海上运输的脱碳:土耳其船队的案例研究

Climate change and global warming are among the most severe threats to the global ecosystem,caused by greenhouse gas emissions.Therefore,all industries that cause environmental emissions should collaborate in the struggle against climate change.In this context,the International Maritime Organization(IMO)approved the initial greenhouse gas strategy at the MEPC 72 session in April 2018 to achieve targets for 2050.With this strategy,the IMO aims to create and improve new regulations that can enhance energy efficiency to achieve their short-term,midterm,and long-term goals.In this study,one of the novel terms,energy efficiency existing ship index(EEXI)values,has been calculated for the Turkish fleet to guide the maritime sector.The Turkish fleet in the study refers to the Turkish-owned vessels both sailing with a national or international flag.In accordance with this regulation,the number of Turkish fleets that were identified as either above or below the IMO reference lines has been determined.Additionally,EEXI values have been recalculated using the engine power limitation(EPL)method for ships that exceed the required limits,and the success rate of this method has been estimated.As a result,the application of EPL increased the number of ships below the Phase 2 reference line from 15.6%to 53.1%.To the best of our knowledge,this research,which has been carried out on all Turkish-owned ships,is the first study intended to serve as a guide for other ship owners in the global maritime industry regarding energy efficiency management.

A research on the energy efficiency operational indicator EEOI calculation tool on M/V NSU JUSTICE of VINIC transportation company,Vietnam

Nowadays,the development of science and technology brings the innovations and resolutions in aims with increasing the benefits and incomes for companies and producers.Additionally,the development of nation’s economic associates with environmental protection.In the field of shipping transportation,the increasing in a number of ships operates then International Maritime Organization IMO’s regulations become gradually more tightly about the environmental protection.EEOI-Energy Efficiency Operational Indicator is an operational measure tool for assessing the ship energy efficiency and CO 2 emission to the environment.Furthermore,the research status is about ships energy efficiency management also has the optimistic trends and more effective.In the world,there are a lot of ships energy efficiency researches in aim with enhancing the energy efficiency and environmental protection.For instance,the voyage optimization,increasing the propulsion efficiency of ships,reducing the resistances,etc.However,in Vietnam,there are a lot of shipping transportation companies with different types of ship but the number of research about ships energy efficiency management also restrict.Since this research was carried out and applied the energy efficiency measure tool of International Maritime Organization(IMO)for assessing all ships in Vietnam.The above workings were conducted by EEOI calculation tool for a certain ship with name M/V NSU JUSTICE 250,000 DWT of the VINIC Shipping Transportation Company,Vietnam.

Investigate the energy efficiency operation model for bulk carriers based on Simulink/Matlab

The world is facing great challenges regarding the energy crisis and environmental pollution.One of the main challenges is the increasing number of ships with a lot of different types and sizes.In this research,it is essential to analyze the operational energy efficiency of sea-going ships since the previous studies have concentrated on the inland river ships.This working is carried out by analyzing of the energy efficiency operation for large size ships such as bulk carriers,container ships,etc.based on the resistance characteristics of different navigation environment factors.A numerical model of main engine energy efficiency operation was considered using the energy efficiency measure of IMO(International Maritime Organization),in particular using the Energy Efficiency Operational Indicator(EEOI)as a monitoring tool in this research.This EEOI numerical model was established and simulated through Simulink/Matlab and verified by the collected data from a certain vessel in Vietnam.The EEOI model was simulated under the different navigation environment conditions including various engine speed,wind speed,wave height,and water speed.After that,the simulation results of this research will be compared with experimental database of a certain bulk carrier namely M/V NSU JUSTICE 250,000 DWT.The results indicated that it is important to consider the main engine speed appropriately in ship operation in order to save energy onboard and reduce the greenhouse gas emission problem.

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.

Simulation and analysis on the ship energy efficiency operational indicator for bulk carriers by Monte Carlo simulation method

The ship energy efficiency management is an important topic in the field of the energy management onboard and the exhaust gases emission nowadays.The advanced model plays a vital role to improve the ship energy efficiency management when considering the variable factors.The establishment of the ship energy efficiency model through energy efficiency operational indicator(EEOI)index has been conducted through Monte Carlo simulation method along with using the operation data of a bulk carrier.A bulk carrier is chosen,namely,M/V NSU JUSTICE 250,000 DWT of VINIC Shipping Transportation Company in Vietnam.This research uses the real operational data to perform a statistical methodology which calculates the various factors used to calculate EEOI.This method is supported by Matlab program through the curve fitting tool.The normal distribution estimation and the kernel density estimation method are used for the parametric curve fitting and non-parametric curve fitting,respectively.The average weather condition(wind speed and wave height)and the fouling condition of hull have been investigated and compared with the research results.The validation of the proposed methods has been conducted through the study of the external factors influencing the research results.The research result shows the optimal operational data for the fuel consumption at each certain voyage.This paper is useful for the ship-owners and the ship-operators in the field of the ship energy efficiency management.

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.

Ship performance and navigation data compression and communication under autoencoder system architecture

Modern vessels are designed to collect,store and communicate large quantities of ship performance and navigation information through complex onboard data handling processes.That data should be transferred to shore based data centers for further analysis and storage.However,the associated transfer cost in large-scale data sets is a major challenge for the shipping industry,today.The same cost relates to the amount of data that are transferring through various communication networks(i.e.satellites and wireless networks),i.e.between vessels and shore based data centers.Hence,this study proposes to use an autoencoder system architecture(i.e.a deep learning approach)to compress ship performance and navigation parameters(i.e.reduce the number of parameters)and transfer through the respective communication networks as reduced data sets.The data compression is done under the linear version of an autoencoder that consists of principal component analysis(PCA),where the respective principal components(PCs)represent the structure of the data set.The compressed data set is expanded by the same data structure(i.e.an autoencoder system architecture)at the respective data center requiring further analyses and storage.A data set of ship performance and navigation parameters in a selected vessel is analyzed(i.e.data compression and expansion)through an autoencoder system architecture and the results are presented in this study.Furthermore,the respective input and output values of the autoencoder are also compared as statistical distributions and sample number series to evaluate its performance.