Abstract
With the vigorous development of offshore energy and mining, offshore fracturing and deep-sea mining necessitate the ship-to-ship and platform-to-ship transfer of solid particles via floating hoses. However, traditional floating hoses designed for oil transportation are inadequate for the long-term and stable conveyance of granular materials, as solid particles are prone to deposition and blockage under excessive bending. Additionally, in large-scale offshore fracturing operations, tension fluctuations in high-pressure hoses accelerate hose wear and compromise structural integrity. To address these challenges, this study proposes a systematic framework integrating neural network prediction with a feedforward–feedback composite control strategy. Spatial Attention–Convolutional Neural Network (SA-CNN) achieves the highest prediction accuracy and the strongest generalization capability across all operating conditions. Then, a feedforward–feedback composite control strategy is formulated, where the feedforward component is derived from SA-CNN predictions and the feedback component is provided by a PID controller, with an adaptive weighting mechanism adjusting their contributions based on prediction confidence. A curvature safety constraint is also incorporated to prevent excessive bending. The results show that the composite control strategy achieves the highest peak tension reduction, while achieving the lowest RMSE. Unlike pure PID, which introduces severe oscillations, the composite control strategy converges smoothly, confirming that feedforward prediction effectively suppresses feedback-induced oscillations. This study provides a theoretical foundation and a practical solution for the safety assurance of floating hoses in high-pressure fracturing fluid delivery applications and offshore solid particle transshipment.