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Online parameter estimation and model maintenance using parameter-aware physics-informed neural network
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DOI：10.1016/j.compchemeng.2025.109403.png)
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Abstract
En 中文
• Process parameters are included as inputs in a physics-informed neural network. • Changing process parameters are identified and estimated in real time. • The framework is demonstrated on CSTR, PMMA reactor, and PSA process case studies.
Keywords:
Parameter estimation
Hybrid model
Physics-informed neural network
Model maintenance
Digital twin
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