KR102326243B1 - Control system for desulfurization apparatus of molten iron and control method thereof - Google Patents

Abstract

A control system for a molten iron desulfurization apparatus is disclosed. The control system of the molten iron|metal desulfurization apparatus is a molten iron|metal desulfurization apparatus which degassing|desulfurizing molten iron|metal; A server for generating an optimal degassing treatment control model for each molten iron for degassing the molten iron under optimal conditions, and extracting information on an optimal degassing treatment condition required for the current molten iron from among the generated optimal degassing treatment control models for each molten iron; and a control device including a processor for processing the extracted optimal degassing treatment condition information required for the extracted current molten iron, wherein the control device responds to processing the extracted optimal degassing treatment condition information required for the extracted current molten iron. It is possible to control the molten iron desulfurization device to desulfurize the.

Description

Control system for molten iron desulfurization apparatus and control method thereof

The disclosed invention relates to a control system for a molten iron desulfurization apparatus and a control method therefor, and more particularly, to a control system and a control method for a molten iron desulfurization apparatus capable of desulfurizing molten iron under the optimal degassing treatment conditions required for current molten iron. it's about

In general, the molten iron produced through the iron making process in a blast furnace undergoes a refining process of controlling the content of some components in the molten iron to finally obtain molten steel and products of desired components.

After undergoing refining to adjust the sulfur and phosphorus contained in the molten iron to below a certain level, it is charged into the converter, and the components are further adjusted through secondary refining by oxygen blowing in the converter.

As a method of degassing molten iron, a mechanical stirring method with relatively high degassing efficiency is used. The mechanical stirring method is a method of degassing through a reaction between molten steel and a degassing agent by immersing and rotating a stirrer called an impeller in a container in which the molten iron is accommodated. A representative method of a degassing method using a mechanical stirring method is a method using a KR facility (Kanvara reactor).

In recent years, when degassing molten iron, research on a molten iron desulfurization apparatus for desulfurizing molten iron is actively conducted under the optimal degassing treatment conditions required for current molten iron. As an example, in the conventional method of degassing molten iron, the operator sensed the optimal degassing treatment conditions required for the current molten iron and degassed the molten iron.

However, in the conventional method of degassing molten iron, the optimal degassing treatment conditions required for the current molten iron, which are grasped by each operator, are different from each other, and thus the molten iron could not be degassed under the optimal degassing conditions.

For the above reasons, an aspect of the disclosed invention is to provide a control system for a molten iron desulfurization apparatus capable of desulfurizing molten iron under the optimal degassing treatment conditions required for current molten iron and a control method thereof.

A control system for a molten iron desulfurization apparatus according to an aspect of the disclosed invention includes: a molten iron desulfurization apparatus for desulfurizing molten iron; a server for generating an optimal degassing treatment control model for each molten iron for degassing the molten iron under optimal conditions, and extracting information on an optimal degassing treatment condition required for the current molten iron from among the generated optimal degassing treatment control models for each molten iron; and a control device including a processor for processing the optimal degassing treatment condition information required for the extracted current molten iron, wherein the control device responds to processing the optimal degassing treatment condition information required for the extracted current molten iron Thus, it is possible to control the molten iron desulfurization device to process the current molten iron.

The server uses a big data model to determine the correlation between the resultant factor of the degassing reaction for each molten iron among the generated optimal degassing treatment control models for each molten iron and the control factor of the degassing reaction for each molten iron based on the operation factor of the degassing reaction for each molten iron. By analyzing it, it is possible to extract information on the optimal degassing treatment conditions required for the current molten iron.

The resultant factor of the degassing reaction for each molten iron may include a degassing efficiency and/or a degassing rate and/or a hit rate of a chemical component of the molten iron.

The operation factor of the degassing reaction for each molten iron may include the temperature and/or the amount of molten iron and/or the concentration of each chemical component of the molten iron.

The control factor of the degassing reaction for each molten iron is, the input amount of the degassing agent and / or the input time of the degassing agent and / or the degassing treatment time and / or the rotation speed of the impeller and / or the motor RPM for the rotation of the impeller and / or the impeller may include motor current for rotation and/or impeller immersion depth and/or blast furnace slag basicity.

The server, the temperature and / or the amount of molten iron and / or the concentration of each chemical component of the molten iron among the operation factors of the degassing reaction for each molten iron, the rotation speed of the impeller and / or the motor RPM and / or the impeller for the rotation of the impeller The correlation between motor current and/or impeller immersion depth and/or degassing time and/or blast furnace slag basicity for rotation of can be further extracted.

The server, the temperature and / or the amount of molten iron and / or the concentration of each chemical component of the molten iron among the operation factors of the degassing reaction for each molten iron, the rotation speed of the impeller and / or the motor RPM and / or the impeller for the rotation of the impeller Further analysis of the correlation between motor current for rotation of and/or impeller immersion depth and/or degassing time and/or blast furnace slag basicity using a multilayer perceptron neural network model to provide information on the optimal degassing treatment conditions required for current molten iron can be further extracted.

The server, the temperature and / or the amount of molten iron and / or the concentration of each chemical component of the molten iron among the operation factors of the degassing reaction for each molten iron, the rotation speed of the impeller and / or the motor RPM and / or the impeller for the rotation of the impeller Further analysis of the correlation between motor current for rotation of and/or impeller deposition depth and/or degassing treatment time and/or blast furnace slag basicity using a deep neural network model to obtain more information on the optimal degassing treatment conditions required for the current molten iron can be extracted.

