Purities prediction in a manufacturing froth flotation plant: the deep learning techniques


Accurate and timely investigation to concentrate grade and recovery is a premise of realizing automation control in a froth flotation process. This study seeks to use deep learning technologies modeling a manufacturing flotation process, forecasting the concentrate purities for iron and the waste silica. Considering the size and temporality of engineering data, we adopted a long short-term memory to form the core part of the deep learning model. To perform this process, 23 variables reflecting a flotation plant were monitored and collected hourly over a half year time span, then wrangled, split, and restructured for deep learning model use. A deep learning model encompassing a stacked long short-term memory architecture was designed, trained, and tested with prepared data. The model’s performance on test data demonstrates the capability of our proposed model to predict real-time concentrate purities for iron and silica. Compared with a traditional machine model typified by a random forest model in this study, the proposed deep learning model is significantly more competent to model a manufacturing froth flotation process. Expected to lay a foundation for realizing automation control of the flotation process, this study should encourage deep learning in mineral processing engineering.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12


  1. 1.

    Jahedsaravani A et al (2016) Froth-based modeling and control of a batch flotation process. Int J Miner Process 146:90–96

    Article  Google Scholar 

  2. 2.

    Wills BA, Finch J (2015) Wills’ mineral processing technology: an introduction to the practical aspects of ore treatment and mineral recovery. Butterworth-Heinemann, Oxford

    Google Scholar 

  3. 3.

    Nakhaei F et al (2012) Recovery and grade accurate prediction of pilot plant flotation column concentrate: neural network and statistical techniques. Int J Miner Process 110:140–154

    Article  Google Scholar 

  4. 4.

    Vieira S, Sousa J, Durão F (2005) Fuzzy modelling strategies applied to a column flotation process. Miner Eng 18(7):725–729

    Article  Google Scholar 

  5. 5.

    McCoy J, Auret L (2019) Machine learning applications in minerals processing: a review. Miner Eng 132:95–109

    Article  Google Scholar 

  6. 6.

    Miriyala SS, Mitra K (2019) Multi-objective optimization of iron ore induration process using optimal neural networks. Mater Manuf Process. https://doi.org/10.1080/10426914.2019.1643476

    Article  Google Scholar 

  7. 7.

    Miriyala SS, Subramanian VR, Mitra K (2018) TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study. Eur J Oper Res 264(1):294–309

    MathSciNet  Article  Google Scholar 

  8. 8.

    Mitra K, Ghivari M (2006) Modeling of an industrial wet grinding operation using data-driven techniques. Comput Chem Eng 30(3):508–520

    Article  Google Scholar 

  9. 9.

    Chelgani SC, Shahbazi B, Rezai B (2010) Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network. Int J Miner Metall Mater 17(5):526–534

    Article  Google Scholar 

  10. 10.

    Yang C et al (2011) Soft sensor of key index for flotation process based on sparse multiple kernels least squares support vector machines. Chin J Nonferr Met 21(12):3149–3154

    Article  Google Scholar 

  11. 11.

    Kaijun, Z., et al., Flotation recovery prediction based on froth features and LS-SVM [J]. Chinese Journal of Scientific Instrument, 2009. 6

  12. 12.

    Chelgani SC, Shahbazi B, Hadavandi E (2018) Support vector regression modeling of coal flotation based on variable importance measurements by mutual information method. Measurement 114:102–108

    Article  Google Scholar 

  13. 13.

    Shahbazi B, Chelgani SC, Matin S (2017) Prediction of froth flotation responses based on various conditioning parameters by random forest method. Colloids Surf A 529:936–941

    Article  Google Scholar 

  14. 14.

    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  15. 15.

    Miriyala SS, Mitra K (2020) Deep learning based system identification of industrial integrated grinding circuits. Powder Technol 360:921–936

    Article  Google Scholar 

  16. 16.

    Filippov L, Severov V, Filippova I (2014) An overview of the beneficiation of iron ores via reverse cationic flotation. Int J Miner Process 127:62–69

    Article  Google Scholar 

  17. 17.

    Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211

    Article  Google Scholar 

  18. 18.

    Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. pp 850–855

  19. 19.

    Cybenko G (1989) Approximations by superpositions of a sigmoidal function. Math Control Signals Syst 2:183–192

    MathSciNet  Article  Google Scholar 

  20. 20.

    Pascanu R et al (2013) How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026

  21. 21.

    Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  22. 22.

    Hua J et al (2004) Optimal number of features as a function of sample size for various classification rules. Bioinformatics 21(8):1509–1515

    Article  Google Scholar 

  23. 23.

    Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22

    Google Scholar 

Download references


This study was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Collaborative Research and Development (CRD) Grant (NSERC RGPIN-2019-04572). And also, supports from Chinese Scholarship Council were gratefully acknowledged.

Author information



Corresponding authors

Correspondence to Yuanyuan Pu or Derek B. Apel.

Ethics declarations

Conflict of interest

There are no conflicts of interest to disclosure.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pu, Y., Szmigiel, A. & Apel, D.B. Purities prediction in a manufacturing froth flotation plant: the deep learning techniques. Neural Comput & Applic 32, 13639–13649 (2020). https://doi.org/10.1007/s00521-020-04773-2

Download citation


  • Froth flotation
  • Deep learning
  • Long short-term memory
  • Concentrate purity