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

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  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

  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

  3. 3

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

  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

  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

  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

  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

  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

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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).

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Correspondence to Jiayang Dai.

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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

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