EP3733876A1 - System und verfahren zur bewertung der betriebsbedingungen eines hochofens - Google Patents

System und verfahren zur bewertung der betriebsbedingungen eines hochofens Download PDF

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Publication number
EP3733876A1
EP3733876A1 EP18893418.6A EP18893418A EP3733876A1 EP 3733876 A1 EP3733876 A1 EP 3733876A1 EP 18893418 A EP18893418 A EP 18893418A EP 3733876 A1 EP3733876 A1 EP 3733876A1
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EP
European Patent Office
Prior art keywords
combustion state
tuyeres
blast furnace
tuyere
index
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP18893418.6A
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English (en)
French (fr)
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EP3733876B1 (de
EP3733876A4 (de
Inventor
Kee-Young SHIN
Young-Hyun Kim
Sang-Woo Choi
Hyung-Woo Kim
Gi-Wan SON
Young-Do Park
Kil-Bong JANG
Sang-Han Son
Ho-Moon BAE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Posco Holdings Inc
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Posco Co Ltd
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Publication of EP3733876A1 publication Critical patent/EP3733876A1/de
Publication of EP3733876A4 publication Critical patent/EP3733876A4/de
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Publication of EP3733876B1 publication Critical patent/EP3733876B1/de
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Classifications

    • 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
    • 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
    • C—CHEMISTRY; METALLURGY
    • C21—METALLURGY OF IRON
    • C21B—MANUFACTURE OF IRON OR STEEL
    • C21B7/00—Blast furnaces
    • C21B7/16—Tuyéres
    • 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
    • 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
    • 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
    • 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
    • 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

