquid solidification rate index, and calculates the optimal inner blowing pressure P(t) and airflow pulse frequency f(t) at the current time slice t in combination with the obtained T0 and P0.

[0046] The control module generates a driving signal sequence according to P(t) and f(t), and sends the driving signal sequence to the electromagnetic valve and the fan for real-time control of the blowing rhythm.

[0047] The fall rate determination module acquires the inner cavity pressure fall curve of the steel pipe after each inner blowing pulse, calculates the actual fall rate dP / dt, and takes the actual fall rate dP / dt as a zinc liquid detachment efficiency determination basis.

[0048] The comparison and optimization module compares the dP / dt with the expected fall curve, and if the deviation is greater than the set threshold ε, the output interval in the fuzzy rule base is corrected in real time, and P(t+1) and f(t+1) in the next period are optimized.

[0049] In the above technical solution, the application provides technical effects and advantages:

[0050] 1. The present application breaks through the limitations of traditional internal blowing process relying on fixed parameters and unable to dynamically adapt to different pipe types and cooling states by constructing a fuzzy self-learning control system based on real-time thermal characteristics (ΔT / Δt) and pressure feedback rate (dP / dt) of steel pipes. By introducing multi-dimensional sensor fusion, fuzzy control model, and self-adaptive rule optimization mechanism, real-time adjustment and periodic optimization of internal blowing pressure P(t) and pulse frequency f(t) are realized, greatly improving the zinc liquid detachment efficiency and the consistency of the inner coating, and significantly reducing the rework rate caused by zinc tumors, zinc hanging, and zinc slag defects.

[0051] 2. The control method provided by the present application has the advantages of high automation, fast response, and fine control, and especially shows stronger stability and robustness under multi-specification steel pipe mixed production and high-speed galvanizing process conditions. In addition, the system has the ability of fault identification, rule self-correction, and control parameter self-evolution, can continuously adapt to changes in production conditions, reduces manual intervention, and improves the operation efficiency of the production line and the energy utilization rate. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings described in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0053] Figure 1 The method flowchart of the present application.

[0054] Figure 2 The system module flowchart of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0056] Embodiment 1, please refer to Figure 1 The galvanized steel pipe internal blowing process automatic control method described in this embodiment includes:

[0057] S1: Obtain the basic parameters of the target steel pipe entering the internal blowing station, match with the historical galvanizing parameter model in the database, and output the corresponding process initial temperature and pressure value pair (T0, P0);

[0058] S2: After the target steel pipe enters the inner blowing position, collect the temperature gradient data of the inner cavity wall surface of the steel pipe, combine the set sampling period At, and establish a temperature change rate vector DT / At;

[0059] S3: Take DT / At as a dynamic zinc liquid solidification rate index, and calculate the optimal inner blowing pressure P(t) and airflow pulse frequency f(t) at the current time slice t in combination with the obtained T0 and P0;

[0060] S4: Generate a driving signal sequence according to P(t) and f(t), and send it to the electromagnetic valve and the fan for real-time control of the blowing rhythm;

[0061] S5: Collect the pressure drop curve of the inner cavity of the steel pipe after each inner blowing pulse, calculate the actual drop rate dP / dt, and use it as a judgment basis for the zinc liquid desorption efficiency;

[0062] S6: Deviation comparison is performed between dP / dt and the expected drop curve, if the deviation is greater than the set threshold value epsilon, the output interval in the fuzzy rule base is corrected in real time, and P(t+1) and f(t+1) of the next period are optimized.

[0063] In the specific implementation process, first, multi-source parameter acquisition is completed before the steel pipe enters the inner blowing station, and the parameters include:

[0064] The structural parameter group: the length L of the steel pipe is measured by the integrated laser displacement sensor on the conveying line; the inner diameter D is obtained by using the ring laser ranging module to obtain the reflection data of the steel pipe port edge and calculating; the wall thickness T is measured by a non-contact ultrasonic thickness gauge in a rotating state of the steel pipe and averaged;

[0065] The extended parameter set M includes the production batch number, material code, forming process identifier and other information of the steel pipe, which is obtained by an RFID radio frequency identification or a two-dimensional code scanner and transmitted to the control system;

[0066] The parameter group (L, D, T, M) together constitutes the feature vector of the steel pipe, which is used as the input for comparison with the sample records in the historical database.

