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Published July 17, 2025 | Version 1.0.0
Conference paper Open

Soft-Sensor-Enhanced Monitoring of an Alkylation Unit via Multi-Fidelity Model Correction

Creators

  • 1. ROR icon Slovak University of Technology in Bratislava
  • 2. ROR icon University of Pisa
  • 3. Slovnaft, a.s.

Description

Industrial process monitoring can benefit from utilizing historical data, providing insights for decision-making and operational efficiency. This study develops a soft-sensor-based approach leveraging multi-fidelity modeling to correct discrepancies between online sensors and laboratory analyses. A Gaussian process-based strategy is used to predict deviations between high-frequency low-fidelity sensor data and less frequent high-fidelity laboratory measurements. By exploring static and dynamic modeling frameworks, we assess their suitability for capturing process dynamics and addressing time-dependent variability. The multi-fidelity soft sensor noticeably improves predictive accuracy, outperforming high-fidelity and low-fidelity methods. This approach demonstrates applicability across various industrial settings where integrating diverse data sources enhances real-time process control and monitoring, reducing reliance on costly laboratory sampling.

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Additional details

Funding

The Vega Science Trust
Efficient control of industrial plants using data VEGA 1/0691/21
Slovak Research and Development Agency
Energy-efficient Safe and Secure Process Control APVV-20-0261
European Commission
FrontSeat - Fostering Opportunities Towards Slovak Excellence in Advanced Control for Smart Industries 101079342

Dates

Accepted
2025-07-06

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