Liquefied Natural Gas Bunkering Research Articles

Structural impact under accidental LNG release on the LNG bunkering ship: Implementation of advanced cryogenic risk analysis

The potential risk associated with the accidental release of Liquefied Natural Gas (LNG) is a notable concern during the design phase of gas-handling facilities. One of the risk is an excessive cryogenic exposure to structures or equipment, leading to hazardous situations. This study aims to introduce a novel method that implements Advanced Cryogenic Risk Analysis (ACRA) to enhance the safety of LNG bunkering ship structure in case of cryogenic flow leakage. The method involves evaluating thermal loads based on the preceding simulated LNG release scenarios using Computational Fluid Dynamics (CFD). These thermal parameters are then employed in structural analysis through the Finite Element (FE) approach.The integration of thermal data from CFD simulations into the FE model was achieved using the Random Forest algorithm. The LNG bunkering structure is represented using DH36-grade steel material. Furthermore, to accurately capture the mechanical properties of DH36 steel, cryogenic tensile tests were conducted across a temperature range from 298.15 K to 113.15 K. The resulting stress-strain data from these tests were utilized to establish a reliable material model for conducting FE analysis.This paper comprehensively presents a novel ACRA procedure by utilizing physical parameter of the gas release cosnequence such as the steel temperature profile to identify the brittle failure under the accidental gas release on the LNG bunkering ship. In addition, quantitative risk analysis (QRA) was also presented regarding the cryogenic spill hazard, to link the probability and consequence aspects of the LNG bunkering risk, particularly the results of steel temperature, stress, and strain experienced by the ship’s structure. Furthermore, the individual risk was also estimated. The implications of the findings are discussed in detail regarding the cryogenic risk, cryogenic tensile test and numerical analyses.

A Comprehensive Review of the Establishment of Safety Zones and Quantitative Risk Analysis during Ship-to-Ship LNG Bunkering

This study comprehensively reviews the current academic literature concerning the safety and risk assessment associated with the utilization of liquefied natural gas (LNG) in ship-to-ship bunkering scenarios. Simultaneously, it explores the complex system of regulations, standards, and guidelines that oversee the thorough evaluation of risks linked to ship-to-ship LNG bunkering procedures. Special attention is given to the scrutiny of legal frameworks that encompass a range of safety considerations, such as storage facilities, transportation, bunkering processes, and the vessels involved in both bunkering and receiving. The research questions are formulated to provide a clear direction and objectives for this study’s journey. The main hazards and risks related to LNG bunkering are identified and analyzed. The legal framework for LNG bunkering risk assessment is analyzed, and opportunities for improvement in these legal documents are identified. The general methodology and procedure for the safety assessment of the LNG bunkering process are summarized and established. From an extensive compilation of scholarly articles, 210 high-quality research papers have been deliberately selected for thorough examination. The research gaps are identified and analyzed. Through this analysis, the highlighted studies and key points are mentioned and analyzed. The research gaps are also outlined to predict the future directions of research on establishing safety zones during LNG ship-to-ship bunkering. Recommendations are made to propose improvements to the legal documents and suggest further research on the establishment of safety zones during ship-to-ship LNG bunkering to relevant authorities.

Risk assessment of LNG bunkering vessel operation based on formal safety assessment method

AbstractWith the increase of Liquefied Natural Gas (LNG) fuel‐powered vessels, LNG bunkering vessels have become an important development direction of LNG bunkering technology for its advantages. However, the LNG bunkering vessel operation risk assessment is relatively few. Formal Safety Assessment (FSA) is a novel structured and systematic risk analysis method for Marine engineering, through the data analysis based on probability theory, people can foresee the risk before the accident and take measures to reduce the risk and avoid heavy loss. In this paper, the LNG bunkering vessel operation process is divided into four subprocesses: the loading process, the navigation process, the bunkering process, and the anchoring process. The FSA method is used to identify and evaluate the risks in each process, judge the negligible risk, reasonable and feasible low risk, unacceptable risk, and put forward corresponding safe measures to provide safety guarantees for the operation of LNG bunkering vessels. Through the standardized assessment steps of FSA, a risk model for the LNG bunkering vessel operation process is established, and reasonable suggestions and measures that can effectively control the risk of the LNG bunkering vessel operation process are proposed, the research results of this paper can provide important technical guidance and reference value for the safe operation of LNG bunkering vessel.

