Self-balancing physics-informed LSTM for soft-sensing of slurry concentration in dredger operation

Ensuring Slurry Concentration (SC) within a target range is critical for stable and safe dredging operations of dredgers, yet direct SC measurements are often unreliable due to sensor degradation caused by harsh environments. In this context, soft-sensing technology provides a robust and reliable approach to accurately estimate SC during dredging processes. In this work, a soft-sensing method is developed based on a novel self-balancing Physics-informed Long-Short Term Memory (PILSTM) Network. First, the Least Absolute Shrinkage and Selection Operator (LASSO) is employed to analyze the correlation between SC and other monitored dredger signals, thereby identifying the most relevant signals for SC estimation. Second, to address the lack of explicit physical dynamics that governs SC evolution, a Deep Hidden Physics Model (DeepHPM) is leveraged to infer the underlying physical relationships from historical data. This model is then integrated with a Long Short-Term Memory Network (LSTM) to form the PILSTM, enabling accurate and physics-consistent SC estimation. Then, a self-balancing strategy is implemented to automatically weigh the two competing objectives during the PILSTM training process: maximizing physics consistency with DeepHPM and improving estimation accuracy on the training dataset. Finally, two datasets collected from actual in-field dredger operations are utilized to verify the performance of the proposed soft-sensing method. The results demonstrate its superior accuracy and enhanced physics-consistency compared to other state-of-the-art approaches, achieving R 2 coefficients over 0.85 and 0.9 in the two case studies, respectively, which highlights its effectiveness in practical applications.