The server further analyzes the motor RPM for the rotation of the impeller and/or the immersion depth and/or the degassing processing time of the impeller among the operation factors of the degassing reaction for each molten iron, using a self-organizing map model to further analyze the current necessary for molten iron. It is possible to further extract optimal degassing treatment condition information.

The server uses a deep deterministic policy gradient model for the motor RPM and/or the impeller's immersion depth and/or the degassing processing time for the rotation of the impeller among the operating factors of the degassing reaction for each molten iron. Degassing efficiency and/or When the chemical component hit ratio of the molten iron is improved, the information on the optimal degassing treatment conditions required for the current molten iron can be further extracted by further analyzing the excess degassing caused by the excessive input of the degassing agent.

The control device, in response to processing the extracted optimal degassing treatment condition information required for the current molten iron, the optimum operating factor and the current An optimal control factor of the molten iron degassing reaction can be further indicated.

The control method of the molten iron desulfurization apparatus according to an aspect of the disclosed invention is, by a server, an optimal degassing treatment control model for each molten iron for degassing the molten iron in an optimal condition, and by the server, the generated molten iron Extract the optimal degassing treatment condition information required for the current molten iron from among the selection optimal degassing treatment control model, and receive, by the control device, the optimal degassing treatment condition information required for the extracted current molten iron, and the extracted current molten iron The molten iron desulfurization apparatus for desulfurizing the molten iron so that the current molten iron is degassed can be controlled by the control device in response to processing the optimal degassing treatment condition information required for the molten iron.

Extracting the optimal degassing treatment condition information required for the current molten iron is, by the server, the result factor of the degassing reaction for each molten iron and the operation factor of the degassing reaction for each molten iron among the generated optimal degassing treatment control models for each molten iron. By analyzing the correlation between the control factors of the degassing reaction for each molten iron based on the big data model, it is possible to extract information on the optimal degassing treatment conditions required for the current molten iron.

Extracting the optimal degassing treatment condition information required for the current molten iron is, by the server, the temperature and/or the amount of molten iron and/or the concentration of each chemical component of the molten iron among the operation factors of the degassing reaction for each molten iron, and the impeller Multiple linear regression control of the correlation between the rotational speed of and/or the motor RPM for the rotation of the impeller and/or the motor current for the rotation of the impeller and/or the immersion depth of the impeller and/or the degassing treatment time and/or the blast furnace slag basicity Further analysis using the model can further extract information on the optimal degassing treatment conditions required for the current molten iron.

Extracting the optimal degassing treatment condition information required for the current molten iron is, by the server, the temperature and/or the amount of molten iron and/or the concentration of each chemical component of the molten iron among the operation factors of the degassing reaction for each molten iron, and the impeller Correlation between the rotation speed and/or motor RPM for the rotation of the impeller and/or the motor current for the rotation of the impeller and/or the immersion depth of the impeller and/or the degassing processing time and/or the blast furnace slag basicity in a multilayer perceptron neural network Further analysis using the model can further extract information on the optimal degassing treatment conditions required for the current molten iron.

Extracting the optimal degassing treatment condition information required for the current molten iron is, by the server, the temperature and/or the amount of molten iron and/or the concentration of each chemical component of the molten iron among the operation factors of the degassing reaction for each molten iron, and the impeller The correlation between the rotational speed of and/or the motor RPM for the rotation of the impeller and/or the motor current for the rotation of the impeller and/or the immersion depth of the impeller and/or the degassing processing time and/or the blast furnace slag basicity was obtained using a deep neural network model. It is possible to further extract information on the optimal degassing treatment conditions required for the current molten iron by further analysis.

Extracting the optimal degassing treatment condition information required for the current molten iron is, by the server, the motor RPM for the rotation of the impeller among the operation factors of the degassing reaction for each molten iron and / or the immersion depth of the impeller and / or the degassing processing time For , further analysis using a self-organizing map model, it is possible to further extract information on the optimal degassing treatment conditions required for the current molten iron.

Extracting the optimal degassing treatment condition information required for the current molten iron is, by the server, the motor RPM for the rotation of the impeller among the operation factors of the degassing reaction for each molten iron and / or the immersion depth of the impeller and / or the degassing processing time of the current molten iron by using a deep deterministic policy gradient model to further analyze the excess degassing caused by the over-injection of the degassing agent when improving the degassing efficiency and/or the chemical component hit rate of molten iron more can be extracted.

By the control device in response to processing the extracted information on the optimal degassing treatment condition required for the current molten iron, the optimum operating factor and the current An optimal control factor of the molten iron degassing reaction can be further indicated.

According to one aspect of the disclosed invention, it is possible to provide a control system and a control method of the molten iron desulfurization apparatus capable of desulfurizing the molten iron under the optimal degassing treatment conditions required for the current molten iron.

1 shows an example of a control system of the molten iron desulfurization apparatus according to an embodiment.
2 shows a configuration of a server according to an embodiment.
3 shows a configuration of a control device according to an embodiment.
Figure 4 shows an example of a control method of the control system of the molten iron desulfurization apparatus according to an embodiment.
5 shows a process of extracting the optimal degassing treatment condition information required for the current molten iron.

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 the 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" with another part, it includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

In addition, when a part "includes" a certain component, this 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 1st, 2nd, etc. are used to distinguish one component from another component, 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. have.

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

1 shows an example of a control system of the molten iron desulfurization apparatus according to an embodiment.