[0067] In order to realize efficient and accurate parameter matching, the present application adopts K-neighbor algorithm (KNN) to calculate the similarity of the steel pipe feature vector. The algorithm is defined as follows:

[0068] The sample records in the historical database that have completed inner blowing are standardized according to the feature vector format, and a vector space is constructed;

[0069] For the feature vector X of the current steel pipe to be processed, the Euclidean distance between X and each historical sample Xi in the database is calculated as a similarity measure;

[0070] Select the K=3 samples with the closest similarity from them to form the matching candidate set;

[0071] Further according to the defect rate R value (such as the zinc tumor residual rate) corresponding to each sample, set its weight W = 1 / (1+ R), so that the sample with lower defect rate has higher weight on the output parameter;

[0072] The recommended initial temperature T0 and pressure P0 of the current steel pipe are calculated by weighted average of the T and P parameters of the three samples, which are used as the basis values of the subsequent internal blowing parameters.

[0073] In addition, in order to ensure that the system can still output reasonable parameters in extreme cases, a similarity threshold θ = 0.65 is set, when the maximum similarity of the K nearest neighbor samples is lower than the threshold, the expert rule base will be triggered, and the parameters will be adjusted according to the material and inner diameter of the steel pipe. The rule table is obtained by manual extraction from historical test data and process manual, such as:

[0074] For the steel pipe with Q235 material and inner diameter less than 80 mm, the recommended initial temperature is 420 degrees Celsius, and the initial air pressure is 0.35 MPa;

[0075] When the material is Q345 and the inner diameter is greater than 100 mm, the recommended initial temperature is 440 degrees Celsius, and the initial air pressure is 0.42 MPa.

[0076] In a preferred embodiment of the present application, an annular positioning frame is arranged at the front end of the internal blowing station, the positioning frame is coaxial with the steel pipe, and at least three groups of infrared temperature sensor arrays are arranged in the annular positioning frame. Each group of arrays is provided with not less than four infrared temperature probes arranged in the axial direction of the steel pipe, which correspond to the front, middle, rear and tail sections of the steel pipe respectively, so as to realize full coverage monitoring of the temperature of the inner wall of the entire steel pipe. The infrared probe is a non-contact temperature sensing element of thermocouple type, which has a performance index of temperature measurement accuracy better than ±0.3℃ and response time less than 200 milliseconds, and is provided with a self-calibration logic based on a fixed black body standard part for zero drift correction at regular intervals every day, so as to ensure long-term stable operation.

[0077] Each infrared probe collects the current temperature value at a time interval Δt The recommended sampling period Δt is 0.5 seconds to 2 seconds, which is adjusted according to the production line speed and zinc liquid cooling characteristics. The data of each group of sensors are collected to the temperature control analysis unit through RS485 bus, and are arranged in synchronous time sequence by the central processing module, and the temperature time sequence is generated according to the sampling order.

[0078] In order to reduce the occasional noise in the measurement process and the local abnormality caused by the probe angle, the present application adopts the least square method curve fitting algorithm to extract the trend of the temperature sequence of each group of collection points, and constructs the axial temperature distribution curve of the steel pipe. The temperature distribution curve of the steel pipe is constructed by the least square method curve fitting algorithm. temperature ) is inputted, a first-order polynomial fitting is performed, and a fitting model is set as , wherein a is a temperature gradient coefficient, and b is an initial temperature intercept value.

