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Author:

Tang, J. (Tang, J..) | Qiao, J. (Qiao, J..) | Liu, Z. (Liu, Z..) | Zhou, X. (Zhou, X..) | Yu, G. (Yu, G..) | Zhao, J. (Zhao, J..) (Scholars:赵京)

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Scopus PKU CSCD

Abstract:

Considering importance of mill load parameter on-line measurement in realization operational optimization and control of the mineral grinding process, ball mill grinding mechanism numerical simulation and mill load parameters soft measurement for the mineral grinding process were reviewed. First, the mill load was described based on the actual grinding process. Then, definitions of mill load, mill load parameters and mechanism model among them were reviewed, and the mill load detection framework was given out. Second, current status of numerical simulation analysis for the grinding process was reviewed, and the difference between the grinding mechanisms of different ball mills was given out and the possibility of soft measuring mill load based on support of numerical simulation was discussed. Third, mill load parameter soft measurement methods & techniques based on multi-component mechanical signals were reviewed in detail. Finally, the developing trend and the problems that need to be solved were summarized. Results show that the soft measurement of ball mill load parameters is difficult using the normally method. An intelligent soft measuring model with characteristic that simulate domain experts' dynamic cognition and compensation mechanism must be constructed. Moreover, the shortcomings of domain experts should be overcome. Thus, the intelligent soft measurement of mill load parameter can be realized. © 2018, Editorial Department of Journal of Beijing University of Technology. All right reserved.

Keyword:

Grinding process; Mechanical signals; Mill load; Soft measurement

Author Community:

  • [ 1 ] [Tang, J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Tang, J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
  • [ 3 ] [Qiao, J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Qiao, J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China
  • [ 5 ] [Liu, Z.]State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110004, China
  • [ 6 ] [Zhou, X.]State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110004, China
  • [ 7 ] [Yu, G.]State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing, 100089, China
  • [ 8 ] [Yu, G.]Beijing Key Laboratory of Process Automation in Mining & Metallurgy, Beijing, 100089, China
  • [ 9 ] [Zhao, J.]State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing, 100089, China
  • [ 10 ] [Zhao, J.]Beijing Key Laboratory of Process Automation in Mining & Metallurgy, Beijing, 100089, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2018

Issue: 11

Volume: 44

Page: 1459-1470

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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