As shown in FIG. 1, the control system 100 of the molten iron desulfurization device 110 is an optimal degassing treatment control model for each molten iron for degassing the current molten iron M1 under optimal conditions (121, see FIG. 2). Controls the molten iron desulfurization device 110 by the control device 130 to degas the molten iron under the optimal degassing treatment conditions required for the current molten iron (M1) using the.

Hereinafter, the control system 100 of the molten iron desulfurization apparatus 110 will be described in detail.

The control system 100 of the molten iron desulfurization apparatus 110 includes a molten iron desulfurization apparatus 110 , a server 120 , and a control apparatus 130 .

The molten iron desulfurization apparatus 110 desulfurizes the molten iron (M1) by using the rotational operation of the impeller 111 and the desulfurization agent (M2).

The molten iron desulfurization device 110 includes a container (L), an impeller 111, a shaft 112, a rotation driving unit 113, an elevating driving unit 114, and a degassing agent input unit 115. can

The container (L) can accommodate the molten iron (M1) and the degassing agent (M2). Since the container L stores the hot molten iron M1, it may include a refractory wall. For example, the container L may be a ladle. Not limited to the bar shown, the container (L) may be provided with a variety of accommodating means.

The impeller 111 may be immersed in the interior of the vessel (L) to stir the molten iron (M1) and the degassing agent (M2). The degassing agent (M2) may be introduced into the vessel (L) to desulfurize the molten iron (M1). The impeller 111 may include four rotary blades. The four rotary blades may be provided to be spaced apart at intervals of 90 degrees, and by rotating while being immersed in the molten iron (M1), it is possible to promote the desulfurization of the molten iron (M1). The four rotary blades may be formed so that the width of the upper end is larger than the width of the lower end. For example, the four rotor blades may be formed to be inclined so that the width becomes narrower from the upper end to the lower end. These four rotary blades may generate a downflow in the molten iron (M1) stirred by the rotation of the impeller (111). Not limited to the illustrated bar, the impeller 111 may be provided in various structures or various shapes capable of sufficiently stirring the molten iron (M1) accommodated in the container (L).

The shaft 112 may be connected to an upper portion of the impeller 111 . The rotation driving unit 113 may be connected to the shaft 112 to rotate the impeller 111 according to the rotational operation of the shaft 112 . For example, the rotation driving unit 113 may rotate the shaft 112 while controlling the rotation speed of the shaft 112 using a reducer and a motor.

The elevating driving unit 114 may be connected to the rotation driving unit 113 to elevate the impeller 111 according to the elevating operation of the rotating driving unit 113 . The elevating driving unit 114 may include sheaves 114a1 , 114a2 and Sheave, a reducer 114b and a motor 114c. The two sheave (114a1, 114a2, Sheave) may be provided to hang the wire rope (W) connected to the rotation driving unit 113 to raise or lower. Not limited to the bar shown, the sheave (114a1, 114a2, Sheave) hangs the wire rope (W) according to the weight of the impeller 111 and the shaft 112 and the rotational driving unit 113 to raise or lower three or more can be provided as The reducer 114b may adjust the speed of pulling up and hanging the wire rope (W) or the speed of hanging and pulling the wire rope (W). The motor 114c may raise or lower the wire rope W. It is not limited to the illustrated bar, and the elevating driving unit 114 may be provided in various structures capable of elevating the impeller 111 .

The degassing agent input unit 115 may be provided to inject the degassing agent M2 into the container L. When the degassing agent input unit 115 is stirred using the impeller 111, the molten iron M1 overcomes inertia and the rotational speed of the impeller 111 becomes a constant speed, the degassing agent M2 can be added. have. For example, the degassing agent input unit 115 may input the degassing agent M2 while controlling the input speed and input amount of the degassing agent M2 using a speed reducer and a motor. The degassing agent input unit 115 may be a hopper.

2 shows a configuration of a server according to an embodiment.

The server 120 generates an optimal degassing treatment control model 121 for each molten iron for degassing the molten iron M1 under optimal conditions. The optimal degassing process control model 121 for each molten iron includes a big data model 121a, a multiple linear regression control model 121b, a multilayer perceptron neural network model 121c, a deep neural network model 121d, and self-organization. It may include a map model 121e and a deep deterministic policy gradient model 121f.

The server 120 extracts the optimal degassing treatment condition information required for the current molten iron M1 from among the generated optimal degassing treatment control model 121 for each molten iron.

For example, the server 120 relates to the resultant factor of the degassing reaction for each molten iron among the generated optimal degassing treatment control model 121 for each molten iron and the control factor of the degassing reaction for each molten iron based on the operation factor of the degassing reaction for each molten iron. By analyzing the relationship using the big data model 121a, it is possible to extract information on the optimal degassing treatment condition required for the current molten iron M1. The server 120 generates a correlation between the result factor of the degassing reaction by molten iron and the control factor of the degassing reaction by molten iron based on the operation factor of the degassing reaction by molten iron using a random forest algorithm as a set of a certain number of data sets, and makes a decision After grouping a certain number of data sets by facility conditions using a tree algorithm, it is possible to extract and analyze information on the optimal degassing treatment conditions required for the current molten iron (M1).

The resultant factors of the degassing reaction for each molten iron may include a degassing efficiency and/or a degassing rate and/or a hit rate of a chemical component of the molten iron (M1). The operating factors of the degassing reaction for each molten iron may include the temperature and/or the amount of molten iron and/or the concentration of each chemical component of the molten iron (M1). The concentration of each chemical component of the molten iron M1 may be a concentration according to a combination of components such as carbon and/or silicon and/or manganese and/or phosphorus and/or sulfur. Control factors of the degassing reaction for each molten iron are, the input amount of the degassing agent (M2) and / or the input time of the degassing agent (M2) and / or the degassing treatment time and / or the rotation speed of the impeller 111 and / or the impeller 111 may include motor RPM for rotation of and/or motor current for rotation of the impeller 111 and/or depth of deposition of the impeller 111 and/or blast furnace slag basicity.

