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Inferential Control of Reactive Destillation Columns – An Algorithmic Approach
Author(s) -
Paramasivan G.,
Kienle A.
Publication year - 2011
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201100141
Subject(s) - benchmark (surveying) , mathematical optimization , binary number , nonlinear programming , integer (computer science) , controller (irrigation) , mathematics , nonlinear system , process (computing) , integer programming , reactive distillation , control theory (sociology) , computer science , distillation , control (management) , artificial intelligence , physics , arithmetic , geodesy , quantum mechanics , agronomy , biology , programming language , geography , operating system , chemistry , organic chemistry
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.