EP3730630A1 - Appareil et procédé de commande de condition de four - Google Patents

Appareil et procédé de commande de condition de four Download PDF

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Publication number
EP3730630A1
EP3730630A1 EP18891914.6A EP18891914A EP3730630A1 EP 3730630 A1 EP3730630 A1 EP 3730630A1 EP 18891914 A EP18891914 A EP 18891914A EP 3730630 A1 EP3730630 A1 EP 3730630A1
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EP
European Patent Office
Prior art keywords
data
blast furnace
action guidance
sensor unit
furnace
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
EP18891914.6A
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German (de)
English (en)
Other versions
EP3730630B1 (fr
EP3730630A4 (fr
Inventor
Kyung-Lyong HAN
Jin-Hwi LEE
Sang-Han Son
Gi-Wan SON
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
Original Assignee
Posco Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
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Publication of EP3730630A1 publication Critical patent/EP3730630A1/fr
Publication of EP3730630A4 publication Critical patent/EP3730630A4/fr
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Publication of EP3730630B1 publication Critical patent/EP3730630B1/fr
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Classifications

    • 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
    • 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
    • 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 specially adapted for furnaces of these types
    • F27B1/26—Arrangements of controlling 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 specially adapted for 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—Arrangement of monitoring devices; Arrangement of safety devices
    • F27D21/0014—Devices for monitoring temperature
    • 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
    • F27D19/00—Arrangements of controlling devices
    • F27D2019/0003—Monitoring the temperature or a characteristic of the charge and using it as a controlling value
    • 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
    • F27D2019/0006—Monitoring the characteristics (composition, quantities, temperature, pressure) of at least one of the gases of the kiln atmosphere and using it as a controlling value
    • F27D2019/0009—Monitoring the pressure in an enclosure or kiln zone
    • 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
    • F27D2019/0028—Regulation
    • F27D2019/0034—Regulation through control of a heating quantity such as fuel, oxidant or intensity of current
    • 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
    • F27D2019/0087—Automatisation of the whole plant or activity
    • 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—Arrangement of monitoring devices; Arrangement of safety devices
    • F27D2021/0007—Monitoring the pressure

