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Optimal control policies of a crystallization process using inverse reinforcement learning
conference contribution
posted on 2022-03-10, 10:31 authored by Paul AnandanPaul Anandan, Chris RiellyChris Rielly, Brahim BenyahiaBrahim BenyahiaCrystallization is widely used in the pharmaceutical industry to purify reaction intermediates and final active pharmaceutical ingredients. This work presents a novel implementation of Inverse Reinforcement Learning (IRL) approach where an agent observes the expert’s optimal control policies of a crystallization process and attempts to mimic its performance. In essence, an Apprenticeship Learning (AL) setup was developed where the expert demonstrates the control task to the IRL agent to help attain effective control performance when compared to the expert. This is achieved through repeated execution of “exploitation policies” that simply maximizes the rewards over the consecutive IRL training episodes. The cooling crystallization of paracetamol is used as a case study and both proportional integral derivative (PID) and Model Predictive Control (MPC) strategies were considered as expert systems. A model based IRL technique is implemented to achieve effective trajectory tracking which ensures final crystal size considered as the critical quality attributes, by reducing the deviation from the optimal reference trajectories namely process temperature, supersaturation, and particle size. The performance of the trained IRL agent was validated against the PID and MPC and tested in presence of noisy measurements and model uncertainties.
Funding
ARTICULAR: ARtificial inTelligence for Integrated ICT-enabled pharmaceUticaL mAnufactuRing
Engineering and Physical Sciences Research Council
Find out more...History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Chemical Engineering
Published in
32nd European Symposium on Computer Aided Process Engineering: ESCAPE-32Pages
1093-1098Source
32nd European Symposium on Computer Aided Process Engineering (ESCAPE32)Publisher
ElsevierVersion
- AM (Accepted Manuscript)
Rights holder
© ElsevierPublisher statement
This paper was accepted for publication in the 32nd European Symposium on Computer Aided Process Engineering: ESCAPE-32 and the definitive published version is available at https://doi.org/10.1016/B978-0-323-95879-0.50183-1.Acceptance date
2022-01-24Publication date
2022-08-01Copyright date
2022ISBN
9780323958790; 9780323958806ISSN
1570-7946Publisher version
Book series
Computer Aided Chemical Engineering; Volume 51Language
- en
Editor(s)
Ludovic Montastruc; Stéphane NegnyLocation
Toulouse, FranceEvent dates
12th June 2022 - 15th June 2022Depositor
Dr Brahim Benyahia. Deposit date: 9 March 2022Usage metrics
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