JP6958488B2 - Plating adhesion control method, hot-dip plating steel strip manufacturing method, plating adhesion control device and plating adhesion control program - Google Patents

Description

The present invention relates to a plating adhesion amount control method, a hot-dip plated steel strip manufacturing method, a plating adhesion amount control device, and a plating adhesion amount control program.

In the continuous plating line of the steel process, the amount of plating adhering to the steel strip (hereinafter referred to as "plating adhesion amount") is the distance from the tip of the gas wiping nozzle to the steel strip (nozzle gap) and the spray from the gas wiping nozzle. It is roughly determined by the gas pressure (nozzle pressure).

In many plants that automatically control the amount of plating adhesion, the nozzle gap and nozzle pressure that achieve the target value of the amount of plating adhesion sent from the host computer (hereinafter referred to as "target plating adhesion amount") are predicted. Automatic control is performed by calculating using a model. In this automatic control of the plating adhesion amount, preset control is performed when the target plating adhesion amount changes (when passing through the welding point of the steel strip), and the error of the target plating adhesion amount generated here is calculated as the plating adhesion amount. This is solved by feedback control using the total detection value.

Here, the above-mentioned plating adhesion amount meter measures the amount of plating actually adhering to the steel strip, and it may be installed several tens to several hundreds of meters behind (downstream side) from the gas wiping nozzle. Since there are many, it takes, for example, 1 to 2 minutes before the plating adhesion amount corresponding to the current nozzle gap and nozzle pressure can be measured. Therefore, if an attempt is made to control the amount of plating adhesion using feedback control, the accuracy of the amount of plating adhesion will decrease over a wide range in the longitudinal direction of the steel strip. Therefore, improvement of preset control control has been conventionally required. For example, in Patent Document 1, highly accurate preset control is performed by appropriately selecting a plurality of preset units and a compensation method for a plating adhesion amount prediction model. The method of doing is proposed.

Japanese Unexamined Patent Publication No. 2008-50680

However, in the method proposed in Patent Document 1, the discrepancy between the plating adhesion amount prediction model and the behavior of the actual plating plant is stored separately as a universal model error and a temporarily generated model error. There is a problem that the logic for controlling the amount of plating adhesion is complicated.

The present invention has been made in view of the above problems, and improves the accuracy of preset control for calculating the operation amount for achieving the target plating adhesion amount of the steel strip, and simplifies the logic for plating adhesion amount control. It is an object of the present invention to provide a plating adhesion amount control method, a method for manufacturing a hot-dip plated steel strip, a plating adhesion amount control device, and a plating adhesion amount control program.

In order to solve the above-mentioned problems and achieve the object, the plating adhesion amount control method according to the present invention is performed by blowing gas from a nozzle onto a steel strip continuously taken out from a hot-dip plating bath. In the plating adhesion amount control method for controlling the plating adhesion amount of the band, the operating conditions are used as explanatory variables, and the corresponding plating adhesion amount is used as the objective variable. It is characterized in that the plating adhesion amount of the steel strip is controlled by classifying into the above groups and estimating the plating adhesion amount at the time of actual operation based on the classification information.

Further, in the above-mentioned invention, the plating adhesion amount control method according to the present invention uses a plurality of data sets including a past operating condition and a corresponding plating adhesion amount, and a plurality of the plating adhesion amounts are set according to the past operating conditions. A learning step to generate a determined tree model by classifying into the group of, an adhesion amount estimation step to estimate the current plating adhesion amount based on the determined tree model and actual operating conditions, and an estimated plating adhesion amount. It is characterized by including an adhesion amount control step of controlling the plating adhesion amount of the steel strip to be a target value by feeding back.

Further, in the method for controlling the amount of plating adhesion according to the present invention, in the above invention, the plurality of data sets are, as the past operating conditions, the steel type which is the character data corresponding to the composition of the steel strip and the steel strip. It is characterized by including a plate width, a plate thickness of the steel strip, a distance from the tip of the nozzle to the steel strip, and a pressure of gas blown from the nozzle.

Further, in the plating adhesion amount control method according to the present invention, in the above invention, the plurality of data sets further, as the past operating conditions, further push the amount of pushing into the steel strip by the roll installed in the hot-dip plating bath. It is characterized by including.

Further, in the above invention, the plating adhesion amount control method according to the present invention is characterized in that, in the learning step, the steel type among the past operating conditions is set as the first branching condition of the decision tree model. And.

Further, in the above invention, in the above-mentioned invention, as the second branching condition of the decision tree model, the plate width of the steel strip and the above-mentioned steel strip width among the past operating conditions are used. Either one of the steel strip thickness is set, and as the third branching condition of the decision tree model, either the steel strip width or the steel strip thickness of the past operating conditions is set. Is characterized by setting.

