[go: up one dir, main page]

WO2019124931A1 - 노황 관리 장치 및 방법 - Google Patents

노황 관리 장치 및 방법 Download PDF

Info

Publication number
WO2019124931A1
WO2019124931A1 PCT/KR2018/016113 KR2018016113W WO2019124931A1 WO 2019124931 A1 WO2019124931 A1 WO 2019124931A1 KR 2018016113 W KR2018016113 W KR 2018016113W WO 2019124931 A1 WO2019124931 A1 WO 2019124931A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
blast furnace
action guidance
sensor unit
algorithm
Prior art date
Application number
PCT/KR2018/016113
Other languages
English (en)
French (fr)
Korean (ko)
Inventor
한경룡
이진휘
손상한
손기완
Original Assignee
주식회사 포스코
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
Application filed by 주식회사 포스코 filed Critical 주식회사 포스코
Priority to CN201880082559.XA priority Critical patent/CN111492070A/zh
Priority to EP18891914.6A priority patent/EP3730630B1/de
Priority to JP2020534232A priority patent/JP7050934B2/ja
Publication of WO2019124931A1 publication Critical patent/WO2019124931A1/ko

Links

Images

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
    • 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 invention relates to an apparatus and method for managing an agar apparatus for managing an agar apparatus for blast furnace.
  • the blast furnace process is a representative process that mainly conducts the manual operation depending on the experience and intuition of the operator so far during the steel making process.
  • the blast furnace is a facility that charges iron ore and coke to the upper part of the blast furnace, blows hot air through the blast furnace tuyere, and produces liquid iron by the oxidation and reduction reaction inside through outlet. Because the inside of the blast furnace can not be measured through the sensor due to high temperature and high pressure, the condition of the blast furnace is predicted indirectly through the thermometer and pressure gauge installed on the outer wall of the blast furnace.
  • the ventilation, breathability, and circumferential balance There are various indicators of the present condition of blast furnace, ie, aging.
  • Three representative examples are the ventilation, breathability, and circumferential balance.
  • furnace heat it refers to an index for predicting the temperature inside the blast furnace through manual measurement of the temperature of the boiler coming out through the outlet.
  • air permeability the state of the hot air moving from the lower part to the upper part inside the blast furnace is indirectly ,
  • the circumference balance is an index for a situation in which a circular blast furnace does not have a large difference in pressure and temperature in the circumferential direction, that is, the balance is maintained.
  • Typical examples include control of pulverized coal (PCI) injection amount, control of air volume of hot wind, control of oxygen amount in air volume, ratio control of iron oxide and coke to be charged, and distribution control of large particle size coke in the center part.
  • PCI pulverized coal
  • the blast furnace operation basically judges the condition of the blast furnace according to the experience of the operator and intuition and the operating standards based on the form data such as the thermometer or the pressure gauge measured value and the information obtained through the atypical data such as CCTV, Based on this, we are taking action.
  • an apparatus and method for controlling a sulfur content management system for guiding pre-existing actions for stably maintaining the sulfur content using various operational and sensor data generated in the blast furnace.
  • an apparatus for managing an anther of a certain type comprising a first sensor unit for imaging at least one of temperature and pressure data of a blast furnace according to a measured position, An action guidance having an artificial intelligence algorithm for outputting an action guidance on blast furnace operation based on the imaged temperature or pressure data from the first sensor unit and the atypical data from the second sensor unit; Section.
  • a method for managing a glaucophore which comprises collecting at least one irregular data of a charge condition, a trough condition and an exit condition of a blast furnace to a blast furnace, A step of inputting the preprocessed data of the artificial intelligence algorithm and outputting an action guidance related to the blast furnace operation, a step of judging re-learning of the artificial intelligence algorithm according to whether the action guidance of the operator is applied or not, And determining the replacement of the artificial intelligence algorithm according to re-learning.
  • FIG. 2 is a view for explaining the concept of artificial intelligence applied to an apparatus for managing aged people according to an embodiment of the present invention.
