Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Journal Article · Wed Sep 24 00:00:00 EDT 2025 · Journal of the American Ceramic Society

DOI:https://doi.org/10.1111/jace.70267· OSTI ID:3002458

Zhang, Pei ^[1]; Ramanuj, Vimal ^[1]; Sankaran, Ramanan ^[1]

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1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 3002458
• Journal Information: Journal of the American Ceramic Society, Journal Name: Journal of the American Ceramic Society Journal Issue: 1 Vol. 109; ISSN 0002-7820; ISSN 1551-2916
• Publisher: American Ceramic Society
• Country of Publication: United States
• Language: English

Related Subjects

autoencoder neural networks
chemical vapor deposition
machine learning
neural network with differential equations
principal component analysis
reduced chemical kinetics