WO2024100771A1

WO2024100771A1 - Raw material charging control device for blast furnace, method for generating opening degree command value, and program

Info

Publication number: WO2024100771A1
Application number: PCT/JP2022/041583
Authority: WO
Inventors: 朋浩池田; 裕作喜松本; 春文合志
Original assignee: 株式会社安川電機
Priority date: 2022-11-08
Filing date: 2022-11-08
Publication date: 2024-05-16
Also published as: WO2024101191A1

Abstract

A raw material charging control device 90 comprises: a data collection unit 118 that accumulates, in a data storage unit, records for learning including the opening degree of a gate 40 for charging raw materials from a bunker 30 containing raw materials at the top of a blast furnace 2, the charging speed of raw materials from the bunker 30, and charging conditions which include characteristics of the raw materials; an off-line learning unit 211 that generates a prediction model representing the relationship between the opening degree, the charging speed, and the charging conditions by means of a multi-stage input-output relationship through machine learning based on a plurality of the records for learning stored in the data storage unit; a command generation unit 220 that generates an opening degree command value for the gate 40 corresponding to a charging target speed on the basis of the charging conditions of newly fed raw material, the charging target speed therefor, and the prediction model; and a gate 40 controller that causes the opening degree to correspond to the opening degree command value.

Description

Blast furnace raw material charging control device, opening command value generation method, and program

This disclosure relates to a raw material charging control device for a blast furnace, a method for generating an opening command value, and a program.

Patent Document 1 discloses a control method in which a model formula expressing the relationship between the number of revolutions of the rotating chute and the opening degree of the flow adjustment gate is prepared, the opening degree of the flow adjustment gate is controlled based on the model formula, and the parameters of the model formula are learned by incorporating the actual results of the opening degree of the flow adjustment gate, the number of revolutions of the rotating chute, the revolution speed, and the weighing value obtained from the results after the charging of the raw materials is completed.

Japanese Patent Application Laid-Open No. 10-251719

This disclosure provides an effective device for controlling the rate at which raw materials are charged from a bunker into a blast furnace with high precision.

A raw material charging control device for a blast furnace according to one aspect of the present disclosure includes a data collection unit that accumulates learning records in a data storage unit, including the gate opening for charging raw materials from a bunker that stores raw materials in the upper part of the blast furnace, the charging rate of the raw materials from the bunker, and charging conditions including raw material characteristics; an offline learning unit that generates a prediction model that expresses the relationship between the opening, the charging rate, and the charging conditions by a multi-stage input/output relationship through machine learning based on the multiple learning records accumulated in the data storage unit; a command generation unit that generates a gate opening command value corresponding to the charging target speed based on the charging conditions and target charging speed of newly added raw materials and the prediction model; and a furnace top controller that corresponds the opening to the opening command value.

A method for generating an opening command value according to another aspect of the present disclosure includes accumulating learning records in a data storage unit, including the opening of a gate for charging raw materials from a bunker that stores raw materials in the upper part of the blast furnace, the charging speed of the raw materials from the bunker, and charging conditions including raw material characteristics; generating a prediction model that represents the relationship between the opening, the charging speed, and the charging conditions using a multi-stage input/output relationship based on the multiple learning records accumulated in the data storage unit; and generating a gate opening command value corresponding to the target charging speed based on the charging conditions and target charging speed of newly added raw materials and the prediction model.

A program according to yet another aspect of the present disclosure causes the device to accumulate learning records in a data storage unit, including the gate opening for charging raw materials from a bunker that stores raw materials in the upper part of the blast furnace, the charging rate of the raw materials from the bunker, and charging conditions including the characteristics of the raw materials; generate a prediction model that represents the relationship between the opening, the charging rate, and the charging conditions using a multi-stage input/output relationship based on the multiple learning records accumulated in the data storage unit; and generate a gate opening command value that corresponds to the target charging rate based on the charging conditions and target charging rate of newly added raw materials and the prediction model.

This disclosure provides an apparatus that is effective in controlling the speed at which raw materials are charged from a bunker into a blast furnace with high precision.

FIG. 2 is a schematic diagram illustrating the configuration of a raw material charging device. FIG. 2 is a block diagram illustrating a functional configuration of a server device and a learning calculation device. FIG. 1 is a schematic diagram illustrating a predictive model generated by deep learning. FIG. 1 is a schematic diagram illustrating a prediction model generated by the random forest method or the gradient boosting method. 4 is a block diagram illustrating a configuration of a command generating unit. FIG. FIG. 2 is a block diagram illustrating a hardware configuration of a server device and a learning calculation device. 13 is a flowchart illustrating a record accumulation procedure. 1 is a flow chart illustrating an offline learning procedure. 4 is a flowchart illustrating an opening control procedure.

The following describes the embodiments in detail with reference to the drawings. In the description, the same elements or elements having the same functions are given the same reference numerals, and duplicate descriptions are omitted.

[Raw material charging device]
The raw material charging device 1 shown in Fig. 1 is a device that charges raw materials into a blast furnace 2 used for steel production or the like. The raw material charging device 1 includes a bunker 30, a gate 40, a collection hopper 50, a distribution chute 60, a hopper 10, and a conveyor 20. The bunker 30 stores raw materials in an upper portion of the blast furnace 2. The raw materials charged into the blast furnace 2 may include multiple types of raw materials.

The raw material charging device 1 may be equipped with multiple bunkers 30, and each of the multiple bunkers 30 may store raw materials in the upper part of the blast furnace 2. In the illustrated example, the raw material charging device 1 has two bunkers 30A, 30B. Above the bunkers 30A, 30B, a distribution device 31 is provided that distributes raw materials charged from above to either the bunker 30A or 30B.

The multiple bunkers 30 are selectively used based on a predetermined order, etc. When the raw materials include multiple types of raw materials, each of the multiple bunkers 30 contains multiple types of raw materials.

Each of the multiple bunkers 30 may be provided with a pressure sensor 32, and the blast furnace 2 may be provided with a pressure sensor 3. The pressure sensor 32 detects the internal pressure of the bunker 30, and the pressure sensor 3 detects the internal pressure of the blast furnace 2.

The gate 40 sends out raw materials from the bunker 30. The opening degree of the gate 40 can be changed by an electric actuator. The opening degree is expressed, for example, as a ratio to the fully open opening degree.

