KR102395430B1 - Control system of steel plated apparatus and control method thereof - Google Patents

Abstract

a steel plate plating apparatus for plating a steel plate; a server that generates an aluminum component change prediction model for plating a steel sheet under optimal conditions, and extracts an aluminum concentration change prediction value in the plating solution existing in the plating bath from the generated aluminum component change prediction model; and a control device for receiving and processing the extracted predicted aluminum concentration change in the plating solution, and controlling the steel plate plating device to plate the steel sheet in response to processing the extracted predicted aluminum concentration change in the plating solution. A control system is disclosed.

Description

A control system for a steel plate plating apparatus and a control method therefor

The disclosed invention relates to a control system for a steel sheet plating apparatus and a control method thereof, and more particularly, to a control system for a steel sheet plating apparatus capable of improving the quality of a plated steel sheet while preventing an operation accident and a control method thereof.

In general, the plating material applied to the surface of the steel sheet in order to strengthen the corrosion resistance of the steel sheet exists in a molten state, and can be naturally applied to the steel sheet while passing through the plating bath.

As the plating operation proceeds, the amount of the plating material contained in the plating tank is reduced, and thus, solid ingots are frequently supplied for continuous operation.

The quality of the plated steel sheet depends on the mixing of 99.7% or more of zinc and less than 0.3% of aluminum in the plating material in the molten state in the plating tank, so the prediction and management of components is a very important factor in the plating process.

In particular, the concentration of aluminum is a factor that determines weldability and corrosion resistance, and it is necessary to accurately predict and put in an ingot of an appropriate component based on this to produce a product of constant quality.

However, it is difficult for an operator performing a plating operation to insert an ingot of an appropriate component into the plating bath at an appropriate time.

Therefore, in the existing hot-dip plating process, an operation accident could occur due to incorrect ingot input, and the quality of the plated steel sheet was deteriorated.

For the above reasons, an aspect of the disclosed invention is to provide a control system for a steel sheet plating apparatus capable of improving the quality of a plated steel sheet while preventing an operation accident by inputting an ingot of an appropriate component at an appropriate time, and a control method thereof.

According to an aspect of the disclosed invention, a steel plate plating apparatus for plating a steel plate; a server that generates an aluminum component change prediction model for plating the steel sheet, and extracts an aluminum concentration change prediction value in a plating solution existing in a plating bath from the generated aluminum component change prediction model; and a control device for receiving and processing the extracted predicted change in aluminum concentration in the plating solution, and controlling the steel plate plating apparatus to plate the steel plate in response to processing the extracted predicted change in concentration of aluminum in the plating solution. and, the server receives the aluminum concentration measurement data present in the plating bath, receives operation factor data for plating the steel sheet, and based on the received aluminum concentration measurement data and the received operation factor data to generate the aluminum component change prediction model, the aluminum component change prediction model learns the correlation between the received aluminum concentration measurement data and the received operation factor data using an artificial intelligence model, and the learning result It is possible to provide a control system for a steel sheet plating apparatus that outputs a predicted value of a change in aluminum concentration in the plating solution for .

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The operation factor data may include at least one of the thickness of the steel sheet, the width of the steel sheet, the input speed of the steel sheet, the plating amount, the temperature of the plating bath, the plating solution level in the plating bath, the ingot input time, and the ingot component.

The control device may determine a type of an ingot for plating the steel sheet, an input amount of the ingot, and an input timing of the ingot in response to processing the extracted predicted change in aluminum concentration in the plating solution.

The control device may control the ingot input device to input the ingot based on the determined type of the ingot, the input amount of the ingot, and the input time of the ingot.

The control device may further display the determined type of the ingot, the input amount of the ingot, and the input timing of the ingot.

According to another aspect of the disclosed invention, an aluminum component change prediction model for plating a steel sheet is generated by the server, and by the server, aluminum in the plating solution existing in the plating bath from the generated aluminum component change prediction model By the control device in response to extracting the predicted value of change in concentration, receiving and processing the predicted value of change in concentration of aluminum in the extracted plating solution by the control device, and processing the predicted value of change in concentration of aluminum in the extracted plating solution, Controlling a steel plate plating apparatus to plate the steel plate, and generating the aluminum component change prediction model includes receiving, by the server, the aluminum concentration measurement data present in the plating bath, and plating the steel plate. Receiving operation factor data for processing, and generating, by the server, the aluminum component change prediction model based on the received aluminum concentration measurement data and the received operation factor data, predicting the aluminum component change Generating the model is to learn the correlation between the received aluminum concentration measurement data and the received operation factor data using an artificial intelligence model, and output an aluminum concentration change prediction value in the plating solution for the learned result. A method of controlling a steel sheet plating apparatus including a method may be provided.

