A Data-Driven Model for Rapid CII Prediction

Mühmer, Markus and La Ferlita, Alessandro and Geber, Evangelos and Ehlers, Sören and Di Nardo, Emanuel and El Moctar, Ould and Ciaramella, Angelo (2024) A Data-Driven Model for Rapid CII Prediction. Journal of Marine Science and Engineering, 12 (11). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/jmse12112048. ISSN 2077-1312.

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Official URL: https://www.mdpi.com/2077-1312/12/11/2048

Abstract

In dem Paper wird eine datenbasierte Methode beschreiben mit deren Hilfe der Carbon Intensity Index (CII) von Schiffen vorhergesagt werden kann. Hierfür werden ausschließlich wenige Betriebsparameter sowie Umweltdaten als Eingangsgrößen benötigt.

Item URL in elib:

https://elib.dlr.de/208454/

Document Type:

Article

Title:

A Data-Driven Model for Rapid CII Prediction

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Mühmer, Markus	Markus.Muehmer (at) dlr.de	https://orcid.org/0000-0003-4621-6870	171784975
La Ferlita, Alessandro	alessandro.laferlita (at) gmail.com	https://orcid.org/0000-0001-5698-9354	UNSPECIFIED
Geber, Evangelos	vag.geber (at) gmail.com	UNSPECIFIED	UNSPECIFIED
Ehlers, Sören	soren.ehlers (at) dlr.de	https://orcid.org/0000-0001-5698-9354	UNSPECIFIED
Di Nardo, Emanuel	emanuel.dinardo (at) uniparthenope.it	UNSPECIFIED	UNSPECIFIED
El Moctar, Ould	ould.el-moctar (at) uni-due.de	UNSPECIFIED	UNSPECIFIED
Ciaramella, Angelo	angelo.ciaramella (at) uniparthenope.it	UNSPECIFIED	UNSPECIFIED

Date:

12 November 2024

Journal or Publication Title:

Journal of Marine Science and Engineering

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

Yes

In SCOPUS:

Yes

In ISI Web of Science:

Yes

Volume:

DOI:

10.3390/jmse12112048

Editors:

Editors	Email	Editor's ORCID iD	ORCID Put Code
Lazakis, Iraklis	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Velasco-Gallego, Christian	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED

Publisher:

Multidisciplinary Digital Publishing Institute (MDPI)

Series Name:

Special Issue Applications of Artificial Intelligence in Marine Machinery

ISSN:

2077-1312

Status:

Published

Keywords:

Marine Machinery, Machine Learning, Artificial Intelligence

HGF - Research field:

Energy

HGF - Program:

Energy System Design

HGF - Program Themes:

Digitalization and System Technology

DLR - Research area:

Energy

DLR - Program:

E SY - Energy System Technology and Analysis

DLR - Research theme (Project):

E - Energy System Technology

Location:

Geesthacht

Institutes and Institutions:

Institute of Maritime Energy Systems > Energy Converters and Systems

Deposited By:

Mühmer, Markus

Deposited On:

15 Nov 2024 09:12

Last Modified:

11 May 2026 08:14

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