Promoting Liquefied Natural Gas (LNG) Bunkering for Maritime Transportation: Should Ports or Ships Be Subsidized?

Qi, Jingwen; Wang, Shuaian; Zheng, Jianfeng

doi:10.3390/su14116647

Cited by 4 publications

(3 citation statements)

References 26 publications

(34 reference statements)

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“…Nonetheless, there is relatively little reported research on forecasting LNG bunker prices despite the abundance of studies on forecasting general bunker fuel prices. However, major global shipping companies as well as universities and research institutes are paying increasing attention to the cost management of LNG bunker fuel owing to the recent expansion of the ship emission control areas centering on Europe and the United States and the IMO's 2020 environmental regulations [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management

Kim

Lim

Lee

et al. 2022

JMSE

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The LNG price is basically determined based on the oil price, but other than that, it is also determined by the influence of the method of LNG transportation; storage; processes; and political, economic, and geographical instability. Liquefied natural gas (LNG) may not reflect its market value if the destination of the purchase is restricted or the purchase contract includes a take-or-pay clause. Furthermore, it is difficult for the buyer to flexibly manage procurement, resulting in the decoupling of oil and natural gas prices. Therefore, as the LNG bunker price is expected to be more volatile than the marine bunker price in the future, shipping companies need to prepare countermeasures based on scientific forecasting techniques. This study aims to be the first to analyze the forecasting of short-term LNG bunker prices using recurrent neural network (RNN) models suitable for highly volatile data such as time series. Predictive analysis was performed using simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) models, which effectively forecast time-series data, and the prediction performance of LSTM among the three models was excellent. LSTM had relatively excellent prediction performance of outliers and beyond. In addition, it was possible to effectively manage ship operating costs with improved forecasting in practice. Furthermore, this study contributes to establishing a systematic strategy for supervisors in global shipping companies, port authorities, and LNG bunkering companies.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management

Kim

Lim

Lee

et al. 2022

JMSE

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“…It is mainly characterized by its susceptibility to leaks that can cause serious accidents [11], especially in coastal areas where there is a lack of complete infrastructure for fuel refueling [12]. Although some government subsidies are widely used [13], LNG prices may change abruptly at any time [14]. It is more difficult to grasp the cost, causing many ship owners to remain in the wait-and-see stage or take the "LNG Ready" option, which is to design for future conversion to LNG.…”

Section: Introductionmentioning

confidence: 99%

New Exploration of Emission Abatement Solution for Newbuilding Bulk Carriers

Huang,

2024

JMSE

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With the implementation of the International Maritime Organization’s (IMO) sulfur cap 2020, shipowners have had to choose suitable sulfur oxide emission abatement solutions to respond to this policy. The use of Very Low Sulfur Fuel Oil (VLSFO) and the installation of scrubbers are the main response solutions for bulk carriers today. In recent years, the epidemic has gradually improved, and the options facing shipowners may change. Based on the Clarkson Shipping Intelligence Network, this paper collects data related to newbuilding bulk carriers after the implementation of this policy, considers several factors affecting shipowners’ decision, and adopts a machine learning approach for the first time to build a model and make predictions on emission abatement solutions to provide some reference for shipowners to choose a more suitable solution. The results of the study show that the Extreme Gradient Boosting (XGBoost) model is more suitable for the problem studied in this paper, and the highest prediction accuracy of about 84.25% with an Area Under the Curve (AUC) value of 0.9019 is achieved using this model with hyperparameter adjustment based on a stratified sampling divided data set. The model makes good predictions for newbuilding bulk carriers. In addition, the deadweight tonnage and annual distance traveled of a ship have a greater degree of influence on the choice of its option, which can be given priority in the decision making. In contrast to traditional cost–benefit analyses, this study incorporates economic and non-economic factors and uses machine learning methods for effective classification, which have the advantage of being fast, comparable, and highly accurate.

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“…In the 1960s, the industry of liquefied natural gas (LNG) was established. In its more than 50-year history, this market has undergone tremendous growth, and future increase is projected [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Approach for Natural Gas Liquefaction Using a Mixed-Multi-Component-Refrigerant

Ismail,

Omar,

Ishak

et al. 2023

J. Phys.: Conf. Ser.

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The natural gas liquefaction is the most expensive and energy-intensive phase in the natural gas-to-liquefied natural gas-to-natural gas chain. In addition, this region has the biggest development potential. As a result, numerous LNG production methods have been developed and are deployed at export facilities around the world. The goal of this study is to describe and assess an innovative approach for mixed-refrigerant (MR) LNG method. The authors have dubbed this technique the MR-X approach. The MR-X process was developed based on the globally proven liquefaction technology C3MR and its large-scale successor AP-X™ (which offers many benefits and flexibility), but with a novel precooling phase construct. In pre-cooling and liquefaction phases, the refrigerant is a combination of methane, nitrogen, propane, ethane, butane, and isobutane. The paper investigates the creation of the MR-X technology, as well as its modelling, energy, and exergy investigations.

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Promoting Liquefied Natural Gas (LNG) Bunkering for Maritime Transportation: Should Ports or Ships Be Subsidized?

Cited by 4 publications

References 26 publications

Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management

Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management

New Exploration of Emission Abatement Solution for Newbuilding Bulk Carriers

An Innovative Approach for Natural Gas Liquefaction Using a Mixed-Multi-Component-Refrigerant

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