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The plating treatment of the steel sheet is performed by the control device in response to processing the extracted predicted value of change in the aluminum concentration in the plating solution, and the type of ingot for plating the steel sheet, the amount of the ingot, and the input time of the ingot may be deciding

The plating treatment of the steel sheet may include inputting the ingot based on the determined type of the ingot, the input amount of the ingot, and the input time of the ingot by the ingot input device.

The plating of the steel sheet may further display, by the control device, the determined type of the ingot, the input amount of the ingot, and the input time of the ingot.

According to one aspect of the disclosed invention, it is possible to provide a control system and a control method of a steel plate plating apparatus capable of improving the quality of a plated steel sheet while preventing an operation accident by inputting an ingot of an appropriate component at an appropriate time.

1 illustrates a configuration of a control system of a steel plate plating apparatus according to an embodiment as an example.
FIG. 2 shows that a chemical reaction occurs between the steel sheet and the zinc solution that have entered the plating bath.
3 shows the dross generated in the plating bath and the behavior of the dross.
Figure 4 shows the configuration of the server in the control system of the steel plate plating apparatus according to an embodiment.
FIG. 5 illustrates outputting a predicted value of a change in aluminum concentration in a plating solution in the aluminum component change prediction model shown in FIG. 4 .
6 shows the change situation of the operation factors occurring during the plating operation for a specific time.
7 shows the change in aluminum concentration in the plating bath according to the input of the ingot.
8 shows a comparison between the aluminum concentration predicted by the aluminum component change prediction model made through learning for a specific period and the actually measured aluminum concentration.
9 shows a statistical representation of the accuracy of an aluminum component change prediction model made through learning for a specific period.
10 shows verifying the accuracy of an aluminum component change prediction model created using new data not used for training.
11 shows statistical representations of the accuracy in the process of verifying the accuracy of the aluminum component change prediction model made using new data not used for training.
12 shows controlling the aluminum concentration based on the aluminum component change prediction model.
13 shows the configuration of the control device in the control system of the steel plate plating apparatus according to an embodiment.
14 is a diagram illustrating the appropriate input of the ingot by determining the type of the ingot, the input amount of the ingot, and the input timing of the ingot by the control device in the control system of the steel plate plating apparatus according to the embodiment.
15 shows a control method using a control system of a steel plate plating apparatus according to an embodiment.

Like reference numerals refer to like elements throughout. This specification does not describe all elements of the embodiments, and general content in the technical field to which the disclosed invention pertains or content overlapping between the embodiments is omitted. The term 'part, module, member, block' used in this specification may be implemented in software or hardware, and according to embodiments, a plurality of 'part, module, member, block' may be implemented as one component, It is also possible for one 'part, module, member, block' to include a plurality of components.

Throughout the specification, when a part is "connected" to another part, it includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

Also, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components, unless otherwise stated.

Throughout the specification, when a member is said to be located "on" another member, this includes not only a case in which a member is in contact with another member but also a case in which another member exists between the two members.

Terms such as first, second, etc. are used to distinguish one component from another, and the component is not limited by the above-mentioned terms.

The singular expression includes the plural expression unless the context clearly dictates otherwise.

In each step, the identification code is used for convenience of description, and the identification code does not describe the order of each step, and each step may be performed differently from the specified order unless the specific order is clearly stated in the context. there is.

Hereinafter, the working principle and embodiments of the disclosed invention will be described with reference to the accompanying drawings.

In the hot-dip plating process, plating materials such as zinc and aluminum that are attached to the surface of the steel sheet in order to improve corrosion resistance must be managed at an appropriate concentration in plating the steel sheet.

