CN111527217B - System and method for evaluating operating state of blast furnace - Google Patents
System and method for evaluating operating state of blast furnace Download PDFInfo
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- CN111527217B CN111527217B CN201880084408.8A CN201880084408A CN111527217B CN 111527217 B CN111527217 B CN 111527217B CN 201880084408 A CN201880084408 A CN 201880084408A CN 111527217 B CN111527217 B CN 111527217B
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/006—Automatically controlling the process
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B7/00—Blast furnaces
- C21B7/16—Tuyéres
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B7/00—Blast furnaces
- C21B7/24—Test rods or other checking devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27B—FURNACES, KILNS, OVENS, OR RETORTS IN GENERAL; OPEN SINTERING OR LIKE APPARATUS
- F27B1/00—Shaft or like vertical or substantially vertical furnaces
- F27B1/10—Details, accessories, or equipment peculiar to furnaces of these types
- F27B1/28—Arrangements of monitoring devices, of indicators, of alarm devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D21/02—Observation or illuminating devices
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B2300/00—Process aspects
- C21B2300/04—Modeling of the process, e.g. for control purposes; CII
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D21/02—Observation or illuminating devices
- F27D2021/026—Observation or illuminating devices using a video installation
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- Manufacturing & Machinery (AREA)
- Materials Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Blast Furnaces (AREA)
- Manufacture Of Iron (AREA)
Abstract
The invention provides a system and a method for evaluating the operating state of a blast furnace. The system for evaluating an operating state of a blast furnace may include: an image acquisition unit that acquires image data of each of a plurality of tuyeres provided on a blast furnace; an image collecting unit that collects image data of each tuyere acquired by the image acquiring unit; a tuyere combustion state judgment unit that classifies a combustion state of each tuyere using the image data of each tuyere and based on artificial intelligence; a tuyere combustion state index generating unit that generates a combustion state index of each tuyere using the combustion state classification result of each tuyere classified by the tuyere combustion state judging unit; and a comprehensive evaluation unit generating a comprehensive combustion state index based on the combustion state index of each tuyere.
Description
Technical Field
The present application relates to a system and method for assessing the operating state of a blast furnace.
Background
In order to evaluate the operating state of the blast furnace, attempts have been made to judge the in-furnace state by analyzing image data captured through a tuyere or the like of the blast furnace, or to judge the in-furnace state by monitoring the operating data.
However, in the related art, an operator qualitatively determines the combustibility or the furnace condition of the blast furnace only by the image data, or determines the in-furnace state only by analyzing the brightness of the image data.
In connection with this, Japanese laid-open patent No. 2015-52148 (published: 2015.03.19) discloses a control method based on blast furnace operation state judgment.
Disclosure of Invention
Technical subject
In the related art, there is a need for a method of quantitatively evaluating a combustion state of a tuyere based on tuyere image data and comprehensively evaluating an operation state of a blast furnace based on the same.
Means for solving the problems
To solve the above problems, one embodiment of the present invention provides a system for evaluating the operating state of a blast furnace.
A system for evaluating an operating state of a blast furnace according to an embodiment of the present invention may include: an image acquisition unit that acquires image data of each of a plurality of tuyeres provided on a blast furnace; the image acquisition unit is used for acquiring the image data of each air opening; a tuyere combustion state judgment unit that classifies a combustion state of each tuyere using the image data of each tuyere and based on artificial intelligence; a tuyere combustion state index generating unit which generates a combustion state index of each tuyere using the combustion state classification result of each tuyere classified by the tuyere combustion state judging unit; and a comprehensive evaluation unit generating a comprehensive combustion state index based on the combustion state index of each tuyere.
In another aspect, another embodiment of the present invention provides a method for evaluating an operating state of a blast furnace.
A method for evaluating an operating state of a blast furnace according to another embodiment of the present invention may include the steps of: collecting image data of each of a plurality of tuyeres provided on a blast furnace; classifying a combustion state of each tuyere using the image data of each tuyere and based on artificial intelligence; generating a combustion state index of each tuyere by using the combustion state classification result of each tuyere; and generating a comprehensive combustion state index based on the combustion state index of each tuyere.
In addition, the solutions to the problems described above do not list all the features of the present invention. Various features of the present invention, together with advantages and effects thereof, may be understood in more detail by reference to the following detailed description.
Effects of the invention
According to an embodiment of the present invention, it is possible to classify the combustion state of the tuyere based on the deep learning using the tuyere image data, and extract an index of the combustion state of each tuyere by additionally using the tuyere image data analysis result and the blast furnace operation data analysis result on the basis of the combustion state classification result using the tuyere, and comprehensively evaluate and control the blast furnace operation state.