Further, in the above invention, when a new steel grade having no operation record in the past is added to the operation conditions, the plating adhesion control method according to the present invention is described in the first branch of the decision tree model in the learning step. It is characterized by adding a subtree having a new steel grade as an apex and updating the decision tree model.

Further, the method for controlling the amount of plating adhesion according to the present invention is characterized in that, in the above-mentioned invention, the amount of plating adhesion is classified into a plurality of groups according to the past operating conditions by using the F test in the learning step. do.

In order to solve the above-mentioned problems and achieve the object, the plating adhesion amount control method according to the present invention is performed by blowing gas from a nozzle onto a steel strip continuously taken out from a hot-dip plating bath. In the plating adhesion amount control method that controls the plating adhesion amount of the band, plating during actual operation using a determination tree model in which the operating conditions including character data are used as explanatory variables and the corresponding plating adhesion amount is used as the objective variable. It is characterized in that the plating adhesion amount of the steel strip is controlled by estimating the adhesion amount.

Further, in the method for controlling the amount of plating adhesion according to the present invention, in the above-mentioned invention, in the decision tree model, the first branching condition is the steel type, and the second branching condition is the plate width of the steel strip and the plate thickness of the steel strip. The third branching condition is one of the steel strip width and the steel strip thickness, and the fourth and subsequent branching conditions are determined based on the F-test. And.

In order to solve the above-mentioned problems and achieve the object, the method for producing a hot-dip plated steel strip according to the present invention is to manufacture a hot-dip plated steel strip whose plating adhesion amount is controlled by the above-mentioned plating adhesion amount control method. It is characterized by.

In order to solve the above-mentioned problems and achieve the object, the plating adhesion amount control device according to the present invention blows gas from a nozzle onto a steel strip continuously taken out from a hot-dip plating bath to achieve the above-mentioned steel. In the plating adhesion amount control device that controls the plating adhesion amount of the band, a plurality of data sets including the past operating conditions and the corresponding plating adhesion amount are used, and the plating adhesion amount is determined by a plurality of groups according to the past operating conditions. A learning unit that generates a determined tree model, an adhesion amount estimation unit that estimates the current plating adhesion amount based on the determined tree model and actual operating conditions, and an estimated plating adhesion amount are fed back. By doing so, it is characterized by including a preset control unit that controls the amount of plating adhesion of the steel strip to be a target value.

Further, in the plating adhesion amount control device according to the present invention, in the above invention, the plurality of data sets are, as the past operating conditions, the steel type which is the character data corresponding to the composition of the steel strip and the steel strip. It is characterized by including a plate width, a plate thickness of the steel strip, a distance from the tip of the nozzle to the steel strip, and a pressure of gas blown from the nozzle.

In order to solve the above-mentioned problems and achieve the object, the plating adhesion amount control program according to the present invention sprays gas from a nozzle onto a steel strip continuously taken out from a hot-dip plating bath to achieve the above-mentioned steel. In the plating adhesion amount control program for controlling the plating adhesion amount of the band, the computer uses a plurality of data sets including the past operating conditions and the corresponding plating adhesion amount, and the plating adhesion amount is determined by the past operating conditions. A learning means for generating a determined tree model by classifying into a plurality of groups, an adhesion amount estimating means for estimating the current plating adhesion amount based on the determined tree model and actual operating conditions, and an estimated plating adhesion amount. It is characterized in that it functions as a preset control means for controlling the plating adhesion amount of the steel strip to be a target value by feeding back.

According to the present invention, the target of the steel strip is obtained by classifying the plating adhesion amount into a plurality of groups according to the operating conditions by using a statistical method and estimating the plating adhesion amount at the time of actual operation based on the classification information. It is possible to improve the accuracy of preset control for calculating the operation amount for realizing the plating adhesion amount and to simplify the logic for plating adhesion amount control.

FIG. 1 is a schematic view showing a configuration of a plating adhesion amount control device and a plating plant according to an embodiment of the present invention. FIG. 2 is a flowchart showing a flow of a plating adhesion amount control method by the plating adhesion amount control device according to the embodiment of the present invention. FIG. 3 is a diagram for explaining a method of generating a decision tree model in the learning step of the plating adhesion amount control method according to the embodiment of the present invention. FIG. 4 updates the decision tree model by adding a steel grade, which is character data corresponding to the composition of the steel strip, as a new operating condition in the learning step of the plating adhesion amount control method according to the embodiment of the present invention. It is a figure which shows an example of the case. FIG. 5 updates the decision tree model by adding a steel grade, which is character data corresponding to the composition of the steel strip, as a new operating condition in the learning step of the plating adhesion amount control method according to the embodiment of the present invention. It is a figure which shows another example of the case. FIG. 6 is a diagram showing an example of the data structure of the decision tree model generated in the learning step of the plating adhesion amount control method according to the embodiment of the present invention.

