•  
  •  
 

Chemical Technology, Control and Management

Abstract

The article analyzes the methods of mathematical modeling of the closed-loop process of fine grinding of ore in order to predict the composition of the crushed raw material. Analyzed modeling methods that consider the control of classification of crushed raw materials and finished products by the size of the recirculating load, simulation modeling methods, methods that use neuro-phase netthe research apparatus, in which the fuzzy logic apparatus serves as a fundamental component for the formation of conclusions. The materials presented in the article reflect the theoretical substantiation and generalization of the accumulated practical experience in modeling and control of the grinding process. The conclusion is that scientific and technical progress in the study of the grinding process can be achieved on the basis of a comprehensive systematic approach based on mathematical modeling, relying on theoretical and practical knowledge of the process and taking into consideration the laws of change in the particle size distribution and process control at a given output of the finished class of commercial products should be combined.

First Page

49

Last Page

62

DOI

https://doi.org/10.51346/tstu-02.21.4-77-0030

References

  1. P.I.Pilov, N.S.Pryadko, “Modeling of closed cycles of ore grinding based on the balance of the control size class”, Metallurgical and Mining Industry, no. 6, pp. 75-80, 2013.
  2. V.N.Bogatikov, Brown-Akway William, Forgor Lempogo, Hardware and software for the process control system for grinding. Internet Magazine «Scientific cultivation», no. 1, [Electronic resource]. Moscow: Scientific Cultivation, 2015.
  3. W.Ray, Technological Process Control Methods. Moscow: Mir, 1983, 368 p.
  4. Olga V. Lazareva, Y.A.Podkamenny, “Automated method of controlling the grinding and classification complex of diamond-bearing ores”, Bulletin of the Irkutsk State Technical University, no. 4 (87), pp. 128-132, 2014.
  5. S.L.Gubin, T.N.Gzogyan, “Modeling and calculation of grinding schemes”, Mining Information and Analytical Bulletin, no. 11, pp. 41-44, 2001
  6. V.G.Bragin, A.V.Rakhimova, Research by simulation modeling of the effectiveness of various control channels of the closed cycle of wet grinding of magnetite ores, Proceedings of the Ural Mining Institute, no. 4, pp. 125-137, 1993.
  7. K.Y.Ulitenko, V.V.Morozov, “Management of grinding and classification operations on the basis of ore typing”, Gornyi inform.-analit. bulletin, no. 3, pp. 162-167, 2014.
  8. Lettiev Oleg Anatolievich, “Research and development of automatic control system for grinding of gold-bearing ores in a ball drum mill”, Dissertation for the degree of Candidate of Technical Sciences, Moscow, 2012.
  9. A.A.Tuz, V.N.Bogatikov, “Construction of a model of the grinding process in a Kovdorsky Mining and Processing Combine with application of neural network models”, Proceedings of the Kola Scientific Center of the Russian Academy of Sciences, no. 5 (18), 2013.
  10. A.G.Kulakov, “Situational management of technological safety of the grinding process”, Dissertation of Candidate of Technical Sciences, Moscow, 2008.
  11. V.N.Bogatikov, A.G.Kulakov, “Investigation of a wet grinding unit with a closed cycle as an object of automatic control”, Information technology in regional development: collection of scientific papers, Apatity: Kola Scientific Center RAS, no. IV. pp.80-91, 2004.
  12. P.V.Kuznetsov, V.N.Bogatikov, A.E.Prorokov, “Neuro-network model for predicting the fracture function of milled material”, Proceedings of the Kola Scientific Center of the Russian Sciences Academy. Information Technology, no. 3, pp. 108-111, 2010.
