TW202211091A - Blast furnace condition determination device, operation method of blast furnace, and manufacturing method for molten iron - Google Patents

Abstract

Provided is a blast furnace condition determination device which automatically determines and classifies a disturbance state in temperature distribution of a blast furnace. Also provided are: an operation method of the blast furnace, capable of achieving an improved operation automation rate by eliminating a disturbance in temperature distribution of the blast furnace on the basis of the determination and classification results of the blast furnace condition determination device; and a corresponding manufacturing method for molten iron. The blast furnace condition determination device (1) is for determining a furnace condition of the blast furnace, said device comprising: a temperature distribution measurement unit (2) for measuring a temperature distribution directly above a raw material charging surface in the blast furnace; a storage unit (3) for storing a learning model learned so as to output multiple classes regarding temperature distribution patterns on the basis of data on the measured temperature distribution; and a determination unit (5) for determining, by using the stored learning model, to which of the multiple classes the temperature distribution measured by the temperature distribution measurement unit corresponds.

Description

Blast furnace state determination device, blast furnace operation method and molten iron manufacturing method

The present invention relates to a blast furnace state determination device, a blast furnace operation method, and a molten iron manufacturing method.

In recent years, blast furnace operations have been performed at low coke ratio (CR) and high pulverized coal injection (PCI) flow rates. Under such an operating environment, it is important to grasp the furnace conditions of the blast furnace as early as possible and accurately. Since the blast furnace has a cylindrical shape, if there is a deviation in the circumferential direction of the charging of raw materials, deviation in the circumferential direction of the reaction, etc., there is a possibility that the tapping state will become uneven in the circumferential direction, and the working state may be deteriorated. Therefore, it is important to detect the deviation in the circumferential direction early and accurately.

As one of the methods of detecting the deviation in the circumferential direction, it is a method of measuring the temperature distribution just above the raw material charging surface. It is known that when at least one of the variation in the charging of the raw materials and the variation in the reaction occurs, the variation in the temperature distribution occurs. Conventionally, the temperature distribution just above the raw material charging surface was often measured by a thermocouple inserted into the furnace. In order to avoid interfering with the work of loading raw materials, thermocouples are usually inserted from four directions, and the temperature of plural points in the radial direction of each thermocouple is measured. However, since it is limited to four directions, it is difficult to grasp the overall temperature distribution. Most of the thermocouples with durability have a longer response time. Therefore, it is difficult to grasp the temperature change of the gas in response to the charging of the raw material by the high-speed rotating raw material chute.

Here, due to recent technological improvements, it has become possible to collect the temperature distribution just above the raw material charging surface at a relatively high sampling cycle. Thereby, it becomes possible to visualize the temperature distribution just above the raw material charging surface in real time, and to grasp the abnormality such as the deviation of the temperature distribution. However, it is difficult to always send people to monitor the visual information such as the temperature distribution screen. Also, when human judgment is abnormal, the standard of judgment belongs to the individual, and it is difficult to always make appropriate judgments. In view of this, Patent Document 1 discloses a blast furnace state determination device that performs appropriate determination by indexing the nonuniformity of temperature. [Prior Art Literature] [Patent Literature]

Patent Document 1: Japanese Patent Laid-Open No. 2018-165399

[Problems to be Solved by Invention]

Here, when it is determined that the state of the blast furnace is abnormal, it is appropriate to control the operation of the blast furnace in order to eliminate the abnormality. However, the control content of the blast furnace for eliminating the abnormality varies depending on the disordered state of the temperature distribution. Therefore, there is a need for a technology that can determine and classify the disordered state of temperature distribution without human intervention in addition to detecting anomalies.

An object of the present invention to solve the above problems is to provide a blast furnace furnace condition state determination device that can automatically determine and classify the disturbance state of the temperature distribution of the blast furnace. Another object of the present invention is to provide a blast furnace operation method and a molten iron manufacturing method, which can eliminate the disturbance of the temperature distribution of the blast furnace according to the results of the determination and classification of the blast furnace state determination device, and reduce the automation ratio of the operation. improve. [Technical means to solve problems]

A blast furnace state determination device according to an embodiment of the present invention is a blast furnace state determination device for determining the furnace state of a blast furnace, and includes a temperature distribution measurement unit, a storage unit, and a determination unit; the temperature distribution measurement unit is a Measure the temperature distribution just above the raw material charging surface in the blast furnace; the storage unit stores the learning after learning by outputting a plurality of patterns for the temperature distribution based on the measured data of the temperature distribution The model; the determining unit uses the stored learning model to determine which of the plurality of types the temperature distribution measured by the temperature distribution measuring unit is.

