Reference : Classification of states and model order reduction of large scale Chemical Vapor Depo...
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/37228
Classification of states and model order reduction of large scale Chemical Vapor Deposition processes with solution multiplicity
English
Koronaki, E.D. []
Gkinis, P.A. []
Beex, Lars mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Bordas, Stéphane mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Theodoropoulos, C. []
Sep-2018
Computers and Chemical Engineering
Elsevier
121
148-157
Yes (verified by ORBilu)
International
0098-1354
1873-4375
Oxford
United Kingdom
[en] Multiplicity of states ; Support Vector Machines ; Classification ; Reduced order modeling ; Artificial Neural Networks ; Data-driven models ; Chemical Vapor Deposition
[en] This paper presents an equation-free, data-driven approach for reduced order modeling of a Chemical Vapor Deposition (CVD) process. The proposed approach is based on process information provided by detailed, high-fidelity models, but can also use spatio-temporal measurements. The Reduced Order Model (ROM) is built using the method-of-snapshots variant of the Proper Orthogonal Decomposition (POD) method and Artificial Neural Networks (ANN) for the identification of the time-dependent coefficients. The derivation of the model is completely equation-free as it circumvents the projection of the actual equations onto the POD basis. Prior to building the model, the Support Vector Machine (SVM) supervised classification algorithm is used in order to identify clusters of data corresponding to (physically) different states that may develop at the same operating conditions due to the inherent nonlinearity of the process. The different clusters are then used for ANN training and subsequent development of the ROM. The results indicate that the ROM is successful at predicting the dynamic behavior of the system in windows of operating parameters where steady states are not unique.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/37228
10.1016/j.compchemeng.2018.08.023

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