  • FIG. 3 is a schematic operation flow diagram of a method for managing an aged population according to an embodiment of the present invention.
  • FIG. 4 is a diagram showing an example of a GUI of a management apparatus for an aged person according to an embodiment of the present invention.
  • FIG. 5 is an image of a thermometer and pressure gauge data applied to a glaze management apparatus according to an embodiment of the present invention.
  • FIG. 1 is a schematic block diagram of an apparatus for managing the effect of the present invention according to an embodiment of the present invention.
  • an apparatus 100 for managing an aged person 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 the temperature and pressure data of the blast furnace according to the 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 respectively installed in the blast furnace, and the plurality of temperature sensors can detect the temperature of the blast furnace at the installed position.
  • the pressure sensor unit 112 may include a plurality of pressure sensors respectively installed in the blast furnace, and the plurality of pressure sensors can detect the pressure in the blast furnace at the installed position.
  • the data processing unit 113 can map the detected temperature data of each of the plurality of temperature sensors of the temperature sensor unit 111 to the detected position and image them. Similarly, the detected pressure data of each of the plurality of pressure sensors of the pressure sensor unit 112 can be mapped and imaged to the detected position. In addition, the detected temperature data of each of the plurality of temperature sensors and the detected pressure data of each of the plurality of pressure sensors can be mapped and imaged to the detected position.
  • the data processing unit 113 may map the detected temperature or pressure data to the detected position to form a two-dimensional image.
  • FIG. 5 is an image of a thermometer and pressure gauge data applied to a glaze management apparatus according to an embodiment of the present invention.
  • sensor data of blast furnace that is, detection data of the temperature sensor unit 111 and the pressure sensor unit 112 are imaged.
  • each black dot represents a temperature sensor.
  • the temperature values of the blast furnace vary instantaneously with an organic correlation.
  • Directional pressure gauges can be divided into four color lines.
  • the horizontal axis represents the pressure value, and the vertical axis represents the height position of the pressure sensor.
  • the imaging technology as shown in this drawing is used to efficiently input the necessary positional information relation to the artificial intelligence.
  • the second sensor unit 120 can detect atypical data of the blast furnace by measuring at least one of the state of the blast furnace, the tuyere state, and the outlet state.
  • the present invention it is possible to determine an aging based on current blast state data through an algorithm based on a deep learning and to suggest an optimal action guidance for maintaining a normal aging state. Since deep-run-based algorithms are data-driven algorithms, a large amount of data is necessary to represent the situation well.
  • the operators have used the data as the basis of the judgment of the blind operation by the naked eye, but the data that can not be used for the control using the computer because of the unstructured data is formulated and applied to the present invention.
  • the first data is data on the particle size of the iron ore and coke charged. This is data related to breathability.
  • the second is to use the data of the combustion zone of the tungsten as a numerical data.
  • tuyu burning zone it is the only part that can observe inside the blast furnace, and it is a facility to blow hot air.
  • the pulverized coal is blown in together, and it functions to monitor the combustion state of the pulverized coal and the combustion / raw material that does not melt at the inner wall of the blast furnace.
  • the third is an instrument for the exit condition, and in particular, the measurement of the char temperature is an important factor.
  • the measurement position is also a place at a distance from the exit, and the degree of measurement of the person is not constant. This value is important data related to the row.
  • the second sensor unit 120 may include a charge state meter 121, a tougue state meter 122, and an exit state meter 123.
  • the charge state meter 121 can measure at least one of the particle size, particle size distribution, and humidity state of the charge placed in the conveyor belt passing through the soft material to be charged into the blast furnace of the blast furnace, and measures the measured unstructured data as the format data And transmits it to the action guidance unit 130.
