Steelworks operation support method, operation support device, display device, and operation support program

Abstract

Provided is an operation assistance method, which is used in ironworks having a blast furnace section, a steel manufacturing section, and an energy section and executed by an information processing device, comprising: inputting data of blast furnace operation specifications and ion distribution influencing the steel manufacturing section and energy section into a learning model; estimating a total cost by the learning model on the basis of the input data of the blast furnace operation specifications and ion distribution; and outputting the estimated total cost. The learning model is a model trained on the basis of record data including records of the blast furnace operation specifications and the total cost.

Description

[Technical Field]

[0001] The present disclosure relates to an operation support method, an operation support device, a display device, and an operation support program for supporting operations in a steelworks. [Background technology]

[0002] Technologies for supporting the operation of steelworks have been known for some time. For example, Patent Document 1 describes a method for determining the conditions that minimize costs by performing optimization processing using the total cost of a steelworks as an objective function and operational conditions such as crude steel production volume, molten iron blending ratio, and the types and amounts of raw materials used as variables in a calculation model. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-55997 Summary of the Invention [Problem to be solved by the invention]

[0004] In actual operation, the material heat balance and power plant efficiency of a blast furnace often deviate from the theoretical values ​​derived by the calculation model. Therefore, the calculation model needs to be tuned each time using the most recent actual results. However, it is difficult to tune the calculation model using the genetic algorithm processing, which is the optimization processing method used in Patent Document 1. As described above, with conventional technology, optimization conditions are searched for based on theoretical values, which may result in an inability to obtain an optimal solution suited to actual operation. Thus, there is room for improvement in technology to support operations at steelworks.

[0005] In view of the above circumstances, an object of the present disclosure is to improve the technology that supports operations in steelworks. [Means for solving the problem]

[0006] (1) An operation support method according to an embodiment of the present disclosure includes: An operation support method executed by an information processing device, used in a steelworks having a blast furnace department, a steelmaking department, and an energy department, Inputting data on blast furnace operation parameters and pig iron distribution that have an impact on the steelmaking sector and the energy sector into a learning model, Estimating a total cost based on the input blast furnace operation specifications and pig iron distribution data using the learning model; outputting the estimated total cost; The learning model is a model trained based on performance data including blast furnace operation specifications and overall cost performance.

[0007] (2) An operation support method according to one embodiment of the present disclosure is the operation support method described in (1), The learning model includes a blast furnace sector model, a steelmaking sector model, and an energy sector model, inputting data on blast furnace operation specifications that have an impact on the steelmaking sector and the energy sector into the blast furnace sector model; Based on the input data of blast furnace operation specifications, the blast furnace department model outputs estimated values ​​of molten iron components and molten iron temperature, and B gas generation conditions, The estimated values ​​of the molten iron composition and the molten iron temperature and the pig iron mixture are input into a steelmaking department model related to the steelmaking department, and the LD gas generation conditions of the steelmaking department are determined from the steelmaking department model based on the total amount of molten iron and the amount of scrap, which are input from outside, and the molten iron composition and the molten iron temperature; The B gas generation conditions and the LD gas generation conditions are input into the energy sector model, and the total cost is estimated by the energy sector model based on the input B gas generation conditions and the LD gas generation conditions; The estimated total cost is output.

[0008] (3) An operation support method according to one embodiment of the present disclosure is the operation support method described in (2), The pig iron ratio is determined based on the amount of crude steel, the type of steel, and the main unit price.

[0009] (4) An operation support method according to one embodiment of the present disclosure is the operation support method described in any one of (1) to (3), the blast furnace division includes a plurality of blast furnaces; The total amount of hot metal is allocated to each blast furnace based on the upper and lower limit of the hot metal production ratio for each furnace. The data of the blast furnace operation specifications for each blast furnace is determined based on the allocated amount of molten iron.

[0010] (5) An operation support method according to one embodiment of the present disclosure is the operation support method described in (2), The learning model further estimates carbon dioxide emissions from the blast furnace sector, the steelmaking sector, and the energy sector based on the input blast furnace operation specifications and pig iron distribution data, The estimated carbon dioxide emission amount is output.

[0011] (6) An operation support method according to an embodiment of the present disclosure is the operation support method described in any one of (1) to (5), The total cost includes the cost equivalent to carbon dioxide emissions.

[0012] (7) An operation support method according to one embodiment of the present disclosure is the operation support method described in any one of (1) to (6), The blast furnace operation parameters that have an impact on the energy sector include reducing material parameters and blast parameters.

[0013] (8) An operation support method according to one embodiment of the present disclosure is the operation support method described in (7), the reducing material specifications include a coke ratio and a pulverized coal ratio, The airflow specifications include airflow humidity, airflow temperature, and airflow oxygen concentration.

[0014] (9) An operation support method according to one embodiment of the present disclosure is the operation support method described in (8), The reducing material specifications include the composition and carbon content of the coke.

[0015] (10) An operation support method according to one embodiment of the present disclosure is the operation support method described in (1), Inputting data of a predetermined range of blast furnace operation specifications into the learning model, and estimating the overall costs of the blast furnace department, the steelmaking department, and the energy department corresponding to each input, Based on the estimated total cost, a plurality of candidate operation data are output.

[0016] (11) An operation support method according to one embodiment of the present disclosure is the operation support method according to (10), The total cost relating to the plurality of candidate operational data satisfies a predetermined standard.

[0017] (12) An operation support method according to one embodiment of the present disclosure is the operation support method according to (10) or (11), The plurality of candidate operational data satisfies a predetermined constraint condition.

[0018] (13) An operation support device according to an embodiment of the present disclosure includes: An operation support device for a steelworks having a blast furnace department, a steelmaking department, and an energy department, the operation support device comprising: The control unit inputting data on blast furnace operation parameters and pig iron distribution that have an impact on the steelmaking sector and the energy sector into a learning model related to the steelworks; Estimating a total cost based on the input blast furnace operation specifications and pig iron distribution data using the learning model; outputting the estimated total cost; The learning model is a model trained based on performance data including blast furnace operation specifications and overall cost performance.

[0019] (14) A display device according to an embodiment of the present disclosure includes: A display device that displays the output of an operation support device equipped with a control unit in a steelworks having a blast furnace department, a steelmaking department, and an energy department, displaying a total cost estimated by the control unit of the operation support device using a learning model related to the steelworks based on blast furnace operation specifications and pig iron distribution data that have an impact on the steelmaking department and the energy department; The learning model is a model trained based on performance data including blast furnace operation specifications and overall cost performance.

[0020] (15) A display device according to an embodiment of the present disclosure is the display device according to (14), Based on the total costs of the blast furnace department, the steelmaking department, and the energy department estimated in response to each input of data on blast furnace operation specifications within a predetermined range that is input into the learning model, multiple candidate operation data are filtered, and the filtered candidate operation data is displayed.

[0021] (16) An operation support program according to an embodiment of the present disclosure, An operation support program for a steelworks having a blast furnace department, a steelmaking department, and an energy department, executed by an information processing device, the program comprising: Inputting data on blast furnace operation parameters and pig iron distribution that have an impact on the steelmaking department and the energy department into a learning model related to the steelworks; Estimating a total cost based on the input blast furnace operation specifications and pig iron distribution data using the learning model; outputting the estimated total cost; Execute The learning model is a model trained based on performance data including blast furnace operation specifications and overall cost performance. [Effects of the Invention]

[0022] According to one embodiment of the present disclosure, technology supporting operations in steel plants can be improved. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a block diagram showing a schematic configuration of an operation support device according to an embodiment of the present disclosure. [Figure 2] 4 is a flowchart illustrating an operation of an operation support device according to an embodiment of the present disclosure. [Figure 3] FIG. 1 is a diagram illustrating a configuration of a learning model according to an embodiment of the present disclosure. [Figure 4] 4 is a flowchart illustrating an operation of an operation support device according to an embodiment of the present disclosure. [Figure 5] These are case study results for six different operating conditions. [Figure 6] These are case study results under different preconditions. [Figure 7] 10 is a flowchart showing the operation of the operation support device when the coke department model is omitted. DETAILED DESCRIPTION OF THE INVENTION

[0024] Hereinafter, an operation support technology for a steelworks according to an embodiment of the present disclosure will be described with reference to the drawings.

