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

Abstract

The invention discloses a galvanized steel pipe internal blowing process automatic control system and method, and belongs to the technical field of hot galvanizing process intelligent control. Steel pipe structure parameters are obtained and matched with a historical process model, and an initial temperature and pressure value is output; the temperature change rate delta T / delta t of the inner cavity of the steel pipe is collected, and a thermal state vector is established; calculating the current optimal internal blowing pressure P (t) and the pulse frequency f (t) by combining delta T / delta t with the initial parameters T0 and P0 through a fuzzy controller; a PWM driving signal is generated to control an electromagnetic valve and a fan to operate, so that the air blowing rhythm is regulated and controlled; by collecting a pressure falling curve after blowing each time, the falling rate dP / dt is calculated, and the desorption efficiency of the zinc liquid is evaluated; when the deviation exceeds a threshold value, the fuzzy rule base is automatically corrected, and self-learning and optimization of control parameters are achieved; the method improves the internal blowing precision and consistency, is high in adaptability, and can be widely applied to the field of intelligent manufacturing of hot-dip galvanized steel pipes.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for hot-dip galvanizing processes, specifically to an automated control system and method for the internal blowing process of galvanized steel pipes. Background Technology

[0002] Galvanized steel pipes are widely used in construction, transportation, power, and other fields. The quality of the galvanization on their inner wall directly affects their corrosion resistance and service life. Traditional internal blowing processes rely mainly on manually setting parameters and using a timer to drive compressed air to purge the inner wall of the steel pipe and remove residual zinc. However, this method has the following serious drawbacks:

[0003] First, the current internal blowing process lacks an adaptive adjustment mechanism to respond to different steel pipe diameters, wall thicknesses, and zinc liquid viscosities. This results in some zinc nodules on the inner wall of the steel pipes not being completely blown away during multiple batches of continuous production, forming micro-corrosion sources such as "cavitation points," which seriously affects product consistency. Second, existing internal blowing control systems generally rely on simple temperature and constant pressure control, which cannot detect changes in the solidification rate of the zinc liquid inside the steel pipe in real time. This makes them highly susceptible to "zinc liquid backflow" under low temperature or low air pressure conditions, causing zinc strands or zinc clumps to accumulate at the tail end of the steel pipe, increasing subsequent cleaning costs.

[0004] Especially in multi-station automated hot-dip galvanizing production lines, due to the dynamic changes in the height of the internal blowing port from the zinc liquid surface, traditional solutions cannot establish a closed-loop regulation model for the internal temperature flow field of the steel pipe. This results in significant differences in the cleanliness of steel pipes of different lengths under the same internal blowing parameters, and in severe cases, it may even induce the risk of zinc dross splashing onto related equipment, reducing the stability and safety of the entire line. Summary of the Invention

[0005] The purpose of this invention is to provide an automated control system and method for the internal blowing process of galvanized steel pipes, so as to overcome the shortcomings of the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Automated control methods for the internal blowing process of galvanized steel pipes include:

[0008] S1: Obtain the basic parameters of the target steel pipe entering the internal blowing station, and output the corresponding initial temperature and pressure value pair (T0, P0) by matching it with the historical galvanizing parameter model in the database.

[0009] S2: After the target steel pipe enters the internal blowing position, collect the temperature gradient data of the inner wall of the steel pipe, and establish the temperature change rate vector ΔT / Δt by combining it with the set sampling period Δt.

[0010] S3: Using ΔT / Δt as the dynamic zinc liquid solidification rate index, and combining the obtained T0 and P0, calculate the optimal internal blowing pressure P(t) and airflow pulse frequency f(t) at the current time slot t;

[0011] S4: Generate a drive signal sequence based on P(t) and f(t), and send it to the solenoid valve and the blower for real-time control of the blowing rhythm;

[0012] S5: After each internal blowing pulse, the pressure drop curve inside the steel pipe is collected, and the actual drop rate dP / dt is calculated as the basis for judging the zinc liquid desorption efficiency.

[0013] S6: Compare the deviation between dP / dt and the expected fallback curve. If the deviation is greater than the set threshold ε, then correct the output range in the fuzzy rule base in real time and optimize P(t+1) and f(t+1) for the next cycle.

