A hybrid prediction model with a selectively updating strategy for iron removal process in zinc hydrometallurgy

This is a preview of subscription content, access via your institution.


  1. 1

    Zhou X, Zhou J J, Yang C H, et al. Set-point tracking and multi-objective optimization-based pid control for the goethite process. IEEE Access, 2018, 6: 36683–36698

    Article  Google Scholar 

  2. 2

    Xie Y F, Xie S W, Chen X F, et al. An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy. Hydrometallurgy, 2015, 151: 62–72

    Article  Google Scholar 

  3. 3

    Zhou X J, Yang C H, Gui W H. State transition algorithm. J Ind Manage Optim, 2012, 8: 1039–1056

    MathSciNet  Article  Google Scholar 

  4. 4

    Chen N, Dai J Y, Yuan X F, et al. Temperature prediction model for roller kiln by ALD-based double locally weighted kernel principal component regression. IEEE Trans Instrum Meas, 2018, 67: 2001–2010

    Article  Google Scholar 

  5. 5

    Chan C L, Chen C L, Ting H W, et al. An agile mortality prediction model: hybrid logarithm least-squares support vector regression with cautious random particle swarm optimization. Int J Comput Intell Syst, 2018, 11: 873–881

    Article  Google Scholar 

  6. 6

    Yuan X F, Ge Z, Huang B, et al. A probabilistic just-in-time learning framework for soft sensor development with missing data. IEEE Trans Control Syst Technol, 2017, 25: 1124–1132

    Article  Google Scholar 

  7. 7

    Tang J, Yu W, Chai T Y, et al. On-line principal component analysis with application to process modeling. Neurocomputing, 2012, 82: 167–178

    Article  Google Scholar 

  8. 8

    Yuan X F, Ge Z, Song Z. Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes. Ind Eng Chem Res, 2014, 53: 13736–13749

    Article  Google Scholar 

Download references


This work was supported by National Natural Science Foundation of China (Grant No. 61673399), Program of Natural Science Foundation of Hunan Province (Grant No. 2017JJ2329), and Fundamental Research Funds for Central Universities of Central South University (Grant No. 2018zzts550).

Author information



Corresponding author

Correspondence to Jiayang Dai.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, N., Dai, J., Gui, W. et al. A hybrid prediction model with a selectively updating strategy for iron removal process in zinc hydrometallurgy. Sci. China Inf. Sci. 63, 119205 (2020). https://doi.org/10.1007/s11432-018-9711-2

Download citation