TW202037735A - System and method for hot-dip galvanization - Google Patents

Abstract

A system for hot dip galvanizing is provided. The system includes an air knife module, a zinc liquid tank and a calculation module. The zinc liquid tank stores zinc liquid, and the steel strip passes through the air knife module after being immersed in the zinc liquid tank. The calculation module is used to obtain process parameters, and predict the thickness of a zinc layer on the steel strip according to the process parameters.

Description

Hot dip galvanizing system and method

The invention relates to a hot-dip galvanizing system and method.

Hot-dip galvanization (HDG) is a common anti-corrosion method for steel. After the steel strip is immersed in the galvanizing tank, an air knife is used to remove excess zinc liquid. The corrosion resistance of galvanized steel sheet mainly depends on the thickness of the zinc layer. The thickness of the zinc layer is usually based on the coating weight (CW). Since the thickness of the zinc layer cannot be measured at high temperatures, the conventional method is to install the sensor at the back end. The sensor is used to measure the cold film thickness of the zinc layer, but because the position of the sensor is different from the position of the air knife (the two may differ by more than 100 meters), the zinc layer cannot be immediately known after adjusting the air knife parameters thickness. How to solve this problem is a topic of concern to those skilled in the art.

The embodiment of the present invention provides a hot-dip galvanizing system, which includes an air knife module, a zinc bath and a calculation module. The zinc liquid tank stores zinc liquid, and the steel strip will pass through the air knife module after being immersed in the zinc liquid tank. The calculation module is used to obtain the process parameters and predict the thickness of the zinc layer on the steel strip according to the process parameters.

In some embodiments, the process parameters include production line speed, steel strip parameters related to the steel strip, zinc liquid tank parameters related to the zinc liquid tank, and air knife parameters related to the air knife module.

In some embodiments, the zinc bath includes an immersion roller, a correction roller, and a stabilization roller. The above-mentioned zinc bath parameters include the roller diameter and roller position of the immersion roller, correction roller, and stabilizer roller.

In some embodiments, the aforementioned steel strip parameters include the steel type, width and thickness of the steel strip.

In some embodiments, the aforementioned air knife module includes a lower knife lip, an upper knife lip and a plurality of motors. These motors correspond to multiple air knife positions to adjust the opening of the air knife module at the air knife position. The aforementioned air knife parameters include opening degree, air inlet pressure, and distances from multiple air knives to the steel belt.

In some embodiments, the calculation module is used to train a regression model for each air knife position, and predict the thickness of the zinc layer at the corresponding air knife position based on the regression model.

In some embodiments, the aforementioned regression model includes a plurality of weak classifiers, and the regression model is expressed as the following equation (1).

F(x)= h ₁ ( x )+ h ₂ ( x )+… h _i ( x )+…+ h _n ( x )...(1)

Where x is a process parameter, h _i ( x ) is the thickness predicted by the corresponding weak classifier, n is the number of weak classifiers, and F(x) is the thickness predicted by the regression model.

In some embodiments, each weak classifier can be expressed as the following equation (2), where w and b are parameters after training.

h _i (x)=w. x+b...(2)

In some embodiments, the calculation module controls the motor to adjust the opening of the air knife module at the corresponding air knife position according to the thickness of the zinc layer.

From another perspective, the embodiment of the present invention proposes a hot-dip galvanizing method, which includes: immersing a steel strip in a zinc bath and passing the steel strip through an air knife module; obtaining process parameters, and predicting the tapping according to the process parameters Take the thickness of the zinc layer; and adjust the opening of the air knife module according to the thickness.

In the above system and method, the thickness of the zinc layer can be predicted in real time, so that the air knife module can be adjusted in time to avoid the delay in the length direction and the uneven thickness of the zinc layer in the width.

In order to make the above-mentioned features and advantages of the present invention more comprehensible, the following specific embodiments are described in detail in conjunction with the accompanying drawings.

100‧‧‧Hot Dip Galvanizing System

110‧‧‧Zinc bath

111‧‧‧ Immersion Roll

112‧‧‧Correction roller

113‧‧‧Stabilizing roller

120‧‧‧Air Knife Module

121‧‧‧Lower Lip

122‧‧‧Upper Sword Lip

123‧‧‧Motor

130‧‧‧Calculation Module

140‧‧‧X-ray galvanized thickness measuring instrument

150‧‧‧Steel belt

401~403、501~503‧‧‧Step

[Figure 1] is a schematic diagram showing a hot dip galvanizing system according to an embodiment.

