Data-driven soft sensors in blast furnace ironmaking: a survey
Author:
Publisher
Zhejiang University Press
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing
Link
https://link.springer.com/content/pdf/10.1631/FITEE.2200366.pdf
Reference123 articles.
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2. An JQ, Peng K, Cao WH, et al., 2016. A soft-sensing method for missing temperature information based on dynamic neural network on BF wall. J Chem Ind Eng, 67(3):903–911 (in Chinese). https://doi.org/10.11949/j.issn.0438-1157.20151941
3. An JQ, Shen XL, Wu M, et al., 2019. A multi-time-scale fusion prediction model for the gas utilization rate in a blast furnace. Contr Eng Pract, 92:104120. https://doi.org/10.1016/j.conengprac.2019.104120
4. Azadi P, Minaabad SA, Bartusch H, et al., 2020. Nonlinear prediction model of blast furnace operation status. Comput Aided Chem Eng, 48:217–222. https://doi.org/10.1016/B978-0-12-823377-1.50037-9
5. Azadi P, Winz J, Leo E, et al., 2022. A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace. Comput Chem Eng, 156:107573. https://doi.org/10.1016/j.compchemeng.2021.107573
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