When the raw material charging device 1 is equipped with multiple bunkers 30, it is equipped with multiple gates 40 corresponding to the multiple bunkers 30, respectively. Each of the multiple gates 40 is provided at the bottom of the corresponding bunker 30. In the illustrated example, the raw material charging device 1 has two gates 40A, 40B corresponding to the two bunkers 30A, 30B, respectively.

The collecting hopper 50 temporarily stores the raw materials sent from the multiple gates 40 and collects them at a single charging port 51. The distribution chute 60 distributes the raw materials that drop from the charging port 51 into the blast furnace 2. For example, the distribution chute 60 tilts and rotates using an electric actuator, distributing the raw materials in a spiral shape inside the blast furnace 2.

The multiple hoppers 10 are provided outside the blast furnace and each store multiple types of raw materials. For example, the multiple hoppers 10 include a hopper 10A that stores relatively small coke lumps, a hopper 10B that stores main raw materials such as multiple types of iron ore, a hopper 10C that stores auxiliary raw materials, and a hopper 10D that stores relatively large coke lumps. Each of the multiple hoppers 10 has a discharge device 11 at the bottom. The discharge device 11 discharges the raw materials stored in the hopper 10 onto the conveyor 20. Hereinafter, as necessary in the explanation, the raw materials discharged by the discharge device 11 will be referred to as "discharged raw materials" to distinguish them from the raw materials stored in the hopper 10.

The multiple hoppers 10 may be provided with weighing scales 12 that detect the weight of each of the multiple types of discharged raw materials. For example, each of the multiple hoppers 10 further has a weighing scale 12. For example, the weighing scale 12 detects the weight of the discharged raw materials based on the difference between the weight of the hopper 10 before the material is discharged by the discharge device 11 and the weight of the hopper 10 after the material is discharged by the discharge device 11. The multiple weighing scales 12 that the multiple hoppers 10 each have detect the weight of each of the multiple types of discharged raw materials before they are put into the bunker 30 described below.

The multiple hoppers 10 may be provided with moisture meters 13 that detect the moisture content of the multiple types of raw materials. For example, each of the multiple hoppers 10 further has a moisture meter 13. For example, the moisture meter 13 detects the moisture content of the raw materials in the hopper 10 using a non-contact method such as infrared. The moisture content is expressed, for example, as the ratio of the weight of the moisture content to the total weight of the raw materials. The moisture content of the raw materials is substantially the same when they are stored in the hopper 10 and when they are discharged from the hopper 10. Therefore, the moisture content detected by the moisture meter 13 represents the moisture content of the discharged raw materials.

The degree of influence of the moisture content on the flow characteristics, etc., may vary depending on the type of raw material. Since there may be types of raw material where the influence of the moisture content can be ignored, each of the multiple hoppers 10 does not necessarily need to have a moisture meter 13.

The conveyor 20 is, for example, a belt conveyor, and transports the multiple types of discharged raw materials discharged from the multiple hoppers 10 to the bunker 30. The multiple types of discharged raw materials transported by the conveyor 20 are loaded into the bunker 30. If the raw material charging device 1 is equipped with multiple bunkers 30, the multiple types of discharged raw materials transported by the conveyor 20 are loaded into one of the multiple bunkers 30 by the sorting device 31.

In the above, an example was given of a case in which multiple gates 40 are provided for multiple bunkers 30 at the entrance side of the collecting hopper 50, but the gates 40 may also be provided at the exit side of the collecting hopper 50. If only one bunker 30 is required, the bunker 30 and the collecting hopper 50 may be integrated together.

The raw material charging control device 90 controls the multiple hoppers 10 and the gate 40. For example, the raw material charging control device 90 has a raw material controller 92, a furnace top controller 93, and a schedule controller 91. The raw material controller 92 controls each of the multiple hoppers 10 so that the discharge device 11 discharges raw material of a weight corresponding to a predetermined target weight. The raw material controller 92 may retain the elapsed time from the time the discharge device 11 starts discharging raw material to the time the discharge device 11 stops discharging raw material as information representing the discharge speed of the raw material.

The furnace top controller 93 adjusts the opening of the gate 40 so that the raw materials are charged (dumped) from the bunker 30 at a charging speed corresponding to a predetermined charging target speed. The charging speed and the charging target speed are expressed, for example, by the weight of raw materials charged per unit time. The charging speed and the charging target speed may be expressed by the charging period of the raw materials from the bunker 30 (the length of time from the start of charging to the completion of charging), assuming that information on the weight of the raw materials put into the bunker 30 is obtained separately. The raw material charging period may also be expressed by the number of rotations and the rotation speed of the distribution chute 60. The furnace top controller 93 may hold the elapsed time from the time when charging of the raw materials from the bunker 30 starts to the time when charging is completed, and use it as information indicating the charging period.

If the raw material charging device 1 is equipped with multiple bunkers 30, the furnace top controller 93 may control the sorting device 31 to load the raw materials transported by the conveyor 20 into one of the multiple bunkers 30. The furnace top controller 93 may retain identification information of the bunker 30 into which the raw materials are loaded, among the multiple bunkers 30.

The schedule controller 91 communicates with the raw material controller 92 and the furnace top controller 93 via a network line 94. The network line 94 may be a local area network for control or a wide area network. For example, the schedule controller 91 receives raw material status information of the multiple hoppers 10 from the raw material controller 92 and receives furnace top status information of the bunker 30 and the gate 40 from the furnace top controller 93 via the network line 94. The raw material status information of the multiple hoppers 10 may include the status of the discharge device 11, the detection result by the weight meter 12, and the detection result by the moisture meter 13, etc. The furnace top status information of the bunker 30 and the gate 40 may include the identification information of the bunker 30 in use, the remaining weight of raw materials in the bunker 30, and the opening degree of the gate 40, etc. The schedule controller 91 calculates the above-mentioned target weight and charging target speed based on a predetermined production plan, the raw material status information of the multiple hoppers 10, and the furnace top status information of the bunker 30 and the gate 40. The schedule controller 91 transmits the target weight to the raw material controller 92 and the target charging speed to the furnace top controller 93 via the network line 94.