[0025] In each drawing, the same or corresponding parts are denoted by the same reference numerals. In the description of this embodiment, the description of the same or corresponding parts will be omitted or simplified as appropriate.

[0026] An overview and configuration of an operation support technology for a steelworks according to this embodiment will be described with reference to FIG. 1. The operation support technology for a steelworks according to this embodiment is executed by an operation support device 10. The operation support device 10 is also referred to as an information processing device. Such a support method is used, for example, in a steelworks having a blast furnace department, a steelmaking department, a coke department, and an energy department. Hereinafter, the support technology according to this embodiment will be described as being used in a steelworks having a blast furnace department, a steelmaking department, a coke department, and an energy department.

[0027] First, an overview of this embodiment will be described, and details will be provided later. Generally, steelworks prepare production plans based on steel product demand, including the amount of steel tapped, the amount of pig iron produced, and the amount of coke produced. Such production plans are prepared for each plant with the aim of maintaining production activities without surpluses or shortages. Furthermore, the energy department, which manages overall energy, prepares supply and demand plans to ensure the supply of utilities required at each plant without shortages. Here, utilities include gas, electricity, steam, water, compressed air, and the like. When preparing operation plans, each department, including the energy department, performs cost management not only to realize the production plan but also to minimize manufacturing costs. Specifically, under conditions that satisfy the production plan, the operation plan is prepared by optimizing various operating conditions, such as the types and amounts of raw materials used, the types and amounts of utilities used, and equipment operating conditions, to minimize manufacturing costs. In steelworks, combustible gases (B gas, C gas, and LD gas) produced as by-products in blast furnaces, coke ovens, and converters are used directly or as a mixture (M gas) within the plant, with surplus gases used in captive power generation facilities managed by the energy department. In captive power generation facilities, the by-product gases and some purchased fuel are combusted, and the resulting flue gas is heat-exchanged with pure water to generate steam, which is then used to power a steam turbine. This process also involves extracting the necessary amount of steam for the steelworks, i.e., steam extraction. Therefore, the supply and demand management of utilities managed by the energy department, particularly gas, electricity, and steam, is deeply related not only to the conditions of energy equipment such as turbines, but also to the operating conditions managed by each plant, particularly the conditions for by-product gas generation. However, gas, electricity, and steam supply and demand plans are typically prepared based on detailed operating plans for each plant, and it is difficult to say that the operating plans adequately take into account the energy department's cost situation. Therefore, the result of aggregating the optimal costs for each department does not necessarily represent the optimal cost for the entire steelworks. Therefore, the operation support technology for steelworks according to this embodiment aims to provide a comprehensive energy simulation technology for blast furnaces, steelmaking, and coke making that enables more accurate cost management.

[0028] When operating a blast furnace, first, operating conditions are determined using a model constructed from material balance and heat balance data. However, in actual operation, due to the influence of changes in the state of the blast furnace or changes in the surrounding environment, which change every day, a discrepancy occurs between the theoretical values ​​calculated from the model and the actual data obtained from actual operation. In this embodiment, in order to suppress this discrepancy, machine learning is performed based on data from actual operation, and the model is corrected.

[0029] Specifically, in the steelworks operation support technology according to this embodiment, the operation support device 10 uses a physical model of the steelworks to perform estimation processing necessary for operation support at the steelworks. The physical model of the steelworks is also referred to as a learning model. The learning model takes blast furnace operation parameter data as input and costs for the blast furnace department, steelmaking department, coke department, and energy department as output. The costs for the blast furnace department, steelmaking department, coke department, and energy department are also referred to as total costs. This learning model is trained using blast furnace operation parameter data and actual data including actual total costs as training data. In this embodiment, the operation support device 10 inputs blast furnace operation parameter data that affect the energy department into the learning model. The operation support device 10 then estimates the total cost using the learning model based on the input blast furnace operation parameter data. The operation support device 10 then outputs the estimated total cost.

[0030] As described above, according to this embodiment, the operation support device 10 inputs data on blast furnace operation parameters that affect the energy sector into a learning model and outputs a total cost. The learning model is trained using actual data as training data. In other words, this embodiment is provided with a feedback function using actual data to correct any discrepancy between actual operation and the theoretical value derived from the model. This improves the accuracy of estimating total costs in a steelworks. In other words, this embodiment can provide a comprehensive blast furnace and energy simulation technology that enables more accurate cost management, thereby improving the technology for supporting operations in a steelworks.

[0031] Here, data on blast furnace operation specifications that affect the energy sector include, for example, reducing material conditions and blast conditions. There are a wide variety of operating conditions that affect the overall costs of the blast furnace sector and the energy sector. For example, in a blast furnace, reducing material conditions such as the coke ratio and pulverized coal ratio, which are closely related to the B gas generation conditions, and blast conditions such as the blast moisture content, blast temperature, and blast oxygen concentration also have a significant impact on the overall cost. In this embodiment, by considering such reducing material conditions and blast conditions as variables, more accurate cost management and the creation of highly feasible operation plans are realized.

[0032] Next, each component of the operation support device 10 will be described in detail.

[0033] The operation support device 10 is any device used by a user, including, for example, a manager or person in charge of a steelworks. For example, a general-purpose electronic device or a dedicated electronic device can be adopted as the operation support device 10. As shown in FIG. 1 , the operation support device 10 includes a control unit 11, a memory unit 12, a communication unit 13, an input unit 14, and an output unit 15.

[0034] The control unit 11 includes at least one processor, at least one dedicated circuit, or a combination of these. The processor is a general-purpose processor such as a central processing unit (CPU) or a graphics processing unit (GPU), or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The control unit 11 controls each part of the operation support device 10 and executes processes related to the operation of the operation support device 10.

[0035] The storage unit 12 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or a combination of at least two of these. The semiconductor memory is, for example, a random access memory (RAM) or a read-only memory (ROM). The RAM is, for example, a static random access memory (SRAM) or a dynamic random access memory (DRAM). The ROM is, for example, an electrically erasable programmable read-only memory (EEPROM). The storage unit 12 functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 12 stores data used in the operation of the operation support device 10 and data obtained by the operation of the operation support device 10. For example, the storage unit 12 stores a learning model. Note that the learning model may be stored in an external device different from the operation support device 10. In this case, the operation support device 10 uses the learning model by accessing the external device storing the learning model via the communication unit 13.

[0036] The communication unit 13 includes at least one external communication interface. The communication interface may be either a wired or wireless communication interface. In the case of wired communication, the communication interface is, for example, a LAN (Local Area Network) interface or a USB (Universal Serial Bus). In the case of wireless communication, the communication interface is, for example, an interface compatible with mobile communication standards such as LTE (Long Term Evolution), 4G (4th generation), or 5G (5th generation), or an interface compatible with short-range wireless communication such as Bluetooth (registered trademark). The communication unit 13 receives data used in the operation of the operation support device 10 and transmits data obtained by the operation of the operation support device 10.