To this end, since it is difficult for an operator performing a plating operation to put an ingot of an appropriate component into the plating bath at an appropriate time, the plating material using an aluminum component change prediction model that can predict the change in the aluminum concentration in the plating solution in advance is managed under optimal conditions.

Hereinafter, a control system of a steel plate plating apparatus capable of improving the quality of a plated steel sheet while preventing an operation accident by being able to inject an ingot of an appropriate component at an appropriate time will be described.

1 illustrates a configuration of a control system of a steel plate plating apparatus according to an embodiment as an example.

As shown in Figure 1, the control system 100 of the steel plate plating apparatus 110 is an aluminum component change prediction model ( 120a (refer to FIG. 4 ), the control device 130 controls the steel plate plating apparatus 110 to plated the steel plate 1 .

Hereinafter, the control system 100 of the steel plate plating apparatus 110 will be described in detail.

The control system 100 of the steel plate plating apparatus 110 may include a steel plate plating apparatus 110 , a server 120 , and a control apparatus 130 .

The steel plate plating apparatus 110 may perform plating on the steel plate 1 using the rotation operation of the sink roll 4 and the plating solution 3 .

The steel plate plating apparatus 110 may include a plating tank 2 , a sink roll 4 , an air knife 5 , a cooling unit 6 , a tension roll 7 , and an ingot input apparatus 9 . For example, the steel sheet plating apparatus 100 may be a hot-dip steel sheet plating apparatus.

The plating bath 2 may accommodate the plating solution 3 to hot-dip the steel sheet 1 . For example, the plating solution 3 may be a zinc solution or a zinc alloy solution. The steel sheet 1 guided to the plating bath 2 may be immersed in the plating solution 3 by the rotational operation of the sink roll 5 provided in the plating bath 2 , and the hot-dip plating process may be performed to coat the surface. . The steel sheet 1 may be moved to the upper part of the plating bath 2 by changing the direction of movement by the sink roll 5 .

The air knife 5 may be provided on one or both sides of the steel sheet 1 along the moving direction of the steel sheet 1 to control the amount of plating of the steel sheet 1 . The air knife 5 may control the thickness of the plating amount by spraying a gas on the plating layer attached to the surface of the steel sheet 1 .

The cooling unit 6 may cool the steel sheet 1 by directly contacting the plating layer attached to the surface of the steel sheet 1 . For example, the cooling unit 6 may include a cooling roll that extends in the width direction of the steel sheet 1 and circulates a cryogenic liquid therein, and is pressurized to the plating layer attached to the surface of the steel sheet 1 to apply cold air. there is. Such cooling rolls may be provided in plurality, and the plurality of cooling rolls may be provided at intervals along the moving direction of the steel sheet 1 .

The tension roll 7 may advance the cooled steel sheet 1 through the cooling unit 6 to the next process. Such a tension roll 7 may be provided in plurality at intervals along the moving direction of the steel plate 1 .

The ingot input device 8 may be provided to input the ingot 9 into the plating bath 2 . The ingot input device 8 may input the ingot 9 into the plating tank 2 using an arm (ARM). For example, the temperature of the plating bath 2 may be managed to maintain a high temperature of 460 degrees. The ingot 9 is made by melting a metal or alloy once and then pouring it into a mold to harden it. It uses a relatively simple mold called an ingot case and is also called an ingot. As the plated steel sheet having corrosion resistance is continuously produced, the aluminum component present in the plating tank 2 is gradually reduced. For the purpose of supplementing this, the ingot input device 8 transfers the solid ingot 9 to the plating tank 2 ) can be added at any time. The component of the ingot 9 may be an aluminum component acting as an adhesive, and the ingot input device 8 is a plating tank according to the input timing of the ingot 9 in various solid state ingots 9 with different aluminum concentrations. (2) can be put into The input timing of the ingot 9 is a very important factor in the plating process because it determines the quality of the plated steel sheet.

FIG. 2 shows that a chemical reaction occurs between the steel sheet and the zinc solution that have entered the plating bath.

As shown in FIG. 2 , since Zn, which is a plating solution, is thinly applied to the surface of the steel sheet 1 containing Fe as a main component, corrosion of the steel sheet 1 as a base material can be prevented. When Fe and Zn are directly bonded, the bonding strength between the two metals is not high, so the Zn layer may fall off from the steel sheet during the processing of the plated product. This is called plating layer peeling.