[0014] Preferably, wherein S1 includes:

[0015] Obtain the length L and inner diameter D of the target steel pipe, and simultaneously collect the wall thickness T to form a basic parameter set (L, D, T).

[0016] Read the production batch number and material identifier of the target steel pipe to generate an extended parameter set M;

[0017] The basic parameter set (L, D, T) and the extended parameter set M are used as input feature vectors and input into a pre-built historical process parameter database. The K-nearest 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 using a weighted average strategy as the initial control parameters for the internal blowing process of the current steel pipe.

[0019] Preferably, wherein S2 includes:

[0020] An array of infrared temperature sensors is arranged at equal intervals on a ring positioning frame around the outlet of the internal blowing nozzle. Each sensor array contains multiple infrared probes arranged along the axial direction of the steel pipe.

[0021] Each group of infrared probes synchronously collects the inner wall surface temperature value of its corresponding monitoring point with a sampling period of Δt seconds. ;

[0022] For each set of temperature sequences collected, least squares curve fitting was performed to obtain the corresponding temperature spatial gradient distribution curve, and the rate of temperature change between adjacent sampling points was calculated along the time axis. ;

[0023] All monitoring points The values ​​are constructed as a set of temperature change rate vectors .

[0024] Preferably, wherein S3 includes:

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

[0026] The fuzzy control model pre-sets several input fuzzy variables, including a pre-set membership function, and output variables: the increase / decrease in airflow pressure ΔP and the pulse frequency adjustment factor Δf;

[0027] Based on the membership degree of the current input variables, inference is performed 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 fluctuation of the ΔT / Δt change trend exceeds the set threshold η over multiple consecutive periods, the membership function parameters will be adjusted and optimized.

[0029] Preferably, wherein S4 includes:

[0030] The output internal blowing pressure P(t) and pulse frequency f(t) are used as control targets and digital signal encoding processing is performed.

[0031] The PWM control signal is generated using duty cycle modulation, where the pulse width is directly proportional to P(t) and the pulse interval is inversely proportional to f(t).

[0032] The PWM control signal is synchronously output to the frequency converter driver and the solenoid valve control, which respectively control the output air pressure of the fan and the opening rhythm of the solenoid valve.

[0033] Preferably, wherein S5 includes:

[0034] Record the data on the change of internal pressure over time to form a complete pressure drop curve P(t);

[0035] The P(t) curve is smoothed using a moving average filtering algorithm, and the fall slope, i.e. the fall rate dP / dt, is extracted by polynomial curve fitting.

[0036] The dP / dt ratio is compared with the set standard reference fall rate value to determine whether the current internal blowing has achieved the expected zinc liquid desorption efficiency. If it is less than the standard value, parameter correction suggestions are triggered.

[0037] Preferably, wherein S6 includes:

[0038] The actual measured rate of return of internal cavity pressure dP / dt is compared with the expected reference curve dP / dt under similar operating conditions. s Perform time-slice comparisons and calculate the deviation function ΔE(t);

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

[0040] Based on the current deviation direction and magnitude, adjust the fuzzy membership function boundary of the corresponding output variable in the fuzzy rule base to correct the control output interval;

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

[0042] The present invention also provides an automated control system for the internal blowing process of galvanized steel pipes, comprising:

[0043] Parameter acquisition module: Acquires the basic parameters of the target steel pipe entering the internal blowing station, matches them with the historical galvanizing parameter model in the database, and outputs the corresponding initial temperature and pressure value pair (T0, P0).

[0044] Temperature change data acquisition module: After the target steel pipe enters the internal blowing position, it acquires the temperature gradient data of the inner wall of the steel pipe, and establishes the temperature change rate vector ΔT / Δt by combining it with the set sampling period Δt.

[0045] Calculation module: Using ΔT / Δt as the dynamic zinc liquid solidification rate index, and combining the obtained T0 and P0, calculate the optimal internal blowing pressure P(t) and airflow pulse frequency f(t) at the current time slot t;

[0046] Control module: Generates a drive signal sequence based on P(t) and f(t), and sends it to the solenoid valve and the blower for real-time control of the blowing rhythm;

[0047] Fallback rate determination module: After each internal blowing pulse, the pressure fallback curve of the steel pipe cavity is collected, and the actual fallback rate dP / dt is calculated as the basis for judging the zinc liquid desorption efficiency.