Accordingly, it is possible to secure stable operation of the blast furnace and to improve productivity by quantitatively evaluating combustibility and furnace condition of the blast furnace.
Drawings
Fig. 1 is a block diagram of a system for evaluating an operating state of a blast furnace according to an embodiment of the present invention.
FIG. 2 is a diagram describing the concept of classifying the tuyere combustion state for the first time based on deep learning according to an embodiment of the present invention.
Fig. 3 and 4 are diagrams describing a concept of accumulating the results of the first classification based on the deep learning into a time series according to a preset time period and determining the combustion state classification of each tuyere based on the accumulated results, according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for evaluating an operating state of a blast furnace according to another embodiment of the present invention.
Detailed Description
Hereinafter, preferred embodiments will be described in detail with reference to the accompanying drawings so that those having ordinary knowledge in the technical field to which the present invention pertains can easily implement the present invention. However, in describing the preferred embodiments of the present invention in detail, if a detailed explanation for a related known function or structure is considered to unnecessarily deviate from the gist of the present invention, the detailed explanation thereof will be omitted. Further, in all the drawings, the same reference numerals are used for components having similar functions and actions.
In addition, throughout the specification, when a component is described as being 'connected to' another component, the component may be 'directly connected to' the other component, or other elements interposed between the component and the other component may also be present. Furthermore, unless explicitly stated to the contrary, the term "comprising" should be understood to mean that other elements are included, and not to exclude any other elements.
Fig. 1 is a block diagram of a system for evaluating an operating state of a blast furnace according to an embodiment of the present invention.
Referring to fig. 1, a system for evaluating an operating state of a blast furnace according to an embodiment of the present invention may be configured to include an image acquisition unit 110, an image collection unit 120, a tuyere combustion state judgment unit 130, a tuyere combustion state index generation unit 140, an operation information collection unit 150, a comprehensive evaluation unit 160, and a furnace condition control unit 170.
The image acquisition unit 110 can acquire image data of each tuyere 11 provided on the blast furnace 10.
For example, the image acquisition unit 110 may include a plurality of cameras installed on each tuyere 11, and image data of each tuyere can be acquired by each camera in real time (e.g., in ms).
The image collection unit 120 can collect image data of each tuyere acquired by the image acquisition unit 110.
For example, the image collecting unit 120 may collect image data obtained in real time for each tuyere from a plurality of cameras included in the image acquiring unit 110.
Further, the image collection unit 120 can map the collected image data with collection environment information including a tuyere number and a data acquisition time, and the like.
Further, the image data on which the mapping has been completed by the image collection unit 120 may be stored to a data storage (not shown) provided in the blast furnace operation state evaluation system 100, or may be transmitted to the tuyere combustion state judgment unit 130 in real time.
The tuyere combustion state determination unit 130 classifies the combustion state of each tuyere using the image data of each tuyere received from the image collection unit 120, and the tuyere combustion state determination unit 130 may be configured to include an AI-based determination unit 131 and an image-processing-based determination unit 132.
The AI-based determination unit 131 can classify the combustion state of each tuyere based on artificial intelligence using image data of each tuyere. For example, the AI-based determination unit 131 can classify the combustion state of each tuyere based on deep learning.
According to one embodiment, the AI-based determination unit 131 may first classify the combustion state of each tuyere based on a Convolutional Neural Network (CNN) using image data of each tuyere.
The AI-based determination unit 131 can determine the tuyere combustion state classification from the accumulated first-time classification results in time series, as necessary, so that the matching of the combustion state classification can be improved.
The concept of classifying and determining the tuyere combustion state by the AI-based determination unit 131 will be described in more detail with reference to fig. 2 to 4.
FIG. 2 is a diagram describing the concept of classifying the tuyere combustion state for the first time based on deep learning according to an embodiment of the present invention.
Referring to fig. 2, the AI-based determination unit 131 can classify combustion states of the first to nth tuyere image data 21 to 2N acquired through each tuyere based on image deep learning (e.g., based on CNN), thereby obtaining first to nth tuyere combustion state classification results 21 'to 2N'. Wherein N represents the number of tuyeres.
Fig. 3 and 4 are diagrams describing the concept of determining a classification of a tuyere combustion state from the accumulated first-time classification results in time series based on deep learning according to an embodiment of the present invention.