[0079] In two consecutive sampling periods, the fitting functions and are recorded respectively, and then the temperature change rate ΔT / Δt at the corresponding moment can be obtained by calculating the difference between the two sets of functions at the same position point x, that is:

[0080] Taking ΔT(x) / Δt at each position point as a component, a temperature change rate vector is constructed. The vector adopts a four-dimensional structure in actual implementation, corresponding to four typical regions of the steel pipe: the front section (0%~25% length), the middle section (25%~50%), the rear section (50%~75%), and the tail section (75%~100%). The ΔT / Δt value of each section is the weighted average of multiple probes in the region, and the weight is set according to the temperature fluctuation stability, and the point with small fluctuation has large weight.

[0081] The finally formed temperature change rate vector is one of the main variables of the current steel pipe thermal state characteristics, and is inputted into the subsequent fuzzy regulator and air pressure control logic. t The values of each dimension of the vector

[0082] If is much smaller than , it indicates that the tail section cools slowly, and the airflow action time needs to be extended.

[0083] If and have a large difference, it may indicate uneven heating or zinc liquid reflux, and the internal blowing angle or rhythm should be adjusted.

[0084] In the production process of hot-dip galvanized steel pipe, the core purpose of the internal blowing process is to strip the residual zinc liquid attached to the inner wall of the pipe through airflow, so as to ensure uniform coating and qualified thickness, and prevent defects such as zinc nodules and zinc hanging wires. In order to realize adaptive regulation and control under different steel pipe parameters and cooling states, the present application calculates the optimal internal blowing pressure P(t) and airflow pulse frequency f(t) at the current time slice t after collecting the temperature change rate vector ΔT / Δt, in combination with the initial process set value T0, P0, and proposes a dynamic adjustment method based on fuzzy logic control algorithm.

[0085] ​The temperature change rate vector ΔT / Δt is a dynamic thermal state index obtained by time-differencing the temperature monitoring data obtained in step S2, and is used to reflect the cooling trend of the zinc liquid in the steel pipe during the transition from liquid to solid. The vector is usually represented in a four-dimensional structure, with the average cooling rate of the front, middle, rear and tail sections of the steel pipe.

[0086] In the present application, ΔT / Δt is defined as the dominant variable affecting the intensity and frequency of internal blowing, and its change trend determines the control boundary of the gas flow action time and pressure intensity.

[0087] The system uses a double-input double-output fuzzy control model, i.e., the input variables are the temperature change rate ΔT / Δt and the initial parameters T0 and P0, and the output variables are the adjusted pressure value P(t) and the gas flow pulse frequency f(t) at the current time slice t.

[0088] Input variable design:

[0089] After the ΔT / Δt vector is normalized, it is decomposed into four regional sub-variables, and the system inputs the average values of the calculation regions;

[0090] T0 is the set initial temperature (unit: Celsius), and P0 is the initial gas flow pressure (unit: megaPascal);

[0091] Fuzzy processing divides ΔT / Δt into three language membership grades: "cooling too slow", "cooling moderate", and "cooling too fast";

[0092] T0 and P0 are used to judge whether there is a starting deviation state, and a correction factor is introduced as a compensation parameter.

[0093] Output variable design:

[0094] Output 1 is P(t), i.e., the internal blowing air pressure adjustment value at the current calculation time;

[0095] Output 2 is f(t), i.e., the frequency value of the current pulse blowing, with the unit being hertz (Hz);

[0096] The output variable uses three fuzzy language labels: "increase", "keep", and "decrease" to express the adjustment trend.

[0097] To realize the logical mapping relationship, the present application predefines a set of not less than 25 fuzzy rules, which are extracted from artificial experience and historical data training results, and the format is as follows:

[0098] IF ΔT / Δt is "cooling too slow" AND initial temperature T0 is "higher", THEN P(t) is "increase" and f(t) is "increase";

[0099] IF ΔT / Δt is "cooling moderate" AND initial pressure P0 is "moderate", THEN P(t) is "hold", f(t) is "hold";

[0100] IF ΔT / Δt is "cooling too fast" AND T0 is "too low", THEN P(t) is "lower", f(t) is "lower".

[0101] The fuzzy reasoning adopts a Mamdani-type fuzzy reasoning system, and the center average method is used for defuzzification to obtain continuous numerical output results P(t) and f(t).