[0048] Comparison and optimization module: Compare the deviation of dP / dt with the expected fallback curve. If the deviation is greater than the set threshold ε, the output range in the fuzzy rule base is corrected in real time to optimize P(t+1) and f(t+1) in the next cycle.

[0049] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0050] 1. This invention overcomes the limitations of traditional internal blowing processes, which rely on fixed parameters and cannot dynamically adapt to different pipe types and cooling states, by constructing a fuzzy self-learning control system based on the real-time thermal characteristics (ΔT / Δt) and pressure feedback rate (dP / dt) of the steel pipe. Through the introduction of multi-dimensional sensor fusion, a fuzzy control model, and an adaptive rule optimization mechanism, real-time adjustment and periodic optimization of the internal blowing pressure P(t) and pulse frequency f(t) are achieved, significantly improving the zinc melt desorption efficiency and the consistency of the inner wall coating, and significantly reducing the rework rate caused by defects such as zinc nodules, zinc buildup, and zinc slag.

[0051] 2. The control method provided by this invention has the advantages of high automation, rapid response, and precise control, especially exhibiting stronger stability and robustness under conditions of mixed production lines for multiple specifications of steel pipes and high-speed galvanizing processes. Furthermore, the system possesses fault identification, rule self-correction, and control parameter self-evolution capabilities, enabling it to continuously adapt to changes in production conditions, reduce manual intervention, and improve production line operating efficiency and energy utilization. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0053] Figure 1 This is a flowchart of the method of the present invention.

[0054] Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1, please refer to Figure 1 As shown in this embodiment, the automated control method for the internal blowing process of galvanized steel pipe includes:

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

[0058] S2: After the target steel pipe enters the internal blowing position, collect the temperature gradient data of the inner wall of the steel pipe, and establish the temperature change rate vector ΔT / Δt by combining it with the set sampling period Δt.

[0059] S3: Using ΔT / Δt as the dynamic zinc liquid solidification rate index, and combining the obtained T0 and P0, calculate the optimal internal blowing pressure P(t) and airflow pulse frequency f(t) at the current time slot t;

[0060] S4: Generate a drive signal sequence based on P(t) and f(t), and send it to the solenoid valve and the blower for real-time control of the blowing rhythm;

[0061] S5: After each internal blowing pulse, the pressure drop curve inside the steel pipe is collected, and the actual drop rate dP / dt is calculated as the basis for judging the zinc liquid desorption efficiency.

[0062] S6: Compare the deviation between dP / dt and the expected fallback curve. If the deviation is greater than the set threshold ε, then correct the output range in the fuzzy rule base in real time and optimize P(t+1) and f(t+1) for the next cycle.

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

[0064] Structural parameters: Length L of the steel pipe is measured by a laser displacement sensor integrated on the conveyor line; Inner diameter D is obtained and calculated by using a ring laser ranging module to acquire edge reflection data of the steel pipe port; Wall thickness T is measured at multiple points and averaged by a non-contact ultrasonic thickness gauge while the steel pipe is rotating.

[0065] Extended parameter set M: includes information such as the production batch number, material code, and forming process identification of the steel pipe, which is obtained by RFID radio frequency identification or QR code scanner and then transmitted to the control system.

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

[0067] To achieve efficient and accurate parameter matching, this invention employs the K-Nearest Neighbors (KNN) algorithm to calculate the similarity of the steel pipe feature vectors. The algorithm is defined as follows:

[0068] The sample records that have completed internal blowing in the historical database are standardized according to the feature vector format to construct a vector space;

[0069] For the feature vector X of the steel pipe to be processed, calculate its Euclidean distance with each historical sample Xi in the database as a similarity measure;

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

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

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

[0073] Furthermore, to ensure the system can still output reasonable parameters under extreme conditions, a similarity threshold θ = 0.65 is set. When the maximum similarity of the K nearest neighbor samples is all below this threshold, the expert rule base will be triggered to adjust parameters based on the material and inner diameter of the steel pipe. The rule table is manually extracted from historical test data and process manuals, such as:

[0074] For steel pipes made of Q235 with an inner diameter of less than 80 mm, the recommended initial temperature is 420 degrees Celsius and the initial wind 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 wind pressure is 0.42 MPa.

