Vessel's trim optimization using IoT data and machine learning models

Theodor Panagiotakopoulos; Ioannis Filippopoulos; Christos Filippopoulos; Evangelos Filippopoulos; Zoran Lajic; Antonis Violaris; Sotirios Panagiotis Chytas; Yiannis Kiouvrekis

doi:10.1109/IISA56318.2022.9904361

Vessel's trim optimization using IoT data and machine learning models

Theodor Panagiotakopoulos
, Ioannis Filippopoulos
, Christos Filippopoulos
, Evangelos Filippopoulos
, Zoran Lajic
, Antonis Violaris
, Sotirios Panagiotis Chytas
, Yiannis Kiouvrekis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The shipping industry is an important source of greenhouse gas emissions, such as carbon dioxide, methane and nitrogen oxides. In the past few years, environmental and policy reasons dictate the immense reduction of greenhouse gas emissions in industries worldwide. Towards this direction, the shipping industry has focused on ship trim optimization in the last few years as an operational measure for better energy efficiency and thus a way to reduce consumption and energy-related emissions. In this paper, we present a machine learning solution to the problem of trim optimization. Specifically, we use Internet of Things (IoT) data for speed, draft, and trim in order to accurately predict shaft power. After our machine learning model is trained, we use its predicting capabilities to create the shaft power surface as part of the trim monitoring user interface of the maritime company infrastructure.

Original language	English
Title of host publication	13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665463904
DOIs	https://doi.org/10.1109/IISA56318.2022.9904361
Publication status	Published - 2022
Externally published	Yes
Event	13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022 - Corfu, Greece Duration: 18 Jul 2022 → 20 Jul 2022

Publication series

Name	13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

Conference

Conference	13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
Country/Territory	Greece
City	Corfu
Period	18/07/22 → 20/07/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Internet of Things
machine learning
Maritime
Shipping
trim optimization

Access to Document

10.1109/IISA56318.2022.9904361

Cite this

Panagiotakopoulos, T., Filippopoulos, I., Filippopoulos, C., Filippopoulos, E., Lajic, Z., Violaris, A., Chytas, S. P., & Kiouvrekis, Y. (2022). Vessel's trim optimization using IoT data and machine learning models. In 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022 (13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IISA56318.2022.9904361

Panagiotakopoulos, Theodor ; Filippopoulos, Ioannis ; Filippopoulos, Christos et al. / Vessel's trim optimization using IoT data and machine learning models. 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022).

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title = "Vessel's trim optimization using IoT data and machine learning models",

abstract = "The shipping industry is an important source of greenhouse gas emissions, such as carbon dioxide, methane and nitrogen oxides. In the past few years, environmental and policy reasons dictate the immense reduction of greenhouse gas emissions in industries worldwide. Towards this direction, the shipping industry has focused on ship trim optimization in the last few years as an operational measure for better energy efficiency and thus a way to reduce consumption and energy-related emissions. In this paper, we present a machine learning solution to the problem of trim optimization. Specifically, we use Internet of Things (IoT) data for speed, draft, and trim in order to accurately predict shaft power. After our machine learning model is trained, we use its predicting capabilities to create the shaft power surface as part of the trim monitoring user interface of the maritime company infrastructure.",

keywords = "Internet of Things, machine learning, Maritime, Shipping, trim optimization",

author = "Theodor Panagiotakopoulos and Ioannis Filippopoulos and Christos Filippopoulos and Evangelos Filippopoulos and Zoran Lajic and Antonis Violaris and Chytas, \{Sotirios Panagiotis\} and Yiannis Kiouvrekis",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022 ; Conference date: 18-07-2022 Through 20-07-2022",

year = "2022",

doi = "10.1109/IISA56318.2022.9904361",

language = "English",

series = "13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022",

}

Panagiotakopoulos, T, Filippopoulos, I, Filippopoulos, C, Filippopoulos, E, Lajic, Z, Violaris, A, Chytas, SP & Kiouvrekis, Y 2022, Vessel's trim optimization using IoT data and machine learning models. in 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022. 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022, Institute of Electrical and Electronics Engineers Inc., 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022, Corfu, Greece, 18/07/22. https://doi.org/10.1109/IISA56318.2022.9904361

Vessel's trim optimization using IoT data and machine learning models. / Panagiotakopoulos, Theodor; Filippopoulos, Ioannis; Filippopoulos, Christos et al.
13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Vessel's trim optimization using IoT data and machine learning models

AU - Panagiotakopoulos, Theodor

AU - Filippopoulos, Ioannis

AU - Filippopoulos, Christos

AU - Filippopoulos, Evangelos

AU - Lajic, Zoran

AU - Violaris, Antonis

AU - Chytas, Sotirios Panagiotis

AU - Kiouvrekis, Yiannis

PY - 2022

Y1 - 2022

N2 - The shipping industry is an important source of greenhouse gas emissions, such as carbon dioxide, methane and nitrogen oxides. In the past few years, environmental and policy reasons dictate the immense reduction of greenhouse gas emissions in industries worldwide. Towards this direction, the shipping industry has focused on ship trim optimization in the last few years as an operational measure for better energy efficiency and thus a way to reduce consumption and energy-related emissions. In this paper, we present a machine learning solution to the problem of trim optimization. Specifically, we use Internet of Things (IoT) data for speed, draft, and trim in order to accurately predict shaft power. After our machine learning model is trained, we use its predicting capabilities to create the shaft power surface as part of the trim monitoring user interface of the maritime company infrastructure.

AB - The shipping industry is an important source of greenhouse gas emissions, such as carbon dioxide, methane and nitrogen oxides. In the past few years, environmental and policy reasons dictate the immense reduction of greenhouse gas emissions in industries worldwide. Towards this direction, the shipping industry has focused on ship trim optimization in the last few years as an operational measure for better energy efficiency and thus a way to reduce consumption and energy-related emissions. In this paper, we present a machine learning solution to the problem of trim optimization. Specifically, we use Internet of Things (IoT) data for speed, draft, and trim in order to accurately predict shaft power. After our machine learning model is trained, we use its predicting capabilities to create the shaft power surface as part of the trim monitoring user interface of the maritime company infrastructure.

KW - Internet of Things

KW - machine learning

KW - Maritime

KW - Shipping

KW - trim optimization

UR - https://www.scopus.com/pages/publications/85141077610

U2 - 10.1109/IISA56318.2022.9904361

DO - 10.1109/IISA56318.2022.9904361

M3 - Conference contribution

AN - SCOPUS:85141077610

T3 - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

BT - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

Y2 - 18 July 2022 through 20 July 2022

ER -

Panagiotakopoulos T, Filippopoulos I, Filippopoulos C, Filippopoulos E, Lajic Z, Violaris A et al. Vessel's trim optimization using IoT data and machine learning models. In 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022). doi: 10.1109/IISA56318.2022.9904361

Vessel's trim optimization using IoT data and machine learning models

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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