[0102] In order to improve the adaptability of the system in a complex dynamic environment, a self-learning fuzzy control mechanism based on error feedback is proposed:

[0103] In a plurality of continuous time slices (such as 3-5), the system calculates the change slope of ΔT / Δt;

[0104] If the slope continuously oscillates (i.e. alternately positive and negative) or the amplitude is greater than a set threshold η (the recommended value is 2.5 degrees Celsius per second square), it indicates that the current control model is not suitable for the cooling state;

[0105] Then automatically trigger the membership function fine-tuning program to adjust the boundary values of the input linguistic variables and the shape of the membership degree curve (using triangular or Gaussian function), so that the control model is refitted to the current thermal field environment;

[0106] The self-learning record will be accumulated in the model as a historical reference for subsequent similar steel pipe parameters, gradually improving the stability of the model.

[0107] In order to avoid excessive fluctuations in the control output signal, the exponential moving average (EMA) is used to smooth P(t) and f(t). Specifically as follows:

[0108] The smoothed result of the current cycle output value is: ;

[0109] Where α is the smoothing coefficient, the recommended value range is 0.3 to 0.6, which is set by the system stability requirement.

[0110] The present application adopts a digital pulse width modulation (PWM) control method to encode the control parameters. The PWM signal is a pulse waveform with variable duty cycle, and its periodicity is determined by the frequency f(t), and the duty cycle is controlled by the control target P(t).

[0111] Definition of PWM signal:

[0112] Period (T): ​The reciprocal of the pulse frequency, in seconds;

[0113] Duty cycle (D): , wherein Pmax is the maximum set air pressure;

[0114] Pulse width (W): , which represents the duration of time in each cycle that the signal is at a high level.

[0115] P(t) and f(t) are converted into a PWM signal by the central control unit, which is output by an FPGA or a high-precision PWM generation module;

[0116] The duty cycle of the P(t) control signal reflects the air pressure output intensity;

[0117] The frequency value of the f(t) control signal reflects the density of the pulse interval and represents the speed of the blowing rhythm.

[0118] The system supports a duty cycle adjustment range of 20% to 80% with a step precision of 1%, meeting the wide range of regulation and control requirements from low pressure and low frequency to high pressure and high frequency.

[0119] After the PWM signal is output, it is sent to a variable frequency drive via an isolation amplification module to control the speed of the fan motor, thereby indirectly adjusting the output air pressure. The variable frequency drive has the following characteristics:

[0120] Supports continuous speed regulation from 0 to 50 Hz with a response time of less than 100 milliseconds;

[0121] Adopts a voltage-type vector control algorithm to adjust the output power in real time;

[0122] The built-in air pressure PID stabilization module prevents airflow from shaking due to rapid changes in P(t).

[0123] The fan receives continuous PWM signal updates during operation and completes air pressure regulation once every time slice (usually 0.5 seconds).

[0124] The solenoid valve is used to realize the "pulsed" opening and closing of the airflow, and its response speed directly affects the stability of the airflow rhythm. The invention uses a transistor array to control the opening and closing state of the solenoid valve and uses a PWM signal to control the opening period of the solenoid valve.

[0125] The solenoid valve response control logic is as follows:

[0126] During the high level period, the valve is opened and the airflow is output;

[0127] During the low level period, the valve is closed and the airflow is stopped;

[0128] The solenoid valve control period is synchronized with the PWM period, and it acts once in each cycle.

[0129] To ensure the stability in the frequent opening and closing state:

[0130] Each solenoid valve is equipped with a current protection diode and a voltage-resistant transistor;

[0131] The working current of the solenoid valve coil is less than 1 ampere, and the response time is not more than 50 milliseconds.

[0132] To ensure the successful execution of the output signal, the system introduces a feedback detection mechanism to judge the response state of the fan and the solenoid valve in real time, and builds a complete closed-loop control.

