Comparative study of machine learning techniques to predict fuel consumption of a marine diesel engine

Yüksel, Onur; Bayraktar, Murat; Sokukcu, Mustafa

doi:10.1016/j.oceaneng.2023.115505

Cited by 16 publications

(3 citation statements)

References 39 publications

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“…In summary, different models are used to study the prediction of ship fuel consumption. The research methods are mainly based on neural networks, support vector machines, LASSO regression, and integrated learning models [12,13]. Under different scenarios and datasets, the best prediction models are different.…”

Section: Related Workmentioning

confidence: 99%

“…It was found that the correlation between the features, such as seawater temperature, wave period, and swell period, and the cumulative CII of the ship is high, which is different from the results of previous studies. In previous studies [12,28], it was found that the meteorological data, such as navigation speed, main engine speed, and wave height, significant influence on the fuel consumption of the ship, and the CII of the ship needs to be further calculated according to the fuel consumption data to be obtained. However, Figure 6 shows that characteristics such as speed and engine speed do not show strong correlation with the cumulative CII of the ship.…”

Section: Feature Correlation Analysismentioning

confidence: 99%

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Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Zhang,

Lu,

Wang

et al. 2023

JMSE

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The Carbon Intensity Index (CII) exerts a substantial impact on the operations and valuation of international shipping vessels. Accurately predicting the CII of ships could help ship operators dynamically evaluate the possible CII grate of a ship at the end of the year and choose appropriate methods to improve its CII grade to meet the IMO requirement with minimum cost. This study developed and compared five CII predicting models with multiple data sources. It integrates diverse data sources, including Automatic Identification System (AIS) data, sensor data, meteorological data, and sea state data from 2022, and extracts 21 relevant features for the vessel CII prediction. Five machine learning methods, including Artificial Neural Network (ANN), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are employed to construct the CII prediction model, which is then applied to a 2400 TEU container ship. Features such as the mean period of total swell, mean period of wind waves, and seawater temperature were considered for inclusion as inputs in the model. The results reveal significant correlations between cumulative carbon emissions intensity and features like cumulative distance, seawater temperature, wave period, and swell period. Among these, the strongest correlations are observed with cumulative distance and seawater temperature, having correlation coefficients of 0.45 and 0.34, respectively. Notably, the ANN model demonstrates the highest accuracy in CII prediction, with an average absolute error of 0.0336, whereas the LASSO model exhibits the highest error of 0.2817. Similarly, the ANN model provides more accurate annual CII ratings for the vessel. Consequently, the ANN model proves to be the most suitable choice for cumulative CII prediction.

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Section: Related Workmentioning

confidence: 99%

Section: Feature Correlation Analysismentioning

confidence: 99%

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Zhang,

Lu,

Wang

et al. 2023

JMSE

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“…The seminal work written in have introduced the Physics Informed Neural Networks (PINN) [5]. These models have been employed also in Lubricants and Marine Diesels leading to a substantial existence of literature for the manipulation of the data extruded from the experimental and in situ activity [6][7][8][9]. In this literature, the Lubricant Viscosity for various operational activities through PINN models is estimated, others have employed productive simulation models of active rotor-bearing systems, the fuel consumption on a marine diesel engine is predicted through ML models like M5 rules algorithm and the wear fault diagnosis for marine diesel Lubricants 2024, 12, 127 2 of 13 engine has been investigated for the most suitable PINN model that can represent this phenomenon and predict the fault with the largest accuracy and reliability in combination with the fast computation of the estimation.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for the Investigation of Metal Ion Concentration on Distillate Marine Diesel Fuels through Feed Forward Neural Networks

Savvides,

Papadopoulos,

Intzirtzis

et al. 2024

Lubricants

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In this work, a set of Feed Forward Neural Networks (FNN) for the estimation of the metal ion concentration of diesel fuels is presented. The dataset vector is obtained through in situ measurements from distillate marine diesel fuel storage tanks all over Greece, in order to reduce the selection bias. It has been demonstrated that the most correlated ions among them are Aluminum (Al), Barium (Ba) and Calcium (Ca). Moreover, the FNN models are the most reliable models to be used for the model construction under discussion. The initial L2 error is relatively small, in the vicinity of 0.3. However, after removing a small dataset that includes 1–2 data points significantly deviating from the model trend, the error is substantially reduced to 0.05, ensuring the reliability and accuracy of the model. If this dataset is cleared, the estimated error is substantially reduced to 0.05, enhancing the reliability and accuracy of the model. The correlation between the sum of the concentrations of the model in relation with the Density and Viscosity are, respectively, 0.15 and 0.29 which are characterized as weak.

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Investigation of Seasonal Effects on Two-Stroke Marine Diesel Engine Performance Parameters and Emissions

Ceylan

2023

J. Marine. Sci. Appl.

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Comparative study of machine learning techniques to predict fuel consumption of a marine diesel engine

Cited by 16 publications

References 39 publications

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Machine Learning Approach for the Investigation of Metal Ion Concentration on Distillate Marine Diesel Fuels through Feed Forward Neural Networks

Investigation of Seasonal Effects on Two-Stroke Marine Diesel Engine Performance Parameters and Emissions

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