Batch-to-batch strategies for cooling crystallization

M. Forgione; A. Mesbah; X. Bombois; P.M.J. Hof, Van den

doi:10.1109/CDC.2012.6426773

Batch-to-batch strategies for cooling crystallization

M. Forgione, A. Mesbah, X. Bombois, P.M.J. Hof, Van den

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), 10-13 December 2012, Maui, Hawai
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	6364-6369
DOIs	https://doi.org/10.1109/CDC.2012.6426773
Publication status	Published - 2012
Event	51st IEEE Conference on Decision and Control, CDC 2012 - Maui, United States Duration: 10 Dec 2012 → 13 Dec 2012 Conference number: 51

Conference

Conference	51st IEEE Conference on Decision and Control, CDC 2012
Abbreviated title	CDC 2012
Country/Territory	United States
City	Maui
Period	10/12/12 → 13/12/12

Access to Document

10.1109/CDC.2012.6426773

Cite this

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title = "Batch-to-batch strategies for cooling crystallization",

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.",

author = "M. Forgione and A. Mesbah and X. Bombois and {Hof, Van den}, P.M.J.",

year = "2012",

doi = "10.1109/CDC.2012.6426773",

language = "English",

pages = "6364--6369",

booktitle = "Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), 10-13 December 2012, Maui, Hawai",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "51st IEEE Conference on Decision and Control, CDC 2012, CDC 2012 ; Conference date: 10-12-2012 Through 13-12-2012",

}

Forgione, M, Mesbah, A, Bombois, X & Hof, Van den, PMJ 2012, Batch-to-batch strategies for cooling crystallization. in Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), 10-13 December 2012, Maui, Hawai. Institute of Electrical and Electronics Engineers, Piscataway, pp. 6364-6369, 51st IEEE Conference on Decision and Control, CDC 2012, Maui, Hawaii, United States, 10/12/12. https://doi.org/10.1109/CDC.2012.6426773

Batch-to-batch strategies for cooling crystallization. / Forgione, M.; Mesbah, A.; Bombois, X. et al.
Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), 10-13 December 2012, Maui, Hawai. Piscataway: Institute of Electrical and Electronics Engineers, 2012. p. 6364-6369.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Batch-to-batch strategies for cooling crystallization

AU - Forgione, M.

AU - Mesbah, A.

AU - Bombois, X.

AU - Hof, Van den, P.M.J.

N1 - Conference code: 51

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1109/CDC.2012.6426773

DO - 10.1109/CDC.2012.6426773

M3 - Conference contribution

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EP - 6369

BT - Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), 10-13 December 2012, Maui, Hawai

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 51st IEEE Conference on Decision and Control, CDC 2012

Y2 - 10 December 2012 through 13 December 2012

ER -

Batch-to-batch strategies for cooling crystallization

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