The actual charging speed at the opening adjusted by the furnace top controller 93 may have an error with respect to the target charging speed. In order to distribute the raw materials evenly in the blast furnace 2, it is desirable that the error be small. However, since the relationship between the opening adjusted by the furnace top controller 93 and the actual charging speed varies greatly depending on various conditions such as the characteristics of the raw materials, it is difficult to reduce the error. Therefore, the raw material charging control device 90 may be configured to execute the following: storing learning records including the opening of the gate 40, the charging speed of the raw materials from the bunker 30, and the charging conditions including the characteristics of the raw materials in a data storage unit; generating a prediction model that represents the relationship between the opening, the charging speed, and the charging conditions by a multi-stage input/output relationship based on the multiple learning records stored in the data storage unit; generating a gate opening command value corresponding to the target charging speed based on the charging conditions and target charging speed of the newly charged raw materials and the prediction model; and making the opening of the gate 40 correspond to the generated opening command value.

By using a prediction model that represents the relationship between the opening, charging speed, and charging conditions using a multi-stage input/output relationship, it is possible to generate an opening command value that corresponds to the target charging speed with higher accuracy. By making the opening correspond to the opening command value generated with high accuracy, it is possible to suppress errors in the charging speed relative to the target charging speed. This is therefore effective in controlling the charging speed of raw materials from the bunker into the blast furnace with high accuracy.

For example, the raw material charging control device 90 further includes a server device 100 and a learning calculation device 200. The server device 100 and the learning calculation device 200 communicate with each other via a network line 94, and communicate with the raw material controller 92 and the furnace top controller 93 via the network line 94. The server device 100 collects the learning records from the raw material controller 92 and the furnace top controller 93 via the network line 94 and stores them in the data storage unit. The learning calculation device 200 acquires multiple learning records stored in the data storage unit via the network line 94, generates the prediction model based on the acquired multiple learning records, and generates a gate opening command value corresponding to the charging target speed based on the charging conditions and charging target speed of the newly charged raw materials and the prediction model. The learning calculation device 200 transmits the opening command value to the furnace top controller 93 via the network line 94.

2 is a block diagram illustrating the functional configuration of the server device 100 and the learning calculation device 200. As shown in FIG. 2, the server device 100 has, as its functional configuration (hereinafter referred to as "functional blocks"), an opening information acquisition unit 111, a speed information acquisition unit 112, a weight information acquisition unit 113, a flow characteristic information acquisition unit 114, a moisture information acquisition unit 115, an environmental information acquisition unit 116, a bunker information acquisition unit 117, a data collection unit 118, and a data storage unit 119. The opening information acquisition unit 111 acquires opening information representing the opening of the gate 40 from the furnace top controller 93. The speed information acquisition unit 112 acquires speed information representing the actual charging speed at the opening represented by the opening information from the furnace top controller 93. For example, the speed information acquisition unit 112 acquires information on the actual charging period at the opening represented by the opening information from the furnace top controller 93.

The weight information acquisition unit 113 acquires type information indicating the type of each of the multiple types of charged raw materials and weight information indicating the weight of each of the multiple types of charged raw materials. For example, the weight information acquisition unit 113 acquires the detection results of the multiple weighing scales 12 from the raw material controller 92, and acquires type information and weight information based on the acquired detection results. The flow characteristic information acquisition unit 114 acquires flow characteristic information indicating the flow characteristic of each of the multiple types of raw materials. For example, the flow characteristic information acquisition unit 114 acquires information indicating the discharge speed when each of the multiple types of raw materials is discharged from the corresponding hopper from the raw material controller 92 as flow characteristic information. The moisture information acquisition unit 115 acquires moisture information indicating the moisture content of the multiple types of raw materials. For example, the moisture information acquisition unit 115 acquires moisture information based on the detection results of the multiple moisture meters 13 that each of the multiple hoppers 10 has from the raw material controller 92.

The environmental information acquisition unit 116 acquires environmental information about the inside of the bunker 30 and the inside of the blast furnace 2 from the furnace top controller 93. For example, the environmental information acquisition unit 116 acquires pressure information including the detection results by the pressure sensor 32 and the detection results by the pressure sensor 3 from the furnace top controller 93, and acquires differential pressure information indicating the pressure difference between the inside of the bunker 30 and the inside of the blast furnace 2 based on the acquired pressure information. The bunker information acquisition unit 117 acquires bunker identification information from the furnace top controller 93 indicating which of the multiple bunkers 30 was used to store and charge multiple types of raw materials. The bunker identification information may be represented by a categorical variable, for example.

The data collection unit 118 accumulates learning records in the data storage unit 119, including the opening degree of the gate 40, the loading speed of the raw materials from the bunker 30, and the loading conditions including the characteristics of the raw materials.

The raw material characteristics may include the above type information and weight information, may include the above flow property information, and may include the above moisture content information. The charging conditions may further include the above environmental information, and may further include the above bunker identification information.

For example, the data collection unit 118 acquires opening information from the opening information acquisition unit 111, speed information from the speed information acquisition unit 112, type information and weight information from the weight information acquisition unit 113, flow characteristics information from the flow characteristics information acquisition unit 114, moisture information from the moisture information acquisition unit 115, environmental information from the environmental information acquisition unit 116, bunker identification information from the bunker information acquisition unit 117, generates a learning record by associating all the acquired information, and accumulates the generated learning record in the data storage unit 119. In this way, when the characteristics of the raw material include weight information, the speed information may be represented by the charging time, assuming the weight of the raw material identified based on the weight information.

When performing machine learning based on learning records containing multiple types of numerical values, numerical values with a large absolute value fluctuation range tend to have a greater impact on the learning results than numerical values with a small absolute value fluctuation range. However, the size of the absolute value fluctuation range does not necessarily correlate with importance in machine learning. Therefore, the data collection unit 118 may be configured to perform standardization processing on at least one type of information to generate learning records. For example, one example of standardization processing on one type of information is for the data collection unit 118 to divide a numerical value representing one type of information by a specified fluctuation range to make it dimensionless.

The learning calculation device 200 has, as functional blocks, an offline learning unit 211, a prediction model storage unit 212, and a command generation unit 220. The offline learning unit 211 generates a prediction model that expresses the relationship between the opening, the charging rate, and the charging conditions by a multi-stage input/output relationship through machine learning based on multiple learning records stored in the data storage unit 119. The input/output relationship may be expressed by a case classification process that classifies the input and generates an output according to the case classification result, or a probabilistic case classification process, etc.

The offline learning unit 211 may generate a prediction model using a neural network based on deep learning. FIG. 3 is a schematic diagram illustrating a prediction model based on deep learning. The prediction model 310 shown in FIG. 3 is a neural network, and has an input layer 311, one or more intermediate layers 313, and an output layer 312.