Definitions

  • the present disclosure relates to a system and a method for evaluating operational conditions of a blast furnace.
  • Japanese Patent Publication No. 2015-52148 discloses a control method based on determination of operational conditions of a furnace.
  • an embodiment of the present disclosure is to provide a system for evaluating operational conditions of a blast furnace.
  • a system and a method for evaluating operational conditions of a blast furnace includes: an image capturing unit for capturing image data according to each of a plurality of tuyeres disposed in a blast furnace; an image collection unit for collecting the image data captured according to each of the tuyeres by the image capturing unit; a tuyere combustion state determination unit for classifying, on the basis of artificial intelligence, combustion states according to each of the tuyeres by using the image data according to each of the tuyeres; a tuyere combustion state index generation unit for generating combustion state indices according to each of the tuyeres by using the result of classifying the combustion states according to each of the tuyeres by the tuyere combustion state determination unit; and an integrated evaluation unit for generating an integrated combustion state index on the basis of the combustion state indices according to each of the tuyeres.
  • another embodiment of the present disclosure is to provide a method for evaluating operating conditions a blast furnace.
  • the method for evaluating operating conditions a blast furnace includes operations of: collecting image data according to a plurality of tuyeres provided in a blast furnace; classifying combustion states according to each of the tuyeres on the basis of artificial intelligence, by using the image data according to each of the tuyeres; generating combustion state indices according to each of the tuyeres by using the result of classifying the combustion state according to a plurality of tuyeres; and generating an integrated combustion state index on the basis of the combustion state indices according to each of the tuyeres.
  • a tuyere combustion state based on deep learning using the tuyere image data, in addition to the result of classifying the result of classifying the tuyere combustion state, and a result of analyzing the tuyere image data and a result of analyzing the blast furnace operational data may be additionally used to extract the tuyere combustion state indices according to each of the tuyeres, and an operational condition of a blast furnace may be integrally evaluated and controlled.
  • the blast furnace combustibility and the blast furnace condition may be quantitatively evaluated to enable stable blast furnace operations, and productivity may be improved.
  • FIG. 1 is a configuration diagram of a system for evaluating operational conditions of a blast furnace according to an embodiment of the present disclosure.
  • a system 100 for evaluating operational conditions of a blast furnace may be configured to include an image capturing unit 110, an image collection unit 120, a tuyere combustion state determination unit 130, a tuyere combustion state index generation unit 140, an operational information collection unit 150, an integrated evaluation unit 160, and a blast furnace condition control unit 170.
  • the image capturing unit 110 may acquire image data according to each of the tuyeres 11 provided in the blast furnace 10.
  • the image capturing unit 110 may include a plurality of cameras installed in each tuyere 11, and may acquire the image data according to each of the tuyeres in real time (e.g., in ms units) through each camera.
  • the image collection unit 120 may collect image data according to each of the tuyeres captured by the image capturing unit 110.
  • the image collection unit 120 may collect image data obtained in real time according to each of the tuyeres from a plurality of cameras included in the image capturing unit 110.
  • the image collection unit 120 may map the collected image data with collection environment information including a tuyere number, data capture time, and the like.
  • the image data which has been mapped by the image collection unit 120, may be stored in a data storage (not shown) provided in a system for evaluating operational conditions of a blast furnace 100, or may be transmitted in real time to the tuyere combustion state determination unit 130.
  • the tuyere combustion state determination unit 130 is for classifying a combustion state according to each of the tuyeres using the image data according to each of the tuyeres transmitted from the image collection unit 120, and may be configured to include an AI-based determination unit 131 and an image processing-based determination unit 132.
  • the AI-based determination unit 1311 may classify the combustion state according to each of the tuyeres based on artificial intelligence using the image data according to each of the tuyeres.
  • the AI-based determination unit 131 may classify the combustion state according to each of the tuyeres based on deep learning.
  • the AI-based determination unit 131 may primarily classify the combustion state according to each of the tuyeres based on a convolutional neural network (CNN) using image data according to each of the tuyeres.
  • CNN convolutional neural network
  • the AI-based determination unit 131 may determine the tuyere combustion state classification based on results of accumulating the results of classifying the combustion states according to each of the tuyeres in time series, primarily classified, thereby further improving consistency of the combustion state classification.
  • FIG. 2 is a view illustrating the concept of primarily classifying a tuyere combustion state based on deep learning according to an embodiment of the present disclosure.
  • the AI-based determination unit 131 may classify the combustion states based on the image deep learning, for example, CNN, for first tuyere image to the Nth tuyere image data (21 to 2N) captured according to each of the tuyeres, thereby obtaining the results of the first tuyere combustion state classification to the Nth tuyere combustion state classification (21'to 2N').
  • N means the number of tuyere.
  • FIGS. 3 and 4 are diagrams illustrating a concept of determining a tuyere combustion state classification based on a result of accumulating a result primarily classified based on deep learning in time series according to an embodiment of the present disclosure.
  • the AI-based determination unit 131 may determine a tuyere combustion state classification according to each of the tuyeres based on first tuyere combustion state classifications 31-1, 31-2, and 31-3, second tuyere combustion state classifications 32-1, 32-2, and 32-3, and Nth tuyere combustion state classifications 3N-1, 3N -2, and 3N-3, and may obtain determined tuyere combustion state classification results 31' to 33'.
  • a result of classifying the plurality of combustion states primarily classified for an arbitrary time period (t-1 to t+1) to determine the tuyere combustion state classification may be determined as the corresponding combustion state classification.
  • the AI-based determination unit 131 may determine tuyere combustion state classification according to each of the tuyeres based on deep learning in time series on first tuyere combustion state classifications 41-1, 41-2, and 41-3, second tuyere combustion state classifications 42-1, 42-2, and 42-3, and Nth tuyere combustion state classifications 4N-1, 4N-2, and 4N-3, and may obtain determined tuyere combustion state classification results 41' to 43'.
  • the AI-based determination unit 131 determine the tuyere combustion state classification according to each of the tuyeres based on a recurrent neural network (RNN) or a recurrent convolutional neural network (RCNN) by using the result of classifying a plurality of combustion states primarily classified according to each of the tuyeres for an arbitrary time period (t-1 to t+1).
  • RNN recurrent neural network
  • RCNN recurrent convolutional neural network
  • the accuracy may be deteriorated to determine the combustion state of the tuyere only at a certain point in time.
  • an image time-series deep learning may be applied to further improve the accuracy of the tuyere combustion state classification.
  • the accuracy of classification may be affected according to the time period (for example, t-1 to t+1) for accumulating the results primarily classified and a start time (t-1) of the corresponding time period.
  • the tuyere combustion state classification may be determined by accumulating the results primarily classified for a time period set by a user.
  • the above-described time period is adjusted according to the elapsed time information from the time at which the tuyere combustion state classification is first detected to the time at which the tuyere combustion state classification transitions to another state, such that the accuracy may be further improved.
  • the tuyere combustion state classified by the AI-based determination unit 131 may include, for example, a normal combustion state, a poor combustion state, pulverized coal non-injection, unreduced molten material falling(raw ore falling), coke turning, and the like.
