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Embedding Dynamic Microkinetic Modeling Information Into Reduced-Order Models Using Gaussian Processes
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DOI：10.1002/aic.70194.png)
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Abstract
En 中文
In this work, a novel generalizable framework is proposed for obtaining dynamic discrepancy reduced-order models (DD-ROMs) that balance the differences between high-fidelity models (HFMs) and reduced-order models (ROMs) using Gaussian Processes (GPs). The proposed framework encompasses fundamental criteria for addressing missing underlying physics and is the first-of-its-kind to offer a comprehensive insight guided by sensitivity and correlation analyses into where the discrepancy terms must be incorporated. The proposed framework is employed to correct dynamic mismatches between a reduced-order model and a high-fidelity microkinetic model of the steam methane reforming (SMR) reactions. The validation results demonstrate that with the discrepancy function added to the equilibrium constant, the DD-ROM is capable of mimicking the dynamic trajectories of the microkinetic model with high accuracy, exhibiting an of 97.86% and an of 0.123, while obtaining a significant computational gain, being 104 times faster per model execution than integrating the HFM model.
Keywords:
Gaussian processes (GPs)
hybrid modeling
microkinetic modeling
model reduction
physics-informed machine learning
steam methane reforming (SMR)
Journal
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4
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1.1W
Citations:
2.9W