CL2023000841A1 - Sistema informático y procedimiento para el control térmico de un alto horno - Google Patents

Sistema informático y procedimiento para el control térmico de un alto horno

Info

Publication number
CL2023000841A1
CL2023000841A1 CL2023000841A CL2023000841A CL2023000841A1 CL 2023000841 A1 CL2023000841 A1 CL 2023000841A1 CL 2023000841 A CL2023000841 A CL 2023000841A CL 2023000841 A CL2023000841 A CL 2023000841A CL 2023000841 A1 CL2023000841 A1 CL 2023000841A1
Authority
CL
Chile
Prior art keywords
domain
thermal control
data
blast furnace
reward
Prior art date
Application number
CL2023000841A
Other languages
English (en)
Inventor
Schockaert Cédric
Hansen Fabrice
Hausemer Lionel
Baniasadi Maryam
Bermes Philipp
Original Assignee
Wurth Paul Sa
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 Wurth Paul Sa filed Critical Wurth Paul Sa
Publication of CL2023000841A1 publication Critical patent/CL2023000841A1/es

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • 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
    • C21B7/24—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
    • F27B1/26—Arrangements of controlling 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
    • F27D19/00—Arrangements of controlling 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Chemical & Material Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Automation & Control Theory (AREA)
  • Medical Informatics (AREA)
  • Mechanical Engineering (AREA)
  • Physiology (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Manufacturing & Machinery (AREA)
  • Materials Engineering (AREA)
  • Metallurgy (AREA)
  • Organic Chemistry (AREA)
  • Manufacture Of Iron (AREA)
  • Feedback Control In General (AREA)
  • Filling Or Discharging Of Gas Storage Vessels (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

Se proporcionan un sistema informático (100), un procedimiento implementado por ordenador y un producto de programa informático para entrenar un modelo de aprendizaje por refuerzo (130) para proporcionar instrucciones operativas para el control térmico de un alto horno. Un modelo de aprendizaje automático de adaptación de dominio (110) genera un primer conjunto de datos invariantes del dominio (22) a partir de datos operativos históricos (21) obtenidos como series temporales multivariadas y que reflejan los estados térmicos de los altos hornos respectivos (BF1 a BFn) de múltiples dominios. Se usa un modelo transitorio (121) de un proceso de alto horno genérico para generar datos operativos artificiales (24a) como series temporales multivariadas que reflejan un estado térmico de un alto horno genérico (BFg) para una acción de control térmico particular (26a). Una red generativa de aprendizaje profundo (122) genera un segundo conjunto de datos invariantes del dominio (23a) mediante la transferencia de las características aprendidas de los datos operativos históricos 21 a los datos operativos artificiales (24a). El modelo de aprendizaje por refuerzo (130) determina (1400) una recompensa (131) por la acción de control térmico particular (26a) en vista de una función objetivo determinada mediante el procesamiento del primer y segundo conjuntos de datos invariantes del dominio combinados (22, 23a). En dependencia de la recompensa (131), el segundo conjunto de datos invariantes del dominio se regenera en base a los parámetros modificados (123-2) y se repite la determinación de la recompensa para aprender instrucciones operativas optimizadas para acciones de control térmico optimizadas que se aplicarán para los estados operativos respectivos de uno o más altos hornos.
CL2023000841A 2020-09-30 2023-03-23 Sistema informático y procedimiento para el control térmico de un alto horno CL2023000841A1 (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
LU102103A LU102103B1 (en) 2020-09-30 2020-09-30 Computer System and Method Providing Operating Instructions for Thermal Control of a Blast Furnace

Publications (1)

Publication Number Publication Date
CL2023000841A1 true CL2023000841A1 (es) 2023-11-17

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
CL2023000841A CL2023000841A1 (es) 2020-09-30 2023-03-23 Sistema informático y procedimiento para el control térmico de un alto horno

Country Status (10)

Country Link
US (1) US20230359155A1 (es)
EP (1) EP4222562B1 (es)
JP (1) JP2023543813A (es)
KR (1) KR20230079093A (es)
CN (1) CN116261690A (es)
BR (1) BR112023004793A2 (es)
CL (1) CL2023000841A1 (es)
LU (1) LU102103B1 (es)
TW (1) TW202232356A (es)
WO (1) WO2022069498A1 (es)

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* Cited by examiner, † Cited by third party
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JP2023161707A (ja) * 2022-04-26 2023-11-08 横河電機株式会社 制御装置、制御方法、および、制御プログラム
US20230394888A1 (en) * 2022-06-01 2023-12-07 The Boeing Company Vehicle Health Management Using a Counterfactual Machine Learning Model
CN114974450B (zh) * 2022-06-28 2023-05-30 苏州沃时数字科技有限公司 基于机器学习与自动化试验装置的操作步骤的生成方法
EP4357997A1 (de) * 2022-10-19 2024-04-24 Siemens Aktiengesellschaft Verfahren zum ermitteln einer fertigungsreihenfolge von fertigungsaufträgen einer fertigungsanlage, fertigungsanlage-system, computerprogramm sowie elektronisch lesbarer datenträger
CN115859508B (zh) * 2022-11-23 2024-01-02 北京百度网讯科技有限公司 流场分析方法、元件模型生成方法、训练方法及装置
CN115796364A (zh) * 2022-11-30 2023-03-14 南京邮电大学 一种面向离散制造系统的智能交互式决策方法
CN116045104B (zh) * 2023-01-10 2024-05-10 浙江伟众科技有限公司 空调软硬管连接密封装置及其方法
CN116011422B (zh) * 2023-03-28 2023-06-09 北京宽客进化科技有限公司 一种结构化表格数据生成方法和系统
TWI826335B (zh) * 2023-06-19 2023-12-11 中國鋼鐵股份有限公司 用於預測高爐爐料使用量的方法及其電腦程式產品
CN117311170B (zh) * 2023-11-29 2024-02-06 江苏美特林科特殊合金股份有限公司 自适应控制的镍铌合金熔炼设备多参数调整方法及系统

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US11915105B2 (en) * 2019-02-05 2024-02-27 Imagars Llc Machine learning to accelerate alloy design

Also Published As

Publication number Publication date
EP4222562C0 (en) 2024-04-03
TW202232356A (zh) 2022-08-16
CN116261690A (zh) 2023-06-13
KR20230079093A (ko) 2023-06-05
EP4222562A1 (en) 2023-08-09
JP2023543813A (ja) 2023-10-18
WO2022069498A1 (en) 2022-04-07
LU102103B1 (en) 2022-03-30
BR112023004793A2 (pt) 2023-04-18
EP4222562B1 (en) 2024-04-03
US20230359155A1 (en) 2023-11-09

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