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Related Experiment Video

Updated: May 26, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Robust graph-transformer soft sensor for radar level estimation in dynamic LPG storage systems.

Songqiao Bai¹, Shidong Fan¹, Pengcheng Wu²

¹School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, China.

Scientific Reports

|May 24, 2026

Summary

This summary is machine-generated.

A new Robust Graph Transformer Networks (RGTNs) soft sensor improves Liquefied Petroleum Gas (LPG) carrier liquid-level monitoring. This advanced system enhances radar accuracy and reliability in dynamic marine conditions.

Keywords:

Graph neural networks LPG carrier Radar level monitoring Soft sensing Transformer architecture

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Area of Science:

Marine Engineering
Sensor Technology
Artificial Intelligence

Background:

Accurate liquid-level monitoring is critical for the safe operation of Liquefied Petroleum Gas (LPG) carriers.
Conventional radar gauges face limitations in accuracy and reliability, especially under dynamic marine conditions.

Purpose of the Study:

To develop a Robust Graph Transformer Networks (RGTNs)-based soft sensor for enhanced radar level estimation in LPG carriers.
To improve the reliability and accuracy of liquid-level monitoring in dynamic marine environments.

Main Methods:

Integration of a graph attention network for spatial dependency modeling.
Utilization of a Transformer for temporal feature extraction to achieve spatiotemporal fusion.
Fusing radar measurements with thermodynamically coupled auxiliary sensors using learned spatial and temporal attention.

Main Results:

The RGTNs model achieved a coefficient of determination (R²) of 0.9704.
Mean Absolute Error (MAE) was 151.12 mm, and Root Mean Square Error (RMSE) was 198.33 mm.
Demonstrated superior performance compared to existing models in real-world experiments on an LPG carrier.

Conclusions:

The proposed RGTNs-based soft sensor offers a scalable and physically interpretable solution for liquid-level monitoring.
The framework effectively addresses the challenges of dynamic marine operating conditions for LPG storage.
Enhanced accuracy and reliability in radar level estimation were achieved, improving operational safety.