[0037] The input unit 14 includes at least one input interface. The input interface may be, for example, a physical key, a capacitive key, or a pointing device. The input interface may also be a touch screen integrated with a display. The input interface may also be, for example, a microphone that accepts voice input, or a camera that accepts gesture input. The input unit 14 accepts operations to input data used in the operation of the operation support device 10. The input unit 14 may be connected to the operation support device 10 as an external input device instead of being provided in the operation support device 10. Any connection method may be used, for example, a Universal Serial Bus (USB), a High-Definition Multimedia Interface (HDMI) (registered trademark), or Bluetooth (registered trademark).

[0038] The output unit 15 includes at least one output interface. The output interface is, for example, a display that outputs information visually, or a speaker that outputs information audibly. The display is, for example, an LCD (liquid crystal display) or an organic EL (electro luminescence) display. The output unit 15 displays and outputs data obtained by the operation of the operation support device 10. The output unit 15 may be connected to the operation support device 10 as an external output device instead of being provided in the operation support device 10. Any connection method can be used, for example, USB, HDMI (registered trademark), or Bluetooth (registered trademark).

[0039] The functions of the operation support device 10 are realized by executing a program according to this embodiment on a processor corresponding to the operation support device 10. That is, the functions of the operation support device 10 are realized by software. The program causes a computer to execute the operations of the operation support device 10, thereby causing the computer to function as the operation support device 10. That is, the computer functions as the operation support device 10 by executing the operations of the operation support device 10 in accordance with the program.

[0040] In this embodiment, the program can be recorded on a computer-readable recording medium. The computer-readable recording medium includes non-transitory computer-readable media, such as a magnetic recording device, an optical disc, a magneto-optical recording medium, or a semiconductor memory. The program can be distributed, for example, by selling, transferring, or lending a portable recording medium, such as a DVD (digital versatile disc) or a CD-ROM (compact disc read only memory), on which the program is recorded. The program can also be distributed by storing the program in the storage of an external server and transmitting the program from the external server to another computer. The program can also be provided as a program product.

[0041] Some or all of the functions of the operation support device 10 may be realized by a dedicated circuit equivalent to the control unit 11. In other words, some or all of the functions of the operation support device 10 may be realized by hardware.

[0042] (Operation example 1 of the operation support device 10) Next, a first operational example of the operation support device 10 according to this embodiment will be described. Fig. 2 is a flowchart showing an example of an operation support method executed by the operation support device 10 according to this embodiment.

[0043] Step S100: The control unit 11 of the operation support device 10 trains the learning model based on the actual data. Specifically, the control unit 11 trains the learning model using actual data including actual blast furnace operation parameters, coke volume, iron mix, and overall cost as training data. Here, iron mix represents the ratio between the total amount of molten iron and the amount of scrap. As described above, a discrepancy may occur between the theoretical value calculated from the physical model and the actual data obtained from actual operation. In this embodiment, to suppress this discrepancy, the learning model is trained based on data from actual operation.

[0044] In this embodiment, the blast furnace operation specifications include reducing agent specifications and blast specifications. The reducing agent specifications include a coke rate and a pulverized coal rate. The reducing agent specifications may also include the composition and carbon content of the coke. The blast specifications include blast moisture, blast temperature, and blast oxygen concentration. All of these blast furnace operation specifications have a significant impact on the energy sector. In other words, these blast furnace operation specifications have a significant impact on cost management in the energy sector, i.e., costs in the energy sector. Therefore, in this embodiment, a learning model is adopted that uses blast furnace operation specifications that have an impact on the energy sector as input. By adopting such a learning model in this embodiment, the estimation accuracy of the total cost at the steelworks is improved. Hereinafter, in this embodiment, the blast furnace operation specifications will be described as including the coke rate, pulverized coal rate, blast moisture, blast temperature, and blast oxygen concentration.

[0045] The total costs include the main costs of the blast furnace department, the main costs of the steelmaking department, the main costs of the coke department, and the main costs of the energy department. The main costs of the blast furnace department include main raw material-related costs and energy costs, and the energy costs include reducing material-related costs and utility costs. Utility costs are the costs of utilities required at each factory, etc. The main costs of the steelmaking department include main raw material-related costs, auxiliary raw material-related costs, and utility costs. The main costs of the coke department include utility costs. The main costs of the energy department include purchased fuel costs and electricity buying and selling costs. In this embodiment, the total costs are determined by the following formula (1). [Number 1] Total cost = Major costs of the blast furnace division + Major costs of the steelmaking division + Major costs of the coke division + Major costs of the energy division = (main raw material cost + auxiliary raw material cost) + (reducing material related costs + utility costs) + (fuel purchase cost + electricity trading cost) (1)

[0046] The learning model may be, for example, a machine learning model generated based on a multilayer perceptron configured with an input layer, a hidden layer, and an output layer, but is not limited to this. The learning model may also be any model generated based on a machine learning algorithm such as a convolutional neural network (CNN), a recurrent neural network (RNN), or other deep learning. The learning model is not limited to a model generated by machine learning. For example, the learning model may be a multiple regression model or a model based on a Kalman filter. The learning model may also be a theoretical model constructed from mass balance and heat balance data. In the present embodiment, "training" includes, but is not limited to, training in a machine learning algorithm. In the present embodiment, "training" includes, for example, optimization or correction of various parameters in a multiple regression model or a theoretical model.

[0047] Step S110: The control unit 11 determines the blast furnace operation parameters, coke volume, and pig iron mix based on conditions determined according to the steelworks' production plan, etc. Conditions determined according to the steelworks' production plan, etc., are also referred to as preconditions. In this embodiment, the coke volume is the coke production volume in the coke department. The preconditions are set, for example, by user input. These preconditions include the pig iron production volume (or pig iron productivity ratio), coke unit price, rolling operation plan, coal market conditions, and crude oil market conditions. Specifically, the control unit 11 determines the blast furnace operation parameters using, for example, a lookup table in which the pig iron production volume and the blast furnace operation parameters are associated. In this case, the memory unit 12 stores the lookup table. The control unit 11 also determines the blast furnace operation parameters corresponding to the preconditions by referring to the memory unit 12. The control unit 11 may determine the pig iron mix based on the crude steel volume, steel type, and major unit price. The crude steel amount, steel type, and main unit price may be included in the preconditions or may be derived from the preconditions. The crude steel amount is the amount of crude steel produced in the blast furnace. The steel type is the type of crude steel produced in the blast furnace. The main unit price includes the unit price of coal, scrap, LNG (Liquefied Natural Gas), etc. The control unit 11 calculates an objective function within a search range set by user input, pig iron By adopting the top specifications including pig iron The allocation may be determined.

[0048] Step S120: The control unit 11 inputs the blast furnace operation specifications, coke amount, and pig iron mix data determined in step S110 into the learning model learned in step S100.

[0049] Step S130: The control unit 11 uses a learning model to estimate the total costs of the blast furnace, steelmaking, coke, and energy departments based on the input data on blast furnace operation specifications, coke volume, and pig iron distribution. The above-mentioned preconditions are used in this estimation process. Specifically, the learning model uses the rolling operation plan, coal market conditions, and crude oil market conditions to estimate the total costs of the blast furnace, steelmaking, coke, and energy departments based on the input data on blast furnace operation specifications, coke volume, and pig iron distribution.

[0050] Step S140: The control unit 11 outputs the total cost estimated in step S130 through the output unit 15.

