通过动态多保真度建模改进过程监控

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI:10.1016/j.ifacol.2025.07.145

Rastislav Fáber , Marco Vaccari , Riccardo Bacci di Capaci , Karol Ľubušký , Gabriele Pannocchia , Radoslav Paulen

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引用次数: 0

摘要

我们研究实时过程监控，其中使用在线传感器产生不准确的信息。采用多保真度（MF）建模方法，将在线低保真度（LF）数据的动态信息与不频繁的高保真度（HF）实验室测量相结合。最后，以实际炼油厂的成分监测问题为例进行了验证。开发的中频模型在低频数据（在线传感器）和高频模型（标准软传感器）的精度方面都有显著提高。结果突出了MF建模的潜力，通过集成不同的数据源来改善过程监测和控制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improving Process Monitoring via Dynamic Multi-Fidelity Modeling

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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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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