Hereinafter, the plating adhesion amount control method, the hot-dip plated steel strip manufacturing method, the plating adhesion amount control device, and the plating adhesion amount control program according to the embodiment of the present invention will be described with reference to the drawings. The present invention is not limited to the embodiments described below.

[Plating plant]
First, the configuration of the plating plant 3 used in the present embodiment will be described. As shown in FIG. 1, the plating plant 3 includes a pot 31, a sink roll 33, a collect roll 34, a guide roll 35, a gas wiping nozzle (hereinafter referred to as “nozzle”) 36, and a plating adhesion meter 37. And have.

The molten metal is stored in the pot 31. The steel strip (steel plate) 32 is continuously conveyed into the pot 31 and immersed in the molten metal. Further, a sink roll 33 and a collect roll 34 are installed in the pot 31.

The collect roll 34 is for suppressing the bending of the steel strip 32 at the position where the nozzle 36 is installed. One roll (lower roll in FIG. 1) and the other roll (upper roll in FIG. 1) in the pair of collect rolls 34 are arranged so as to be offset from each other in height position, and one roll is fixed. On the other hand, by pushing the other roll, the steel strip 32 is sandwiched and its curvature is suppressed. In this embodiment, the amount of pushing of the other roll with respect to one of the rolls at that time is defined as "roll pushing amount C".

The steel strip 32 is supported by the sink roll 33 and the collect roll 34 inside the pot 31, and is supported by the guide roll 35 outside the pot 31. After the steel strip 32 is once immersed in the molten metal in the pot 31, high-pressure gas is blown from the nozzle 36 to the pulling rod. In this way, the unnecessary molten metal adhering to the steel strip 32 is scraped off to control the plating adhering amount to a desired value (target plating adhering amount). The amount of plating adhered to the steel strip 32 is the distance from the tip of the nozzle 36 to the surface of the steel strip 32 (hereinafter referred to as "nozzle gap D") and the pressure of the gas blown from the nozzle 36 (hereinafter referred to as "nozzle pressure P"). ), Which is largely determined by.

Specifically, the steel strip 32 is configured by connecting a plurality of steel strips by welding points PW. This welding point PW corresponds to the switching point of the target plating adhesion amount in the plating adhesion amount control described later.

The plating adhesion meter 37 measures the actual plating adhesion amount of the steel strip 32. The plating adhesion meter 37 is attached at a position separated from the position of the nozzle 36 by several tens to several hundreds of meters rearward (downstream side). Further, since the plating adhesion amount meter 37 measures the steel strip 32 cyclically (periodically) in the width direction, for example, before measuring the plating adhesion amount corresponding to the current nozzle gap D and nozzle pressure P, for example. It takes 1-2 minutes.

[Plating adhesion amount control device]
Hereinafter, the configuration of the plating adhesion amount control device 1 according to the present embodiment will be described. The plating adhesion amount control device 1 controls the plating plant 3 described above, and adheres plating of a desired thickness to the steel strip 32. That is, the plating adhesion amount control device 1 controls the plating adhesion amount of the steel strip 32 by blowing gas from the nozzle 36 onto the steel strip 32 continuously taken out from the hot-dip plating bath (pot 31). ..

In terms of hardware, the plating adhesion amount control device 1 is composed of an arithmetic processing device such as a CPU (Central Processing Unit) and a storage device such as a hard disk device. By executing a computer program, the above-mentioned arithmetic processing unit functions as a preset control unit (preset control means) 11, an adhesion amount estimation unit (adhesion amount estimation means) 12, and a learning unit (learning means) 13. Further, the above-mentioned storage device functions as a learning result storage unit 14. Although not shown in FIG. 1, the plating adhesion amount control device 1 has an input unit for taking in various information from the host computer 2 via a network and an output unit for outputting various processing information (for example, a display). Equipment, printing equipment, etc.) and are provided separately.

The preset control unit 11 obtains manufacturing information such as the target plating adhesion amount of each steel strip 32 connected at the welding point PW and the plate width and plate thickness of each steel strip 32 via the above-mentioned input unit. Taking in from 2, the control command value at the operation end is calculated so that a new target plating adhesion amount is realized at the transition of the steel strip 32. Here, the above-mentioned "control command value" is specifically a control command value relating to the nozzle gap D and the nozzle pressure P of the nozzle 36.

The preset control unit 11 controls the plating adhesion amount of the steel strip 32 to be the target plating adhesion amount by feeding back the current plating adhesion amount estimated by the adhesion amount estimation unit 12 described later. Specifically, the preset control unit 11 calculates the control command values related to the nozzle gap D and the nozzle pressure P of the nozzle 36 based on the estimated current plating adhesion amount and the target plating adhesion amount.

