The Hydrocyclones Performance Monitoring Based on Vibration Wave Analysis at Sarcheshmeh Processing Plant

Document Type : Research - Paper

Authors

1 M.Sc, Dept. of Mining, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

2 Associate Professor, Dept. of Mining, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

3 M.Sc, Research and Development Center, Sarcheshmeh Copper Complex, Rafsanjan, Iran

Abstract

Hydrocyclone is one of the main important equipment applied for the classification of materials in mineral processing plants. Presence of coarse particles in the hydrocyclone overflow adversely affects the performance of the downstream processes. This research was carried out to provide a solution for these problems by using vibrational wave analysis. The results of vibrometer analysis showed that the average rate of vibrational signal increased from 6 dB at normal condition to 11 dB at the chock condition and variation increased more than 2 times. To stabilize the hydrocyclone vibratory behavior, a rubber layer was fitted under the hydrocyclone base and tightened with an equal torque. In order to reduce the cost and increase the accuracy of the work a piezoelectric sensor was manufactured and installed by designing the printed circuit board and installing an alarm. Analysis of piezoelectric sensor data showed that the average vibrational amplitude increases from 13 dB at normal time to 23 dB during clogging. The size distribution results showed that the amount of K80 (the size at which 80% of the particles are smaller) increased from 119 microns under normal conditions to 725 microns during clogging due to the miss classification of materials transferred to the overflow. This result indicated by using these sensors the efficiency of mineral processing circuits increases significantly.

Keywords

References

  1. Poursaeedi, M., Shamsadini, R., Arghavani, E., and Banisi, S. (2012). “Increasing Efficiency of Initial Grinding Circuit of Share-Babak Copper Complex Concentration Plan”. Journal of Analytical and Numerical Methods in Mining Engineering, 2(4): 88-92. (In Persian).
  2. Gupta, A., and Yan, D. S. (2006). “Mineral Processing Design and Operation: An introduction”. Elsevier, 354-400.
  3. Wills, B. A., and Finch, J. A. (2015). “Wills’ Mineral Processing Technology, Eighth Edition”. Elsevier.
  4. Ranjbar, M., and Sam, A. (2011). “Hydrocyclone Diameter Selection Using a Modified Particle Density Correction Factor”. Iranian Journal of Mining Engineering, 6(11): 43-48. (In Persian).
  5. Gigliaa, K. C., and Aldrich C. (2020). “Operational state detection in hydrocyclones with convolutional neural networks and transfer learning”. Minerals Engineering, 149: 106211.
  6. Williams, R. A., Jia, X., West, R. M., Wang, M., Cullivan, J. C., Bond, J., and Payton, D. (1999). “Industrial monitoring of hydrocyclone operation using electrical resistance tomography”. Minerals Engineering, 12(10): 1245-1252.
  7. Gutiérrez, J. A., Dyakowski, T., Beck, M. S., and Williams, R. A. (2000). “Using electrical impedance tomography for controlling hydrocyclone underflow discharge”. Powder Technology, 108(2): 180-184
  8. Hou, R., Hunt, A., and Williams, R. A. (2002). “Acoustic monitoring of hydrocyclones”. Powder Technology, 124(3): 176-187. DOI: https://doi.org/10.1016/S0032-5910(02)00025-6.
  9. Cirulis, D., and Russell, J. (2011). “Cyclone Monitoring System Improves Operations at KUC’s Copperton Concentrator”. Engineering and Mining Journal, 212(10): 44-49.
  10. Cirulis, D., and Jerin, R. (2017). “Cidra cyclonetrac SM at Kennecott Utah copper”. International Mining, 1-20.
  11. Buttler, B. (2016). “New mining sensors go wireless, smart cyclone delivers new opportunities”. FLSmidth wireless wear detection and roping detection sensors, FLSmidth catalogs, https://www.flsmidth.com.
  12. Neesse, T., Schneider, M., Golyk, V., and Tiefel, H. (2004). “Measuring the operating state of the hydrocyclone”. Minerals Engineering, 17(5): 697-703.
  13. MINTEK, (2011). “CyLas: Keeping an eye on your cyclone [Brochure]”. Retrieved from. http://www.mintek.co.za/wp-content/uploads/2011/09/CyLas-brochure-en.pdf.
  14. Murphy, K. P. (2012). “Machine learning : a probabilistic perspective”. MIT Press, Cambridge, MA, pp. 1067.
  15. Petersen, K. R. P., Aldrich, C., van Deventer, J. S. J., McInnes, C., and Stange, W. W. (1996). “Hydrocyclone underflow monitoring using image processing methods”. Minerals Engineering, 9(3): 301-315. DOI: https://doi.org/10.1016/0892-6875(96)00015-5.
  16. Van Deventer, J. S. J., Feng, D., Petersen, K. R. P., and Aldrich, C. (2003). “Modelling of hydrocyclone performance based on spray profile analysis”. International Journalof Mineral Processing, 70(1): 183-203. DOI: https://doi.org/10.1016/S0301-7516(03)00002-4.
  17. Janse van Vuuren, M. J., Aldrich, C., and Auret, L. (2011). “Detecting changes in the operational states of hydrocyclones”. Minerals Engineering, 24(14): 1532-1544. DOI: https://doi.org/10.1016/j. mineng.2011.08.002.
  18. Janse van Vuuren, M. J., Aldrich, C., Auret, L., Bezuidenhoudt, C., and De Jager, C. (2010). “Online monitoring of hydrocyclones by use of image analysis”. IFAC Proceedings Volumes, 43(9): 87-91. DOI: https://doi.org/10.3182/20100802-3-ZA-2014.00021.
  19. Krizhevsky, A., Sutskever, I., and Hinton, G. (2017). “ImageNet classification with deep convolutional neural networks”. Communicationsof the ACM, 60(6): 84-90. https://doi.org/10.1145/3065386.
  20. حجت، ی.، شیرکش، م.؛ 1398؛ "اصول و کاربردهای پیزوالکتریک 1". انتشارات دانشگاه تربیت مدرس، 168 صفحه.

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History

  • Receive Date: 15 August 2020
  • Revise Date: 06 March 2021
  • Accept Date: 06 March 2021

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