For another example, the server 120 is the temperature and / or the amount of molten iron (M1) and / or the concentration of each chemical component of the molten iron (M1) among the operation factors of the degassing reaction for each molten iron, and the rotational speed of the impeller (111) and/or the motor RPM for the rotation of the impeller 111 and/or the motor current for the rotation of the impeller 111 and/or the immersion depth of the impeller 111 and/or the degassing treatment time and/or the correlation between the blast furnace slag basicity By further analyzing the relationship using the multiple linear regression control model 121b, it is possible to further extract information on the optimal degassing treatment condition required for the current molten iron M1. After the server 120 analyzes and extracts the optimal degassing treatment condition information required for the current molten iron M1 using the big data model 121a, the temperature and / or the amount of molten iron and / or the generated molten iron (M1) A certain number of data sets for the concentration of each chemical component of the molten iron (M1), the rotation speed of the impeller 111 and / or the motor RPM for the rotation of the impeller 111 and / or the motor for the rotation of the impeller 111 A certain number of data sets for the current and/or the immersion depth of the impeller 111 and/or the degassing time and/or the blast furnace slag basicity are analyzed using the multiple linear regression control model 121b to obtain the optimum required for the current molten iron M1. It can be further extracted by re-analyzing the degassing treatment condition information of

For another example, the server 120 is the temperature and / or the amount of molten iron (M1) of the operation factors of the degassing reaction for each molten iron and / or the concentration of each chemical component of the molten iron (M1), and the rotation of the impeller 111 Between the speed and/or motor RPM for the rotation of the impeller 111 and/or the motor current for the rotation of the impeller 111 and/or the deposition depth of the impeller 111 and/or the degassing treatment time and/or the blast furnace slag basicity By further analyzing the correlation using the multilayer perceptron neural network model 121c, it is possible to further extract information on the optimal degassing treatment condition required for the current molten iron M1. After the server 120 analyzes and extracts the optimal degassing treatment condition information required for the current molten iron M1 using the multiple linear regression control model 121b, the temperature and / or molten iron amount of the generated molten iron M1 and / or a certain number of data sets for the concentration of each chemical component of the molten iron (M1), and the rotation speed of the impeller 111 and / or the rotation of the motor RPM and / or the impeller 111 for the rotation of the impeller 111 A certain number of data sets for the motor current and/or the immersion depth of the impeller 111 and/or the degassing processing time and/or the blast furnace slag basicity for the It can be further extracted by re-analyzing the necessary optimal degassing treatment condition information.

For another example, the server 120 is the temperature and / or the amount of molten iron (M1) of the operation factors of the degassing reaction for each molten iron and / or the concentration of each chemical component of the molten iron (M1), and the rotation of the impeller 111 Between the speed and/or motor RPM for the rotation of the impeller 111 and/or the motor current for the rotation of the impeller 111 and/or the deposition depth of the impeller 111 and/or the degassing treatment time and/or the blast furnace slag basicity By further analyzing the correlation using the deep neural network model (121d), it is possible to further extract information on the optimal degassing treatment condition required for the current molten iron (M1). The server 120 analyzes and extracts the optimal degassing treatment condition information required for the current molten iron (M1) using the multi-layer perceptron neural network model (121c), and then the generated temperature and / or the amount of molten iron (M1) and / or a certain number of data sets for the concentration of each chemical component of the molten iron (M1), and the rotation speed of the impeller 111 and / or the rotation of the motor RPM and / or the impeller 111 for the rotation of the impeller 111 A certain number of data sets for the motor current and/or the immersion depth of the impeller 111 and/or the degassing processing time and/or the blast furnace slag basicity for the It can be further extracted by re-analyzing the degassing treatment condition information of

For another example, the server 120 self-organizes the motor RPM for rotation of the impeller 111 and/or the immersion depth and/or degassing processing time of the impeller 111 among the operating factors of the degassing reaction for each molten iron. Further analysis using the map model (121e) can be further extracted information on the optimal degassing treatment conditions required for the current molten iron (M1). After the server 120 analyzes and extracts the optimal degassing treatment condition information required for the current molten iron (M1) using the deep neural network model 121d, the motor RPM and / or the impeller 111 for the rotation of the impeller 111 ) of immersion depth and/or degassing time, grouping similar factor conditions using the self-organizing map model 121e for a data set that is not differentiated, and then the optimal degassing treatment conditions required for the current molten iron (M1) The information can be re-analyzed to extract more. The server 120 uses the self-organizing map model 121e to determine the motor RPM for the rotation of the impeller 111 and/or the immersion depth and/or the degassing processing time of the impeller 111 to determine the degassing efficiency and/or molten iron (M1). ) can be further analyzed how it affects the hit rate of chemical components.