[0051] Although the example above describes the output of the learning model as a total cost, this is not limited to this. The learning model may further estimate carbon dioxide emissions from the blast furnace, steelmaking, coke, and energy sectors based on input data on blast furnace operation specifications, coke volume, and pig iron distribution. Carbon dioxide emissions include carbon dioxide emissions from reducing materials, carbon dioxide emissions from purchased fuel, and carbon dioxide emissions from purchased electricity. In addition, carbon dioxide emissions from sold electricity are subtracted from carbon dioxide emissions. In other words, carbon dioxide emissions are determined, for example, by the following formula (2): [Number 2] Carbon dioxide emissions = Carbon dioxide emissions from reducing materials +CO2 emissions from purchased fuel +CO2 emissions from purchased electricity -Carbon dioxide emissions from sold electricity (2)

[0052] When estimating the amount of carbon dioxide emissions, the actual data in step S100 described above includes the actual amount of carbon dioxide emissions. That is, in step S100, the control unit 11 trains the learning model using actual data including the blast furnace operation specifications, coke volume, pig iron mix, total cost, and carbon dioxide emissions as training data. In addition, in step S130, the control unit 11 estimates the total cost of the blast furnace department, steelmaking department, coke department, and energy department, and the amount of carbon dioxide emissions using the learning model based on the input data of the blast furnace operation specifications. Furthermore, in step S140, the control unit 11 outputs the estimated total cost and carbon dioxide emissions.

[0053] The total cost may also include the cost equivalent to carbon dioxide emissions. The cost equivalent to carbon dioxide emissions is determined by the amount of carbon dioxide emissions and the unit price of carbon dioxide. In this case, the above-mentioned preconditions include the unit price of carbon dioxide. Hereinafter, the total cost including the cost equivalent to carbon dioxide emissions will also be referred to as the total cost including carbon assessment. The total cost including carbon assessment is determined, for example, by the following formula (3). [Number 3] Total cost including carbon assessment = Total cost + carbon dioxide emissions × carbon dioxide unit cost (3)

[0054] The learning model has been described as a single model corresponding to a steelworks including a blast furnace department, a steelmaking department, a coke department, and an energy department, but is not limited to this. For example, the learning model may be a combination of four models: a model corresponding to the blast furnace department, i.e., a blast furnace department model, a model corresponding to the steelmaking department, i.e., a steelmaking department model, a model corresponding to the coke department, i.e., a coke department model, and a model corresponding to the energy department, i.e., an energy department model. Furthermore, the learning model may be a model that combines models that are further subdivided from the blast furnace department model, the steelmaking department model, the coke department model, and the energy department model.

[0055] An example of the configuration of a learning model is shown with reference to FIG. 3. The learning model shown in FIG. 3 includes a blast furnace department model 100, a steelmaking department model 200, a coke department model 300, and an energy department model 400. The blast furnace department model 100 includes a blast furnace operation model 110 and a hot stove model 120. The blast furnace operation model 110 and the hot stove model 120 are models based on the material and heat balance of the blast furnace and the hot stove, respectively. The control unit 11 trains the blast furnace operation model 110 and the hot stove model 120 using actual data as training data. Blast furnace operation parameters based on preconditions are input to the blast furnace operation model 110 and the hot stove model 120. Such preconditions include a tapping plan. The tapping plan includes plans such as the amount of tapping. As described above, the control unit 11 determines the blast furnace operation parameters to be input to the model based on the tapping plan and the look-up table. The blast furnace operation model 110 and the hot stove model 120 output data necessary for calculating the overall cost and data on molten iron conditions, including molten iron composition and molten iron temperature, based on the input blast furnace operation specifications. The data necessary for calculating the overall cost is used as input to the energy department model 400. The data necessary for calculating the overall cost includes, for example, data on B gas, which is a combustible gas by-produced in the blast furnace. The data on B gas includes the amount of B gas generated and the heat value per unit volume of B gas. The data on molten iron conditions, including molten iron composition and molten iron temperature, is used as input to the steelmaking department model 200.

[0056] The steelmaking department model 200 includes a converter operation model 210 and an electric furnace operation model 220. The converter operation model 210 and the electric furnace operation model 220 are models based on the material and heat balances of the converter and the electric furnace, respectively. The control unit 11 trains the converter operation model 210 and the electric furnace operation model 220 using actual data as training data. The converter operation model 210 receives input data on molten iron conditions, including the total amount of molten iron, the amount of scrap, and the molten iron composition and temperature, based on preconditions. The electric furnace operation model 220 receives input data on the amount of scrap based on preconditions. The converter operation model 210 and the electric furnace operation model 220 output data necessary for calculating the overall cost based on the input preconditions. This data is used as input for the energy department model 400. The data necessary for calculating the overall cost includes, for example, data on LD gas, a combustible gas by-produced in the converter. The data related to LD gas includes the amount of LD gas generated and the calorific value per unit volume of LD gas.

[0057] The coke department model 300 is a model based on the utility balance of coke ovens and peripheral equipment. The control unit 11 trains the coke department model 300 using actual data as training data. Data on the amount of coke produced based on preconditions is input to the coke department model 300. The coke department model 300 outputs data necessary for calculating the overall cost based on the input preconditions. This data is used as input to the energy department model 400. The data necessary for calculating the overall cost includes, for example, data related to C gas, which is a combustible gas produced as a by-product in coke ovens. The data related to C gas includes the amount of C gas produced and the heat value of C gas per unit volume.

[0058] The energy sector model 400 includes a utility balance logic 410. The control unit 11 trains the utility balance logic 410 using actual data as training data. The utility balance logic 410 outputs the total cost and carbon dioxide emissions based on the input preconditions and data related to B gas, LD gas, and C gas. These preconditions include the rolling operation plan, coal market conditions, and crude oil market conditions. In other words, the energy sector model 400 receives the preconditions, the output of the blast furnace sector model 100, the output of the steelmaking sector model 200, and the output of the coke sector model 300 as inputs, and outputs the total cost and carbon dioxide emissions.

[0059] (Operation example 2 of the operation support device 10) Next, a second operational example of the operation support device 10 according to this embodiment will be described. FIG. 4 is a flowchart showing an example of an operation support method executed by the operation support device 10 according to this embodiment. Here, an example of an operation in which the operation support device 10 outputs multiple operation data candidates based on the overall cost estimated by the learning model will be described. The multiple operation data candidates are also referred to as candidate operation data. It is assumed that the operation support device 10 uses a learning model that has been trained by the processing in step S100 of FIG. 3.

[0060] Step S210: The control unit 11 of the operation support device 10 determines representative values ​​of the blast furnace operation parameters, coke rate, and pig iron ratio based on the preconditions. The representative values ​​of the blast furnace operation parameters, coke rate, and pig iron ratio are also referred to as base conditions. These preconditions are set, for example, by user input. The preconditions include pig iron production rate (or pig iron production ratio), coke price, rolling operation schedule, coal market conditions, and crude oil market conditions. Specifically, the control unit 11 determines the base conditions using, for example, a lookup table in which pig iron production rate and base conditions are associated. In this case, the memory unit 12 stores the lookup table. The control unit 11 also determines the base conditions corresponding to the preconditions by referring to the memory unit 12. The base conditions include the coke rate, pulverized coal rate, blast moisture, blast temperature, and blast oxygen concentration. Note that at least some of the base conditions may be set manually by the user.