[0076] In a preferred embodiment of the present invention, an annular positioning frame is provided at the front end of the internal blowing station. The positioning frame is coaxial with the steel pipe, and at least three sets of infrared temperature sensor arrays are distributed in a ring inside. Each array has no fewer than four infrared temperature probes along the axial direction of the steel pipe, corresponding to the front, middle, rear, and tail sections of the steel pipe, respectively, thereby achieving full coverage monitoring of the temperature of the entire inner wall of the steel pipe. The infrared probes are thermopile-type non-contact temperature sensing elements, with performance indicators of temperature measurement accuracy better than ±0.3℃ and response time less than 200 milliseconds. They are equipped with a self-calibration logic based on a fixed blackbody standard, which performs zero drift correction daily to ensure long-term stable operation.

[0077] Each infrared probe acquires the current temperature value at time intervals of Δt. The recommended sampling period Δt is 0.5 to 2 seconds, which should be adjusted according to the production line speed and the cooling characteristics of the zinc liquid. The data from each group of sensors are collected to the temperature control and analysis unit via RS485 bus, where the central processing module performs synchronous timing processing and generates a temperature time series according to the sampling order.

[0078] To reduce incidental noise and local anomalies caused by probe angle during the measurement process, this invention employs a least squares curve fitting algorithm to extract the trend of the temperature sequence at each set of data points, constructing an axial temperature distribution curve for the steel pipe. Each set of data points (location) is then used to... ,temperature Using as input, perform a first-order polynomial fitting, with the fitting model set as follows: , where a is the temperature gradient coefficient and b is the initial temperature intercept value.

[0079] The fitted function was recorded in two consecutive sampling periods. and Then, the rate of temperature change ΔT / Δt at the corresponding moment can be obtained by calculating the difference between the two sets of functions at the same location point x, that is: ;

[0080] Construct a temperature change rate vector with ΔT(x) / Δt as components at each location point. In practical implementation, this vector adopts a four-dimensional structure, corresponding to four typical regions of the steel pipe: the front section (0%~25% of length), the middle section (25%~50%), the rear section (50%~75%), and the tail section (75%~100%). The ΔT / Δt value of each segment is a weighted average of multiple probes within that region, with the weights set based on the stability of temperature fluctuations; points with smaller fluctuations have larger weights.

[0081] The final temperature change rate vector As one of the main variables of the current thermal characteristics of the steel pipe, it is input into the subsequent fuzzy regulator and wind pressure control logic. t The values ​​of each dimension can be used to determine the cooling trend of the molten zinc on the inner wall of each section of the steel pipe, for example:

[0082] like much smaller This indicates that the tail section cools slowly, requiring a longer airflow duration;

[0083] like and Large differences may indicate uneven heating or zinc liquid backflow, and the internal blowing angle or rhythm should be adjusted.

[0084] In the production of hot-dip galvanized steel pipes, the core purpose of the internal blowing process is to peel off residual zinc liquid adhering to the inner wall of the pipe through airflow, so as to ensure uniform coating, qualified thickness, and prevent defects such as zinc nodules and zinc wires. To achieve adaptive control of different steel pipe parameters and cooling states, this invention, after acquiring the temperature change rate vector ΔT / Δt, combines it with the initial process setpoints T0 and P0 to calculate the optimal internal blowing pressure P(t) and airflow pulse frequency f(t) at the current time slice t, 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-difference processing of the temperature monitoring data obtained in step S2. It is used to reflect the cooling trend of the molten zinc in the inner wall of the steel pipe during the transition from liquid to solid state. This vector is usually represented by a four-dimensional structure, which represents the average cooling rates of the front, middle, rear, and tail sections of the steel pipe.

[0086] In this invention, ΔT / Δt is defined as the dominant variable affecting the intensity and frequency of internal blowing, and its changing trend determines the control boundary of the airflow action time and pressure intensity.