Comparative analysis on vapor cloud dispersion between LNG/liquid NH3 leakage on the ship to ship bunkering for establishing safety zones

This study presents a comparative analysis of vapor cloud dispersion in the case of accidental fuel release from a bunkering hose to enable the establishment of a safety zone during the bunkering process. The pipeline can carry marine fuels such as liquefied natural gas (LNG) or liquid ammonia (NH3). The safety zone can be regarded as the 3-dimensional area wherein the leak event takes place and where, under certain circumstances, there is a recognized potential to endanger crew members or damage equipment. The primary objective of this research was to investigate the characteristics of leakage dispersion and determine the safety distance during ship-to-ship LNG or NH3 bunkering. Mathematical models discussing the dispersion process for LNG/NH3 leakage during ship-to-ship bunkering are presented. Numerical studies were conducted for 26 scenarios, considering diverse operational and environmental conditions, leak hole diameters, leak rates, wind speeds, and wind directions. Findings suggest that under equivalent operational conditions, NH3 exhibited a greater dispersion range for the vapor cloud compared to LNG. Consequently, NH3 demands a more extensive safety zone distance. The dispersion time of NH3 was longer than that of LNG. The parametric study revealed that the toxicity and flammability limit range were important parameters that affect the extent of safety zones. In addition, weather conditions (wind speed and wind direction) and leak characteristics (leak rate and leak duration) are critical parameters. The data and insights regarding the prediction of danger areas can serve as a vital reference for both qualitative and quantitative risk assessments concerning emergency management in ship-to-ship LNG/NH3 leakage incidents.

Determination of cryogenic temperature loads for finite-element model of LNG bunkering ship under LNG release accident

Abstract The rising demand for liquefied natural gas (LNG)-fueled ships requires the LNG bunkering facility that partially uses a ship-to-ship operation. The bunkering process of LNG fuel may have a greater risk due to LNG volatility. The cryogenic temperature of LNG poses a threat to the personnel and structural embrittlement to ships. Therefore, cryogenic spill protection optimization was introduced concerning the structural strength analysis using finite element (FE) by utilizing cryogenic temperature loads provided by the computational fluid dynamics (CFD) model of an LNG release. This study aims to build a platform for transferring the temperature load profile from CFD to FE software accurately. The CFD model usually uses a structured Cartesian grid, and the FE method adopts an unstructured tetrahedral or hexahedral mesh. As a result, both configurations store results at different positions, and it is not preferred for the load profile to be transferred directly. The error will be greater due to the variance of positions. Random Forest, a machine learning method, has been employed that uses a regression technique to deal with a continuous variable. An accurate load profile for the FE model can be obtained by adopting decision tree learning in Random Forest. The procedure for determining the temperature load profile is presented in this article.

Simulating Operating Performance of Alternative Configurations of LNG Bunkering Stations