For another example, the server 120 determines the depth of the motor RPM and/or the immersion depth and/or the degassing processing time of the impeller 111 for the rotation of the impeller 111 among the operating factors of the degassing reaction for each molten iron. When the degassing efficiency and/or the chemical component hit rate of the molten iron (M1) is improved using the theoretical policy gradient model (121f), the over-desorption due to the over-injection of the degassing agent (M2) is further analyzed, and the current required for the molten iron (M1) is It is possible to further extract optimal degassing treatment condition information. After the server 120 analyzes and extracts the optimal degassing treatment condition information required for the current molten iron (M1) using the self-organizing map model 121e, the motor RPM and / or the impeller for the rotation of the impeller 111 ( 111) for immersion depth and/or degassing processing time of molten iron (M1) for controlling the input amount of degassing agent (M2) when improving degassing efficiency and/or chemical component hit rate of molten iron (M1) using a reinforcement learning algorithm ) of the temperature and/or the amount of molten iron and/or the concentration of each chemical component of the molten iron (M1) can be further analyzed. Reinforcement learning algorithms can learn both good and bad cases by repeatedly performing optimal selection and random selection. Optimal selection can extract the optimal degassing treatment condition information required for the best current molten iron (M1) among the learned results, and random selection is the optimal degassing treatment condition information required for any current molten iron (M1) regardless of learning. can be extracted. The reinforcement learning algorithm may further optimize and extract information on the optimal degassing treatment condition required for the current molten iron (M1) as reinforcement learning progresses.

3 shows a configuration of a control device according to an embodiment.

3 , 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.

In response to processing the optimal degassing treatment condition information required for the extracted current molten iron (M1, see FIG. 1), the processor 131 is configured to desulfurize the current molten iron (M1, see FIG. 1) to desulfurize the molten iron 110. can control Processor 131 in response to processing the optimal degassing treatment condition information required for the extracted current molten iron (M1, see FIG. 1), the rotation drive unit 113 and / or the elevating drive 114 and / or the degassing agent input The unit 115 may be controlled. The rotation driving unit 113 and/or the elevating driving unit 114 and/or the degassing agent input unit 115 of the current molten iron degassing reaction corresponding to the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1). It can be driven based on the optimal control factor. The rotation driving unit 113 has an optimum degassing processing time and/or an optimum rotation speed of the impeller 111 and/or an optimum motor RPM for rotation of the impeller 111 and/or optimum rotation of the impeller 111 . It can be driven based on motor current and/or optimum blast furnace slag basicity. The elevating driving unit 114 may be driven based on the optimum immersion depth of the impeller 111 . The degassing agent input unit 115 may be driven based on the optimal input amount of the degassing agent (M2, see FIG. 1) and/or the optimal input time of the degassing agent (M2, see FIG. 1).

In response to processing the optimal degassing treatment condition information required for the extracted current molten iron (M1, see FIG. 1), the control device 130 responds to the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1). An optimal operating factor of the corresponding current molten iron degassing reaction and an optimal control factor of the current molten iron degassing reaction may be further displayed. The control device 130 may notify the operator and/or the manager of the optimal operation factor of the current molten iron degassing reaction and the optimal control factor of the current molten iron degassing reaction through a pop-up message on the screen. The optimal operating factors for the current molten iron degassing reaction may include the optimum temperature and/or the optimum amount of molten iron (M1, see Fig. 1) and/or the optimum chemical component concentration of the molten iron (M1, see Fig. 1). have. The optimal control factors for the current molten iron degassing reaction are the optimal dosage of the degassing agent (M2, see Fig. 1) and/or the optimal dosing time of the degassing agent (M2, see Fig. 1) and/or the optimal degassing treatment time and/or the impeller. (111, see FIG. 1) and/or the optimum motor RPM for the rotation of the impeller (111, see FIG. 1) and/or the optimum motor current for the rotation of the impeller (111, see FIG. 1) and and/or an optimal immersion depth of the impeller 111 (see FIG. 1 ) and/or an optimal blast furnace slag basicity.

The processor 131 generates a signal for adjusting the rotation speed of the shaft 112 (see FIG. 1) and/or generates a signal for rotating the shaft 112 (see FIG. 1) and/or a wire rope (W, FIG. 1) 1) generating a signal for regulating the speed of hooking and pulling and/or generating a signal for regulating the speed of hooking and pulling the wire rope (W, see FIG. ) generating a signal for hanging and pulling up and/or generating a signal for hanging and pulling a wire rope (W, see Fig. 1) and/or a signal for regulating the rate of dosing of the deleaking agent (M2, see Fig. 1) It may include a micro control unit (MCU) that generates and/or generates a signal for controlling the input amount of the degassing agent (M2, see FIG. 1).

The memory 132 generates a signal for regulating the rotational speed of the shaft 112 (see FIG. 1) and/or generates a signal for rotating the shaft 112 (see FIG. 1) and/or a wire rope (W, FIG. 1) 1) generating a signal for regulating the speed of hooking and pulling and/or generating a signal for regulating the speed of hooking and pulling the wire rope (W, see FIG. ) generating a signal for hanging and pulling up and/or generating a signal for hanging and pulling a wire rope (W, see Fig. 1) and/or a signal for regulating the rate of dosing of the deleaking agent (M2, see Fig. 1) It is possible to store a program and/or data for generating a signal for generating and/or regulating the dosage of the degassing agent (M2, see FIG. 1 ).

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.

Figure 4 shows an example of a control method of the control system of the molten iron desulfurization apparatus according to an embodiment. 5 shows a process of extracting the optimal degassing treatment condition information required for the current molten iron.

Referring to FIG. 4, the server 120 (refer to FIG. 2) generates an optimal degassing control model (121, see FIG. 2) for each molten iron for degassing the molten iron (M1, see FIG. 1) under optimal conditions. (410). The optimal degassing treatment control model for each molten iron (121, see FIG. 2) is a big data model (121a, see FIG. 2), a multiple linear regression control model (121b, see FIG. 2), and a multilayer perceptron neural network model (121c, 2), a deep neural network model 121d (see FIG. 2), a self-organizing map model 121e (see FIG. 2), and a deep deterministic policy gradient model 121f (see FIG. 2).