[0087] This system adopts a dual-input dual-output fuzzy control model, where the input variables are the temperature change rate ΔT / Δt and the initial parameters T0 and P0; and the output variables are the regulating pressure value P(t) and the airflow 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 calculates the regional average value of these sub-variables and inputs it.

[0090] T0 is the initial set temperature (in degrees Celsius), and P0 is the initial airflow pressure (in megapascals).

[0091] The fuzzing process divides ΔT / Δt into three linguistic membership levels: "cooling too slowly", "cooling moderately", and "cooling too fast".

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

[0093] Output variable design:

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

[0095] Output 2 is f(t), which is the frequency value of the current pulse blowing, in Hertz (Hz).

[0096] The output variables use three vague language labels, "increase", "remain", and "decrease", to express the adjustment trend.

[0097] To achieve the logical mapping relationship, this invention pre-defines a set of no fewer than 25 fuzzy rules, extracted jointly from human experience and historical data training results, with the following format:

[0098] If ΔT / Δt means "cooling too slowly" and the initial temperature T0 means "too high", then P(t) means "increased" and f(t) means "increased";

[0099] If ΔT / Δt is “moderate cooling” and the initial pressure P0 is “moderate”, then P(t) is “maintained” and f(t) is “maintained”;

[0100] If ΔT / Δt means "cooling too fast" and T0 means "too low", then P(t) means "reduced" and f(t) means "reduced".

[0101] The fuzzy inference adopts the Mamdani-type fuzzy inference system and uses the central averaging method for defuzzification to obtain continuous numerical output results P(t) and f(t).

[0102] To improve the system's adaptability in complex dynamic environments, a self-learning fuzzy control mechanism based on error feedback is proposed:

[0103] In multiple consecutive time slices (e.g., 3 to 5), the system calculates the slope of change of ΔT / Δt;

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

[0105] Then the membership function fine-tuning program will be automatically triggered to adjust the boundary values ​​and membership curve shape of the input linguistic variables (using triangular or Gaussian functions) so that the control model can refit the current thermal environment.

[0106] The self-learning records will accumulate in the model and serve as a historical reference for the parameters of similar steel pipes in the future, gradually improving the stability of the model.

[0107] To avoid excessive fluctuations in the control output signal, P(t) and f(t) are smoothed using an exponential moving average (EMA). The details are as follows:

[0108] Current period output value Smoothing results for: ;

[0109] Where α is the smoothing coefficient, and it is recommended to take a value between 0.3 and 0.6, depending on the system stability requirements.

[0110] This invention employs digital pulse width modulation (PWM) control to encode the control parameters. A PWM signal is a pulse waveform with a variable duty cycle, the periodicity of which 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): That is, the reciprocal of the pulse frequency, in seconds;

[0113] Duty cycle (D): ,in Maximum set wind pressure;

[0114] Pulse width (W): This indicates the duration of the signal being at a high level in each cycle.

[0115] The central control unit converts P(t) and f(t) into PWM signals, which are then output through an FPGA or a high-precision PWM generator module.

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

[0117] f(t) is the frequency value of the control signal, which 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 accuracy of 1%, to meet the wide range of control requirements from low voltage and low frequency to high voltage and high frequency.

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

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

[0121] Employing a voltage-type vector control algorithm, the output power can be adjusted in real time;

[0122] The built-in PID voltage stabilization module prevents airflow vibration caused by excessively rapid changes in P(t).

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

[0124] Solenoid valves are used to achieve "pulse-like" opening and closing of airflow, and their response speed directly affects the stability of the airflow rhythm. This invention uses a transistor array to control the opening and closing state of the solenoid valve and utilizes a PWM signal to control the opening cycle of the solenoid valve.

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

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

[0127] The valve is closed during the low-level period, stopping the airflow;

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

[0129] To ensure stability under frequent start-up and shutdown:

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

[0131] The operating current of the solenoid valve coil is less than 1 amp, and the response time is no more than 50 milliseconds.

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

[0133] The changes in motor current are collected and compared with the target current for PWM regulation;

[0134] If the current fan current deviation value ΔI exceeds the threshold (It is recommended to set it to 0.3 amps). The problem is identified as a voltage regulation malfunction, and the system records fault code F01.

