Implementation of a Radial Basis Function control strategy for the crystallization of Ibuprofen under uncertainty

Frederico Montes, Krist V. Gernaey, Gürkan Sin*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Batch crystallization is still one of the most commonly used processes in the pharmaceutical industry. Indeed, due to the flexibility of the process and the possibility of changing the product specification, the batch process is still the main choice. However, crystallization is a highly non-linear process and therefore difficult to model and simulate. Moreover, uncertainty in physical parameters and uncertainty propagation from previous process outputs has an important and critical influence on the process risk and therefore the control strategy needed to achieve robust process performance.

To this end, a two dimensional population balance model of ibuprofen batch crystallization is developed and used to analyse and study the operability space, where uncertainty is included. The uncertainty comes from previous synthesis steps and from reported uncertainty in the process parameters, described in the literature. A nonlinear control strategy is then applied, comparing different radial basis functions (quadratic, cubic, Gaussian…), in order to achieve the desired mean size by manipulating and updating the cooling profile of the same process.

The resulting three operation strategies are benchmarked with respect to key performance metrics (reference trajectory, deviation and input variation): open-loop, open-loop strategy under process uncertainties, and closed loop operation subject to process uncertainties. The final mean crystal size (MCS) is reported as a confidence interval, in order to be used for further downstream processing.

Original languageEnglish
Title of host publicationProceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018
EditorsMario R. Eden, Marianthi G. Ierapetritou, Gavin P. Towler
Volume44
PublisherElsevier
Publication date2018
Pages565-570
ISBN (Electronic)978-0-444-64241-7
DOIs
Publication statusPublished - 2018
Event13th International Symposium on Process Systems Engineering (PSE 2018) - San DIego, United States
Duration: 1 Jul 20185 Jul 2018

Conference

Conference13th International Symposium on Process Systems Engineering (PSE 2018)
CountryUnited States
CitySan DIego
Period01/07/201805/07/2018
SeriesComputer Aided Chemical Engineering
ISSN1570-7946

Keywords

  • Modelling
  • Ibuprofen crystallization
  • Process control
  • Process uncertainty

Cite this

Montes, F., Gernaey, K. V., & Sin, G. (2018). Implementation of a Radial Basis Function control strategy for the crystallization of Ibuprofen under uncertainty. In M. R. Eden, M. G. Ierapetritou, & G. P. Towler (Eds.), Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018 (Vol. 44, pp. 565-570). Elsevier. Computer Aided Chemical Engineering https://doi.org/10.1016/B978-0-444-64241-7.50089-6
Montes, Frederico ; Gernaey, Krist V. ; Sin, Gürkan. / Implementation of a Radial Basis Function control strategy for the crystallization of Ibuprofen under uncertainty. Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. editor / Mario R. Eden ; Marianthi G. Ierapetritou ; Gavin P. Towler. Vol. 44 Elsevier, 2018. pp. 565-570 (Computer Aided Chemical Engineering).
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Montes, F, Gernaey, KV & Sin, G 2018, Implementation of a Radial Basis Function control strategy for the crystallization of Ibuprofen under uncertainty. in MR Eden, MG Ierapetritou & GP Towler (eds), Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. vol. 44, Elsevier, Computer Aided Chemical Engineering, pp. 565-570, 13th International Symposium on Process Systems Engineering (PSE 2018), San DIego, United States, 01/07/2018. https://doi.org/10.1016/B978-0-444-64241-7.50089-6

Implementation of a Radial Basis Function control strategy for the crystallization of Ibuprofen under uncertainty. / Montes, Frederico ; Gernaey, Krist V.; Sin, Gürkan.

Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. ed. / Mario R. Eden; Marianthi G. Ierapetritou; Gavin P. Towler. Vol. 44 Elsevier, 2018. p. 565-570 (Computer Aided Chemical Engineering).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AU - Montes, Frederico

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AU - Sin, Gürkan

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N2 - Batch crystallization is still one of the most commonly used processes in the pharmaceutical industry. Indeed, due to the flexibility of the process and the possibility of changing the product specification, the batch process is still the main choice. However, crystallization is a highly non-linear process and therefore difficult to model and simulate. Moreover, uncertainty in physical parameters and uncertainty propagation from previous process outputs has an important and critical influence on the process risk and therefore the control strategy needed to achieve robust process performance.To this end, a two dimensional population balance model of ibuprofen batch crystallization is developed and used to analyse and study the operability space, where uncertainty is included. The uncertainty comes from previous synthesis steps and from reported uncertainty in the process parameters, described in the literature. A nonlinear control strategy is then applied, comparing different radial basis functions (quadratic, cubic, Gaussian…), in order to achieve the desired mean size by manipulating and updating the cooling profile of the same process.The resulting three operation strategies are benchmarked with respect to key performance metrics (reference trajectory, deviation and input variation): open-loop, open-loop strategy under process uncertainties, and closed loop operation subject to process uncertainties. The final mean crystal size (MCS) is reported as a confidence interval, in order to be used for further downstream processing.

AB - Batch crystallization is still one of the most commonly used processes in the pharmaceutical industry. Indeed, due to the flexibility of the process and the possibility of changing the product specification, the batch process is still the main choice. However, crystallization is a highly non-linear process and therefore difficult to model and simulate. Moreover, uncertainty in physical parameters and uncertainty propagation from previous process outputs has an important and critical influence on the process risk and therefore the control strategy needed to achieve robust process performance.To this end, a two dimensional population balance model of ibuprofen batch crystallization is developed and used to analyse and study the operability space, where uncertainty is included. The uncertainty comes from previous synthesis steps and from reported uncertainty in the process parameters, described in the literature. A nonlinear control strategy is then applied, comparing different radial basis functions (quadratic, cubic, Gaussian…), in order to achieve the desired mean size by manipulating and updating the cooling profile of the same process.The resulting three operation strategies are benchmarked with respect to key performance metrics (reference trajectory, deviation and input variation): open-loop, open-loop strategy under process uncertainties, and closed loop operation subject to process uncertainties. The final mean crystal size (MCS) is reported as a confidence interval, in order to be used for further downstream processing.

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DO - 10.1016/B978-0-444-64241-7.50089-6

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VL - 44

T3 - Computer Aided Chemical Engineering

SP - 565

EP - 570

BT - Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018

A2 - Eden, Mario R.

A2 - Ierapetritou, Marianthi G.

A2 - Towler, Gavin P.

PB - Elsevier

ER -

Montes F, Gernaey KV, Sin G. Implementation of a Radial Basis Function control strategy for the crystallization of Ibuprofen under uncertainty. In Eden MR, Ierapetritou MG, Towler GP, editors, Proceedings of the 13th International Symposium on Process Systems Engineering – PSE 2018. Vol. 44. Elsevier. 2018. p. 565-570. (Computer Aided Chemical Engineering). https://doi.org/10.1016/B978-0-444-64241-7.50089-6