Bunker consumption of containerships considering sailing speed and wind conditions

Medina, Josep R.; Molines, Jorge; González-Escrivá, Jose A.; Aguilar, José

doi:10.1016/j.trd.2020.102494

Cited by 16 publications

(6 citation statements)

References 27 publications

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“…) is the characteristic coefcients, and ε is the error term. Equation (13) shows the quantitative estimation model based on the MRV daily fuel consumption.…”

Section: Fuel Consumption Comparisonmentioning

confidence: 99%

“…Te model will frst estimate the resistance in calm water and add the additional resistance caused by external factors such as wind and waves. Ten, the total engine power needed to overcome the total resistance can be calculated along with the corresponding fuel consumptions and carbon emissions [12]: Medina et al [13] developed an analytical model with an empirical formula by considering wind values along the sailing route to fnd the fuel consumption of container ship. Tillig et al [14] proposed the model for a speed reduction optimization plan during sail by considering weather impact for minimizing fuel consumption over voyages.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

Ren

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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The International Maritime Organization (IMO) had made effort to reduce the ship’s energy consumption and carbon emission by optimizing the ship’s operational measures such as speed and weather routing. However, existing fuel consumption models were relatively simple without considering the quantified effect of weather conditions. In this paper, a knowledge-based ridge regression-based algorithm is presented for enabling automated fuel consumption estimation under varying weather conditions during voyages. Wind speed, wave height, ship speed, draught, AIS segment distance, and ship’s heading (HDG) are used as input to predict the fuel consumption value from the MRV report. In this work, 3 types of models are tested: AIS-based model, MRV-based model, and MRV-based normalized model. In AIS based model, weather conditions are divided into nine categories based on wind speed, wave height, and wind directions then trained separately. In MRV-based mode, the daily weather condition was used, and the MRV-normalized model used the normalized daily weather data. The proposed ridge regression models (11 models total) were tested with 4 container ships for a period of one year, and the result shows that compared to real fuel consumption, MRV-based model could achieve the best result with an average error less than 3% comparing to real MRV report.

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“…) is the characteristic coefcients, and ε is the error term. Equation (13) shows the quantitative estimation model based on the MRV daily fuel consumption.…”

Section: Fuel Consumption Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

Ren

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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Section: Slow Steaming As a Mitigation Measure For Ghg Abatementmentioning

confidence: 99%

“…A very important review regarding the ship voyage optimization based on the control of emissions is presented within [29]. In [29] a careful review of the recent articles regarding the voyage optimization driven by emissions is presented and the investigation of the state-of-art and additional identification of possible future work are presented. Lashgari et al [30] proposed a scenario-based stochastic linear integer programming model which considers routing, sailing speed, and bunkering policy under the uncertainty of fuel price together.…”

Section: Slow Steaming As a Mitigation Measure For Ghg Abatementmentioning

confidence: 99%

GHG Abatement Potential Due to the Implementation of Slow Steaming

Degiuli¹,

Farkas²

2022

JMS

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All branches of industry as well as the maritime transport industry are encountering the challenge of reducing greenhouse gas (GHG) emissions to pre-vent hazardous climate change. The ratification of the Paris agreement has set a restrain for global average temperature significantly lower than 2 °C and preferably restrains the growth to 1.5 °C compared to preindustrial levels. Therefore, International Maritime Organization (IMO) has imposed an aim to decrease carbon dioxide (CO2) emissions per transport work by a minimum of 40 % by 2030 and to 70 % by 2050 compared to 2008. IMO has proposed various short-term, mid-term, and long-term measures for accomplishing this aim. Speed reduction i.e., slow steaming is a crucial short-term measure, that can be effortlessly implemented, and does not depend upon any supplementary infrastructure. Even a slight speed reduction will lead to substantial fuel reduction and therefore substantial CO2 abatement considering the hypothesis that ship speed and fuel oil consumption are related with the cubic function. The implementation of slow steaming leads to a larger voyage time and consequently to a larger number of ships to attain yearly transport work constant. Therefore, it is essential to analyse the increase in fuel oil consumption and CO2 emissions due to the larger number of ships engaged in maritime transport. This paper provides an extensive review of slow steaming and GHG abatement potential and points out the disadvantages of its application.

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“…In addition to econometric studies like these ones, studies focused on reducing costs by optimizing speed based on the relationship between bunker price and bunker consumption have also attracted great interest in the literature (e.g. Wang et al, 2013;Sheng et al, 2014;Aydin et al, 2017;Wang et al, 2019;Medina et al, 2020). These studies have developed and proposed optimization models using various bunker price and speed scenario, which includes bunker cost minimization and freight income maximization.…”

Section: Literature Reviewmentioning

confidence: 99%

Başlıca Ticari Gemi Tiplerinin Ortalama Filo Hızlarının Modellenmesi ve Analizi

Açık

2021

Gemi Ve Deniz Teknolojisi

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Bu çalışmada, deniz taşımacılığında ana piyasa türleri olan konteyner, kuru yük ve ham petrol filolarının ortalama hızlarını navlun oranı, yakıt fiyatı, filo büyüklüğü ve faiz oranı gibi dış faktörleri kullanarak tahmin ve analiz edilmiştir. Teorik çerçeveye uygun olarak, navlun ve faiz oranları ortalama hızları olumlu yönde etkilerken, yakıt fiyatı ve filo büyüklüğü olumsuz yönde etkilemektedir. Navlun oranı en çok konteyner filosunun ortalama hızını etkilerken, yakıt fiyatı, filo büyüklüğü ve faiz oranı en çok ham petrol filosunun ortalama hızını etkilemektedir; kuru yük filosunun ortalama hızı yakıt fiyatından ve filo büyüklüğünden en az etkilenmektedir; ve konteyner filosunun ortalama hızı faiz oranından en az etkilenmektedir. Genel olarak, faktörler arasındaki katsayı büyüklükleri göz önüne alındığında, üç pazarın ortalama hızlarında en etkili faktör filo büyüklükleridir. Böylece navlun ve yakıt fiyatı çerçevesinde sıkışmış olan ampirik literatüre yeni boyutlar eklenmiştir.

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Bunker consumption of containerships considering sailing speed and wind conditions

Cited by 16 publications

References 27 publications

Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

GHG Abatement Potential Due to the Implementation of Slow Steaming

Başlıca Ticari Gemi Tiplerinin Ortalama Filo Hızlarının Modellenmesi ve Analizi

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