Development of a Fuel Consumption Prediction Model Based on Machine Learning Using Ship In-Service Data

Kim, Young-Rong; Jung, Min Kyo; Park, Jun-Bum

doi:10.3390/jmse9020137

Cited by 63 publications

(26 citation statements)

References 38 publications

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“…At the same time, if the distance of the ship is reduced by 1 NM then the LSS tariff will increase by Rp 886,212.00. This result also corresponds with research by Kim et al, who describes the equation of fuel efficiency, that sailing distance is directly proportional with fuel consumption [52]. It can be interpreted that the destination port distinguishes the need for fuel consumption because the farther the destination, the more fuel is needed, and a higher LSS tariff will be charged, and vice versa.…”

Section: Multiple Linear Regression Model Interpretationsupporting

confidence: 88%

Analisis Pengaruh Waktu Dan Jarak Perjalanan Kapal Terhadap Biaya Low Sulphur Surcharge

Kurnia

Nawadir

2021

j. manaj. ind. dan logist.

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The danger of ship emission caused by HFO (Heavy Fuel Oil) fuel type has become a serious matter due to its high containment of sulphur as much as 3.50% m/m. The IMO (International Maritime Organization) took action on this problem by releasing new regulation to limit sulphur in the ship fuel as low as 0.50% m/m. This regulation leads to an additional tariff called the LSS (Low Sulphur Surcharge). As an impact, shipping companies charge this fee to customers and ocean freight forwarders, hence there is an increase of the total shipping charges. Meanwhile, the dominant variable which determines the LSS charge amount is not yet known, so it is still uninformative for the public and the academic field. The purpose of this study is to analyse which variable gives the most influence on the amount of the LSS tariff. By using multiple linear regression method, the study finds that the shipping distance variable is the dominant variable with a contribution value of 86.48% and has positive relationship with the LSS tariff. On the other hand, though the voyage time also has influence on the tariff, the effect is weak and it shows negative relationship with the LSS tariff.

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Section: Multiple Linear Regression Model Interpretationsupporting

confidence: 88%

Analisis Pengaruh Waktu Dan Jarak Perjalanan Kapal Terhadap Biaya Low Sulphur Surcharge

Kurnia

Nawadir

2021

j. manaj. ind. dan logist.

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“…This is comparable to some of other methods, used for reduction of fuel consumption for ships, such as changes in hull and propulsion plant design, additional waste heat recovery, change of fuel type and energy management (Lassesson, Andersson 2009;Yan et al 2020). Fuel consumption prediction and optimisation models usually involve evaluation of multiple parameters including hull resistance, weather influence, currents, engine load and many others and by selecting optimal speed, course and load can reach up to 2…7% in fuel consumption reduction (Panapakidis et al 2020;Yan et al 2020;Kim et al 2021;Moreira et al 2021). But in very few it is noted, that fuel quality parameters also influence efficiency and that the effect can be different for each engine model, as it was also shown in this study (Lundh et al 2016;Panapakidis et al 2020).…”

Section: (supporting

confidence: 52%

Influence of Marine Fuel Properties on Ignition, Injection Delay and Energy Efficiency

et al. 2021

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According to the International Council on Combustion Engines (CIMAC) and International Maritime Organization (IMO) statistics, the rational selection of Marine Bunker Fuel (MBF) properties is an effective way to improve operating conditions and energy efficiency of all types of marine Diesel Engines (DEs). The publication presents the results of studies on the influence of heavy and distillate MBF properties on the characteristics of different DE types: high-speed (Caterpillar 3512B, MTU 8V 396TB), medium-speed (SKL VDS 48/42, ChN 26.5/31) ir low-speed (MAN B&W 6S60MC). The aim of work is to form a methodological framework for assessing the influence of marine fuel properties on the energy performance of different types of ship power plants. Numerical methods show that in the case of unfavourable selection of the density and viscosity of marine fuels regulated by the standard ISO 8217:2017, the changes in specific fuel consumption be reach up to 10% low-speed, 4…7% medium-speed, and 2…3% high-speed DEs. As the density varies from light grades to 1010 kg/m3, the change in be is 3…4%. At low viscosity, as the density increases to 1030 kg/m3, the low-speed engine comparative fuel consumption increases by 5%. It is recommended not to use fuel with a density >1010 kg/m3 and a viscosity <300…400 mm2/s. Developed solutions for the rational selection of bunkered marine fuel properties for a specific DE model trough the influence of density and viscosity on fuel injection and combustion characteristics based on multiparametric diagrams of relative fuel consumption change.

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“…Cheliotis et al [15] detected abnormal symptoms of ships using machine learning. Kim et al [16] predicted the fuel consumption rate of container ships using a deep neural network and presented the optimal operating conditions accordingly. Song et al [17] conducted a study on the detection of ships through synthetic aperture radar (SAR) images and automatic identification system (AIS) information using a convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Reinforcement Learning to Predict Shaft Deformation Considering Hull Deformation of Medium-Sized Oil/Chemical Tanker

Choi

Lee

Park

2021

JMSE

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The enlargement of ships has increased the relative hull deformation owing to draft changes. Moreover, design changes such as an increased propeller diameter and pitch changes have occurred to compensate for the reduction in the engine revolution and consequent ship speed. In terms of propulsion shaft alignment, as the load of the stern tube support bearing increases, an uneven load distribution occurs between the shaft support bearings, leading to stern accidents. To prevent such accidents and to ensure shaft system stability, a shaft system design technique is required in which the shaft deformation resulting from the hull deformation is considered. Based on the measurement data of a medium-sized oil/chemical tanker, this study presents a novel approach to predicting the shaft deformation following stern hull deformation through inverse analysis using deep reinforcement learning, as opposed to traditional prediction techniques. The main bearing reaction force, which was difficult to reflect in previous studies, was predicted with high accuracy by comparing it with the measured value, and reasonable shaft deformation could be derived according to the hull deformation. The deep reinforcement learning technique in this study is expected to be expandable for predicting the dynamic behavior of the shaft of an operating vessel.

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Development of a Fuel Consumption Prediction Model Based on Machine Learning Using Ship In-Service Data

Cited by 63 publications

References 38 publications

Analisis Pengaruh Waktu Dan Jarak Perjalanan Kapal Terhadap Biaya Low Sulphur Surcharge

Analisis Pengaruh Waktu Dan Jarak Perjalanan Kapal Terhadap Biaya Low Sulphur Surcharge

Influence of Marine Fuel Properties on Ignition, Injection Delay and Energy Efficiency

Application of Deep Reinforcement Learning to Predict Shaft Deformation Considering Hull Deformation of Medium-Sized Oil/Chemical Tanker

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