The server 120 (refer to FIG. 2) extracts the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1) from among the generated optimal degassing treatment control models 121 for each molten iron (see FIG. 2) (420) .

For example, referring to FIG. 5 , the server 120 (refer to FIG. 2 ) determines the result factor of the degassing reaction for each molten iron among the generated optimal degassing treatment control model 121 for each molten iron (see FIG. 2 ) and the degassing reaction for each molten iron. The correlation between the control factors of the degassing reaction for each molten iron based on the operating factors is analyzed using the big data model (121a, see FIG. 2) to extract the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1). can (421). The resultant factors of the degassing reaction for each molten iron may include a degassing efficiency and/or a degassing rate and/or a hit rate of a chemical component of the molten iron (M1, see FIG. 1 ). The operating factors of the degassing reaction for each molten iron may include the temperature and/or the amount of molten iron (M1, see FIG. 1) and/or the concentration of each chemical component of the molten iron (M1, see FIG. 1). The concentration of each chemical component of molten iron (M1, see FIG. 1) may be a concentration according to a combination of components such as carbon and/or silicon and/or manganese and/or phosphorus and/or sulfur. The control factors of the degassing reaction for each molten iron are the input amount of the degassing agent (M2, see FIG. 1) and/or the input time of the degassing agent (M2, see FIG. 1) and/or the degassing treatment time and/or the impeller (111, FIG. 1) see) and/or motor RPM for rotation of the impeller (111, see FIG. 1) and/or motor current for rotation of the impeller (111, see FIG. 1) and/or impeller (111, see FIG. 1) of immersion depth and/or blast furnace slag basicity.

As another example, the server 120 (see FIG. 2) analyzes and extracts the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1) using the big data model (121a, see FIG. 2). , Among the operating factors of the degassing reaction for each molten iron, the temperature and / or the amount of molten iron (M1, see Fig. 1) and / or the concentration of each chemical component of the molten iron (M1, see Fig. 1), and the impeller (111, see Fig. 1) of the rotation speed and/or motor RPM for rotation of the impeller (111, see FIG. 1) and/or motor current for rotation of the impeller (111, see FIG. 1) and/or deposition of the impeller (111, see FIG. 1) The correlation between depth and/or degassing time and/or blast furnace slag basicity was further analyzed using a multiple linear regression control model (121b, see FIG. 2) to further analyze the optimal degassing treatment required for the current molten iron (M1, see FIG. 1). Condition information may be further extracted ( 422 ).

As another example, the server 120 (see FIG. 2) analyzes the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1) using a multiple linear regression control model (121b, see FIG. 2). After extraction, the temperature and / or the amount of molten iron (M1, see Fig. 1) and / or the concentration of each chemical component of the molten iron (M1, see Fig. 1) among the operating factors of the degassing reaction for each molten iron and the impeller (111, Fig. 1) 1) and/or motor RPM for rotation of the impeller (111, see FIG. 1) and/or motor current for rotation of the impeller (111, see FIG. 1) and/or the impeller (111, see FIG. 1) ) of the immersion depth and/or the degassing time and/or the blast furnace slag basicity were further analyzed using a multilayer perceptron neural network model (121c, see FIG. It is possible to further extract the degassing treatment condition information of (423).

As another example, the server 120 (refer to FIG. 2) analyzes the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1) using a multi-layer perceptron neural network model (121c, see FIG. 2). After extraction, the temperature and/or the amount of molten iron (M1, see FIG. 1) and/or the amount of molten iron (M1, see FIG. 1) among the operating factors of the degassing reaction for each molten iron and/or the concentration by chemical component of the molten iron (M1, see FIG. 1), and the impeller (111, FIG. 1) and/or motor RPM for rotation of the impeller (111, see FIG. 1) and/or motor current for rotation of the impeller (111, see FIG. 1) and/or the impeller (111, see FIG. 1) ) of the immersion depth and/or the degassing time and/or the blast furnace slag basicity were further analyzed using a deep neural network model (121d, see FIG. Processing condition information may be further extracted ( 424 ).

As another example, the server 120 (see FIG. 2) analyzes and extracts the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1) using a deep neural network model (121d, see FIG. 2). In, among the operating factors of the degassing reaction for each molten iron, the motor RPM for the rotation of the impeller (111, see FIG. 1) and/or the immersion depth and/or the degassing processing time of the impeller (111, see FIG. 1), self-organization map By further analysis using the model (121e, see FIG. 2), it is possible to further extract optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1) (425).

As another example, the server 120 (see FIG. 2) analyzes and extracts the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1) using a self-organizing map model (121e, see FIG. 2). After that, for the motor RPM and / or the impeller (111, see FIG. 1) for the rotation of the impeller (111, see FIG. 1) of the operating factors of the degassing reaction for each molten iron and / or the immersion depth and / or the degassing processing time, in-depth determination When the degassing efficiency and/or the chemical component hit rate of the molten iron (M1, see FIG. 1) is improved using the theoretical policy gradient model (121f, see FIG. 2) By further analyzing the degassing, it is possible to further extract optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1) (426).

Referring to FIG. 3 , the control device 130 receives the optimal degassing treatment condition information required for the extracted current molten iron (M1, see FIG. 1 ) ( 430 ).