  13. P.V.Kuznetsov, V.N.Bogatikov, A.E.Prorokov, “Algorithm of information neuro-model creation for control optimization of technological processes of grinding and classification”, Proceedings of the Kola Scientific Center of the Russian Academy of Sciences. Information Technologies, no. 3, pp.112-115, 2010.
  14. V.V.Kafarov, I.N.Dorokhov, System analysis of chemical technology processes. Processes of Grinding and Mixing of Bulk Materials, Moscow: Nauka, 1985. 440 p.
  15. X.Chen, Q.Li, S.Fei, “Constrained model predictive control in a ball mill grinding process”, Powder Tech., no.186 (2008), pp. 31-39, 2008.
  16. A.J.Linch, Crushing and grinding cycles. Modeling, optimization, design and management. Moscow: Nedra, 1981, 243 p.
  17. S.P.Bobkov, D.O.Bytev, Modeling of systems: tutorial. Ivanovo: Ivanovo State Chemical Engineering University, 2008. 156 p.
  18. А.N.Chokhonelidze, F.Lempogo, W.Brown-Ackway, Development of software for controlling the grinding circuit, Internet magazine «Science Cultivation», no. 3 (22), [Electronic resource], Moscow: Science Cultivation, 2014.
  19. А.N.Chokhonelidze, F.Lempogo, W.Brown-Acquaye, Analysis of cement production process and review of control strategies and methods [Electronic resource], Science Cultivation internet magazine, 2014. no. 2 (24), Access mode: http://naukovedenie.ru/sbornik24/ 26TAVN514.pdf.
  20. K.Y.Ulitenko, I.V.Sokolov, R.P.Markin, A.P.Naidenov, Automation of grinding processes in enrichment and metallurgy, http://www.scma.ru
  21. V.A.Oleynikov, O.N.Tikhonov, Automatic control of technological processes in the dressing industry. L.: Nedra, 1966. 356 p.
  22. Oleg Anatolievich Lettiev, “Research and development of an automatic control system for grinding gold-bearing ores in a ball drum mill”, D. thesis for the degree of Candidate of Technical Sciences, Moscow, 2012.
  23. P.Zkhou and T.Y.Chai, “Intelligent control setting with CBR for ore grinding classification system” J. Northeastern Univ. (Natural Science), vol. 28, no. 5, pp. 613–616, 2007.
  24. Pomerleau, D.Hodouin, A.Desbiens, and E.Gagnon, “A survey of grinding circuit control methods: From decentralized PID controllers to multivariable predictive controller”, Powder Technol., vol. 108, no. 2-3, pp. 103-115, 2000.
  25. Chai, Tianyou & Wang, Hong, “Intelligent Optimal-Setting Control for Grinding Circuits of Mineral Processing Process”, Automation Science and Engineering, IEEE Transactions, no. 6, pp. 730 – 743, 2009.
  26. Ma, T.-Y & Gui, W.-H., “Optimal control for continuous bauxite grinding process in ball-mill”, Control Theory and Applications, no. 29, 1339-1347, 2012.
  27. D. Wei, I. Craig, “Grinding mill circuits – a survey of control and economic concerns”, Int. J. Mineral Processing, no. 90 (2009), pp. 56-66, 2009.
  28. X. Chen, Q. Li, S. Fei, “Constrained model predictive control in a ball mill grinding process”, Powder Tech., no. 186 (2008), pp. 31-39, 2008.
  29. M. Ramasamy, S. S. Narayanan, C. D. P. Rao, “Control of ball mill grinding circuit using model predictive control scheme”, J. Process Control, no. 15 (2005), pp. 273-283, 2005.
  30. Remes, J. Aaltonen, Kh. Koivo, “Grinding circuit modeling and simulation of particle size control at Siilinj¨arvi concentrator”, Int. J. Mineral Processing, no. 96 (2010), pp. 70-78, 2010.
  31. D.Morton, S.Dunstull, “Using the Web to increase the availability of EM-based mill modelling”, Minerals Engineering, no. 17, pp. 1199-1207, 2004.
  32. R.D.Morrison, P.W.Cleary, “Using DEM to model ore breakage within a pilot scale SAG mill”, Minerals Engineering, no. 17, pp.1117-1124, 2004.