[0135] Record the valve drive response and actual opening status in each cycle;

[0136] If no solenoid valve coil energization signal is detected for three consecutive cycles or the valve body does not open, it is determined as "solenoid valve malfunction", triggering code F02 and stopping the blowing.

[0137] The controller has built-in "anomaly tolerance logic". When an anomaly is detected continuously but does not exceed the set limit (e.g., 3 times), it will first attempt to recover.

[0138] If the maximum number of attempts is exceeded, the internal blowing action will be stopped, a fault log will be recorded, and an alarm will be triggered to notify the maintenance personnel.

[0139] In the internal blowing process of hot-dip galvanized steel pipes, the thorough removal of residual zinc liquid from the inner cavity of the pipe is a crucial step in ensuring the uniformity of the coating and surface quality. Traditional methods often rely on timed blowing, lacking dynamic detection methods for the actual removal effect of zinc liquid, leading to frequent occurrences of insufficient or excessive blowing, which affects process consistency and wastes energy.

[0140] This invention proposes a method for evaluating the desorption efficiency of molten zinc based on the pressure fall rate dP / dt inside the steel pipe. By collecting the transient pressure change on the inner wall of the steel pipe after each airflow pulse, and analyzing the resistance effect of residual molten zinc on airflow rebound, a real-time quantitative judgment of the internal blowing effect can be achieved.

[0141] To obtain accurate dynamic feedback of airflow within the steel pipe cavity, this invention deploys a set of differential pressure sensors at the end of the internal blowing station. These sensors employ a mechanically telescopic probe structure, automatically aligning and inserting approximately 10-20 mm into the steel pipe cavity, avoiding interference from external airflow. Their performance specifications are as follows:

[0142] Sampling accuracy better than ±10 Pascals;

[0143] Response time no more than 10 milliseconds;

[0144] It can withstand positive air pressure of up to 0.5 MPa without affecting measurement stability.

[0145] Within the first control time slot 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, and the sampling time span is 0.5 seconds to 1 second. The pressure change process from pulse cutoff to air pressure stabilization is completely recorded, forming a time series curve P(t), which is the pressure drop curve of the inner cavity of the steel pipe.

[0146] To eliminate potential high-frequency noise and equipment vibration interference during the sampling process, the system uses a moving average filtering algorithm to smooth the P(t) curve data. The algorithm window length is set to 5-10 sampling points, depending on the sampling frequency and noise level.

[0147] The processed P(t) data is then reconstructed into a continuous curve using a cubic spline interpolation algorithm, resulting in a physically smooth pressure change curve with high fitting accuracy. Cubic spline interpolation has the advantages of inter-segment continuity and derivative continuity, making it suitable for fitting nonlinear, slowly changing trends.

[0148] The instantaneous pressure drop rate at the initial stage of the decline is extracted by differentiating the fitted P(t) curve within the key time window using numerical differentiation. The specific formula is as follows:

[0149] exist Time (i.e., pulse cutoff point) to Select multiple points between time points;

[0150] calculate , ;

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

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

[0153] This invention uses the fall rate dP / dt as the core physical indicator for measuring the efficiency of airflow in removing zinc liquid. Its basic principle is as follows:

[0154] If the amount of zinc liquid adhering to the liquid is large, after the airflow impact, there will still be a large amount of residual liquid that dampens the backflow, the pressure will drop slowly, and the dP / dt value will be small.

[0155] If the zinc liquid desorption is good, there is no obvious resistance in the cavity, the gas pressure is released rapidly, and the dP / dt value is large.

[0156] To establish judgment criteria, this invention introduces a dynamic judgment threshold mechanism based on a multi-parameter reference model:

[0157] The modeling variables include: steel pipe material (such as Q235, Q345), pipe diameter D, initial inner wall temperature 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, which outputs the recommended reference fall rate dP / dt. s ;

[0159] The currently measured dP / dt and dP / dt s If the comparison is made and the value is lower than 90% of the reference value, the system determines that the "de-attachment is insufficient" and issues a parameter optimization prompt.

[0160] If three consecutive pulses are below the threshold, it is recommended to adjust the internal air pressure or extend the duration of a single pulse.