[0061] Step S220: The control unit 11 inputs data on the blast furnace operation parameters, coke amount, and pig iron mixture within a predetermined range into the learning model based on the base conditions. That is, the control unit 11 inputs data on the blast furnace operation parameters, coke amount, and pig iron mixture within a predetermined range into the learning model, using the five operation factors included in the base conditions, namely, coke rate, pulverized coal rate, blast moisture, blast temperature, and blast oxygen concentration. A predetermined range is set for each of the five operation factors. Specifically, the predetermined range for the coke rate has a lower limit of 280 kg / ton and an upper limit of 450 kg / ton. The predetermined range for the pulverized coal rate has a lower limit of 80 kg / ton and an upper limit of 260 kg / ton. The predetermined range for the blast moisture has a lower limit of 5 g / Nm 3 and the upper limit is 70g / Nm 3 The predetermined range of the blast temperature has a lower limit of 900°C and an upper limit of 1200°C. The predetermined range of the blast oxygen concentration has a lower limit of 0% and an upper limit of 10%. These predetermined ranges are determined based on the upper and lower limit values ​​of operational performance in a steady state. The predetermined ranges may also be determined based on constraints in facility management. For example, the predetermined range of the blast temperature is determined to be, for example, the above range from the viewpoint of maintaining stable function of the hot stove.

[0062] Furthermore, the control unit 11 inputs all combinations of operating factors within a predetermined range into the learning model based on a specified mesh, i.e., an increment within the predetermined range. For example, the predetermined range for the air temperature is set to 900°C to 1200°C. The mesh can be specified as 50°C. When the predetermined range and mesh are specified in this way, air temperatures of 900°C, 950°C, ... 1200°C are input into the learning model. In other words, the control unit 11 performs a grid search for the five operating factors, using a predetermined range for each operating factor, for example, the settable upper and lower limit values ​​for each operating factor, as the search range. The mesh size is determined appropriately depending on the calculation load associated with the grid search.

[0063] Step S230: The control unit 11 estimates the total cost corresponding to each input input in step S220 using the learning model.

[0064] Step S240: The control unit 11 outputs multiple candidate operation data through the output unit 15 based on the total cost estimated in step S230. In other words, the control unit 11 outputs multiple candidate operation data by searching using the total cost as an objective function. The multiple candidate operation data are, for example, the top 20 operation data in descending order of total cost estimated corresponding to each input. The operation data includes the coke rate, pulverized coal rate, blast moisture, blast temperature, and blast oxygen concentration. In other words, the user can easily identify the operation data in the top 20 with the lowest total cost. Furthermore, the user can select operational data that can be actually used from the operation data.

[0065] While the example above describes an example in which the output of the learning model is the total cost, this is not limiting. The learning model may further estimate a total carbon dioxide emission amount based on the input data of blast furnace operation specifications. The carbon dioxide emission amount is expressed by the above formula (2). In this case, in steps S220 to S240, the control unit 11 outputs multiple candidate operation data by searching using the total cost and carbon dioxide emission amount as objective functions. In this case, the candidate operation data is the top 20 operation data based on a ranking determined based on the total cost and carbon dioxide emission amount estimated corresponding to each input. The ranking determined based on the total cost and carbon dioxide emission amount is appropriately determined based on an evaluation value based on the total cost and carbon dioxide emission amount. For example, the learning model may also estimate a total cost including a carbon evaluation. The total cost including a carbon evaluation is determined, for example, by the above formula (3). In this case, in steps S220 to S240, the control unit 11 outputs multiple candidate operation data by searching using the total cost including a carbon evaluation as an objective function. In this case, the candidate operational data is, for example, the top 20 operational data in order from lowest to highest total cost including carbon assessment estimated in response to each input.

[0066] As described above, according to this embodiment, the operation support device 10 inputs data on blast furnace operation parameters, coke volume, and pig iron distribution that affect the energy sector into a learning model and outputs total costs, etc. The learning model is trained based on actual data. Therefore, this embodiment can improve the accuracy of estimating total costs, etc. in a steelworks. Furthermore, according to this embodiment, the operation support device 10 inputs data on a predetermined range of blast furnace operation parameters, coke volume, and pig iron distribution, estimates total costs, etc. corresponding to each input, and outputs multiple candidate operation data. Therefore, it is possible to output multiple candidate operation data that are desirable from the perspective of total costs, etc., from among data on blast furnace operation parameters, coke volume, and pig iron distribution within a predetermined range. Thus, this embodiment can improve the technology for supporting operations in a steelworks.

[0067] Here, there are a wide variety of operating conditions that affect the overall cost, etc. For example, in a blast furnace, reducing material conditions such as the coke ratio and pulverized coal ratio, which are closely related to the B gas generation conditions, and blast conditions such as the blast moisture content, blast temperature, and blast oxygen concentration also have a significant effect on the overall cost and carbon dioxide emissions. According to this embodiment, these reducing material conditions and blast conditions are considered as variables, allowing for more accurate cost management and the creation of a more feasible operation plan.

[0068] Figure 5 shows the results of a case study in which six different blast furnace operating data and coke rates were used for trial operation under the same preconditions and pig iron mix. All six blast furnace operating parameters were thermally equivalent, combining multiple parameter changes equivalent to a coke rate of 2 kg / ton. In the case study results shown in Figure 5, the cost benefits of the blast furnace, coke, and energy departments are shown in bar graphs. Note that the cost benefit is calculated by multiplying the cost by -1. The overall cost benefit, calculated by combining the cost benefits of the blast furnace, coke, and energy departments, is shown in a line graph. The overall cost benefit is the sum of the cost benefits of the blast furnace, coke, and energy departments. As shown in Figure 5, the cost benefits of the blast furnace, coke, and energy departments are greatest in Cases III and VI for the blast furnace department, Cases IV, V, and VI for the coke department, and Case II for the energy department. On the other hand, the overall cost benefit is greatest in Case III. As described above, the case where the cost benefit of the blast furnace department is greatest, the case where the cost benefit of the coke department is greatest, the case where the cost benefit of the energy department is greatest, and the case where the overall cost benefit is greatest do not necessarily coincide. In other words, it is necessary to determine the blast furnace specifications based on the overall cost, not just the individual costs of the blast furnace department, the coke department, and the energy department. By estimating the overall cost using the operation support device 10 according to this embodiment, it is possible to optimize the entire steelworks.

[0069] Figure 6 shows the results of a case study of trial operation under different assumptions. The six blast furnace operating parameters in Figure 6 were all thermally equivalent, combining multiple parameter changes equivalent to a coke rate of 2 kg / t. In other words, Figure 6 shows the results of a case study of trial operation under different assumptions, with the same coke volume and pig iron mix. Rolling operation plans A and B represent the cases where all major rolling mills are operated and none of the major rolling mills are operated, respectively. The normal and elevated coke prices and crude oil market prices are appropriately set within the normal and elevated price ranges. Figure 6 also highlights the cells with the highest and second-highest overall cost benefits. As Figure 6 shows, the cases with the highest cost benefits are different for all six assumptions. In other words, if the assumptions change, the overall costs in the blast furnace and energy sectors must be reevaluated. According to the operation support device 10 of this embodiment, the total cost can be easily estimated, so that even if the preconditions change, recalculation related to optimization of the entire steelworks can be performed in a timely manner.

[0070] In step S240, the multiple candidate operational data are described as being the top 20 operational data based on the total cost, but this is not limited to this. The total cost of the multiple candidate operational data may be operational data that meets a predetermined criterion. For example, the predetermined criterion may be based on ranking, or the total cost may be lower than a predetermined threshold. The predetermined threshold may be a fixed value, or may be determined based on the average value of the total costs corresponding to the inputs found by grid search.

