ISO 15016:2015-Based Method for Estimating the Fuel Oil Consumption of a Ship

Kim, Ki-Su; Roh, Myung-Il

doi:10.3390/jmse8100791

Cited by 16 publications

(11 citation statements)

References 8 publications

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“…The datadriven methodologies can be used to calculate the propulsion power and fuel consumption for a ship cruise. Support vector regression (SVR) was used in a study in this area to predict propulsion power more accurately than conventional methods [5,41]. Another study indicated that machine learning approaches outperform the ANN method in specific cases for predicting shaft power onboard ships using AIS data and weather data [42].…”

Section: Methodsmentioning

confidence: 99%

“…Various rules and regulations were enacted by the International Maritime Organization (IMO) to limit emissions in the shipping industry [3,4]. Therefore, several indices such as the Energy Efficiency Design Index (EEDI), Ship Energy Efficiency Management Plan (SEEMP), and Energy Efficiency Operational Indicator (EEOI) have been proposed to determine and enhance the energy efficiency level of the global fleet of marine vessels [5]. In terms of shipping companies, it can be claimed that energy efficiency begins at the design stage [6] and that ship energy efficiency may be boosted through operational ways in addition to numerous design approaches [7][8][9][10].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

et al. 2022

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In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of guidelines that increase energy efficiency and reduce shipborne emissions. The main engine shaft power (MESP) and fuel consumption (FC) are the critical components used in ship energy efficiency calculations. Errors in ship energy efficiency calculation methodologies are also caused by misinterpretation of these values. This study aims to predict the MESP and FC of a container ship with the help of data-driven methodologies utilizing actual voyage data to assist in the calculation process of the ship’s energy efficiency indexes appropriately. The algorithms’ prediction success was measured using the RMSE, MAE, and R2 error metrics. When the simulation results were analyzed, the Deep Neural Network and Bayes algorithms predicted MESP best with 0.000001 and 0.000002 RMSE, 0.000987 and 0.000991 MAE, and 0.999999 R2, respectively, while the Multiple-Linear Regression and Kernel Ridge algorithms estimated FC best with 0.000208 and 0.000216 RMSE, 0.001375 and 0.001471 MAE, and 0.999999 R2, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

et al. 2022

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“…The simplest method is to estimate fuel consumption using total engine power and specific fuel consumption. Kim et al [17] proposed a method for estimating fuel consumption based on theoretical resistance calculations, which applied methods [18,19] and the ship's propulsion parameters, among others. Bialystocki and Konovessis [20] proposed an operational approach to estimate fuel consumption and speed based on the main influencing factors, such as ship draught and displacement, wind speed and direction, hull and propeller roughness.…”

Section: The Estimation Of Fuel Consumptionmentioning

confidence: 99%

The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships

Cepowski

Chorab

2021

Energies

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The 2007–2008 financial crisis, together with rises in fuel prices and stringent pollution regulation, led to the need to update the methods concerning ship propulsion system design. In this article, a set of artificial neural networks was used to update the design equations to estimate the engine power and fuel consumption of modern tankers, bulk carriers, and container ships. Deadweight or TEU capacity and ship speed were used as the inputs for the ANNs. This study shows that even a linear ANN with two neurons in the input and output layers, with purelin activation functions, offers an accurate estimation of ship propulsion parameters. The proposed linear ANNs have simple mathematical structures and are straightforward to apply. The ANNs presented in the article were developed based on the data of the most recent ships built from 2015 to present, and could have a practical application at the preliminary design stage, in transportation or air pollution studies for modern commercial cargo ships. The presented equations mirror trends found in the literature and offer much greater accuracy for the features of new-built ships. The article shows how to estimate CO2 emissions for a bulk carrier, tanker, and container carrier utilizing the proposed ANNs.

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“…where C a is the air resistance coefficient as a function of γ R ; the relative angle between wind direction and the ship's motion, ρ a is air density, A is the ship surface area opposed to the wind, v is the ship's speed over ground, and w R is the relative wind speed. C a was fitted for this study according to laden conditions of a container ship described in [36], which was then used to model the air resistance coefficients dependency on wind direction for all of the studied ships. This was deemed reasonable, as the shape of the function does not differ substantially across ship types, and C a functions were not publicly available for the studied ships specifically.…”

Section: Ship Performance Modelmentioning

confidence: 99%

Convex Optimisation Model for Ship Speed Profile: Optimisation under Fixed Schedule

Huotari

Manderbacka

Ritari

et al. 2021

JMSE

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We present a novel convex optimisation model for ship speed profile optimisation under varying environmental conditions, with a fixed schedule for the journey. To demonstrate the efficacy of the proposed method, a combined speed profile optimisation model was developed that employed an existing dynamic programming approach, along the novel convex optimisation model. The proposed model was tested with 5 different ships for 20 journeys from Houston, Texas to London Gateway, with differing environmental conditions, which were retrieved from actual weather forecasts. As a result, it was shown that the combined model with both dynamic programming and convex optimisation was approximately 22% more effective in developing a fuel saving speed profile compared to dynamic programming alone. Overall, average fuel savings for the studied voyages with speed profile optimisation was approximately 1.1% compared to operation with a fixed speed and 3.5% for voyages where significant variance in environmental conditions was present. Speed profile optimisation was found to be especially beneficial in cases where detrimental environmental conditions could be avoided with minor speed adjustments. Relaxation of the fixed schedule constraint likely leads to larger savings but makes comparison virtually impossible as a lower speed leads to lower propulsion energy needed.

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ISO 15016:2015-Based Method for Estimating the Fuel Oil Consumption of a Ship

Cited by 16 publications

References 8 publications

Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships

Convex Optimisation Model for Ship Speed Profile: Optimisation under Fixed Schedule

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