Classification of blast furnace internal state based on FLS and its application in furnace temperature prediction

Wang Gao-peng; Zhai Hai-peng; Yan Zhen-yu; Zheng Rui-ji

doi:10.1051/e3sconf/202125202041

All issues

Volume 252 (2021)

E3S Web Conf., 252 (2021) 02041

References

Open Access

Issue		E3S Web Conf. Volume 252, 2021 2021 International Conference on Power Grid System and Green Energy (PGSGE 2021)


Article Number		02041
Number of page(s)		7
Section		Research and Development of Electrical Equipment and Energy Nuclear Power Devices
DOI		https://doi.org/10.1051/e3sconf/202125202041
Published online		23 April 2021

Chen, M., Yi, Y.X., Zhu, Q., Zhang, H.G. (2014) Prediction of blast furnace temperature based on distributed parameter model. Control theory and Application., 31(09): 1232–1237. [Google Scholar]
Henrik, S., Gao, C.H., Gao, Z.W. (2013) Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—a review. IEEE Transactions on Industrial Informatics., 9(4): 2213–2225. [Google Scholar]
Barbasova, T.A., Zagoskina, E.V. (2019) Blast-Furnace Melting Blast Control. In: 2019 IEEE Russian Workshop on Power Engineering and Automation of Metallurgy. Russian. 13–17. [Google Scholar]
Wang, G.P. (2018) Silicon Prediction Model of Blast Furnace Based on ARX and PCR. In: 2018 13th World Congress on Intelligent Control and Automation (WCICA). China. 1214–1220. [Google Scholar]
Zhou, P., Zhang, L., Li, W.P., Dai, P., Chai, T.Y. (2018) Modeling of multiple hot metal quality in blast furnace based on stochastic weight neural network integrating self coding and PCA. Journal of automation, 44(10): 1799–1811. [Google Scholar]
Cui, J.M., Jiang, Z.G., Zhan, W.P., Gu, J.H. (2015) Predict ion of blast furnace temperature based on time series neural network. Metallurgical Automation, 39 (05): 15–21. [Google Scholar]
Zhou, P., Li, R.F., Guo, D. W., Wang, H., Chai, T.Y. (2016) Multi output support vector regression modeling of multiple hot metal quality indexes in blast furnace ironmaking process. Control theory and Application, 33(06): 727–734. [Google Scholar]
Jiang, C.H., Dong, M.L., Gui, W.H., Yang. C.H, Xie, Y.F. (2016) Two dimensional prediction of silicon content in hot metal of blast furnace based on Bootstrap. Journal of automation, 36(50): 331–336. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] Chen, M., Yi, Y.X., Zhu, Q., Zhang, H.G. (2014) Prediction of blast furnace temperature based on distributed parameter model. Control theory and Application., 31(09): 1232–1237. [Google Scholar]

[2] Henrik, S., Gao, C.H., Gao, Z.W. (2013) Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—a review. IEEE Transactions on Industrial Informatics., 9(4): 2213–2225. [Google Scholar]

[3] Barbasova, T.A., Zagoskina, E.V. (2019) Blast-Furnace Melting Blast Control. In: 2019 IEEE Russian Workshop on Power Engineering and Automation of Metallurgy. Russian. 13–17. [Google Scholar]

[4] Wang, G.P. (2018) Silicon Prediction Model of Blast Furnace Based on ARX and PCR. In: 2018 13th World Congress on Intelligent Control and Automation (WCICA). China. 1214–1220. [Google Scholar]

[5] Zhou, P., Zhang, L., Li, W.P., Dai, P., Chai, T.Y. (2018) Modeling of multiple hot metal quality in blast furnace based on stochastic weight neural network integrating self coding and PCA. Journal of automation, 44(10): 1799–1811. [Google Scholar]

[6] Cui, J.M., Jiang, Z.G., Zhan, W.P., Gu, J.H. (2015) Predict ion of blast furnace temperature based on time series neural network. Metallurgical Automation, 39 (05): 15–21. [Google Scholar]

[7] Zhou, P., Li, R.F., Guo, D. W., Wang, H., Chai, T.Y. (2016) Multi output support vector regression modeling of multiple hot metal quality indexes in blast furnace ironmaking process. Control theory and Application, 33(06): 727–734. [Google Scholar]

[8] Jiang, C.H., Dong, M.L., Gui, W.H., Yang. C.H, Xie, Y.F. (2016) Two dimensional prediction of silicon content in hot metal of blast furnace based on Bootstrap. Journal of automation, 36(50): 331–336. [Google Scholar]