[0071] The plurality of candidate operational data may be operational data that satisfy predetermined constraints. In other words, constraints on the operation or facility management of each of the blast furnace division, steelmaking division, coke division, and energy division may be set, and if candidate operational data conflicts with the constraints, it may be excluded when ranking. Such constraints include tuyere temperature or blast volume. For example, the lower and upper limits of the tuyere temperature may be specified as 2250°C and 2320°C, respectively. Also, for example, the lower and upper limits of the blast volume may be specified as 6500 Nm 3 / min, 7400Nm 3 / min. The constraints may also include a minimum amount of heavy oil used in the power generation facility. The plurality of candidate operational data may be limited to data that satisfy these constraints.

[0072] The control unit 11 may highlight and output candidate operational data that is identical or similar to the operating conditions of the performance data, among the plurality of candidate operational data output by the output unit 15. This allows the user to more easily select operational data that can actually be used from the candidate operational data.

[0073] In the present embodiment, the learning model outputs either the total cost, the total cost and carbon dioxide emissions, or the total cost including carbon assessment. However, this is not limited to this. The learning model may also output the carbon dioxide emissions. In other words, the operation support device 10 may estimate and output only the carbon dioxide emissions without estimating the total cost. In this case, in step S130 of the above-described Operational Example 1, the control unit 11 estimates the carbon dioxide emissions of the blast furnace department, steelmaking department, coke department, and energy department using the learning model based on the input blast furnace operation specifications, coke volume, and pig iron mix data. Then, in step S140, the control unit outputs the estimated carbon dioxide emissions via the output unit 15. Also, in step S230 of the above-described Operational Example 2, the control unit 11 estimates the carbon dioxide emissions of the blast furnace department, steelmaking department, coke department, and energy department corresponding to each input using the learning model. Then, in step S240, the control unit 11 outputs multiple candidate operation data via the output unit 15 based on the estimated carbon dioxide emissions. In other words, in this case, the control unit 11 performs a search using the carbon dioxide emission amount as an objective function, and outputs a plurality of candidate operational data.

[0074] In the present embodiment, the operation support device 10 includes the output unit 15, and the control unit 11 of the operation support device 10 outputs the results of calculations from the output unit 15. However, the present invention is not limited to this. For example, the technology according to the present embodiment may be implemented in a Software as a Service (SaaS) format. In this case, the operation support device 10 functions as a cloud server. The operation support device 10 outputs the calculation results on a display device separate from the operation support device 10. The display device includes a communication unit, receives the calculation results from the operation support device 10 via the communication unit, and displays information such as total costs. In other words, the display device displays the total costs of the blast furnace department, steelmaking department, coke department, and energy department estimated by the control unit 11 of the operation support device 10 using a learning model related to the steelworks, based on data on blast furnace operation parameters, coke volume, and pig iron distribution that affect the energy department. Alternatively, the display device displays carbon dioxide emissions from the blast furnace department, steelmaking department, coke department, and energy department estimated by the control unit 11 provided in the operation support device 10 using a learning model related to the steelworks, based on data on blast furnace operation specifications, coke volume, and pig iron distribution that have an impact on the energy department. Note that any method can be used to request the above calculation results from the operation support device 10. For example, the display device may include an input unit that accepts input from a user, and may send a request for the calculation results to the operation support device 10 based on the input content accepted by the input unit.

[0075] This device is a device for outputting blast furnace operating parameters, coke volume, and pig iron ratios in order to optimize the overall costs and carbon dioxide emissions of the blast furnace, steelmaking, coke, and energy divisions combined. This embodiment is intended for blast furnace, steelmaking, and coke operators to consider and implement changes to blast furnace operating parameters, coke volume, and pig iron ratios accordingly, and is therefore different from devices that optimize the supply and demand of gas, steam, or electricity only within a specific division, such as the energy division.

[0076] The display device may output multiple candidate operation data based on the estimated total cost. That is, the operation support device 10, functioning as a cloud server, inputs a predetermined range of blast furnace operation specification data into the learning model and estimates the total costs of the blast furnace department, steelmaking department, energy department, etc. corresponding to each input. Based on the estimated total cost, the display device may display multiple candidate operation data. The display device may filter the multiple candidate operation data based on filtering conditions and display the filtered candidate operation data. The filtering conditions may be appropriately set. In other words, the display device may filter from the multiple candidate operation data based on the total costs of the blast furnace department, steelmaking department, energy department, etc. estimated corresponding to each input of a predetermined range of blast furnace operation specification data input into the learning model, and display the filtered candidate operation data. The filtering conditions may include, for example, the ranking of the total cost. Specifically, for example, the filtering conditions may be the top five rankings of the total cost. In this way, the display device may display only candidate operation data whose total cost is at or above a predetermined ranking.

[0077] In this embodiment, the C gas condition is output by the coke department model and used to calculate the total cost. However, this is not limited to this. For example, in the operation support according to this embodiment, the coke department model may be omitted from the learning model. That is, the operation support device 10 according to this embodiment may be used in a steelworks having a blast furnace department, a steelmaking department, and an energy department. In this case, the operation support device 10 inputs data on blast furnace operation parameters and iron distribution that affect the steelmaking department and the energy department into the learning model, and estimates the total cost using the learning model based on the input data on the blast furnace operation parameters. The operation support device 10 then outputs the estimated total cost.

[0078] More specifically, the operation support device 10 according to this embodiment calculates the total cost using three models: a blast furnace department model, a steelmaking department model, and an energy department model. In this case, the learning model includes the blast furnace department model, the steelmaking department model, and the energy department model. The operation support device 10 inputs data on blast furnace operation specifications that affect the steelmaking department and the energy department into the blast furnace department model, and based on the input data on blast furnace operation specifications, outputs estimated values ​​of molten iron composition and molten iron temperature, and B gas generation conditions from the blast furnace department model. The operation support device 10 also inputs estimated values ​​of molten iron composition and molten iron temperature and iron ratio into the steelmaking department model, and determines the LD gas generation conditions for the steelmaking department from the steelmaking department model based on the total amount of molten iron, scrap amount, molten iron composition, and molten iron temperature input from outside. The operation support device 10 also inputs the B gas generation conditions and the LD gas generation conditions into the energy sector model, and estimates the overall cost based on the input B gas generation conditions and LD gas generation conditions using the energy sector model.The operation support device 10 then outputs the estimated overall cost. Figure 7 shows a flowchart illustrating an example of an operation support method executed by the operation support device 10 according to this embodiment when the coke sector model is omitted.

[0079] Step S310: The control unit 11 of the operation support device 10 trains the learning model based on the performance data. Specifically, the control unit 11 trains the learning model using performance data including performance data of blast furnace operation specifications, iron ore distribution, and total cost as training data.

[0080] Step S320: The control unit 11 determines the blast furnace operation parameters and the pig iron ratio based on the preconditions. The preconditions are set, for example, by user input. Specifically, the control unit 11 determines the blast furnace operation parameters using, for example, a lookup table in which the pig iron production rate is associated with the blast furnace operation parameters.

[0081] Step S330: The control unit 11 inputs the blast furnace operation specifications and pig iron distribution data determined in step S210 into the learning model learned in step S310.

[0082] Step S340: The control unit 11 estimates the total costs of the blast furnace department, steelmaking department, and energy department using a learning model based on the input blast furnace operation specifications and pig iron distribution data. The above-mentioned preconditions are used in this estimation process. Specifically, the learning model uses the rolling operation plan, coal market conditions, and crude oil market conditions, and estimates the total costs of the blast furnace department, steelmaking department, and energy department based on the input blast furnace operation specifications and pig iron distribution data.

[0083] Step S350: The control unit 11 outputs the total cost estimated in step S340 through the output unit 15.

[0084] In this embodiment, the blast furnace department in the steelworks has been described as including one blast furnace, but this is not limited thereto. The blast furnace department may include multiple blast furnaces. In this case, the total amount of molten iron may be allocated to each blast furnace based on the upper and lower limit productivity ratios of each blast furnace. Furthermore, the blast furnace operation specifications for each blast furnace may be determined based on the allocated amount of molten iron.

