Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy

The suction-lifting system of cutter suction dredgers consumes a large amount of energy. Optimizing their performance is of great significance for enhancing the overall efficiency of dredgers. This study proposes the effective specific cutting energy, a new indicator suitable for evaluating the energy consumption of the cutting system of cutter suction dredgers. It reflects the cooperation state between the cutter system and the pump-pipe system and has important reference value for improving construction efficiency. The calculation method of the effective specific cutting energy is given, which is calculated by the cutter motor power, slurry concentration, and slurry flow rate. Based on the machine learning framework, a model framework for predicting the specific cutting energy according to the relevant parameters of the suction-lifting system is constructed. Real ship data from the cutter suction dredger “Changshi 12” are used for experiments. First, eigenvalue screening is carried out based on the dredging knowledge and mechanism, then outliers are removed, and finally data processing is performed using Spearman correlation coefficient and PCA dimensionality reduction techniques. Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. The experimental results show that the Random Forest and Stacking models have high prediction accuracy for slurry concentration, cutter motor power, and slurry flow rate, verifying the feasibility of this method.