430
Views
1
CrossRef citations to date
0
Altmetric
Research Article

A Convolutional Neural Network for Classification of Froth Mobility in an Industrial Flotation Cell

, & ORCID Icon

References

  • Al-Thyabat, S. 2008. On the optimization of froth flotation by the use of an artificial neural network. Journal of China University of Mining and Technology 18 (3):418–26. doi:10.1016/S1006-1266(08)60087-5.
  • Aldrich, C., and X. Liu. 2021. Monitoring of flotation systems by use of multivariate froth image analysis. Minerals 11 (7):683. doi:10.3390/min11070683.
  • Aldrich, C., C. Marais, B. J. Shean, and J. J. Cilliers. 2010. Online monitoring and control of froth flotation systems with machine vision: A review. International Journal of Mineral Processing 96 (1–4):1–13. doi:10.1016/j.minpro.2010.04.005.
  • Aldrich, C., D. Moolman, S.-J. Bunkell, M. Harris, and D. Theron. 1997. Relationship between surface froth features and process conditions in the batch flotation of a sulphide ore. Minerals Engineering 10 (11):1207–18. doi:10.1016/S0892-6875(97)00107-6.
  • Aldrich, C., L. K. Smith, D. I. Verrelli, W. J. Bruckard, and M. Kistner. 2018. Multivariate image analysis of realgar–orpiment flotation froths. Mineral Processing and Extractive Metallurgy 127 (3):146–56. doi:10.1080/03719553.2017.1318570.
  • Ali, D., M. B. Hayat, L. Alagha, and O. K. Molatlhegi. 2018. An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal. Advanced Powder Technology 29 (12):3493–506. doi:10.1016/j.apt.2018.09.032.
  • Allahkarami, E., O. Salmani Nuri, A. Abdollahzadeh, B. Rezai, and B. Maghsoudi. 2017. Improving estimation accuracy of metallurgical performance of industrial flotation process by using hybrid genetic algorithm–artificial neural network (GA-ANN). Physicochemical Problems of Mineral Processing 53 (1):366–78.
  • Barbian, N., J. J. Cilliers, S. H. Morar, and D. J. Bradshaw. 2007. Froth imaging, air recovery and bubble loading to describe flotation bank performance. International Journal of Mineral Processing 84 (1–4):81–88. doi:10.1016/j.minpro.2006.10.009.
  • Benford, P. M., and J. A. Meech. 1992. An advisory package for flotation operators using a real-time expert system. Minerals Engineering 5 (10–12):1325–31. doi:10.1016/0892-6875(92)90168-9.
  • Bhondayi, C. 2022. Flotation froth phase bubble size measurement. Mineral Processing and Extractive Metallurgy Review 43 2 251–273.
  • Boracchi, G., V. Caglioti, and A. Giusti, 2007. Ball position and motion reconstruction from blur in a single perspective image. In 14th International Conference on Image Analysis and Processing, Modena, Italy, pp. 87–92.
  • Chelgani, S. C., B. Shahbazi, and R. Bahram. 2010. Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network. International Journal of Minerals, Metallurgy, and Materials 17 (5):526–34. doi:10.1007/s12613-010-0353-1.
  • Coelho, L. P. 2012. Mahotas: Open source software for scriptable computer vision. arXiv preprint arXiv:1211.4907.
  • Cutting, G. W. 1989. Effect of froth structure and mobility on plant performance. Mineral Processing and Extractive Metallurgy Review 5 (1–4):169–201. doi:10.1080/08827508908952649.
  • Dwicahya, J. A., N. Ramadijanti, and A. Basuki. 2018. Moving object velocity detection based on motion blur on photos using gray level. In 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing, Nusa Tenggara Barat, Indonesia, pp. 192–98
  • Figueroa, L., E. Peragallo, A. Gomez, and F. Orrante. 2009. Determination of rougher froth velocity profiles and their implementation through expert systems.” In 6th International Mineral Processing Seminar, Santiago, Chile, pp. 395–401
  • Forbes, G. 2007. Texture and bubble size measurements for modelling concentrate grade in flotation froth systems. (Doctoral dissertation, University of Cape Town).