The blast furnace operation method according to one embodiment of the present invention changes the operation conditions according to the blast furnace condition state determined by the blast furnace condition state determination device.

The manufacturing method of molten iron which concerns on one Embodiment of this invention manufactures molten iron using the blast furnace operated by the operation method of the said blast furnace. [Effect of invention]

According to the present invention, it is possible to provide a blast furnace state determination device capable of automatically determining and classifying the disordered state of the temperature distribution of the blast furnace. Furthermore, according to the present invention, it is possible to provide a blast furnace operation method and a molten iron manufacturing method, which can eliminate the disturbance of the temperature distribution of the blast furnace according to the results of the determination and classification of the blast furnace state determination device, thereby reducing the automation ratio of the operation. improve.

Hereinafter, one embodiment of the blast furnace furnace state determination device 1 for determining the furnace state state of a blast furnace, a blast furnace operation method, and a molten iron manufacturing method will be described using the present invention.

(Configuration of the device) As shown in FIG. 1 , the blast furnace state determination device 1 of the present embodiment includes a temperature distribution measurement unit 2 , a storage unit 3 , a determination unit 5 , and a display unit 6 . Furthermore, the blast furnace state determination device 1 is configured to be able to communicate with the learning model generating device 10 , thereby performing input and output of learning models and learning materials described later between the learning model generating device 10 and the learning model generating device 10 . In the present embodiment, the learning model generating device 10 includes a learning model generating unit 4 .

The temperature distribution measuring unit 2 measures the temperature distribution in the space immediately above the raw material charging surface, which is charged from the top of the blast furnace inside a blast furnace for producing pig iron using iron ore as a raw material. The uppermost surface of the layer composed of the raw material. The temperature distribution measuring unit 2 may be various sensors or devices that measure the temperature distribution just above the charging surface of the raw material in the blast furnace at high speed (for example, within 10 seconds). In the present embodiment, the temperature distribution measuring unit 2 is a temperature measuring device that utilizes the temperature dependence of the speed of sound. The temperature distribution measuring unit 2 includes, for example, eight transceivers provided on the circumference of the blast furnace roof, transmits and receives sound waves toward the raw material loading surface, and measures the temperature based on the time from the transmission of the sound waves to the reception of the sound waves.

The storage unit 3 stores a learning model that has been learned so as to output a plurality of patterns for the temperature distribution based on the measured temperature distribution data. The learning model is generated (learned) by the learning model generating unit 4, and is stored in the storage unit 3 after the learning is completed. Further, the storage unit 3 stores data of the temperature distribution measured by the temperature distribution measuring unit 2 . The temperature distribution immediately above the raw material charging surface in the blast furnace is measured by the temperature distribution measuring unit 2 , and the storage unit 3 stores the data of the temperature distribution. When the learning model generating unit 4 generates the learning model, the data of the temperature distribution stored in the storage unit 3 is used as the learning data. When the determination unit 5 determines the state of the blast furnace, the determination unit 5 acquires the data of the current temperature distribution from the temperature distribution measuring unit 2 through the storage unit 3 . When the determination unit 5 determines the state of the blast furnace, the learning model stored in the storage unit 3 is acquired by the determination unit 5 .

The learning model generation unit 4 generates a plurality of types of learning models for outputting patterns of temperature distribution based on the temperature distribution data stored in the storage unit 3 . Type is a collection or group that categorizes patterns of temperature distribution. Details of the type used in this embodiment will be described later.

The determination part 5 uses the learning model (learning completed model) stored in the storage part 3 to determine the furnace state of the blast furnace. In this embodiment, in order to detect an abnormality in the furnace state of the blast furnace, the determination unit 5 determines which of a plurality of types the temperature distribution measured by the temperature distribution measurement unit 2 is. In the present embodiment, the plurality of types include at least two types in which patterns of abnormal temperature distribution are classified. That is, when the determination part 5 acquires the data of an abnormal temperature distribution, it does not simply determine an abnormality, but determines which one of a plurality of types is classified according to a pattern. Here, the pattern of abnormal temperature distribution in the present embodiment includes at least a pattern of temperature distribution that may occur due to variations in raw material charging and variations in reaction in the circumferential direction of the blast furnace.

