Adaptation of Dynamic Data?Driven Models for Real?Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning
نویسندگان
چکیده
In the absence of knowledge about challenging dynamic phenomena involved in batch distillation processes, e.g., complex flow regimes or appearing and vanishing phases, generation accurate mechanistic models is limited. Real plant data containing this missing information scarce, also limiting use data-driven models. To exploit contained measurement a related but inaccurate first-principles model, transfer learning from simulated to real analyzed. For case column, adapted model provides more predictions than trained exclusively on scarce data. Its enhanced convergence lower computational cost make it suitable for optimization real-time.
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ژورنال
عنوان ژورنال: Chemie Ingenieur Technik
سال: 2023
ISSN: ['0009-286X', '1522-2640']
DOI: https://doi.org/10.1002/cite.202200228