First, referring to FIG. 3, the AI-based determination unit 131 can determine a tuyere combustion state classification of each tuyere based on the accumulated results of the primary classification in time series, i.e., based on the first tuyere combustion state classification (31-1, 31-2, 31-3), the second tuyere combustion state classification (32-1, 32-2, 32-3) and the Nth tuyere combustion state classification (3N-1, 3N-2, 3N-3), so that the determined tuyere combustion state classification results 31 'to 33' can be obtained.
In the present embodiment, in determining the tuyere combustion state classification, if any combustion state classification occurs a predetermined number of times or more based on a plurality of combustion state classification results for any period of time (t-1 to t +1) for which the first classification has been performed, the combustion state classification may be determined. Therefore, the accuracy of classification of the combustion state of the tuyere can be further improved.
Next, referring to fig. 4, the AI-based determination unit 131 can determine the tuyere combustion state classification of each tuyere according to the combustion state classification results accumulated in time series for the first classification, that is, according to the first tuyere combustion state classification (41-1, 41-2, 41-3), the second tuyere combustion state classification (42-1, 42-2, 42-3) and the nth tuyere combustion state classification (4N-1, 4N-2, 4N-3), based on the image time series deep learning, and can obtain determination results 41 'to 43' of the classification of the determined tuyere combustion state.
For example, the AI-based determination unit 131 can determine the tuyere combustion state classification of each tuyere based on a Recurrent Neural Network (RNN) or a Recurrent Convolutional Neural Network (RCNN) using a plurality of combustion state classification results that are first classified according to each tuyere for an arbitrary period of time (t-1 to t + 1).
Since the combustion state of the tuyere continuously changes with time, the accuracy thereof may be low if the combustion state of the tuyere is determined only at a certain point of time.
Therefore, according to the present embodiment, in order to determine the tuyere combustion state classification on the basis of comprehensively considering the combustion state varying with time, deep learning may be applied to further improve the accuracy of the tuyere combustion state classification.
On the other hand, as shown in FIGS. 3 and 4, when determining the tuyere combustion state classification based on the time-series accumulation result, the time period (e.g., t-1 to t +1) for accumulating the first classification result and the start point (t-1) of the corresponding time period may affect the accuracy of the classification.
According to one embodiment, at the initial execution, the primary classification results accumulated within a user-preset time period may be accumulated from a time point at which the classification of the tuyere combustion state is initially sensed, and the tuyere combustion state may be determined therefrom.
Further, after the determination results of the tuyere combustion state classification are accumulated, the above-described time period may be adjusted based on the elapsed time information from the time point at which the tuyere combustion state classification is initially sensed to the time point at which the tuyere combustion state classification is transitioned to another state, so that the accuracy of the classification may be further improved.
The tuyere combustion state classified by the AI-based determination unit 131 may include: for example, normal combustion state, poor combustion state, no pulverized coal blowing, falling of unreduced melt (raw ore falling), and coke swirling. Here, not blowing in the pulverized coal means to judge whether or not the pulverized coal is not blown in, not reduced melt (raw ore falling) means to judge whether or not the unreduced raw material melt falls because the raw material that should be reduced in the upper part of the furnace is not reduced, and the coke whirling means to judge whether or not the coke whirls in the furnace.
The image-processing-based determination unit 132 may diagnose the tuyere device by performing image processing on the image data of each tuyere, thereby determining the tuyere combustion state.
According to the embodiment, the image processing-based determining unit 132 may determine the abnormal condition of the tuyere device including whether there is tuyere damage, tuyere deposit, tuyere blockage, lance bending or burning through the image processing of the image data for each tuyere.
Further, the image processing-based determination unit 132 may extract the combustion area and the combustion brightness by performing image processing on the image data of each tuyere.
Further, when the combustion state is normal, the image-processing-based determination unit 132 may determine the pulverized coal flow rate by performing image processing on the image data of each tuyere.
The image processing-based determination unit 132 may apply various image processing techniques known to those skilled in the art when performing image processing on the image data of each tuyere, and a detailed description thereof is omitted herein.
The determinations by the AI-based determination unit 131 and the image-processing-based determination unit 132 may be performed in parallel.
The combustion state classification result and the tuyere device diagnosis result of each tuyere classified by the tuyere combustion state judgment unit 130 can be mapped together with the image data and the collection environment information of each tuyere for storage and management.
The tuyere combustion state index generating unit 140 may generate the combustion state index of each tuyere by using the combustion state classification result of each tuyere classified by the tuyere combustion state judging unit 130.