  33. B.K.Mishra, C.Murty, “On the determination of contact parameters for realistic DEM simulations of ball mills”, Powder Technology, no. 115, pp. 290-297, 2001.
  34. A.Refakhi, J.Aghazadeh Mohandesi, B.Rezai, “Comparison between bond crushing energy and fracture energy of rocks in a jaw crusher using numerical simulation”, J. of the South. African Inst. of Mining and Metallurgy, vol. 109, pp. 709-717, 2009.
  35. A.Somani, et al., “Pre-treatment of rocks prior to comminution – A critical review of present practices”, International Journal of Mining Science and Technology, no. 27(2), pp. 339-348, 2017.
  36. N.Chimwani, et al., “Scale-up of batch grinding data for simulation of industrial milling of platinum group minerals ore”, Minerals Engineering, no. 63, pp. 100-109, 2014.
  37. G.Danha, “Identifying Opportunities for Increasing the Milling Efficiency of a Bushveld Igneous Complex (BIC) Upper Group (UG) 2 Ore”, University of Witwatersrand, 2013.
  38. V.Denız, “Computer Simulation of Product Size Distribution of a Laboratory Ball Mill”, Particulate Science and Technology, no. 29(6), pp. 541-553, 2011.
  39. L.Ergün, et al., “Optimization of Çayeli (Çbi) Grinding Circuit by Modelling and Simulation”, The 19th International Mining Congress and Fair of Turkey, JMCET 2005. Izmer, Turkey: pp. 309-320, 2005.
  40. D.M.Francioli, Effect of Operational Variables on Ball Milling, Universidade Federal do Rio de Jeneiro, 2015.
  41. D.W.Fuerstenau, et al., “Simulation of the grinding of coarse/fine (heterogeneous) systems in a ball mill”, International Journal of Mineral Processing, no. 99(1–4), pp. 32-38, 2011.
  42. J.Luis-Bakhena, “Modelling and Simulation of the Grinding Circuit at 'ELPILON' Mine”, JKMRC Conference. Brisbane, pp. 181-196, 2001.
  43. E.Rybinski, et al., “Optimisation and continuous improvement of Antamina comminution circuit SAG”, Winnipeg, 2011.
  44. TAN Lu-min, FENG Xin-gang, “Design of Automatic Control System of Ball Mill Based on Fuzzy PID Control”, Coal Mine Machinery, no. 33(2), pp. 170-172, 2012.
  45. Dang Hong-she, Zhang Ying, Wang Gang, “Ball Mill Control System Design, Process Automation Instrumentation”, no. 4 (2014), pp. 26-29, 2014.
  46. P. Zhou, H. Yue, X.-P. Zheng, and T.-Y. Chai, “Multivariable fuzzy supervisory control for mineral grinding process”, Control and Decision, vol. 23, no. 6, pp. 685-688, 2008.
  47. J. Ding, H. Yue, Y. Qi, T. Chai, and X. Zheng, “NN soft-sensor for particle size of grinding circuit based GA”, Chinese Journal of Scientific Instrument, vol. 27, no. 9, pp. 981-984, 2006.
  48. G.C.He, Y.P.Mao, and W.Ni, “Grinding size soft sensor model based on neural network”, Metal Mines, vol. 344, no. 2, pp. 47-49, 2005.
  49. X. Li and Y. Rong, “Framework of grinding process modeling and simulation based on microscopic interaction analysis”, Robotics and Computer-Integrated Manufacturing, vol. 27, no. 2, pp. 471-478, 2011.
  50. J. S. Wang, X. W. Gao, and S. F. Sun, “Data-driven integrated modeling and intelligent control methods of grinding process”, in Advances in Neural Networks, vol. 7368 of Lecture Notes in Computer Science, pp. 396-405, Springer, Berlin, Germany, 2012.
  51. X.-G. Wu, M.-Z. Yuan, and H.-B. Yu, “Product flow rate control in ball mill grinding process using fuzzy logic controller”, in Proceedings of the IEEE International Conference on Machine Learning and Cybernetics, vol. 2, pp. 761-764, July 2009.
  52. C. R. Costea , E. Gergely , G. Husi , Laura Coroiu , Helga Silaghi , A. Bara, “A control design technique for grinding systems with feedforward undercompensation and feedback control”, The scientific bulletin of electrical engineering faculty. 10.1515/sbeef-2016-0012.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.