[Fig. 2] is a schematic diagram showing the air knife module according to an embodiment.

[Fig. 3] is a schematic diagram showing the distance between the air knife and the steel belt according to an embodiment.

[Fig. 4] is a schematic diagram of training multiple weak classifiers according to an embodiment.

[Fig. 5] is a flowchart of a hot-dip galvanizing method according to an embodiment.

Fig. 1 is a schematic diagram illustrating a hot-dip galvanizing system according to an embodiment. Please refer to Figure 1, the hot-dip galvanizing system 100 includes a zinc bath 110, an air knife die The group 120 and the calculation module 130 also include an X-ray coating weight gauge 140 in some embodiments, but in some embodiments, the X-ray coating weight gauge 140 may also be omitted. The zinc bath 110 is also provided with an immersion roller 111, a correction roller 112, and a stabilization roller 113. The zinc liquid tank 110 stores zinc liquid, and the steel strip 150 is immersed in the zinc liquid tank 110 to form a zinc layer on the steel strip 150. The correction roller 112 and the stabilizer roller 113 can be used to form a force on the steel belt 150, and the tension can be adjusted by changing the position of the correction roller 112. After the steel strip 150 passes through the zinc liquid tank 110, the surface of the steel strip 150 has unsolidified zinc liquid. Then the steel strip 150 will pass through the air knife module 120, and the air knife module 120 will blow out gas to remove the excess Zinc liquid.

The calculation module 130 can be any computer, server, or controller for obtaining at least one process parameter and predicting the thickness of the zinc layer on the steel strip 150 according to the process parameter. The calculation module 130 can control the air knife module 120 according to the predicted thickness to adjust the pressure at the outlet of the air knife, thereby adjusting the thickness of the zinc layer. In the prior art, the thickness of the zinc layer is measured by the X-ray galvanizing thickness gauge 140, but it can be seen from FIG. 1 that the position of the X-ray galvanizing thickness gauge 140 is different from that of the air knife module 120. Therefore, even if the air knife module 120 is adjusted according to the measured thickness, the thickness of the zinc layer cannot be changed in time. This phenomenon is called a measurement delay in the length direction. Therefore, compared with the prior art, this embodiment has at least the effect of real-time control of the air knife module 120, and can solve the above-mentioned problem of the measurement delay in the length direction.

More specifically, the calculation module 130 establishes a regression model. The input of the regression model is the aforementioned process parameters, and the output of the regression model is the thickness of the zinc layer. During the training phase, the thickness of the zinc layer is measured through X-ray plating Zinc thickness gauge 140 is used to measure. The measured data is time series. However, because the production line speed can change at any time, it is impossible to accurately calculate the required time, so the steel strip position can be used as the basis. The method is to change the production line speed Integrate the time to calculate the corresponding strip position, as shown in the following equation (1).

Local= ∫ v _S Δ t …(1)

Where Local represents the position of the steel strip, and v _s is the production line speed (speed of the steel strip 150). In other words, through the above equation (1), each measured thickness can correspond to a specific position on the steel strip. Each position on the steel strip has corresponding process parameters. These process parameters may include the production line speed, the steel strip parameters related to the steel strip, the zinc liquid tank parameters related to the zinc liquid tank 110, and the air knife parameters related to the air knife module 120.

The steel strip parameters may include the steel type, width and thickness of the steel strip 150. In some embodiments, the thickness of the steel strip 150 is not uniform. Therefore, the thickness of the steel strip at various positions will also be obtained. Later, when considering the air knife parameters, Used, the following detailed description. In some embodiments, the steel strip parameters may also include the order number, the parent steel coil number, and upstream related parameters (such as annealing temperature, annealing time). In addition, the parameters of the zinc bath may include the roller diameter and roller position of the immersion roller 111, the correction roller 112, and the stabilizer roller 113.

