Combining First Principles Dynamic Modelling and Plant Identification for Design of an Industrial MPC Application to a Polymer Reactor

Abstract This paper presents the design ot model predictive control systems on the basis ot combined use of first principles dynamic simulation models and process identification techniques to develop the models ultimately applied in the control system. As an example the application to a fluidised bed gas phase polymerisation reactor is discussed. The use of both modelling approaches is motivated from the requirements on the models imposed by the controller at one hand and the restrictions in process operation related to testing at the other hand. Aspects related to moving from the simulation environment to the plant environment to bridge the gap between true polymer reactor dynamics and the dynamics represented by the simulation model are discussed. Suggestions are given for further improvement of the approach.

[1]  Lennart Ljung,et al.  Tools for Semi-Physical Modeling , 1994 .

[2]  Daniel E. Rivera,et al.  Systematic techniques for determining modelling requirements for SISO and MIMO feedback control , 1995 .

[3]  A.C.P.M. Backx,et al.  Identification for the control of MIMO industrial processes , 1991 .

[4]  T. Johansen,et al.  Semi-empirical modeling of non-linear dynamic systems through identification of operating regimes and local models , 1995 .

[5]  J. Brian Froisy.  Model predictive control: Past, present and future , 1994 .

[6]  Hm Heinz Falkus,et al.  Parametric uncertainty in system identification , 1994 .

[7]  Daniel E. Rivera,et al.  SYSTEMATIC TECHNIQUES FOR DETERMINING MODELING REQUIREMENTS FOR SISO AND MIMO FEEDBACK CONTROL PROBLEMS , 1994 .

[8]  Sigurd Skogestad,et al.  Modelling and Identification for Robust Control of Ill-Conditioned Plants - a Distillation Case Study , 1991, 1991 American Control Conference.

[9]  Wei Li,et al.  Control relevant identification of ill-conditioned systems: Estimation of gain directionalyty , 1996 .

[10]  Herbert J. A. F. Tulleken,et al.  Grey-box modelling and identification using physical knowledge and bayesian techniques , 1993, Autom..

[11]  James B. Rawlings,et al.  Model predictive control with linear models , 1993 .

[12]  R. Tousain,et al.  SSQP for the solution of large-scale dynamic-economic optimization problems , 1999 .

[13]  Tor Arne Johansen,et al.  Identification of non-linear systems using empirical data and prior knowledge - an optimization approach , 1996, Autom..

[14]  Daniel Berckmans,et al.  Building a grey box model to model the energy and mass transfer in an imperfectly mixed fluid by using experimental data , 1996 .

[15]  Andreas Hans Kemna,et al.  Identification of combined physical and empirical models using nonlinear A priori knowledge , 1994 .

[16]  Lennart Ljung,et al.  Tools for semiphysical modelling , 1995 .