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Self‐tuning run to run optimization of fed‐batch processes using unfold‐PLS
Author(s) -
Camacho José,
Picó Jesús,
Ferrer Alberto
Publication year - 2007
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.11205
Subject(s) - robustness (evolution) , heuristic , process (computing) , computer science , nonlinear system , mathematical optimization , partial least squares regression , process optimization , work in process , mathematics , engineering , machine learning , chemistry , biochemistry , operations management , quantum mechanics , environmental engineering , gene , operating system , physics
Unfold Partial‐Least Squares (u‐PLS) is a modeling method successfully applied to batch‐process monitoring and end quality prediction. This method is integrated in a self‐tuning optimization algorithm, based on extremum‐seeking control. The optimization is driven by the gradient obtained by means of an adaptive u‐PLS model. Since this is an empirical model, no first‐principles based knowledge of the process is necessary. Heuristic rules are used to constrain the gradient taking into account nonlinearity and unknown causes of variability. Extensions to model the variability in initial conditions, to optimize several performance indices, and to handle inequality constraints are presented. The optimization algorithm is tested on a complex comprehensive simulated‐process model for the fed‐batch cultivation of Sacharomyces cerevisiae. Results show the performance and versatility of the proposed approach, as well as its robustness to process changes. © 2007 American Institute of Chemical Engineers AIChE J, 2007