Batch-to-batch strategies for cooling crystallization

Author

Forgione, Marco ; Mesbah, Ali ; Bombois, Xavier ; Van den Hof, Paul M. J.

Author_Institution

Delft Center for Syst. & Control, Delft Univ. of Technol., Delft, Netherlands

fYear

2012

fDate

10-13 Dec. 2012

Firstpage

6364

Lastpage

6369

Abstract

Two batch-to-batch (B2B) algorithms for supersaturation control in cooling crystallization are presented in this paper. In Iterative Learning Control (ILC) a nominal process model is adjusted with an additive correction term which depends on the error in the last batch. In Iterative Identification Control (IIC) the physical parameters of the process model are recursively estimated by adopting a Bayesian identification framework. Both B2B algorithms compute an optimized input for the next batch that is fed to a lower level PI feedback controller in order to reject the process disturbances. The tracking performance of these B2B+PI control schemes is investigated in a simulation study.

Keywords

PI control; batch processing (industrial); cooling; crystallisation; feedback; process control; B2B algorithms; B2B+PI control schemes; Bayesian identification framework; PI feedback controller; additive correction term; batch-to-batch algorithms; batch-to-batch strategies; cooling crystallization; iterative learning control; nominal process model; physical parameters; process disturbances; tracking performance; Computational modeling; Crystallization; Equations; Mathematical model; Temperature control; Temperature measurement; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control (CDC), 2012 IEEE 51st Annual Conference on

Conference_Location

Maui, HI

ISSN

0743-1546

Print_ISBN

978-1-4673-2065-8

Electronic_ISBN

0743-1546

Type

conf

DOI

10.1109/CDC.2012.6426773

Filename

6426773