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dc.contributor.authorAlhazmi, Khalid
dc.contributor.authorAlbalawi, Fahad
dc.contributor.authorSarathy, Mani
dc.date.accessioned2021-07-14T11:46:56Z
dc.date.available2021-05-11T10:36:05Z
dc.date.available2021-07-14T11:46:56Z
dc.date.issued2021-07-03
dc.date.submitted2020-06-18
dc.identifier.citationAlhazmi, 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.2021.130993
dc.identifier.issn1385-8947
dc.identifier.doi10.1016/j.cej.2021.130993
dc.identifier.urihttp://hdl.handle.net/10754/669169
dc.description.abstractEconomic 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.
dc.description.sponsorshipThis work was funded by the KAUST, Saudi Arabia Office of Sponsored Research (Grant OSR-2019-CRG7-4077).
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S1385894721025766
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Chemical Engineering Journal. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemical Engineering Journal, [428, , (2021-07-03)] DOI: 10.1016/j.cej.2021.130993 . © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleA reinforcement learning-based economic model predictive control framework for autonomous operation of chemical reactors
dc.typeArticle
dc.contributor.departmentChemical Engineering
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentChemical Engineering Program
dc.contributor.departmentClean Combustion Research Center
dc.identifier.journalChemical Engineering Journal
dc.rights.embargodate2023-07-06
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
dc.identifier.volume428
dc.identifier.pages130993
dc.identifier.arxivid2105.02656
kaust.personAlhazmi, Khalid
kaust.personAlbalawi, Fahad
kaust.personSarathy, Mani
kaust.grant.numberOSR-2019-CRG7-4077
dc.date.accepted2021-06-19
dc.identifier.eid2-s2.0-85109162947
refterms.dateFOA2021-05-11T10:36:30Z
kaust.acknowledged.supportUnitCRG
kaust.acknowledged.supportUnitOffice of Sponsored Research
kaust.acknowledged.supportUnitOSR


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