  • pulverized coal non-injection means that it is determined whether or not pulverized coal is injected, unreduced molten material falling(raw ore falling)means that it is determined whether or not an unreduced raw material in a molten state in which raw materials that need to be reduced in an upper part of the furnace are unreduced and fall, and coke turning means whether coke turns in a middle part of the coke.
  • the image processing-based determination unit 132 may diagnose a tuyere facility through image processing for image data according to each of the tuyeres, and determine the tuyere combustion state.
  • the image processing-based determination unit 132 may determine a tuyere facility abnormal condition including presence or absence of a curvature of a tuyere, presence or absence of a tuyere attachment, clogging or a tuyere, lance banding or burning, or the like, through image processing of the image data according to each of the tuyeres.
  • the image processing-based determination unit 132 may extract a combustion area and combustion brightness (i.e., luminance) through image processing of image data according to each of the tuyeres.
  • the image processing-based determination unit 132 may determine a pulverized coal flow rate through image processing of the image data according to each of the tuyeres.
  • the determination by the AI-based determination unit 131 and the image processing-based determination unit 132 described above may be performed in parallel.
  • the combustion condition classification result according to each of the tuyeres classified by the tuyere combustion state determination unit 130 and the tuyere facility diagnosis result may be mapped and stored and managed together with image data and collection environment information according to each of the tuyeres.
  • the tuyere combustion state index generation unit 140 may generate a combustion state index according to each of the tuyeres by using the combustion state classification result according to each of the tuyeres classified by the tuyere combustion state determination unit 130.
  • the combustion state index according to each of the tuyeres generated by the tuyere combustion state index generation unit 140 may include a combustion state defect index, a pulverized coal non-injection index, an unreduced molten material falling(raw ore falling) index, a coke turning index, a combustion state level index, a pulverized coal flow rate index, a tuyere raceway index, and the like.
  • the tuyere combustion state index generation unit 140 may count the number of times that an arbitrary classification result has occurred based on the combustion state classification results according to each of the tuyeres by the tuyere combustion state determination unit 130 for every predetermined period, and generate a related index by scoring it according to the number of times counted for each corresponding period.
  • the tuyere combustion state index generation unit 150 may score the combustion state level index according to a combustion area and combustion brightness (i.e., luminance) extracted by the tuyere combustion state determination unit 130, combine the calculated scores for a predetermined period to generate a combustion state level index.
  • reference information used to generate the combustion state level index can be updated according to the input signal by the administrator. Accordingly, the updated reference information may be reflected in real time to generate index information reflecting the blast furnace condition.
  • the tuyere combustion state index generation unit 140 may generate a tuyere facility abnormality index by scoring the results of the tuyere facility diagnosis determined by the tuyere combustion state determination unit 130.
  • the tuyere facility abnormality index may include a tuyere curvature index, a tuyere attachment index, a tuyere blockage index, a lance damage index, and the like.
  • An operational information collection unit 150 is for collecting operational information generated during a blast furnace operation in real time.
  • the operational information may include, for example, a blast furnace body temperature, pressure, a cooling water flow rate, and the like.
  • the operational information collected in real time by the operational information collection unit 150 may be mapped with the tuyere combustion state index information generated by the tuyere combustion state index information unit 140 described above and stored and managed.
  • An integrated evaluation unit 160 may be integrally evaluated in a circumferential direction of the blast furnace based on the tuyere operational state index information generated according to each of the tuyeres by the tuyere combustion state index generation unit 140 and operational information collected by an operational information collection unit 150.
  • the integrated evaluation unit 160 may generate an integrated combustion state index by comprehensively considering the tuyere combustion state index information generated according to each of the tuyeres by the tuyere combustion state index generation unit 140.
  • the integrated combustion state index may include an integrated combustion state index, matched 1:1 to the integrated combustion state index generated according to each of the tuyeres such as an integrated combustion state defect index, an integrated pulverized coal non-inj ection index, an integrated unreduced molten material falling(raw ore falling)index, and the like.
  • the integrated evaluation unit 160 may generate a circumferential balance index based on tuyere raceway indices generated according to each of the tuyeres.
  • the integrated evaluation unit 160 may generate an integrated tuyere facility abnormality index based on the tuyere facility abnormality index generated according to each of the tuyeres.
  • a blast furnace condition control unit 170 may perform at least one of pulverized coal injection control, N2 purge control, and blast furnace charge control, based on the tuyere combustion state index information generated according to each of the tuyeres by the tuyere combustion state index generation unit 140 or the integrated combustion state index generated by the integrated evaluation unit 160 to control the blast furnace condition.
  • the blast furnace condition control unit 170 may perform pulverized coal injection control when a pulverized coal non-injection index for an arbitrary tuyere exceeds a predetermined reference value.
  • the blast furnace condition control unit 170 may perform blast furnace charging control when a unreduced molten material falling (raw ore falling)index exceeds a predetermined reference value due to occurrence of raw ore falling in any tuyere region.
  • the blast furnace condition control unit 170 may integrally control a plurality of tuyeres based on information of an integrated combustion state index or a circumferential balance index.
  • the blast furnace control unit 170 may control a blast furnace charging, for example, by changing distribution of charges to change a direction in which the charges fall, when raw ore falling occurs in only one direction.
  • the system for evaluating operational conditions of a blast furnace 100 described above with reference to FIG. 1 applies an artificial intelligence algorithm to input data and performs image processing, and may be implemented by combination of a processing device capable of calculating various indices, and a control device capable of performing blast furnace control.
  • FIG. 5 is a flowchart of a method for evaluating operational conditions of a blast furnace according to another embodiment of the present disclosure.
  • image data according to each of the tuyeres provided in a blast furnace may be collected in real time by an image capturing unit 110 and an image collection unit 120 (S510).
  • a combustion state according to each of the tuyeres may be classified using the image data according to each of the tuyeres (S520) .
  • an AI-based determination unit 131 after primarily classifying the tuyere combustion state based on artificial intelligence using the image data according to each of the tuyeres (S521), the classification of the tuyere combustion state may be determined based on the result of classifying the combustion states (S522).
  • an image processing-based determination unit 132 in addition to classifying the combustion state according to each of the tuyeres through image processing for the image data according to each of the tuyeres, a tuyere facility can be diagnosed (S525).
  • a combustion state index is generated based on the result of classifying the combustion state according to each of the tuyeres (S530) , and by an integrated evaluation unit 160, an operational condition of a blast furnace may be integrally evaluated in a circumferential direction based on the generated combustion state index according to each of the tuyeres (S540) .
  • a blast furnace condition may be controlled based on the integrally evaluated operational condition (S550).