The input layer 311 outputs an input vector to the next intermediate layer 313. The intermediate layer 313 transforms the input from the previous layer using an activation function and outputs it to the next layer. The output layer 312 transforms the input from the intermediate layer 313, which is the furthest from the input layer 311, using an activation function and outputs the transformation result as an output vector. In the prediction model 310, the activation functions in each intermediate layer 313 and the activation function in the output layer 312 are an example of the multi-stage input/output relationship described above. According to the process of transforming the input using an activation function, the transformation result changes depending on the input. In other words, since the transformation result changes depending on the input case, transformation using an activation function is an example of the case classification process or probabilistic case classification process described above.

The offline learning unit 211 may generate a prediction model using the random forest method or the gradient boosting method. FIG. 4 is a schematic diagram illustrating a prediction model using the random forest method or the gradient boosting method. The prediction model 320 shown in FIG. 4 includes multiple decision trees 321 and an output selection unit 322. The decision tree 321 represents the relationship between the input and the output as conditional branching in multiple stages of branches 323. The output selection unit 322 selects one of the outputs of the multiple decision trees 321 by majority vote. In the random forest method, the offline learning unit 211 generates the multiple decision trees 321 by bagging. In the gradient boosting method, the offline learning unit 211 generates the multiple decision trees 321 by boosting.

In the prediction model 320, the decision tree 321 and the output selection unit 322 are an example of the multi-stage input/output relationship described above. According to the decision tree 321, the output changes depending on the input case, so the input/output conversion by the decision tree 321 is an example of case classification processing or probabilistic case classification processing. Furthermore, the multi-stage branch 323 in the decision tree 321 is also an example of the multi-stage input/output relationship described above. According to the branch 323, the branch 323 to which the transition is made in the subsequent stage changes depending on the input case, so the conditional branch by the branch 323 is an example of case classification processing or probabilistic case classification processing.

In addition, according to the multiple regression model, the relationship between the opening, the charging speed, and the charging conditions is expressed as a single function. Therefore, the multiple regression model does not correspond to a prediction model that expresses the relationship between the opening, the charging speed, and the charging conditions as a multi-stage input/output relationship.

The offline learning unit 211 stores the generated prediction model in the prediction model storage unit 212. The command generation unit 220 generates an opening command value for the gate 40 corresponding to the target charging speed based on the charging conditions and target charging speed of the newly charged raw materials and the prediction model stored in the prediction model storage unit 212. The newly charged raw materials refer to the raw materials that are charged into the bunker 30 after the prediction model is generated. The command generation unit 220 acquires the charging conditions of the newly charged raw materials, for example, from the data collection unit 118.

The command generating unit 220 transmits the generated opening command value to the furnace top controller 93. The furnace top controller 93 makes the opening of the gate 40 correspond to the opening command value.

As described above, the offline learning unit 211 may generate a prediction model based on the multiple learning records, each of which includes type information and weight information as charging conditions, and the command generating unit 220 may generate an opening command value based on the charging conditions including type information and weight information of the newly added raw materials, the charging target speed, and the prediction model.

As described above, the offline learning unit 211 may generate a prediction model based on multiple learning records, each of which includes weight information obtained from the weighing scale 12 as the charging conditions, and the command generating unit 220 may generate an opening command value based on the charging conditions including weight information obtained from the weighing scale 12 for newly added raw materials, the target charging speed, and the prediction model.

As described above, the offline learning unit 211 may generate a prediction model based on multiple learning records, each of which includes flow characteristic information as a charging condition, and the command generating unit 220 may generate an opening command value based on the charging conditions including the flow characteristic information of the newly charged raw material, the target charging speed, and the prediction model.

As described above, the offline learning unit 211 may generate a prediction model based on multiple learning records, each of which includes flow characteristic information obtained based on the discharge rate as the charging condition, and the command generating unit 220 may generate an opening command value for newly added raw material based on the charging condition including the flow characteristic information obtained based on the discharge rate, the charging target rate, and the prediction model.

As described above, the offline learning unit 211 may generate a prediction model based on multiple learning records, each of which includes moisture information as a charging condition, and the command generating unit 220 may generate an opening command value based on the charging conditions including moisture information of the newly added raw material, the target charging speed, and the prediction model.

As described above, the offline learning unit 211 may generate a prediction model based on multiple learning records, each of which includes environmental information as a charging condition, and the command generating unit 220 may generate an opening command value based on the charging conditions including environmental information when the newly charged raw materials are stored in the bunker 30, the target charging speed, and the prediction model.

As described above, the offline learning unit 211 may generate a prediction model based on multiple learning records, each of which includes differential pressure information as a charging condition, and the command generating unit 220 may generate an opening command value based on the charging conditions including differential pressure information when newly added raw materials are stored in the bunker 30, the target charging speed, and the prediction model.

As described above, the offline learning unit 211 may generate a prediction model based on multiple learning records, each of which includes bunker identification information as a loading condition, and the command generating unit 220 may generate an opening command value based on the loading conditions including bunker identification information indicating which of the multiple bunkers 30 will be used as the bunker 30 for the newly added raw material, the loading target speed, and the prediction model.

For example, the offline learning unit 211 may generate a prediction model based on multiple learning records, each of which includes all of the type information, weight information, flow property information, moisture information, environmental information, and bunker identification information, and the command generating unit 220 may generate an opening command value based on the charging conditions, which include all of the type information, weight information, flow property information, moisture information, environmental information, and bunker identification information of the newly input raw material, the target charging speed, and the prediction model.

The offline learning unit 211 may generate a prediction model to output the opening degree according to the input of the charging conditions and the charging speed. The command generating unit 220 may input the charging conditions and the charging speed of the newly added raw materials to the prediction model to calculate the opening degree command value.

The offline learning unit 211 may generate a prediction model to output a charging speed according to input of charging conditions and an opening degree. The command generating unit 220 may input the charging conditions of newly added raw materials and an opening degree command value into the prediction model to calculate a charging speed, and may calculate an opening degree command value corresponding to a charging target speed by repeating changing the opening degree command value. For example, as shown in FIG. 5, the command generating unit 220 may have an initial value generating unit 221, a simulation unit 222, and a command value changing unit 223.

The initial value generating unit 221 generates an initial value for the opening command value. The simulation unit 222 inputs the charging conditions for the newly charged raw materials and the opening command value into a prediction model to calculate the charging speed. Hereinafter, the charging speed calculated by the simulation unit 222 is referred to as the "estimated speed." The command value changing unit 223 changes the opening command value.

