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An Hysys Simulation of a Dynamic Process using Linear Offset Free MPC with an Empirical Model

Affiliations

  • Department of Chemical Engineering, Universiti Teknologi Petronas, 32160 Tronoh, Perak, Malaysia

Abstract


Objectives: In advanced process control, especially model predictive control (MPC), a model is needed to calculate the input (manipulated variable) to the plant to track the set-point. The model used for MPC is usually empirical model, usually a state space model in the open literature, identified by system identification. The problem is that the empirical model is never completely accurate to represent the plant, a reason that brings about an offset in set-point tracking by MPC. In addition, in the presence of disturbance, the accuracy becomes much worse. Method: In this work, we recommend state estimation for the state prediction according to measured output at each iteration calculation to obtain an equal output prediction with output measurement from the plant. Findings: It was found out that integrating MPC and Kalman filter could facilitate linear offset free MPC. Application: The success of this approach is demonstrated using an integrated MPC and Kalman filter in Simulink- Matlab to control the dynamic Depropanizer process in Hysys.

Keywords

Kalman Filter,Linear Offset Free MPC, Matlab-Hysys Interface.

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References


  • Pannocchia G, Rawlings JB. Disturbance Models for OffsetFree Model-Predictive Control. AIChE Journal. 2003; 49(2):426–37. Crossref
  • Pannocchia G, Bemporad A. Combined Design of Disturbance Model and Observer for Offset-Free Model Predictive Control. IEEE Transactions Automatic Control. 2007; 52(6):1048–53. Crossref
  • Maeder U, Borrelli F, Morari M. Linear Offset-Free Model Predictive Control. Automatica. 2009; 45(10): 2214–22. Crossref 4. Maeder U, Morari M. Offset-Free Reference Tracking with Model Predictive Control. Automatica. 2010; 46(9):1469–76. Crossref 5. Morari M H, Lee J. Model Predictive Control: Past, Present and Future. Computers and Chemical Engineering. 1999; 23(4–5):667–82. Crossref
  • Simon D. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. John Wiley & Sons; 2006. p.555. Crossref PMid:17048242
  • Lee JH, Ricker NL. Extended Kalman Filter Based Nonlinear Model Predictive Control. Industrial and Engineering Chemistry Research. 1994; 33(6):1530–41. Crossref
  • Huang R, Patwardhan SC, Biegler LT. Robust Stability of Nonlinear Model Predictive Control Based on Extended Kalman Filter. Journal of Process Control. 2012; 22(1):82–9. Crossref
  • Jacob NC, Dhib R. Unscented Kalman Filter Based Nonlinear Model Predictive Control of a LDPE Autoclave Reactor. Journal of Process Control. 2011; 21(9):1332–44. Crossref
  • Tuan TT, Tufa LD, Mutalib MIA, Abdallah AFM. Control of Depropanizer in Dynamic Hysys Simulation Using MPC in Matlab-Simulink. Procedia Engineering. 2016; 148:1104–11. Crossref
  • A toolbox for using MATLAB as an activeX/COM controller for Hysys, Matlab Central. http://www.pvv.org/~olafb/ hysyslib/. Date accessed: 15/02/2017.
  • FluidChe 2017 Available from: http://fluidsche.ump.edu.my/index.php/en/
  • The Center of Excellence for Advanced Research in Fluid Flow (CARIFF) Available from: http://cariff.ump.edu.my/
  • Natural resources products prospects - International Conference on Fluids and Chemical Engineering FluidsChE 2017 Malaysia, ). Indian Journal of science and technology. 2017; S2(1).
  • University Malaysia Pahang. Available from: www.ump.edu.my

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