According to one embodiment, the combustion state index of each tuyere generated by the tuyere combustion state index generating unit 140 includes: an index of a poor combustion state, an index of a pulverized coal not blown, an index of a falling of unreduced smelt (raw ore falling), an index of a coke swirling, an index of a combustion state level, an index of a pulverized coal flow rate, an index of a tuyere swirling area (Raceway), and the like.
For example, the tuyere combustion state index generating unit 140 counts the number of occurrences of a result arbitrarily classified based on the combustion state classification result of each tuyere by the tuyere combustion state judging unit 130 by a set period, and can generate a correlation index by scoring the counted number of times for each period.
Further, the tuyere combustion state index generating unit 140 scores the combustion state level index according to the combustion area and the combustion brightness extracted by the tuyere combustion state judging unit 130, and may generate the combustion state level index by integrating the scores calculated within the preset period. Wherein the reference information used for generating the combustion state level index may be updated according to a signal input by an administrator. Since the updated reference information is reflected in real time, index information reflecting the state of the blast furnace can be generated.
Further, the tuyere combustion condition index generating unit 140 may generate a tuyere device abnormality index by scoring the tuyere device diagnosis result judged by the tuyere combustion state judging unit 130. Here, the tuyere device abnormality index may include a tuyere buckling loss index, a tuyere attachment index, a tuyere clogging index, a lance damage index, and the like.
The operation information collecting unit 150 serves to collect operation information generated during the operation of the blast furnace in real time. The operation information may include, for example, blast furnace body temperature, pressure, cooling water flow, etc.
The operation information collected in real time by the operation information collecting unit 150 may be stored and managed in mapping with the tuyere combustion state index information generated by the tuyere combustion state index generating unit 140 as described above.
The comprehensive evaluation unit 160 performs comprehensive evaluation along the circumferential direction of the blast furnace based on the combustion state index information of each tuyere generated by the tuyere combustion state index generating unit 140 and the operation information collected by the operation information collecting unit 150.
According to one embodiment, the comprehensive evaluation unit 160 may generate the comprehensive combustion state index by comprehensively considering the combustion state index information of each tuyere generated by the tuyere combustion state index generation unit 140. For example, the comprehensive burning state indexes may include a poor burning comprehensive index, a non-pulverized coal blowing-in comprehensive index, a non-reduced melt falling (raw ore falling) comprehensive index, and the like, which are mapped one-to-one with the tuyere burning state index generated in each tuyere.
Further, the comprehensive evaluation unit 160 may generate a circumferential balance index based on a tuyere convolution (radial) index generated on each tuyere.
Further, the comprehensive evaluation unit 160 may generate a tuyere device abnormality index based on the tuyere device abnormality index generated on each tuyere.
The furnace condition control unit 170 performs at least one of the implementation of pulverized coal injection control, N2 purge control, and blast furnace charging control to control the blast furnace condition based on the combustion state index information of each tuyere generated by the tuyere combustion state index generation unit 140 or the comprehensive combustion state index generated by the comprehensive evaluation unit 160.
According to one embodiment, the furnace condition control unit 170 may perform the pulverized coal injection control in the case where the pulverized coal non-injection index of any tuyere exceeds a preset reference value.
Further, the furnace condition control unit 170 may perform blast furnace charging control in the case where the falling index of unreduced melt exceeds a preset reference value due to the occurrence of raw ore falling in any tuyere region.
According to another embodiment, the furnace condition control unit 170 may perform integrated control of the plurality of tuyeres based on the integrated combustion state index or the circumferential balance index.
For example, when the raw ore is dropped in only one direction, the direction in which the filler is dropped may be changed by changing the filler distribution, for example, and the charging control of the blast furnace may be performed.
The system 100 for evaluating the operating state of the blast furnace described above with reference to fig. 1 may be implemented by applying an artificial intelligence algorithm to input data, performing image processing, incorporating processing means that can calculate various indexes, and control means for performing blast furnace control.
Fig. 5 is a flowchart of a method for evaluating an operating state of a blast furnace according to another embodiment of the present invention.
Referring to fig. 5, according to a method for evaluating an operating state of a blast furnace according to another embodiment of the present invention, image data of each tuyere provided on the blast furnace may be collected in real time by the image acquisition unit 110 and the image collection unit 120 (S510).
