Explore the vast range of titles available.

Liquefied natural gas (LNG) is a commercially attractive phase of the commodity that facilitates the efficient handling and transportation of natural gas around the world. The LNG industry, using technologies proven over decades of development, continues to expand its markets, diversify its supply chains and increase its share of the global natural gas trade. The Handbook is a timely book as the industry is currently developing new large sources of supply and the technologies have evolved in recent years to enable offshore infrastructure to develop and handle resources in more remote and harsher environments. It is the only book of its kind, covering the many aspects of the LNG supply chain from liquefaction to regasification by addressing the LNG industries' fundamentals and markets, as well as detailed engineering and design principles.

With the rising geopolitical tensions, predicting future trade partners has become a critical topic for the global community. Liquefied natural gas (LNG), recognized as the cleanest burning hydrocarbon, plays a significant role in the transition to a cleaner energy future. As international trade in LNG becomes increasingly volatile, it is essential to assist governments in identifying potential trade partners and analyzing the trade network. Traditionally, forecasts of future mineral and energy resource trade networks have relied on similarity indicators (e.g., CN, AA). This study employs complex network theory to illustrate the characteristics of nodes and edges, as well as the evolution of global LNG trade networks from 2001 to 2020. Utilizing node and edge data from these networks, this research applies machine learning algorithms to predict future links based on local and global similarity-based indices (e.g., CN, JA, PA). The findings indicate that random forest and decision tree algorithms, when used with local similarity-based indices, demonstrate strong predictive performance. The reliability of these algorithms is validated through the Receiver Operating Characteristic Curve (ROC). Additionally, a graph attention network model is developed to predict potential links using edge and motif data. The results indicate robust predictive performance. This study demonstrates that machine learning algorithms—specifically random forest and decision tree—outperform in predicting links within the global LNG trade network based on local information proximity, while the graph attention network, a deep learning model, exhibits stable optimization and effective feature learning. These findings suggest that machine learning approaches hold significant promise for mineral trade network analysis.

Natural gas is considered a transitional energy source in the transition to clean energy owing to its clean, efficient, and ecologically beneficial properties. The trade of liquefied natural gas (LNG) serves as the backbone of the global natural gas trade and significantly influences the dynamics of the global energy trade system. This paper constructs long-term global LNG trade networks, and explores the spatiotemporal evolution and topological structures of the international LNG trade by utilizing multilevel network analysis methods, to provide insights for comprehensively understanding the market dynamics of the global LNG trade system. The findings indicate the following: (1) The global LNG trade volume shows an overall upward trend, and the global LNG trade network exhibits uneven spatial distribution, clear hierarchical differentiation, and an increasingly complicated structure. Global LNG trade is gradually changing from regionalization to globalization, and the international LNG market is undergoing structural reshaping. (2) The global LNG trade network continues to expand in size and density, and the rapidly growing LNG supply and trade relations are driving the formation of the global natural gas market. (3) Global LNG trade is still in a phase of rapid change, with the global efficiency of the network increasing and then decreasing. The trade network has traditionally been centered on ten countries, including Japan, South Korea, the United States, and Qatar. (4) The global LNG trade network exhibits clear core-periphery structures with considerable polarization effects, and the trade network structure is continuously evolving and is growing unbalanced. Finally, we put forward relevant policy suggestions to promote global LNG trade interconnectivity and enhance environmental protection and respond to global climate change.

Large-scale construction projects, such as port construction and reclamation endeavors, can alter inshore wave dynamics, leading to severe coastal erosion. In South Korea, recent large-scale reclamation projects have resulted in severe sand erosion along nearby coastlines. This study focused on Wolcheon Beach, where complete sand loss had occurred due to robust long-shore sediment transport (LST) induced by a reclamation project for construction of the nearby Samcheok liquefied natural gas terminal in Gangwon Province. A shoreline change model was employed to simulate this phenomenon, and the results were validated using satellite imagery. Model accuracy was assessed by comparing the LST rate vectors indirectly estimated from the changes in the shoreline delineated in the satellite images with those directly derived from the model. Furthermore, a response methodology was proposed using the parabolic bay-shaped equation, which can effectively mitigate coastal erosion by controlling LST by installing a small-scale groin group on the adjacent beach before commencing reclamation or port projects. These findings are expected to contribute significantly to averting catastrophic coastal erosion issues, such as those witnessed at Wolcheon Beach, before large-scale construction in coastal regions is performed.

