A Statistical Control of the Ship Fuel Consumption

Bocchetti, Dario; Lepore, Antonio; Palumbo, Biagio; Vitiello, Luigi

doi:10.3940/rina.pass.2013.01

Cited by 9 publications

(9 citation statements)

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“…However, as noon reports are completed manually, their accuracy cannot be guaranteed. Furthermore, only one record is made per day, which reduces the data quality and quantity (Bocchetti et al, 2013(Bocchetti et al, , 2015Erto et al, 2015;Chaal, 2018;Farag and Ölçer, 2020).…”

Section: Input Data Overviewmentioning

confidence: 99%

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Yan

Wang

Psaraftis

2021

Transportation Research Part E: Logistics and Transportation Re

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Section: Input Data Overviewmentioning

confidence: 99%

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Yan

Wang

Psaraftis

2021

Transportation Research Part E: Logistics and Transportation Re

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“…Two of these categories are related to the type of data being captured: environmental or oceanographic (outside of the vessel) and propulsion related (inside of the vessel). Both types of data are needed to build fuel oil consumption (FOC) forecasting models using a statistical approach, − a machine learning approach, − or combined with optimization methods . A third group integrates other devices that help data integration or produce data relevant for environmental and propulsion forecasting models (e.g., timestamps or vessel position).…”

Section: Methods For a Fishing Vessel Monitoring Systemmentioning

confidence: 99%

Toward Digitalization of Fishing Vessels to Achieve Higher Environmental and Economic Sustainability

Uriondo,

Fernandes-Salvador,

Reite

et al. 2024

ACS Environ. Au

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Fishing vessels need to adapt to and mitigate climate changes, but solution development requires better information about the environment and vessel operations. Even if ships generate large amounts of potentially useful data, there is a large variety of sources and formats. This lack of standardization makes identification and use of key data challenging and hinders its use in improving operational performance and vessel design. The work described in this paper aims to provide cost-effective tools for systematic data acquisition for fishing vessels, supporting digitalization of the fishing vessel operation and performance monitoring. This digitalization is needed to facilitate the reduction of emissions as a critical environmental problem and industry costs critical for industry sustainability. The resulting monitoring system interfaces onboard systems and sensors, processes the data, and makes it available in a shared onboard data space. From this data space, 209 signals are recorded at different frequencies and uploaded to onshore servers for postprocessing. The collected data describe both ship operation, onboard energy system, and the surrounding environment. Nine of the oceanographic variables have been preselected to be potentially useful for public scientific repositories, such as Copernicus and EMODnet. The data are also used for fuel prediction models, species distribution models, and route optimization models.

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“…Thanh et al [12] develop simplified ship fuel consumption model for ocean going container vessels by different sizes based on regression models. Bocchetti et al [13] presents a statistical model which allows prediction of fuel consumption from set of navigational parameters and mission profile. Li et al [14] presents calculation model of ship fuel consumption and provide energy consumption assessment index for single ship.…”

Section: Introductionmentioning

confidence: 99%

“…Figure13 Histogram of the generated sludge per day from fuel during mixed operations for period 02/2020 -08/2020 -M/V Vessel Source: Authors…”

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confidence: 99%

Quantitative Analysis of Fuel Consumption and Fuel Sediments Separation as a Function of Fuel Type and Ship Operation Mode on Product/Chemical Tanker Example

Dujmović

Miculinić

2023

Pomorstvo (Online)

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Fuel consumption onboard is influenced on several factors but most crucial is ship operation mode and fuel type in use. Conventual fuel preparation process onboard consists of several stages. One of the most essential processes is centrifugal separation treatment operation onboard, which consists of introducing fuel into the field of high centrifugal field, where impurities and sediments are separated from fuels. The quantity of separated sediments and impurities from the fuel is essential for accurate calculating the remaining fuel quantity on board (ROB). The purpose of this paper is to determine and verify the influence of potential factors that can affect the quantity of fuel consumption and separated sediments quantity from the fuel based on actual data from real product/chemical tanker powered by conventional fuels. By means of statistical analysis the average values, minimums, maximums, standard deviations, and variations, median and modes, standard errors for separated sludge from fuel, heavy fuel consumption (HSHFO) and diesel fuel consumption (LSMGO) during entire observed period and during specific operations modes are obtained. Furthermore, regression formulas between separated sediments from fuel vs fuel consumption during each ship operation mode are generated. Finally, by means of single and multifactorial analysis significant influence of ship operations mode on fuel consumption and separated quantity has been confirmed. This research contributed to find relations more accurately between separated sediments quantity to fuel consumption that might be used for more accurate emission calculation.

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A Statistical Control of the Ship Fuel Consumption

Cited by 9 publications

References 0 publications

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Toward Digitalization of Fishing Vessels to Achieve Higher Environmental and Economic Sustainability

Quantitative Analysis of Fuel Consumption and Fuel Sediments Separation as a Function of Fuel Type and Ship Operation Mode on Product/Chemical Tanker Example

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