[0161] A multi-parameter reference model is established based on historical data to determine the expected fallback curves dP / dt under different conditions such as material, size, and initial temperature (T0). s This curve can be generated by a multiple linear regression model, support vector machine, or neural network prediction model, and has good generalization ability.

[0162] Assuming within the time window Internal sampling yields the actual fallback curve The reference curve is Then the deviation function ΔE(t) is defined as: ΔE(t) = average value This refers to the mean square error (MSE) within the time window. The larger this value, the more serious the deviation between the actual zinc bath desorption effect and the expectation.

[0163] The threshold ε is a dynamically fluctuating value, defined by the bias distribution of historical similar samples extracted from the reference database by the statistical model: Where μ is the average historical deviation and σ is the standard deviation, ensuring that abnormal situations are identified in more than 95% of operating conditions.

[0164] When ΔE(t) exceeds the set threshold ε, the fuzzy rule adjustment procedure within the control system is triggered. This process includes the following key steps:

[0165] The controller backtracks the fuzzy rules and their input-output mapping combinations (i.e. rule "trajectories") invoked during the current control cycle, marking which inputs led to the deviation output.

[0166] In the currently active fuzzy rule, the membership function of the output variable will undergo boundary fine-tuning. The specific method is as follows:

[0167] For Gaussian membership functions: adjust the center value μ and standard deviation σ to shift the output curve upward or downward;

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

[0169] Adjust the step size to 2% to 5% of the relative output range to prevent excessive drift.

[0170] The revised fuzzy rules are stored in a short-term rule set through a local caching mechanism and are given priority for use under similar operating conditions. If deviation control is successful for five or more consecutive times, the version of the rule is written into the main control rule base to form the iterative learning result.

[0171] Based on the revised fuzzy rules, the system recalculates the adjustment outputs P(t+1) and f(t+1) for the next control cycle. The specific steps are as follows:

[0172] Re-fuzzify the current inputs such as ΔT / Δt, T0, and P0;

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

[0174] P(t+1) and f(t+1) are calculated using defuzzification, and the trend of change is buffered by the moving average method to prevent structural shocks caused by drastic adjustments.

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

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

[0177] If ΔE(t) increases instead of decreases within two consecutive periods after the rule is updated, or if changes in P(t+1) and f(t+1) exceed the maximum allowable fluctuation threshold (e.g., 20%) are detected.

[0178] Then it will automatically restore the fuzzy rule version used in the previous control cycle;

[0179] Simultaneously, "learning failure" events are recorded, and a delayed relearning state is entered, with a cooldown period of 3 to 5 cycles.

[0180] Example 2, please refer to Figure 2 As shown, the automated control system for the internal blowing process of galvanized steel pipes in this embodiment includes:

[0181] Parameter acquisition module: Acquires the basic parameters of the target steel pipe entering the internal blowing station, matches them with the historical galvanizing parameter model in the database, and outputs the corresponding initial temperature and pressure value pair (T0, P0).

[0182] Temperature change data acquisition module: After the target steel pipe enters the internal blowing position, it acquires the temperature gradient data of the inner wall of the steel pipe, and establishes the temperature change rate vector ΔT / Δt by combining it with the set sampling period Δt.

[0183] Calculation module: Using ΔT / Δt as the dynamic zinc liquid solidification rate index, and combining the obtained T0 and P0, calculate the optimal internal blowing pressure P(t) and airflow pulse frequency f(t) at the current time slot t;

[0184] Control module: Generates a drive signal sequence based on P(t) and f(t), and sends it to the solenoid valve and the blower for real-time control of the blowing rhythm;

[0185] Fallback rate determination module: After each internal blowing pulse, the pressure fallback curve of the steel pipe cavity is collected, and the actual fallback rate dP / dt is calculated as the basis for judging the zinc liquid desorption efficiency.

[0186] Comparison and optimization module: Compare the deviation of dP / dt with the expected fallback curve. If the deviation is greater than the set threshold ε, the output range in the fuzzy rule base is corrected in real time to optimize P(t+1) and f(t+1) in the next cycle.

[0187] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this 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 period.

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 periods 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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