In order to prevent such peeling of the plating layer, Al serving as an adhesive between the base material and the plating solution may be used. When Al is added to the Zn solution, Fe and Al are chemically combined on the surface of the steel sheet 1 to form an Fe2Al5 layer, and a pure Zn layer may be applied to the surface of the Fe2Al5 layer. The bonding strength between the Fe2Al5 layer and the pure Zn layer is greatly increased, so that the plating layer peeling phenomenon may be eliminated during the processing of the steel sheet 1 .

The concentration of Al used to prevent delamination of the plating layer is very low, 0.2 to 0.22%wt%, and in the process of manufacturing the ingot 9 that is put into the plating tank 2, Al is introduced into the Zn solution to exist as a compound. can

If the ratio of Al contained in the molten Zn is excessively high, in electric welding of the plated steel sheet, a problem of insufficient welding may occur by preventing the passage of current into the steel sheet. Conversely, if the ratio of Al is too low, it may not sufficiently act as an adhesive and may become a factor causing the plating layer peeling phenomenon. Therefore, the concentration of Al is an important factor influencing the quality of the plated steel sheet and must be precisely managed within a certain range.

3 shows the dross generated in the plating bath and the behavior of the dross.

As shown in FIG. 3 , when the steel sheet 1 enters the Zn solution in the plating bath 2 , the Zn solution is applied to the steel sheet 1 and at the same time the Fe component is eluted from the surface of the steel sheet 1 . . Fe eluted in this way is easily combined with Al molecules present in the Zn solution to form a compound of Fe2Al5, which is called dross.

Unlike the same compound Fe2Al5 that exists between the steel sheet 1 and the Zn layer, which acts as an adhesive, the dross becomes an impurity that adversely affects the product as well as the equipment. Fe2Al5 has a lower specific gravity than the Zn solution, so it floats to the surface after time passes, which is called top dross (10).

On the other hand, some of the Fe eluted from the steel sheet 1 is combined with Zn to generate another dross, which is an Fe-Zn-based compound. FeZn7 has a higher specific gravity than the Zn solution, and as time passes, sinks and accumulates in This is called a bottom dross 11 .

The amount of top dross 10 composed of Fe2Al5 depends on the solubility of Fe according to the temperature of the plating bath 2 . Therefore, as the amount of Fe2Al5 produced increases, the concentration of Al in Zn is lowered, so it will affect the Fe2Al5 layer, which is an adhesive component between the steel sheet 1 and the Zn layer, and weaken the bonding strength of the plating layer. can

It can be seen that in order to keep the components in the plating tank 2 constant, it is necessary to predict the change trend of the components in advance in consideration of the operating conditions and prepare in advance.

As such, there are very many factors affecting the change in aluminum concentration, and it is not known exactly how each factor affects the change. For this reason, it is very difficult to predict in advance the change in the aluminum concentration in the plating solution 3 present in the plating bath 2 .

Accordingly, the control system 100 of the steel plate plating apparatus 110 according to an embodiment is an aluminum component change prediction model 120a that can predict in advance the change in the aluminum concentration in the plating solution 3 existing in the plating bath 2 . , see FIG. 4 ) to control the steel plate plating apparatus 110 by the control apparatus 130 to plate the steel plate 1 .

Figure 4 shows the configuration of the server in the control system of the steel plate plating apparatus according to an embodiment.

As shown in FIG. 4 , the server 120 may generate an aluminum component change prediction model 120a for plating the steel sheet 1 under optimal conditions.

In other words, the server 120 may receive the aluminum concentration measurement data existing in the plating bath (2). The aluminum concentration measurement unit 111 may be provided on the plating bath 2 to transmit the aluminum concentration measurement data in the plating bath 2 to the server 120 . The server 120 may receive the aluminum concentration measurement data for the aluminum concentration change obtained by the aluminum concentration measurement unit 111 in real time. The server 120 may receive operation factor data for plating the steel sheet 1 from the control device 130 . The control device 130 may set operation factor information for manufacturing the plated steel sheet under specific operating conditions. The operation factor information may be the thickness of the steel sheet, the width of the steel sheet, the input speed of the steel sheet, the plating amount, the temperature of the plating bath, the plating solution level in the plating bath, the ingot input time, and the ingot component. The server 120 may generate an aluminum component change prediction model 120a based on the received aluminum concentration measurement data and the received operation factor data.