Recently, due to the great need to promote environmentally sustainable maritime transport, alternative energy sources to traditional fossil fuels have been proposed to reduce ship emissions. Among few alternative scenarios, most experts recognize Liquefied Natural Gas (LNG) as the most promising solution in the short to medium term. However, there are still some critical issues related to the future expansion of bunkering stations and the LNG-fuelled fleet. Firstly, there is the need for a sufficiently extensive international network of bunkering facilities. Secondly, the layout and location of bunkering stations impact the efficiency of ship bunkering operations, cost reduction and the safety of the surrounding areas. Last, the in-progress Russian-Ukrainian conflict is causing serious unbalances in gas supply and prices, especially for Europe. Specifically, in the case of Italy, gas imports represent the seventh most imported commodity. Due to the changed geopolitical scenarios, interest has arisen in investigating the technical and operational characteristics of LNG bunkering stations and comparing different configurations with a view to increasing Italy’s independence from other foreign countries, focusing on degasifies that could promote new infrastructures that make available LNG in ports. In this paper we highlight the importance of reducing ship emissions and investigate some technical and operational characteristics of LNG bunkering stations. We present a simulation study to analyse quantitatively the operating performance of different LNG bunkering technologies in a port terminal and their impact on the efficiency and overall cost within the whole goods’ supply chain. In particular, we evaluate and compare bunkering time, throughput and refuelling costs in alternative layouts, referring to marine terminals located near urban areas. The aim of this research is to verify whether ports with infrastructure embedded in metropolitan areas could provide, safely, a valuable contribution to the green transition by efficiently handling an adequate level of LNG supply, especially referring to the present Italian interest in increasing independence from foreign countries. For this purpose, we present four dynamic discrete event simulations of all the main LNG bunkering configurations and present their dynamic performance sampled over two consecutive years after a warmup period of 6 months. The simulation conceptual models have been created by the authors based on analyses of those configurations and then processed and implemented within the simulation software Witness Horizon 23®, used for experimentation. This is the first time that a simulation study is presented for comparing different configuration of LNG bunkering stations. The results presented here confirm that simulation is a key science to address these complex problems and it represents a major added value for the development of new infrastructures embedded in supply chains and able to favour green transition. Concerning the present study, the simulation output reveals that, although the increase in the price of LNG over the past year has had a strong negative impact on the propensity to activate LNG refuelling stations at maritime terminals, Truck-To-Ship, or Ship-to-Ship with small feeder ships, and Port-To-Ship configurations appear to be flexible and particularly suitable for port terminals located near urban areas. However, the final the choice of the most suitable LNG bunkering station requires further and specific inside investigation as well as considerations on the Decision Maker Strategies and Attitudes.

Fostering Sustainable LNG Bunkering Operations: Development of Regulatory Framework

Liquefied natural gas (LNG) is a liquid form of natural gas, predominantly composed of methane, that has emerged as a promising alternative to traditional hydrocarbon fuels in the maritime transportation sector due to its lower greenhouse gas (GHG) emissions. As the world shifts towards renewable energy sources, LNG is increasingly recognized as a bridge fuel that can help mitigate global warming. However, the production and use of LNG can result in methane emissions, which have a higher global warming potential than carbon dioxide. To address this issue, competent authorities aim to develop regulatory frameworks to reduce the potential environmental impact of LNG and promote its use as a cleaner fuel in the maritime sector. We used a systematic approach to selecting and synthesizing the sources relevant to the LNG bunkering process to provide an overview of the current state of regulations, standards, guidance, and trends in LNG bunkering to minimize the potential adverse environmental impacts.

Understanding core determinants in LNG bunkering port selection: Policy implications for the maritime industry

This study identifies the core determinants for selecting Liquefied Natural Gas (LNG) bunkering ports from the view of liner shipping companies in South Korea. Interpretive structural modeling (ISM) is applied to understand the holistic formation of interactional relationships among the core decision determinants. The results confirm that the 11 LNG bunkering port selection components indicate interactive contextual characteristics that can be grouped into three categories identified in this study: “components for basic requirements,” “components for port attractiveness (differentiation),” and “core components for LNG bunking port selection.” The three determinants at the top level are recognized as core determinants that may directly influence shipping companies’ decision-making for the LNG bunkering selection individually rather than interactively. The findings obtained from this study can be a good reference for policymakers and ports that need to establish such a good LNG bunkering port strategy by realizing the perspective of shipping companies.