The air knife parameters are described as follows. FIG. 2 is a schematic diagram illustrating the air knife module according to an embodiment. 2, the air knife module 120 includes a lower knife lip 121, an upper knife lip 122 and a plurality of motors 123. The air is input from the inlet (not shown) of the air knife module 120, and finally from the upper knife lip 122 and The outlet between the lower knife lips 121 is sprayed to the steel belt 150. These motors 123 are set at different air knife positions to adjust the opening at the corresponding position (that is, the distance between the upper knife lip 122 and the lower knife lip 121). Changing the opening of the knife lip can change the outlet of the air knife. The above-mentioned air knife parameters include these openings and the air inlet pressure of the air knife. In addition, FIG. 3 is a schematic diagram showing the distance between the air knife and the steel belt according to an embodiment. Please refer to FIGS. 2 and 3. The air knife is sprayed from the paper surface of FIG. 2 and shoots toward the steel belt 150 in FIG. 3. Since the thickness of the steel strip 150 may be uneven, the distance between the air knife 120 and the steel strip 150 will be different at different positions. For example, the distance Z ₀ will be greater than the distance Z ₁ , and the distance Z ₁ will be greater than the distance Z _{2 is} large. In some embodiments, the thickness of the steel strip can be obtained after the steel strip 150 is rolled, and there is no need to provide an additional sensor to measure the thickness of the steel strip, and it can be determined according to the installation position of the air knife module 120 The distance from the air knife 120 to the steel belt 150 is obtained. The aforementioned air knife parameters also include the distance from the air knife to the steel belt at each air knife position (ie, the distances Z ₀ , Z ₁ , Z _2, etc.).

In some embodiments, since each motor 123 is independently controlled, a regression model can be independently trained for each air knife position (that is, the position of the motor 123). In some embodiments, there are 14 groups of motors 123. , So a total of 14 regression models will be trained. The above production line speed, steel strip parameters, and zinc bath parameters are the same for all air knife positions, so they will all be used as the input of the above 14 sets of regression models. However, parameters such as the opening of the knife lip and the distance from the air knife to the steel belt will be grouped according to the position of the air knife and input into the corresponding regression model.

In some embodiments, each regression model includes multiple weak classifiers, which can be expressed as the following equation (2).

F(x)= h ₁ ( x )+ h ₂ ( x )+… h _i ( x )+…+ h _n ( x )...(2)

Where x is the vector formed by the above-mentioned process parameters. In other words, the vector x may include the production line speed, the steel strip parameters, the zinc bath parameters, and the air knife parameters at the corresponding positions. h _i ( x ) is the thickness predicted by the corresponding weak classifier, n is the number of weak classifiers, and F(x) is the thickness predicted by the regression model. In some embodiments, each weak classifier is support vector regression (SVR), expressed as the following equation (3).

h _i (x)=w. x+b…(3)

Among them, w and b are the parameters after training. However, those with ordinary knowledge in the field should understand the support vector regression, which will not be repeated here.

The above method of training multiple weak classifiers is to make these weak classifiers complementary to each other, so that a stronger classifier is combined. Therefore, after training a weak classifier each time, the next weak classifier will be based on the prediction Wrong information to train. Specifically, FIG. 4 is a schematic diagram of training multiple weak classifiers according to an embodiment. Please refer to Figure 4, the thickness of the zinc layer measured during the training phase is ground truth, which is marked as y below. In step 401, training is performed based on the collected process parameters x and the measured thickness y A weak classifier h ₁ ( x ). Next, subtract the prediction result h ₁ ( x ) of the first weak classifier from the thickness y, and train the second weak classifier h ₂ according to the process parameters x and y - h ₁ ( x ) in step 402 ( x ). Similarly, in step 403 the process parameters x and _{y - h 1 (x) -} h 2 (x) to train a third weak classifiers h ₃ (x). Such training will continue until the error between the predicted thickness and the actual thickness is within a preset range. Since there are many training data, in some embodiments the above error may be the root mean square error. , MSE), but the present invention is not limited to this.

In the testing phase, the calculation module 130 can predict the thickness of the zinc layer in real time. In addition, the calculation module 130 can also control the motor 123 to adjust the opening of the air knife module 120 at the corresponding air knife position according to the thickness of the zinc layer. For example, if the thickness of the zinc layer is greater than a critical value, the opening of the corresponding position can be reduced, thereby increasing the air knife pressure and removing excess zinc liquid, which can reduce the consumption of zinc liquid. However, the present invention does not limit how to control the opening of the air knife.

In other embodiments, the aforementioned regression model may also be linear regression, Least Absolute Selection Shrinkage Operator (LASSO) algorithm, logistic regression (Logistic regression) and other regression algorithms. The present invention is not This limit.