The steel type, plate width, plate thickness and target plating adhesion amount of the steel strip 32 are input to the preset control unit 11 from the host computer 2, and the plating adhesion amount is estimated from the adhesion amount estimation unit 12 (hereinafter, “estimated”). "Plating adhesion amount") is input. In response to this, the preset control unit 11 calculates the above-mentioned control command value and outputs the control command value to the nozzle 36.

The adhesion amount estimation unit 12 takes in the actual value of the nozzle gap D of the nozzle 36 and the actual value of the nozzle pressure P of the gas sprayed from the nozzle 36 to the steel strip 32 in the plating plant 3, and adheres to the plating. Estimate the current plating adhesion amount based on the amount prediction model. Here, the above-mentioned "plating adhesion amount prediction model" specifically indicates a decision tree model described later. The adhesion amount estimation unit 12 estimates the current plating adhesion amount based on the decision tree model and the actual operating conditions.

The nozzle gap D, the nozzle pressure P, the roll pushing amount C, and the plating adhesion amount measured by the plating adhesion amount meter 37 are input to the adhesion amount estimation unit 12 as actual values from the plating plant 3. In response to this, the adhesion amount estimation unit 12 estimates the plating adhesion amount described above, and outputs the estimated plating adhesion amount to the preset control unit 11.

The learning unit 13 receives information from the host computer 2 and the plating plant 3 via the adhesion amount estimation unit 12, and mechanically learns the correlation between the plating adhesion amount and the operating conditions using the information. .. Specifically, the learning unit 13 uses a plurality of data sets including the past operating conditions and the corresponding plating adhesion amount, and determines the plating adhesion amount by classifying the plating adhesion amount into a plurality of groups according to the past operating conditions. Generate a tree model.

Further, when a new operation condition is added, the learning unit 13 updates the decision tree model by adding a new operation condition as a branch condition (explanatory variable) to the generated decision tree model. .. Here, as an example of the above-mentioned "operating conditions", a steel type which is character data corresponding to the composition of the steel strip 32, a plate width of the steel strip 32, a plate thickness of the steel strip 32, a nozzle gap D, and the like. The nozzle pressure P and the roll pushing amount C can be mentioned.

The learning unit 13 received the plating adhesion amount measured by the steel type, plate width, plate thickness, nozzle gap D, nozzle pressure P, roll pushing amount C, and plating adhesion amount meter 37 of the steel strip 32 from the adhesion amount estimation unit 12. Is entered. In response to this, the learning unit 13 generates the above-mentioned decision tree model and outputs the decision tree model to the learning result storage unit 14. The method of generating the decision tree model by the learning unit 13 and the structure of the decision tree model will be described later.

[Plating adhesion control method]
Hereinafter, the plating adhesion amount control method by the plating adhesion amount control device 1 according to the present embodiment will be described with reference to FIGS. 2 to 6. The plating adhesion amount control method described below can be widely applied to, for example, plating adhesion amount control in a steel process line.

As will be described later, the plating adhesion amount control method according to the present embodiment uses a statistical method in which the operating conditions are used as explanatory variables and the corresponding plating adhesion amount is used as the objective variable, and a plurality of plating adhesion amounts are set according to the operating conditions. The plating adhesion amount of the steel strip 32 is controlled by estimating the plating adhesion amount at the time of actual operation based on the classification information.

Further, as described later, the plating adhesion amount control method according to the present embodiment uses a decision tree model in which the operating conditions including character data such as steel grade are used as explanatory variables and the corresponding plating adhesion amount is used as the objective variable. By estimating the amount of plating adhesion during actual operation, the amount of plating adhesion on the steel strip 32 is controlled.

In the plating adhesion amount control method according to the present embodiment, specifically, as shown in FIG. 2, the learning step S1, the adhesion amount estimation step S2, and the adhesion amount control step S3 are performed in this order.

<Learning step>
In the learning step S1, the learning unit 13 uses a plurality of data sets including the past operating conditions and the corresponding plating adhesion amount, and classifies the plating adhesion amount into a plurality of groups according to the past operating conditions. Generate a decision tree model.

In the learning step S1, the F-test is used to classify the plating adhesion amount into a plurality of groups according to the past operating conditions. That is, in the learning step S1, as shown in FIG. 3, an F-test is performed for each explanatory variable (operating condition) for the entire population (parent data) of the plating adhesion amount measured by the plating adhesion amount meter 37. Then, the branching condition is determined by searching for the division point of the data that can minimize the p-value. As shown in the figure, the child data classified from the parent data of the plating adhesion amount is indicated by the average plating adhesion amount average value of each of the classified plating adhesion amounts.

