Improving Process Monitoring via Dynamic Multi-Fidelity Modeling Article Swipe
Rastislav Fáber
,
Marco Vaccari
,
Riccardo Bacci di Capaci
,
Karol Ľubušký
,
Gabriele Pannocchia
,
Radoslav Paulen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2025.07.145
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2025.07.145
We study real-time process monitoring, where employed online sensors yield inaccurate information. A multi-fidelity (MF) modeling approach is adopted that integrates dynamic information from online, low-fidelity (LF) data with infrequent, high-fidelity (HF) laboratory measurements. The proposed methodology is demonstrated on a composition monitoring problem derived from real oil refinery operations. The developed MF model exhibits a significant improvement in accuracy with respect to both LF data (online sensor) and the HF model (standard soft sensor). The results highlight the potential of MF modeling for improving process monitoring and control through the integration of diverse data sources.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2025.07.145
- OA Status
- diamond
- Cited By
- 1
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413168301
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