<listing id="l9bhj"><var id="l9bhj"></var></listing>
<var id="l9bhj"><strike id="l9bhj"></strike></var>
<menuitem id="l9bhj"></menuitem>
<cite id="l9bhj"><strike id="l9bhj"></strike></cite>
<cite id="l9bhj"><strike id="l9bhj"></strike></cite>
<var id="l9bhj"></var><cite id="l9bhj"><video id="l9bhj"></video></cite>
<menuitem id="l9bhj"></menuitem>
<cite id="l9bhj"><strike id="l9bhj"><listing id="l9bhj"></listing></strike></cite><cite id="l9bhj"><span id="l9bhj"><menuitem id="l9bhj"></menuitem></span></cite>
<var id="l9bhj"></var>
<var id="l9bhj"></var>
<var id="l9bhj"></var>
<var id="l9bhj"><strike id="l9bhj"></strike></var>
<ins id="l9bhj"><span id="l9bhj"></span></ins>
Volume 39 Issue 9
Sep.  2017
Turn off MathJax
Article Contents
WU Cheng-rui, JIA Yao, WANG Lin-yan. A hardware-in-the-loop simulation system for the mixed separation process[J]. Chinese Journal of Engineering, 2017, 39(9): 1412-1420. doi: 10.13374/j.issn2095-9389.2017.09.015
Citation: WU Cheng-rui, JIA Yao, WANG Lin-yan. A hardware-in-the-loop simulation system for the mixed separation process[J]. Chinese Journal of Engineering, 2017, 39(9): 1412-1420. doi: 10.13374/j.issn2095-9389.2017.09.015

A hardware-in-the-loop simulation system for the mixed separation process

doi: 10.13374/j.issn2095-9389.2017.09.015
  • Received Date: 2016-11-05
  • A representative hardware-in-the-loop simulation system was established for the mixed separation process to meet the demands of industrial process control technologies with complex characteristics. A hardware-in-the-loop simulation system was developed composed of a virtual object computer, a controller-design computer, a monitoring computer, virtual actuators, virtual detecting instruments, and a control system to achieve mixed separation. The control algorithm of the system was based on actual industrial software, and Matlab was use to design the virtual object, virtual actuators, virtual detecting instruments, and online identification system. The mechanism modeling of controlled object, parameter identification of controller design model, controller design and controller performance evaluation were completed on the virtual object computer, controller-design computer and monitoring computer. The proposed system lays the foundation for future industrial applications of control algorithms for complex industrial processes.

     

  • loading
  • [3]
    Liu Y, Spencer S. Dynamic simulation of grinding circuits. Miner Eng, 2004, 17(11-12):1189
    [5]
    Li H B, Chai T Y, Zhang L Y. Hybrid intelligent optimal control for flotation processes//2012 American Control Conference. Montréal, 2012:4891
    [6]
    Geng Z X, Chai T Y, Yue H. A method of hybrid intelligent optimal setting control for flotation process//7th World Congress on Intelligent Control&Automation. Chongqing, 2008:4713
    [7]
    Dai W, Zhou P, Zhao D Y, et al. Hardware-in-the-loop simulation platform for supervisory control of mineral grinding process. Powder Technol, 2016, 288:422
    [9]
    Radhakrishnan V R. Model based supervisory control of a ball mill grinding circuit. J Process Control, 1999, 9(3):195
    [10]
    Bartlett J T. Process simulation and optimization using metsim//Mineral Resources Management by Personal Computer. Littleton, 1987:105
    [12]
    Liu F Z, Gao H J, Qiu J B, et al. Networked multirate output feedback control for setpoints compensation and its application to rougher flotation process. IEEE T Ind Electron, 2014, 61(1):460
    [13]
    Chen Z M, Hu Y J, Wang X, et al. Design of hardware-in-theloop simulation system for LTE-A system based on USRP//2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA). Hefei, 2016:2081
    [14]
    Rajamani R K, Herbst J A. Optimal control of a ball mill grinding circuit——I. Grinding circuit modeling and dynamic simulation. Chem Eng Sci, 1991, 46(3):861
    [15]
    Durance M V, Guillaneau J C, Villeneuve J, et al. Computer simulation of mineral and hydrometallurgical processes, USIM PAC2, a single software from design to optimization//Proceedings of International Symposium on Modelling Simulation Control of Hydrometallurgical Processes. Quebec, 1993:109
    [16]
    Cecati C, Kolbusz J, Różycki P, et al. A novel RBF training algorithm for short-term electric load forecasting and comparative studies. IEEE T Ind Electron, 2015, 62(10):6519
    [17]
    Yu H, Xie T T, Paszczynski S, et al. Advantages of radial basis function networks for dynamic system design. IEEE T Ind Electron, 2011, 58(12):5438
    [18]
    Kolodner J L. An introduction to case-based reasoning. Artif Intell Rev, 1992, 6(1):3
    [19]
    Riesbeck C K, Schank R C. Inside Case-based Reasoning. New Jersey:Lawrence Erlbaum Associates, 2013
    [20]
    Zhou P, Chai T Y, Wang H. Intelligent optimal-setting control for grinding circuits of mineral processing process. IEEE Trans Autom Sci Eng, 2009, 6(4):730
    [21]
    Sidrak Y L. Control of the thickener operation in alumina production. Control Eng Pract, 1997, 5(10):1417
    [22]
    Chai T Y, Jia Y, Li H B, et al. An intelligent switching control for a mixed separation thickener process. Control Eng Pract, 2016, 57:61
    [23]
    Li H B, Chai T Y, Fu J, et al. Adaptive decoupling control of pulp levels in flotation cells. Asian J Control, 2013, 15(5):1434
  • 加載中

Catalog

    通訊作者: 陳斌, bchen63@163.com
    • 1. 

      沈陽化工大學材料科學與工程學院 沈陽 110142

    1. 本站搜索
    2. 百度學術搜索
    3. 萬方數據庫搜索
    4. CNKI搜索
    Article views (761) PDF downloads(10) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return
    久色视频