Kinetics of Impurity Removal in Zinc Hydrometallurgy Based on Parameter Estimation

Qianqian Wang; Minan Tang; Aimin An; Jiawei Lu; Yingying Zhao

doi:10.1051/e3sconf/202126702051

All issues

Volume 267 (2021)

E3S Web Conf., 267 (2021) 02051

References

Open Access

Issue		E3S Web Conf. Volume 267, 2021 7^th International Conference on Energy Science and Chemical Engineering (ICESCE 2021)


Article Number		02051
Number of page(s)		7
Section		Environmental Chemistry Research and Chemical Preparation Process
DOI		https://doi.org/10.1051/e3sconf/202126702051
Published online		04 June 2021

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[1] Sun B, Zhang B, Yang C, et al. (2017) Discussion on modeling and optimal control of nonferrous metallurgical purification process. ACTA AUTOMATICA SINICA, 43:880–892. [Google Scholar]

[2] Zhang Z. (2020) Study on industrial application hydrometallurgy of zinc purification system to high impurity raw materials. Lanzhou University of Technology. [Google Scholar]

[3] Gui W, Yang C. (2010) Intelligent modeling, control and optimization of complex nonferrous metallurgy production process. Science Press, Beijing. [Google Scholar]

[4] Sun B, Gui W, Wu T, et al. (2013) An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential. Hydrometallurgy, 140:102–110. [Google Scholar]

[5] Sun B, Gui W, Yang C, et al. (2016) Online estimation of impurity ion concentration in solution purification process. IFAC-PapersOnLine, 49:178–183. [Google Scholar]

[6] Zhou F, Li C, Zhu H, et al. (2019) Determination of trace ions of cobalt and copper by UV-vis spectrometry in purification process of zinc hydrometallurgy. Optik, 184:227–233. [Google Scholar]

[7] Wu T, Yang C, Li Y, et al. (2014) Fuzzy operational-pattern based operating parameters collaborative optimization of cobalt removal process with arsenic salt. ACTA AUTOMATICA SINICA, 40:1690–1698. [Google Scholar]

[8] Wang X, Zhou X, Yang C. (2020) Chance constrained optimization for copper removal process under uncertainty in zinc hydrometallurgy. CIESC Journal, 71:1226–1233. [Google Scholar]

[9] Xie S, Xie Y, Li F, et al. (2018) Optimal setting and control for iron removal process based on adaptive neural network soft-sensor. IEEE Transactions on Systems Man & Cybernetics Systems, 99:1–13. [Google Scholar]

[10] Xie S, Xie Y, Ying H, et al. (2018) A hybrid control strategy for real-time control of the iron removal process of the zinc hydrometallurgy plants. IEEE Transactions on Industrial Informatics, 14:5278–5288. [Google Scholar]

[11] Sun B, He M, Wang Y, et al. (2018) A data-driven optimal control approach for solution purification process. Journal of Process Control, 68:171–185. [Google Scholar]

[12] Chen N, Zhou J, Gui W, et al. (2020) Two-layer optimal control for goethite iron precipitation process. Control Theory & Applications, 37:222–228. [Google Scholar]

[13] Gui W, Yang C, Chen X, et al. (2013) Modeling and optimization problems and challenges arising in nonferrous metallurgical processes. ACTA AUTOMATICA SINICA, 39:197–207. [Google Scholar]

[14] Wang L Y, Gui W H, Teo K L, et al. (2009) Time delayed optimal control problems with multiple characteristic time points: Computation and industrial applications. Journal of Industrial & Management Optimization, 5:705–718. [Google Scholar]

[15] Wu W, Gao L. (2021) Inertia parameter identification of biped robot using ZMP feedback. Journal of Harbin Institute of Technology. [Google Scholar]

[16] Sang M, Ding Y, Bao M, et al. (2021) Propagation dynamics model considering the characteristics of 2019-nCoV and prevention-control measurements. System Engineering-Theory & Practice, 41:124–133. [Google Scholar]