LNG Bunkering Station Deployment Problem—A Case Study of a Chinese Container Shipping Network

Liquefied natural gas (LNG) is a promising measure to reduce shipping emissions and alleviate air pollution problem, especially in coastal areas. Currently, the lack of a complete infrastructure system is preventing the extensive application of dual-fueled ships that are mainly LNG-powered. Given that groups of LNG bunkering stations are under establishment in various countries and areas, the construction plan becomes critical. In this paper, we focus on the LNG bunkering station deployment problem, which identifies the locations of the stations to be built. A large-scale case study of China’s container shipping network was conducted. The problem scale of this case paper exceeds those in previous academic studies. Thus, this study better validates the model and solution method proposed than numerical experiments that are randomly generated. Sensitive analyses on the LNG price, bunkering station construction costs, and total budget were carried out. The results yielded provide practical suggestions and managerial insights for the competent department. In addition to building a complete bunkering system, subsidies to ship operators for consuming LNG and higher production efficiency in bunkering station construction also help promote the application of LNG as marine fuel.

Alternatif Deniz Yakıtı Olarak LNG Kullanımında Emniyet Tedbirleri

International Maritime Organisation has changed the emission rates in the MARPOL Annex VI rules as of 2020 and reduced the sulfur rate from 3.5 percent to 0.5 percent and led the maritime sector to alternative fuels. Compared to other alternative fuels, liquefied natural gas (LNG) is an attractive option with almost zero emission rate, ease of access, availability of ports that currently supply LNG and low price in the fuel market. In addition, according to the life cycle assessment, which is a methodology created by ISO 14040: 2006 to examine the impact of a product on the environment, in order for a fuel to be environmentally sustainable, it has to be associated not only with low emissions during fuel combustion, but also with the entire fuel life cycle, starting with raw material extraction, fuel production, distribution and finally combustion. The life cycle environmental performance of LNG as a marine fuel has been observed to have its impact on climate change in the range of 20% to 5%, significantly reducing SO2 and NOX emissions compared to heavy fuel oil. LNG bunkering is specified in the IGC code. In addition, there are standards brought by class organizations. This study shows us how to avert from the risk of LNG leakage accident. For this reason, risk analysis should be done correctly, ISM checklists should be created and applied on ships accordingly.

A branch-and-price heuristic algorithm for the bunkering operation problem of a liquefied natural gas bunkering station in the inland waterways

Liquefied natural gas (LNG) bunkering stations are areas for bunkering LNG-powered ships via a flexible hose from either a shoreside facility, shore-based/pontoon tank, or an LNG truck. The operation management of LNG bunkering stations is complex because many factors affect the operational performance, including station layout, bunkering technology, and frequent interactions among trucks, tanks, and ships. In this study, we consider the bunkering operation problem (BOP) of an LNG bunkering station in the inland waterways. The problem involves decisions of assigning ships to tanks, managing the inventory of the tanks, and scheduling LNG trucks for bunkering ships and replenishing tanks. We first formulate the problem as a mixed-integer linear programming (MILP) model that aims to minimize the cost incurred by the bunkering operations. As the BOP is NP-hard, the MILP model for the problem with practical size is generally difficult to solve. We thus reformulate it into an equivalent task-based model and develop a tailored branch-and-price heuristic (BPH) algorithm to solve the new model. Several enhancement techniques are also presented to improve the solution efficiency of the BPH algorithm. Numerical experiments demonstrate the satisfactory performance of the solution algorithm. Some managerial implications are also obtained to provide scientific guidance for station operators to make operational decisions. In particular, our model can help determine the best combination of bunkering modes and replenishment modes as well as the optimal truck fleet.

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

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.