[0133] Collect the motor current change and compare it with the PWM regulated target current;

[0134] If the current fan current deviation ΔI exceeds the threshold value (recommended to be set to 0.3 ampere), it is judged as abnormal pressure regulation, and the system records the fault code F01.

[0135] Record the valve drive response and actual opening state every cycle;

[0136] If the solenoid valve coil energizing signal or the valve body is not opened within 3 consecutive cycles, it is determined that the solenoid valve is out of control, the code F02 is triggered and the blowing is stopped.

[0137] The controller has an "abnormal tolerance logic", when continuously detecting abnormality but not exceeding the set number of upper limit (such as 3 times), first try self-recovery;

[0138] If the number of upper limit is exceeded, the internal blowing action is stopped, the fault log is recorded and the operation and maintenance personnel are prompted.

[0139] In the internal blowing process of hot galvanized steel pipe, the complete removal of residual zinc liquid in the steel pipe cavity is the key link to ensure the uniformity of the coating and the surface quality. The traditional method relies on the time blowing mode, and lacks dynamic detection means for the actual stripping effect of the zinc liquid, resulting in frequent blowing shortage or overblowing, which affects the process consistency and wastes energy.

[0140] The present application proposes a zinc liquid stripping efficiency evaluation method based on the pressure drop rate dP / dt of the steel pipe cavity, which collects the transient pressure change of the steel pipe inner wall after each air pulse, and analyzes the resistance effect of residual zinc liquid on air rebound, to realize real-time quantitative judgment of internal blowing effect.

[0141] To obtain the real air flow dynamic feedback of the steel pipe cavity, a group of differential pressure type pressure sensors are arranged at the end of the internal blowing station. The sensor adopts a mechanical telescopic probe structure, which can automatically center and insert about 10~20 millimeters into the steel pipe cavity, avoiding external air interference. Its performance indicators are as follows:

[0142] Sampling accuracy is better than ± 10 pascal;

[0143] Response time is not more than 10 milliseconds;

[0144] Can withstand air flow positive pressure of 0.5 Mpa, does not affect the stability of measurement.

[0145] In the first control time slice after each internal blowing pulse ends, the control system triggers the sensor to start the high-speed sampling mode, the sampling frequency is not less than 200 Hz, the sampling time span is 0.5 seconds to 1 second, the pressure change process from pulse cutoff to air pressure stabilization is recorded completely, and a time sequence curve P(t) is formed, that is, the steel pipe cavity pressure drop curve.

[0146] In order to eliminate the high-frequency noise and equipment vibration interference that may exist in the sampling process, the system uses the moving average filtering algorithm to smooth the P(t) curve data. The algorithm window length is set to 5-10 sampling points, which depends on the sampling frequency and noise level.

[0147] The processed P(t) data is then reconstructed by a continuous curve through a cubic spline interpolation algorithm, and a physically smooth and high-precision pressure change curve is obtained. The cubic spline interpolation has the advantages of segment continuity and derivative continuity, and is suitable for fitting of nonlinear slow-changing trend.

[0148] The fitted P(t) curve is differentiated in the key time window by using numerical differentiation method, and the instantaneous pressure drop rate at the initial drop stage is extracted. The specific formula is as follows:

[0149] At the moment (i.e. pulse cutoff point) to Select multiple points between the moments

[0150] Calculate , ;

[0151] The drop rate dP / dt is defined as ΔP divided by Δt, and the unit is pascal per second.

[0152] The default time window Δt is 100-300 milliseconds, which is automatically adjusted according to the air flow response speed. Multiple dP / dt values can be used to generate average drop rate or maximum drop slope.

[0153] The invention takes the drop rate dP / dt as the core physical index to measure the efficiency of air flow in removing zinc liquid. The basic principle is as follows:

[0154] If the zinc liquid adhesion amount is large, there will be a large amount of residual liquid to dampen the backflow after air flow impact, the pressure drop is slow, and the dP / dt value is small;

[0155] ​​If the zinc solution is well attached, there is no obvious resistance in the cavity, the air pressure is rapidly released, and the value of dP / dt is large.

