Open Access
Issue
E3S Web Conf.
Volume 267, 2021
7th 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
  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]

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.