dc.contributor.author | Chaffart, Donovan | |
dc.contributor.author | Ricardez-Sandoval, Luis A. | |
dc.date.accessioned | 2018-10-26 17:13:52 (GMT) | |
dc.date.available | 2018-10-26 17:13:52 (GMT) | |
dc.date.issued | 2018-11-02 | |
dc.identifier.uri | https://dx.doi.org/10.1016/j.compchemeng.2018.08.029 | |
dc.identifier.uri | http://hdl.handle.net/10012/14071 | |
dc.description | The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.compchemeng.2018.08.029 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
dc.description.abstract | This work details the construction and evaluation of a low computational cost hybrid multiscale thin film deposition model that couples artificial neural networks (ANNs) with a mechanistic (first-principles) multiscale model. The multiscale model combines continuum differential equations, which describe the transport of the precursor gas phase, with a stochastic partial differential equation (SPDE) that predicts the evolution of the thin film surface. In order to allow the SPDE to accurately predict the thin film growth over a range of system parameters, an ANN is developed and trained to predict the values of the SPDE coefficients. The fully-assembled hybrid multiscale model is validated through comparison against a kinetic Monte Carlo-based thin film multiscale model. The model is subsequently applied to a series of optimization and control studies to test its performance under different scenarios. These studies illustrate the computational efficiency of the proposed hybrid multiscale model for optimization and control applications. | en |
dc.description.sponsorship | Natural Sciences and Engineering Research Council of Canada | en |
dc.language.iso | en | en |
dc.publisher | Elsevier | en |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Artificial neural networks | en |
dc.subject | Hybrid modelling | en |
dc.subject | Multiscale modelling | en |
dc.subject | Stochastic partial differential equations | en |
dc.subject | Thin film deposition | en |
dc.title | Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach | en |
dc.type | Article | en |
dcterms.bibliographicCitation | Chaffart, D., & Ricardez-Sandoval, L. A. (2018). Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach. Computers & Chemical Engineering, 119, 465–479. doi:10.1016/j.compchemeng.2018.08.029 | en |
uws.contributor.affiliation1 | Faculty of Engineering | en |
uws.contributor.affiliation2 | Chemical Engineering | en |
uws.typeOfResource | Text | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Reviewed | en |
uws.scholarLevel | Faculty | en |