A Study on the Estimation of Facilities in LNG Bunkering Terminal by Simulation—Busan Port Case

Human Error Probability Assessment for LNG Bunkering Based on Fuzzy Bayesian Network-CREAM Model

Liquified natural gas (LNG) as a marine fuel has gained momentum as the maritime industry moves towards a sustainable future. Since unwanted LNG release may lead to severe consequences, performing quantitative risk assessment (QRA) for LNG bunkering operations has become mandatory according to some regulations. Human error is a main contributor to the risks, and the human error probabilities (HEPs) are essential for inclusion in a QRA. However, HEPs data are unavailable in the LNG bunkering industry so far. Therefore, this study attempts to infer HEPs through on-site safety philosophical factors (SPFs). The cognitive reliability and error analysis method (CREAM) was adopted as a basic model and modified to make it suitable for HEP assessment in LNG bunkering. Nine common performance condition (CPC) indicators were identified based on the fuzzy ranking of 23 SPF indicators (SPFIs). A Bayesian network (BN) was built to simulate the occurrence probabilities of different contextual control modes (COCOMs), and a conditional probability table (CPT) for the COCOM node with 19,683 possible combinations in the BN was developed according to the CREAM’s COCOM matrix. The prior probabilities of CPCs were evaluated using the fuzzy set theory (FST) based on data acquired from an online questionnaire survey. The results showed that the prior HEP for LNG bunkering is 0.009841. This value can be updated based on the re-evaluation of on-site SPFIs for a specific LNG bunkering project to capture the dynamics of HEP. The main innovation of this work is realizing the efficient quantification of HEP for LNG bunkering operations by using the proposed fuzzy BN-CREAM model.

Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence

Ship exhaust emission is the main cause of coastal air pollution, leading to premature death from cardiovascular cancer and lung cancer. In light of public health and climate change concerns, the International Maritime Organization (IMO) and several governments are reinforcing policies to use clean ship fuels. In January 2020, the IMO reduced the acceptable sulfur content in ship fuel to 0.5% m/m (mass/mass) for sustainability. The use of liquified natural gas (LNG) as a ship fuel is currently the most likely measure to meet this regulation, and LNG bunkering infrastructure investment and network planning are underway worldwide. Therefore, the aim of this study is to predict the LNG bunkering demand for investment and planning. So far, however, there has been little quantitative analysis of LNG bunkering demand prediction. In this study, first, the global LNG bunkering demand was predicted using meta-regression analysis. Global demand for LNG bunkering is forecast to increase from 16.6 million tons in 2025 to 53.2 million tons in 2040. Second, LNG bunkering prediction by country and region was performed through analogy and artificial intelligence methods. The information and insights gained from this study may facilitate policy implementation and investments.

Analytic Hierarchy Process Analysis for Industrial Application of LNG Bunkering: A Comparison of Japan and South Korea

From January 2020, the International Maritime Organization has regulated ship emissions to reduce sulfur content. As an alternative to this, LNG bunkering was proposed, and infrastructure and ships were deployed. Therefore, we used analytic hierarchy process AHP techniques to determine optimal methods of LNG bunkering for shipyard safety. First, we conducted a literature survey on the concept and type of LNG bunkering, global LNG bunkering trends, and features of Japan and South Korea cases and compared them. Thereafter, an expert survey was conducted, and survey data was analyzed using AHP techniques. Finally, we derived optimal methods applicable to shipyard industry. The analytical results revealed that the derived priority of the optimal LNG bunkering method of shipyard was in the order of the STS method, TTS method, and the PTS method. The result of this study can serve as a theoretical basis to make LNG bunkering safer and more economical in shipyards to prepare for the expansion of demand of LNG-fueled ships and LNG. However, this study inevitably has limitations of ranking reversals paradox as it was conducted by experts, assuming no weights to STS, TTS, or PTS.

Evolution Process of Liquefied Natural Gas from Stratification to Rollover in Tanks of Coastal Engineering with the Influence of Baffle Structure

During the storage process, liquefied natural gas (LNG) may undergo severe evaporation, stratification, and rollover in large storage tanks due to heat leakage, aging, or charging, causing major safety risks. Therefore, this article theoretically analyzes the causes and inducing factors of the LNG stratification and rollover phenomenon in the storage tank of coastal engineering. The computational fluid dynamics was used to establish a numerical model for the heat and mass transfer of LNG multicomponent materials in the imaginary layered interface of the storage tank, and the evolution process of LNG from spontaneous stratification to rollover was simulated. The accuracy of the mathematical model is verified by comparing numerical results with experimental data from open literature. The effects of the density difference between upper and lower layers, layering parameters, heat leakage parameters, and the baffles structure on the rollover process were studied. The effects of the interfacial surface variations are not included in this study. The results show that different baffle structures will form different boundary velocity fields, which will only affect the severity of the rollover, not the occurrence time. The larger the layering density difference, the earlier the rollover occurs. Under current conditions, the baffle structure that has the best suppression of rollover and the minimum boundary velocity is at 0.5 m above the stratified interface with the installation of the baffle at 5 degrees.