In the learning step S1, the F-test is performed for all the explanatory variables (operating conditions), and among the explanatory variables, the explanatory variable that can make the parent data most homogeneous (homoscedastic) is the first (first). ) Is determined as a branching condition. Then, in the learning step S1, such a branching condition determination process is executed for each child data, and an explanatory variable capable of making the branched child data most homogeneous is determined as the next branching condition. A decision tree model as shown in FIG. 3 is generated. In the learning step S1, the branching of the child data is terminated when it is determined by the F-test that the child data is not homoscedastic (not homogeneous).

In the decision tree model, as the operating conditions constituting the branching condition, as described above, in addition to the numerical information such as the plate width, plate thickness, nozzle gap D, nozzle pressure P and roll pushing amount C of the steel strip 32, the steel type Since character data such as, etc. can also be used, there is an advantage that branching conditions other than numerical information can be imported without being converted into numerical information.

(Data structure of decision tree model)
In the learning step S1, for example, a decision tree model as shown in FIGS. 4 and 5 can be generated. In the decision tree model shown in FIG. 4, the steel type (steel type A or steel type B) is set as the first branching condition, and the plate width (whether the plate width is larger or smaller than X mm) is set as the second branching condition. Has been done. Here, in the case where the decision tree model shown on the “existing” side of the figure has already been generated, for example, when a new steel grade C having no operation record in the past is added to the operation conditions, the same in learning step S1. As shown on the "new" side of the figure, the decision tree model is updated by adding a new subtree with the steel type C as the apex to the first branch of the decision tree model. As shown in the figure, when the steel type is set as the first branching condition, there is an advantage that it is possible to distinguish between the existing and the new.

On the other hand, in the decision tree model shown in FIG. 5, the plate width (whether the plate width is larger or smaller than X mm) is set as the first branching condition, and the steel type (steel type A or steel type B) is set as the second branching condition. Is set. Here, in the case where the decision tree model shown on the “existing” side of the figure has already been generated, for example, when a new steel grade C having no operation record in the past is added to the operation conditions, the same in learning step S1. As shown on the "new" side of the figure, the decision tree model is updated by adding a new subtree with steel type C as the apex to the second branch of the decision tree model. When the steel type is set as the second branching condition as shown in the figure, it is necessary to modify each branch of the first branching (plate width).

Here, in the learning step S1, as shown in FIG. 6, the branching condition is manually determined for the upper branch close to the parent data without using the F test, and the F test is performed for the lower branch far from the parent data. It is preferable to generate a decision tree model by automatically determining the branching condition using.

In the determination tree model shown in FIG. 6, the steel type is set as the first branching condition, the plate width of the steel strip 32 is set as the second branching condition, and the plate thickness of the steel strip 32 is set as the third branching condition. The roll pushing amount C and nozzle pressure (front) are set as the fourth branch condition, the nozzle pressure (back) and nozzle gap (front) are set as the fifth branch condition, and the sixth branch condition is set. Nozzle pressure (table) is set. Of these, the first to third branching conditions are manually determined without using the F-test, and the fourth and subsequent (fourth to sixth) branching conditions are based on the F-test. It was decided automatically.

The "nozzle pressure (table)" described above refers to the nozzle pressure P of the nozzle (the nozzle on the left side of FIG. 1) arranged on the front side of the steel strip 32 among the pair of nozzles 36. The "nozzle pressure (back)" indicates the nozzle pressure P of the nozzle (the nozzle on the right in the figure) arranged on the back side of the steel strip 32 among the pair of nozzles 36, and the above-mentioned "nozzle gap (nozzle gap)". “Table)” indicates the nozzle gap D between the nozzle arranged on the front side of the steel strip 32 (the nozzle on the left side in the figure) and the steel strip 32 among the pair of nozzles 36.

In this way, in the decision tree model generated in the learning step S1, by setting the steel grade at the upper level of the branching condition, as shown in FIG. 4 described above, when a new steel grade is added as the operating condition. Even if there is, the decision tree model can be extended without modifying the existing decision tree model. Therefore, the reusability of the existing decision tree model is improved. Hereinafter, the description after the adhesion amount estimation step S2 will be continued.

<Adhesion amount estimation step>
In the adhesion amount estimation step S2, the adhesion amount estimation unit 12 estimates the current plating adhesion amount based on the decision tree model and the actual operating conditions.

<Adhesion amount control step>
In the adhesion amount control step S3, the preset control unit 11 feeds back the estimated current plating adhesion amount to control the plating adhesion amount of the steel strip 32 to be the target plating adhesion amount.

As described above, in the plating adhesion amount control method according to the present embodiment, the actual data of the steel type, the plate width, the plate thickness, the nozzle gap D, the nozzle pressure P and the roll pushing amount C of the steel strip 32, and the nozzle 36 The correlation between the actual data of the plating adhesion amount measured by the plating adhesion amount meter 37 in the subsequent stage and the actual data of the plating adhesion amount is periodically and mechanically learned (learning step S1). Then, the current plating adhesion amount is estimated based on the decision tree model which is the learning result (adhesion amount estimation step S2), and the actual plating adhesion amount is controlled based on the estimated plating adhesion amount (adhesion amount control step S3). ).