  • the exit meter status measuring device 123 measures the temperature of the molten iron leaving the blast furnace in real time and measures the amount of emission or the like based on the angle and thickness of the molten steel stem and converts the measured irregular data into the formatted data, 130).
  • FIG. 2 is a view for explaining the concept of artificial intelligence applied to an apparatus for managing aged people according to an embodiment of the present invention.
  • the action guidance unit 130 may include a learning unit 131, a control unit 132, and a reinforcement learning unit 133.
  • FIG. 3 is a schematic operation flow diagram of a method for managing an aged population according to an embodiment of the present invention.
  • the learning unit 131 may include an action guidance on-line algorithm, and the action guidance on-line algorithm may include a temperature and pressure from the first sensor unit 110, Based on the data S10 and S11 and the state of the charge of the blast furnace formulated from the second sensor unit 120, the toughening state and the exit state state measurement data S10 and S12, and the action guidance (S20, S30).
  • the action guidance on-line algorithm may include a temperature and pressure from the first sensor unit 110, Based on the data S10 and S11 and the state of the charge of the blast furnace formulated from the second sensor unit 120, the toughening state and the exit state state measurement data S10 and S12, and the action guidance (S20, S30).
  • the control unit 132 receives the action guidance of the learning unit ( , And whether or not the operator's action guidance is accepted can be fed back to the reinforcement learning unit 133 (S40).
  • the reinforcement learning unit 133 may include an action guidance offline algorithm configured by a deep learning based algorithm, and the action guidance offline algorithm may enhance the algorithm learning by receiving an action guidance not accepted by the worker.
  • the control unit 132 may determine whether to re-learn the action guidance online algorithm and replace the action guidance online algorithm with the action guidance offline algorithm of the reinforcement learning unit 133.
  • the deep learning algorithm is operated based on the learned model Provide guidance on the action to be performed by the user.
  • the operator judges acceptance of such action guidance, and the deep learning algorithm uses it as feedback to utilize the algorithm to enhance the performance.
  • re-learning is performed to maintain the artificial intelligence algorithm for the current blast condition to optimize the performance.
  • the on-line learning or reinforcement learning is performed by receiving as a feedback value a result of whether the operator accepts the AI action guidance when there is an offline offline control algorithm (S60). That is, in the case of the deep learning-based action guidance offline (off-line) algorithm, the action guidance value inputted as the feedback according to the acceptance of the former operator is compensated (S50) and used for the algorithm reinforcement.
  • the deep learning-based action guidance offline algorithm has a reinforcement learning part, which is used to improve the algorithm performance in case of misidentification of the deep learning-based action guidance on-line algorithm.
  • the compensation value falls below a predetermined level or the characteristics of the data are learned, the re-learning is judged and the re-learning is performed if necessary (S70).
  • the deep learning-based action guidance on-line algorithm is replaced with the newly learned action guidance offline algorithm on the system. Therefore, it is possible to maintain the algorithm that responds to the blast situation, and the more the operation is performed, the better the action guidance performance can be realized.
  • FIG. 4 is a diagram showing an example of a GUI (Graphic User Interface) of an apparatus for managing the aged population according to an embodiment of the present invention.
  • GUI Graphic User Interface
  • the action guidance unit 130 can present actions related to blast furnace operation such as air volume, oxygen, pulverized coal, loading / raw material cost, and distribution of center coke. For example, through the illustrated GUI, an action guidance value necessary for air volume control can be confirmed and the trend of related data can be confirmed. If necessary, manual operation can also be carried out.
  • the present invention it is possible to produce stable blast furnace by guiding the action of the operator who needs to maintain a stable sulfur content, thereby improving the efficiency of the blast furnace.
  • since the operation is automated and standardized it is possible not only to reduce the load of the operator, but also to formulate the tactile know-how and experiential experience such that the tactic can be shared with the propagation.

