Arrhenius plot; Fe CVD reactor; Hierarchical clustering; Polynomial chaos expansion; Sensitivity analysis; Uncertainty quantification; Environmental Chemistry; Chemistry (all); Chemical Engineering (all); Industrial and Manufacturing Engineering
Abstract :
[en] This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interplay of physical mechanism that dominate in each one of them. Through this work, we address three key objectives. Firstly, our methodology relies on process outcomes, derived by a detailed CFD model, to identify clusters of “outcomes” corresponding to distinct process regimes, wherein the relative influence of input variables undergoes notable shifts. This phenomenon is experimentally validated through Arrhenius plot analysis, affirming the efficacy of our approach. Secondly, we demonstrate the development of an efficient surrogate model, based on Polynomial Chaos Expansion (PCE), that maintains accuracy, facilitating streamlined computational analyses. Finally, as a result of PCE, sensitivity analysis is made possible by means of Sobol’ indices, that quantify the impact of process inputs across identified regimes. The insights gained from our analysis contribute to the formulation of hypotheses regarding phenomena occurring beyond the transition regime. Notably, the significance of temperature even in the diffusion-limited regime, as evidenced by the Arrhenius plot, suggests activation of gas phase reactions at elevated temperatures. Importantly, our proposed methods yield insights that align with experimental observations and theoretical principles, aiding decision-making in process design and optimization. By circumventing the need for costly and time-consuming experiments, our approach offers a pragmatic pathway toward enhanced process efficiency. Moreover, this study underscores the potential of data-driven computational methods for innovating reactor design paradigms.
Disciplines :
Chemical engineering
Author, co-author :
LOACHAMIN SUNTAXI, Geremy ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) ; NTUA - National Technical University of Athens > School of Chemical Engineering
PAPAVASILEIOU, Paris ; University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Engineering > Team Stéphane BORDAS ; School of Chemical Engineering, National Technical University of Athens, Attiki, Greece
KORONAKI, Eleni ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Giovanis, Dimitrios G. ; Department of Civil & Systems Engineering, Johns Hopkins University, Baltimore, United States
Gakis, Georgios ; School of Chemical Engineering, National Technical University of Athens, Attiki, Greece
Aviziotis, Ioannis G. ; School of Chemical Engineering, National Technical University of Athens, Attiki, Greece
POZZETTI, Gabriele ; University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Engineering ; CERATIZIT Luxembourg S.à r.l. Mamer, Luxembourg
Czettl, Christoph; CERATIZIT Austria GmbH Reutte, Austria
Bordas, Stéphane P.A. ; Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
Boudouvis, Andreas G. ; School of Chemical Engineering, National Technical University of Athens, Attiki, Greece
External co-authors :
yes
Language :
English
Title :
Discovering deposition process regimes: Leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
This research was funded by the Luxembourg National Research Fund (FNR) , grant reference 16758846 .This research was funded by the Luxembourg National Research Fund (FNR) , grant reference 16758846 . For the purpose of open access, the authors have applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.
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