Consequence Analysis of Accidental LNG Release on the Collided Structure of 500 cbm LNG Bunkering Ship

The growing demand for liquefied natural gas (LNG)-fueled ships necessitates the establishment of an LNG bunkering facility. Ship-to-ship (STS) is one of the most practical forms of LNG bunkering systems. Although there are benefits to the LNG bunkering of ships, risk and safety issues are a concern due to the volatile cargo. Ship collision could result in accidental LNG release. The purpose of this study was to build LNG leakage scenarios, establish critical zones based on gas concentrations, and estimate the temperature reduction in a bunkering ship’s structure resulting from the use of cryogenic fluid. The condition of a target ship’s structure, both intact and when damaged due to collision, was considered. Leak size, leak direction, leak position, release rate, and reservoir pressure were included as leak parameters, and environmental parameters, such as the wind direction, wind speed, and ambient temperature, were also included. The release duration was set based on the shutdown duration of the emergency shutdown valve (ESD). A total of 72 leakage scenarios were generated for the main CFD analysis. Convergence tests were conducted to determine the appropriate grid and iteration numbers for a computational fluid dynamics (CFD) simulation. The gas dispersion characteristics and the cryogenic flow impact on the LNG bunkering ship’s structure are discussed through a parametric study.

Development of Fire Consequence Prediction Model in Fuel Gas Supply System Room with Changes in Operating Conditions during Liquefied Natural Gas Bunkering

Recently, various dynamic risk analysis methods have been suggested for estimating the risk index by predicting the possibility of accidents and damage in real time. It is necessary to quickly estimate the risk of an accident by predicting the probability and consequences of accidents, which are quantitative criteria for ship risk assessment. This study aimed to develop an algorithm for predicting the consequences of accidents in real time to perform a dynamic risk assessment and, using this algorithm, formulate a ship accident–response procedure that can be used in fire accidents during LNG bunkering. The risk of fire was estimated by predicting the hole size due to changes in the liquefied natural gas (LNG) transport conditions and the consequence based on the fire accidents scenario in LNG-fueled ships. The prediction model was trained with the hole prediction data using Ansys CFX and with the consequence data using DNV Phast, and the consequences of fire were compared and evaluated by applying the trained results. A method for estimating the size of the fire based on the predicted consequence is proposed, which supports fast decision making in fire accidents during LNG bunkering by identifying the potential size of the fire at the beginning of an accident.

Comparison of FLACS and BASiL Model for Ro-Pax Ferry LNG Bunkering Leak Analysis

Performing liquefied natural gas (LNG) bunkering involves the risk of accidental leakage. When released from containment, LNG rapidly vaporizes into flammable natural gas and could lead to flash fire and explosion. Hence, LNG bunkering needs to take place in an area without an ignition source called a safety zone. This study compares the safety zone estimated by the Bunkering Area Safety Information for LNG (BASiL) model with that of the computational fluid dynamic (CFD) software FLACS, for Ro-Pax ferry bunkering. Horizontal leaks covering different wind speeds in eight wind directions were compared between the two models. Additionally, a grid refinement study was performed systematically to quantify the discretization error uncertainty in the CFD. Of 24 leak cases, FLACS and the BASiL model results agreed on 18 cases. In three cases validation was inconclusive due to the CFD error uncertainty. The BASiL model underestimated the safety zone distance in three cases compared with FLACS. Future work would be to perform a higher grid refinement study to confirm inconclusive comparison and examine ways to reduce gas dispersion spread for the worst result.

Formation of Dataset for Fuzzy Quantitative Risk Assessment of LNG Bunkering SIMOPs

New international regulations aimed at decarbonizing maritime transportation are positively contributing to attention being paid to the use of liquefied natural gas (LNG) as a ship fuel. Scaling up LNG-fueled ships is highly dependent on safe bunkering operations, particularly during simultaneous operations (SIMOPs); therefore, performing a quantitative risk assessment (QRA) is either mandated or highly recommended, and a dynamic quantitative risk assessment (DQRA) has been developed to make up for the deficiencies of the traditional QRA. The QRA and DQRA are both data-driven processes, and so far, the data of occurrence rates (ORs) of basic events (BEs) in LNG bunkering SIMOPs are unavailable. To fill this gap, this study identified a total of 41 BEs and employed the online questionnaire method, the fuzzy set theory, and the Onisawa function to the investigation of the fuzzy ORs for the identified BEs. Purposive sampling was applied when selecting experts in the process of online data collection. The closed-ended structured questionnaire garnered responses from 137 experts from the industry and academia. The questionnaire, the raw data and obtained ORs, and the process of data analysis are presented in this data descriptor. The obtained data can be used directly in QRAs and DQRAs. This dataset is first of its kind and could be expanded further for research in the field of risk assessment of LNG bunkering.