Fig. 5 is a flowchart of a hot-dip galvanizing method according to an embodiment. Referring to FIG. 5, in step 501, the steel strip is immersed in the zinc bath and then the steel strip passes through the air knife module. In step 502, the process parameters are obtained, and the thickness of the zinc layer on the steel strip is predicted according to the process parameters. In step 503, the opening of the air knife module is adjusted according to the thickness. However, each step in FIG. 5 has been described in detail as above, and will not be repeated here. In addition, the process of FIG. 5 can be implemented as a program code, executed by a computer system, or can also be implemented as a circuit, and the present invention is not limited thereto.

In the hot-dip galvanizing system and method proposed above, the thickness of the zinc layer can be predicted in real time according to the process parameters without waiting for the zinc layer to cool down and solidify, so that the problem of the measurement delay in the length direction can be solved. In addition, since the air knife module has multiple groups of motors, and each group of motors is independently controlled, the problem of uneven thickness of the zinc layer across the width can be solved.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the technical field can make some changes and modifications without departing from the spirit and scope of the present invention. The scope of protection of the present invention shall be determined by the scope of the attached patent application.

100‧‧‧Hot Dip Galvanizing System

110‧‧‧Zinc bath

111‧‧‧ Immersion Roll

112‧‧‧Correction roller

113‧‧‧Stabilizing roller

120‧‧‧Air Knife Module

130‧‧‧Calculation Module

140‧‧‧X-ray galvanized thickness measuring instrument

150‧‧‧Steel belt

Claims (10)

A hot-dip galvanizing system includes: an air knife module; a zinc liquid tank storing zinc liquid, wherein a steel strip passes through the air knife module after being immersed in the zinc liquid tank; and a calculation module for obtaining At least one process parameter, and at least one thickness of a zinc layer on the steel strip is predicted according to the at least one process parameter. The hot-dip galvanizing system described in item 1 of the scope of patent application, wherein the at least one process parameter includes production line speed, at least one steel strip parameter related to the steel strip, and at least one zinc bath related to the zinc bath The slot parameter and at least one air knife parameter related to the air knife module. The hot-dip galvanizing system described in item 2 of the scope of patent application, wherein the zinc bath includes: an immersion roller; a correction roller; and a stabilizer roller, wherein the at least one zinc bath parameter includes the immersion roller, the Correction roller and the roller diameter and roller position of the stabilizer roller. The hot-dip galvanizing system described in item 2 of the scope of patent application, wherein the parameters of the at least one steel strip include the steel type, width and thickness of the steel strip. The hot-dip galvanizing system described in item 2 of the scope of patent application, wherein the air knife module includes: a lower knife lip; an upper knife lip; and a plurality of motors corresponding to a plurality of air knife positions for adjusting the air knife The openings of the module at the air knife positions, wherein the at least one air knife parameter includes the openings, an air inlet pressure, and the distances from the air knife to the steel belt. The hot-dip galvanizing system described in item 1 of the scope of patent application, wherein the air knife module includes an upper knife lip, a lower knife lip and a plurality of motors, the motors correspond to a plurality of air knife positions, and the calculation module It is used to train a regression model for each of the air knife positions, and predict the thickness of the zinc layer at the corresponding air knife position according to the regression model. The hot-dip galvanizing system described in item 6 of the scope of patent application, wherein the regression model includes a plurality of weak classifiers, and the regression model is expressed as the following equation (1), F(x) = h ₁ ( x ) + h ₂ ( x )+… h _i ( x )+…+ h _n ( x )...(1) where x is the at least one process parameter, and h _i ( x ) is the prediction of the corresponding weak classifier Thickness, n is the number of the weak classifiers, and F(x) is the thickness predicted by the regression model. For the hot-dip galvanizing system described in item 7 of the scope of patent application, each of the weak classifiers can be expressed as the following equation (2), h _i (x) = w. x+b...(2) where w and b are parameters after training. The hot-dip galvanizing system described in item 1 of the scope of patent application, wherein the air knife module includes an upper knife lip, a lower knife lip and a plurality of motors, and the motors correspond to a plurality of air knife positions. The at least one thickness is respectively corresponding to the air knife positions, wherein the calculation module controls the motors to adjust the opening of the air knife module at the corresponding air knife positions according to the thickness of the zinc layer. A hot-dip galvanizing method includes: immersing a steel strip in a zinc bath and passing the steel strip through an air knife module; obtaining at least one process parameter, and predicting a zinc on the steel strip based on the at least one process parameter At least one thickness of the layer; and adjusting at least one opening of the air knife module according to the at least one thickness.

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