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • 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)
EP18893418.6A 2017-12-26 2018-12-24 System und verfahren zur bewertung der betriebsbedingungen eines hochofens Active EP3733876B1 (de)

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KR1020170180170A KR102075223B1 (ko) 2017-12-26 2017-12-26 고로 조업 상황 평가 시스템 및 방법
PCT/KR2018/016580 WO2019132476A1 (ko) 2017-12-26 2018-12-24 고로 조업 상황 평가 시스템 및 방법

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WO2021182041A1 (ja) * 2020-03-12 2021-09-16 Ｊｆｅスチール株式会社 高炉の異常判定方法、安定期モデルの学習方法、高炉の操業方法および高炉の異常判定装置
CN112094974A (zh) * 2020-08-21 2020-12-18 广东韶钢松山股份有限公司 高炉铁口喷雾控制方法、系统及其计算机可读存储介质
CN112501368B (zh) * 2020-11-17 2022-07-08 中冶南方工程技术有限公司 一种高炉冶炼方法及计算机设备
JP7380604B2 (ja) * 2021-01-12 2023-11-15 Ｊｆｅスチール株式会社 学習モデル生成方法、学習モデル生成装置、高炉の制御ガイダンス方法、及び溶銑の製造方法
CN113177364B (zh) * 2021-05-21 2023-07-14 东北大学 一种高炉风口回旋区温度软测量建模方法

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KR950014631B1 (ko) * 1993-12-28 1995-12-11 포항종합제철주식회사 인공신경회로망을 이용한 고로용선온도 예측 및 액션 제어량 가이드 장치
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EP3733876B1 (de) 2023-06-07
KR20190078319A (ko) 2019-07-04
CN111527217A (zh) 2020-08-11
JP2021509440A (ja) 2021-03-25
EP3733876A4 (de) 2021-01-20
WO2019132476A1 (ko) 2019-07-04
KR102075223B1 (ko) 2020-02-07
JP7064598B2 (ja) 2022-05-10
CN111527217B (zh) 2022-08-16

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