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Advantage of Low‐Cost Predictive Control: Study Case on a Train of Distillation Columns
Author(s) -
Muresan Cristina I.,
Ionescu Clara M.,
Dulf Eva H.,
Rusu-Both Roxana,
Folea Silviu
Publication year - 2018
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.201700529
Subject(s) - multivariable calculus , model predictive control , control theory (sociology) , decoupling (probability) , distillation , settling time , pid controller , controller (irrigation) , train , process (computing) , internal model , fractionating column , control engineering , process control , computer science , engineering , control (management) , temperature control , chemistry , step response , chromatography , artificial intelligence , agronomy , cartography , biology , geography , operating system
The process of enriching the 13 C isotope, performed in trains of cryogenic distillation columns, exhibits large settling times, nonlinearities, large dead‐times, and are difficult to model precisely. Such equipment has been developed in Romania, with concentration increasing up to 70 %. A control analysis for a single unit has already been done including a decentralized multivariable PI controller and two decoupling control algorithms based on the internal model control (IMC) approach. Here, a multivariable predictive controller, the extended prediction self‐adaptive controller is proposed. The simulation results, considering significant modeling errors, demonstrate that this represents a more suitable choice than the previously designed strategies. Comparisons are included to support this idea.