[Plating adhesion control program]
The plating adhesion amount control program according to the present embodiment is for controlling the plating adhesion amount of the steel strip 32 by blowing gas from the nozzle 36 onto the steel strip 32 continuously taken out from the hot-dip plating bath. The computer functions as a learning means (learning unit 13), an adhesion amount estimation means (adhesion amount estimation unit 12), and a preset control means (preset control unit 11).

The plating adhesion amount control program according to the present embodiment may be stored and distributed in a computer-readable recording medium such as a hard disk, a flexible disk, or a CD-ROM, or may be distributed via a network. May be good.

[Manufacturing method of hot-dip plated steel strip]
The method for producing a hot-dip plated steel strip according to the present embodiment manufactures a hot-dip plated steel strip whose plating adhesion amount is controlled by the plating adhesion amount control method according to the present embodiment described above.

According to the plating adhesion amount control method, the hot-dip plated steel strip manufacturing method, the plating adhesion amount control device 1, and the plating adhesion amount control program according to the present embodiment as described above, the plating adhesion amount is used by a statistical method. Is classified into a plurality of groups according to the operating conditions, and the amount of plating adhered during actual operation is estimated based on the classification information (decision tree model). It is possible to improve the accuracy of preset control for calculating the amount of plating and to simplify the logic for controlling the amount of plating adhesion.

Further, in the plating adhesion amount prediction model used in the prior art (Patent Document 1), the plating adhesion amount is predicted only by three items of nozzle pressure, nozzle gap and plate speed. Therefore, when other operating conditions (for example, steel type of steel strip, plate width, plate thickness, etc.) fluctuate, it is necessary to correct the model itself, and in order to make this correction, it is composed of multiple logics such as a storage unit. There is a need to.

Further, in the conventional technique, when other operating conditions are newly added and the difference between the plating adhesion amount prediction model and the actual result becomes large, it is necessary to recreate the plating adhesion amount prediction model. In this case, in the prior art, it is assumed that the model accuracy is improved by incorporating a large number of additional elements such as composition data of the steel grade into the plating adhesion amount prediction model and performing multiple regression. However, in this case, it is necessary to recreate a new plating adhesion amount prediction model each time additional elements are added. In addition, since only numerical data can be used in multiple regression, the steel grade (steel grade symbol) itself, which is the character data corresponding to the composition of the steel strip, cannot be reflected in the plating adhesion amount prediction model, and the steel grade is plated and adhered. In order to reflect it in the amount prediction model, it is necessary to create a plating adhesion amount prediction model for each steel type.

In addition, since the composition of the steel strip usually changes significantly when the steel type changes, when trying to incorporate the composition data into a mathematical model such as a regression equation, for example, C, Si, Mn, S, P, Al, Cr, Ni, It is necessary to include all the concentrations of many additive alloy elements such as Cu, W, Mo, V, Co, Ti, Nb, B, Sb, and In, which increases the amount of calculation for numerical analysis and increases the number of steps.

On the other hand, in the plating adhesion amount control method, the hot-dip plated steel strip manufacturing method, the plating adhesion amount control device 1, and the plating adhesion amount control program according to the present embodiment, plating is performed based on the result of periodic learning by the learning unit 13. Since the adhesion amount prediction model (decision tree model) is updated, the stability discrimination logic and the like as in the prior art are not required, and the logic is simplified. Further, in the present embodiment, since it is possible to use the steel type symbol itself as the branching condition instead of the composition of the steel type, the decision tree model generated by the learning unit 13 is treated as one model including the steel type symbol. Is possible.

Further, in the plating adhesion amount control method, the hot-dip plating steel strip manufacturing method, the plating adhesion amount control device 1 and the plating adhesion amount control program according to the present embodiment, character data such as steel type can be obtained by using the determination tree model. Can be taken in as an explanatory variable, and the steel type that affects the plating adhesion amount can also be used in model creation to improve the estimation accuracy of the plating adhesion amount, which is the objective variable.

Further, in the plating adhesion amount control method, the hot-dip plating steel strip manufacturing method, the plating adhesion amount control device 1 and the plating adhesion amount control program according to the present embodiment, the steel type is determined as shown in FIGS. 4 and 6 described above. By setting it as the first branch of the tree model, even if a new steel grade is added, the decision tree model can be updated simply by adding a new subtree without destroying the existing decision tree model. Therefore, it is possible to reuse the existing decision tree model. Further, by using the decision tree model as the plating adhesion amount prediction model, the visibility is improved, and for example, program verification and horizontal deployment to other facilities become easy.

