A reinforcement learning-based economic model predictive control framework for autonomous operation of chemical reactors
Name:
RL_EMPC_Paper_CEJ.pdf
Size:
1.328Mb
Format:
PDF
Description:
Accepted manuscript
Embargo End Date:
2023-07-06
Type
ArticleKAUST Department
Chemical EngineeringChemical Engineering Program
Clean Combustion Research Center
Combustion and Pyrolysis Chemistry (CPC) Group
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Physical Science and Engineering (PSE) Division
KAUST Grant Number
OSR-2019-CRG7-4077Date
2021-07-03Embargo End Date
2023-07-06Submitted Date
2020-06-18Permanent link to this record
http://hdl.handle.net/10754/669169
Metadata
Show full item recordAbstract
Economic model predictive control (EMPC) is a promising methodology for optimal operation of dynamical processes that has been shown to improve process economics considerably. However, EMPC performance relies heavily on the accuracy of the process model used. As an alternative to model-based control strategies, reinforcement learning (RL) has been investigated as a model-free control methodology, but issues regarding its safety and stability remain an open research challenge. This work presents a novel framework for integrating EMPC and RL for online model parameter estimation of a class of nonlinear systems. In this framework, EMPC optimally operates the closed loop system while maintaining closed loop stability and recursive feasibility. At the same time, to optimize the process, the RL agent continuously compares the measured state of the process with the model's predictions (nominal states), and modifies model parameters accordingly. The major advantage of this framework is its simplicity; state-of-the-art RL algorithms and EMPC schemes can be employed with minimal modifications. The performance of the proposed framework is illustrated on a network of reactions with challenging dynamics and practical significance. This framework allows control, optimization, and model correction to be performed online and continuously, making autonomous reactor operation more attainable.Citation
Alhazmi, K., Albalawi, F., & Sarathy, S. M. (2021). A reinforcement learning-based economic model predictive control framework for autonomous operation of chemical reactors. Chemical Engineering Journal, 130993. doi:10.1016/j.cej.2021.130993Sponsors
This work was funded by the KAUST, Saudi Arabia Office of Sponsored Research (Grant OSR-2019-CRG7-4077).Publisher
Elsevier BVJournal
Chemical Engineering JournalarXiv
2105.02656Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S1385894721025766ae974a485f413a2113503eed53cd6c53
10.1016/j.cej.2021.130993