VLCC’s fuel consumption prediction modeling based on noon report and automatic identification system

It is extremely important for fuel saving by taking the correct decisions where cost efficiency and environmental friendliness are top priorities. The fuel consumption rate of the ship is influenced by many parameters, such as average daily sailing speed, ship displacement, cargo, ballast water and...

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Autores principales:	Ali Akbar Safaei, Hassan Ghassemi, Mahmoud Ghiasi
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	fuel consumption sea state multiple linear regressions noon report data automatic identification system vlcc Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/a84e472800db433f9ab88503361df112
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spelling	oai:doaj.org-article:a84e472800db433f9ab88503361df1122021-11-04T15:51:55ZVLCC’s fuel consumption prediction modeling based on noon report and automatic identification system2331-191610.1080/23311916.2019.1595292https://doaj.org/article/a84e472800db433f9ab88503361df1122019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1595292https://doaj.org/toc/2331-1916It is extremely important for fuel saving by taking the correct decisions where cost efficiency and environmental friendliness are top priorities. The fuel consumption rate of the ship is influenced by many parameters, such as average daily sailing speed, ship displacement, cargo, ballast water and bunker, trim and sea conditions (wind, wave and current) in a complicated way. In this study, noon report (NR) and automatic identification system (AIS) datum of four Very Large Crude Carriers (VLCC) are widely used to establish a prediction model. Needless to say that, the accuracy of statistical models depends on consistency and quality of collected datum, hence a novel combination methodology applied to NR and AIS datum to prepare a series of pure valid data population of vessel speed, fuel consumption and sea state. Then the consistency of populations are enriched by eliminating the out ranged or junkie members in different methods, i.e., T-test, normality control and outlier score base (OSB). Finally, multiple linear regressions are applied considering all fuel consumption influential parameters. Results show a high correlation between the independent and dependent variables. Consequently, generated formula predicts fuel consumption of vessels at all variable conditions in good agreement with recorded fuel consumption data.Ali Akbar SafaeiHassan GhassemiMahmoud GhiasiTaylor & Francis Grouparticlefuel consumptionsea statemultiple linear regressionsnoon report dataautomatic identification systemvlccEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuel consumption sea state multiple linear regressions noon report data automatic identification system vlcc Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	fuel consumption sea state multiple linear regressions noon report data automatic identification system vlcc Engineering (General). Civil engineering (General) TA1-2040 Ali Akbar Safaei Hassan Ghassemi Mahmoud Ghiasi VLCC’s fuel consumption prediction modeling based on noon report and automatic identification system
description	It is extremely important for fuel saving by taking the correct decisions where cost efficiency and environmental friendliness are top priorities. The fuel consumption rate of the ship is influenced by many parameters, such as average daily sailing speed, ship displacement, cargo, ballast water and bunker, trim and sea conditions (wind, wave and current) in a complicated way. In this study, noon report (NR) and automatic identification system (AIS) datum of four Very Large Crude Carriers (VLCC) are widely used to establish a prediction model. Needless to say that, the accuracy of statistical models depends on consistency and quality of collected datum, hence a novel combination methodology applied to NR and AIS datum to prepare a series of pure valid data population of vessel speed, fuel consumption and sea state. Then the consistency of populations are enriched by eliminating the out ranged or junkie members in different methods, i.e., T-test, normality control and outlier score base (OSB). Finally, multiple linear regressions are applied considering all fuel consumption influential parameters. Results show a high correlation between the independent and dependent variables. Consequently, generated formula predicts fuel consumption of vessels at all variable conditions in good agreement with recorded fuel consumption data.
format	article
author	Ali Akbar Safaei Hassan Ghassemi Mahmoud Ghiasi
author_facet	Ali Akbar Safaei Hassan Ghassemi Mahmoud Ghiasi
author_sort	Ali Akbar Safaei
title	VLCC’s fuel consumption prediction modeling based on noon report and automatic identification system
title_short	VLCC’s fuel consumption prediction modeling based on noon report and automatic identification system
title_full	VLCC’s fuel consumption prediction modeling based on noon report and automatic identification system
title_fullStr	VLCC’s fuel consumption prediction modeling based on noon report and automatic identification system
title_full_unstemmed	VLCC’s fuel consumption prediction modeling based on noon report and automatic identification system
title_sort	vlcc’s fuel consumption prediction modeling based on noon report and automatic identification system
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/a84e472800db433f9ab88503361df112
work_keys_str_mv	AT aliakbarsafaei vlccsfuelconsumptionpredictionmodelingbasedonnoonreportandautomaticidentificationsystem AT hassanghassemi vlccsfuelconsumptionpredictionmodelingbasedonnoonreportandautomaticidentificationsystem AT mahmoudghiasi vlccsfuelconsumptionpredictionmodelingbasedonnoonreportandautomaticidentificationsystem
_version_	1718444732215984128

VLCC’s fuel consumption prediction modeling based on noon report and automatic identification system

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