The server 120 may extract a predicted change in aluminum concentration in the plating solution 3 existing in the plating bath 2 from the generated aluminum component change prediction model 120a.

FIG. 5 illustrates outputting a predicted value of a change in aluminum concentration in a plating solution in the aluminum component change prediction model shown in FIG. 4 . Fig. 6 shows changes in operating factors occurring during a plating operation for a specific time.

5, the aluminum component change prediction model 120a learns the correlation between the received aluminum concentration measurement data D1 and the received operation factor data D2 using an artificial intelligence model (M). can The operation factor data (D2) is the thickness of the steel sheet (D21), the width (D22) of the steel sheet, the feed rate (D23) of the steel sheet, the plating amount (D24), the temperature of the plating bath (D25), the plating solution level of the plating bath (D26), At least one of an ingot input time (D27) and an ingot component (D28) may be included.

The artificial intelligence model (M) shows the aluminum concentration measurement data (D1), the thickness of the steel plate (D21), the width of the steel plate (D22), the input speed of the steel plate (D23), the amount of coating (D24) , the temperature of the plating bath (D25), the plating solution level (D26) of the plating bath, the time of ingot input (D27), and the ingot component (D28) are input, and a neural network with a large number of hidden layers is constructed, and plating on the learned result The predicted value R of the aluminum concentration change in the solution 3 can be output. At this time, Figure 6 shows the respective output waveforms for the aluminum concentration measurement data, which are operation factors that fluctuate during the plating operation, the thickness of the steel plate, the width of the steel plate, the input speed of the steel plate, the amount of plating, the temperature of the plating bath, and the plating solution level of the plating bath. is indicating

7 shows the change in the aluminum concentration in the plating bath according to the input of the ingot.

As shown in FIG. 7 , it can be seen that the Al concentration in the plating bath is rapidly increased whenever a high concentration of Al 5% ingot is input. This shows that the Al concentration in the ingot has a high correlation with the Al concentration in the plating bath.

In this way, the server 120 reflects the influence of various operation factors (thickness of steel sheet, width of steel sheet, input speed of steel sheet, plating amount, temperature of plating bath, plating solution level of plating bath, etc.) which are input variables, but input variables When learning is performed using a sufficient amount of big data to reflect the various conditions of , the predicted change in aluminum concentration in the plating solution 3 can be output from the aluminum component change prediction model 120a.

8 shows a comparison between the aluminum concentration predicted by the aluminum component change prediction model made through learning for a specific period and the actually measured aluminum concentration.

As shown in FIG. 8 , it can be seen that the predicted Al concentration made through learning for a period of 2 months is similar to the measured Al concentration. The data learned over the two-month period reflects most of the conditions that can be produced at the plant, so it can be judged to be sufficient.

9 shows a statistical representation of the accuracy of the aluminum component change prediction model made through learning for a specific period.

As shown in FIG. 9 , the predicted Al concentration made through learning for a period of 2 months is in agreement with the measured Al concentration by about 96%. At this time, the amount of data used for learning is 112,921, which is a sufficient size.

10 shows verifying the accuracy of an aluminum component change prediction model created using new data not used for training.

In general, when the aluminum component change prediction model is created through the learning process, the accuracy of the aluminum component change prediction model made using completely new data not used for training is verified.

As shown in FIG. 10 , only operation data was input to the aluminum component change prediction model made previously using new data for about one month, and the aluminum concentration was predicted accordingly. It can be seen that the Al concentration predicted by the aluminum component change prediction model predicts the change trend well when compared with the measured Al concentration.

11 shows statistical representations of the accuracy in the process of verifying the accuracy of the aluminum component change prediction model made using new data not used for training.

As shown in FIG. 11 , the Al concentration predicted by the aluminum component change prediction model is in agreement with the measured Al concentration by about 92%. At this time, the amount of data used in the verification process is 40,965 pieces, which is about 1/3 of the learning process, which corresponds to the amount of data at the level suggested by the theory of the artificial intelligence model. The number of data presented in the learning process and verification process means that different input variables exist for each data.