Development of Model of Risk-Based Approach to the Formation of Industry Standard in the Field of Occupational Safety and Health at Liquefied Natural Gas Bunkering Facilities

Development of Model of Risk-Based Approach to the Formation of Industry Standard in the Field of Occupational Safety and Health at Liquefied Natural Gas Bunkering Facilities

Uncertainties in failure rates in the LNG bunkering risk assessment

The increasing use of Liquefied Natural Gas (LNG) in shipping has led to an increased interest in marine bunkering safety in ports. The main tool for risk management and siting of bunkering facilities is quantitative risk assessment. This paper addresses the uncertainties in the available data on bunker equipment (loading and unloading arms and hoses), safety system failure rates, and ignition probabilities required for risk assessment. Using the search engines Scopus and Google, a literature search was conducted for studies and papers on the risk assessment of LPG bunkering. Analysis of the relevant studies revealed that different sources of data were used for equipment failure rates and ignition probabilities. The analysis of failure rates for leaks and ruptures of arms and hoses revealed that there are several problems with comparability, but also that there is a high degree of uncertainty, with data ranging from two to four orders of magnitude. The analysis of ignition probabilities showed that the uncertainty is about two orders of magnitude for small releases and within one order of magnitude for large releases. The failure rates of the emergency shutdown systems are within one order of magnitude. A wide range of arm and hose failure rates represents a large uncertainty for a reliable risk assessment. The way forward appears to be a transparent data collection system that describes the scope and the various assumptions.

Small-scale LNG supply chain optimization for LNG bunkering in Turkey

LNG (Liquefied Natural Gas) provides a viable option to comply with emission control measures as an alternative marine fuel. Supply chain optimization is critical for LNG bunkering development in the maritime context as it requires high capital-expenditure. This study proposes a model for optimizing the ship-to-ship LNG bunkering supply chain. The related problem is defined as a Multiple Period Maritime Fleet Size and Routing Problem. The proposed mathematical model has been solved for various demand scenarios obtained by multiple regression and forecasting as a case study of ship-to-ship LNG bunker deliveries in Turkey. The model presents an optimal solution as a tactical and strategic decision-making tool, finds the number and size of the LNG bunker barges and the optimum allocation of the barges and the distribution network within a ship-to-ship bunkering framework. Moreover, it provides a commercial framework for shipowners and suppliers by determining the breakeven point for investment decisions.

Reduced order models for uncertainty quantification of gas plumes from leakages during LNG bunkering

The impacts of uncertainty in wind conditions on the spread of hazardous plume resulting from a jet leak during a Liquefied Natural Gas (LNG) bunkering operation were investigated. Computational Fluid Dynamics (CFD) using the Reynolds-Averaged Navier Stokes (RANS) solver with multi-species transport and a transient leak model for keyhole leak was used for the simulation of a simplified bunkering station. Following detailed validation & verification, the sensitivity of the safety zone extents to the wind conditions was demonstrated. CFD results reinforced the strong dependence of the maximum spread distance on wind conditions and enclosure geometry. To quantify the impact of input uncertainty from wind conditions on the plume spread, a reduced-order model (ROM) was developed using the proper orthogonal decomposition (POD) of CFD results on sampled conditions. ROM-POD enables a fast evaluation of the plume under changing wind conditions and acts as an efficient forward model for uncertainty quantification using Polynomial Chaos Expansion (PCE) technique. The spatial distribution of plume residence time under the same input uncertainty was also obtained from the proposed approach showing its potential in risk assessment and design of bunkering facilities.