For example, the command value change unit 223 calculates the deviation between the charging target speed and the estimated speed, calculates a correction value for the opening command value so as to reduce the deviation, and adds the correction value to the opening command value to change the opening command value. The command value change unit 223 may calculate the correction value by adding a sign in the direction of reducing the deviation to a predetermined absolute value for one step. For example, the command value change unit 223 may add a positive sign to the absolute value when the deviation is a positive value (when the estimated speed is smaller than the charging target speed), and may add a negative sign to the absolute value when the deviation is a negative value. The command value change unit 223 may calculate the correction value by performing a proportional operation, a proportional-integral operation, or a proportional-integral-differential operation on the deviation.

The simulation unit 222 and the command value modification unit 223 calculate the estimated speed, calculate the deviation, and modify the opening command value to reduce the deviation, starting with the opening command value as the initial value, and repeating this process until the deviation becomes equal to or less than a predetermined threshold value. The command value modification unit 223 transmits the opening command value at the point in time when the deviation becomes equal to or less than the threshold value to the furnace top controller 93 as the result of generating the opening command value.

By setting the above initial value close to the opening command value that is ultimately sent to the furnace top controller 93, the number of times that the simulation unit 222 and the command value change unit 223 repeat the calculation can be reduced. Therefore, the learning calculation device 200 may further include a profile generation unit 231 and a profile storage unit 232.

The profile generating unit 231 repeatedly inputs the charging conditions and the opening command value into the prediction model to calculate the charging speed, and changes at least one of the charging conditions and the opening command value, to generate a profile that represents the relationship between the charging conditions, the charging speed, and the opening command value, and stores the profile in the profile storage unit 232. For example, the profile generating unit 231 generates a profile that represents the relationship between the charging conditions, the charging speed, and the opening command value as a one-stage input/output relationship. For example, the profile generating unit 231 may generate the profile as a function, or may generate it as a discrete reference table. The profile generating unit 231 may generate a profile that outputs an opening command value according to the input of the charging conditions and the charging speed.

In a configuration in which the learning calculation device 200 further includes a profile generation unit 231 and a profile storage unit 232, the initial value generation unit 221 may calculate an initial value of the opening command value based on the profile stored in the profile storage unit 232 and the charging target speed. This makes it possible to reduce the number of times that the simulation unit 222 and the command value change unit 223 repeat the calculation, since the initial value is set to a value close to the opening command value that is ultimately sent to the furnace top controller 93.

FIG. 6 is a block diagram illustrating an example of the hardware configuration of the server device 100 and the learning calculation device 200. As shown in FIG. 6, the server device 100 has a circuit 190. The circuit 190 has a processor 191, a memory 192, a storage 193, and a communication port 194. The processor 191 includes one or more calculation elements, and the memory 192 includes one or more memory elements such as a random access memory. The storage 193 is a non-volatile storage device. Specific examples of the storage 193 include a hard disk, a flash memory, a read-only memory, and the like. The storage 193 may be a portable storage medium such as a USB memory, an optical disk, or a magnetic disk.

Storage 193 stores a program for causing server device 100 to accumulate the learning records. For example, storage 193 stores a program for causing server device 100 to configure each of the functional blocks described above.

Memory 192 temporarily stores programs and the like loaded from storage 193. Processor 191 executes the programs loaded into memory 192 while temporarily storing the results of calculations in memory 192, and configures each of the functional blocks described above in server device 100. Communication port 194 communicates via network line 94 in response to a request from processor 191.

The learning calculation device 200 has a circuit 290. The circuit 290 has a processor 291, a memory 292, a storage 293, and a communication port 294. The processor 291 includes one or more calculation elements, and the memory 292 includes one or more memory elements such as a random access memory. The storage 293 is a non-volatile storage device. Specific examples of the storage 293 include a hard disk, a flash memory, a read-only memory, etc. The storage 293 may be a portable storage medium such as a USB memory, an optical disk, or a magnetic disk.

Storage 293 stores a program for causing the learning calculation device 200 to generate the above-mentioned prediction model based on a plurality of learning records stored in storage 193 of the server device 100, and to generate an opening command value corresponding to the target charging speed based on the charging conditions and target charging speed of newly added raw materials and the prediction model. For example, storage 293 stores a program for causing the learning calculation device 200 to configure each of the above-mentioned functional blocks.

The memory 292 temporarily stores programs and the like loaded from the storage 293. The processor 291 executes the programs loaded into the memory 292 while temporarily storing the calculation results in the memory 292, and configures each of the above-mentioned functional blocks in the learning calculation device 200. The communication port 294 communicates via the network line 94 in response to a request from the processor 291. The above hardware configuration is merely an example and can be changed as appropriate. For example, at least a part of the server device 100 may be incorporated into the learning calculation device 200. Furthermore, at least a part of the server device 100 and the learning calculation device 200 may be incorporated into the furnace top controller 93 or the raw material controller 92.

[Control Procedure]
The control procedure executed by the raw material charging control device 90 is illustrated. This control procedure includes a procedure for generating an opening command value including storing the learning records in the data storage unit 119, generating the prediction model based on the multiple learning records stored in the data storage unit 119, generating an opening command value for the gate 40 corresponding to the charging target speed based on the charging conditions and the charging target speed of the newly charged raw materials and the prediction model, and making the opening of the gate 40 correspond to the opening command value. Below, this control procedure is illustrated by dividing it into a record storage procedure, an offline learning procedure, and an opening control procedure.

(Record Storage Procedure)
As shown in Fig. 7, the server device 100 executes steps S01, S02, S03, and S04. In step S01, the loading of raw materials from the bunker 30 is executed, and the data collection unit 118 waits for various information to be acquired by the opening information acquisition unit 111, the speed information acquisition unit 112, the weight information acquisition unit 113, the flow property information acquisition unit 114, the moisture information acquisition unit 115, the environmental information acquisition unit 116, and the bunker information acquisition unit 117. In step S02, the data collection unit 118 acquires opening information from the opening information acquisition unit 111, speed information from the speed information acquisition unit 112, type information and weight information from the weight information acquisition unit 113, flow property information from the flow property information acquisition unit 114, moisture information from the moisture information acquisition unit 115, environmental information from the environmental information acquisition unit 116, and bunker identification information from the bunker information acquisition unit 117. In step S03, the data collection unit 118 performs standardization processing on at least one type of information. In step S04, the data collection unit 118 generates learning records by associating all information including the standardized information, and stores the generated learning records in the data storage unit 119. After that, the server device 100 returns the processing to step S01. The server device 100 repeatedly executes the above processing.

