Operator Learning for Loss Parameter Estimation in Dredging Operations
To optimize the suction production on Trailing Suction Hopper Dredgers
M. Kielhöfer (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A. Heinlein – Mentor (TU Delft - Numerical Analysis)
G. Jongbloed – Graduation committee member (TU Delft - Statistics)
M.B. van Gijzen – Graduation committee member (TU Delft - Numerical Analysis)
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Abstract
Accurate modeling of vacuum dynamics in Trailing Suction Hopper Dredgers (TSHDs) is critical for optimizing suction production and mitigating sensor anomalies. This study proposes a data-driven, physics-guided operator learning framework to estimate the vacuum pressure loss parameter θ, a variable derived from physical principles in dredging operations. Leveraging a modified Deep Operator Network (DeepONet), we introduce attention-based interactions between branches and the trunk network to capture complex dependencies in the sensor data. A local trunk mechanism is introduced to preserve temporal locality across dredging trips.
Due to the nature of a lagging density sensor, we integrate a real-time rolling mean error correction mechanism. This addresses training biases for refined predictions, as well as offering an anomaly detection mechanism. The model is trained and validated on real-world vessel data, including synthetic simulations of vacuum processes, and evaluated using trip-wise and global metrics. Experimental results show that the proposed architecture significantly outperforms the rolling mean baseline setups and the classical DeepONet across accuracy metrics such as the root mean square error (RMSE).
This work demonstrates the value of combining domain knowledge with operator learning techniques in maritime engineering. The proposed framework offers a scalable framework, allowing application across entire fleets for real-time suction production estimation and anomaly detection, contributing to efficient dredging operations.