Inferential control of reactive distillatiion columns - An algorithmic approach

Decentralized control system design comprises the selection of a suitable control structure and controller parameters. Here, mixed integer optimization is used to determine the optimal control structure and the optimal controller parameters simultaneously. The process dynamics is included explicitly into the constraints using a rigorous nonlinear dynamic process model. Depending on the objective function, which is used for the evaluation of competing control systems, two different formulations are proposed which lead to mixed-integer dynamic optimization (MIDO) problems. A MIDO solution strategy based on the sequential approach is adopted in the present paper. Here, the MIDO problem is decomposed into a series of nonlinear programming (NLP) subproblems (dynamic optimization) where the binary variables are fixed, and mixed-integer linear programming (MILP) master problems which determine a new binary configuration for the next NLP subproblem. The proposed methodology is applied to inferential control of reactive distillation columns as a challenging benchmark problem for chemical process control.

[1]  Muhammad A. Al-Arfaj,et al.  Comparison of Alternative Control Structures for an Ideal Two-Product Reactive Distillation Column , 2000 .

[2]  David Kendrick,et al.  GAMS, a user's guide , 1988, SGNM.

[3]  J. D. Perkins,et al.  Selection of process control structure based on economics , 1994 .

[4]  Michael F. Malone,et al.  Measurement of Residue Curve Maps and Heterogeneous Kinetics in Methyl Acetate Synthesis , 1998 .

[5]  Devrim B. Kaymak,et al.  Evaluation of a two-temperature control structure for a two-reactant/two-product type of reactive distillation column , 2006 .

[6]  A. M. Geoffrion.  Generalized Benders decomposition , 1972 .

[7]  N. Kaistha,et al.  Steady-State Multiplicity and Its Implications on the Control of an Ideal Reactive Distillation Column , 2008 .

[8]  K. Sundmacher,et al.  Reactive distillation : status and future directions , 2003 .

[9]  Cheng-Ching Yu,et al.  Control of different reactive distillation configurations , 2006 .

[10]  Michael F. Malone,et al.  Reactive distillation for methyl acetate production , 2003, Comput. Chem. Eng..

[11]  W. Luyben,et al.  Tuning PI controllers for integrator/dead time processes , 1992 .

[12]  Antonio Flores-Tlacuahuac,et al.  Simultaneous mixed-integer dynamic optimization for integrated design and control , 2007, Comput. Chem. Eng..

[13]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Devrim B. Kaymak,et al.  Comparison of two types of two-temperature control structures for reactive distillation columns , 2005 .

[15]  Efstratios N. Pistikopoulos,et al.  Recent advances in optimization-based simultaneous process and control design , 2004, Comput. Chem. Eng..

[16]  W. E. Stewart,et al.  Sensitivity analysis of initial value problems with mixed odes and algebraic equations , 1985 .

[17]  Jinsong Zhao,et al.  An overview on controllability analysis of chemical processes , 2011 .

[18]  Pu Li,et al.  Chance constrained programming approach to process optimization under uncertainty , 2008, Comput. Chem. Eng..

[19]  Cheng-Ching Yu,et al.  Relay Feedback Tests for Highly Nonlinear Processes: Reactive Distillation , 2006 .

[20]  Achim Kienle,et al.  Nonlinear computation in DIVA — methods and applications , 2000 .

[21]  E. Bristol.  On a new measure of interaction for multivariable process control , 1966 .