Silicon content prediction of hot metal in blast furnace based on attention mechanism and CNN-IndRNN model

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

doi:10.1051/e3sconf/202125202025

All issues

Volume 252 (2021)

E3S Web Conf., 252 (2021) 02025

References

Open Access

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


Article Number		02025
Number of page(s)		5
Section		Research and Development of Electrical Equipment and Energy Nuclear Power Devices
DOI		https://doi.org/10.1051/e3sconf/202125202025
Published online		23 April 2021

Yang, Y. L., Zhang, S. and Yin, Y. X. (2016). A modified elm algorithm for the prediction of silicon content in hot metal, Neural Comput & Applic 27(1): 241–247. DOI 10.1007/s00521–014–1775-x. [Google Scholar]
Jiang Chaohui, Dong Menglin, Gui Weihua, et al. Two-dimensional prediction of silicon content in blast furnace hot metal based on Bootstrap. Acta Automatica Sinica, 2016, 42(5):715–723. [Google Scholar]
Chen, J. (2001). Predictive system for blast furnaces by integrating a neural network with qualitative analysis, Engineering Applications of Artificial Intelligence 14(1): 77–85. DOI 10.1016/S0952–1976(00)00062–2. [Google Scholar]
Yin Juping, Jiang Chaohui. Data-based prediction of silicon content in blast furnace hot metal[J]. Design and Research of Nonferrous Metallurgy, 2015, 36(03): 36–38+41. [Google Scholar]
Sun Jie, Cui Tingting, Xu Bin, Wang Xingnan. Prediction of silicon content in molten iron based on IGA-ELM[J]. Journal of North China University of Technology (Natural Science Edition), 2020, 42(01): 25–29. [Google Scholar]
Jian, L., Song, Y. Q., Shen, S. Q., Wang, Y. and Yin, H. Q. (2015). Adaptive least squares support vector machine predictor for blast furnace ironmaking process, ISIJ International 55(4): 845–850. DOI 10.2355/isijinternational.55.845. [Google Scholar]
Ye Fei. Prediction method of blast furnace silicon content based on improved BP neural network [D]. Anhui University of Technology, 2019. [Google Scholar]
Yang, Y. L., Zhang, S. and Yin, Y. X. (2016). A modified elm algorithm for the prediction of silicon content in hot metal, Neural Comput & Applic 27(1): 241–247. DOI 10.1007/s00521–014–1775-x. [Google Scholar]
Li Zelong, Yang Chunjie, Liu Wenhui, Zhou Heng, Li Yuxuan. Prediction of silicon content in molten iron based on LSTM-RNN model[J]. CIESC Journal, 2018, 69(03): 992–997. [Google Scholar]
Li S, Li W, Cook C, et al. Independently recurrent neura network (IndRNN): Building a longer and deeper rnn[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 5457–5466. [Google Scholar]
Vaswani A, Shazeer N, Parmar N, et al. Attention Is All You Need[J]. arXiv, 2017. [Google Scholar]
Dhar, S. and Dash, P. K. (2017). Adaptive threshold based new active islanding protection scheme for multiple PV based microgrid application, Iet Generation Transmission & Dis- tribution 11(1): 118–132. DOI 10.1049/iet-gtd.2016.0679. [Google Scholar]
Acar, Y., Dogan, H., Basar, E. and Panayirci, E. (2016). Interpolation based pilot-aided channel estimation for STBC spatial modulation and performance analysis under imperfect CSI, Iet Communications 14(10): 1820–1828. DOI 10.1049/iet-com.2016.0151. [Google Scholar]

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[1] Yang, Y. L., Zhang, S. and Yin, Y. X. (2016). A modified elm algorithm for the prediction of silicon content in hot metal, Neural Comput & Applic 27(1): 241–247. DOI 10.1007/s00521–014–1775-x. [Google Scholar]

[2] Jiang Chaohui, Dong Menglin, Gui Weihua, et al. Two-dimensional prediction of silicon content in blast furnace hot metal based on Bootstrap. Acta Automatica Sinica, 2016, 42(5):715–723. [Google Scholar]

[3] Chen, J. (2001). Predictive system for blast furnaces by integrating a neural network with qualitative analysis, Engineering Applications of Artificial Intelligence 14(1): 77–85. DOI 10.1016/S0952–1976(00)00062–2. [Google Scholar]

[4] Yin Juping, Jiang Chaohui. Data-based prediction of silicon content in blast furnace hot metal[J]. Design and Research of Nonferrous Metallurgy, 2015, 36(03): 36–38+41. [Google Scholar]

[5] Sun Jie, Cui Tingting, Xu Bin, Wang Xingnan. Prediction of silicon content in molten iron based on IGA-ELM[J]. Journal of North China University of Technology (Natural Science Edition), 2020, 42(01): 25–29. [Google Scholar]

[6] Jian, L., Song, Y. Q., Shen, S. Q., Wang, Y. and Yin, H. Q. (2015). Adaptive least squares support vector machine predictor for blast furnace ironmaking process, ISIJ International 55(4): 845–850. DOI 10.2355/isijinternational.55.845. [Google Scholar]

[7] Ye Fei. Prediction method of blast furnace silicon content based on improved BP neural network [D]. Anhui University of Technology, 2019. [Google Scholar]

[8] Yang, Y. L., Zhang, S. and Yin, Y. X. (2016). A modified elm algorithm for the prediction of silicon content in hot metal, Neural Comput & Applic 27(1): 241–247. DOI 10.1007/s00521–014–1775-x. [Google Scholar]

[9] Li Zelong, Yang Chunjie, Liu Wenhui, Zhou Heng, Li Yuxuan. Prediction of silicon content in molten iron based on LSTM-RNN model[J]. CIESC Journal, 2018, 69(03): 992–997. [Google Scholar]

[10] Li S, Li W, Cook C, et al. Independently recurrent neura network (IndRNN): Building a longer and deeper rnn[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 5457–5466. [Google Scholar]

[11] Vaswani A, Shazeer N, Parmar N, et al. Attention Is All You Need[J]. arXiv, 2017. [Google Scholar]

[12] Dhar, S. and Dash, P. K. (2017). Adaptive threshold based new active islanding protection scheme for multiple PV based microgrid application, Iet Generation Transmission & Dis- tribution 11(1): 118–132. DOI 10.1049/iet-gtd.2016.0679. [Google Scholar]

[13] Acar, Y., Dogan, H., Basar, E. and Panayirci, E. (2016). Interpolation based pilot-aided channel estimation for STBC spatial modulation and performance analysis under imperfect CSI, Iet Communications 14(10): 1820–1828. DOI 10.1049/iet-com.2016.0151. [Google Scholar]