Online measurement method of FeO content in sinter based on infrared machine vision and convolutional neural network-Reference-Cited by-同舟云学术

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Online measurement method of FeO content in sinter based on infrared machine vision and convolutional neural network

Published:2022-10 Issue: Volume:202 Page:111849
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Zhang Ning,Chen Xuling,Huang Xiaoxian,Fan Xiaohui,Gan Min,Ji Zhiyun,Sun Zengqing,Peng Zitang

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference35 articles.

1. Iron ore sintering: raw materials and granulation;Fernández-González;Mineral Process Extr. Metal. Rev,2017

2. Reducibilities of wüstite and calcio-wüstite in terms of high temperature X-ray diffraction analysis;Cai;ISIJ Int.,2017

3. Effect of coke reactivity on sinter softening-melting property by simultaneous evaluation method of carbonaceous and ferrous burdens in blast furnace;Sunahara;ISIJ Int.,2011

4. A multilevel prediction model of carbon efficiency based on the differential evolution algorithm for the iron ore sintering process;Hu;IEEE Trans. Ind. Electron,2018

5. Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process;Hu;J. Process Control,2022

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3. Softening and Melting of Wüstite: Insights from a Multiscale Study;Metallurgical and Materials Transactions B;2024-08-19

4. Identification of working conditions and prediction of FeO content in sintering process of iron ore fines;Journal of Iron and Steel Research International;2024-06-04

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