Definitions

  • the present disclosure relates to furnace condition control apparatus and method for controlling conditions of a blast furnace.
  • a blast furnace process is a typical process, which is mainly dependent on the experience and intuition of operators to manually perform operations, among iron making processes.
  • a blast furnace is a facility for charging iron ore and coke in an upper portion of the blast furnace and blowing hot air through a tuyere to produce molten iron through a taphole using internal oxidation and reduction reactions . Due to a high temperature and high pressure in the blast furnace, a measurement cannot be performed through a sensor. Therefore, a situation of the blast furnace is indirectly predicted through a thermometer, a pressure gage, and the like, mounted on an external wall of the blast furnace, and operators perform operations based on the prediction of the situation of the blast furnace.
  • the furnace heat is an index obtained by manually measuring a temperature of molten iron, coming out through a taphole, to predict an internal temperature of the blast furnace.
  • the air permeability is an index to indirectly infer a state of hot air, flowing from a lower portion to an upper portion in the blast furnace, with an air permeability index, or the like, through measurement of a pressure gage on an external wall.
  • the circumferential balance is an index on a state in which there is no significant difference in pressure and temperature in a circumferential direction of a circular blast furnace, for example, balance is maintained.
  • Operators take actions to maintain the above-described three indices at desired values.
  • Representative examples of the actions are control of a pulverized coal injection (PCI) rate, control of hot air volume, control of the amount of oxygen contained in hot air, control of a ratio of charged iron ore and coke, control of distribution of coke having a large grain size entering a central portion, and the like.
  • PCI pulverized coal injection
  • An aspect of the present disclosure is to provide a furnace control apparatus and method for guiding proactive actions to stably maintain a furnace condition using various operations, occurring in a blast furnace, and sensor data.
  • a furnace condition control apparatus includes a first sensor unit configured to image at least one of temperature data and pressure data of a blast furnace depending on a measured location, a second sensor unit configured to detect unstructured data of the blast furnace, and an action guidance unit having an artificial intelligence algorithm outputting action guidance regarding a blast furnace operation based on imaged temperature or pressure data from the first sensor unit and unstructured data from the second sensor unit.
  • a furnace condition control method includes collecting, by a data preprocessing unit, at least one of a charging material state, a tuyere state, and a taphole state of a blast furnace as unstructured data and imaging temperature data and pressure data of the blast furnace depending on a measured location, receiving, by an artificial intelligence algorithm, preprocessed data to output action guidance regarding a blast furnace operation, determining relearning of the artificial intelligence algorithm depending on whether an operator employs the action guidance, and determining replacement of the artificial intelligence algorithm depending on whether or not to perform relearning of a corresponding artificial intelligence algorithm.
  • stable production in a blast furnace may be achieved, efficiency of the blast furnace may be improved, a condition of the blast furnace may be controlled to maintain constant performance, and operations may be automated and standardized.
  • FIG. 1 is a schematic block diagram of a furnace condition control apparatus according an example embodiment of the present disclosure.
  • a furnace condition control apparatus 100 may include a first sensor unit 110, a second sensor unit 120, and an action guidance unit 130.
  • the first sensor unit 110 may image at least one of temperature data and pressure data of a blast furnace depending on a measured position.
  • the first sensor unit 110 may include a temperature sensor unit 111, a pressure sensor unit 112, and a data processing unit 113.
  • the temperature sensor unit 111 may include a plurality of temperature sensors mounted in the blast furnace.
  • the plurality of temperature sensors may detect temperatures of the blast furnace in mounted locations thereof, respectively.
  • the pressure sensor unit 111 may include a plurality of pressure sensors mounted in the blast furnace.
  • the plurality of temperature sensors may detect pressures of the blast furnace in mounted locations thereof, respectively.
  • the data processing unit 113 may map data on the temperature, detected by each of the plurality of temperature sensors of the temperature sensor unit 111, to the detected location and may image the mapped data. Similarly, the data processing unit 113 may map data on the pressure, detected by each of the plurality of pressure sensors of the pressure sensor unit 112, to the detected location and may image the mapped data. In addition, the data processing unit 113 may map data on the temperature and data on the pressure, detected by each of the plurality of temperature sensors and each of the plurality of pressure sensors of the temperature sensor unit 111, to the detected locations and may image the mapped data.
  • the data processing unit 113 may map data on a detected temperature or pressure to a detected location and may two-dimensionally image the mapped data.
  • FIG. 5 illustrates imaged data of a thermometer and a pressure gage applied to a furnace condition control apparatus according an example embodiment of the present disclosure.
  • FIG. 5 an example of imaging sensor data of the blast furnace, that is, detected data of the temperature sensor unit 111 and the pressure sensor unit 112 may be illustrated.
  • a left image shows a heatmap drawn under an assumption that a plurality of temperature sensors are distributed on a surface of a cylindrical blast furnace and then cut and expanded at a zero degree.
  • a horizontal direction of the drawing is an angle at which the temperature sensors are distributed.
  • a height-dependent distribution of the temperature sensors corresponded to a height in the drawing.
  • each black dot expresses a temperature sensor.
  • temperatures values of the blast furnace change every moment while having an organic interrelationship.
  • a directional pressure gage may be divided into four colored lines.
  • a horizontal axis denotes a pressure value, and a vertical axis denotes a height location of the pressure sensor.
  • an imaging technology illustrated in the drawing is used to efficiently input such a required location information relationship to artificial intelligence.
  • the second sensor unit 120 may measure at least one of a state of a charging material, a state of a tuyere, and a state of a taphole of the blast furnace to detect unstructured data.
  • the present disclosure may propose optimal action guidance for determining a furnace state based on current state data of the blast furnace through a deep learning-based algorithm and maintaining a normal furnace condition. Since the deep learning-based algorithm is a data-driven algorithm, a large amount of data, capable of representing a condition well, is necessary.
  • operators structure data, which is not used in a control operation using a computer because it is not structured while being contents used as a basis for determining operations of a blast furnace with naked eyes, and apply the structured data to the present disclosure.
  • First data is data generated by measuring grain sizes of charged iron ore and coke.
  • the first data is related to air permeability.
  • Second is data used as numerical data on conditions of a combustion zone of a tuyere.
  • the combustion zone of the tuyere is the only facility allowed to observe the inside of the blast furnace and blowing hot air.
