Application of Modified Multi Model Predictive Control Algorithm to Fluid Catalytic Cracking Unit

This paper presents a modified multi model predictive control algorithm for the control of riser outlet temperature and regenerator temperature for the fluid catalytic cracking unit (FCCU). The models of the fluid catalytic cracking unit are estimated using subspace identification (N4SID) algorithm. The PRBS signal is applied as an input signal to estimate the FCCU models. Since the estimated model does not give 100% fit; especially for nonlinear systems having more than one operating conditions, multi-model approach is proposed. In multi model, more than one model of FCCU used in MPC design. The main advantages of proposed method are that it can handle hard input and output constraints and it can be used for multi input multi output processes (MIMO) without increasing the complexity in control design. MATLAB/Simulink is used to estimate the models of FCCU and simulate the results for the controller. The simulation results show that the proposed algorithm provides better result for both reference tracking and disturbance rejection.

[1]  O. Nelles.  Nonlinear System Identification , 2001 .

[2]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[3]  P. Eykhoff.  System Identification Parameter and State Estimation , 1974 .

[4]  A. T. Boum,et al.  Observer based and Quadratic dynamic matrix control of a fluid catalytic cracking unit: A comparison study , 2013 .

[5]  Keith R. Godfrey,et al.  Perturbation signals for system identification , 1993 .

[6]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[7]  J. Corriou,et al.  Modelling and Control of a Riser Type Fluid Catalytic Cracking (FCC) Unit , 1997 .

[8]  Nuno M.C. Oliveira,et al.  Fluid Catalytic Cracking (FCC) Process Modeling, Simulation, and Control , 2012 .

[9]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[10]  Mircea Vasile Cristea,et al.  Simulation and model predictive control of a UOP fluid catalytic cracking unit , 2003 .

[11]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[12]  Zoltan K. Nagy,et al.  Dynamic modeling and nonlinear model predictive control of a fluid catalytic cracking unit , 2005 .

[13]  S. Billings.  Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains , 2013 .

[14]  Cristina Popa,et al.  The model predictive control system for the fluid catalytic cracking unit , 2010 .