Hereinafter, the present invention will be described in more detail with reference to examples. In this embodiment, as shown in Table 1 below, learning was performed including the steel type which is character data as the operating condition (branch condition) (learning step S1), and learning was performed without including the steel type. In each case, the estimation accuracy of the plating adhesion amount was confirmed.

As shown in Table 1, when a decision tree model is generated by learning including the steel type in the operating conditions and the plating adhesion amount is estimated based on the decision tree model, the estimated value of the plating adhesion amount is "488. 89 ”, the actual difference indicating the difference in the estimated value from the actual value of the plating adhesion amount was“ 0.0644730 ”, and the difference ratio was“ 1.0188% ”. On the other hand, when a decision tree model is generated by learning without including the steel type in the operating conditions and the plating adhesion amount is estimated based on the decision tree model, the estimated value of the plating adhesion amount is "488.88", which is the actual result. The difference was "0.0462449" and the difference ratio was "1.1345%". Therefore, it was confirmed that the estimation accuracy of the plating adhesion amount was improved by learning by including the steel type in the operating conditions.

The method for controlling the amount of plating adhesion, the method for manufacturing a hot-dip plated steel strip, the device for controlling the amount of plating adhesion, and the program for controlling the amount of plating adhesion according to the present invention have been specifically described above with reference to embodiments and examples for carrying out the invention. , The gist of the present invention is not limited to these descriptions, and must be widely interpreted based on the description of the scope of claims. Needless to say, various changes, modifications, etc. based on these descriptions are also included in the gist of the present invention.

For example, in the plating adhesion amount control device 1 shown in FIG. 1 described above, the decision tree model generated by the learning unit 13 was temporarily stored in the learning result storage unit 14, but the learning unit 13 once stored the generated decision tree. The model may be output to the adhesion amount estimation unit 12 as it is without being stored in the learning result storage unit 14.

Further, in the decision tree model shown in FIG. 6 described above, the steel type is set as the first branching condition, the plate width of the steel strip 32 is set as the second branching condition, and the steel strip 32 is set as the third branching condition. However, the plate thickness of the steel strip 32 may be set as the second branching condition, and the plate width of the steel strip 32 may be set as the third branching condition.

Further, in the above-mentioned plating adhesion amount control method, a steel type is given as an example as character data that can be included in an operating condition (branch condition). For example, in addition to the steel type, "GI" representing a hot-dip galvanized steel sheet , Other character data such as "GA" representing an alloyed hot-dip galvanized steel sheet may be used as an operating condition.

Further, in the above-mentioned plating adhesion amount control method, as the timing for updating the decision tree model, for example, a case where a new steel grade having no operation record in the past is added to the operation conditions is illustrated (see FIGS. 4 and 5). ), The decision tree model may be updated at, for example, a set predetermined time interval or an operation stop timing such as a periodic inspection, in addition to the case where operating conditions such as a steel type and a new steel plate size are added.

1 Plating adhesion amount control device 11 Preset control unit (preset control means)
12 Adhesion amount estimation unit (adhesion amount estimation means)
13 Learning department (learning means)
14 Learning result storage unit 2 High-level computer 3 Plating plant 31 Pot 32 Steel strip (steel plate)
33 Sink roll 34 Collect roll 35 Guide roll 36 Nozzle (gas wiping nozzle)
37 Plating adhesion meter PW Welding point

Claims (12)