[0085] In addition, the operation support technology according to this embodiment may take into account the inventory amount of coke produced in the coke department. In other words, the coke amount may include the coke production amount and the coke inventory amount in the coke department. Here, the coke inventory amount has a lower limit and an upper limit. The lower limit may be determined, for example, based on the coke required to continuously operate the steelworks. On the other hand, the upper limit may be determined, for example, based on the size of the coke storage area in the steelworks. In particular, if the coke inventory amount falls below the lower limit, it may become necessary to purchase coke from an external source, which may have an impact on the operating costs of the steelworks. Therefore, the operation support device 10 may provide operation support based on a total cost that also takes into account the coke inventory amount.

[0086] The above-mentioned predetermined constraints may include a condition related to the amount of coke. That is, the predetermined constraints may include a condition related to the amount of coke produced and the amount of coke in the coke department. As described above, the amount of coke in stock has a lower limit and an upper limit. Since the trading price of coke can be high, by setting the condition related to the amount of coke as a predetermined constraint, it is possible to maintain an appropriate amount of coke and reduce overall costs.

[0087] The relationship between coke volume and preconditions is explained below. Coke consumption in steelworks is also affected by preconditions. Coke consumption may be included in blast furnace operation parameters. For example, when crude steel production is high, almost without exception, pig iron production and coke consumption are also high. Therefore, B gas, C gas, and LD gas generation are also roughly proportionally high. Meanwhile, the rolling process is also operating at nearly full capacity, resulting in high electricity consumption. Gas consumption in steelworks is strongly dependent on pig iron production, coke production, and rolling operation time, so gas consumption is also high in this case. On the other hand, when crude steel production is low, almost all elements are low. However, because the decline in electricity and gas consumption in the rolling process does not have the same impact as the decline in production, electricity and gas shortages are likely. Furthermore, if the decline in pig iron production is large and the decline in B gas generation is significant, gas shortages may occur in generators that are nearly exclusively B gas-fired. As a result, more external electricity may need to be purchased than necessary. For these reasons, when crude steel production is at a low level, the proportion of externally purchased energy sources such as electricity, LNG, or heavy oil is likely to increase due to a shortage of by-product gases.

[0088] Furthermore, when at least some coke ovens are repaired, i.e., when coke ovens are shut down, this leads to a decrease in coke production and a decrease in C gas generation, regardless of the production level. This often leads to an increase in externally purchased energy due to gas shortages and a decrease in coke inventory. Furthermore, when rolling mills are shut down, it is common to plan for a certain amount of reduction in production level, but even in such a situation, the impact of electricity and gas demand being reduced to zero due to the rolling mill shutdown is significant, and often results in a surplus of by-product gas. This can lead to a decrease in externally purchased energy. In some cases, electricity may even have to be sold to external parties.

[0089] It is preferable to perform coke inventory management based on the relationship between the coke amount and the prerequisites. An example of coke inventory management will be described below.

[0090] (Example 1 of coke inventory management) At a steelworks with a coke inventory management lower limit of 10,000 tons and upper limit of 80,000 tons, the current inventory was 15,000 tons. The coke ovens are scheduled to be shut down next month, resulting in a 10,000 tons reduction in coke production. Therefore, the inventory must be restored to at least 20,000 tons by the end of the current month. Therefore, the coke strength was improved for this month only, and the coke rate (the amount of coke used per unit of blast furnace production) was reduced. For example, when the production rate is 900,000 tons per month, a decrease in the coke rate of 6 kg / ton would increase the coke inventory by 5,400 tons.

[0091] (Example 2 of coke inventory management) In the above case, the inventory can be recovered by increasing the coke production rate rather than reducing the coke rate in the blast furnace, provided that the upper limit of coke production has not already been reached.

[0092] (Example 3 of coke inventory management) In the above case, the inventory amount can be restored by combining the first and second embodiments.

[0093] (Example 4 of coke inventory management) At a steel mill where the lower limit for coke inventory management is 10,000 tons and the upper limit is 80,000 tons, the current inventory is 15,000 tons. The coke ovens are scheduled to be shut down in the month after next, resulting in a reduction in coke production of 20,000 tons. The main production plans for the next three months are as follows:

[0094] [Table 1]

[0095] If we assume that the coke rate is 360 kg / ton, the change in coke inventory at the end of the month will be: 36×0.9-(88×360 / 1000)=+7,200 tons Similarly, the coke inventory change for the next month will be ±0,000 tons, and the coke inventory change for the month after that will be -18,000 tons. Therefore, measures to recover the coke inventory equivalent to 10,800 tons will be required. The coke inventory recovery measures are, as described in Examples 1 to 3, a reduction in the coke rate, an increase in coke production, or a combination of these.

[0096] According to the production plan, a rolling shutdown is scheduled for the current month. From an energy supply and demand perspective, by-product gas is more likely to be needed the following month when there is no rolling shutdown than the current month when there is a rolling shutdown, so it is desirable that increased coke production result in a high production of C gas in the following month. On the other hand, from the perspective of blast furnace permeability, it is easier to reduce the coke rate in the current month when iron production is relatively low. For these reasons, it is desirable from an overall operational strategy perspective to reduce the coke rate in the current month and increase coke production the following month.

[0097] In this embodiment, the production plan is scheduled on a monthly basis, but may be scheduled on various other basis such as a weekly or bimonthly basis. In this embodiment, the current month in which the pig iron production rate is low may be replaced by a first period. The following month in which the pig iron production rate is high may be replaced by a second period. A coke rate reduction may be performed in the first period in which the pig iron production rate is low, and a coke rate increase may be performed in the second period in which the pig iron production rate is high.

[0098] (Example 5 of Coke Inventory Management) The steelworks' coke inventory management limits are 10,000 tons and 80,000 tons, but the current inventory is 60,000 tons. A large-scale reduction in iron production is planned for the month after next, and production is expected to return to normal within the next few months. The main production plans for the next three months are as follows:

[0099] [Table 2]

[0100] If we assume that the coke rate is 360 kg / ton, the amount of surplus coke in the month after next will be: 36×0.9-(75×360 / 1000)=+36,000 tons Therefore, a coke inventory adjustment of at least 16,000 tons would be necessary. After comparing the total costs of two plans, one to reduce coke production for the current and next month by 9,000 tons / month and the other to increase the coke rate for the current and next month by 10 kg / ton, i.e., to use surplus 9,000 tons / month, the latter was selected.

[0101] In other words, an increase in the coke rate may be implemented if the cost of increasing the coke rate to adjust the coke inventory is less than the cost of decreasing the coke production rate, and conversely, a decrease in the coke production rate may be implemented if the cost of decreasing the coke production to adjust the coke inventory is less than the cost of increasing the coke rate.

[0102] (Example 6 of coke inventory management) Steelworks A, which has a coke inventory management lower limit of 10,000 tons and an upper limit of 80,000 tons, has currently seen its inventory fall to 15,000 tons. Even with the upper limit of coke production, the production plan for this month still results in a coke shortage, with the inventory expected to fall to 7,000 tons by the end of the month. Poor ventilation conditions in the blast furnace mean that a reduction in the coke rate is not expected, and if this continues, the company will be forced to purchase expensive coke from outside. Meanwhile, Steelworks B, which has a coke inventory management lower limit of 10,000 tons and an upper limit of 100,000 tons, has a stable inventory of around 40,000 tons, and still has room to increase coke production. Therefore, it has been decided to reduce coke purchases from outside by increasing coke production at Steelworks B by 10,000 tons per month and shipping it to Steelworks A.