(Offline learning procedure)
This procedure is executed after a sufficient number of learning records for machine learning are accumulated in the data storage unit 119. As shown in Fig. 8, the learning calculation device 200 first executes steps S11 and S12. In step S11, the offline learning unit 211 generates a prediction model that represents the relationship between the opening degree, the charging rate, and the charging conditions by a multi-stage input/output relationship through machine learning based on the multiple learning records accumulated in the data storage unit 119. In step S12, the offline learning unit 211 stores the generated prediction model in the prediction model storage unit 212.

Next, the learning calculation device 200 executes steps S13 and S14. In step S13, the profile generation unit 231 repeatedly inputs the charging conditions and the opening command value into the prediction model to calculate the charging rate, and changes at least one of the charging conditions and the opening command value, to generate a profile that represents the relationship between the charging conditions, the charging rate, and the opening command value. In step S14, the profile storage unit 232 stores the generated profile in the profile storage unit 232. This completes the offline learning procedure.

(Opening control procedure)
As shown in Fig. 9, the learning calculation device 200 first executes steps S21, S22, and S23. In step S21, the data collection unit 118 acquires type information and weight information from the weight information acquisition unit 113, acquires flow property information from the flow property information acquisition unit 114, acquires moisture information from the moisture information acquisition unit 115, acquires environmental information from the environmental information acquisition unit 116, and acquires bunker identification information from the bunker information acquisition unit 117 for the newly charged raw material. In step S22, the data collection unit 118 performs standardization processing similar to step S03 on the information acquired in step S21. In step S23, the initial value generation unit 221 calculates an initial value of the opening command value based on the charging target speed and the profile stored in the profile storage unit 232.

Next, the learning calculation device 200 executes steps S24, S25, and S26. In step S24, the simulation unit 222 inputs the charging conditions for the newly charged raw materials and the opening command value into the prediction model to calculate the charging speed (the above-mentioned estimated speed). In step S25, the command value change unit 223 calculates the deviation between the charging target speed and the estimated speed. In step S26, the command value change unit 223 checks whether the deviation is equal to or less than a threshold value.

If it is determined in step S26 that the deviation exceeds the threshold value, the learning calculation device 200 executes step S27. In step S27, the command value change unit 223 changes the opening command value so as to reduce the deviation. Thereafter, the learning calculation device 200 returns the process to step S24. Thereafter, the calculation of the estimated speed, the calculation of the deviation, and the change of the opening command value so as to reduce the deviation are repeated until the deviation becomes equal to or less than the threshold value.

If it is determined in step S26 that the deviation is equal to or less than the threshold value, the learning calculation device 200 executes steps S31 and S32. In step S31, the command value change unit 223 transmits the opening command value at the time when the deviation is equal to or less than the threshold value to the top controller 93 as the opening command value generation result. In step S32, the top controller 93 makes the opening of the gate 40 correspond to the opening command value. This completes the opening control procedure.

〔summary〕
The above-described embodiment includes the following configurations.
(1) A raw material charging control device 90 for a blast furnace 2, comprising: a data collection unit 118 that accumulates learning records in a data storage unit, the learning records including the opening of a gate 40 through which raw materials are charged from a bunker 30 that accommodates raw materials at the top of the blast furnace 2, the charging rate of the raw materials from the bunker 30, and charging conditions including characteristics of the raw materials; an offline learning unit 211 that generates a prediction model that represents the relationship between the opening, the charging rate, and the charging conditions by a multi-stage input-output relationship through machine learning based on the multiple learning records accumulated in the data storage unit; a command generation unit 220 that generates an opening command value for the gate 40 corresponding to the target charging speed based on the charging conditions and target charging speed of newly charged raw materials and the prediction model; and a gate 40 controller that causes the opening to correspond to the opening command value.
By using a prediction model that expresses the relationship between the opening, the charging speed, and the charging conditions as a multi-stage input/output relationship, it is possible to generate an opening command value corresponding to the target charging speed with higher accuracy. By making the opening correspond to the opening command value generated with high accuracy, it is possible to suppress the error of the charging speed relative to the target charging speed. Therefore, it is effective in controlling the charging speed of the raw materials from the bunker 30 into the blast furnace 2 with high accuracy.

(2) The raw materials include multiple types of raw materials, the offline learning unit 211 generates a prediction model based on multiple learning records each including, as charging conditions, type information representing each of the multiple types of raw materials and weight information representing each of the multiple types of raw materials, and the command generating unit 220 generates an opening command value based on the charging conditions including the type information and weight information of the newly charged raw materials.
The effect of the raw material composition on the relationship between the opening and the charging rate can be reflected in the prediction model, thereby further improving the reliability of the prediction model.

(3) A weighing scale 12 is provided to detect the weight of each of multiple types of raw materials before the raw materials are charged into the bunker 30, the raw material charging control device 90 further includes a weight information acquisition unit that acquires weight information based on the detection results by the weighing scale 12, the offline learning unit 211 generates a predictive model based on multiple learning records each including weight information acquired from the weighing scale 12 as charging conditions, and the command generation unit 220 generates an opening command value based on the charging conditions including the weight information acquired from the weighing scale 12 for the newly charged raw materials.
The weight information of the raw material before it is put into the bunker 30 does not include errors caused by the environment inside the bunker 30. This improves the reliability of the weight information in each learning record. This further improves the reliability of the prediction model.

(4) A raw material charging control device 90 for a blast furnace 2 described in (2) or (3), in which the offline learning unit 211 generates a prediction model based on a plurality of learning records, each of which includes the flow characteristics of a plurality of types of raw materials as charging conditions, and the command generating unit 220 generates an opening command value based on the charging conditions including the flow characteristics of the newly charged raw materials.
The reliability of the prediction model can be further improved by including the flow characteristics of each component in the charging conditions in addition to information describing the raw material composition.

