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Hybrid Energy Systems for Vehicle Industry
Published in Yatish T. Shah, Hybrid Energy Systems, 2021
CO2 emissions from world shipping are directly related to the fuel consumption of the fleet. In 2007, approximately 277 million tons of fuel were consumed by international shipping. Three categories of ship account for almost two-thirds of this consumption. The liquid bulk sector accounts for about 65 million tons fuel/ year, container vessels for about 55 million tons fuel/year, and the dry bulk sector for about 53 million tons fuel/year [19, p. 42]. Many of the present efforts to reduce CO2 emissions from global shipping are aimed at the container vessel sector since this contains relatively large vessels travelling at comparatively high speeds, leading to high fuel consumptions. Significant reductions in fuel consumption and hence emissions may be made through introducing slower operational speeds in a practice referred to as “slow steaming” [20,21]. Less attention has been paid to the dry and liquid bulk sectors, where operational speeds are much slower and vessel design optimized over many decades.
Vessel logistics and shipping operations management
Published in Dong-Ping Song, Container Logistics and Maritime Transport, 2021
Ship speed optimisation aims to determine the ship sailing speed at each sea leg (from one portcall to the next portcall) in the shipping service route by optimising a specific objective, subject to certain constraints on the ship’s operation. The ship speed is often planned at the tactical level but may be adjusted at the operational or real-time level. Slow steaming refers to the practice that ships are planned to sail at a speed significantly lower than their designed speeds to cut down fuel consumption and absorb the overcapacity. Technically, slow steaming in the liner shipping sector also concerns the determination of the ship sailing speed in the service route but with the possibility of deploying extra ships in the service route to maintain the service frequency. If the ship speed optimisation problem also incorporates the number of ships to be deployed in the service route as a decision variable, then ship speed optimisation and slow steaming essentially serve the same purpose.
Happiness – Wind of change for shipping companies, a new way to measure their performance
Published in Petar Georgiev, C. Guedes Soares, Sustainable Development and Innovations in Marine Technologies, 2019
Martin Stopford (2009) the father of Maritime economy developed the concept of slow steaming to enhance sustainable shipping. Meanwhile IMO has set a goals to reduce GHG emissions from existing vessels up to 50% by 2050 and enforces clean-fuels burning for all vessels from 2020 onwards. So up to 70,000 ships would be affected by the regulation according to IMO estimates. In the literature it is described as a tectonic shift in maritime industry. To achieve this goals, IMO suggested three basic approaches: the enlargement of vessel size, the reduction of voyage speed, and the application of new technologies. So we can say that slow steaming could be helpful in reducing operating costs and GHG emissions (Woo & Moon, 2014). Many studies in the Maritime literature pay attention on reduce CO2 emission in shipping operations by reducing the speed of vessels (Lekhavat et al., 2015).
Pathways towards carbon reduction through technology transition in liner shipping
Published in Maritime Policy & Management, 2023
Yuzhe Zhao, Yu Chen, Kjetil Fagerholt, Elizabeth Lindstad, Jingmiao Zhou
Secondly, how should liner transport services promote carbon reduction by flexible operational measures? Considering cost-effectiveness, ship retrofitting is an important way to technology transition. This result is also suggested by Lagemann et al. (2022). In the long run, slow steaming should be regarded as an important strategy to reduce the carbon footprint of containerships. This result agrees with the opinion of Zhen et al. (2020). Christodoulou and Cullinane (2022) put forward that technology maturity and technology applicability (reliable refueling infrastructure) have a great impact on future operation. In our research, except for the price of alternative fuels, different preferences for speed optimization also arise with the degree of fuel promotion under short-sea and deep-sea modes. In addition, the use of shore power can reduce the carbon emissions during the port berthing stage (Zhen et al., 2020b). Our experiment results show that, it will exert an important remedial effect, if ships have not switched to non-fossil fuels, or face stricter environmental regulations.
Modeling the interactions among green shipping policies
Published in Maritime Policy & Management, 2022
Lixian Fan, Yuhan Xu, Meifeng Luo, Jingbo Yin
Although slow steaming can help reduce emissions for individual ships or voyages (Woo and Woo and Daniel 2013; Fagerholt et al. 2015; Corbett, Wang, and Winebrake 2009), it recently suffered various criticisms for its ineffectiveness in emission reduction from a macroeconomic perspective (Doudnikoff and Lacoste 2014). The Third IMO GHG Study analyzed that ‘A reduction in speed and the associated reduction in fuel consumption do not relate to an equivalent percentage increase in efficiency, because a greater number of ships (or more days at sea) are required to do the same amount of transport work’ (IMO 2014). Because of these controversial discussions on the effectiveness of slow steaming on emission reduction, the model in Figure 2 does not include the impact of V3 on V1 or V2.
Neural network-based fuel consumption estimation for container ships in Korea
Published in Maritime Policy & Management, 2020
Luan Thanh Le, Gunwoo Lee, Keun-Sik Park, Hwayoung Kim
This study predicted the fuel consumption for five container ship groups by designing an MLP ANN architecture featuring four inputs (average sailing speed, voyage time, cargo weight, and ship capacity). All optimum hyper-parameters were selected to ensure a balance between the convergence value and the execution time based on the trial-and-error rule. Through this study’s testing measures, the selected ANN models exhibited impressive performance compared to statistical models. All of the error indexes of the ANN with four inputs were the lowest—in fact, much lower than the three other models. This not only confirms the extraordinary forecasting advantage of the ANN method but also indicates the quantified effect of cargo weight on fuel consumption. Our work addresses the abovementioned research gap created by prior studies, which have not constructed an ANN that utilizes real operational data from hundreds of container ships. This paper also contributes to the existing literature by providing a hybrid method to determine the slow-steaming level corresponding to each specific set of simple inputs for every case of a certain round-trip. The optimized solution not only demonstrates the crucial role of the slow-steaming method in reducing fuel consumption and emissions but can also provide shipping companies with the specific level of slow-steaming required to achieve their energy efficiency goals.