Landscapes

  • 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)
PCT/KR2018/016113 2017-12-19 2018-12-18 노황 관리 장치 및 방법 WO2019124931A1 (ko)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN201880082559.XA CN111492070A (zh) 2017-12-19 2018-12-18 炉况控制设备和方法
EP18891914.6A EP3730630B1 (de) 2017-12-19 2018-12-18 Vorrichtung und verfahren zur steuerung von ofenbedingungen
JP2020534232A JP7050934B2 (ja) 2017-12-19 2018-12-18 炉況管理装置及び方法

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
KR10-2017-0175537 2017-12-19

Publications (1)

Publication Number Publication Date
WO2019124931A1 true WO2019124931A1 (ko) 2019-06-27

Family

ID=66994076

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2018/016113 WO2019124931A1 (ko) 2017-12-19 2018-12-18 노황 관리 장치 및 방법

Country Status (5)

Country Link
EP (1) EP3730630B1 (de)
JP (1) JP7050934B2 (de)
KR (1) KR102075210B1 (de)
CN (1) CN111492070A (de)
WO (1) WO2019124931A1 (de)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114185976A (zh) * 2021-11-01 2022-03-15 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

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
EP4001440A1 (de) * 2020-11-18 2022-05-25 Primetals Technologies Austria GmbH Charakterisierung eines verhüttungsprozesses
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
JP7644337B2 (ja) 2021-03-30 2025-03-12 日本製鉄株式会社 高炉操業用機械学習装置、それに用いられるプログラム、高炉操業予測モデルの生成方法及び高炉の操業方法
CN113921427B (zh) * 2021-10-09 2024-09-24 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
WO2023171501A1 (ja) * 2022-03-07 2023-09-14 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、溶銑温度予測システムおよび端末装置
WO2023187501A1 (en) * 2022-03-29 2023-10-05 Tata Steel Limited System and method for measuring burden profile in a metallurgical furnace
EP4276550A1 (de) * 2022-05-12 2023-11-15 Primetals Technologies Austria GmbH Verfahren und computersystem zum steuern eines prozesses in einer metallurgischen anlage

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH05156327A (ja) * 1991-12-06 1993-06-22 Nkk Corp 高炉炉熱制御装置
JPH0726127B2 (ja) * 1987-11-20 1995-03-22 日本鋼管株式会社 高炉炉熱自動制御システム
KR950014631A (ko) 1993-11-27 1995-06-16 전성원 자동차의 타이밍 벨트 장력조절장치
KR0146785B1 (ko) * 1995-11-27 1998-11-02 김종진 전문가 시스템을 이용한 고로 노황 이상 예측장치 및 방법
KR20030063487A (ko) * 2000-12-28 2003-07-28 신닛뽄세이테쯔 카부시키카이샤 고로의 조업 상태 감시 방법, 장치 및 프로그램
KR20120087559A (ko) * 2011-01-28 2012-08-07 현대제철 주식회사 연화융착대 근부위치 추정방법