(5) A raw material charging control device 90 for a blast furnace 2 described in (4) is provided around the blast furnace 2 with a plurality of hoppers 10 each storing a plurality of types of raw materials, and a conveyor 20 for transporting the plurality of types of raw materials discharged from the plurality of hoppers 10 to a bunker 30. The raw material charging control device 90 further includes a flow characteristic acquisition unit that acquires flow characteristics based on the discharge speed at which each of the plurality of types of raw materials is discharged from the corresponding hopper 10. The offline learning unit 211 generates a prediction model based on a plurality of learning records each including, as a charging condition, the flow characteristics acquired based on the discharge speed. The command generation unit 220 generates an opening command value for newly charged raw materials based on the charging conditions including the flow characteristics acquired based on the discharge speed.
The reliability of the prediction model can be further improved by including in the charging conditions the flow characteristics actually measured immediately before charging into the bunker 30.

(6) A raw material charging control device 90 for a blast furnace 2 described in any one of (2) to (5), in which the offline learning unit 211 generates a prediction model based on a plurality of learning records, each of which includes moisture content information representing the moisture content of each of a plurality of types of raw materials as a charging condition, and the command generating unit 220 generates an opening command value based on the charging conditions including the moisture content information of newly charged raw materials.
The reliability of the prediction model can be further improved by including information on the moisture content of each component in the charging conditions in addition to information representing the raw material composition.

(7) A raw material charging control device 90 for a blast furnace 2 described in any one of (1) to (6), in which the offline learning unit 211 generates a prediction model based on a plurality of learning records each including environmental information inside the bunker 30 and inside the blast furnace 2 as charging conditions, and the command generating unit 220 generates an opening command value based on the charging conditions including environmental information when the newly charged raw materials are contained in the bunker 30.
By including environmental information inside the bunker 30 and inside the blast furnace 2 in the extraction conditions, the reliability of the prediction model can be further improved.

(8) The offline learning unit 211 generates a prediction model based on a plurality of learning records, each of which includes information on the pressure difference between the inside of the bunker 30 and the inside of the blast furnace 2 as a charging condition, and the command generating unit 220 generates an opening command value based on the charging condition including information on the pressure difference when the newly charged raw materials are contained in the bunker 30.
By including differential pressure information in the extraction conditions, the reliability of the prediction model can be further improved.

(9) A raw material charging control device 90 for a blast furnace 2 described in any one of (1) to (8), in which a plurality of bunkers 30 are provided at the upper part of the blast furnace 2, an offline learning unit 211 generates a predictive model based on a plurality of learning records, each of which includes bunker 30 identification information indicating which of the plurality of bunkers 30 has been used as the bunker 30 as a charging condition, and a command generating unit 220 generates an opening command value based on the charging conditions including bunker 30 identification information indicating which of the plurality of bunkers 30 will be used as the bunker 30 for newly charged raw materials.
The effect of individual differences between the bunkers 30 can be reflected in the prediction model, thereby further improving the reliability of the prediction model.

(10) A raw material charging control device 90 for a blast furnace 2 described in any one of (1) to (9), in which an offline learning unit 211 generates a prediction model to output a charging speed according to inputs of charging conditions and an opening degree, and a command generating unit 220 inputs the charging conditions of newly added raw materials and an opening degree command value into the prediction model to calculate a charging speed and changes the opening degree command value, thereby calculating an opening degree command value corresponding to a target charging speed.
The search range of the opening command value corresponding to the charging target speed can be freely adjusted, which prevents the prediction model from generating a low-reliability opening command value in a range where the reliability of the opening command value is insufficient, and allows the opening of the gate 40 to be controlled with higher reliability.

(11) The raw material charging control device 90 for the blast furnace 2 described in (10) further includes a profile generating unit 231 that generates a profile representing the relationship between the charging conditions, the charging rate, and the opening command value by repeating inputting the charging conditions and the opening command value into the prediction model to calculate the charging rate and changing at least one of the charging conditions and the opening command value, and an initial value calculating unit that calculates an initial value of the opening command value based on the profile and the target charging rate, wherein the command generating unit 220 starts the repetition of inputting the charging conditions of newly charged raw materials and the opening command value into the prediction model to calculate the charging rate and changing the opening command value of the gate 40, using the opening command value as an initial value.
The time required to search for the opening command value corresponding to the target charging speed can be shortened.

(12) The raw material charging control device 90 for a blast furnace 2 according to any one of (1) to (11), wherein the prediction model is a neural network, and the offline learning unit 211 generates the prediction model by deep learning.
The reliability of the prediction model can be further improved.

(13) The raw material charging control device 90 for a blast furnace 2 according to any one of (1) to (11), wherein the offline learning unit 211 generates a prediction model by a gradient boosting method.
The reliability of the prediction model can be further improved.

(14) The raw material charging control device 90 for a blast furnace 2 according to any one of (1) to (11), wherein the offline learning unit 211 generates a predictive model by a random forest method.
The reliability of the prediction model can be further improved.

(15) A method for generating an opening command value, comprising: storing learning records in a data storage unit, the learning records including the opening of a gate 40 for charging raw materials from a bunker 30 that stores raw materials in the upper part of a blast furnace 2, the charging rate of the raw materials from the bunker 30, and charging conditions including raw material characteristics; generating a prediction model that represents the relationship between the opening, the charging rate, and the charging conditions using a multi-stage input/output relationship based on the multiple learning records stored in the data storage unit; and generating an opening command value for the gate 40 that corresponds to the target charging rate based on the charging conditions and target charging rate of newly added raw materials and the prediction model.

(16) A program for causing an apparatus to execute the following: accumulating learning records in a data storage unit, including the opening of a gate 40 through which raw materials are charged from a bunker 30 that stores raw materials in the upper part of a blast furnace 2, the charging rate of the raw materials from the bunker 30, and charging conditions including the characteristics of the raw materials; generating a prediction model that represents the relationship between the opening, the charging rate, and the charging conditions using a multi-stage input/output relationship based on the multiple learning records stored in the data storage unit; and generating an opening command value for the gate 40 that corresponds to the target charging rate based on the charging conditions and target charging rate of newly added raw materials and the prediction model.

2... blast furnace, 90... raw material charging control device, 30... bunker, 40... gate, 10... hopper, 12... weighing scale, 20... conveyor, 118... data collection unit, 211... offline learning unit, 220... command generation unit, 221... initial value generation unit, 231... profile generation unit.