12 shows controlling the aluminum concentration based on the aluminum component change prediction model.

As shown in FIG. 12 , the aluminum component change prediction model completed through the learning process and the verification process may generate an aluminum concentration change prediction value through the generation process S1 . A is the Al concentration change (solid line) measured in the plating bath, and B is the Al concentration change (bold line) predicted by the aluminum component change prediction model. The fact that B follows A well means that the accuracy of the aluminum component change prediction model is excellent.

The aluminum component change prediction model, which generated the predicted aluminum concentration change, determines the operating conditions (thickness of steel sheet, width of steel sheet, input speed of steel sheet, plating amount, temperature of plating bath, plating solution level in plating bath, etc.) through the application process (S2). By reflecting this, the change trend of the Al concentration can be predicted. When the predicted Al concentration value is predicted to be close to the lower limit (C), in order to prevent this, several ingots having a high Al concentration must be added to adjust the Al component. It is possible to control the concentration of Al by properly injecting the ingot in the direction of increasing the Al concentration as in D.

As such, the aluminum component change prediction model 120a is for controlling the concentration of Al to properly manage the aluminum component in the plating tank 2 , and the control device 130 plated the steel sheet 1 under optimum conditions. It can be used to determine the type of the ingot 9 for processing, the input amount of the ingot 9 and the input timing of the ingot 9 .

13 shows the configuration of the control device in the control system of the steel plate plating apparatus according to an embodiment.

13 , the control device 130 may include a processor 131 and a memory 132 . For example, the control device 130 may be a local control panel or a PC or a tablet PC or a notebook computer.

The processor 131 may receive and process the aluminum concentration change predicted value in the plating solution 3 extracted by the server 120 . The processor 131 may control the steel plate plating apparatus 110 to plate the steel plate 1 in response to processing the predicted change in aluminum concentration in the plating solution 3 extracted by the server 120 .

The processor 131 responds to processing the predicted change in aluminum concentration in the plating solution 3 extracted by the server 120 , the type of ingot and the input amount of the ingot for plating the steel sheet 1 under optimal conditions. and ingot input timing can be determined.

The processor 131 may control the ingot input device 8 to input the ingot 9 based on the determined type of ingot, the input amount of the ingot, and the input timing of the ingot. The processor 131 may control the ingot input device 8 to input the ingot 9 during the utilization process (S2, see FIG. 12 ).

The ingot 9 may be prepared in various solid states so that the concentration of aluminum is different from each other. Although the concentration of aluminum present in the plating solution 3 is as low as 0.2 to 0.22 wt %, making the concentration of aluminum present in the ingot 9 as high as 0.3 to 5 wt % is high in terms of the occurrence of dross. This is to consider the amount of aluminum consumed by The ingot input device 8 may seat various ingots 9 to keep the aluminum concentration in the plating tank 2 constant.

14 is a diagram illustrating the appropriate input of the ingot by determining the type of the ingot, the input amount of the ingot, and the input timing of the ingot by the control device in the control system of the steel plate plating apparatus according to an embodiment.

As shown in FIG. 14 , the ingot input device 8 controls the ingot 9 seated in order to continuously produce the plated steel sheet 1 having corrosion resistance, the type of ingot and the ingot determined by the control device 130 . It can be put into the plating tank 2 based on the input amount of the ingot and the input time of the ingot. At this time, since the aluminum component present in the plating tank 2 is gradually consumed, the ingot input device 8 may inject the seated ingot 9 into the plating tank 2 for the purpose of supplementing it.

The control device 130 may further display the determined type of the ingot, the input amount of the ingot, and the input time of the ingot. The control device 130 may notify the operator and/or manager of the type of the ingot, the input amount of the ingot, and the input timing of the ingot through a pop-up message on the screen.

The processor 131 includes a digital signal processor that receives and processes the predicted change in the aluminum concentration in the plating solution 3 extracted by the server 120, and the determined type of ingot, the input amount of the ingot, and the ingot based on the input timing of the ingot. It may include a micro control unit (MCU) for generating a drive signal for driving the input device (8).