[0156] To establish the judgment standard, the dynamic decision threshold mechanism based on the multi-parameter reference model is introduced:

[0157] The modeling variables include: steel pipe material (such as Q235, Q345), pipe diameter D, initial temperature of inner wall T0, wall thickness T, and current fuzzy control parameters;

[0158] Based on the above variables and historical dP / dt measurement data, a multiple regression prediction model is constructed, and the recommended reference falling rate dP / dt s ;

[0159] Compare the current actual measured dP / dt with dP / dt s , if it is lower than 90% of the reference value, the system determines that the "attachment is insufficient", and sends a parameter optimization prompt;

[0160] If the continuous 3 times pulse is lower than the threshold value, the system is suggested to adjust the internal blowing pressure or prolong the single pulse duration.

[0161] Based on historical data, a multi-parameter reference model is established, and the expected falling curve dP / dt s corresponding to different materials, sizes, initial temperature (T0) and other conditions is established. The curve can be generated by a multiple linear regression model, a support vector machine or a neural network prediction model, and has good generalization ability.

[0162] Assume that the actual falling curve is sampled within the time window , and the reference curve is , then the deviation function ΔE(t) is defined as: ΔE(t) = average value ; that is, the mean square error (MSE) in the time window. The larger the value is, the more serious the deviation between the actual zinc solution attachment effect and the expectation is.

[0163] The threshold value ε is a dynamic floating value, which is extracted from the deviation distribution of historical similar samples in the reference database by a statistical model, and is defined as: ; wherein μ is the average value of historical deviation, and σ is the standard deviation, which ensures that the abnormal situation is identified in more than 95% of the working conditions.

[0164] When ΔE(t) is greater than the set threshold value ε, the fuzzy rule adjustment program in the control system is triggered. The process includes the following key steps:

[0165] The controller traces back the fuzzy rules called and their input-output mapping combinations (i.e. rule "trajectory") in the current control period, and marks which inputs cause the deviation output.

[0166] Among the currently activated fuzzy rules, the output variable membership function will be fine-tuned at the boundary. The specific method is as follows:

[0167] For Gaussian membership function: adjust the center value μ and the standard deviation σ, so that the output curve is shifted upward or downward;

[0168] For triangular membership function: move the midpoint position or scale the slope to increase or decrease the control amplitude.

[0169] The adjustment step is set to 2% to 5% of the relative output range, to prevent excessive drift.

[0170] The revised fuzzy rule is stored in the short-term rule set through the local cache mechanism, and is preferentially used for similar working conditions. If the deviation control is successful for more than 5 times in succession, the version of the rule is written into the main control rule library, forming the iterative learning result.

[0171] According to the revised fuzzy rule, the system recalculates the adjustment output P(t+1), f(t+1) of the next control period. The specific steps are as follows:

[0172] Re-fuzzify the current inputs ΔT / Δt, T0, P0, etc.

[0173] Activate the updated fuzzy rule to obtain a new output trend;

[0174] De-fuzzify to calculate P(t+1), f(t+1), and buffer the change trend through the moving average method to prevent structural impact caused by drastic adjustment.

[0175] This mechanism ensures that even if multiple materials and various specifications of steel pipes are put into production, the adaptive optimization of control parameters can be completed in a very short time without human intervention.

[0176] To prevent the learning mechanism from causing a decrease in control quality, an abnormal rollback strategy is introduced:

[0177] If ΔE(t) does not decrease but increases within 2 consecutive periods after updating the rule, or if P(t+1), f(t+1) changes more than the maximum allowed fluctuation threshold (such as 20%);

[0178] Then automatically restore the fuzzy rule version used in the last control period;

[0179] At the same time, record the "learning failure" event, and enter the delayed relearning state, with a cooling period of 3 to 5 periods.