  • Fu, Y., and C. Aldrich. 2018. Froth image analysis by use of transfer learning and convolutional neural networks. Minerals Engineering 115:68–78. doi:10.1016/j.mineng.2017.10.005.
  • Fu, Y., and C. Aldrich. 2019. Flotation froth image recognition with convolutional neural networks. Minerals Engineering 132:183–90. doi:10.1016/j.mineng.2018.12.011.
  • Giakoumoglou, N. 2021. Accessed January 26, 2022. PyFeats: Open source software for image feature extraction. https://github.com/giakou4/pyfeats
  • Glorot, X., and Y. Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In 13th International Conference on Artificial Intelligence and Statistics, Sardinia, Italy, pp. 249–56.
  • Holtham, P. N., and K. K. Nguyen. 2002. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam. International Journal of Mineral Processing 64 (2–3):163–80. doi:10.1016/S0301-7516(01)00070-9.
  • Horn, Z. C., L. Auret, J. T. McCoy, C. Aldrich, and B. M. Herbst. 2017. Performance of convolutional neural networks for feature extraction in froth flotation sensing. IFAC-PapersOnLine 50 (2):13–18. doi:10.1016/j.ifacol.2017.12.003.
  • Ioffe, S., and C. Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. [ Online]. Available: arXiv preprint arXiv:1502.03167.
  • Jahedsaravani, A., M. H. Marhaban, and M. Massinaei. 2014. Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks. Minerals Engineering 69:137–45. doi:10.1016/j.mineng.2014.08.003.
  • Jahedsaravani, A., M. Massinaei, and M. H. Marhaban. 2016. Application of image processing and adaptive neuro-fuzzy system for estimation of the metallurgical parameters of a flotation process. Chemical Engineering Communications 203 (10):1395–402. doi:10.1080/00986445.2016.1198897.
  • Jung, A. 2017. Imgaug: A library for image augmentation in machine learning experiments. [Online]. Available: https://github.com/aleju/imgaug 4 January 2021
  • Kingma, D. P., and J. Ba. 2014. Adam: A method for stochastic optimization. [ Online]. Available: arXiv preprint arXiv:1412.6980.
  • Kistner, M., G. T. Jemwa, and C. Aldrich. 2013. Monitoring of mineral processing systems by using textural image analysis. Minerals Engineering 52:169–77. doi:10.1016/j.mineng.2013.05.022.
  • Lin, H. Y. 2005. Vehicle speed detection and identification from a single motion blurred image. In 7th IEEE Workshops on Applications of Computer Vision, Breckenridge, USA, pp. 461–67
  • Massinaei, M., A. Jahedsaravani, and H. Mohseni. 2020. Recognition of process conditions of a coal column flotation circuit using computer vision and machine learning. International Journal of Coal Preparation and Utilization 1–15. doi:10.1080/19392699.2020.1823843.
  • Massinaei, M., A. Jahedsaravani, E. Taheri, and J. Khalilpour. 2019. Machine vision based monitoring and analysis of a coal column flotation circuit. Powder Technology 343:330–41. doi:10.1016/j.powtec.2018.11.056.
  • Mehrabi, A., N. Mehrshad, and M. Massinaei. 2014. Machine vision based monitoring of an industrial flotation cell in an iron flotation plant. International Journal of Mineral Processing 133:60–66. doi:10.1016/j.minpro.2014.09.018.
  • Mesa, D., and P. R. Brito-Parada. 2019. Scale-up in froth flotation: A state-of-the-art review. Separation and Purification Technology 210:950–62. doi:10.1016/j.seppur.2018.08.076.
  • Moolman, D. W., C. Aldrich, J. S. J. Van Deventer, and D. J. Bradshaw. 1995. The interpretation of flotation froth surfaces by using digital image analysis and neural networks. Chemical Engineering Science 50 (22):/ 3501–3513. doi:10.1016/0009-2509(95)00190-G.
