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dc.contributor.authorKimaev, Grigoriy
dc.contributor.authorRicardez-Sandoval, Luis A.
dc.date.accessioned2020-02-28 18:57:43 (GMT)
dc.date.available2020-02-28 18:57:43 (GMT)
dc.date.issued2019-11-02
dc.identifier.urihttps://doi.org/10.1016/j.ces.2019.07.044
dc.identifier.urihttp://hdl.handle.net/10012/15674
dc.descriptionThe final publication is available at Elsevier via https://doi.org/10.1016/j.ces.2019.07.044. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.description.abstractThe purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-driven models that would enable the shrinking horizon nonlinear model predictive control of a computationally intensive stochastic multiscale system. The system of choice was a simulation of thin film formation by chemical vapour deposition. Two ANNs were trained to estimate the system’s observables. The ANNs were subsequently employed in a shrinking horizon optimization scheme to obtain the optimal time-varying profiles of the manipulated variables that would meet the desired thin film properties at the end of the batch. The resulting profiles were validated using the stochastic multiscale system and a good agreement with the predictions of the ANNs was observed. Due to their observed computational efficiency, accuracy, and the ability to reject disturbances, the ANNs seem to be a promising approach for online optimization and control of computationally demanding multiscale process systems.en
dc.description.sponsorshipThe authors would like to gratefully acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC) for the financial support.en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectnonlinear model predictive controlen
dc.subjectshrinking horizon optimizationen
dc.subjectmultiscale stochastic systemen
dc.subjectmachine learningen
dc.subjectartificial neural networken
dc.titleNonlinear model predictive control of a multiscale thin film deposition process using artificial neural networksen
dc.typeArticleen
dcterms.bibliographicCitationG. Kimaev, L.A. Ricardez-Sandoval, Nonlinear Model Predictive Control of a Multiscale Thin Film Deposition Process Using Artificial Neural Networks, Chemical Engineering Science (2019), doi: https://doi.org/10.1016/j.ces.2019.07.044en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Chemical Engineeringen
uws.typeOfResourceTexten
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen
uws.scholarLevelGraduateen


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