The display unit 6 presents the determination result of the determination unit 5 to the operator through an image. The display unit 6 may further have a function of outputting a sound, and may send an alarm to the operator together with an image. The operator is, for example, an operator who works on a blast furnace. The furnace state of the blast furnace determined by the blast furnace state determination device 1 can be used to change the operation conditions in the blast furnace operation method. The operator who knows that the furnace condition is abnormal based on the image displayed on the display unit 6 changes the working conditions of the blast furnace in order to suppress the deviation of the charging of raw materials and the deviation of the reaction in the circumferential direction of the blast furnace. Changes in blast furnace operating conditions may include, for example, changes in coke ratio. Changes in blast furnace operating conditions may include, for example, changes in air flow rates. As another example, when the charge control mode for charging the raw materials in the circumferential direction of the blast furnace is set to a plurality of modes in advance, the change of the operating conditions of the blast furnace may include the change of the charge control mode. Moreover, the operation of the blast furnace described above can be performed as a part of the production method for producing molten iron. In a blast furnace, iron ore as a raw material is melted and reduced to become pig iron, and iron is tapped in the state of molten iron. In the molten iron after tapping from the blast furnace, impurities such as sulfur and phosphorus are removed by a molten iron preliminary treatment process. Further, it is refined in a converter to remove carbon.

The storage unit 3 , the determination unit 5 , and the display unit 6 of the blast furnace state determination device 1 can be realized by a computer that acquires data of the temperature distribution measured from the temperature distribution measurement unit 2 . The computer includes, for example, a memory (storage device), a CPU (processing device), a hard disk drive (HDD), and a display control unit that controls a display device such as a display. An operating system (OS) and applications for implementing various processes may be stored in a hard disk drive and loaded into memory from the hard disk drive when executed by the CPU. As necessary, the CPU controls the display control unit to cause the display to display necessary images. As for the data being processed, it is stored in the memory and, if necessary, in the HDD. Various functions are realized by organically cooperating hardware such as CPU and memory with the OS and necessary applications. The storage unit 3 can be realized by, for example, a memory and a hard disk drive. The determination unit 5 can be realized by, for example, a CPU. Further, the display unit 6 can be realized by, for example, a display control unit and a display.

The learning model generating unit 4 of the learning model generating device 10 can be realized by a computer different from the blast furnace state determination device 1 . The learning model generation unit 4 can be realized by, for example, a CPU. In the present embodiment, the learned model is stored in the storage unit 3 as described above. As another example, the learned model may be stored in the memory or the hard disk drive of the learned model generation device 10 . In this case, when the determination unit 5 is to determine the furnace state of the blast furnace, the learning model can be read by accessing the memory or hard disk of the learning model generating device 10 . In the present embodiment, the learning data is stored in the storage unit 3 as described above, but as another example, it may be stored in the memory or the hard disk drive of the learning model generating apparatus 10 . In this case, the learning model generating unit 4 can access the memory or hard disk of the learning model generating device 10 to read the learning data.

Here, the configuration of the blast furnace state determination device 1 shown in FIG. 1 is merely an example, and some of the constituent elements may not be included. Further, the blast furnace state determination device 1 may include other components. For example, the blast furnace state determination device 1 may be configured by omitting the display unit 6 . In this case, the blast furnace state determination device 1 may include a communication unit for outputting the determination result of the determination unit 5 to the operator's terminal device via a network.

(Blast Furnace Condition State Judgment Method) FIG. 2 is a schematic flow chart showing a method for determining the state of a blast furnace state executed by the blast furnace state determination device 1 . The blast furnace state determination method is divided into the steps of learning, determination and display. In FIG. 2 , on-line indicates processing performed as a part of blast furnace operation. In contrast, off-line refers to processing performed separately from the blast furnace operation.

The blast furnace state determination device 1 allows the learning model generating device 10 to generate a learning model in an offline manner. For example, using a command from the blast furnace condition determination device 1 as a trigger, the learning model generation unit 4 first acquires a selected person from the temperature distribution data stored in the storage unit 3 as labeled image data. The learning model generating unit 4 generates a learning model using the label-attached image data (step S1). Details of the learning model and learning will be described later. As another example, the learning model generating device 10 may start generating the learning model without waiting for an instruction from the blast furnace condition determination device 1 . That is, as long as the learned model is stored in the storage unit 3 before the determination unit 5 determines the furnace condition state, step S1 can be executed with the learned model generation device 10 as the main body.

