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On the use of detailed models in the MPC algorithm: The pressure‐swing distillation case
Author(s) -
Fissore Davide,
Pin Marco,
Barresi Antonello A.
Publication year - 2006
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.10953
Subject(s) - swing , model predictive control , distillation , sensitivity (control systems) , control theory (sociology) , reduction (mathematics) , algorithm , stability (learning theory) , state variable , energy (signal processing) , calibration , function (biology) , computer science , steady state (chemistry) , pressure swing adsorption , mathematical optimization , engineering , mathematics , control (management) , chemistry , machine learning , mechanical engineering , artificial intelligence , physics , geometry , organic chemistry , statistics , electronic engineering , evolutionary biology , biology , thermodynamics , hydrogen
A novel approach to the design of a model predictive control (MPC) algorithm for a complex plant (with energy integration and mass recycles) is given. Sensitivity analysis and steady‐state optimization are used to determine the manipulated variables that have the strongest influence on the objective function of the operation. This allows a reduction of the number of variables that are optimized on‐line, as well as the use of detailed, first‐principle–based models in the MPC algorithm, thus resulting in more reliable predictions. Moreover, the same algorithm can be used to control plants of different size, without the need of a new calibration of the parameters of the model. The application of this procedure to a pressure‐swing distillation unit is given as an example. © 2006 American Institute of Chemical Engineers AIChE J, 2006