The control device 130 responds to processing the optimal degassing treatment condition information required for the extracted current molten iron (M1, see FIG. 1), and the molten iron desulfurization device 110 to process the current molten iron (M1, see FIG. 1). ) can be controlled (440). The control device 130 responds to processing the optimal degassing treatment condition information required for the extracted current molten iron (M1, see FIG. 1), the rotation drive unit 113 and/or the elevating drive unit 114 and/or the degassing agent The input unit 115 may be controlled. The rotation driving unit 113 and/or the elevating driving unit 114 and/or the degassing agent input unit 115 of the current molten iron degassing reaction corresponding to the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1). It can be driven based on the optimal control factor. The rotation driving unit 113 has an optimum degassing processing time and/or an optimum rotation speed of the impeller 111 and/or an optimum motor RPM for rotation of the impeller 111 and/or optimum rotation of the impeller 111 . It can be driven based on motor current and/or optimum blast furnace slag basicity. The elevating driving unit 114 may be driven based on the optimum immersion depth of the impeller 111 . The degassing agent input unit 115 may be driven based on the optimal input amount of the degassing agent (M2, see FIG. 1) and/or the optimal input time of the degassing agent (M2, see FIG. 1).

In response to processing the optimal degassing treatment condition information required for the extracted current molten iron (M1, see FIG. 1), the control device 130 responds to the optimal degassing treatment condition information required for the current molten iron (M1, see FIG. 1). An optimal operating factor of the corresponding current molten iron degassing reaction and an optimal control factor of the current molten iron degassing reaction may be further displayed ( 431 ). The control device 130 may notify the operator and/or the manager of the optimal operation factor of the current molten iron degassing reaction and the optimal control factor of the current molten iron degassing reaction through a pop-up message on the screen. The optimal operating factors for the current molten iron degassing reaction may include the optimum temperature and/or the optimum amount of molten iron (M1, see Fig. 1) and/or the optimum chemical component concentration of the molten iron (M1, see Fig. 1). have. The optimal control factors for the current molten iron degassing reaction are the optimal dosage of the degassing agent (M2, see Fig. 1) and/or the optimal dosing time of the degassing agent (M2, see Fig. 1) and/or the optimal degassing treatment time and/or the impeller. (111, see FIG. 1) and/or the optimum motor RPM for the rotation of the impeller (111, see FIG. 1) and/or the optimum motor current for the rotation of the impeller (111, see FIG. 1) and and/or an optimal immersion depth of the impeller 111 (see FIG. 1 ) and/or an optimal blast furnace slag basicity.

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 generate program modules to perform operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

The computer-readable recording medium includes any type of recording medium in which instructions readable by the computer 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 110: molten iron desulfurization device
111: impeller 112: shaft
113: rotation driving unit 114: elevating driving unit
115: degassing agent input unit 120: server
130: control unit 131: processor
132: memory L: container
M1: molten iron M2: degassing agent

Claims (19)