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0730368B2 (ja) * 1988-02-12 1995-04-05 日本鋼管株式会社 高炉炉熱制御装置
ES2085285T3 (es) * 1988-12-20 1996-06-01 Nippon Steel Corp Metodo y aparato para la gestion del funcionamiento de un alto horno.
JPH0733531B2 (ja) * 1990-04-25 1995-04-12 日本鋼管株式会社 高炉々熱制御装置の支援システム
JPH0598325A (ja) * 1991-10-07 1993-04-20 Nkk Corp 高炉装入物分布制御装置
CN1038146C (zh) 1993-07-21 1998-04-22 首钢总公司 利用人工智能专家系统控制高炉冶炼的方法
JP3814143B2 (ja) 2000-12-28 2006-08-23 新日本製鐵株式会社 高炉操業における操業監視方法、装置、及びコンピュータ読み取り可能な記録媒体
JP4586129B2 (ja) * 2008-03-25 2010-11-24 独立行政法人沖縄科学技術研究基盤整備機構 制御器、制御方法および制御プログラム
CN105886680B (zh) * 2016-05-11 2017-12-29 东北大学 一种高炉炼铁过程铁水硅含量动态软测量系统及方法
KR101858860B1 (ko) * 2016-12-22 2018-05-17 주식회사 포스코 고로 노열 제어 장치
CN106844636A (zh) * 2017-01-21 2017-06-13 亚信蓝涛（江苏）数据科技有限公司 一种基于深度学习的非结构化数据处理方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0726127B2 (ja) * 1987-11-20 1995-03-22 日本鋼管株式会社 高炉炉熱自動制御システム
JPH05156327A (ja) * 1991-12-06 1993-06-22 Nkk Corp 高炉炉熱制御装置
KR950014631A (ko) 1993-11-27 1995-06-16 전성원 자동차의 타이밍 벨트 장력조절장치
KR0146785B1 (ko) * 1995-11-27 1998-11-02 김종진 전문가 시스템을 이용한 고로 노황 이상 예측장치 및 방법
KR20030063487A (ko) * 2000-12-28 2003-07-28 신닛뽄세이테쯔 카부시키카이샤 고로의 조업 상태 감시 방법, 장치 및 프로그램
KR20120087559A (ko) * 2011-01-28 2012-08-07 현대제철 주식회사 연화융착대 근부위치 추정방법

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3730630A4

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114185976A (zh) * 2021-11-01 2022-03-15 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
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

Also Published As

Publication number Publication date
EP3730630A4 (de) 2021-01-13
EP3730630B1 (de) 2022-05-18
EP3730630A1 (de) 2020-10-28
JP2021507115A (ja) 2021-02-22
KR102075210B1 (ko) 2020-02-07
CN111492070A (zh) 2020-08-04
KR20190074132A (ko) 2019-06-27
JP7050934B2 (ja) 2022-04-08

Similar Documents

Publication Publication Date Title
WO2019124931A1 (ko) 노황 관리 장치 및 방법
KR101397244B1 (ko) 팔레트 대차의 누풍 검지 시스템, 팔레트 대차의 누풍 검지 방법
CN205845067U (zh) 基于图像动静态特征的转炉炼钢吹炼状态识别系统
CN106987675A (zh) 一种转炉出钢过程的控制系统及控制方法
CN111985112A (zh) 一种基于Unity3D的高炉数字孪生系统
KR102757587B1 (ko) 생산 설비의 감시 방법, 생산 설비의 감시 장치, 및 생산 설비의 조업 방법
CN113984125A (zh) 陶瓷烧结气氛状态监测系统
CN112633049A (zh) 一种高炉风口工作状态监控的系统
WO2019117493A1 (ko) 고로의 송풍 제어 장치 및 그 방법
CN210765379U (zh) 一种用于转炉智能出钢的装置
CN104090557B (zh) 一种基于现场总线元件的脱碳炉信息系统及其控制方法
CN112458231A (zh) 一种转炉下渣检测方法及系统
KR102741775B1 (ko) 생산 설비의 조업 방법 및 조업 시스템
CN113110322A (zh) 一种虚拟工长决策控制方法、装置、系统及存储介质
CN202849470U (zh) 一种三通道炉风口炉况监测装置
KR100376525B1 (ko) 고로 연소대 감시장치 및 그 방법
CN115997096A (zh) 用于监测和控制炉的系统和方法
CN116519164B (zh) 气缸套生产用铁水温度智能测定系统
JPH0598325A (ja) 高炉装入物分布制御装置
CN114235164B (zh) 一种用于钢包空包及出钢过程的热状态监测系统及方法
CN118134224B (zh) 基于智慧矿产的三维可视化预警系统及方法
CN110907039A (zh) 一种管系温度检测系统及检测方法
JPS6142898Y2 (de)
CN209513087U (zh) 一种焦炉直行测温装置
CN217953683U (zh) 一种基于红外热像仪的高炉风口温度报警系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18891914

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2020534232

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2018891914

Country of ref document: EP

Effective date: 20200720