Characterization of predictive control based on model (MPC) in multivariable process of milling in a mineral concentrator plant

Juan Tisza, Mario Chauca

Research output: Contribution to journalConference articlepeer-review


In this article, the simulation level characterization of the predictive control system - based in multivariable model (MPC) is developed, without restrictions in a milling process of a mineral concentrator plant. The multivariability of the process is considered and is evaluated the interaction between the variables. The control strategy that integrates all the control actions is developed, assessing the robustness in the application against disturbances. Generalized predictive control (GPC) is used, presenting the methodology in accordance with the philosophy of predictive control MPC, based on an initial modeling of the process, developed in reference [1]. The case study includes the use of a ball mill in a process of 4 input variables by 4 output (MIMO 4x4), where one of the output variables of greater control is the size of the mineral particles in an iron mine. The results of the control are evaluated observing and discussing the temporal responses in all the variables, the robustness of the control system is evaluated considering the response of the system before the application of multiple disturbances. Results are presented in various simulation scenarios using MATLAB.

Original languageEnglish
Article number012082
JournalIOP Conference Series: Materials Science and Engineering
Issue number1
StatePublished - 20 Jul 2020
Externally publishedYes
Event2nd Tarumanagara International Conference on the Applications of Technology and Engineering, TICATE 2019 - Jakarta, Indonesia
Duration: 21 Nov 201922 Nov 2019


  • Disturbances
  • Generalized predictive control (GPC)
  • Law of control
  • Model-based predictive control (MPC)
  • Multivariate system (MIMO) 4x4
  • Objective function
  • Robustness


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