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

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

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

Acknowledgements

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

Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China
    Ning Chen, Jiayang Dai, Weihua Gui, Yuqian Guo & Jiaqi Zhou

Authors

  1. Ning Chen
  2. Jiayang Dai
  3. Weihua Gui
  4. Yuqian Guo
  5. Jiaqi Zhou

Corresponding author

Correspondence toJiayang Dai.

Electronic supplementary material

Rights and permissions

About this article

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