| 1. |
- Lang, Xiao, 1992, et al.
(författare)
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A Machine Learning Ship’s Speed Over Ground Prediction Model and Sailing Time Control Strategy
- 2022
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Ingår i: International Journal of Offshore and Polar Engineering. - : International Society of Offshore and Polar Engineers. - 1053-5381. ; 32:4, s. 386-393
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a machine learning–based ship speed over a ground prediction model, driven by the eXtreme Gradient Boosting (XGBoost) algorithm. The data set is acquired from a world-sailing chemical tanker with five years of full-scale measurements. The model is trained using encountered metocean environments and ship operation profiles in two scenarios: through propeller revolutions per minute (RPM) or propulsion power. This model is further combined with the particle swarm optimization algorithm to integrate a sailing time control method. It optimizes constant RPM or power operation strategy to meet the requirements of a fixed estimated time of arrival.
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| 2. |
- Lang, Xiao, 1992, et al.
(författare)
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A Machine Learning Ship's Speed Prediction Model and Sailing Time Control Strategy
- 2022
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Ingår i: Proceedings of the International Offshore and Polar Engineering Conference. - 1098-6189 .- 1555-1792. ; , s. 3598-3605
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a machine learning based ship speed over ground prediction model, driven by the eXtreme Gradient Boosting (XGBoost) algorithm. The dataset is acquired from a world-sailing chemical tanker with five years of full-scale measurements. The model is trained using encountered metocean environments and ship operation profiles in two scenarios, i.e., through RPM or propulsion power. This model is further combined with the particle swarm optimization (PSO) algorithm to integrate a sailing time control method. It optimizes constant RPM or power operation strategy to meet the requirements of fixed ETA.
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| 3. |
- Lang, Xiao, 1992, et al.
(författare)
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Benchmark Study of Supervised Machine Learning Methods for a Ship’s Speed-Power Prediction at Sea
- 2021
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Ingår i: Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE.
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Konferensbidrag (refereegranskat)abstract
- The development and evaluation of energy efficiency measures to reduce air emissions from shipping strongly depends on reliable description of a ship’s performance when sailing at sea. Normally, model tests and semi-empirical formulas are used to model a ship's performance but they are either expensive or lack accuracy. Nowadays, a lot of ship performance-related parameters have been recorded during a ship's sailing, and different data driven machine learning methods have been applied for the ship speed-power modelling. This paper compares different supervised machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), neural network, support vector machine, and some statistical regression methods, for the ship speed-power modelling. A worldwide sailing chemical tanker with full-scale measurements is employed as the case study vessel. A general data pre-processing method for the machine learning is presented. The machine learning models are trained using measurement data including ship operation profiles and encountered metocean conditions. Through the benchmark study, the pros and cons of different machine learning methods for the ship’s speed-power performance modelling are identified. The accuracy of various algorithms based models for ship performance during individual voyages is also investigated.
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| 4. |
- Lang, Xiao, 1992, et al.
(författare)
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Data-driven ship fatigue assessment based on pitch and heave motions
- 2023
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Ingår i: Advances in the Analysis and Design of Marine Structures - Proceedings of the 9th International Conference on Marine Structures (MARSTRUCT 2023). - London : CRC Press. - 9781032506364 ; , s. 95-103
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Konferensbidrag (refereegranskat)abstract
- Ocean-crossing ship structures continuously suffer from wave-induced loads when sailing at sea. The encountered wave loads cause significant variations in ship structural stresses, leading to accumulated fatigue damage. It is common today to use the spectral method for direct fatigue calculation when evaluating ship fatigue, where large inherent uncertainties still exist. This paper investigates the machine learning technique to establish model for a 2800TEU container vessel fatigue assessment. The measurement data of three years cross-Atlantic sailing demonstrates and validates the machine learning model. In this investigation, the motions of the ship are used as inputs to build machine learning model. The fatigue damage amounts predicted using machine learning model were compared with those obtained from full-scale measurements and direct fatigue calculation. The pros and cons of the methods are compared in terms of capability, robustness, and accuracy of the prediction.