[0180] Example 2, please refer to Figure 2 The galvanized steel pipe internal blowing process automatic control system described in the embodiment comprises:

[0181] Parameter acquisition module: obtain the basic parameters of the target steel pipe entering the inner blowing station, match with the historical galvanizing parameter model in the database, and output the corresponding process initial temperature and pressure value pair (T0, P0);

[0182] Temperature change data acquisition module: after the target steel pipe enters the inner blowing position, collect the temperature gradient data of the steel pipe inner cavity wall surface, combine the set sampling period Δt, and establish the temperature change rate vector ΔT / Δt;

[0183] Calculation module: take ΔT / Δt as a dynamic zinc liquid solidification rate index, and combine T0 and P0 to calculate the optimal inner blowing pressure P(t) and airflow pulse frequency f(t) at the current time slice t;

[0184] Control module: generate a driving signal sequence according to P(t) and f(t), and send it to the electromagnetic valve and the fan for real-time control of the blowing rhythm;

[0185] Fall rate determination module: collect the steel pipe inner cavity pressure fall curve after each inner blowing pulse, calculate the actual fall rate dP / dt, and use it as a zinc liquid detachment efficiency judgment basis;

[0186] Comparison and optimization module: compare the deviation of dP / dt and the expected fall curve, if the deviation is greater than the set threshold ε, then real-time correct the output interval in the fuzzy rule base, and optimize P(t+1) and f(t+1) in the next cycle.

[0187] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for automatic control of the inside blowing process of galvanized steel pipes, characterized by: Comprise: S1: obtain the basic parameters of the target steel pipe entering the internal blowing station, match with the historical galvanizing parameter model in the database, and output the corresponding process initial temperature and pressure value pair (T0, P0); S2: after the target steel pipe enters the internal blowing position, collect the steel pipe inner cavity wall temperature gradient data, combine the set sampling period Δt, and establish the temperature change rate vector ΔT / Δt; S3: take ΔT / Δt as a dynamic zinc liquid solidification rate index, and calculate the optimal internal blowing pressure P(t) and airflow pulse frequency f(t) at the current time slice t combined with the obtained T0 and P0; S4: generate a driving signal sequence according to P(t) and f(t), and send it to the electromagnetic valve and the fan for real-time control of the blowing rhythm; S5: collect the steel pipe inner cavity pressure drop curve after each internal blowing pulse, calculate the actual drop rate dP / dt, and use it as the zinc liquid detachment efficiency judgment basis; S6: compare the deviation of dP / dt and the expected drop curve, if the deviation is greater than the set threshold ε, then real-time correct the output interval in the fuzzy rule base, and optimize P(t+1) and f(t+1) in the next cycle.

2. The method of automatic control of the inside blowing process of galvanized steel pipes according to claim 1, characterized in that: Wherein the S1 comprises: Obtain the length L and inner diameter D of the target steel pipe, and synchronously collect the wall thickness T to form a basic parameter group (L, D, T); Read the production batch number and material quality identification of the target steel pipe to generate an extended parameter set M; Take the basic parameter group (L, D, T) and the extended parameter set M as the input feature vector, input into the pre-constructed historical process parameter database, and use K-neighbor algorithm for multi-dimensional similarity comparison; Based on the similarity score, extract the top three historical records with the highest similarity, and calculate the optimal initial temperature and pressure value pair (T0, P0) as the initial control parameters of the internal blowing process of the current steel pipe according to the weighted average strategy.

3. The method of automatic control of the inside blowing process of galvanized steel pipes according to claim 1, characterized in that: Wherein the S2 comprises: Equidistantly arrange an infrared temperature sensor array on the annular positioning frame around the internal blowing nozzle outlet, and each sensor array contains a plurality of infrared probes arranged along the steel pipe axis; Each infrared probe synchronously collects the temperature value of the inner wall surface of the corresponding monitoring point in a sampling period of Δt seconds ; The least square method is used for curve fitting on the temperature sequence of each group of collection points to obtain the corresponding temperature spatial gradient distribution curve, and the temperature change rate between adjacent two sampling points is calculated in the time axis direction ; The values of all monitoring points are constructed as a set of temperature rate of change vectors .​ 4. The method of automatic control of the inside blowing process of galvanized steel pipes according to claim 1, characterized in that: Wherein the S3 comprises: Based on the temperature change rate vector ΔT / Δt and the initial temperature and pressure value pair (T0, P0), a fuzzy control model is constructed; The fuzzy control model presets several input fuzzy variables, including a preset membership function, and output variables: airflow pressure increase and decrease amplitude ΔP and pulse frequency adjustment factor Δf; According to the current input variable membership degree, inference is carried out through the fuzzy rule base to output the internal blowing pressure adjustment value P(t) and the airflow pulse frequency f(t) at the current time slice t; If the change trend fluctuation of ΔT / Δt in continuous multiple cycles exceeds the set threshold η, adjust and optimize the membership function parameters.