[0103] In other words, if the coke inventory cannot be restored by reducing the coke ratio and increasing coke production at the steelworks that is the target of operational support, coke may be transported from another steelworks to restore the coke inventory.

[0104] (Example 7 of coke inventory management) Steelworks A, which has a coke inventory management lower limit of 10,000 tons and an upper limit of 80,000 tons, has currently seen its inventory fall to 15,000 tons. Even with the upper limit set for coke production, the company's production plan for this month still results in a coke shortage, and it expects the inventory to fall to 7,000 tons by the end of the month. Because of poor ventilation in the blast furnace and no prospect of reducing the coke rate, the company will be forced to purchase expensive coke from outside if things continue as they are.

[0105] On the other hand, at Steelworks B with a lower limit of 15,000 tons and an upper limit of 100,000 tons for coke inventory management, the inventory has been stable at around 40,000 tons, and there is still room for increasing coke production. Furthermore, at Steelworks C with a lower limit of 8,000 tons and an upper limit of 60,000 tons for coke inventory management, the inventory has been stable at around 30,000 tons, and there is still room for increasing coke production. To restore the coke inventory at Steelworks A, the transportation of coke from Steelworks B or C was considered. Due to distance or location conditions, etc., the transportation cost is Steelworks B < Steelworks C, but in the comparison of costs related to energy supply and demand within the steelworks, it was Steelworks B > Steelworks C. As a result of comprehensively judging these, it was decided to transport coke from Steelworks C to Steelworks A.

[0106] In other words, when there are other steelworks that can transport coke to the steelworks targeted for operation support, including the first steelworks and the second steelworks, even if the coke transportation cost from the first steelworks is higher than the coke transportation cost from the second steelworks, when the cost related to energy supply and demand within the first steelworks is lower than the cost related to energy supply and demand within the second steelworks, the transportation of coke from the first steelworks may be executed.

[0107] Although the present disclosure has been described based on the drawings and examples, it should be noted that those skilled in the art can make various modifications and alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included in the scope of the present disclosure. For example, the functions, etc. included in each means or each step, etc. can be rearranged so as not to be logically contradictory, and a plurality of means or steps, etc. can be combined into one or divided.

Description of Reference Numerals

[0108] 10 Operation support device 11 Control unit 12 Storage unit 13 Communication unit 14 Input unit 15 Output unit 100 Blast furnace department model 110 Blast furnace operation model 120 Hot air stove model 200 Steelmaking Division Model 300 Coke Division Model 400 Energy Sector Model 410 Utility Balance Logic

Claims

1. An operation support method executed by an information processing device, used in a steelworks having a blast furnace department, a steelmaking department, and an energy department, Inputting data on blast furnace operation parameters and pig iron distribution within a predetermined range that have an impact on the steelmaking sector and the energy sector into a learning model; Based on the input blast furnace operation specifications and pig iron distribution data, the learning model is used to estimate the overall costs of the blast furnace department, the steelmaking department, and the energy department corresponding to each input; outputting the estimated total cost; outputting a plurality of candidate operation data based on the estimated total cost; The operation support method, wherein the learning model is a model trained based on performance data including blast furnace operation specifications and overall cost performance.

2. The learning model includes a blast furnace sector model, a steelmaking sector model, and an energy sector model, inputting data on blast furnace operation specifications that have an impact on the steelmaking sector and the energy sector into the blast furnace sector model; Based on the input data of blast furnace operation specifications, the blast furnace department model outputs estimated values ​​of molten iron components and molten iron temperature, and B gas generation conditions; the estimated values ​​of the molten iron components and the molten iron temperature, and the pig iron mixture are input into a steelmaking department model for the steelmaking department, and LD gas generation conditions for the steelmaking department are determined from the steelmaking department model based on the total amount of molten iron and the amount of scrap, which are input from outside, and the molten iron components and the molten iron temperature; inputting the B gas generation conditions and the LD gas generation conditions into the energy sector model, and estimating the total cost based on the input B gas generation conditions and the LD gas generation conditions using the energy sector model; The operation support method according to claim 1 , further comprising outputting the estimated total cost.

3. The operation support method according to claim 2, wherein the pig iron allocation is determined based on the amount of crude steel, the type of steel, and the main unit price.

4. the blast furnace division includes a plurality of blast furnaces; The total amount of hot metal is allocated to each blast furnace based on the upper and lower limit of the hot metal production ratio for each furnace. The operation support method according to claim 1 , wherein the data of the blast furnace operation parameters for each blast furnace is determined based on the allocated amount of molten iron.

5. The learning model further estimates carbon dioxide emissions from the blast furnace sector, the steelmaking sector, and the energy sector based on the input blast furnace operation specifications and pig iron distribution data, The operation support method according to claim 2 , further comprising outputting the estimated carbon dioxide emission amount.

6. The operation support method according to claim 1 , wherein the total cost includes a cost equivalent to carbon dioxide emissions.

7. The operation support method according to claim 1 , wherein the blast furnace operation specifications that have an impact on the energy sector include reducing material specifications and air blast specifications.

8. the reducing material specifications include a coke ratio and a pulverized coal ratio, The operation support method according to claim 7 , wherein the air blowing parameters include air blowing humidity, air blowing temperature, and air blowing oxygen concentration.

9. The operation support method according to claim 8 , wherein the reducing material specifications include a composition and a carbon content of coke.

10. The operation support method according to claim 1 , wherein a total cost relating to the plurality of candidate operation data satisfies a predetermined standard.

11. The operation support method according to claim 1 or 10, wherein the plurality of candidate operational data satisfy a predetermined constraint condition.

12. An operation support device for a steelworks having a blast furnace department, a steelmaking department, and an energy department, the operation support device comprising: The control unit inputting data on blast furnace operation parameters and pig iron distribution within a predetermined range that have an impact on the steelmaking sector and the energy sector into a learning model related to the steelworks; Based on the input blast furnace operation specifications and pig iron distribution data, the learning model is used to estimate the overall costs of the blast furnace department, the steelmaking department, and the energy department corresponding to each input; outputting the estimated total cost; outputting a plurality of candidate operation data based on the estimated total cost; The learning model is an operation support device that is a model trained based on performance data including blast furnace operation specifications and overall cost performance.

13. A display device that displays the output of an operation support device equipped with a control unit in a steelworks having a blast furnace department, a steelmaking department, and an energy department, displaying a total cost estimated by the control unit of the operation support device using a learning model related to the steelworks based on blast furnace operation specifications and pig iron distribution data that have an impact on the steelmaking department and the energy department; filtering a plurality of candidate operation data based on the total costs of the blast furnace department, the steelmaking department, and the energy department estimated in response to each input of data of blast furnace operation specifications within a predetermined range input into the learning model, and displaying the filtered candidate operation data; The learning model is a model trained based on actual data including blast furnace operation specifications and overall cost results, Display device.

14. An operation support program for a steelworks having a blast furnace department, a steelmaking department, and an energy department, executed by an information processing device, the program comprising: inputting data on blast furnace operation parameters and pig iron distribution within a predetermined range that have an impact on the steelmaking department and the energy department into a learning model related to the steelworks; Estimating, using the learning model, the overall costs of the blast furnace department, the steelmaking department, and the energy department corresponding to each input based on the input blast furnace operation specifications and pig iron distribution data; outputting the estimated total cost; outputting a plurality of candidate operation data based on the estimated total cost; Execute The learning model is an operation support program that is a model trained based on performance data including blast furnace operation specifications and overall cost performance.

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