Assessment of ship speed, operational carbon intensity indicator penalty and charterer profit of time charter ships

Sun, Ling; Wang, Xinghe; Lu, Youshui; Hu, Zijiang

doi:10.1016/j.heliyon.2023.e20719

Cited by 5 publications

(2 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once operational measures are exhausted, owners must consider introducing advanced technologies like wind-assisted propulsion, air lubrication, waste heat recovery systems, shaft generators, and engine room hybridization, such as using batteries or fuel cells (MMMCZCS, 2023b). Additionally, speed becomes an important parameter for CII compliance, understanding that commercial aspects also need to be considered (Sun et al, 2023).…”

Section: CIImentioning

confidence: 99%

The impact of maritime decarbonization on ship design: State-of-the-Art Report

McKenney

2024

IMDC

View full text Add to dashboard Cite

The maritime industry faces a critical challenge to decarbonize and meet the ambitious goal of net-zero emissions by 2050. This transition requires innovative ship design strategies to address the increasing complexity and multiplicity of technical solutions amidst dynamic regulatory, geopolitical, and market uncertainties. This paper examines the maritime decarbonization challenge's impact on ship design and decision-making under uncertainty, highlighting the necessity for collaboration between researchers and practitioners to tackle this emerging challenge. To navigate the uncertainty, stakeholders can integrate advanced design methods and decision-making processes considering the full lifecycle and fleet-level implications. This paper promotes taking a holistic approach that incorporates regulatory compliance, technological advancements, and commercial considerations, as well as a blend of methods to manage decision-making under uncertainty. Continued research in specific areas is essential to develop and refine frameworks that optimize design and operation for the industry's sustainable future.

show abstract

Section: CIImentioning

confidence: 99%

The impact of maritime decarbonization on ship design: State-of-the-Art Report

McKenney

2024

IMDC

View full text Add to dashboard Cite

show abstract

“…Elkafas found that carbon intensity values depend on the number of trips per year, the number of passengers carried, and the amount of fuel consumed, and that proper deceleration reduces the ship's emission rate [22]. Sun modeled the speed of a time-chartered vessel with CII penalties included and found that the larger the vessel, the more carbon emissions, and that carbon intensity and CII penalties are reduced when the charter speed is reduced for the same amount of time [23].…”

Section: Related Workmentioning

confidence: 99%

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

Zhang,

Lu,

Wang

et al. 2023

JMSE

View full text Add to dashboard Cite

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.

show abstract