  • pulverized coal is blown together, and the combustion zone serves to monitor a combustion state of the pulverized coal or a fuel and a raw material falling from an internal wall of the blast furnace without melting.
  • Third is a measuring device for measuring a state of a taphole, and measurement of a temperature of molten iron is an especially important factor.
  • a temperature of molten iron tapped from the blast furnace is manually measured once every one or two hours. Since a measurement location is also spaced apart from a taphole by a predetermined distance and the degree of measurement taken by a person is also not constant, disturbance is considerably included in a measured value. This value is important data related to furnace heat.
  • the second sensor unit 120 may include a charging material state measuring device 121, a tuyere state measuring device 122, and a taphole state measuring device 123.
  • the charging material state measuring device 121 is disposed on a conveyor belt, along which a fuel and a raw material charged in the blast furnace pass, to measure at least one of a grain size of a charging material, a grain size distribution, and a humidity state of the blast furnace and to convert measured unstructured data into structured data and transfer the structured data to the action guidance unit 130.
  • the tuyere state measuring device 122 may measure at least one of a pulverized coal injection state and a raw ore falling state of the blast furnace through a plurality of tuyere cameras, and may convert measured unstructured data into structured data and transfer the structured data to the action guidance unit 130.
  • the taphole state measuring device 123 may measure a temperature of the molten iron tapped from the blast furnace in real time, and may measure the amount of the tapped molten iron with an angle, a thickness, or the like, of a branch of the molten iron. Then, the taphole state measuring device 123 may convert measured unstructured data into structures data and may transfer the structured data to the action guidance unit 130.
  • the action guidance unit 130 may output action guidance regarding a blast furnace operation, based on imaged temperature or pressure data from the first sensor unit 110 and unstructured data from the second sensor unit 120.
  • FIG. 2 illustrates a concept of artificial intelligence (AI) applied to a furnace condition control apparatus according an example embodiment of the present disclosure.
  • AI artificial intelligence
  • the action guidance unit 130 may include a learning unit 131, a control unit 132, and a reinforcement learning unit 133.
  • FIG. 3 illustrates a schematic operation flow of a furnace condition control method according an example embodiment of the present disclosure.
  • the learning unit 131 may include an action guidance on-line algorithm.
  • the action guidance on-line algorithm learns based on two-dimensionally imaged temperature data and pressure data (S10, S11) from the first sensor unit 110 and structured data of a charging material state, a tuyere state, and a taphole state of the blast furnace (S10, S12) form the second sensor unit 120, and may generate action guidance regarding the blast furnace operation (S20 and S20)
  • the action guidance on-line algorithm may include a deep learning-based algorithm to learn input data X to generate action guidance C .
  • the control unit 132 may output the action guidance C of the learning unit, and whether an operator accepts the action guidance may be feedbacked to the reinforcement learning unit 133 (S40) .
  • the reinforcement learning unit 133 may include an action guidance off-line algorithm including a deep learning-based algorithm.
  • the action guidance off-line algorithm may receive action guidance, unaccepted by an operation, to reinforce algorithm learning.
  • the control unit 132 may determine relearning of the action guidance on-line algorithm and whether or not to replace the action guidance on-line algorithm with an action guidance off-line algorithm of the reinforcement learning unit 133.
  • the deep learning algorithm proposes guidance for an action that an operation should take for stable furnace condition control based on a learned model.
  • the operator determine whether or not to accept such action guidance, and the deep learning algorithm uses the determination as a feedback and utilize the feedback in an algorithm for improving performance.
  • an artificial intelligence algorithm appropriate to a current blast furnace condition, is maintained through relearning to optimize performance.
  • data collected by summing unstructured data, structured and then input, and structured data, directly input is preprocessed and then input to the deep learning-based action guidance algorithm.
  • the algorithm proposes action guidance based on its own model. The operator determines whether the proposed action guidance is appropriate to a blast furnace operation, and then accepts or rejects the proposed action guidance. An operation using a first algorithm is performed through such a repeated loop.
  • the operator receives a result of whether or not to accept artificial intelligence action guidance as a feedback value (S60) to perform on-line learning or reinforcement learning.
  • the deep learning-based guidance off-line algorithm compensates for an action guidance value feedbacked and input depending on whether a previous operator accepts action guidance (S50) to be used in algorithm reinforcement.
  • a reinforcement learning part is basically present in a deep learning-based action guidance off-line algorithm. In the case in which the deep learning-based action guidance off-line algorithm make an erroneous determination, the reinforcement learning part reflects and uses the erroneous determination to improve algorithm performance.
  • FIG. 4 illustrates an example of a graphic user interface (GUI) of a furnace condition control apparatus according to an example embodiment of the present disclosure.
  • GUI graphic user interface
  • the action guidance unit 130 may propose an action regarding a blast furnace operation such as air volume, oxygen, pulverized coal, charging fuel/raw material cost, a center coke distribution, and the like.
  • a blast furnace operation such as air volume, oxygen, pulverized coal, charging fuel/raw material cost, a center coke distribution, and the like.
  • an action guidance value required to control the air volume may be confirmed through the illustrated GUI, and a trend of related data may be confirmed.
  • an operation may be manually performed, as necessary.
  • an action of an operator required to maintain a stable furnace condition may be guided to achieve stable production of a blast furnace.
  • efficiency of the blast furnace may be improved.
  • a furnace condition control system maintaining constant performance using a method of maintaining an algorithm able to response to operating conditions and blast furnace conditions varying depending on time, may be implemented.
  • operations may be automated and standardized to reduce the load of the operator and to change tacit knowledge, such as know-how, experience, and the like, of the operator, into spredable and shareable explicit knowledge.
  • stable production of a blast furnace may be achieved, efficiency of the blast furnace may be improved, furnace conditions may be controlled to maintain constant performance, and operations may be automated and standardized.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Materials Engineering (AREA)
  • Metallurgy (AREA)
  • Organic Chemistry (AREA)
  • Manufacture Of Iron (AREA)
  • Waste-Gas Treatment And Other Accessory Devices For Furnaces (AREA)
  • Blast Furnaces (AREA)
  • Vertical, Hearth, Or Arc Furnaces (AREA)
EP18891914.6A 2017-12-19 2018-12-18 Appareil et procédé de commande de condition de four Active EP3730630B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR1020170175537A KR102075210B1 (ko) 2017-12-19 2017-12-19 노황 관리 장치 및 방법
PCT/KR2018/016113 WO2019124931A1 (fr) 2017-12-19 2018-12-18 Appareil et procédé de commande de condition de four