a vessel containing the molten iron and the desulfurization agent; an impeller immersed in the vessel to stir the molten iron and the desulfurization agent; a shaft connected to an upper portion of the impeller; a rotation driving unit connected to the shaft to rotate the impeller according to a rotation operation of the shaft; and a molten iron desulfurization device connected to the rotary driving unit to desulfurize the molten iron, including an elevating driving unit for elevating and lowering the impeller according to the elevating operation of the rotating driving unit;
An optimal degassing treatment control model for each molten iron is generated for degassing the molten iron under optimal conditions, and the resultant factor of the degassing reaction for each molten iron among the generated optimal degassing treatment control models for each molten iron and the operation of the degassing reaction for each molten iron a server that analyzes the correlation between the control factors of the degassing reaction for each molten iron based on the factors using a big data model to extract information on the optimal degassing treatment conditions required for the current molten iron; and
Includes a control device including a processor for processing the optimal degassing treatment condition information required for the extracted current molten iron,
The control system, in response to processing the optimal degassing treatment condition information required for the extracted current molten iron, the control system of the molten iron desulfurization apparatus for controlling the molten iron desulfurization apparatus to desulfurize the current molten iron. delete According to claim 1,
The resultant factor of the degassing reaction for each molten iron is a control system of a molten iron desulfurization device comprising at least one selected from the group consisting of degassing efficiency, degassing rate, and chemical component hit rate of molten iron. According to claim 1,
The operation factor of the degassing reaction for each molten iron is a control system of a molten iron desulfurization device comprising one or more selected from the temperature and amount of molten iron and the concentration of each chemical component of the molten iron. According to claim 1,
The control factors of the degassing reaction for each molten iron are the input amount of the degassing agent, the input time of the degassing agent, the degassing treatment time, the rotation speed of the impeller, the motor RPM for the rotation of the impeller, the motor current for the rotation of the impeller, and the immersion depth of the impeller and a control system for a molten iron desulfurization device comprising at least one selected from among blast furnace slag basicity. According to claim 1,
The server is, among the operating factors of the degassing reaction for each molten iron, the temperature and the amount of molten iron, the concentration by chemical composition of the molten iron, the rotation speed of the impeller, the motor RPM for the rotation of the impeller, and the motor current and the impeller for the rotation of the impeller Control of the molten iron desulfurization apparatus that further analyzes the correlation between the deposition depth, the degassing treatment time, and the basicity of two or more of the blast furnace slag basicity, using a multiple linear regression control model to further extract information on the optimal degassing treatment condition required for the current molten iron system. 7. The method of claim 6,
The server is, among the operating factors of the degassing reaction for each molten iron, the temperature and the amount of molten iron, the concentration by chemical composition of the molten iron, the rotation speed of the impeller, the motor RPM for the rotation of the impeller, and the motor current and the impeller for the rotation of the impeller Control of the molten iron desulfurization device that further analyzes the correlation between the deposition depth, the degassing treatment time, and the basicity of two or more selected among the blast furnace slag basicity using a multi-layer perceptron neural network model to further extract information on the optimal degassing treatment conditions required for the current molten iron system. 8. The method of claim 7,
The server is, among the operating factors of the degassing reaction for each molten iron, the temperature and the amount of molten iron, the concentration by chemical composition of the molten iron, the rotation speed of the impeller, the motor RPM for the rotation of the impeller, and the motor current and the impeller for the rotation of the impeller A control system for a molten iron desulfurization device that further analyzes the correlation between the deposition depth, the degassing treatment time, and the basicity of two or more of the blast furnace slag basicity, using a deep neural network model to further extract information on the optimal degassing treatment conditions required for the current molten iron. 9. The method of claim 8,
The server uses a self-organizing map model to further analyze the selected one or more of the motor RPM for the rotation of the impeller, the immersion depth of the impeller, and the degassing processing time among the operating factors of the degassing reaction for each molten iron. A control system for molten iron desulfurization equipment that further extracts the necessary optimal degassing treatment condition information. 10. The method of claim 9,
The server, among the operating factors of the degassing reaction for each molten iron, is selected as one or more of the motor RPM for the rotation of the impeller, the immersion depth of the impeller, and the degassing processing time, using a deep deterministic policy gradient model for degassing efficiency and molten iron A control system for a molten iron desulfurization device that further analyzes over-desorption due to over-injection of a degassing agent when at least one of the chemical component hit ratios of According to claim 1,
The control device, in response to processing the extracted optimal degassing treatment condition information required for the current molten iron, the optimum operating factor and the current The control system of the molten iron desulfurization unit further indicating the optimal control factor of the molten iron desulfurization reaction. By the server, an optimal degassing control model for each molten iron is generated for degassing under optimal conditions,
By the server, the correlation between the result factor of the degassing reaction for each molten iron among the generated optimal degassing treatment control model for each molten iron and the control factor of the degassing reaction for each molten iron based on the operation factor of the degassing reaction for each molten iron is a big data model. Extract the optimal degassing treatment condition information required for the current molten iron by analyzing it using
By the control device, receiving the optimal degassing treatment condition information required for the extracted current molten iron,
Controlling the molten iron desulfurization device for desulfurizing the molten iron so that the current molten iron is degassed by the control device in response to processing the optimal degassing treatment condition information required for the extracted current molten iron,
The molten iron desulfurization device,
a container for accommodating the molten iron and the desulfurization agent;
an impeller immersed in the vessel to stir the molten iron and the desulfurization agent;
a shaft connected to an upper portion of the impeller;
a rotation driving unit connected to the shaft to rotate the impeller according to a rotation operation of the shaft; and
A control method of a molten iron desulfurization device including a lift driving unit connected to the rotary drive unit for elevating the impeller in accordance with the elevating operation of the rotary drive unit. delete 13. The method of claim 12,
Extracting the optimal degassing treatment condition information required for the current molten iron is,
By the server, among the operating factors of the degassing reaction for each molten iron, the temperature and the amount of molten iron, the concentration by chemical composition of the molten iron, the rotation speed of the impeller, the motor RPM for the rotation of the impeller, and the motor current and the impeller for the rotation of the impeller of the molten iron desulfurization apparatus that further analyzes the correlation between the deposition depth of control method. 15. The method of claim 14,
Extracting the optimal degassing treatment condition information required for the current molten iron is,
By the server, among the operating factors of the degassing reaction for each molten iron, the temperature and the amount of molten iron, the concentration by chemical composition of the molten iron, the rotation speed of the impeller, the motor RPM for the rotation of the impeller, and the motor current and the impeller for the rotation of the impeller of the molten iron desulfurization apparatus, which further analyzes the correlation between the deposition depth of control method. 16. The method of claim 15,
Extracting the optimal degassing treatment condition information required for the current molten iron is,
By the server, among the operating factors of the degassing reaction for each molten iron, the temperature and the amount of molten iron, the concentration by chemical composition of the molten iron, the rotation speed of the impeller, the motor RPM for the rotation of the impeller, and the motor current and the impeller for the rotation of the impeller A control method of a molten iron desulfurization apparatus that further analyzes the correlation between the deposition depth of . 17. The method of claim 16,
Extracting the optimal degassing treatment condition information required for the current molten iron is,
By the server, among the operation factors of the degassing reaction for each molten iron, one or more of the motor RPM for the rotation of the impeller, the immersion depth of the impeller, and the degassing processing time are further analyzed using a self-organizing map model to further analyze the current molten iron A control method of the molten iron desulfurization device to further extract the information on the optimal degassing treatment conditions required for 18. The method of claim 17,
Extracting the optimal degassing treatment condition information required for the current molten iron is,
By the server, among the operating factors of the degassing reaction for each molten iron, the degassing efficiency and the degassing efficiency and A control method of a molten iron desulfurization apparatus that further analyzes over-dehydration due to over-injection of a degassing agent when at least one of the chemical component hit ratios of molten iron is improved to further extract information on optimal de-gassing conditions required for the current molten iron. 13. The method of claim 12,
By the control device in response to processing the extracted information on the optimal degassing treatment condition required for the current molten iron, the optimum operating factor and the current The control method of the molten iron|metal desulfurization apparatus which further displays the optimal control factor of molten iron|metal desulfurization reaction.

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