5. The method of automatic control of the inside blowing process of galvanized steel pipes according to claim 1, characterized in that: Wherein the S4 comprises: Take the output internal blowing pressure P(t) and pulse frequency f(t) as control targets, and carry out digital signal coding processing; Generate a PWM control signal using a duty cycle modulation method, wherein the signal pulse width is proportional to P(t) and the pulse interval is inversely proportional to f(t); The PWM control signal is output to the frequency converter driver and the electromagnetic valve control synchronously to control the fan output air pressure and the electromagnetic valve rhythm opening respectively.

6. The method of automatic control of the inside blowing process of galvanized steel pipes according to claim 1, characterized in that: Wherein the S5 comprises: Data of the inner cavity pressure change over time is recorded to form a complete pressure drop curve P(t); The P(t) curve is smoothed by using a sliding average filtering algorithm, and the drop slope, i.e. the drop rate dP / dt, is extracted by polynomial curve fitting; The dP / dt is compared with a standard reference drop rate value, and it is determined whether the current inner blowing achieves the expected zinc liquid desorption efficiency. If it is less than the standard value, a parameter correction suggestion is triggered.

7. The method of automatic control of the inside blowing process of galvanized steel pipes according to claim 1, characterized in that: The S6 comprises: The actually measured inner cavity pressure fall rate dP / dt is compared with the expected reference curve dP / dt under the same working condition s The bias function ΔE(t) is calculated by time slice by time slice comparison; If ΔE(t) exceeds a set deviation threshold ε, a rule correction mechanism of the fuzzy controller is triggered to extract the current input features and output responses of the historical records; Based on the current deviation direction and amplitude, the fuzzy membership function boundaries of the corresponding output variables in the fuzzy rule base are adjusted to correct the control output interval; The updated rule base is used for parameter calculation in the next control cycle to output the corrected P(t+1) and f(t+1) values.

8. A galvanized steel tube internal blowing process automatic control system for implementing the galvanized steel tube internal blowing process automatic control method according to any one of claims 1 to 7, characterized in that: It comprises: A parameter acquisition module: basic parameters of the target steel pipe entering the inner blowing station are acquired, and corresponding process initial temperature and pressure value pairs (T0, P0) are output by matching with historical galvanizing parameter models in a database; A temperature change data acquisition module: after the target steel pipe enters the inner blowing position, steel pipe inner cavity wall surface temperature gradient data are collected, and a temperature change rate vector ΔT / Δt is established in combination with a set sampling period Δt; A calculation module: ΔT / Δt is taken as a dynamic zinc liquid solidification rate index, and T0 and P0 are obtained to calculate optimal inner blowing pressure P(t) and airflow pulse frequency f(t) at the current time slice t; A control module: drive signal sequences are generated according to P(t) and f(t), and are sent to electromagnetic valves and fans for real-time control of blowing rhythm; A drop rate determination module: after each inner blowing pulse, the steel pipe inner cavity pressure drop curve is collected, and the actual drop rate dP / dt is calculated as a zinc liquid desorption efficiency determination basis; A comparison and optimization module: dP / dt is compared with an expected drop curve. If the deviation is greater than a set threshold ε, the output interval in the fuzzy rule base is corrected in real time to optimize P(t+1) and f(t+1) in the next cycle.

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