  • Morar, S. H., M. C. Harris, and D. J. Bradshaw. 2012. The use of machine vision to predict flotation performance. Minerals Engineering 36:31–36. doi:10.1016/j.mineng.2012.02.010.
  • Nakhaei, F., M. Irannajad, and S. Mohammadnejad. 2019. Column flotation performance prediction: PCA, ANN and image analysis-based approaches. Physicochemical Problems of Mineral Processing 55 (5):1298–310.
  • Nakhaei, F., M. R. Mosavi, A. Sam, and Y. Vaghei. 2012. Recovery and grade accurate prediction of pilot plant flotation column concentrate: Neural network and statistical techniques. International Journal of Mineral Processing 110:140–54. doi:10.1016/j.minpro.2012.03.003.
  • Núñez, F., and A. Cipriano. 2009. Visual information model based predictor for froth speed control in flotation process. Minerals Engineering 22 (4):366–71. doi:10.1016/j.mineng.2008.10.005.
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research 12:2825–30.
  • Runge, K., J. McMaster, M. Wortley, D. La Rosa, and O. Guyot. 2007. A correlation between Visiofroth™ measurements and the performance of a flotation cell. In 9th Mill Operators’ Conference, Fremantle, Australia, pp. 79–86
  • Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15 (1):1929–58.
  • Supomo, A., E. Yap, X. Zheng, G. Banini, J. Mosher, and A. Partanen. 2008. PT Freeport Indonesia’s mass-pull control strategy for rougher flotation. Minerals Engineering 21 (12–14):808–16. doi:10.1016/j.mineng.2008.07.002.
  • Tan, J., L. Liang, Y. Peng, and G. Xie. 2016. The concentrate ash content analysis of coal flotation based on froth images. Minerals Engineering 92:9–20. doi:10.1016/j.mineng.2016.02.006.
  • Tang, M., C. Zhou, N. Zhang, C. Liu, J. Pan, and S. Cao. 2021. Prediction of the ash content of flotation concentrate based on froth image processing and BP neural network modeling. International Journal of Coal Preparation and Utilization 41 3 191–202 https://doi.org/10.1080/19392699.2018.1458716 .
  • Van Olst, M., N. Brown, P. Bourke, and S. Ronkainen. 2000. Improving flotation plant performance at Cadia by controlling and optimising the rate of froth recovery using Outokumpu FrothMaster. In 7th Mill Operators’ Conference, Melbourne, Australia, pp. 127–35.
  • Wold, S., K. Esbensen, and P. Geladi. 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2 (1–3):37–52. doi:10.1016/0169-7439(87)80084-9.
  • Yianatos, J., I. Panire, and L. Vinnett. 2016. A new method for flotation rate characterization using top-of-froth grades and the froth discharge velocity. Minerals Engineering 92:242–47. doi:10.1016/j.mineng.2016.03.026.
  • Zarie, M., A. Jahedsaravani, and M. Massinaei. 2020. Flotation froth image classification using convolutional neural networks. Minerals Engineering 155:106443. doi:10.1016/j.mineng.2020.106443.
  • Zhang, J., Z. Tang, J. Liu, Z. Tan, and P. Xu. 2016. Recognition of flotation working conditions through froth image statistical modeling for performance monitoring. Minerals Engineering 86:116–29. doi:10.1016/j.mineng.2015.12.008.
  • Zhang, J., Z. Tang, Y. Xie, M. Ai, and W. Gui. 2019. Flotation fault diagnosis method using statistical approaches. In Seventh International Conference on Advanced Cloud and Big Data, Suzhou, China, pp. 266–71
  • Zhang, H., Z. Tang, Y. Xie, Q. Chen, X. Gao, and W. Gui. 2020. FR-R net: A Light-weight deep neural network for performance monitoring in the froth flotation. IEEE Transactions on Industrial Informatics 17 (12):8406–17. doi:10.1109/TII.2020.3046278.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.