Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.

Fault Tree Analysis of Fire and Explosion Accidents for Dual Fuel （Diesel/Natural Gas） Ship Engine Rooms

In recent years, China's increased interest in environmental protection has led to a promotion of energy-efficient dual fuel(diesel/natural gas) ships in Chinese inland rivers. A natural gas as ship fuel may pose dangers of fire and explosion if a gas leak occurs. If explosions or fires occur in the engine rooms of a ship, heavy damage and losses will be incurred. In this paper, a fault tree model is presented that considers both fires and explosions in a dual fuel ship; in this model, dual fuel engine rooms are the top events. All the basic events along with the minimum cut sets are obtained through the analysis.The primary factors that affect accidents involving fires and explosions are determined by calculating the degree of structure importance of the basic events.According to these results, corresponding measures are proposed to ensure and improve the safety and reliability of Chinese inland dual fuel ships.

An adaptive hyper parameter tuning model for ship fuel consumption prediction under complex maritime environments

An accurate prediction of ship fuel consumption is critical for speed,trim,and voyage optimisation etc.While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors,research on the impact of environmental factors on fuel consumption has been lacking.In addition,although recent research efforts have widely focused on machine learning methods to predict fuel consumption,studies on hyperparameter values that are suitable for these prediction models are limited.To compensate for this deficiency in existing literature,an adaptive hyperparameter tuning method is proposed,and the effects of maritime environmental factors on fuel consumption are taken into account.Through experimentation,the proposed adaptive hyperparameter tuning method was validated via artificial neural network(ANN),support vector regression(SVR),random forest(RF),and least absolute shrink-age and selection operator(Lasso).The hyperparameter tuning proportionally increased the amplitudes of the coefficients of determination(R 2)of these algorithms.The increase of the amplitude demonstrated the following trend,in the order of the largest increase to the lowest increase:ANN,Lasso,SVM,and RF.The rates of increase were between 0.0773%and 2.1653%.Furthermore,after the environmental factors were considered,the prediction accuracies of the ANN and Lasso increased;however,the opposite was observed for the SVR and RF.As such,we confirmed that the use of Bayesian optimisation for hyper-parameter tuning can effectively improve the fuel consumption prediction accuracy,and our proposed model can therefore serve as a significant reference for calculating fuel consumption.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅱ-Voyage report data,AIS data and meteorological data

When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources.These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models.Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments.For each ship,nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested.Experimental results revealed the benefits of fusing voyage report data,AIS data,and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate.Over the best datasets,the performances of several decision tree-based models are promising,including Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG).With the best datasets,their R^(2) values over the training sets are all above 0.96 and mostly reach the level of 0.99-1.00,while their R^(2) values over the test sets are in the range from 0.75 to 0.90.Fit errors of ET,AB,GB,and XG on daily bunker fuel consumption,measured by RMSE and MAE,are usually between 0.8 and 4.5 ton/day.These results are slightly better than our previous study,which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data,compared with the estimated geographical positions derived from the great circle route,in retrieving weather and sea conditions the ship sails through.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅰ-Voyage report data and meteorological data

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimization,weather routing,and the virtual arrival policy.The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed,displacement/draft,trim,weather conditions,and sea conditions.Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection.To overcome this issue,this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution.Eleven widelyadopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company.The best datasets found reveal the benefits of fusing voyage report data and meteorological data,as well as the practically acceptable quality of voyage report data.Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG)present the best fit and generalization performances.Their R^(2) values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set,and 0.74 to 0.90 for the test set.Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day.These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅲ-Sensor data and meteorological data

Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.