Claims

The opening degree of a gate for charging raw materials from a bunker that stores raw materials at the top of the blast furnace;
The charging rate of the raw material from the bunker; and
Charging conditions including raw material characteristics;
A data collection unit that accumulates learning records including the above in a data storage unit;
An offline learning unit that generates a prediction model that represents the relationship between the opening degree, the charging rate, and the charging conditions by a multi-stage input/output relationship through machine learning based on a plurality of learning records stored in the data storage unit;
A command generating unit that generates an opening command value of the gate corresponding to the target charging speed based on the charging conditions and the target charging speed of the newly charged raw material and the prediction model;
a furnace top controller that causes the opening to correspond to an opening command value;
A raw material charging control device for a blast furnace.
The raw material includes a plurality of raw materials,
The offline learning unit generates the prediction model based on the plurality of learning records, each of which includes type information representing the type of each of the plurality of types of raw materials and weight information representing the weight of each of the plurality of types of raw materials, as the charging conditions;
The command generating unit generates the opening command value based on the charging conditions including the type information and the weight information of the newly charged raw material.
The raw material charging control device for a blast furnace according to claim 1.
a weight scale is provided to detect the weight of each of the plurality of types of raw materials before the raw materials are put into the bunker;
The raw material charging control device further includes a weight information acquisition unit that acquires the weight information based on the detection result by the weighing scale,
The offline learning unit generates the prediction model based on the plurality of learning records each including the weight information acquired from the weighing scale as the charging condition,
The command generating unit generates the opening command value based on the charging conditions including the weight information acquired from the weighing scale for the newly charged raw material.
The raw material charging control device for a blast furnace according to claim 2.
The offline learning unit generates the prediction model based on the plurality of learning records, each of which includes flow characteristics of the plurality of types of raw materials as the charging conditions, and
The command generating unit generates the opening command value based on the charging conditions including the flow characteristics of the newly charged raw material.
The raw material charging control device for a blast furnace according to claim 2.
A plurality of hoppers for accommodating the plurality of types of raw materials, respectively, and a conveyor for transporting the plurality of types of raw materials discharged from the plurality of hoppers to the bunker are provided around the blast furnace,
The raw material charging control device further includes a flow characteristic acquisition unit that acquires the flow characteristics based on the discharge speed when each of the plurality of types of raw materials is discharged from the corresponding hopper,
The offline learning unit generates the prediction model based on the plurality of learning records, each of which includes the flow characteristics acquired based on the discharge rate as the charging condition;
The command generating unit generates the opening command value based on the charging conditions including the flow characteristics acquired based on the discharge rate for the newly charged raw material.
The raw material charging control device for a blast furnace according to claim 4.
the offline learning unit generates the prediction model based on the plurality of learning records, each of which includes moisture content information representing a moisture content of each of the plurality of types of raw materials as the charging conditions;
The command generating unit generates the opening command value based on the charging conditions including the moisture information of the newly charged raw material.
The raw material charging control device for a blast furnace according to claim 2.
The offline learning unit generates the prediction model based on the plurality of learning records each including environmental information in the bunker and in the blast furnace as the charging conditions,
The command generating unit generates the opening command value based on the charging conditions including the environmental information in a state in which the newly charged raw material is stored in the bunker.
The raw material charging control device for a blast furnace according to any one of claims 1 to 6.
The offline learning unit generates the prediction model based on the plurality of learning records each including information on a differential pressure between the inside of the bunker and the inside of the blast furnace as the charging condition,
The command generating unit generates the opening command value based on the charging conditions including information on the differential pressure in a state in which the newly charged raw material is stored in the bunker.
The raw material charging control device for a blast furnace according to claim 7.
A plurality of bunkers are provided at the upper portion of the blast furnace,
The offline learning unit generates the prediction model based on the plurality of learning records, each of which includes bunker identification information indicating which of the plurality of bunkers was used as the bunker as the charging condition,
The command generating unit generates the opening command value based on the charging conditions including the bunker identification information indicating which of the plurality of bunkers is to be used as the bunker for the newly charged raw material.
The raw material charging control device for a blast furnace according to any one of claims 1 to 6.
The offline learning unit generates the prediction model so as to output the charging rate in response to input of the charging conditions and the opening degree,
The command generating unit calculates the charging speed by inputting the charging conditions of the newly charged raw material and the opening command value into the prediction model, and repeats changing the opening command value to calculate the opening command value corresponding to the charging target speed.
The raw material charging control device for a blast furnace according to any one of claims 1 to 6.
A profile generating unit that generates a profile representing the relationship between the charging condition, the charging rate, and the opening command value by repeatedly inputting the charging condition and the opening command value into the prediction model to calculate the charging rate and changing at least one of the charging condition and the opening command value;
Further provided is an initial value calculation unit that calculates an initial value of the opening command value based on the profile and the charging target speed;
The command generating unit inputs the charging conditions of the newly charged raw material and the opening command value into the prediction model to calculate the charging speed and changes the gate opening command value, and starts repeating the process of changing the gate opening command value with the opening command value as an initial value.
The raw material charging control device for a blast furnace according to claim 10.
the predictive model is a neural network;
The offline learning unit generates the prediction model by deep learning.
The raw material charging control device for a blast furnace according to any one of claims 1 to 6.
The offline learning unit generates the prediction model by a gradient boosting method.
The raw material charging control device for a blast furnace according to any one of claims 1 to 6.
The offline learning unit generates the prediction model by a random forest method.
The raw material charging control device for a blast furnace according to any one of claims 1 to 6.
The opening degree of a gate for charging raw materials from a bunker that stores raw materials at the top of the blast furnace;
The charging rate of the raw material from the bunker; and
Charging conditions including raw material characteristics;
storing a learning record including the learning record in a data storage unit;
Generating a prediction model that represents the relationship between the opening degree, the charging speed, and the charging conditions by a multi-stage input/output relationship based on a plurality of learning records stored in the data storage unit;
Generating an opening command value of the gate corresponding to the target charging speed based on the charging conditions and the target charging speed of the newly charged raw material and the prediction model;
A method for generating an opening command value, comprising:
The opening degree of a gate for charging raw materials from a bunker that stores raw materials at the top of the blast furnace;
The charging rate of the raw material from the bunker; and
Charging conditions including raw material characteristics;
accumulating a learning record including the above in a data storage unit;
Generating a prediction model that represents the relationship between the opening degree, the charging speed, and the charging conditions by a multi-stage input/output relationship based on a plurality of learning records stored in the data storage unit;
Generating an opening command value of the gate corresponding to the target charging speed based on the charging conditions and the target charging speed of the newly charged raw material and the prediction model;
A program for causing a device to execute the above.