Thereafter, the combustion state of each tuyere may be classified using the image data of each tuyere based on the tuyere combustion state judgment unit 130 (S520). Specifically, the primary tuyere combustion state classification can be performed according to the AI-based determination unit 131, i.e., based on artificial intelligence using image data of each tuyere (S521), and then the tuyere combustion state classification can be determined based on the classification result (S522). Further, the tuyere device diagnosis may be performed while classifying the combustion state of each tuyere by the image processing of the image data of each tuyere according to the determination unit 132 based on the image processing (S525).
Thereafter, the combustion state index of each tuyere is generated based on the combustion state classification result of each tuyere by the tuyere combustion state index generation unit 140 (S530), and the blast furnace operation state is comprehensively evaluated in the circumferential direction based on the generated combustion state index of each tuyere by the comprehensive evaluation unit 160 (S540).
Thereafter, the furnace condition may be controlled by the furnace condition control unit 170 based on the comprehensively evaluated operation state (S550).
Since a specific execution method of each step described above with reference to fig. 5 is the same as that described above with reference to fig. 1 to 4, a repetitive description will be omitted.
The invention is not limited to the embodiments and figures described above. However, it will be apparent to those skilled in the art that various modifications and variations can be made without departing from the spirit and scope of the invention.
Claims (4)
1. A system for assessing the operating status of a blast furnace, comprising:
an image acquisition unit that acquires image data of each of a plurality of tuyeres provided on a blast furnace;
an image collecting unit that collects image data of each tuyere acquired by the image acquiring unit;
a tuyere combustion state judgment unit that classifies a combustion state of each tuyere using the image data of each tuyere and based on artificial intelligence;
a tuyere combustion state index generating unit that generates a combustion state index of each tuyere using the combustion state classification result of each tuyere classified by the tuyere combustion state judging unit; and
a comprehensive evaluation unit generating a comprehensive combustion state index based on the combustion state index of each tuyere;
wherein the combustion state classification includes a normal combustion state, a poor combustion state, a state in which no pulverized coal is blown in, a state in which unreduced melt falls, and a coke swirling state;
wherein the burning state index of each tuyere includes at least one of a poor burning state index, a pulverized coal non-blowing-in index, a non-reduced melt falling index, a coke swirling index, a burning state level index, a pulverized coal flow rate index, and a tuyere swirling zone index;
wherein, the wind gap combustion state judgement unit includes: an AI-based determination unit that classifies a combustion state of each tuyere using the image data of each tuyere and based on deep learning;
wherein the AI-based determination unit accumulates classification results of the combustion state of each tuyere classified based on the deep learning into a time series by a preset time period, and determines the combustion state classification of each tuyere based on the accumulated results;
wherein the time period is adjusted according to time lapse information during a period from a time point at which the tuyere combustion state classification is initially sensed to when the tuyere combustion state classification is transferred to another state;
wherein the AI-based determination unit determines an arbitrary combustion state classification as the combustion state classification when the arbitrary combustion state classification occurs a predetermined number of times or more during the time period,
wherein the AI-based determination unit determines the combustion state classification based on time-series deep learning with respect to the combustion state classification result for each tuyere accumulated during the time period.
2. The system for evaluating an operating state of a blast furnace according to claim 1, wherein the tuyere combustion state judging unit further comprises:
an image processing-based determination unit that diagnoses the tuyere device and determines the tuyere combustion state by performing image processing on the image data of each tuyere.
3. The system for evaluating an operating state of a blast furnace according to claim 1, further comprising a furnace condition control unit that implements pulverized coal injection control, N, based on the combustion state index of each tuyere or the comprehensive combustion state index 2 At least one of purging control and blast furnace charging control.
4. A method for assessing the operating state of a blast furnace, comprising the steps of:
collecting image data of each of a plurality of tuyeres provided on a blast furnace;
classifying a combustion state of each tuyere using the image data of each tuyere and based on artificial intelligence;
generating a combustion state index of each tuyere by using the combustion state classification result of each tuyere; and
generating a comprehensive combustion state index based on the combustion state index of each tuyere;
wherein the step of classifying the combustion state of each tuyere includes the steps of:
classifying a combustion state of each tuyere using the image data of each tuyere and based on deep learning;
accumulating the classification result of the combustion state of each tuyere classified based on the deep learning into a time series by a preset time period and determining the combustion state classification of each tuyere based on the accumulated result, and;
adjusting the time period according to time lapse information during a period from a time point when the tuyere combustion state classification is initially sensed to a time point when the tuyere combustion state classification is transferred to another state,
wherein the combustion state classification is determined when an arbitrary combustion state classification occurs a predetermined number of times or more during the time period,
wherein the combustion state classification is determined based on time-series deep learning with respect to the combustion state classification result of each tuyere accumulated during the time period,
wherein the combustion state classification includes a normal combustion state, a poor combustion state, a state in which no pulverized coal is blown in, a state in which unreduced melt falls, and a coke swirling state;
wherein the combustion state index of each tuyere includes at least one of a poor combustion state index, a pulverized coal non-blowing-in index, a non-reduced melt falling index, a coke swirling index, a combustion state level index, a pulverized coal flow rate index, and a tuyere swirling zone index.