The memory 132 includes a program and/or data for the processor 131 to receive and process the aluminum concentration change predicted value in the plating solution 3 extracted by the server 120, and the processor 121 to generate a driving signal may store programs and/or data for

The memory 132 temporarily stores the predicted change in aluminum concentration in the plating solution 3 extracted by the server 120 , and the aluminum concentration in the plating solution 3 extracted by the server 120 of the processor 131 . The processing result of the change predicted value can be stored temporarily.

The memory 132 includes not only volatile memories such as S-RAM and D-RAM, but also flash memory, read-only memory (ROM), erasable programmable read only memory (EPROM), etc. of non-volatile memory.

15 shows a control method using a control system of a steel plate plating apparatus according to an embodiment.

Referring to FIG. 15 , the server 120 may generate an aluminum component change prediction model 120a for plating the steel sheet 1 under optimal conditions ( 1510 ). The server 120 may receive the aluminum concentration measurement data present in the plating bath 2 . The aluminum concentration measurement unit 111 may be provided on the plating bath 2 to transmit the aluminum concentration measurement data in the plating bath 2 to the server 120 . The server 120 may receive the aluminum concentration measurement data for the aluminum concentration change obtained by the aluminum concentration measurement unit 111 in real time. The server 120 may receive operation factor data for plating the steel sheet 1 from the control device 130 . The server 120 may generate an aluminum component change prediction model 120a based on the received aluminum concentration measurement data and the received operation factor data.

The aluminum component change prediction model 120a may learn a correlation between the received aluminum concentration measurement data D1 and the received operation factor data D2 using the artificial intelligence model M. The artificial intelligence model (M) consists of the aluminum concentration measurement data (D1), the thickness of the steel plate (D21), the width of the steel plate (D22), the input speed of the steel plate (D23), and the amount of coating (D24), which are operational factors that vary during the plating operation. , the temperature of the plating bath (D25), the plating solution level of the plating bath (D26), the time of ingot input (D27), and the ingot component (D28) are input, and a neural network with a plurality of hidden layers is constructed, and plating on the learned result The predicted value R of the aluminum concentration change in the solution 3 can be output.

The server 120 may extract a predicted change in aluminum concentration in the plating solution 3 existing in the plating bath 2 from the generated aluminum component change prediction model 120a ( 1520 ). The server 120 may transmit the predicted change in the aluminum concentration in the extracted plating solution 3 to the control device 130 .

The control device 130 may receive and process an aluminum concentration change predicted value in the plating solution 3 extracted by the server 120 ( 1530 ).

The control device 130 may control the steel plate plating device 110 to plate the steel sheet 1 in response to processing the predicted value of change in the aluminum concentration in the plating solution 3 extracted by the server 120 . (1540).

In response to processing the predicted value of change in the aluminum concentration in the plating solution 3 extracted by the server 120, the control device 130 determines the type of ingot and the ingot for plating the steel sheet 1 under optimal conditions. It is possible to determine the input amount and the input timing of the ingot.

The control device 130 may control the ingot input device 8 (see FIG. 14 ) to input the ingot 9 (see FIG. 14 ) based on the determined type of ingot, the input amount of the ingot, and the input time of the ingot. The control device 130 may control the ingot input device 8 (refer to FIG. 14 ) to input the ingot 9 (see FIG. 14 ) during the utilization process ( S2 , see FIG. 12 ).

The ingot input device (8, see FIG. 14) can seat various ingots (9, see FIG. 14) for maintaining a constant aluminum concentration in the plating tank (2, see FIG. 14).

The ingot input device (8, see FIG. 14) controls the seated ingot (9, see FIG. 14) to continuously produce a plated steel sheet (1, see FIG. 14) having corrosion resistance, the ingot determined by the control device 130 It can be put into the plating tank (2, see FIG. 14) based on the type of the ingot, the input amount of the ingot, and the input time of the ingot.

The control device 130 may further display the determined type of the ingot, the input amount of the ingot, and the input time of the ingot. The control device 130 may notify the operator and/or manager of the type of the ingot, the input amount of the ingot, and the input timing of the ingot through a pop-up message on the screen.

As described above, the control system 100 of the steel plate plating apparatus 110 according to an embodiment can input an ingot of an appropriate component at an appropriate time, thereby preventing an operation accident and improving the quality of the plated steel sheet.

Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing instructions executable by a computer. Instructions may be stored in the form of program code, and when executed by a processor, may create a program module to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

The computer-readable recording medium includes all types of recording media in which computer-readable instructions are stored. For example, there may be a read only memory (ROM), a random access memory (RAM), a magnetic tape, a magnetic disk, a flash memory, an optical data storage device, and the like.

The disclosed embodiments have been described with reference to the accompanying drawings as described above. Those of ordinary skill in the art to which the present invention pertains will understand that the present invention may be practiced in other forms than the disclosed embodiments without changing the technical spirit or essential features of the present invention. The disclosed embodiments are illustrative and should not be construed as limiting.

100: control system of the steel plate plating apparatus 110: steel plate plating apparatus
111: aluminum concentration measurement unit 120: server
120a: aluminum component change prediction model 130: control device
131: processor 132: memory

Claims (13)

a steel plate plating apparatus for plating a steel plate;
a server that generates an aluminum component change prediction model for plating the steel sheet, and extracts an aluminum concentration change prediction value in a plating solution existing in a plating bath from the generated aluminum component change prediction model; and
and a control device for controlling the steel plate plating apparatus to receive and process the extracted predicted change in aluminum concentration in the plating solution, and to plate the steel plate in response to processing the extracted predicted change in concentration of aluminum in the plating solution, and ,
The server receives the aluminum concentration measurement data present in the plating tank, receives operation factor data for plating the steel sheet, and based on the received aluminum concentration measurement data and the received operation factor data, the create a model for predicting changes in aluminum components,
The aluminum component change prediction model learns the correlation between the received aluminum concentration measurement data and the received operation factor data using an artificial intelligence model, and outputs an aluminum concentration change prediction value in the plating solution for the learned result control system of steel plate plating equipment. delete delete According to claim 1,
The operation factor data includes at least one of the thickness of the steel sheet, the width of the steel sheet, the input speed of the steel sheet, the plating amount, the temperature of the plating bath, the plating solution level of the plating bath, the ingot input time, and the ingot component. control system. According to claim 1,
The control system is a control system of a steel plate plating apparatus for determining a type of an ingot for plating the steel sheet, an input amount of the ingot, and an input time of the ingot in response to processing the extracted aluminum concentration change predicted value in the plating solution. 6. The method of claim 5,
The control device is a control system of a steel plate plating apparatus for controlling the ingot input device to input the ingot based on the determined type of the ingot, the input amount of the ingot, and the input time of the ingot. 6. The method of claim 5,
The control device is a control system of the steel plate plating apparatus for further displaying the determined type of the ingot, the input amount of the ingot, and the input time of the ingot. By the server, an aluminum component change prediction model for plating a steel sheet is generated,
Extracting, by the server, an aluminum concentration change prediction value in the plating solution existing in the plating bath from the generated aluminum component change prediction model,
The control device receives and processes the predicted value of aluminum concentration change in the extracted plating solution,
Controlling the steel sheet plating apparatus by the control apparatus in response to processing the extracted aluminum concentration change predicted value in the plating solution to perform plating on the steel sheet,
Generating the aluminum component change prediction model is,
The server receives, by the server, the aluminum concentration measurement data present in the plating bath, receives operation factor data for plating the steel sheet, and by the server, the received aluminum concentration measurement data and the received operation Comprising generating the aluminum component change prediction model based on the factor data,
Generating the aluminum component change prediction model is,
Learning the correlation between the received aluminum concentration measurement data and the received operation factor data using an artificial intelligence model, and outputting an aluminum concentration change predicted value in the plating solution based on the learned result of a steel plate plating apparatus control method. delete delete 9. The method of claim 8,
The plating treatment of the steel sheet,
The type of ingot for plating the steel sheet, the input amount of the ingot, and the input timing of the ingot are determined by the control device in response to processing the extracted predicted change in the aluminum concentration in the plating solution. control method. 12. The method of claim 11,
The plating treatment of the steel sheet,
By the ingot input device, the control method of the steel sheet plating apparatus to input the ingot based on the determined type of ingot, the input amount of the ingot, and the input time of the ingot. 12. The method of claim 11,
The plating treatment of the steel sheet,
The control method of the steel plate plating apparatus to further display, by the control device, the determined type of the ingot, the input amount of the ingot, and the input time of the ingot.

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