The dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation that can be used in emergency situations is essential. The characteristic spare mud concentration meter is particularly critical. In this study, a data-driven soft sensor method is proposed that can predict the mud concentration in real time and can mitigate current marine mud concentration meter malfunctions, which affects continuous construction. This sensor can also replace the mud concentration meter when the construction is stable, thereby extending its service life. The method is applied to two actual construction cases, and the results show that the stacking generalization (SG) model has a good prediction effect in the two cases, and its goodness of fit R² values are as high as 0.9774 and 0.9919, indicating that this method can successfully detect the mud concentration.

Pulsed waterjets are widely used in exploitation of fossil fuels for their high efficiency. With the aim to further clarify the impact performance of Helmholtz self-excited oscillation waterjets (HSEOW), numerical and experimental studies were conducted. The morphological characteristics of the erosion surfaces between conical and HSEOW nozzles were compared and the cavitation evolution was obtained. Results show that the cavitation damage caused by the HSEOW nozzle on the specimen was mainly caused by the jet cavitation cloud under submerged conditions. The cavitation effect produced by the HSEOW nozzle had a much greater destructive effect than that of a conical nozzle. The mass loss caused by HSEOW nozzles increased first with the increase of standoff distance, then decreased rapidly after reaching the maximum value. Moreover, the density of holes and the damage intensity weakened with the increase of radial distance. A dimensionless cavity length of 2 and a dimensionless cavity diameter of 8 was the optimal structure that led to maximum mass loss. These results provide a further understanding of cavitation mechanism which leads to the impact performance of pulsed water jets and optimal working parameters in the field of energy exploitation.

Based on low-cost and easy to enlarge, the bubble column device has been widely concerned in chemical industry. This paper focuses on bubble plumes in laboratory-scale three-dimensional rectangular air-water columns. Static behavior has been investigated in many experiments and simulations, and our present investigations consider the dynamic behavior of bubble plume offset in three dimensions. The investigations are conducted with a set of closure models by the Euler–Euler approach, and subsequently, literature data for rectangular bubble columns are analyzed for comparison purposes. Moreover, the transient evolution characteristics of the bubble plume in the bubble column and the gas phase distribution in sections are introduced, and the offset characteristics and the oscillation period of the plume are analyzed. In addition, the distributions of the vector diagram of velocity and vortex intensity in the domain are given. The effects of different fluxes and column aspect ratios on bubble plumes are studied, and the offset and plume oscillation period (POP) characteristics of bubbles are examined. The investigations reveal quantitative correlations of operating conditions (gas volume flux) and aspect ratios that have not been reported so far, and the simulated and experimental POP results agree well. An interesting phenomenon is that POP does not occur under conditions of a high flux and aspect ratio, and the corresponding prediction values for the conditions with and without POP are given as well. The results reported in this paper may open up a new way for further study of the mass transfer of bubble plumes and development of chemical equipment.

Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of the dredger under abnormal working conditions. In this paper, a data-driven method for predicting the vacuum of the underwater pump of the cutter suction dredger (CSD) is proposed with the help of big data, machine learning, data mining, and other technologies, and based on the historical data of “Hua An Long” CSD. The method eliminates anomalous data, standardizes the data set, and then relies on theory and engineering experience to achieve feature extraction using the Spearman correlation coefficient. Then, six machine learning methods were employed in this study to train and predict the data set, namely, lasso regression (lasso), elastic network (Enet), gradient boosting decision tree (including traditional GBDT, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM)), and stacking. The comparison of the indicators obtained through multiple rounds of feature number iteration shows that the LightGBM model has high prediction accuracy, a good running time, and a generalization ability. Therefore, the methodological framework proposed in this paper can help to improve the efficiency of underwater pumps and issue timely warnings in abnormal working conditions.

In the current implementation process for dredging projects, due to the lack of an accurate understanding of underwater soil characteristics, construction teams often find it difficult to accurately understand the soil condition. Not only does this lead to a reduced dredger operation efficiency, but it may also cause delays to the project’s progress, as well as increasing its cost. Therefore, there is an urgent need to closely integrate soil analysis technology with dredging construction to ensure that projects can be completed efficiently and to a high standard. Therefore, this paper proposes a soil modeling and prediction method based on a three-dimensional point cloud model. The research objective is to propose a new method of soil mass identification with a strong generalization ability and function, which can not only be applied to dredging engineering but can also identify and analyze statistics of land soil and its mass. The accuracy of this method, based on a convergent grid, can reach 95%.