The determination unit 5 reads the stored learning model from the storage unit 3 . Moreover, the determination part 5 acquires the data of the real-time temperature distribution from the temperature distribution measurement part 2 through the storage part 3. FIG. The determination part 5 inputs the image data of the real-time temperature distribution into the learning model in an online method, and determines the furnace condition of the blast furnace (step S2). In the present embodiment, the determination unit 5 determines whether or not the current temperature distribution is classified into a pattern of abnormal temperature distribution as the determination of the furnace condition.

Then, the display unit 6 displays the determination result of the determination unit 5 as an image (step S3). The image is not limited to a specific form as long as it is of a type that allows the operator to grasp whether or not it is classified as a pattern of abnormal temperature distribution. Operators who know that the furnace condition is abnormal from the image can change the working conditions of the blast furnace.

(Learning Model) It is known that when the raw materials are normally charged, that is, when there is no variation in the raw material charging, the temperature distribution immediately above the raw material charging surface becomes a concentric distribution. It is also known that when there is no variation in the response, the distribution becomes concentric. Conversely, when there is a variation in the raw materials, or when the falling speed of the raw materials is not uniform and there is a deviation in the charging of the raw materials, the temperature distribution will be disordered and cannot be concentric. Therefore, the disturbance of the temperature distribution is imaged and classified, and the blast furnace condition determination device 1 determines which type of disturbance the image of the immediate temperature distribution belongs to, thereby realizing automatic determination of the furnace condition without human intervention. . In the present embodiment, the learning model to be used for determination is generated by learning a person having a turbulent temperature distribution selected from the temperature distribution data stored in the storage unit 3 as the learning data.

When the data of the temperature distribution selected as the learning material is imaged, it includes a pattern having an abnormal temperature distribution described below. The pattern of abnormal temperature distribution described below is known to cause abnormality in blast furnace operation.

In the pattern of the first abnormal temperature distribution, the high temperature part is deviated from the center of the raw material charging surface. Since the blast furnace has a cylindrical shape, the shape of the raw material charging surface is circular, and this mode is a mode in which the high temperature portion does not include its center. In blast furnaces, ventilation is important in order to stabilize the operation. Therefore, the raw material is distributed so that the gas can easily pass through the center of the blast furnace. Under normal circumstances, the temperature distribution just above the raw material loading surface is that the temperature in the center is higher. The pattern of the first abnormal temperature distribution may be caused by the variation in the distribution of the charging of the raw materials. For example, the operation of the blast furnace can be normalized by correcting the distribution of the charging of raw materials. Hereinafter, the type to which the pattern of the temperature distribution classified as the pattern of the first abnormal temperature distribution belongs is referred to as the first type.

In the pattern of the second abnormal temperature distribution, the high temperature portion extends from the center to the periphery of the raw material charging surface. In this mode, the high temperature portion includes the center of the raw material charging surface, but the high temperature portion extends to the periphery (circumferential portion) of the raw material charging surface, and the temperature distribution is not concentric. As described above, in a normal case, since the temperature of the central portion is high, the temperature of the intermediate portion (between the central portion and the periphery) is relatively low. The pattern of the second abnormal temperature distribution should be caused by at least one of the variation in the charging of the raw materials and the variation in the reaction. Hereinafter, the type to which the pattern of the temperature distribution classified as the pattern of the second abnormal temperature distribution belongs is referred to as the second type.

The data of the temperature distribution selected as the learning material is "labeled" as described above. For example, the label "1" may be assigned to the image data classified as the temperature distribution of the first type, and the label "2" may be assigned to the image data classified as the temperature distribution of the second type.

(study) FIG. 3 is a flowchart showing the learning performed by the learning model generation unit 4 . The learning model generation unit 4 acquires the above-mentioned learning materials, creates input materials, and performs learning to generate a learning model. In the present embodiment, the learning model generation unit 4 uses a method of deep learning, that is, a convolutional neural network (hereinafter referred to as CNN) to perform learning. CNN is an image processing technique for recognizable images. Here, the learning method of the learning model generation unit 4 is not limited to CNN, and may be other methods for recognizing pattern images.

Here, the learning model generation unit 4 multivalues the temperature distribution selected as the learning data using one or more thresholds, and then uses it as the image data. In the present embodiment, the learning model generation unit 4 uses binarized image data. As shown in FIG. 3 , in an example of an image serving as a learning material, a portion with a temperature equal to or higher than a threshold value is shown in black corresponding to a high temperature portion. The threshold value is 150° C. as an example, but is not limited to this. The threshold value can be set as a temperature between the temperature of the center part and the temperature of the middle part of the raw material charging surface of the blast furnace in normal operation.

