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Control of batch and continuous crystallization processes using reinforcement learning

chapter
posted on 19.07.2021, 12:50 by Brahim Benyahia, Paul Anandan, Chris Rielly
In crystallization processes, the control of particle size distribution, shape and purity are crucial to achieve the targeted critical quality attributes of the final drug product and meet the pharmaceutical regulatory requirements. This work presents novel optimal trajectory tracking control strategies for batch and continuous cooling crystallization processes using reinforcement learning (RL). The cooling crystallization of paracetamol in water was used as a case study. A model-based reinforcement learning technique is implemented to achieve large crystal size by reducing the deviation from targeted reference trajectories namely process temperature, supersaturation and particle size. This multioutput tracking control strategy was development to address quality and performance challenges commonly encountered in batch and continuous crystallization processes. Various training strategies and reward functions were investigated to enhance the learning capabilities and robustness of the reinforcement-learning-based control. Despite the computational costs inherent to reinforcement learning, the later demonstrated robust control capabilities compared the benchmark control strategies such as model predictive control.

Funding

EPSRC (EP/R032858/1) ARTICULAR project

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Computer Aided Chemical Engineering

Volume

50

Pages

1371 - 1376

Source

31st European Symposium on Computer Aided Process Engineering (ESCAPE-31)

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This book chapter was accepted for publication in the book Computer Aided Chemical Engineering, volume 50, and the definitive published version is available at https://doi.org/10.1016/B978-0-323-88506-5.50211-4.

Acceptance date

05/03/2021

Publication date

2021-07-18

Copyright date

2021

ISBN

9780323885065

ISSN

1570-7946

Book series

Computer Aided Chemical Engineering; 50

Language

en

Editor(s)

Metin Türkay; Rafiqul Gani

Location

Istanbul,Turkey

Event dates

6th June 2021 - 9th June 2021

Depositor

Dr Brahim Benyahia. Deposit date: 16 July 2021