Search Results (3)

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. Full article

The electrical submersible pump (ESP) well system is widely used in the oil industry due to its advantages of high displacement and lift capability. However, it is associated with significant energy consumption. In order to conserve electrical energy and enhance the efficiency of petroleum companies, a deep learning-based energy consumption calculation method is proposed and utilized to optimize the most energy-efficient operating regime. The energy consumption of the ESP well system is precisely determined through the application of the Pearson correlation coefficient analysis method, which is utilized to examine the relationship between production parameters and energy usage. This process aids in identifying the input parameters of the model. Following this, an energy consumption prediction model is developed using the dual-stage attention-based recurrent neural network (DA-RNN) algorithm. To evaluate the accuracy of the DA-RNN model, a comparison of its errors is carried out in comparison to three other deep learning algorithms: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transform. Lastly, an orthogonal experiment is executed using the chosen model to pinpoint the most energy-efficient operating regime. Analysis of 325 ESP wells in the Bohai PL oil field indicated that ten parameters, including choke diameter, casing pressure, pump inlet pressure, pump outlet pressure, motor temperature, frequency, oil production, gas production, water production, and GOR significantly impact the energy consumption of the ESP well system. Consequently, these parameters were selected as input variables for the deep learning model. Due to the attention mechanisms employed in the encoding and decoding stages, the DA-RNN algorithm achieved the best performance during model evaluation and was chosen for constructing the energy consumption prediction model. Furthermore, the DA-RNN algorithm demonstrates better model generalization capabilities compared to the other three algorithms. Based on the energy consumption prediction model, the operating regime of the ESP system was optimized to save up to 12% of the maximum energy. The energy consumption of the ESP well system is affected by numerous parameters, and it is difficult to comprehensively evaluate and predict quantitatively. Thus, this work proposes a data-driven model based on the DA-RNN algorithm, which has a dual-stage attention mechanism to rapidly and accurately predict the energy consumption of the ESP well system. Optimization of production parameters using this model can effectively reduce energy consumption. Full article

The polymetallic nodules in the deep-sea multi-stage lifting motor pump will undergo repeated impeller blade impact and fragmentation, which will change the particle size, thereby affecting the number of ores that can be recovered on the surface and the design parameters of the processing equipment. A new calculation method of degradation rate is proposed. The degradation model of multiple impacts of particles is improved to quantitatively calculate the final particle size distribution (PSD) of polymetallic nodules transported from the Clarion Clipperton Zone (CCZ) to the ground through a series of multi-stage lifting electric pumps. The newly proposed calculation method is obtained by analyzing the degradation of experimental data of polymetallic nodules when they pass through the six-stage lifting motor pump experimental system many times. The improved model is used to predict the PSD of the nodules after running for 10 min in the experimental system, and compared with the experimental test results, the deviation is small. The new method can estimate the change in PSD of nodules due to degradation during transportation, reducing design costs for land processing equipment. Full article