The learning model generation unit 4 divides the above-mentioned image data into an appropriate size (pixel size), averages each divided data, and uses the data as the input data of the CNN. The learning model generation unit 4 can increase the size of the divided image within the range in which the pattern of the temperature distribution can be grasped. As an example, the learning model generation unit 4 may divide the image data into ten parts in the vertical direction and the horizontal direction, respectively. Conversely, in the case where the performance of the CPU for realizing the learning model generation unit 4 is sufficiently high, when the CNN-based learning can be performed in a realistic time, the learning model generation unit 4 may not divide the image data but use pixel units as input material.

The CNN input layer shown in Figure 3 corresponds to the above input data. Again, the middle layer corresponds to the feature quantity. In addition, the output layer determines which of a plurality of types the pattern corresponding to the temperature distribution is classified into. The learning model generation unit 4 generates a learning model by such a method. The learning model generation unit 4 may store the generated learning model in the storage unit 3 . Here, the image data used as the learning material is the temperature distribution of the raw material charging surface of the blast furnace having a center-symmetric structure. Therefore, the data obtained by rotating the image data about the center of the material loading surface as the axis can also be used for learning. That is, the learning model generation unit 4 can efficiently learn by increasing the number of input data by using the original image data and the rotated image data.

(judgment of abnormality) The determination unit 5 uses the learning model generated by the learning model generation unit 4 to determine the furnace state of the blast furnace. When judging the furnace state of the blast furnace, the judging unit 5 may read the learning model from the storage unit 3 . Further, the determination unit 5 acquires the data of the current temperature distribution from the temperature distribution measurement unit 2 through the storage unit 3 . Similar to the learning model generation unit 4 , the determination unit 5 multi-values the acquired temperature distribution using one or more thresholds, and uses it as image data. Similarly to the learning model generation unit 4, the determination unit 5 divides the image data into appropriate sizes, averages the divided data, and inputs the data into the learning model. As the output of the learning model, the judgment result is obtained. In the present embodiment, the outputs of the learning model are plural types of patterns of abnormal temperature distribution (“1” corresponding to the first type and “2” corresponding to the second type). For example, when the current temperature distribution is a pattern in which the high temperature part is off-center, the learning model outputs "1". For another example, when the current temperature distribution is a pattern in which the high temperature portion extends from the center of the raw material loading surface to the periphery, the learning model outputs "2".

The display unit 6 presents the determination result of the determination unit 5 to the operator through an image. The display unit 6 can display, for example, an image of the temperature distribution and a pattern of the abnormal temperature distribution to which it belongs. For example, when the learning model outputs "1", the display unit 6 may display an image of the temperature distribution and "first type". Here, the display unit 6 may display the image of the temperature distribution and the "third type" for the mode determined to be neither the first type nor the second type.

The operator can operate the blast furnace according to the judgment result. For example, in the case of the first type (mode in which the high temperature part is off-center), when a different load control mode is available, the operator performs an operation to change the load control mode. For example, in cases other than the first type, the operator can change the operating conditions so as to stabilize the subsequent work. Changes in operating conditions to stabilize subsequent operations include, for example, an increase in the coke ratio, a decrease in the PCI ratio, and a decrease in the air flow rate. Example

Hereinafter, the present invention will be specifically described using examples. The temperature distribution measuring unit 2 is a temperature measuring device that utilizes the temperature dependence of the speed of sound, and is installed in a blast furnace for producing pig iron. The sampling period for the measurement of the temperature distribution was 10 seconds.

In the present embodiment, detection is performed for the pattern of the first abnormal temperature distribution and the pattern of the second abnormal temperature distribution. Here, the pattern of the first abnormal temperature distribution is a pattern in which the high temperature portion does not include the center. In the second abnormal temperature distribution pattern, although the high temperature portion includes the center of the raw material charging surface, the high temperature portion extends to the periphery of the raw material charging surface, and the temperature distribution is not concentric.

First, for these patterns of anomalous temperature distributions, a CNN is used for learning. As the learning data, about 50 samples each belonging to the patterns of these abnormal temperature distributions are selected by human from the data of temperature distribution stored in the storage unit 3 . The image data of the selected temperature distribution data is rotated by 30° successively, and the number of samples is increased for learning. The total number of original samples and samples obtained by rotation is about 7000. Furthermore, in this embodiment, in order to make it easier to understand the abnormal mode, a threshold value is set for the temperature and binarized. The threshold for binarization was set to 150°C. However, the binarization operation is not a necessary process of the present invention. That is, binarization can be omitted.