In a general high-dimensional process, a large number of process parameters or quality characteristics is found to be featured through their dependencies and relevance. The features that have similar characteristics or behaviors in the process operation can be categorized into multiple groups. Thus, when a few quality characteristics in the process change, it is highly probable that the process shift would have occurred in a few relevant groups. Recently, several advanced statistical process control techniques are developed to monitor the changes in high-dimensional processes under sparsity. However, monitoring schemes that utilize the grouped pattern of the quality characteristics are sparse. This paper proposes a new method to monitor the high-dimensional process when the grouped structure of the process data is observed. The proposed method identifies the potentially changed groups and individual variables within the groups based on a modified sparse group LASSO (MSGL) model. Then, a monitoring statistic is obtained using MSGL-based likelihood function to test abnormality of the process. Extensive numerical studies are conducted to demonstrate the effectiveness and efficiency of the proposed method. In addition, a real-life application of a liquefied natural gas process is presented to illustrate the proposed method.

Due to growing environmental concerns and stringent emissions regulations, optimizing the fuel consumption of marine propulsion systems is crucial. This work deals with the potential in an LNG ship propulsion system to reduce fuel consumption through controlled load distribution between engines in Dual-Fuel Diesel Electric (DFDE) plant. Based on cyclical data acquisition measured onboard and using an optimization model, this study evaluates different load distribution strategies between setups according to the optimization model results and automatic (equal) operation to determine their effectiveness in improving fuel efficiency. The analysis includes scenarios with different fuel types, including LNG, MDO and HFO, at different engine loads. The results indicate that load distribution adjustment based on the optimization model results significantly improves fuel efficiency compared to conventional methods of uniform load distribution controlled by power management systems in almost all load intervals. This research contributes to the maritime industry by demonstrating that strategic load management can achieve significant fuel savings and reduce environmental impact, which is in line with global sustainability goals. This work not only provides a framework for the implementation of more efficient energy management systems on LNG vessels, but also sets a benchmark for future innovations in maritime energy optimization as well as in the view of exhaust emission reduction.

This study is concerned with the critical issue of ensuring safety in the maritime transportation of hazardous materials, specifically LPG and LNG. These gases, which are increasingly adopted as cleaner alternatives to traditional fuels, pose significant risks due to their flammable and hazardous nature, thus making safety a priority for both policy and practice. In order to address these challenges, this research employs the Delphi method to gather expert opinions and Fault Tree Analysis (FTA) to systematically analyze potential fault points in loading, unloading, and storage processes. The findings of the study indicate that human faults, such as inadequate maintenance and overwork, are the most significant contributors to accidents, followed by environmental factors like adverse weather and machinery faults. The study offers actionable recommendations, including the enhancement of training programs, the implementation of advanced monitoring technologies, and the strengthening of safety protocols. These findings offer critical insights to policymakers and practitioners, with a view to mitigating risks in LPG and LNG operations.

As countries scramble for cleaner energy production and to meet carbon reduction targets, natural gas seems to become an increasingly attractive option with liquified natural gas (LNG) as a popular transportation choice. In this paper, we first conduct a literature review and discuss the various factors affecting the global natural gas market, its recent history, current state, and future. Then we look at the possibility of East Asia becoming an alternative market to Europe for Russian LNG. We also bring in the US both as a political force that employs economic sanctions and as a potential LNG supplier. As a case study, we define a 3-player game between Russia, The United States, and Japan which results in relative market stability. In the case of sanctions against Russia, we conclude that it will lose its foothold in the Japanese market in the long term. Finally, we discuss the potential of LNG trading as the first step for East Asia’s energy transition to a low-carbon economy.

There is little dispute about the need to urgently reduce the use of fossil (a.k.a. 'natural') gas, as part of the global effort to address human contributions to climate change. Researchers have established that human wellbeing and the satisfaction of basic needs do not require dependence on fossil fuels, and that a good life for all is possible while remaining within planetary boundaries (Millward-Hopkins et al. 2020; O'Neill et al. 2018). Yet this is not happening in practice. No country is 'even close to achieving sufficient need satisfaction within sustainable levels of energy use' (Vogel et al. 2021:12).