Data mining based feedback regulation in operation of hematite ore mineral processing plant

  • Authors:

  • Jinliang Ding;Qi Chen;Tianyou Chai;Hong Wang;Chun-Yi Su

  • Affiliations:

  • Key Laboratory of Integrated Automation of Process Industry, Ministry of Education and Research Center of Automation, Northeastern University, Shenyang, China;Key Laboratory of Integrated Automation of Process Industry, Ministry of Education and Research Center of Automation, Northeastern University, Shenyang, China;Key Laboratory of Integrated Automation of Process Industry, Ministry of Education and Research Center of Automation, Northeastern University, Shenyang, China;Control Systems Centre, The University of Manchester, Manchester, UK;Department of Mechanical Engineering, Concordia University, Montreal, Quebec, Canada

  • Venue:
  • ACC'09 Proceedings of the 2009 conference on American Control Conference
  • Year:
  • 2009

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Abstract

To deal with the variation of production operation of the mineral processing plant, the data-mining based feedback regulation strategy is proposed to compensate the open loop steady state setting of the production unit at the plant-wide level. Rough set and increment association rule learning are used for the feedback regulation rule extraction from the historical operation data. To realize the feedback compensator two steps are carried out: (1) Determining the variables to be compensated based on rough set, (2) Mining the compensation rules through the increment association rule learning and rough set. The efficiency of the proposed strategy is proven by the experiments.