A Novel Hybrid Modeling Framework for Polymer Reaction Engineering with Application to Real-Time Process Monitoring

Hybrid modeling frameworks that integrate knowledge-driven and data-driven approaches offer a promising solution for modeling complex physical systems, especially when data are limited and domain knowledge is well established. In this work, a hybrid modeling strategy is proposed, coupling Gaussian P...

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Bibliographic Details
Main Author:	Cindy Trinh (22573197) (author)
Other Authors:	Maria Prokopidou (22573200) (author), Konstantinos Chatzilygeroudis (13946142) (author), Dimitrios Meimaroglou (22573203) (author)
Published:	2025
Subjects:	Medicine Biological Sciences not elsewhere classified Information Systems not elsewhere classified maintain physical plausibility hybrid modeling strategy constraint handling strategies allowing fast adaptation polymer reaction engineering based hybrid models kinetic model serves future work aimed driven approaches offer coupling gaussian processes additional process variables kinetic polymerization model proposed framework enables reaction temperatures framework ’ dd models radical polymerization polymerization systems model updates wide range well established varying degrees typically required simple example results highlight regression process promising solution predictions accordingly neural networks mean function integrate knowledge incorporate uncertainty improved robustness extensive training domain knowledge constrained predictions broader applications
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A Novel Hybrid Modeling Framework for Polymer Reaction Engineering with Application to Real-Time Process Monitoring

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