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| KR1020170180170A KR102075223B1 (en) | 2017-12-26 | 2017-12-26 | System and method of evaluating furnace operation state |
| KR10-2017-0180170 | 2017-12-26 | ||
| PCT/KR2018/016580 WO2019132476A1 (en) | 2017-12-26 | 2018-12-24 | System and method for evaluating operational conditions of blast furnace |
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| CN111527217B true CN111527217B (en) | 2022-08-16 |
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| JP (1) | JP7064598B2 (en) |
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| WO2021182041A1 (en) * | 2020-03-12 | 2021-09-16 | Ｊｆｅスチール株式会社 | Abnormality determination method for blast furnace, training method for stabilization period model, operation method for blast furnace, and abnormality determination device for blast furnace |
| CN112094974A (en) * | 2020-08-21 | 2020-12-18 | 广东韶钢松山股份有限公司 | Blast furnace taphole spray control method, system and computer readable storage medium thereof |
| CN112501368B (en) * | 2020-11-17 | 2022-07-08 | 中冶南方工程技术有限公司 | Blast furnace smelting method and computer equipment |
| JP7380604B2 (en) * | 2021-01-12 | 2023-11-15 | Ｊｆｅスチール株式会社 | Learning model generation method, learning model generation device, blast furnace control guidance method, and hot metal manufacturing method |
| CN113177364B (en) * | 2021-05-21 | 2023-07-14 | 东北大学 | Soft measurement modeling method for temperature of blast furnace tuyere convolution zone |
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| JP2015048508A (en) * | 2013-09-02 | 2015-03-16 | 新日鐵住金株式会社 | Method and device for observing state of blast furnace tuyere |
| JP2015052148A (en) * | 2013-09-06 | 2015-03-19 | 新日鐵住金株式会社 | Method for controlling blast furnace by determining operational situation thereof |
| CN105308191A (en) * | 2013-06-19 | 2016-02-03 | 杰富意钢铁株式会社 | Method for detecting abnormality in blast furnace, and method for operating blast furnace |
| CN105392904A (en) * | 2013-07-29 | 2016-03-09 | 杰富意钢铁株式会社 | Abnormality detection method and blast-furnace operation method |
| JP2016060931A (en) * | 2014-09-17 | 2016-04-25 | 新日鐵住金株式会社 | Blast furnace tuyere condition observation method and blast furnace tuyere condition observation device |
| CN106191350A (en) * | 2016-08-30 | 2016-12-07 | 武汉钢铁股份有限公司 | Bottom house air port working condition appraisal procedure based on fixed point radar |
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| CN2589484Y (en) * | 2002-12-12 | 2003-12-03 | 武汉钢铁(集团)公司 | Blast furnace tuyere combustion status monitoring device |
| CN2913380Y (en) * | 2006-06-22 | 2007-06-20 | 重庆大学 | On-line monitoring device for blast furnace tuyere operating mode |
| CN105308191A (en) * | 2013-06-19 | 2016-02-03 | 杰富意钢铁株式会社 | Method for detecting abnormality in blast furnace, and method for operating blast furnace |
| CN105392904A (en) * | 2013-07-29 | 2016-03-09 | 杰富意钢铁株式会社 | Abnormality detection method and blast-furnace operation method |
| JP2015048508A (en) * | 2013-09-02 | 2015-03-16 | 新日鐵住金株式会社 | Method and device for observing state of blast furnace tuyere |
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Also Published As
| Publication number | Publication date |
|---|---|
| JP2021509440A (en) | 2021-03-25 |
| EP3733876B1 (en) | 2023-06-07 |
| KR102075223B1 (en) | 2020-02-07 |
| EP3733876A4 (en) | 2021-01-20 |
| KR20190078319A (en) | 2019-07-04 |
| WO2019132476A1 (en) | 2019-07-04 |
| JP7064598B2 (en) | 2022-05-10 |
| EP3733876A1 (en) | 2020-11-04 |
| CN111527217A (en) | 2020-08-11 |
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