For the learning model obtained by the above-mentioned learning (CNN), the determination accuracy is verified using data different from the learning data. The verification results obtained a judgment accuracy of 90.3%. FIG. 4 illustrates the determination result of this verification.

It was also confirmed that the pattern of the temperature distribution which did not belong to either the first type nor the second type (classified as the third type) could be distinguished.

As described above, according to the blast furnace state determination device 1 of the present invention, the disturbance state of the temperature distribution of the blast furnace can be automatically determined and classified. Furthermore, in the blast furnace operation method and the molten iron manufacturing method, the disturbance of the temperature distribution of the blast furnace can be eliminated according to the results of the determination and classification of the blast furnace state determination device 1, and the automation ratio of the operation can be improved.

As mentioned above, although this invention was demonstrated based on drawing and an Example, it should be understood that various deformation|transformation and correction|amendment based on this invention are easy as long as those skilled in the art are informed. Therefore, these deformation|transformation and correction are also included in the scope of the present invention. For example, functions, etc. included in each means, each step, etc. can be rearranged in a logical manner, and a plurality of means, steps, etc. can be combined into one or divided.

In the learning of the above-described embodiment, as the pattern of the first abnormal temperature distribution, image data in which the high temperature portion does not include the center are selected. That is, the selection of the image data is performed according to whether or not the high temperature portion includes the center point of the raw material loading surface. Here, the image data may be selected not according to the center point, but according to whether the center part having a certain size and the high temperature part overlap.

Also, as shown in FIG. 5 , the blast furnace state determination device 1 may be configured to further include a learning model generation unit 4 . That is, the blast furnace state determination device 1 may include a temperature distribution measurement unit 2 , a storage unit 3 , a learning model generation unit 4 , a determination unit 5 , and a display unit 6 .

1: Blast furnace condition state determination device 2: Temperature distribution measurement section 3: Storage Department 4: Learning model generation part 5: Judgment Department 6: Display part 10: Learning Model Generation Device

Fig. 1 shows an example of the configuration of a blast furnace furnace state determination device according to an embodiment of the present invention. Fig. 2 is a flowchart showing learning, determination, and display performed by the blast furnace state determination device according to an embodiment of the present invention. [Fig. 3] is a flowchart showing learning using a convolutional neural network. [ Fig. 4 ] is an example of the determination result. [ Fig. 5] Fig. 5 is a diagram showing a configuration example of a blast furnace state determination device according to a modification.

1: Blast furnace condition state determination device

2: Temperature distribution measurement section

3: Storage Department

4: Learning model generation part

5: Judgment Department

6: Display part

10: Learning Model Generation Device

Claims (8)

A blast furnace state determination device, which is a blast furnace state determination device for determining the furnace state of a blast furnace, which is provided with a temperature distribution measurement part, a storage part, and a determination part; The temperature distribution measuring unit measures the temperature distribution just above the raw material charging surface in the blast furnace; The storage unit stores a learning model that has been learned in a manner of outputting a plurality of types for the pattern of the temperature distribution according to the measured data of the temperature distribution; The determination unit uses the stored learning model to determine which of the plurality of types the temperature distribution measured by the temperature distribution measurement unit is. The blast furnace furnace condition state determination device according to claim 1, wherein, The storage unit stores the measured data of the temperature distribution, The blast furnace state determination device further includes a learning model generation unit that generates a plurality of types of learning models that output the pattern of the temperature distribution based on the stored data. The blast furnace condition state determination device according to claim 2, wherein, The aforementioned learning model generation section uses a convolutional neural network to generate the aforementioned learning model. The blast furnace furnace condition state determination device according to claim 2 or 3, wherein, The learning model generation unit and the determination unit multi-value the temperature distribution using one or more thresholds. The blast furnace furnace condition state determination device according to any one of claims 1 to 4, wherein, The learning model outputs plural types of abnormal temperature distribution patterns. The blast furnace furnace condition state determination device according to any one of claims 1 to 5, wherein, The plurality of types include: a pattern of abnormal temperature distribution in which the high temperature portion deviates from the center of the raw material charging surface, and an abnormal temperature distribution pattern in which the high temperature portion extends from the center to the periphery of the raw material charging surface of the type. An operation method of a blast furnace, in which operation conditions are changed according to the furnace state of the blast furnace determined by the blast furnace state state determination device according to any one of claims 1 to 6. A method for producing molten iron using a blast furnace operated by the blast furnace operation method described in claim 7.

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