In the plating adhesion amount control method for controlling the plating adhesion amount of the steel strip by blowing gas from a nozzle onto the steel strip continuously taken out from the hot-dip plating bath.
Using a decision tree model with the operating conditions as the explanatory variable and the corresponding plating adhesion amount as the objective variable, the plating adhesion amount is determined using a plurality of data sets including the past operating conditions and the corresponding plating adhesion amount. It is classified into a plurality of groups according to the operating conditions, and the plating adhesion amount at the time of actual operation is estimated based on the classification information, and the estimated plating adhesion amount and the preset target value of the plating adhesion amount are set. Based on this, the plating adhesion amount of the steel strip is controlled by calculating the control command value for controlling the plating adhesion amount of the steel strip.
The plurality of data sets are, as the past operating conditions, from the steel type which is the character data corresponding to the composition of the steel strip, the plate width of the steel strip, the plate thickness of the steel strip, and the tip of the nozzle. A method for controlling an amount of adhesion of plating, which comprises a distance to the steel strip and a pressure of a gas blown from the nozzle. Using said plurality of data sets, by classifying into a plurality of groups by the coating weight the past operating conditions, a learning step of generating the decision tree model,
An adhesion amount estimation step for estimating the current plating adhesion amount based on the decision tree model and actual operating conditions, and an adhesion amount estimation step.
By calculating the control command value for controlling the plating adhesion amount of the steel strip based on the estimated plating adhesion amount estimated value and the preset target value of the plating adhesion amount, the steel strip Adhesion amount control step that controls the plating adhesion amount to the target value,
The plating adhesion amount control method according to claim 1, further comprising. The plating adhesion according to claim 1 or 2, wherein the plurality of data sets further include an amount of pushing into the steel strip by a roll installed in a hot-dip plating bath as the past operating conditions. Quantity control method. The plating adhesion amount control method according to claim 2, wherein in the learning step, the steel type among the past operating conditions is set as the first branching condition of the decision tree model. In the learning step, as the second branching condition of the decision tree model, either one of the plate width of the steel strip and the plate thickness of the steel strip in the past operating conditions is set, and the decision tree model is set. The plating adhesion amount according to claim 4, wherein as the third branching condition, either one of the plate width of the steel strip and the plate thickness of the steel strip among the past operating conditions is set. Control method. When adding a new steel grade that has not been operated in the past to the operating conditions, in the learning step, a subtree having the new steel grade as the apex is added to the first branch of the decision tree model, and the decision tree model is added. The plating adhesion amount control method according to claim 4 or 5, wherein the method is updated. The plating according to any one of claims 4 to 6, wherein in the learning step, the plating adhesion amount is classified into a plurality of groups according to the past operating conditions by using the F test. Adhesion amount control method. In the plating adhesion amount control method for controlling the plating adhesion amount of the steel strip by blowing gas from a nozzle onto the steel strip continuously taken out from the hot-dip plating bath.
A decision tree model generated using a plurality of data sets including past operating conditions and the corresponding plating adhesion amount, with the operating conditions including character data as explanatory variables and the corresponding plating adhesion amount as the objective variable. By using this, the plating adhesion amount during actual operation is estimated, and the plating adhesion amount of the steel strip is controlled based on the estimated plating adhesion amount estimated value and the preset target value of the plating adhesion amount. By calculating the control command value for this, the amount of plating adhesion on the steel strip is controlled.
The plurality of data sets are, as the past operating conditions, from the steel type which is the character data corresponding to the composition of the steel strip, the plate width of the steel strip, the plate thickness of the steel strip, and the tip of the nozzle. A method for controlling an amount of adhesion of plating, which comprises a distance to the steel strip and a pressure of a gas blown from the nozzle. In the decision tree model, the first branching condition is the steel type, the second branching condition is either the steel strip width or the steel strip thickness, and the third branching condition is the steel strip plate. The plating adhesion amount control method according to claim 8, wherein the fourth and subsequent branching conditions are determined based on the F-test, which is either the width or the plate thickness of the steel strip. A method for producing a hot-dip plated steel strip, which comprises manufacturing a hot-dip plated steel strip whose plating adhesion amount is controlled by the plating adhesion amount control method according to any one of claims 1 to 9. In a plating adhesion amount control device that controls the plating adhesion amount of the steel strip by blowing gas from a nozzle onto the steel strip continuously taken out from the hot-dip plating bath.
A learning unit that generates a decision tree model by classifying the plating adhesion amount into a plurality of groups according to the past operating conditions using a plurality of data sets including the past operating conditions and the corresponding plating adhesion amount. ,
An adhesion amount estimation unit that estimates the current plating adhesion amount based on the decision tree model and actual operating conditions, and an adhesion amount estimation unit.
By calculating the control command value for controlling the plating adhesion amount of the steel strip based on the estimated plating adhesion amount estimated value and the preset target value of the plating adhesion amount, the steel strip A preset control unit that controls the amount of plating adhesion to the target value,
With
The plurality of data sets are, as the past operating conditions, from the steel type which is the character data corresponding to the composition of the steel strip, the plate width of the steel strip, the plate thickness of the steel strip, and the tip of the nozzle. A plating adhesion amount control device comprising a distance to the steel strip and a pressure of a gas blown from the nozzle. In a plating adhesion control program that controls the plating adhesion amount of the steel strip by blowing gas from a nozzle onto the steel strip continuously taken out from the hot-dip plating bath, a computer is used.
A learning means for generating a decision tree model by using a plurality of data sets including past operating conditions and corresponding plating adhesion amounts and classifying the plating adhesion amounts into a plurality of groups according to the past operating conditions.
Adhesion amount estimation means for estimating the current plating adhesion amount based on the decision tree model and actual operating conditions,
By calculating the control command value for controlling the plating adhesion amount of the steel strip based on the estimated plating adhesion amount estimated value and the preset target value of the plating adhesion amount, the steel strip Preset control means that controls the amount of plating adhesion to the target value,
To function as
The plurality of data sets are, as the past operating conditions, from the steel type which is the character data corresponding to the composition of the steel strip, the plate width of the steel strip, the plate thickness of the steel strip, and the tip of the nozzle. A plating adhesion control program that includes the distance to the steel strip and the pressure of the gas blown from the nozzle.

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