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| 5. |
- Lang, Xiao, 1992, et al.
(författare)
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Fatigue assessment comparison between a ship motion-based data-driven model and a direct fatigue calculation method
- 2023
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Ingår i: Journal of Marine Science and Engineering. - 2077-1312. ; 11:12, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Ocean-crossing ship structures continuously suffer from wave-induced loads when sailing at sea. The encountered wave loads cause significant variations in ship structural stresses, leading to accumulated fatigue damage. Where large inherent uncertainties still exist, it is now common to use spectral methods for direct fatigue calculation when evaluating ship fatigue. This paper investigates the use of a machine learning technique to establish a model for 2800TEU container vessel fatigue assessment. Measurement data from 3 years of cross-Atlantic sailing demonstrated and validated the machine learning model. In this investigation, the ship’s motions were used as inputs to build a machine learning model. The fatigue damage amounts predicted using a machine learning model were compared with those obtained from full-scale measurements and direct fatigue calculation. The pros and cons of the methods are compared in terms of their capability, robustness, and prediction accuracy.
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| 8. |
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| 9. |
- Jiang, Junfei, et al.
(författare)
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Partial oxidation of filter cake particles from biomass gasification process in the simulated product gas environment
- 2018
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Ingår i: Energy & Fuels. - : American Chemical Society (ACS). - 0887-0624 .- 1520-5029. ; 32:2, s. 1703-1710
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Tidskriftsartikel (refereegranskat)abstract
- Filtration failure occurs when filter media is blocked by accumulated solid particles. Suitable operating conditions were investigated for cake cleaning by partial oxidation of filter-cake particles (FCP) during biomass gasification. The mechanism of the FCP partial oxidation was investigated in a ceramic filter and by using thermo-gravimetric analysis through a temperature-programmed route in a 2 vol.% O2–N2 environment. Partial oxidation of the FCP in the simulated product gas environment was examined at 300–600°C in a ceramic filter that was set and heated in a laboratory-scale fixed reactor. Four reaction stages, namely drying, pre-oxidation, complex oxidation and non-oxidation, occurred in the FCP partial oxidation when the temperature increased from 30°C to 800°C in a 2 vol.% O2–N2 environment. Partial oxidation was more effective for FCP mass loss from 275 to 725°C. Experimental results obtained in a ceramic filter indicated that the best operating temperature and FCP loading occurred at 400°C and 1.59 g/cm2, respectively. The FCP were characterized by Fourier-transform infrared spectroscopy, scanning electron microscopy and Brunaeur–Emmett–Teller before and after partial oxidation. Fourier-transform infrared spectroscopy analysis revealed that partial oxidation of the FCP can result in a significant decrease in C–Hn (alkyl and aromatic) groups and an increase in C=O (carboxylic acids) groups. The scanning electron microscopy and Brunaeur–Emmett–Teller analysis suggests that during partial oxidation, the FCP underwent pore or pit formation, expansion, amalgamation and destruction.
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| 10. |
- Lang, Xiao, 1992, et al.
(författare)
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Physics-informed machine learning models for ship speed prediction
- 2024
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Ingår i: Expert Systems with Applications. - 0957-4174. ; 238
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a novel physics-informed machine learning method to build grey-box model (GBM) predicting ship speed for ocean crossing ships. In this method, the expected ship speed in calm water is first modeled by the physics-informed neural networks (PINNs) based on speed-power model tests. Then the eXtreme Gradient Boosting (XGBoost) machine learning algorithm is integrated to estimate ship speed reduction under actual weather conditions. The proposed GBM has been compared against the traditional black-box model (BBM) using performance monitoring data from two ships. The results show that when the amount of data is sufficient for modeling, the GBM can increase the accuracy of speed prediction by about 30%. When data volume is limited, the GBM can also significantly improve the prediction results. Finally, the GBM is validated by checking its implementation for the ETA predictions of cross-Pacific or North Atlantic voyages. The highest cumulative error of sailing time estimated by the GBM is 5 h among all the study cases.
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