Publications (3)

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EP3730630A1 true EP3730630A1 (fr) 2020-10-28
EP3730630A4 EP3730630A4 (fr) 2021-01-13
EP3730630B1 EP3730630B1 (fr) 2022-05-18

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EP (1) EP3730630B1 (fr)
JP (1) JP7050934B2 (fr)
KR (1) KR102075210B1 (fr)
CN (1) CN111492070A (fr)
WO (1) WO2019124931A1 (fr)

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CN113921427A (zh) * 2021-10-09 2022-01-11 杭州中欣晶圆半导体股份有限公司 一种具有非接触式SiC舟改善体高效能的退火炉系统
EP4001440A1 (fr) * 2020-11-18 2022-05-25 Primetals Technologies Austria GmbH Caractérisation d'un procédé de fusion
WO2023187501A1 (fr) * 2022-03-29 2023-10-05 Tata Steel Limited Système et procédé de mesure de profil de lit de fusion dans un four métallurgique
WO2023217967A1 (fr) * 2022-05-12 2023-11-16 Primetals Technologies Austria GmbH Procédé et système informatique pour commander un processus d'une installation métallurgique

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JP6897723B2 (ja) * 2019-07-19 2021-07-07 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、高炉の溶銑温度制御方法、高炉の溶銑温度制御ガイダンス方法、及び溶銑の製造方法
CN112257590B (zh) * 2020-10-22 2023-08-01 中冶南方工程技术有限公司 一种高炉铁口工作状态的自动检测方法、系统及存储介质
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
CN114185976B (zh) * 2021-11-01 2024-03-26 中冶南方工程技术有限公司 一种高炉可视化智能感知平台
JP7384326B1 (ja) * 2022-03-07 2023-11-21 Ｊｆｅスチール株式会社 高炉の溶銑温度予測方法、高炉の溶銑温度予測モデルの学習方法、高炉の操業方法、高炉の溶銑温度予測装置、高炉の溶銑温度予測システムおよび端末装置

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4001440A1 (fr) * 2020-11-18 2022-05-25 Primetals Technologies Austria GmbH Caractérisation d'un procédé de fusion
WO2022106454A1 (fr) * 2020-11-18 2022-05-27 Primetals Technologies Austria GmbH Caractérisation d'un procédé de fusion
CN113921427A (zh) * 2021-10-09 2022-01-11 杭州中欣晶圆半导体股份有限公司 一种具有非接触式SiC舟改善体高效能的退火炉系统
WO2023187501A1 (fr) * 2022-03-29 2023-10-05 Tata Steel Limited Système et procédé de mesure de profil de lit de fusion dans un four métallurgique
WO2023217967A1 (fr) * 2022-05-12 2023-11-16 Primetals Technologies Austria GmbH Procédé et système informatique pour commander un processus d'une installation métallurgique

Also Published As

Publication number Publication date
KR20190074132A (ko) 2019-06-27
KR102075210B1 (ko) 2020-02-07
JP7050934B2 (ja) 2022-04-08
CN111492070A (zh) 2020-08-04
EP3730630B1 (fr) 2022-05-18
WO2019124931A1 (fr) 2019-06-27
JP2021507115A (ja) 2021-02-22
EP3730630A4 (fr) 2021-01-13

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