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 ener...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/3/598 |
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| author | Junlang Yuan Ke Yang Taiwei Yang Haoran Xu Ting Xiong Shidong Fan |
| author_facet | Junlang Yuan Ke Yang Taiwei Yang Haoran Xu Ting Xiong Shidong Fan |
| author_sort | Junlang Yuan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b7c86e6e38c44a29afaa82e831858775 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-b7c86e6e38c44a29afaa82e8318587752025-08-20T01:49:04ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113359810.3390/jmse13030598Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting EnergyJunlang Yuan0Ke Yang1Taiwei Yang2Haoran Xu3Ting Xiong4Shidong Fan5School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaThe 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.https://www.mdpi.com/2077-1312/13/3/598specific cutting energycutterheadcutter suction dredgerdata mining |
| spellingShingle | Junlang Yuan Ke Yang Taiwei Yang Haoran Xu Ting Xiong Shidong Fan Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy Journal of Marine Science and Engineering specific cutting energy cutterhead cutter suction dredger data mining |
| title | Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy |
| title_full | Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy |
| title_fullStr | Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy |
| title_full_unstemmed | Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy |
| title_short | Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy |
| title_sort | predictive study on the cutting energy efficiency of dredgers based on specific cutting energy |
| topic | specific cutting energy cutterhead cutter suction dredger data mining |
| url | https://www.mdpi.com/2077-1312/13/3/598 |
| work_keys_str_mv | AT junlangyuan predictivestudyonthecuttingenergyefficiencyofdredgersbasedonspecificcuttingenergy AT keyang predictivestudyonthecuttingenergyefficiencyofdredgersbasedonspecificcuttingenergy AT taiweiyang predictivestudyonthecuttingenergyefficiencyofdredgersbasedonspecificcuttingenergy AT haoranxu predictivestudyonthecuttingenergyefficiencyofdredgersbasedonspecificcuttingenergy AT tingxiong predictivestudyonthecuttingenergyefficiencyofdredgersbasedonspecificcuttingenergy AT shidongfan predictivestudyonthecuttingenergyefficiencyofdredgersbasedonspecificcuttingenergy |