Nonlinear model-based control algorithm for a distillation column using software sensor.

This paper presents the design of model-based globally linearizing control (GLC) structure for a distillation process within the differential geometric framework. The model of a nonideal binary distillation column, whose characteristics were highly nonlinear and strongly interactive, is used as a real process. The classical GLC law is comprised of a transformer (input-output linearizing state feedback), a nonlinear state observer, and an external PI controller. The tray temperature based short-cut observer (TTBSCO) has been used as a state estimator within the control structure, in which all tray temperatures were considered to be measured. Accordingly, the liquid phase composition of each tray was calculated online using the derived temperature-composition correlation. In the simulation experiment, the proposed GLC coupled with TTBSCO (GLC-TTBSCO) outperformed a conventional PI controller based on servo performances with and without measurement noise as well as on regulatory behaviors. In the subsequent part, the GLC law has been synthesized in conjunction with tray temperature based reduced-order observer (GLC-TTBROO) where the distillate and bottom compositions of the distillation process have been inferred from top and bottom product temperatures respectively, which were measured online. Finally, the comparative performance of the GLC-TTBSCO and the GLC-TTBROO has been addressed under parametric uncertainty and the GLC-TTBSCO algorithm provided slightly better performance than the GLC-TTBROO. The resulting control laws are rather general and can be easily adopted for other binary distillation columns.

[1]  J. B. Riggs,et al.  Basic Principles and Calculations in Chemical Engineering , 1974 .

[2]  J. S. Rowlinson,et al.  Molecular Thermodynamics of Fluid-Phase Equilibria , 1969 .

[3]  C. Kravaris,et al.  Synthesis of multivariable nonlinear controllers by input/output linearization , 1990 .

[4]  Michael A. Henson,et al.  Nonlinear model predictive control: current status and future directions , 1998 .

[5]  Riccardo Scattolini,et al.  A stabilizing model-based predictive control algorithm for nonlinear systems , 2001, Autom..

[6]  Masoud Soroush,et al.  Discrete‐time nonlinear feedback control of multivariable processes , 1996 .

[7]  Sirish L. Shah,et al.  Constrained nonlinear MPC using hammerstein and wiener models: PLS framework , 1998 .

[8]  James B. Rawlings,et al.  Nonlinear Model Predictive Control: A Tutorial and Survey , 1994 .

[9]  B. Wayne Bequette,et al.  Extension of dynamic matrix control to multiple models , 2003, Comput. Chem. Eng..

[10]  C. Kravaris,et al.  Nonlinear State Feedback Synthesis by Global Input/Output Linearization , 1986, 1986 American Control Conference.

[11]  F. Fontes.  A General Framework to Design Stabilizing Nonlinear Model Predictive Controllers , 2001 .

[12]  Masoud Soroush,et al.  Synthesis of discrete-time nonlinear feedforward/feedback controllers , 1994 .

[13]  Saibal Ganguly,et al.  Nonlinear control of a multicomponent distillation process coupled with a binary distillation model as an EKF predictor. , 2006, ISA transactions.

[14]  Massimo Morbidelli,et al.  Development of a composition estimator for binary distillation columns. Application to a pilot plant , 1995 .

[15]  Masoud Soroush,et al.  Discrete‐time nonlinear controller synthesis by input/output linearization , 1992 .

[16]  William L. Luyben,et al.  Process Modeling, Simulation and Control for Chemical Engineers , 1973 .