Title:
高炉の炉況学習方法、炉況学習装置、異常検出方法、異常検出装置、及び操業方法
Document Type and Number:
Japanese Patent JP6939930
Kind Code:
B2
Abstract:
This blast-furnace furnace condition learning method comprises: a first step in which image data of a raceway section of a blast furnace, captured during an image-capture period that includes a period in which a furnace condition abnormality of the blast furnace occurred, is learned by a teacherless neural network; a second step in which, for each neuron constituting the teacherless neural network after learning, a correlation coefficient between an ignition value of the neuron and an index indicating furnace abnormality is calculated; and a third step in which the neurons to be used to detect furnace abnormalities are extracted as neurons for abnormality detection on the basis of the correlation coefficient.
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Inventors:
Naofumi Yamadaira
Application Number:
JP2020041449A
Publication Date:
September 22, 2021
Filing Date:
March 11, 2020
Export Citation:
Assignee:
jfe Steel Corporation
International Classes:
C21B5/00; C21B7/24; F27D21/00; G05B23/02
Domestic Patent References:
| JP202015938A | ||||
| JP202015934A | ||||
| JP6119454A |
Foreign References:
| CN110544261A |
Attorney, Agent or Firm:
Sakai International Patent Office